FN Clarivate Analytics Web of Science
VR 1.0
PT J
AU Chekanov, SV
Gavalian, G
Graf, NA
AF Chekanov, S. V.
Gavalian, G.
Graf, N. A.
TI Jas4pp? A data-analysis framework for physics and detector studies ? ,
?? , ?
SO COMPUTER PHYSICS COMMUNICATIONS
LA English
DT Article
DE End-user data analysis; Software frameworks; Python; Jython; Java;
Groovy
AB This paper describes the Jas4pp framework for exploring physics cases and for detector-performance studies of future particle collision experiments. Jas4pp is a multi-platform Java program for numeric calculations, scientific visualization in 2D and 3D, storing data in various file formats and displaying collision events and detector geometries. It also includes complex data-analysis algorithms for function minimization, regression analysis, event reconstruction (such as jet reconstruction), limit settings and other libraries widely used in particle physics. The framework can be used with several scripting languages, such as Python/Jython, Groovy and JShell. Several benchmark tests discussed in the paper illustrate significant improvements in the performance of the Groovy and JShell scripting languages compared to the standard Python implementation in C. The improvements for numeric computations in Java are attributed to recent enhancements in the Java Virtual Machine. Program summary Program title: Jas4pp CPC Library link to program files: https://doi.org/10.17632/jzvddk26cy.1 Developer & rsquo;s repository link: https://atlaswww.hep.anl.gov/asc/jas4pp/ Licensing provisions: GNU General Public License 3 Programming language: Java, Jython, Groovy Nature of problem: Develop a platform-independent data-analysis framework for high-energy and nuclear physics (HEP and NP) with a support of fast dynamically-typed scripting languages, comprehensive data-visualisation and I/O libraries. Solution method: The solution adopted here is to use Java and the scripting languages integrated with Java VM. Additional comments: All 3rd party Java libraries included with this program are licensed by GPLv3, GNU Lesser General Public License (LGPL) or by other licenses compatible with the GPLv3 license, and adhere to Mendeley Data approved open-source software licenses. These licenses files are includes with the program.
Published by Elsevier B.V.
C1 [Chekanov, S. V.] Argonne Natl Lab, HEP Div, 9700 S Cass Ave, Argonne, IL 60439 USA.
[Gavalian, G.] Jefferson Lab, 12000 Jefferson Ave, Newport News, VA 23602 USA.
[Graf, N. A.] SLAC Linear Accelerator Lab, 2575 Sand Hill Rd, Menlo Pk, CA 94025 USA.
RP Chekanov, SV (corresponding author), Argonne Natl Lab, HEP Div, 9700 S Cass Ave, Argonne, IL 60439 USA.
EM chekanov@anl.gov; gavalian@jlab.org; Norman.Graf@slac.stanford.edu
FU Argonne, a U.S. Department of Energy Office of Science laboratoryUnited
States Department of Energy (DOE) [DE-AC0206CH11357]; U.S. Department of
Energy, Office of High Energy PhysicsUnited States Department of Energy
(DOE) [DE-AC02-06CH11357]
FX We thank Marco Lucchini for help with debugging the Jas4pp program. We
gratefully acknowledge the computing resources provided on a
high-performance computing cluster operated by the Laboratory Computing
Resource Center at Argonne National Laboratory. The submitted manuscript
has been created by UChicago Argonne, LLC, Operator of Argonne National
Laboratory (``Argonne''). Argonne, a U.S. Department of Energy Office of
Science laboratory, is operated under Contract No. DE-AC0206CH11357. The
U.S. Government retains for itself, and others acting on its behalf, a
paid-up nonexclusive, irrevocable worldwide license in said article to
reproduce, prepare derivative works, distribute copies to the public,
and perform publicly and display publicly, by or on behalf of the
Government. The Department of Energy will provide public access to these
results of federally sponsored research in accordance with the DOE
Public Access Plan.
http://energy.gov/downloads/doe-public-access-plan.Argonne National
Laboratory's work was funded by the U.S. Department of Energy, Office of
High Energy Physics under contract DE-AC02-06CH11357. All authors
approved the version of the manuscript to be published.
CR Ablikim M, 2010, NUCL INSTRUM METH A, V614, P345, DOI 10.1016/j.nima.2009.12.050
Abramowicz H., 2013, TECH REP ILC REPORT, V4
Allison J, 2016, NUCL INSTRUM METH A, V835, P186, DOI 10.1016/j.nima.2016.06.125
[Anonymous], 2020, XROOTD HIGH PERFORMA
[Anonymous], 2020, HIPO 4 HIGH PERFORMA
[Anonymous], 2008, ATLAS COLLABORATION, V3, DOI [10.1088/1748-0221/3/08/s08003, DOI 10.1088/1748-0221/3/08/S08003]
Antcheva I, 2009, COMPUT PHYS COMMUN, V180, P2499, DOI 10.1016/j.cpc.2009.08.005
Apache Software Foundation, 2020, APACHE COMMONS MATH
Ballaminut A, 2001, COMPUT PHYS COMMUN, V140, P266, DOI 10.1016/S0010-4655(01)00277-6
Baltzell N, 2020, NUCL INSTRUM METH A, V959, DOI 10.1016/j.nima.2020.163421
Baltzell N, 2017, NUCL INSTRUM METH A, V859, P69, DOI 10.1016/j.nima.2017.03.061
Behnke T., 2013, ARXIV13066327
Benedikt M., 2013, P5 WORKSH FUT HIGH E
Blyth D, 2019, COMPUT PHYS COMMUN, V241, P98, DOI 10.1016/j.cpc.2019.03.018
BOCK R, 1987, COMPUT PHYS COMMUN, V45, P181, DOI 10.1016/0010-4655(87)90154-8
Cacciari M, 2008, J HIGH ENERGY PHYS, DOI 10.1088/1126-6708/2008/04/063
Cacciari M, 2012, EUR PHYS J C, V72, DOI 10.1140/epjc/s10052-012-1896-2
Chekanov S., 2016, NUMERIC COMPUTATION, P620
Chekanov SV, 2019, NUCL INSTRUM METH A, V931, P92, DOI 10.1016/j.nima.2019.04.031
Chekanov SV, 2017, COMPUT PHYS COMMUN, V220, P91, DOI 10.1016/j.cpc.2017.06.017
Chekanov SV, 2017, J INSTRUM, V12, DOI 10.1088/1748-0221/12/06/P06009
Chekanov SV, 2015, ADV HIGH ENERGY PHYS, V2015, DOI 10.1155/2015/136093
Chekanov SV, 2014, COMPUT PHYS COMMUN, V185, P2629, DOI 10.1016/j.cpc.2014.06.016
da Costa J.a.B. Guimaraes, 2018, CEPC CONCEPTUAL DESI, V2
Donszelmann M., 2005, WIRED 4 A GENERIC EV, DOI [10.5170/CERN-2005-002.369, DOI 10.5170/CERN-2005-002.369]
Gaede F., 2003, ARXIV0306114
Gavalian G., 2020, GROOT JAVA DATA VISU
Gilman J., 2020, IMPROVING JAVA MATH
Graf N.A., 2012 IEEE NUCL SCI S
Groovy, MULTIFACETED LANGUAG
Harris CR, 2020, NATURE, V585, P357, DOI 10.1038/s41586-020-2649-2
Hocquet S, 2008, J PHYS CONF SER, V112, DOI 10.1088/1742-6596/112/3/032016
JAS3, 2020, JAVA ANAL STUDIO
JAS4pp, 2020, JAVA ANAL STUDIO PAR
Johnson A., 1996, JAVA BASED ANAL ENV
Linssen L., 2012, PHYS DETECTORS CLIC, DOI [10.5170/CERN-2012-003, DOI 10.5170/CERN-2012-003]
Perl J., 2000, SLAC PUB 8332
Repond J., 2018, 24 INT WORKSH DEEP I, P179, DOI [10.22323/1.316.0179, DOI 10.22323/1.316.0179]
Roberts A, 2019, J INSTRUM, V14, DOI 10.1088/1748-0221/14/06/P06001
Romer D, 2020, PLOS ONE, V15, DOI 10.1371/journal.pone.0227545
Tang J., 2015, ARXIV150703224
Yeh C., 2019, POS ICHEP2018, P905, DOI [10.22323/1.340.0905, DOI 10.22323/1.340.0905]
NR 42
TC 0
Z9 0
U1 1
U2 1
PU ELSEVIER
PI AMSTERDAM
PA RADARWEG 29, 1043 NX AMSTERDAM, NETHERLANDS
SN 0010-4655
EI 1879-2944
J9 COMPUT PHYS COMMUN
JI Comput. Phys. Commun.
PD MAY
PY 2021
VL 262
AR 107857
DI 10.1016/j.cpc.2021.107857
PG 11
WC Computer Science, Interdisciplinary Applications; Physics, Mathematical
SC Computer Science; Physics
GA RD3EE
UT WOS:000633365000008
DA 2021-04-21
ER
PT J
AU Granelli, A
Moffat, K
Perez-Gonzalez, YF
Schulz, H
Turner, J
AF Granelli, A.
Moffat, K.
Perez-Gonzalez, Y. F.
Schulz, H.
Turner, J.
TI ULYSSES: Universal LeptogeneSiS Equation Solver
SO COMPUTER PHYSICS COMMUNICATIONS
LA English
DT Article
DE Python; Boltzmann equation; Leptogenesis; Neutrino physics
AB ULYSSES is a python package that calculates the baryon asymmetry produced from leptogenesis in the context of a type-I seesaw mechanism. The code solves the semi-classical Boltzmann equations for points in the model parameter space as specified by the user. We provide a selection of predefined Boltzmann equations as well as a plugin mechanism for externally provided models of leptogenesis. Furthermore, the ULYSSES code provides tools for multi-dimensional parameter space exploration. The emphasis of the code is on user flexibility and rapid evaluation. It is publicly available at https://gith ub.com/earlyuniverse/ulysses.
Program summary
Program Title: ULYSSES
CPC Library link to program files: https://doi.org/10.17632/rzd24f34h2.1
Developer's repository link: github.com/earlyuniverse/ulysses
Licensing provisions: MIT
Programming language: python3
Nature of problem: Solving semi-classical momentum averaged Boltzmann equations for leptogenesis in the context of a type-I seesaw mechanism.
Solution method: Numerically solving coupled differential equations that can be complex. (c) 2020 Elsevier B.V. All rights reserved.
C1 [Granelli, A.] SISSA INFN, Via Bonomea 265, I-34136 Trieste, Italy.
[Moffat, K.] Univ Durham, Inst Particle Phys Phenomenol, Durham, England.
[Perez-Gonzalez, Y. F.] Fermilab Natl Accelerator Lab, Batavia, IL 60510 USA.
[Perez-Gonzalez, Y. F.] Northwestern Univ, Dept Phys & Astron, Evanston, IL 60208 USA.
[Perez-Gonzalez, Y. F.] Colegio Fis Fundamental & Interdisciplinaria Amer, 254 Norzagaray St, San Juan, PR 00901 USA.
[Schulz, H.; Turner, J.] Univ Durham, Dept Phys, Inst Particle Phys Phenomenol, South Rd, Durham DH1 3LE, England.
RP Turner, J (corresponding author), Univ Durham, Dept Phys, Inst Particle Phys Phenomenol, South Rd, Durham DH1 3LE, England.
EM jessicaturner.5390@gmail.com
OI Perez Gonzalez, Yuber Ferney/0000-0002-2020-7223; Granelli,
Alessandro/0000-0002-0941-8126
FU Fermi National Accelerator Laboratory (Fermilab), a U.S. Department of
Energy, Office of Science, HEP User Facility; European Research Council
under the European UnionEuropean Research Council (ERC) [617143]; Fermi
Research Alliance, LLC (FRA) [DE-AC02-07CH11359]; U.S. Department of
Energy, Office of Science, Office of Advanced Scientific Computing
Research, Scientific Discovery through Advanced Computing (SciDAC)
program [1013935]; U.S. Department of EnergyUnited States Department of
Energy (DOE) [DE-AC02-76SF00515]
FX We are deeply grateful to Serguey T. Petcov for useful discussions and
suggestions. It is a pleasure to thank Marco Drewes for helpful
discussions on this code. This research was supported by the Fermi
National Accelerator Laboratory (Fermilab), a U.S. Department of Energy,
Office of Science, HEP User Facility. K.M. acknowledges the (partial)
support from the European Research Council under the European Union
Seventh Framework Programme (FP/2007-2013)/ERC Grant NuMass agreement n.
[617143]. Fermilab is managed by Fermi Research Alliance, LLC (FRA),
acting under Contract No. DE-AC02-07CH11359. This material is based upon
work supported by the U.S. Department of Energy, Office of Science,
Office of Advanced Scientific Computing Research, Scientific Discovery
through Advanced Computing (SciDAC) program, grant HEP Data Analytics on
HPC, No. 1013935. It was supported by the U.S. Department of Energy
under contracts DE-AC02-76SF00515.
CR Ade PAR, 2016, ASTRON ASTROPHYS, V594, DOI 10.1051/0004-6361/201525830
Akhmedov EK, 1998, PHYS REV LETT, V81, P1359, DOI 10.1103/PhysRevLett.81.1359
Bambhaniya G, 2017, PHYS REV D, V95, DOI 10.1103/PhysRevD.95.095016
Biondini S, 2016, J HIGH ENERGY PHYS, DOI 10.1007/JHEP09(2016)126
Biondini S, 2016, J HIGH ENERGY PHYS, DOI 10.1007/JHEP03(2016)191
Biondini S, 2013, J HIGH ENERGY PHYS, DOI 10.1007/JHEP12(2013)028
Blanchet S, 2013, J COSMOL ASTROPART P, DOI 10.1088/1475-7516/2013/01/041
Buchmuller W, 2011, NUCL PHYS B, V851, P481, DOI 10.1016/j.nuclphysb.2011.06.004
Buchmuller W, 2005, ANN PHYS-NEW YORK, V315, P305, DOI 10.1016/j.aop.2004.02.003
Buchner J, 2014, ASTRON ASTROPHYS, V564, DOI 10.1051/0004-6361/201322971
Casas JA, 2001, NUCL PHYS B, V618, P171, DOI 10.1016/S0550-3213(01)00475-8
Ciolfi R, 2018, INT J MOD PHYS D, V27, DOI 10.1142/S021827181842004X
COHEN AG, 1991, NUCL PHYS B, V349, P727, DOI 10.1016/0550-3213(91)90395-E
Dalcin L, 2005, J PARALLEL DISTR COM, V65, P1108, DOI 10.1016/j.jpdc.2005.03.010
Dalcin L, 2008, J PARALLEL DISTR COM, V68, P655, DOI 10.1016/j.jpdc.2007.09.005
Dalcin LD, 2011, ADV WATER RESOUR, V34, P1124, DOI 10.1016/j.advwatres.2011.04.013
Dev Bhupal, 2018, INT J MODERN PHYS A, V33
Dev PSB, 2018, INT J MOD PHYS A, V33, DOI 10.1142/S0217751X18420010
Dev PSB, 2015, NUCL PHYS B, V891, P128, DOI 10.1016/j.nuclphysb.2014.12.003
Dev PSB, 2014, NUCL PHYS B, V886, P569, DOI 10.1016/j.nuclphysb.2014.06.020
Dorsch GC, 2017, J COSMOL ASTROPART P, DOI 10.1088/1475-7516/2017/05/052
Dutta B, 2018, J COSMOL ASTROPART P, DOI 10.1088/1475-7516/2018/10/025
Esteban I, 2019, J HIGH ENERGY PHYS, DOI 10.1007/JHEP01(2019)106
Feroz F, 2009, MON NOT R ASTRON SOC, V398, P1601, DOI 10.1111/j.1365-2966.2009.14548.x
Feroz F., 2013, IMPORTANCE NESTED SA
Fowlie A, 2016, EUR PHYS J PLUS, V131, DOI 10.1140/epjp/i2016-16391-0
FUKUGITA M, 1986, PHYS LETT B, V174, P45, DOI 10.1016/0370-2693(86)91126-3
Garbrecht B, 2020, J HIGH ENERGY PHYS, DOI 10.1007/JHEP02(2020)117
Garbrecht B, 2014, J COSMOL ASTROPART P, DOI 10.1088/1475-7516/2014/10/012
Garbrecht B, 2013, J HIGH ENERGY PHYS, DOI 10.1007/JHEP04(2013)099
Gell-Mann M., 1979, Supergravity, P315
Ghisoiu I, 2014, J COSMOL ASTROPART P, DOI 10.1088/1475-7516/2014/12/032
Hagedorn C, 2018, INT J MOD PHYS A, V33, DOI 10.1142/S0217751X1842006X
Laine M, 2013, J HIGH ENERGY PHYS, DOI 10.1007/JHEP08(2013)138
Lam Siu Kwan, 2015, P 2 WORKSH LLVM COMP
Lopez-Pavon J, 2015, J HIGH ENERGY PHYS, DOI 10.1007/JHEP11(2015)030
Lopez-Pavon J, 2013, PHYS REV D, V87, DOI 10.1103/PhysRevD.87.093007
Marzola Luca, 2012, LEPTOGENESIS FLAVOUR
McDonald J, 2014, J COSMOL ASTROPART P, DOI 10.1088/1475-7516/2014/09/027
MINKOWSKI P, 1977, PHYS LETT B, V67, P421, DOI 10.1016/0370-2693(77)90435-X
Moffat K, 2018, PHYS REV D, V98, DOI 10.1103/PhysRevD.98.015036
Nardi E, 2006, J HIGH ENERGY PHYS
Oliphant T.E., 2006, A GUIDE TO NUMPY, VVolume 1
Patrignani C, 2016, CHINESE PHYS C, V40, DOI 10.1088/1674-1137/40/10/100001
Pilaftsis A, 2004, NUCL PHYS B, V692, P303, DOI 10.1016/j.nuclphysb.2004.05.029
Salvio A, 2011, J HIGH ENERGY PHYS, DOI 10.1007/JHEP08(2011)116
SHAPOSHNIKOV ME, 1987, NUCL PHYS B, V287, P757, DOI 10.1016/0550-3213(87)90127-1
van der Walt S, 2011, COMPUT SCI ENG, V13, P22, DOI 10.1109/MCSE.2011.37
Virtanen P, 2020, SciPy 1.0: fundamental algorithms for scientific computing in Python. Nat. Methods, V17, P261
Weckesser Warren, 2014, ODEINTW COMPLEX MATR
Yanagida Tsutomu, 1979, P WORKSH UN THEOR BA, VC7902131, P95
NR 51
TC 0
Z9 0
U1 1
U2 1
PU ELSEVIER
PI AMSTERDAM
PA RADARWEG 29, 1043 NX AMSTERDAM, NETHERLANDS
SN 0010-4655
EI 1879-2944
J9 COMPUT PHYS COMMUN
JI Comput. Phys. Commun.
PD MAY
PY 2021
VL 262
AR 107813
DI 10.1016/j.cpc.2020.107813
PG 12
WC Computer Science, Interdisciplinary Applications; Physics, Mathematical
SC Computer Science; Physics
GA RD3EE
UT WOS:000633365000001
DA 2021-04-21
ER
PT J
AU Dingel, K
Huhnstock, R
Knie, A
Ehresmann, A
Sick, B
AF Dingel, Kristina
Huhnstock, Rico
Knie, Andre
Ehresmann, Arno
Sick, Bernhard
TI AdaPT: Adaptable Particle Tracking for spherical microparticles in lab
on chip systems
SO COMPUTER PHYSICS COMMUNICATIONS
LA English
DT Article
DE Particle tracking; Machine learning; Python code; Superparamagnetic
beads; Janus particle; Magnetic particle
AB Due to its rising importance in science and technology in recent years, particle tracking in videos presents itself as a tool for successfully acquiring new knowledge in the field of life sciences and physics. Accordingly, different particle tracking methods for various scenarios have been developed. In this article, we present a particle tracking application implemented in Python for, in particular, spherical magnetic particles, including superparamagnetic beads and Janus particles. In the following, we distinguish between two sub-steps in particle tracking, namely the localization of particles in single images and the linking of the extracted particle positions of the subsequent frames into trajectories. We provide an intensity-based localization technique to detect particles and two linking algorithms, which apply either frame-by-frame linking or linear assignment problem solving. Beyond that, we offer helpful tools to preprocess images automatically as well as estimate parameters required for the localization algorithm by utilizing machine learning. As an extra, we have implemented a technique to estimate the current spatial orientation of Janus particles within the x-y-plane. Our framework is readily extendable and easy-to-use as we offer a graphical user interface and a command-line tool. Various output options, such as data frames and videos, ensure further analysis that can be automated. Program summary Program Title: AdaPT CPC Library link to program files: https://doi.org/10.17632/xxpnsbv3cs.1 Developer's repository link: https://git.ies.uni-kassel.de/adapt/adapt Licensing provisions: MPL-2.0 Programming language: Python 3.6 Supplementary material: We provide supplementary material to increase the traceability of the provided example. It consists of an exemplary input video, the corresponding annotated video with tracked particles, a data frame including the tracking information, and a plot displaying the trajectories. Nature of problem: Particle tracking in videos is an important tool for acquiring new knowledge in diverse fields. Several particle tracking methods have been developed for these diverse applications. The presented particle tracking software has been developed for the motion analysis of spherical or close to spherical magnetic particles. Up until now, no easily extensible automated particle tracking software for close to spherical microparticles and their current positioning status is available. Solution method: AdaPT is an extensible, easy-to-use microparticle tracking application developed explicitly for lab on chip applications but easily extensible to other applications and further functionalities. Currently implemented linking algorithms are a frame-by-frame linking approach as well as an approach solving linear assignment problems. In addition to many assistance possibilities for the user in the form of estimates of parameter values through machine learning, we offer the particular option to determine the orientation and rotation of spherical polymer particles with hemispherical metallic caps (Janus particles). The application can be used via console and graphical user interface. Additional comments including restrictions and unusual features: This software requires video data with spherical or close to spherical magnetic particles. It was tested on videos containing spherical superparamagnetic and magnetic Janus particles. Only mobile particles are detected; immobile particles are ignored by the software, reducing the amount of output data considerably.
As a unique feature, the spatial orientation within the x-y-plane of Janus particles can be determined. The application has been tested on a variety of two-dimensional particle motion patterns. The latest version of AdaPT can be found here: https://git.ies.uni- kassel.de/adapt/adapt. (c) 2021 Elsevier B.V. All rights reserved.
C1 [Dingel, Kristina; Sick, Bernhard] Univ Kassel, Dept Elect Engn & Comp Sci, Intelligent Embedded Syst, Wilhelmshoher Allee 73, D-34121 Kassel, Germany.
[Huhnstock, Rico; Knie, Andre; Ehresmann, Arno] Univ Kassel, Inst Phys, Heinrich Plett Str 40, D-34132 Kassel, Germany.
[Huhnstock, Rico; Knie, Andre; Ehresmann, Arno] Univ Kassel, Ctr Interdisciplinary Nanostruct Sci & Technol, Heinrich Plett Str 40, D-34132 Kassel, Germany.
[Dingel, Kristina; Huhnstock, Rico; Knie, Andre; Ehresmann, Arno; Sick, Bernhard] Berlin HZB, Joint Lab Helmholtzzentrum Mat & Energie, Artificial Intelligence Methods Expt Design AIM E, Hahn Meitner Pl 1, D-14109 Berlin, Germany.
[Dingel, Kristina; Huhnstock, Rico; Knie, Andre; Ehresmann, Arno; Sick, Bernhard] Univ Kassel, Hahn Meitner Pl 1, D-14109 Berlin, Germany.
RP Dingel, K (corresponding author), Univ Kassel, Dept Elect Engn & Comp Sci, Intelligent Embedded Syst, Wilhelmshoher Allee 73, D-34121 Kassel, Germany.; Dingel, K (corresponding author), Berlin HZB, Joint Lab Helmholtzzentrum Mat & Energie, Artificial Intelligence Methods Expt Design AIM E, Hahn Meitner Pl 1, D-14109 Berlin, Germany.; Dingel, K (corresponding author), Univ Kassel, Hahn Meitner Pl 1, D-14109 Berlin, Germany.
EM kristina.dingel@uni-kassel.de
OI Huhnstock, Rico/0000-0002-3326-8084
FU Hesse State initiative LOEWE 3, Germany (HAProject) [576/1758];
Helmoltzzentrum fur Materialien und Energie, Berlin; University of
Kassel
FX Parts of this research were supported by the Hesse State initiative
LOEWE 3, Germany (HAProject No. 576/1758) . We gratefully acknowledge
the assistance and support of the Joint Laboratory Artificial
Intelligence Methods for Experiment Design (AIM-ED) between
Helmoltzzentrum fur Materialien und Energie, Berlin and the University
of Kassel.
CR Abdulkarim M, 2015, EUR J PHARM BIOPHARM, V97, P230, DOI 10.1016/j.ejpb.2015.01.023
Allan D.B, 2018, TRACKPY TRACKPY V041
Bradski G., 2008, LEARNING OPENCV
Breiman L, 2001, MACH LEARN, V45, P5, DOI 10.1023/A:1010933404324
Crocker JC, 1996, J COLLOID INTERF SCI, V179, P298, DOI 10.1006/jcis.1996.0217
Ehresmann A, 2004, J MAGN MAGN MATER, V280, P369, DOI 10.1016/j.jmmm.2004.03.048
Ehresmann A, 2015, SENSORS-BASEL, V15, P28854, DOI 10.3390/s151128854
Gaul A, 2016, J APPL PHYS, V120, DOI 10.1063/1.4958847
Gay G, 2013, LAP TRACKER GITHUB R
Holzinger D, 2015, ACS NANO, V9, P7323, DOI 10.1021/acsnano.5b02283
HORN BKP, 1977, ARTIF INTELL, V8, P201, DOI 10.1016/0004-3702(77)90020-0
Jaqaman K, 2008, NAT METHODS, V5, P695, DOI 10.1038/nmeth.1237
Kelley DH, 2011, AM J PHYS, V79, P267, DOI 10.1119/1.3536647
Kompenhans J., 1999, PARTICLE IMAGE VELOC
Mougin A, 2001, J APPL PHYS, V89, P6606, DOI 10.1063/1.1354578
Newby JM, 2018, P NATL ACAD SCI USA, V115, P9026, DOI 10.1073/pnas.1804420115
Ouellette NT, 2006, EXP FLUIDS, V40, P301, DOI 10.1007/s00348-005-0068-7
REID DB, 1979, IEEE T AUTOMAT CONTR, V24, P843, DOI 10.1109/TAC.1979.1102177
Savin T, 2005, BIOPHYS J, V88, P623, DOI [10.1529/biophysj.104.042457, 10.1529/biophysj.104.04245]
Shen H, 2017, CHEM REV, V117, P7331, DOI 10.1021/acs.chemrev.6b00815
Xiao X, 2016, MED IMAGE ANAL, V32, P157, DOI 10.1016/j.media.2016.03.007
NR 21
TC 0
Z9 0
U1 1
U2 1
PU ELSEVIER
PI AMSTERDAM
PA RADARWEG 29, 1043 NX AMSTERDAM, NETHERLANDS
SN 0010-4655
EI 1879-2944
J9 COMPUT PHYS COMMUN
JI Comput. Phys. Commun.
PD MAY
PY 2021
VL 262
AR 107859
DI 10.1016/j.cpc.2021.107859
PG 11
WC Computer Science, Interdisciplinary Applications; Physics, Mathematical
SC Computer Science; Physics
GA QT4QZ
UT WOS:000626574900002
DA 2021-04-21
ER
PT J
AU Santra, R
Obermeyer, M
AF Santra, Robin
Obermeyer, Michael
TI A first encounter with the Hartree-Fock self-consistent-field method
SO AMERICAN JOURNAL OF PHYSICS
LA English
DT Article
AB This paper is intended to serve as a bridge between introductory textbooks on quantum mechanics, which typically do not cover the Hartree-Fock self-consistent-field method, and more advanced ones which treat this important computational method for fermionic many-body systems in an abstract and formal way. We derive the Hartree-Fock equation for the 1s orbital of a realistic two-electron atom. By employing a two-dimensional basis-set representation, we avoid the use of variational calculus and are able to visualize key aspects of the method. We explain the basic self-consistent-field algorithm and provide a python script to illustrate how the algorithm works in practice. Utilizing perturbation theory, we perform an analysis of the convergence behavior of the self-consistent-field algorithm, thereby facilitating a deeper understanding of the numerical examples presented. We expect that this work will be useful for teaching computational techniques to physics students.
C1 [Santra, Robin] Deutsch Elektronen Synchrotron DESY, Ctr Free Electron Laser Sci, Notkestr 85, D-22607 Hamburg, Germany.
Univ Hamburg, Dept Phys, Jungiusstr 9, D-20355 Hamburg, Germany.
RP Santra, R (corresponding author), Deutsch Elektronen Synchrotron DESY, Ctr Free Electron Laser Sci, Notkestr 85, D-22607 Hamburg, Germany.
CR ALMLOF J, 1983, J CHEM PHYS, V79, P2284, DOI 10.1063/1.446079
Baldo M, 2012, REP PROG PHYS, V75, DOI 10.1088/0034-4885/75/2/026301
Behringer E, 2017, AM J PHYS, V85, P325, DOI 10.1119/1.4981900
Bender M, 2003, REV MOD PHYS, V75, P121, DOI 10.1103/RevModPhys.75.121
Bethe H.A., 2008, QUANTUM MECH ONEAND
BLINDER SM, 1965, AM J PHYS, V33, P431, DOI 10.1119/1.1971665
Boas M.L., 1983, MATH METHODS PHYS SC
Booth GH, 2013, NATURE, V493, P365, DOI 10.1038/nature11770
BUNGE CF, 1992, PHYS REV A, V46, P3691, DOI 10.1103/PhysRevA.46.3691
Chonacky N, 2008, AM J PHYS, V76, P327, DOI 10.1119/1.2837811
Condon E.U., 1999, THEORY ATOMIC SPECTR
Devaney, 1992, 1 COURSE CHAOTIC DYN
FACELLI JC, 1982, J CHEM PHYS, V77, P1076, DOI 10.1063/1.443922
Friedrich H., 1998, THEORETICAL ATOMIC P
GAZQUEZ JL, 1977, J CHEM PHYS, V67, P1887, DOI 10.1063/1.435119
Golub G.H., 1992, SCI COMPUTING DIFFER
Golub GH., 1996, MATRIX COMPUTATIONS
Gottfried K., 2003, QUANTUM MECH FUNDAME
Haken H., 2004, MOL PHYS ELEMENTS QU
HARRISS DK, 1980, J CHEM EDUC, V57, P491, DOI 10.1021/ed057p491
Hogaasen H, 2010, AM J PHYS, V78, P86, DOI 10.1119/1.3236392
Johnson W.R., 2007, ATOMIC STRUCTURE THE
Kandula DZ, 2010, PHYS REV LETT, V105, DOI 10.1103/PhysRevLett.105.063001
Khalili K, 2019, STRUCT DYN-US, V6, DOI 10.1063/1.5097653
Koopmans T., 1933, Physica, V1, P104
Krumnow C, 2016, PHYS REV LETT, V117, DOI 10.1103/PhysRevLett.117.210402
Kuleff AI, 2016, PHYS REV LETT, V117, DOI 10.1103/PhysRevLett.117.093002
LATHAM WP, 1973, AM J PHYS, V41, P1258, DOI 10.1119/1.1987540
Loh ZH, 2020, SCIENCE, V367, P179, DOI 10.1126/science.aaz4740
Mazziotti DA, 2016, PHYS REV LETT, V117, DOI 10.1103/PhysRevLett.117.153001
Messina M, 1999, J CHEM EDUC, V76, P1439, DOI 10.1021/ed076p1439
Michel N, 2008, J MATH PHYS, V49, DOI 10.1063/1.2830976
Morris TD, 2018, PHYS REV LETT, V120, DOI 10.1103/PhysRevLett.120.152503
MOSHINSKY M, 1968, AM J PHYS, V36, P52, DOI 10.1119/1.1974410
NOGAMI Y, 1976, AM J PHYS, V44, P886, DOI 10.1119/1.10291
RAMAKER DE, 1975, PHYS REV LETT, V34, P812, DOI 10.1103/PhysRevLett.34.812
ROOTHAAN CCJ, 1960, REV MOD PHYS, V32, P186, DOI 10.1103/RevModPhys.32.186
Sakurai J., 1994, MODERN QUANTUM MECH
Schwabl F., 2007, QUANTUM MECH
Shankar R., 2008, PRINCIPLES QUANTUM M
STANTON RE, 1981, J CHEM PHYS, V75, P3426, DOI 10.1063/1.442451
STANTON RE, 1968, J CHEM PHYS, V48, P257, DOI 10.1063/1.1667913
STANTON RE, 1981, J CHEM PHYS, V75, P5416, DOI 10.1063/1.441942
Strogatz S. H., 2018, NONLINEAR DYNAMICS C
Theel F, 2017, CHAOS, V27, DOI 10.1063/1.5001681
VAUTHERIN D, 1972, PHYS REV C, V5, P626, DOI 10.1103/PhysRevC.5.626
Veeraraghavan S, 2015, PHYS REV A, V92, DOI 10.1103/PhysRevA.92.022512
Veeraraghavan S, 2014, PHYS REV A, V89, DOI 10.1103/PhysRevA.89.010502
Virtanen P, 2020, NAT METHODS, V17, P261, DOI 10.1038/s41592-019-0686-2
NR 49
TC 0
Z9 0
U1 1
U2 1
PU AMER INST PHYSICS
PI MELVILLE
PA 1305 WALT WHITMAN RD, STE 300, MELVILLE, NY 11747-4501 USA
SN 0002-9505
EI 1943-2909
J9 AM J PHYS
JI Am. J. Phys.
PD APR
PY 2021
VL 89
IS 4
BP 426
EP 436
DI 10.1119/10.0002644
PG 11
WC Education, Scientific Disciplines; Physics, Multidisciplinary
SC Education & Educational Research; Physics
GA RA2KI
UT WOS:000631244300013
DA 2021-04-21
ER
PT J
AU May, S
AF May, Simon
TI minimal-lagrangians: Generating and studying dark matter model
Lagrangians with just the particle content
SO COMPUTER PHYSICS COMMUNICATIONS
LA English
DT Article
DE Quantum field theory; Lagrangians; Model building; Beyond the Standard
Model; Dark matter; Neutrino masses; SARAH
AB minimal-lagrangians is a Python program which allows one to specify the field content of an extension of the Standard Model of particle physics and, using this information, to generate the most general renormalizable Lagrangian that describes such a model. As the program was originally created for the study of minimal dark matter models with radiative neutrino masses, it can handle additional scalar or Weyl fermion fields which are SU(3)(C) singlets, SU(2)(L) singlets, doublets or triplets, and can have arbitrary U(1)(Y) hypercharge. It is also possible to enforce an arbitrary number of global U(1) symmetries (with Z(2) as a special case) so that the new fields can additionally carry such global charges. In addition to human-readable and (LTEX)-T-A output, the program can generate SARAH model files containing the computed Lagrangian, as well as information about the fields after electroweak symmetry breaking (EWSB), such as vacuum expectation values (VEVs) and mixing matrices. This capability allows further detailed investigation of the model in question, with minimal-lagrangians as the first component in a tool chain for rapid phenomenological studies of "minimal" dark matter models requiring little effort and no unnecessary input from the user.
Program summary
Program title: minimal-lagrangians
CPC Library link to program files: https://doi.org/10.17632/4mm2zk5r84.1
Licensing provisions: GPLv3
Programming language: Python
Nature of problem: Given a quantum field theory's gauge group, it is sufficient to specify the particle (field) content in order to identify the full renormalizable theory, up to the parameters in its Lagrangian. However, the process of determining the Lagrangian manually is not only tedious and error-prone, but also involves additional complications such as redundant terms or the question of whether the theory is anomaly-free.
Solution method: minimal-lagrangians generates the complete renormalizable Lagrangian for a given model with the Standard Model gauge group SU(3)(C) x SU(2)(L) x U(1)(Y), including interaction terms. Redundant terms in the Lagrangian are eliminated in order to avoid duplicated parameters. The particle content is also checked for gauge anomalies, including the Witten SU(2) anomaly [1]. The model will automatically be modified to make fermions vector-like if necessary. The generated Lagrangian can be output in SARAH [2,3] model file format so that the model is immediately available for detailed phenomenological study using the capabilities of SARAH.
Additional comments including restrictions and unusual features: Instead of manually determining the details of a model, the only input to the program minimal-lagrangians is the particle content. Using the output to SARAH, minimal-lagrangians thus forms the first step in a tool chain which enables the complete implementation and study of a new model with minimal effort and no "boilerplate'' user input. The focus is on "minimal" dark matter models, i.e. those with the Standard Model gauge group (no additional gauge fields), where the new fields are color singlets and at most triplets under SU(2)(L). (C) 2020 Elsevier B.V. All rights reserved.
C1 [May, Simon] Max Planck Inst Astrophys, Karl Schwarzschild Str 1, D-85741 Garching, Germany.
RP May, S (corresponding author), Max Planck Inst Astrophys, Karl Schwarzschild Str 1, D-85741 Garching, Germany.
EM simon.may@mpa-garching.mpg.de
OI May, Simon/0000-0002-2781-6304
CR Allanach BC, 2009, COMPUT PHYS COMMUN, V180, P8, DOI 10.1016/j.cpc.2008.08.004
Belanger G, 2018, COMPUT PHYS COMMUN, V231, P173, DOI 10.1016/j.cpc.2018.04.027
Belanger G, 2002, COMPUT PHYS COMMUN, V149, P103, DOI 10.1016/S0010-4655(02)00596-9
Cheung C, 2014, J COSMOL ASTROPART P, DOI 10.1088/1475-7516/2014/02/011
Dreiner HK, 2010, PHYS REP, V494, P1, DOI 10.1016/j.physrep.2010.05.002
Esch S, 2018, J HIGH ENERGY PHYS, DOI 10.1007/JHEP10(2018)055
Farzan Y, 2009, PHYS REV D, V80, DOI 10.1103/PhysRevD.80.073009
Fiaschi J, 2019, J HIGH ENERGY PHYS, DOI 10.1007/JHEP05(2019)015
Kanemura S, 2012, PHYS REV D, V85, DOI 10.1103/PhysRevD.85.115009
Ma E, 2006, PHYS REV D, V73, DOI 10.1103/PhysRevD.73.077301
May S., 2018, ARXIV200304157
Porod W, 2003, COMPUT PHYS COMMUN, V153, P275, DOI 10.1016/S0010-4655(03)00222-4
Porod W, 2012, COMPUT PHYS COMMUN, V183, P2458, DOI 10.1016/j.cpc.2012.05.021
Restrepo D, 2013, J HIGH ENERGY PHYS, DOI 10.1007/JHEP11(2013)011
Skands P, 2004, J HIGH ENERGY PHYS, DOI 10.1088/1126-6708/2004/07/036
Staub F., 2015, ADV HIGH ENERGY PHYS, V2015, DOI [10.1155/2015/840780, DOI 10.1155/2015/840780]
Staub F, 2015, ADV HIGH ENERGY PHYS, V2015, DOI 10.1155/2015/840780
Staub F, 2014, COMPUT PHYS COMMUN, V185, P1773, DOI 10.1016/j.cpc.2014.02.018
The Unicode Standard, 2019, 10646 ISOIEC
WITTEN E, 1982, PHYS LETT B, V117, P324, DOI 10.1016/0370-2693(82)90728-6
NR 20
TC 0
Z9 0
U1 1
U2 1
PU ELSEVIER
PI AMSTERDAM
PA RADARWEG 29, 1043 NX AMSTERDAM, NETHERLANDS
SN 0010-4655
EI 1879-2944
J9 COMPUT PHYS COMMUN
JI Comput. Phys. Commun.
PD APR
PY 2021
VL 261
AR 107773
DI 10.1016/j.cpc.2020.107773
PG 14
WC Computer Science, Interdisciplinary Applications; Physics, Mathematical
SC Computer Science; Physics
GA QH4ZO
UT WOS:000618285100004
OA Green Accepted
DA 2021-04-21
ER
PT J
AU Robertson, EJ
Sibalic, N
Potvliege, RM
Jones, MPA
AF Robertson, E. J.
Sibalic, N.
Potvliege, R. M.
Jones, M. P. A.
TI ARC 3.0: An expanded Python toolbox for atomic physics calculations
SO COMPUTER PHYSICS COMMUNICATIONS
LA English
DT Article
DE Alkaline earth atoms; Divalent atoms; Alkali atoms; Rydberg states;
Atom-surface van der Waals interaction; Quantum technologies;
Neutral-atom quantum computing; Atom-based sensors
AB ARC 3.0 is a modular, object-oriented Python library combining data and algorithms to enable the calculation of a range of properties of alkali and divalent atoms. Building on the initial version of the ARC library (Sibalic et al., 2017), which focused on Rydberg states of alkali atoms, this major upgrade introduces support for divalent atoms. It also adds new methods for working with atom-surface interactions, for modelling ultracold atoms in optical lattices and for calculating valence electron wave functions and dynamic polarisabilities. Such calculations have applications in a variety of fields, e.g., in the quantum simulation of many-body physics, in atom-based sensing of DC and AC fields (including in microwave and THz metrology) and in the development of quantum gate protocols. ARC 3.0 comes with an extensive documentation including numerous examples. Its modular structure facilitates its application to a wide range of problems in atom-based quantum technologies. (c) 2021 The Authors. Published by Elsevier B.V.
C1 [Robertson, E. J.; Potvliege, R. M.; Jones, M. P. A.] Univ Durham, Joint Quantum Ctr JQC Durham Newcastle, Dept Phys, South Rd, Durham DH1 3LE, England.
[Sibalic, N.] Univ Paris Saclay, Inst Opt, Lab Charles Fabry, CNRS,Grad Sch, F-91127 Palaiseau, France.
RP Sibalic, N (corresponding author), Univ Paris Saclay, Inst Opt, Lab Charles Fabry, CNRS,Grad Sch, F-91127 Palaiseau, France.
EM nikolasibalic@physics.org
RI Sibalic, Nikola/B-7622-2016
OI Sibalic, Nikola/0000-0001-9198-1630
FU H2020 Marie Sklodowska-Curie Actions (COQUDDE, H2020-MSCA-IF-2017)
[786702]; EPSRCUK Research & Innovation (UKRI)Engineering & Physical
Sciences Research Council (EPSRC) [EP/R002061/1, EP/R035482/1]; project
EMPIR-USOQS (EMPIR project - European Union's Horizon 2020 research and
innovation programme); project EMPIR-USOQS (EMPIR project - EMPIR)
FX We thank Tsz-Chun Tsui and Trey Porto for tabulating the ytterbium data.
We also thank Hei Yin Andrea Kam for the use of her code to check the
calculations of strontium dipole matrix elements, Paul Huillery for help
with Stark map calculations, and Ifan Hughes for suggesting the
bootstrap method. N. S. is supported by the H2020 Marie Sklodowska-Curie
Actions (COQUDDE, H2020-MSCA-IF-2017 Grant Agreement No. 786702). The
project was supported by the EPSRC Platform grant EP/R002061/1 and
Standard grant EP/R035482/1. We also acknowledge funding from the
project EMPIR-USOQS (EMPIR projects are co-funded by the European
Union's Horizon 2020 research and innovation programme and the EMPIR
Participating States).
CR Adams CS, 2020, J PHYS B-AT MOL OPT, V53, DOI 10.1088/1361-6455/ab52ef
[Anonymous], 2020, J PHYS B AT MOL OPT, V53
Archimi M, 2019, PHYS REV A, V100, DOI 10.1103/PhysRevA.100.030501
ARMSTRONG JA, 1979, J OPT SOC AM, V69, P211, DOI 10.1364/JOSA.69.000211
AYMAR M, 1980, J PHYS B-AT MOL OPT, V13, P1089, DOI 10.1088/0022-3700/13/6/016
Baig MA, 1998, OPT COMMUN, V156, P279, DOI 10.1016/S0030-4018(98)00467-2
Barredo D, 2020, PHYS REV LETT, V124, DOI 10.1103/PhysRevLett.124.023201
BEIGANG R, 1982, PHYS SCRIPTA, V26, P183, DOI 10.1088/0031-8949/26/3/007
BEIGANG R, 1982, OPT COMMUN, V42, P19, DOI 10.1016/0030-4018(82)90082-7
BEIGANG R, 1983, PHYS SCRIPTA, V27, P172, DOI 10.1088/0031-8949/27/3/005
Bernien H, 2017, NATURE, V551, P579, DOI 10.1038/nature24622
Bowden W, 2017, JOINT CONF IEEE INT, P154, DOI 10.1109/FCS.2017.8088831
Budker D, 2007, NAT PHYS, V3, P227, DOI 10.1038/nphys566
Busche H, 2017, NAT PHYS, V13, P655, DOI [10.1038/NPHYS4058, 10.1038/nphys4058]
Couturier L, 2019, PHYS REV A, V99, DOI 10.1103/PhysRevA.99.022503
Cox KC, 2018, PHYS REV LETT, V121, DOI 10.1103/PhysRevLett.121.110502
DeSalvo BJ, 2015, PHYS REV A, V92, DOI 10.1103/PhysRevA.92.031403
Downes LA, 2020, PHYS REV X, V10, DOI 10.1103/PhysRevX.10.011027
Dudin YO, 2012, SCIENCE, V336, P887, DOI 10.1126/science.1217901
Dunning FB, 2016, J PHYS B-AT MOL OPT, V49, DOI 10.1088/0953-4075/49/11/112003
Dutta SK, 2000, PHYS REV LETT, V85, P5551, DOI 10.1103/PhysRevLett.85.5551
ESHERICK P, 1977, PHYS REV A, V15, P1920, DOI 10.1103/PhysRevA.15.1920
Esherick P, 1977, OPT LETT, V1, P19, DOI 10.1364/OL.1.000019
Kien FL, 2013, EUR PHYS J D, V67, DOI 10.1140/epjd/e2013-30729-x
FICHET M, 1995, PHYS REV A, V51, P1553, DOI 10.1103/PhysRevA.51.1553
Fritzsche S, 2019, COMPUT PHYS COMMUN, V240, P1, DOI 10.1016/j.cpc.2019.01.012
Gaj A, 2014, NAT COMMUN, V5, DOI 10.1038/ncomms5546
Gallagher T.F, 1994, RYDBERG ATOMS, DOI [10.1017/CBO9780511524530, DOI 10.1017/CBO9780511524530]
GENTILE TR, 1990, PHYS REV A, V42, P440, DOI 10.1103/PhysRevA.42.440
Gross C, 2017, SCIENCE, V357, P995, DOI 10.1126/science.aal3837
Hostetter J, 2015, PHYS REV A, V91, DOI 10.1103/PhysRevA.91.012507
Hughes I. G., 2010, MEASUREMENTS THEIR U
Hunter JD, 2007, COMPUT SCI ENG, V9, P90, DOI 10.1109/MCSE.2007.55
Jones MPA, 2017, J PHYS B-AT MOL OPT, V50, DOI 10.1088/1361-6455/aa5d06
Kozlov MG, 2015, COMPUT PHYS COMMUN, V195, P199, DOI 10.1016/j.cpc.2015.05.007
Labuhn H, 2016, NATURE, V534, P667, DOI 10.1038/nature18274
Lehec H, 2018, PHYS REV A, V98, DOI 10.1103/PhysRevA.98.062506
Madjarov IS, 2020, NAT PHYS, V16, P857, DOI 10.1038/s41567-020-0903-z
MAEDA H, 1992, PHYS REV A, V45, P1732, DOI 10.1103/PhysRevA.45.1732
Meurer A, 2017, PEERJ COMPUT SCI, DOI 10.7717/peerj-cs.103
Meyer DH, 2020, J PHYS B-AT MOL OPT, V53, DOI 10.1088/1361-6455/ab6051
Millen J, 2011, J PHYS B-AT MOL OPT, V44, DOI 10.1088/0953-4075/44/18/184001
Mitroy J, 2010, J PHYS B-AT MOL OPT, V43, DOI 10.1088/0953-4075/43/20/202001
Miyabe M, 2006, J PHYS SOC JPN, V75, DOI 10.1143/JPSJ.75.034302
Newville M, 2019, LMFIT LMFIT PY 1, DOI [10.5281/zenodo.3588521, DOI 10.5281/ZENODO.3588521]
Oliphant TE, 2007, COMPUT SCI ENG, V9, P10, DOI 10.1109/MCSE.2007.58
OUMAROU B, 1988, PHYS REV A, V37, P1885, DOI 10.1103/PhysRevA.37.1885
Peyronel T, 2012, NATURE, V488, P57, DOI 10.1038/nature11361
Peyrot T, 2019, PHYS REV A, V100, DOI 10.1103/PhysRevA.100.022503
Pritchard JD, 2010, PHYS REV LETT, V105, DOI 10.1103/PhysRevLett.105.193603
Robicheaux F, 2019, J PHYS B-AT MOL OPT, V52, DOI 10.1088/1361-6455/ab4c22
RUBBMARK JR, 1978, PHYS SCRIPTA, V18, P196, DOI 10.1088/0031-8949/18/4/002
Saffman M, 2016, J PHYS B-AT MOL OPT, V49, DOI 10.1088/0953-4075/49/20/202001
Sansonetti JE, 2010, J PHYS CHEM REF DATA, V39, DOI 10.1063/1.3449176
Sedlacek JA, 2012, NAT PHYS, V8, P819, DOI [10.1038/NPHYS2423, 10.1038/nphys2423]
Shah V, 2007, NAT PHOTONICS, V1, P649, DOI 10.1038/nphoton.2007.201
Sibalic N, 2017, COMPUT PHYS COMMUN, V220, P319, DOI 10.1016/j.cpc.2017.06.015
Sibalic N., 2018, RYDBERG PHYS, P2399, DOI [10.1088/978-0-7503-1635-4, DOI 10.1088/978-0-7503-1635-4]
Sibalic N., ARC DOCUMENTATION
Sibalic N., ARC GITHUB PAGE
Vaillant CL, 2015, PHYS REV A, V92, DOI 10.1103/PhysRevA.92.042705
Vaillant CL, 2014, J PHYS B-AT MOL OPT, V47, DOI 10.1088/0953-4075/47/15/155001
Vaillant CL, 2012, J PHYS B-AT MOL OPT, V45, DOI 10.1088/0953-4075/45/13/135004
Vaillant C.L., 2014, THESIS U DURHAM
Wade CG, 2017, NAT PHOTONICS, V11, P40, DOI [10.1038/NPHOTON.2016.214, 10.1038/nphoton.2016.214]
Weber S, 2017, J PHYS B-AT MOL OPT, V50, DOI 10.1088/1361-6455/aa743a
Ye S, 2013, PHYS REV A, V88, DOI 10.1103/PhysRevA.88.043430
Younge KC, 2010, PHYS REV LETT, V104, DOI 10.1103/PhysRevLett.104.173001
Zelener BB, 2019, JETP LETT+, V110, P761, DOI 10.1134/S0021364019240093
Zhi MC, 2001, CHINESE PHYS, V10, P929, DOI 10.1088/1009-1963/10/10/309
NR 70
TC 0
Z9 0
U1 0
U2 0
PU ELSEVIER
PI AMSTERDAM
PA RADARWEG 29, 1043 NX AMSTERDAM, NETHERLANDS
SN 0010-4655
EI 1879-2944
J9 COMPUT PHYS COMMUN
JI Comput. Phys. Commun.
PD APR
PY 2021
VL 261
AR 107814
DI 10.1016/j.cpc.2020.107814
PG 15
WC Computer Science, Interdisciplinary Applications; Physics, Mathematical
SC Computer Science; Physics
GA QH4ZO
UT WOS:000618285100014
OA Green Accepted, Other Gold
DA 2021-04-21
ER
PT J
AU Sartore, L
Schienbein, I
AF Sartore, Lohan
Schienbein, Ingo
TI PyR@TE 3
SO COMPUTER PHYSICS COMMUNICATIONS
LA English
DT Article
DE Renormalization group equations; Quantum field theory; Running coupling
constants; Model building; Physics beyond the Standard Model
AB We present a new version of PyR@TE, a Python tool for the computation of renormalization group equations for general, non-supersymmetric gauge theories. Its new core relies on a recent paper by Poole & Thomsen (2019) to compute the beta-functions. In this framework, gauge kinetic mixing is naturally implemented, and the Weyl consistency relations between gauge, quartic and Yukawa couplings are automatically satisfied. One of the main new features is the possibility for the user to compute the gauge coupling beta-functions up to the three-loop order. Large parts of the PyR@TE code have been rewritten and improved, including the group theory module PyLie. As a result, the overall performance in terms of computation speed was drastically improved and the model file is more flexible and user-friendly.
Program summary
Program Title: PyR@TE 3
CPC Library link to program files: https://doi.org/10.17632/8h454kdd5n.2
Licensing provisions: Apache 2.0
Programming language: Python 3
Journal reference of previous version: PyR@TE [1], PyR@TE 2 [2]
Does the new version supersede the previous version?: Yes.
Reasons for new version: The software was essentially rewritten and new functionalities were added. The performance in terms of computation speed was improved by a factor of 100 to 10000 compared to the previous version. The code now relies on Python 3 instead of the deprecated Python 2.
Summary of revisions: The core of the software was rewritten, based on a new formalism. One of the major new features is the possibility of computing the 3-loop RGEs for gauge couplings. The structure and the syntax of the model file were enhanced. The output of the software was improved.
Nature of problem : Computing the renormalization group equations for any renormalizable, 4dimensional, non-supersymmetric quantum field theory.
Solution method: Group theory, tensor algebra. (C) 2020 Elsevier B.V. All rights reserved.
C1 [Sartore, Lohan; Schienbein, Ingo] Univ Grenoble Alpes, CNRS, IN2P3, Lab Phys Subatom & Cosmol, 53 Ave Martyrs, F-38026 Grenoble, France.
RP Sartore, L (corresponding author), Univ Grenoble Alpes, CNRS, IN2P3, Lab Phys Subatom & Cosmol, 53 Ave Martyrs, F-38026 Grenoble, France.
EM sartore@lpsc.in2p3.fr; schien@lpsc.in2p3.fr
OI Sartore, Lohan/0000-0003-3278-5423
FU IN2P3 project "Theorie - BSMGA'', France
FX We are indebted to Florian Lyonnet from whom we have taken over the
development of PyR@TE.This work would not have been possible without the
foundations laid by him in the previous versions of the code. We are
also grateful to Colin Poole and Anders Eller Thomsen for many useful
discussions. Finally, we would like to thank Jan Kwapisz, Aaron Held and
Fabien Besnard for their helpful contributions in testing and validating
PyR@TE3. We also wish to thank Tom Steudtner for his help with the
validation of the SUSY toy model from [13]. This work was supported in
part by the IN2P3 project "Theorie - BSMGA'', France.
CR Alloul A, 2014, COMPUT PHYS COMMUN, V185, P2250, DOI 10.1016/j.cpc.2014.04.012
Antipin O, 2013, PHYS REV D, V87, DOI 10.1103/PhysRevD.87.125017
Jack I, 2015, J HIGH ENERGY PHYS, DOI 10.1007/JHEP01(2015)138
JACK I, 1983, J PHYS A-MATH GEN, V16, P1101, DOI 10.1088/0305-4470/16/5/026
JACK I, 1990, NUCL PHYS B, V343, P647, DOI 10.1016/0550-3213(90)90584-Z
Litim DF, 2016, J HIGH ENERGY PHYS, DOI 10.1007/JHEP01(2016)081
Lyonnet F, 2017, COMPUT PHYS COMMUN, V213, P181, DOI 10.1016/j.cpc.2016.12.003
Lyonnet F, 2014, COMPUT PHYS COMMUN, V185, P1130, DOI 10.1016/j.cpc.2013.12.002
OSBORN H, 1991, NUCL PHYS B, V363, P486, DOI 10.1016/0550-3213(91)80030-P
OSBORN H, 1989, PHYS LETT B, V222, P97, DOI 10.1016/0370-2693(89)90729-6
Pickering AGM, 2001, PHYS LETT B, V510, P347, DOI 10.1016/S0370-2693(01)00624-4
Poole C, 2019, J HIGH ENERGY PHYS, DOI 10.1007/JHEP09(2019)055
Sartore L, 2020, PHYS REV D, V102, DOI 10.1103/PhysRevD.102.076002
Schienbein I, 2019, NUCL PHYS B, V939, P1, DOI 10.1016/j.nuclphysb.2018.12.001
Thomsen A.E., CURRENTLY DEV
NR 15
TC 1
Z9 1
U1 1
U2 1
PU ELSEVIER
PI AMSTERDAM
PA RADARWEG 29, 1043 NX AMSTERDAM, NETHERLANDS
SN 0010-4655
EI 1879-2944
J9 COMPUT PHYS COMMUN
JI Comput. Phys. Commun.
PD APR
PY 2021
VL 261
AR 107819
DI 10.1016/j.cpc.2020.107819
PG 14
WC Computer Science, Interdisciplinary Applications; Physics, Mathematical
SC Computer Science; Physics
GA QH4ZO
UT WOS:000618285100007
OA Green Accepted
DA 2021-04-21
ER
PT J
AU Singh, V
Herath, U
Wah, B
Liao, XY
Romero, AH
Park, H
AF Singh, Vijay
Herath, Uthpala
Wah, Benny
Liao, Xingyu
Romero, Aldo H.
Park, Hyowon
TI DMFTwDFT: An open-source code combining Dynamical Mean Field Theory with
various density functional theory packages
SO COMPUTER PHYSICS COMMUNICATIONS
LA English
DT Article
DE DFT; DMFT; Strongly correlated materials; Python; Condensed matter
physics; Many-body physics
AB Dynamical Mean Field Theory (DMFT) is a successful method to compute the electronic structure of strongly correlated materials, especially when it is combined with density functional theory (DFT). Here, we present an open-source computational package (and a library) combining DMFT with various DFT codes interfaced through the Wannier90 package. The correlated subspace is expanded as a linear combination of Wannier functions introduced in the DMFT approach as local orbitals. In particular, we provide a library mode for computing the DMFT density matrix. This library can be linked and then internally called from any DFT package, assuming that a set of localized orbitals can be generated in the correlated subspace. The existence of this library allows developers of other DFT codes to interface with our package and achieve the charge-self-consistency within DFT+DMFT loops. To test and check our implementation, we computed the density of states and the band structure of well-known solidstate correlated materials, namely LaNiO3, SrVO3, and NiO. The obtained results are compared to those obtained from other DFT+DMFT implementations.
Program summary
Program title: DMFTwDFT
CPC Library link to program files: https://doi.org/10.17632/y27fngtkdw.1
Licensing provisions: GNU General Public License 3
Programming language: Python2/3, C++, and FORTRAN
External routines: MPI, FFTW, BIAS, LAPACK, Numpy, Scipy, mpi4py, Glib, gsl, weave, PyProcar, and PyChemia
Subprograms used: Wannier90 (v3.0), Siesta (v4.1-b4), VASP (v5.4.4), Quantum Espresso (v6.5), CTQMC
Nature of problem: Need for a simple, efficient, higher-level, and open-source package to study strongly correlated materials interfacing to various DFT codes regardless of basis sets used in DFT.
Solution method: We present an open-source Python code which can be easily interfaced with Wannier90 and different DFT packages and perform a full charge-self-consistent DFT+DMFT calculation using a modern continuous-time quantum Monte Carlo (CTQMC) impurity solver. (C) 2020 Elsevier B.V. All rights reserved.
C1 [Singh, Vijay; Wah, Benny; Liao, Xingyu; Park, Hyowon] Univ Illinois, Dept Phys, Chicago, IL 60607 USA.
[Singh, Vijay; Herath, Uthpala; Romero, Aldo H.] West Virginia Univ, Dept Phys & Astron, Morgantown, WV 26506 USA.
RP Singh, V (corresponding author), Univ Illinois, Dept Phys, Chicago, IL 60607 USA.
EM vsingh83@uic.edu
RI romero, aldo/B-2344-2016
OI romero, aldo/0000-0001-5968-0571; Herath, Uthpala/0000-0002-4585-3002;
Singh, Vijay/0000-0002-4985-2445
FU NSF SI2-SSE Grant [1740112]; DMREF-NSFNational Science Foundation
(NSF)NSF - Directorate for Computer & Information Science & Engineering
(CISE) [1434897]; DOEUnited States Department of Energy (DOE)
[DE-SC0016176]; ACS-PRF grant [60617]; National Science
FoundationNational Science Foundation (NSF) [ACI-1053575, TG-PHY190035];
Texas Advances Computer Center (Stampede2 supercomputer); Texas Advances
Computer Center (Bridges supercomputer)
FX The authors thank Javier Junquera from Cantabria University for
insightful discussions and help with the interface with Siesta. This
work is supported by the NSF SI2-SSE Grant 1740112. Uthpala Herath and
Aldo H. Romero are also supported by DMREF-NSF 1434897 and DOE
DE-SC0016176 grants. Xingyu Liao is supported by ACS-PRF grant 60617.
This work used the XSEDE which is supported by National Science
Foundation grant number ACI-1053575 and allocation number TG-PHY190035.
The authors also acknowledge the support from the Texas Advances
Computer Center (with the Stampede2 and Bridges supercomputers). We
acknowledge the West Virginia University supercomputing clusters (Spruce
Knob and Thorny Flat) and the Advanced Cyberinfrastructure for Education
and Research (ACER) group at the University of Illinois at Chicago for
providing HPC resources which were used for the development of the
library.
CR Aichhorn M, 2016, COMPUT PHYS COMMUN, V204, P200, DOI 10.1016/j.cpc.2016.03.014
Amadon B, 2012, J PHYS-CONDENS MAT, V24, DOI 10.1088/0953-8984/24/7/075604
Amadon B, 2008, PHYS REV B, V77, DOI 10.1103/PhysRevB.77.205112
ANDERSEN OK, 1975, PHYS REV B, V12, P3060, DOI 10.1103/PhysRevB.12.3060
ANISIMOV VI, 1991, PHYS REV B, V44, P943, DOI 10.1103/PhysRevB.44.943
Anisimov VI, 1997, J PHYS-CONDENS MAT, V9, P767, DOI 10.1088/0953-8984/9/4/002
ARYASETIAWAN F, 1995, PHYS REV LETT, V74, P3221, DOI 10.1103/PhysRevLett.74.3221
Avella A., 2011, SPRINGER SERIES SOLI
BLOCHL PE, 1994, PHYS REV B, V50, P17953, DOI 10.1103/PhysRevB.50.17953
BRANDOW BH, 1977, ADV PHYS, V26, P651, DOI 10.1080/00018737700101443
Bredow T, 2000, PHYS REV B, V61, P5194, DOI 10.1103/PhysRevB.61.5194
Bulla R, 2008, REV MOD PHYS, V80, P395, DOI 10.1103/RevModPhys.80.395
Burke K, 1997, INT J QUANTUM CHEM, V61, P287, DOI 10.1002/(SICI)1097-461X(1997)61:2<287::AID-QUA11>3.0.CO;2-9
CEPERLEY DM, 1980, PHYS REV LETT, V45, P566, DOI 10.1103/PhysRevLett.45.566
CHAMBERL.BL, 1971, J SOLID STATE CHEM, V3, P243, DOI 10.1016/0022-4596(71)90035-1
Choi S, 2019, COMPUT PHYS COMMUN, V244, P277, DOI 10.1016/j.cpc.2019.07.003
Cococcioni M, 2005, PHYS REV B, V71, DOI 10.1103/PhysRevB.71.035105
Cox P., 2010, INT SERIES MONOGRAPH
Dang HT, 2014, PHYS REV B, V89, DOI 10.1103/PhysRevB.89.161113
Dobin AY, 2003, PHYS REV B, V68, DOI 10.1103/PhysRevB.68.113408
Dudarev SL, 1998, PHYS REV B, V57, P1505, DOI 10.1103/PhysRevB.57.1505
Eguchi R, 2009, PHYS REV B, V79, DOI 10.1103/PhysRevB.79.115122
Faleev SV, 2004, PHYS REV LETT, V93, DOI 10.1103/PhysRevLett.93.126406
Gaenko A, 2017, COMPUT PHYS COMMUN, V213, P235, DOI 10.1016/j.cpc.2016.12.009
Galitskii V., 1958, ZH EKSP TEOR FIZ+, V34
GARCIAMUNOZ JL, 1992, PHYS REV B, V46, P4414, DOI 10.1103/PhysRevB.46.4414
Georges A, 1996, REV MOD PHYS, V68, P13, DOI 10.1103/RevModPhys.68.13
Giannozzi P, 2009, J PHYS-CONDENS MAT, V21, DOI 10.1088/0953-8984/21/39/395502
Gonze X, 2016, COMPUT PHYS COMMUN, V205, P106, DOI 10.1016/j.cpc.2016.04.003
Gonze X, 2009, COMPUT PHYS COMMUN, V180, P2582, DOI 10.1016/j.cpc.2009.07.007
Gonze X, 2002, COMP MATER SCI, V25, P478, DOI 10.1016/S0927-0256(02)00325-7
Gou GY, 2011, PHYS REV B, V84, DOI 10.1103/PhysRevB.84.144101
Granas O, 2012, COMP MATER SCI, V55, P295, DOI 10.1016/j.commatsci.2011.11.032
Gull E, 2011, REV MOD PHYS, V83, P349, DOI 10.1103/RevModPhys.83.349
Hafner J, 2008, J COMPUT CHEM, V29, P2044, DOI 10.1002/jcc.21057
Haule K., 2016, PHYS REV B, V94, DOI 10.103/PhysRevB.94.195146
Haule K, 2007, PHYS REV B, V75, DOI 10.1103/PhysRevB.75.155113
Haule K, 2015, PHYS REV LETT, V115, DOI 10.1103/PhysRevLett.115.196403
Haule K, 2014, PHYS REV B, V90, DOI 10.1103/PhysRevB.90.075136
Haule K, 2010, PHYS REV B, V81, DOI 10.1103/PhysRevB.81.195107
Hepting M, 2020, NAT MATER, V19, P381, DOI 10.1038/s41563-019-0585-z
Herath U, 2020, COMPUT PHYS COMMUN, V251, DOI 10.1016/j.cpc.2019.107080
HOHENBERG P, 1964, PHYS REV B, V136, pB864, DOI 10.1103/PhysRev.136.B864
Horiba K, 2007, PHYS REV B, V76, DOI 10.1103/PhysRevB.76.155104
Huang L, 2015, COMPUT PHYS COMMUN, V195, P140, DOI 10.1016/j.cpc.2015.04.020
Imada M, 1998, REV MOD PHYS, V70, P1039, DOI 10.1103/RevModPhys.70.1039
JARRELL M, 1989, PHYS REV LETT, V63, P2504, DOI 10.1103/PhysRevLett.63.2504
JARRELL M, 1992, PHYS REV LETT, V69, P168, DOI 10.1103/PhysRevLett.69.168
Jauch W, 2004, PHYS REV B, V70, DOI 10.1103/PhysRevB.70.195121
Kang B., 2019, ARXIV PREPRINT ARXIV
KOHN W, 1960, PHYS REV, V118, P41, DOI 10.1103/PhysRev.118.41
Kotliar G, 2006, REV MOD PHYS, V78, P865, DOI 10.1103/RevModPhys.78.865
KRESSE G, 1993, PHYS REV B, V47, P558, DOI 10.1103/PhysRevB.47.558
Kresse G, 1996, COMP MATER SCI, V6, P15, DOI 10.1016/0927-0256(96)00008-0
Kuo CY, 2017, EUR PHYS J-SPEC TOP, V226, P2445, DOI 10.1140/epjst/e2017-70061-7
Lechermann F, 2006, PHYS REV B, V74, DOI 10.1103/PhysRevB.74.125120
Leonov I, 2014, PHYS REV LETT, V112, DOI 10.1103/PhysRevLett.112.146401
Li DF, 2019, NATURE, V572, P624, DOI 10.1038/s41586-019-1496-5
Li JL, 2005, PHYS REV B, V71, DOI 10.1103/PhysRevB.71.193102
Lu Y, 2017, EUR PHYS J-SPEC TOP, V226, P2549, DOI 10.1140/epjst/e2017-70042-4
LUTTINGER J, 1961, PHYS REV, V121, P942, DOI 10.1103/PhysRev.121.942
LUTTINGER JM, 1960, PHYS REV, V118, P1417, DOI 10.1103/PhysRev.118.1417
Maier T, 2005, REV MOD PHYS, V77, P1027, DOI 10.1103/RevModPhys.77.1027
Mandal S, 2019, PHYS REV B, V100, DOI 10.1103/PhysRevB.100.245109
Marzari N, 2012, REV MOD PHYS, V84, DOI 10.1103/RevModPhys.84.1419
Massidda S, 1997, PHYS REV B, V55, P13494, DOI 10.1103/PhysRevB.55.13494
MATTHEISS LF, 1972, PHYS REV B-SOLID ST, V5, P306, DOI 10.1103/PhysRevB.5.306
MONKHORST HJ, 1976, PHYS REV B, V13, P5188, DOI 10.1103/PhysRevB.13.5188
Mostofi AA, 2014, COMPUT PHYS COMMUN, V185, P2309, DOI 10.1016/j.cpc.2014.05.003
Nekrasov IA, 2006, PHYS REV B, V73, DOI 10.1103/PhysRevB.73.155112
Nekrasov IA, 2005, PHYS REV B, V72, DOI 10.1103/PhysRevB.72.155106
Nowadnick EA, 2015, PHYS REV B, V92, DOI 10.1103/PhysRevB.92.245109
Ouellette DG, 2010, PHYS REV B, V82, DOI 10.1103/PhysRevB.82.165112
Parcollet O, 2015, COMPUT PHYS COMMUN, V196, P398, DOI 10.1016/j.cpc.2015.04.023
Park H, 2020, PHYS REV B, V101, DOI 10.1103/PhysRevB.101.195125
Park H, 2014, PHYS REV B, V90, DOI 10.1103/PhysRevB.90.235103
Park H, 2014, PHYS REV B, V89, DOI 10.1103/PhysRevB.89.245133
Pashov D, 2020, COMPUT PHYS COMMUN, V249, DOI 10.1016/j.cpc.2019.107065
Pavarini E, 2004, PHYS REV LETT, V92, DOI 10.1103/PhysRevLett.92.176403
Perdew JP, 2008, PHYS REV LETT, V100, DOI 10.1103/PhysRevLett.100.136406
Petukhov AG, 2003, PHYS REV B, V67, DOI 10.1103/PhysRevB.67.153106
Pickett WE, 1998, PHYS REV B, V58, P1201, DOI 10.1103/PhysRevB.58.1201
Pizzi G., 2019, ARXIV190709788
Rasander M, 2015, J CHEM PHYS, V143, DOI 10.1063/1.4932334
Ren X, 2006, PHYS REV B, V74, DOI 10.1103/PhysRevB.74.195114
Savrasov SY, 2004, PHYS REV B, V69, DOI 10.1103/PhysRevB.69.245101
SAWATZKY GA, 1984, PHYS REV LETT, V53, P2339, DOI 10.1103/PhysRevLett.53.2339
Schuler TM, 2005, PHYS REV B, V71, DOI 10.1103/PhysRevB.71.115113
Schwarz K, 2003, COMP MATER SCI, V28, P259, DOI 10.1016/S0927-0256(03)00112-5
Sekiyama A, 2004, PHYS REV LETT, V93, DOI 10.1103/PhysRevLett.93.156402
SHEN ZX, 1991, PHYS REV B, V44, P3604, DOI 10.1103/PhysRevB.44.3604
Sherrill CD, 1999, ADV QUANTUM CHEM, V34, P143, DOI 10.1016/S0065-3276(08)60532-8
Shinaoka H, 2017, EUR PHYS J-SPEC TOP, V226, P2499, DOI 10.1140/epjst/e2017-70050-x
Si QM, 1996, PHYS REV LETT, V77, P3391, DOI 10.1103/PhysRevLett.77.3391
Soler JM, 2002, J PHYS-CONDENS MAT, V14, P2745, DOI 10.1088/0953-8984/14/11/302
Stewart MK, 2011, PHYS REV B, V83, DOI 10.1103/PhysRevB.83.075125
SVANE A, 1990, PHYS REV LETT, V65, P1148, DOI 10.1103/PhysRevLett.65.1148
Takizawa M, 2009, PHYS REV B, V80, DOI 10.1103/PhysRevB.80.235104
Tjernberg O, 1996, PHYS REV B, V54, P10245, DOI 10.1103/PhysRevB.54.10245
Toschi A, 2007, PHYS REV B, V75, DOI 10.1103/PhysRevB.75.045118
TROULLIER N, 1991, PHYS REV B, V43, P1993, DOI 10.1103/PhysRevB.43.1993
VANDERBILT D, 1990, PHYS REV B, V41, P7892, DOI 10.1103/PhysRevB.41.7892
von Lohneysen H, 2007, REV MOD PHYS, V79, P1015, DOI 10.1103/RevModPhys.79.1015
VOSKO SH, 1980, CAN J PHYS, V58, P1200, DOI 10.1139/p80-159
Wallerberger M, 2019, COMPUT PHYS COMMUN, V235, P388, DOI 10.1016/j.cpc.2018.09.007
Wang X, 2012, PHYS REV B, V86, DOI 10.1103/PhysRevB.86.195136
Wirth S, 2016, NAT REV MATER, V1, DOI 10.1038/natrevmats.2016.51
XU XQ, 1993, PHYS REV B, V48, P1112, DOI 10.1103/PhysRevB.48.1112
Yoshida T, 2005, PHYS REV LETT, V95, DOI 10.1103/PhysRevLett.95.146404
Yoshida T, 2010, PHYS REV B, V82, DOI 10.1103/PhysRevB.82.085119
Zantout K, 2019, PHYS REV LETT, V123, DOI 10.1103/PhysRevLett.123.256401
NR 111
TC 2
Z9 2
U1 5
U2 5
PU ELSEVIER
PI AMSTERDAM
PA RADARWEG 29, 1043 NX AMSTERDAM, NETHERLANDS
SN 0010-4655
EI 1879-2944
J9 COMPUT PHYS COMMUN
JI Comput. Phys. Commun.
PD APR
PY 2021
VL 261
AR 107778
DI 10.1016/j.cpc.2020.107778
PG 21
WC Computer Science, Interdisciplinary Applications; Physics, Mathematical
SC Computer Science; Physics
GA QH4ZA
UT WOS:000618283700005
DA 2021-04-21
ER
PT J
AU Chatzipapas, KP
Papadimitroulas, P
Loudos, G
Papanikolaou, N
Kagadis, GC
AF Chatzipapas, Konstantinos P.
Papadimitroulas, Panagiotis
Loudos, George
Papanikolaou, Niko
Kagadis, George C.
TI IDDRRA: A novel platform, based on Geant4-DNA to quantify DNA damage by
ionizing radiation
SO MEDICAL PHYSICS
LA English
DT Article; Early Access
DE DNA damage response ‐ DDR; Geant4‐ DNA; microdosimetry;
Monte Carlo simulations
AB Purpose This study proposes a novel computational platform that we refer to as IDDRRA (DNA Damage Response to Ionizing RAdiation), which uses Monte Carlo (MC) simulations to score radiation induced DNA damage. MC simulations provide results of high accuracy on the interaction of radiation with matter while scoring the energy deposition based on state-of-the-art physics and chemistry models and probabilistic methods.
Methods The IDDRRA software is based on the Geant4-DNA toolkit together with new tools that were developed for the purpose of this study, including a new algorithm that was developed in Python for the design of the DNA molecules. New classes were developed in C++ to integrate the GUI and produce the simulation's output in text format. An algorithm was also developed to analyze the simulation's output in terms of energy deposition, Single Strand Breaks (SSB), Double Strand Breaks (DSB) and Cluster Damage Sites (CDS). Finally, a new tool was developed to implement probabilistic SSB and DSB repair models using MC techniques.
Results This article provides the first benchmarks that the user of the IDDRRA tool can use to validate the functionality of the software as well as to provide a starting point to produce different types of DNA simulations. These benchmarks incorporate different kind of particles (e-, e+, protons, electron spectrum) and DNA molecules.
Conclusion We have developed the IDDRRA tool and demonstrated its use to study various aspects of the modeling and simulation of a DNA irradiation experiment. The tool is expandable and can be expanded by other users with new benchmarks and applications based on the user's needs and experience. New functionality will be added over time, including the quantification of the indirect damage.
C1 [Chatzipapas, Konstantinos P.; Kagadis, George C.] Univ Patras, Sch Med, Dept Med Phys, 3dmi Res Grp, Rion 26504, Greece.
[Papadimitroulas, Panagiotis; Loudos, George] Bioemiss Technol Solut BIOEMTECH, Athens 11472, Greece.
[Papanikolaou, Niko] Univ Texas San Antonio, Hlth Sci Ctr, San Antonio, TX 78229 USA.
RP Kagadis, GC (corresponding author), Univ Patras, Sch Med, Dept Med Phys, 3dmi Res Grp, Rion 26504, Greece.
EM gkagad@gmail.com
RI Papadimitroulas, Panagiotis/AAN-8203-2020
OI Papadimitroulas, Panagiotis/0000-0002-5981-6149; Chatzipapas,
Konstantinos/0000-0003-4006-9304
FU European Union (European Social Fund-ESF)European Social Fund
(ESF)European Commission [MIS-5000432]; European Regional Development
Fund (ERDF), Greek General Secretariat for Research and Innovation,
Operational Programme "Competitiveness, Entrepreneurship and Innovation"
(EPAnEK) [POPEYE T11EPA4-00055]; Greek Diaspora Fellowship Program Cycle
IIGreek Ministry of Development-GSRT; Greek Diaspora Fellowship Program
Cycle IVGreek Ministry of Development-GSRT
FX This research is financed by Greece and the European Union (European
Social Fund-ESF) through the Operational Programme << Human Resources
Development, Education and Lifelong Learning >> in the context of the
project "Strengthening Human Resources Research Potential via Doctorate
Research" (MIS-5000432), implemented by the State Scholarships
Foundation (IKY). This work was supported by the European Regional
Development Fund (ERDF), Greek General Secretariat for Research and
Innovation, Operational Programme "Competitiveness, Entrepreneurship and
Innovation" (EPAnEK), under the frame of ERA PerMed (project POPEYE
T11EPA4-00055. The authors would also like to thank Afroditi Toufa for
her technical support on programming. Furthermore, this study has been
partly financed by the Greek Diaspora Fellowship Program Cycles II, and
IV.
CR Abril I, 2013, ADV QUANTUM CHEM, V65, P129, DOI 10.1016/B978-0-12-396455-7.00006-6
Allison J, 2016, NUCL INSTRUM METH A, V835, P186, DOI 10.1016/j.nima.2016.06.125
[Anonymous], DOXYGEN
[Anonymous], 2020, VIRTUALBOX
[Anonymous], 2020, VGATE
Berman HM, 2000, NUCLEIC ACIDS RES, V28, P235, DOI 10.1093/nar/28.1.235
Bernal MA, 2015, PHYS MEDICA, V31, P861, DOI 10.1016/j.ejmp.2015.10.087
Binder K., 1995, MONTE CARLO MOL DYNA
Boscolo D, 2018, CHEM PHYS LETT, V698, P11, DOI 10.1016/j.cplett.2018.02.051
Bustin S, 2015, INT J MOL SCI, V160, P28123
Chatzipapas KP, 2020, CANCERS, V12, DOI 10.3390/cancers12040799
Chatzipapas KP, 2019, MED PHYS, V46, P405, DOI 10.1002/mp.13290
Cucinotta FA, 2008, RADIAT RES, V169, P214, DOI 10.1667/RR1035.1
Cumberworth A, 2018, J CHEM PHYS, V149, DOI 10.1063/1.5051835
Delage E, 2015, COMPUT PHYS COMMUN, V192, P282, DOI 10.1016/j.cpc.2015.02.026
Emfietzoglou D, 2013, J APPL PHYS, V114, DOI 10.1063/1.4824541
Foray N, 1998, INT J RADIAT BIOL, V74, P551, DOI 10.1080/095530098141122
FRANKENBERG D, 1986, INT J RADIAT BIOL, V50, P727, DOI 10.1080/09553008614551121
Friedland W, 1998, RADIAT RES, V150, P170, DOI 10.2307/3579852
Friedland W, 2012, INT J RADIAT BIOL, V88, P129, DOI 10.3109/09553002.2011.611404
Friedland W, 2011, MUTAT RES-FUND MOL M, V711, P28, DOI 10.1016/j.mrfmmm.2011.01.003
Garcia-Molina R, 2017, SURF INTERFACE ANAL, V49, P11, DOI 10.1002/sia.5947
Georgakilas AG, 2013, RADIAT RES, V180, P100, DOI 10.1667/RR3041.1
Hall EJ., 2012, RADIOBIOLOGY RADIOBI
Henthorn NT, 2018, SCI REP-UK, V8, DOI 10.1038/s41598-018-21111-8
Incerti S, 2019, J APPL PHYS, V125, DOI 10.1063/1.5083208
KRAMER M, 1994, RADIAT ENVIRON BIOPH, V33, P91, DOI 10.1007/BF01219334
Kyriakou I, 2019, PHYS MEDICA, V58, P149, DOI 10.1016/j.ejmp.2019.01.001
Lai YF, 2020, MED PHYS, V47, P1971, DOI 10.1002/mp.14036
Lampe N, 2018, PHYS MEDICA, V48, P135, DOI 10.1016/j.ejmp.2018.02.011
LANGRIDGE R, 1957, J BIOPHYS BIOCHEM CY, V3, P767, DOI 10.1083/jcb.3.5.767
Liu RR, 2019, MED PHYS, V46, P5314, DOI 10.1002/mp.13813
Liu RR, 2019, INT J RADIAT BIOL, V95, P1484, DOI 10.1080/09553002.2019.1642537
Liu W, 2018, RADIAT ENVIRON BIOPH, V57, P179, DOI 10.1007/s00411-018-0730-0
Margis S, 2020, PHYS MED BIOL, V65, DOI 10.1088/1361-6560/ab6b47
Mavragani IV, 2019, CANCERS, V11, DOI 10.3390/cancers11111789
McNamara A, 2017, PHYS MEDICA, V33, P207, DOI 10.1016/j.ejmp.2016.12.010
Meylan S, 2017, SCI REP-UK, V7, DOI 10.1038/s41598-017-11851-4
Murray PJ, 2016, J R SOC INTERFACE, V13, DOI 10.1098/rsif.2015.0679
Nikitaki Z, 2016, FREE RADICAL RES, V50, pS64, DOI 10.1080/10715762.2016.1232484
Nikitaki Z, 2016, RADIAT PHYS CHEM, V128, P26, DOI 10.1016/j.radphyschem.2016.06.020
Nikjoo H, 1999, RADIAT ENVIRON BIOPH, V38, P31, DOI 10.1007/s004110050135
Nikjoo H, 2001, RADIAT RES, V156, P577, DOI 10.1667/0033-7587(2001)156[0577:CAFDTS]2.0.CO;2
Nikjoo H, 1997, INT J RADIAT BIOL, V71, P467, DOI 10.1080/095530097143798
Okada S, 2019, MED PHYS, V46, P1483, DOI 10.1002/mp.13370
Pater P, 2014, MED PHYS, V41, DOI 10.1118/1.4901555
Plante I, 2019, GENES-BASEL, V10, DOI 10.3390/genes10110936
Plante I, 2011, RADIAT ENVIRON BIOPH, V50, P405, DOI 10.1007/s00411-011-0368-7
Plante I, 2011, RADIAT ENVIRON BIOPH, V50, P389, DOI 10.1007/s00411-011-0367-8
Qt Company Ltd, 2020, QT TOOLK
Rahmanian S, 2014, DNA REPAIR, V22, P89, DOI 10.1016/j.dnarep.2014.07.011
Rogers DWO, 2006, PHYS MED BIOL, V51, pR287, DOI 10.1088/0031-9155/51/13/R17
Sakata D, 2019, PHYS MEDICA, V63, P98, DOI 10.1016/j.ejmp.2019.05.023
Sakata D, 2019, PHYS MEDICA, V62, P152, DOI 10.1016/j.ejmp.2019.04.010
Sarrut D, 2014, MED PHYS, V41, DOI 10.1118/1.4871617
Schipler A, 2013, NUCLEIC ACIDS RES, V41, P7589, DOI 10.1093/nar/gkt556
Schuemann J, 2019, RADIAT RES, V191, P125, DOI 10.1667/RR15226.1
Shin WG, 2019, J APPL PHYS, V126, DOI 10.1063/1.5107511
Stepan V, 2014, EUR PHYS J D, V68, DOI 10.1140/epjd/e2014-50068-8
Stewart RD, 2015, PHYS MED BIOL, V60, P8249, DOI 10.1088/0031-9155/60/21/8249
Stewart RD, 2011, RADIAT RES, V176, P587, DOI 10.1667/RR2663.1
Taleei R, 2015, MUTAT RES-GEN TOX EN, V779, P5, DOI 10.1016/j.mrgentox.2015.01.007
Taleei R, 2013, RADIAT RES, V179, P530, DOI 10.1667/RR3123.1
Taleei R, 2012, INT J RADIAT BIOL, V88, P948, DOI 10.3109/09553002.2012.695098
Tang N, 2019, MED PHYS, V46, P1501, DOI 10.1002/mp.13405
Tang N, 2019, INT J MOL SCI, V20, DOI 10.3390/ijms20246204
Tsai MY, 2020, MED PHYS, V47, P1958, DOI 10.1002/mp.14037
Warmenhoven JW, 2020, DNA REPAIR, V85, DOI 10.1016/j.dnarep.2019.102743
WATSON JD, 1953, NATURE, V171, P737, DOI 10.1038/171737a0
NR 69
TC 0
Z9 0
U1 0
U2 0
PU WILEY
PI HOBOKEN
PA 111 RIVER ST, HOBOKEN 07030-5774, NJ USA
SN 0094-2405
EI 2473-4209
J9 MED PHYS
JI Med. Phys.
DI 10.1002/mp.14817
PG 13
WC Radiology, Nuclear Medicine & Medical Imaging
SC Radiology, Nuclear Medicine & Medical Imaging
GA RF3VE
UT WOS:000634768400001
PM 33657650
DA 2021-04-21
ER
PT J
AU Lu, L
Meng, XH
Mao, ZP
Karniadakis, GE
AF Lu, Lu
Meng, Xuhui
Mao, Zhiping
Karniadakis, George Em
TI DeepXDE: A Deep Learning Library for Solving Differential Equations
SO SIAM REVIEW
LA English
DT Article
DE education software; DeepXDE; differential equations; deep learning;
physics-informed neural networks; scientific machine learning
ID NEURAL-NETWORKS; ALGORITHM
AB Deep learning has achieved remarkable success in diverse applications; however, its use in solving partial differential equations (PDEs) has emerged only recently. Here, we present an overview of physics-informed neural networks (PINNs), which embed a PDE into the loss of the neural network using automatic differentiation. The PINN algorithm is simple, and it can be applied to different types of PDEs, including integro-differential equations, fractional PDEs, and stochastic PDEs. Moreover, from an implementation point of view, PINNs solve inverse problems as easily as forward problems. We propose a new residual-based adaptive refinement (RAR) method to improve the training efficiency of PINNs. For pedagogical reasons, we compare the PINN algorithm to a standard finite element method. We also present a Python library for PINNs, DeepXDE, which is designed to serve both as an educational tool to be used in the classroom as well as a research tool for solving problems in computational science and engineering. Specifically, DeepXDE can solve forward problems given initial and boundary conditions, as well as inverse problems given some extra measurements. DeepXDE supports complex-geometry domains based on the technique of constructive solid geometry and enables the user code to be compact, resembling closely the mathematical formulation. We introduce the usage of DeepXDE and its customizability, and we also demonstrate the capability of PINNs and the user-friendliness of DeepXDE for five different examples. More broadly, DeepXDE contributes to the more rapid development of the emerging scientific machine learning field.
C1 [Lu, Lu] MIT, Dept Math, Cambridge, MA 02139 USA.
[Meng, Xuhui; Karniadakis, George Em] Brown Univ, Div Appl Math, Providence, RI 02912 USA.
[Mao, Zhiping] Xiamen Univ, Sch Math Sci, Xiamen 361005, Fujian, Peoples R China.
[Karniadakis, George Em] Pacific Northwest Natl Lab, Richland, WA 99354 USA.
RP Lu, L (corresponding author), MIT, Dept Math, Cambridge, MA 02139 USA.
EM lu_lu@mit.edu; xuhui_meng@brown.edu; zpmao@xmu.edu.cn;
George_Karniadakis@brown.edu
RI Lu, Lu/AAG-7335-2019
OI Lu, Lu/0000-0002-5476-5768
FU DOE PhILMs projectUnited States Department of Energy (DOE)
[de-sc0019453]; AFOSRUnited States Department of DefenseAir Force Office
of Scientific Research (AFOSR) [FA9550-17-1-0013]; DARPA-AIRA
[HR00111990025]
FX This work was supported by DOE PhILMs project de-sc0019453, by AFOSR
grant FA9550-17-1-0013, and by DARPA-AIRA grant HR00111990025.
CR Abadi M, 2016, PROCEEDINGS OF OSDI'16: 12TH USENIX SYMPOSIUM ON OPERATING SYSTEMS DESIGN AND IMPLEMENTATION, P265
Ainsworth M., 2011, POSTERIORI ERROR EST
Baeza A, 2006, INT J NUMER METH FL, V52, P455, DOI 10.1002/fld.1191
Baker M, 2019, PARIS REV, P209
BAYDIN A, 2017, J MACH LEARN RES, V18, P5595, DOI DOI 10.5555/3122009.3242010
Beck C, 2019, J NONLINEAR SCI, V29, P1563, DOI 10.1007/s00332-018-9525-3
Berg J, 2018, NEUROCOMPUTING, V317, P28, DOI 10.1016/j.neucom.2018.06.056
Betancourt M., 2018, PREPRINT
Bettencourt J., 2019, NEURIPS 2019 WORKSH
Blum A, 1989, ADV NEURAL INFORM PR, V2, P494
Bottou L., 2008, ADV NEURAL INFORM PR, V20, P161, DOI DOI 10.7751/mitpress/8996.003.0015
BYRD RH, 1995, SIAM J SCI COMPUT, V16, P1190, DOI 10.1137/0916069
Chen Y., 2019, PREPRINT
Ciarlet, 2002, CLASSICS APPL MATH, DOI 10.1137/1.9780898719208
DISSANAYAKE MWMG, 1994, COMMUN NUMER METH EN, V10, P195, DOI 10.1002/cnm.1640100303
Finn C., 2017, P MACH LEARN RES 06, P1126, DOI DOI 10.5555/3305381.3305498
Grohs P., 2018, PREPRINT
Han J, 2018, P NATL ACAD SCI USA, V115, P8505, DOI 10.1073/pnas.1718942115
He J., 2018, PREPRINT
He Q., 2019, PREPRINT
Hughes TJR, 2005, COMPUT METHOD APPL M, V194, P4135, DOI 10.1016/j.cma.2004.10.008
Jagtap A. D., 2019, PREPRINT
Jagtap AD, 2020, J COMPUT PHYS, V404, DOI 10.1016/j.jcp.2019.109136
Jin P., 2019, PREPRINT
Johnson C., 2012, NUMERICAL SOLUTION P
Karniadakis G., 2013, SPECTRAL HP ELEMENT
Khoo Y., 2017, PREPRINT
Kingma D. P., 2015, 3 INT C LEARN REPR 2
Lagaris IE, 1998, IEEE T NEURAL NETWOR, V9, P987, DOI 10.1109/72.712178
Lagaris IE, 2000, IEEE T NEURAL NETWOR, V11, P1041, DOI 10.1109/72.870037
LeCun Y, 2015, NATURE, V521, P436, DOI 10.1038/nature14539
Long Z., 2018, INT C MACH LEARN, P3214
Lu L., 2019, PREPRINT
Lu L., 2018, PREPRINT
Mao ZP, 2020, COMPUT METHOD APPL M, V360, DOI 10.1016/j.cma.2019.112789
Margossian CC, 2019, WIRES DATA MIN KNOWL, V9, DOI 10.1002/widm.1305
MEADE AJ, 1994, MATH COMPUT MODEL, V19, P1, DOI 10.1016/0895-7177(94)90095-7
Meng X., 2019, PREPRINT
Nabian M. A., 2018, PREPRINT
Pang GF, 2019, SIAM J SCI COMPUT, V41, pA2603, DOI 10.1137/18M1229845
Paszke A., 2017, NIPS 2017 WORKSH AUT
Pinkus A., 1999, Acta Numerica, V8, P143, DOI 10.1017/S0962492900002919
Poggio T, 2017, INT J AUTOM COMPUT, V14, P503, DOI 10.1007/s11633-017-1054-2
Rahaman N., 2019, INT C MACH LEARN, P5301
Raissi M, 2019, J COMPUT PHYS, V378, P686, DOI 10.1016/j.jcp.2018.10.045
Raissi M, 2020, SCIENCE, V367, P1026, DOI 10.1126/science.aaw4741
RUMELHART DE, 1986, NATURE, V323, P533, DOI 10.1038/323533a0
Sirignano J, 2018, J COMPUT PHYS, V375, P1339, DOI 10.1016/j.jcp.2018.08.029
Tartakovsky A. M., 2018, PREPRINT
VANMILLIGEN BP, 1995, PHYS REV LETT, V75, P3594, DOI 10.1103/PhysRevLett.75.3594
Weinan E, 2018, COMMUN MATH STAT, V6, P1, DOI 10.1007/s40304-018-0127-z
Winovich N, 2019, J COMPUT PHYS, V394, P263, DOI 10.1016/j.jcp.2019.05.026
Xu Z.Q. J., 2019, PREPRINT
Yang L., 2018, PREPRINT
Zhang D., 2019, PREPRINT
Zhang DK, 2019, J COMPUT PHYS, V397, DOI 10.1016/j.jcp.2019.07.048
Zhu Y., 2019, PREPRINT
Zoph B, 2016, ARXIV161101578
NR 58
TC 1
Z9 1
U1 10
U2 10
PU SIAM PUBLICATIONS
PI PHILADELPHIA
PA 3600 UNIV CITY SCIENCE CENTER, PHILADELPHIA, PA 19104-2688 USA
SN 0036-1445
EI 1095-7200
J9 SIAM REV
JI SIAM Rev.
PD MAR
PY 2021
VL 63
IS 1
BP 208
EP 228
DI 10.1137/19M1274067
PG 21
WC Mathematics, Applied
SC Mathematics
GA QD7RZ
UT WOS:000615712100007
OA Bronze
DA 2021-04-21
ER
PT J
AU Roest, LI
van Heijst, SE
Maduro, L
Rojo, J
Conesa-Boj, S
AF Roest, Laurien, I
van Heijst, Sabrya E.
Maduro, Louis
Rojo, Juan
Conesa-Boj, Sonia
TI Charting the low-loss region in electron energy loss spectroscopy with
machine learning
SO ULTRAMICROSCOPY
LA English
DT Article
DE Transmission electron microscopy; Electron energy loss spectroscopy;
Neural networks; Machine learning; Transition metal dichalcogenides;
Bandgap
AB Exploiting the information provided by electron energy-loss spectroscopy (EELS) requires reliable access to the low-loss region where the zero-loss peak (ZLP) often overwhelms the contributions associated to inelastic scatterings off the specimen. Here we deploy machine learning techniques developed in particle physics to realise a model-independent, multidimensional determination of the ZLP with a faithful uncertainty estimate. This novel method is then applied to subtract the ZLP for EEL spectra acquired in flower-like WS2 nanostructures characterised by a 2H/3R mixed polytypism. From the resulting subtracted spectra we determine the nature and value of the bandgap of polytypic WS2, finding E-BG = 1.6(-0.2)(+0.3) eV with a clear preference for an indirect bandgap. Further, we demonstrate how this method enables us to robustly identify excitonic transitions down to very small energy losses. Our approach has been implemented and made available in an open source PYTHON package dubbed EELSfitter.
C1 [Roest, Laurien, I; van Heijst, Sabrya E.; Maduro, Louis; Conesa-Boj, Sonia] Delft Univ Technol, Kavli Inst Nanosci, NL-2628 CJ Delft, Netherlands.
[Roest, Laurien, I; Rojo, Juan] Nikhef Theory Grp, Sci Pk 105, NL-1098 XG Amsterdam, Netherlands.
[Rojo, Juan] VU, Dept Phys & Astron, NL-1081 HV Amsterdam, Netherlands.
RP Conesa-Boj, S (corresponding author), Delft Univ Technol, Kavli Inst Nanosci, NL-2628 CJ Delft, Netherlands.
EM s.conesaboj@tudelft.nl
OI Roest, Laurien/0000-0003-3135-7929; van Heijst,
Sabrya/0000-0001-5436-4019; Maduro, Louis/0000-0003-3776-2802; Rojo,
Juan/0000-0003-4279-2192
FU ERC through the Starting Grant ``TESLA'' [805021]; Netherlands
Organizational for Scientific Research (NWO) through the Nanofront
program; NWO, The NetherlandsNetherlands Organization for Scientific
Research (NWO)Netherlands Government
FX S. E. v. H. and S. C.-B. acknowledge financial support from the ERC
through the Starting Grant ``TESLA'', grant agreement no. 805021. L. M.
acknowledges support from the Netherlands Organizational for Scientific
Research (NWO) through the Nanofront program. The work of J. R. has been
partially supported by NWO, The Netherlands.
CR Abadi M, TENSORFLOW LARGE SCA
Abdul Khalek R., NNNPDF2 0 QUARK FLAV
[Anonymous], 2007, PHYS STATUS SOLIDI, V214
Ball RD, 2017, EUR PHYS J C, V77, DOI 10.1140/epjc/s10052-017-5199-5
Ball RD, 2015, J HIGH ENERGY PHYS, DOI 10.1007/JHEP04(2015)040
Ball RD, 2013, NUCL PHYS B, V867, P244, DOI 10.1016/j.nuclphysb.2012.10.003
Ball RD, 2009, NUCL PHYS B, V809, P1, DOI 10.1016/j.nuclphysb.2008.09.037
Bangert U., 1998, J MICROSC-PARIS, V188, P237
Bertone V, 2018, EUR PHYS J C, V78, DOI 10.1140/epjc/s10052-018-6130-4
Bertone V, 2017, EUR PHYS J C, V77, DOI 10.1140/epjc/s10052-017-5088-y
Bhandavat R, 2012, J PHYS CHEM LETT, V3, P1523, DOI 10.1021/jz300480w
Bolhuis M, 2020, NANOSCALE, V12, P10491, DOI 10.1039/d0nr00755b
Bosman M, 2006, ULTRAMICROSCOPY, V106, P1024, DOI 10.1016/j.ultramic.2006.04.016
Braga D, 2012, NANO LETT, V12, P5218, DOI 10.1021/nl302389d
Chhowalla M, 2013, NAT CHEM, V5, P263, DOI [10.1038/NCHEM.1589, 10.1038/nchem.1589]
de Haan K, 2019, SCI REP-UK, V9, DOI 10.1038/s41598-019-48444-2
Del Debbio L, 2007, J HIGH ENERGY PHYS, DOI 10.1088/1126-6708/2007/03/039
Dorneich AD, 1998, J MICROSC-OXFORD, V191, P286
Egerton RF, 2009, REP PROG PHYS, V72, DOI 10.1088/0034-4885/72/1/016502
Egerton RF, 2002, ULTRAMICROSCOPY, V92, P47, DOI 10.1016/S0304-3991(01)00155-3
Egerton RF., 1996, ELECT ENERGY LOSS SP, V2nd edn
Erni R, 2005, MICRON, V36, P369, DOI 10.1016/j.micron.2004.12.011
Forte S, 2002, J HIGH ENERGY PHYS
Freitag B, 2005, ULTRAMICROSCOPY, V102, P209, DOI 10.1016/j.ultramic.2004.09.013
Fung KLY, 2020, ULTRAMICROSCOPY, V217, DOI 10.1016/j.ultramic.2020.113052
Gao J, 2018, PHYS REP, V742, P1, DOI 10.1016/j.physrep.2018.03.002
de Abajo FJG, 2010, REV MOD PHYS, V82, P209, DOI 10.1103/RevModPhys.82.209
GEIGER J, 1967, PHYS STATUS SOLIDI, V24, P457, DOI 10.1002/pssb.19670240207
Gordon O. M., 2020, MACHINE LEARNING SCI, V1
Granerod CS, 2018, ULTRAMICROSCOPY, V184, P39, DOI 10.1016/j.ultramic.2017.08.006
Hachtel JA, 2018, SCI REP-UK, V8, DOI 10.1038/s41598-018-23805-5
Haider M, 1998, NATURE, V392, P768, DOI 10.1038/33823
Hanbicki AT, 2015, SOLID STATE COMMUN, V203, P16, DOI 10.1016/j.ssc.2014.11.005
Held JT, 2020, ULTRAMICROSCOPY, V210, DOI 10.1016/j.ultramic.2019.112919
Jany BR, 2017, NANO LETT, V17, P6520, DOI 10.1021/acs.nanolett.7b01789
Jo S, 2014, NANO LETT, V14, P2019, DOI 10.1021/nl500171v
KAM KK, 1982, J PHYS CHEM-US, V86, P463, DOI 10.1021/j100393a010
Kaviraj B., 2019, RSC ADV
Khalek RA, 2019, EUR PHYS J C, V79, DOI 10.1140/epjc/s10052-019-6983-1
Kothleitner G, 2003, MICRON, V34, P211, DOI 10.1016/S0968-4328(03)00037-4
Lazar S, 2003, ULTRAMICROSCOPY, V96, P535, DOI 10.1016/S0304-3991(03)00114-1
Lee JU, 2016, ACS NANO, V10, P1948, DOI 10.1021/acsnano.5b05831
McCreary A, 2016, J MATER RES, V31, P931, DOI 10.1557/jmr.2016.47
Na W, 2019, 2D MATER, V6, DOI 10.1088/2053-1583/aae61c
Nocera ER, 2014, NUCL PHYS B, V887, P276, DOI 10.1016/j.nuclphysb.2014.08.008
Park J, 2009, ULTRAMICROSCOPY, V109, P1183, DOI 10.1016/j.ultramic.2009.04.005
Rafferty B, 1998, PHYS REV B, V58, P10326, DOI 10.1103/PhysRevB.58.10326
Rafferty B, 2000, J ELECTRON MICROSC, V49, P517, DOI 10.1093/oxfordjournals.jmicro.a023838
Reed BW, 2002, ULTRAMICROSCOPY, V93, P25, DOI 10.1016/S0304-3991(02)00146-8
Rojo J., 2018, 13 C QUARK CONF HARD, V9
Schaffer B, 2009, MICRON, V40, P269, DOI 10.1016/j.micron.2008.07.004
Schamm S, 2003, ULTRAMICROSCOPY, V96, P559, DOI 10.1016/S0304-3991(03)00116-5
Shi HL, 2013, PHYS REV B, V87, DOI 10.1103/PhysRevB.87.155304
Splendiani A, 2010, NANO LETT, V10, P1271, DOI 10.1021/nl903868w
Stoger-Pollach M, 2008, MICRON, V39, P1092, DOI 10.1016/j.micron.2008.01.023
TENAILLEAU H, 1992, J MICROSC-OXFORD, V166, P297, DOI 10.1111/j.1365-2818.1992.tb01529.x
Terauchi M, 1999, J MICROSC-OXFORD, V194, P203
Tinoco M, 2019, SCI REP-UK, V9, DOI 10.1038/s41598-019-52119-3
van Benthem K, 2001, J APPL PHYS, V90, P6156, DOI 10.1063/1.1415766
van Heijst SE, 2021, ANN PHYS-BERLIN, V533, DOI 10.1002/andp.202000499
Xia J, 2017, FLATCHEM, V4, P1, DOI 10.1016/j.flatc.2017.06.007
Young S., 2015, P WORKSH MACH LEARN
Zhang Y, 2019, NATURE, V570, P484, DOI 10.1038/s41586-019-1319-8
Zhao WJ, 2013, ACS NANO, V7, P791, DOI 10.1021/nn305275h
Zhu BR, 2015, SCI REP-UK, V5, DOI 10.1038/srep09218
Ziatdinov M, 2017, ACS NANO, V11, P12742, DOI 10.1021/acsnano.7b07504
NR 66
TC 0
Z9 0
U1 2
U2 2
PU ELSEVIER
PI AMSTERDAM
PA RADARWEG 29, 1043 NX AMSTERDAM, NETHERLANDS
SN 0304-3991
EI 1879-2723
J9 ULTRAMICROSCOPY
JI Ultramicroscopy
PD MAR
PY 2021
VL 222
AR 113202
DI 10.1016/j.ultramic.2021.113202
PG 17
WC Microscopy
SC Microscopy
GA QK0MZ
UT WOS:000620077200001
PM 33453606
OA Green Published, Other Gold
DA 2021-04-21
ER
PT J
AU Lestandi, L
AF Lestandi, Lucas
TI Numerical Study of Low Rank Approximation Methods for Mechanics Data and
Its Analysis
SO JOURNAL OF SCIENTIFIC COMPUTING
LA English
DT Article
DE Low rank approximation; Tensor decomposition; HOSVD; ST-HOSVD; Tensor
train; QTT; HT; Hierarchical; Canonical decomposition; RPOD; POD; SVD
AB This paper proposes a comparison of the numerical aspect and efficiency of several low rank approximation techniques for multidimensional data, namely CPD, HOSVD, TT-SVD, RPOD, QTT-SVD and HT. This approach is different from the numerous papers that compare the theoretical aspects of these methods or propose efficient implementation of a single technique. Here, after a brief presentation of the studied methods, they are tested in practical conditions in order to draw hindsight at which one should be preferred. Synthetic data provides sufficient evidence for dismissing CPD, T-HOSVD and RPOD. Then, three examples from mechanics provide data for realistic application of TT-SVD and ST-HOSVD. The obtained low rank approximation provides different levels of compression and accuracy depending on how separable the data is. In all cases, the data layout has significant influence on the analysis of modes and computing time while remaining similarly efficient at compressing information. Both methods provide satisfactory compression, from 0.1% to 20% of the original size within a few percent error in L-2 norm. ST-HOSVD provides an orthonormal basis while TT-SVD doesn't. QTT is performing well only when one dimension is very large. A final experiment is applied to an order 7 tensor with (4 x 8 x 8 x 64 x 64 x 64 x 64) entries (32 GB) from complex multi-physics experiment. In that case, only HT provides actual compression (50%) due to the low separability of this data. However, it is better suited for higher order d. Finally, these numerical tests have been performed with pydecomp , an open source python library developed by the author.
C1 [Lestandi, Lucas] Nanyang Technol Univ, Sch Phys & Math Sci, Singapore, Singapore.
[Lestandi, Lucas] ASTAR, IHPC, Engn Mech Dept, Singapore, Singapore.
RP Lestandi, L (corresponding author), Nanyang Technol Univ, Sch Phys & Math Sci, Singapore, Singapore.; Lestandi, L (corresponding author), ASTAR, IHPC, Engn Mech Dept, Singapore, Singapore.
EM Lucas_Lestandi@ihpc.a-star.edu.sg
FU NTUNanyang Technological University [MOE2018-T2-1-05]; Science and
Engineering Research Council, A*STAR, SingaporeAgency for Science
Technology & Research (ASTAR) [A19E1a0097]
FX Financial support was provided by Grant MOE2018-T2-1-05 in the context
of the author's research fellowship at NTU in the team of Pr. Wang
Li-Lian. Financial support was provided by the Science and Engineering
Research Council, A*STAR, Singapore (Grant No. A19E1a0097) in the
context of the author's new position as a Scientist at A*STAR, IHPC,
Engineering Mechanics Department, Singapore.
CR Alimi J.M., 2012, INT C HPC NETW STOR
Allier P-E., 2015, ADV MODEL SIMULAT EN, V2, P17, DOI [10.1186/s40323-015-0038-4, DOI 10.1186/S40323-015-0038-4]
Austin W, 2016, INT PARALL DISTRIB P, P912, DOI 10.1109/IPDPS.2016.67
Azaiez M., 2016, ADV MODEL SIMULAT EN, V3, P1
Azaiez M, 2019, CISM COURSES LECT, V592, P187, DOI 10.1007/978-3-030-17012-7_5
Bader, 2017, MATLAB TENSOR TOOLBO
Ballani J., 2012, FAST EVALUATION NEAR
Ballani J, 2015, SIAM-ASA J UNCERTAIN, V3, P852, DOI 10.1137/140960980
Ballani J, 2013, LINEAR ALGEBRA APPL, V438, P639, DOI 10.1016/j.laa.2011.08.010
Bergmann M., 2004, OPTIMISATION AERODYN
Bigoni D, 2016, SIAM J SCI COMPUT, V38, pA2405, DOI 10.1137/15M1036919
Carlberg K, 2013, J COMPUT PHYS, V242, P623, DOI 10.1016/j.jcp.2013.02.028
CARROLL JD, 1970, PSYCHOMETRIKA, V35, P283, DOI 10.1007/BF02310791
Cazemier W, 1998, PHYS FLUIDS, V10, P1685, DOI 10.1063/1.869686
Chinesta F, 2014, SPRINGERBR APPL SCI, P1, DOI 10.1007/978-3-319-02865-1
Chinesta F., 2014, FUND APPL INT CTR ME, V554, P24
Cichocki A, 2014, ARXIV PREPRINT ARXIV, P1
Daulbaev T., 2019, REDUCED ORDER MODELI
De Lathauwer L, 2000, SIAM J MATRIX ANAL A, V21, P1253, DOI 10.1137/S0895479896305696
De Lathauwer L, 2000, SIAM J MATRIX ANAL A, V21, P1324, DOI 10.1137/S0895479898346995
DEANE AE, 1991, PHYS FLUIDS A-FLUID, V3, P2337, DOI 10.1063/1.857881
Demchik V., 2019, OUT OF CORE SINGULAR, P1
Dunton AM, 2020, J COMPUT PHYS, V423, DOI 10.1016/j.jcp.2020.109704
Eckart C, 1936, PSYCHOMETRIKA, V1, P211, DOI 10.1007/BF02288367
Fahl M, 2001, TRUST REGION METHODS
Falco A., 2015, GEOMETRIC STRUCTURES, P1
Gorodetsky A., 2016, THESIS MIT
Gorodetsky A, 2019, COMPUT METHOD APPL M, V347, P59, DOI 10.1016/j.cma.2018.12.015
Grasedyck L., 2011, COMPUT METHODS APPL, V11, P291, DOI [DOI 10.2478/CMAM-2011-0016, 10.2478/cmam-2011-0016]
Grasedyck L., 2013, GAMM MITT, V36, P53, DOI DOI 10.1002/GAMM.201310004
Grasedyck L, 2010, SIAM J MATRIX ANAL A, V31, P2029, DOI 10.1137/090764189
Hackbusch W., 2012, TENSOR SPACES NUMERI, DOI [10.1007/978-3-642-28027-6, DOI 10.1007/978-3-642-28027-6]
Harshman R.A., 1970, MULTIMODAL FACTOR AN, V16, P1
Hitchcock F., 1927, THE J, V7, P39, DOI [DOI 10.1002/SAPML9287139, 10.1002/sapm19287139, DOI 10.1002/SAPM19287139]
Hotelling H, 1933, J EDUC PSYCHOL, V24, P417, DOI 10.1037/h0071325
Iollo A, 2000, THEOR COMP FLUID DYN, V13, P377, DOI 10.1007/s001620050119
Ito K, 1998, J COMPUT PHYS, V143, P403, DOI 10.1006/jcph.1998.5943
Khoromskij BN, 2011, CONSTR APPROX, V34, P257, DOI 10.1007/s00365-011-9131-1
Kolda TG, 2009, SIAM REV, V51, P455, DOI 10.1137/07070111X
Kosambi DD., 1943, STAT FUNCTION SPACES, P115
Kressner D., 2013, HTUCKER MATLAB TOOLB, P1
Kressner D, 2011, SIAM J MATRIX ANAL A, V32, P1288, DOI 10.1137/100799010
Lee K, 2020, J COMPUT PHYS, V404, DOI 10.1016/j.jcp.2019.108973
Lestandi L., 2018, LOW RANK APPROXIMATI
Lestandi L, 2018, J MATH STUDY, V51, P150, DOI 10.4208/jms.v51n2.18.03
Lestandi L, 2018, COMPUT FLUIDS, V166, P86, DOI 10.1016/j.compfluid.2018.01.038
Loeve M., 1977, PROBABILITY THEORY, V9
Lu LX, 2018, ACTA MATER, V144, P801, DOI 10.1016/j.actamat.2017.11.033
Lumley J., 1981, P T TURBULENCE, V1, P215, DOI [10.1016/B978-0-12-493240-1.50017-X, DOI 10.1016/B978-0-12-493240-1.50017-X]
Nouy A, 2015, LOW RANK TENSOR METH, P1
Novikov A., 2018, TENSOR TRAIN DECOMPO
Oseledets I., 2009, TENSOR TREE DECOMPOS
Oseledets IV, 2013, CONSTR APPROX, V37, P1, DOI 10.1007/s00365-012-9175-x
Oseledets IV, 2011, SIAM J SCI COMPUT, V33, P2295, DOI 10.1137/090752286
Oseledets IV, 2010, SIAM J MATRIX ANAL A, V31, P2130, DOI 10.1137/090757861
Oseledets I.V., 2018, TTPY
Pearson K, 1901, PHILOS MAG, V2, P559, DOI 10.1080/14786440109462720
Philippe B., 2014, TECHNIQUES LING NIEU
Quesada C, 2016, INT J NUMER METH ENG, V108, P1230, DOI 10.1002/nme.5252
Rabani E., 2001, P 10 SIAM C PAR PROC, V572, P1
Saad Y., 1992, NUMERICAL METHODS LA
Sengupta Tapan K., 2018, Advanced Modeling and Simulation in Engineering Sciences, V5, DOI 10.1186/s40323-018-0119-2
SIROVICH L, 1987, Q APPL MATH, V45, P561, DOI 10.1090/qam/910462
Stabile G, 2018, COMPUT FLUIDS, V173, P273, DOI 10.1016/j.compfluid.2018.01.035
TUCKER LR, 1966, PSYCHOMETRIKA, V31, P279, DOI 10.1007/BF02289464
Vannieuwenhoven N, 2012, SIAM J SCI COMPUT, V34, pA1027, DOI 10.1137/110836067
Wu LF, 2017, SIAM J SCI COMPUT, V39, pS248, DOI 10.1137/16M1082214
NR 67
TC 0
Z9 0
U1 0
U2 0
PU SPRINGER/PLENUM PUBLISHERS
PI NEW YORK
PA 233 SPRING ST, NEW YORK, NY 10013 USA
SN 0885-7474
EI 1573-7691
J9 J SCI COMPUT
JI J. Sci. Comput.
PD FEB 24
PY 2021
VL 87
IS 1
AR 14
DI 10.1007/s10915-021-01421-2
PG 43
WC Mathematics, Applied
SC Mathematics
GA QP2QV
UT WOS:000623684100002
DA 2021-04-21
ER
PT J
AU Martinez, A
Nieves, C
Rua, A
AF Martinez, Alexuan
Nieves, Christian
Rua, Armando
TI Implementing Raspberry Pi 3 and Python in the Physics Laboratory
SO PHYSICS TEACHER
LA English
DT Article
AB Many physics projects recently designed for high school teachers use Arduino as the main tool for managing sensors and data acquisition. This is a low-cost integrated development environment programmed with a simplified version of the C++ language. In comparison, the Raspberry Pi 3 platform, which also allows for the design of physics projects, can expose students to the use of the most trending language in the field: Python. With this in mind, we have developed a project to measure the acceleration of objects due to gravity near the Earth's surface using a Raspberry Pi 3 computer and Python as the programming language. It utilizes an infrared sensor connected to the Raspberry Pi 3, a monitor with an HDMI connection, a mouse, and a keyboard. The experiment yields results with a percentage difference of 2.8% on average for an estimated value of the gravitational acceleration of 9.8 m/s(2).
C1 [Martinez, Alexuan; Nieves, Christian; Rua, Armando] Univ Puerto Rico, Mayaguez, PR 00682 USA.
RP Martinez, A (corresponding author), Univ Puerto Rico, Mayaguez, PR 00682 USA.
FU UPRM College of Arts and Sciences; PR-LSAMP, NSF [HRD-2008186]
FX The authors are pleased to acknowledge support for this work by the UPRM
College of Arts and Sciences and PR-LSAMP, NSF Award No. HRD-2008186.
CR Braun N., 2017, Journal of Physics: Conference Series, V898, DOI 10.1088/1742-6596/898/7/072020
Broberg D, 2018, COMPUT PHYS COMMUN, V226, P165, DOI 10.1016/j.cpc.2018.01.004
Carvalho PS, 2016, PHYS TEACH, V54, P244, DOI 10.1119/1.4944370
Galeriu C, 2013, PHYS TEACH, V51, P156, DOI 10.1119/1.4792011
Hahn MD, 2019, PHYS TEACH, V57, P114, DOI 10.1119/1.5088475
Hernandez D, 2015, J PHYS CONF SER, V582, DOI 10.1088/1742-6596/582/1/012007
McCaughey M, 2017, PHYS TEACH, V55, P274, DOI 10.1119/1.4981032
Mumford Stuart J., 2015, Computational Science and Discovery, V8, DOI 10.1088/1749-4699/8/1/014009
Price-Whelan AM, 2018, ASTRON J, V156, DOI 10.3847/1538-3881/aabc4f
Wicaksono M. F., 2020, IOP Conference Series: Materials Science and Engineering, V879, DOI 10.1088/1757-899X/879/1/012022
NR 10
TC 0
Z9 0
U1 2
U2 2
PU AMER ASSN PHYSICS TEACHERS
PI COLLEGE PK
PA 5110 ROANOKE PLACE SUITE 101, COLLEGE PK, MD 20740 USA
SN 0031-921X
EI 1943-4928
J9 PHYS TEACH
JI Phys. Teach.
PD FEB
PY 2021
VL 59
IS 2
BP 134
EP 135
DI 10.1119/10.0003472
PG 2
WC Education, Scientific Disciplines; Physics, Multidisciplinary
SC Education & Educational Research; Physics
GA QK1JR
UT WOS:000620136400023
DA 2021-04-21
ER
PT J
AU George, LT
Kale, R
Wadadekar, Y
AF George, Lijo T.
Kale, Ruta
Wadadekar, Yogesh
TI An upper limit calculator (UL-CALC) for undetected extended sources with
radio interferometers: radio halo upper limits
SO EXPERIMENTAL ASTRONOMY
LA English
DT Article; Early Access
DE Galaxies:clusters:general; Galaxies: clusters: intracluster medium;
Radiation mechanisms: non-thermal; Radio continuum:general; Methods:data
analysis; Software: data analysis
AB Radio halos are diffuse, extended sources of radio emission detected primarily in massive, merging galaxy clusters. In smaller and/or relaxed clusters, where no halos are detected, one can instead place upper limits to a possible radio emission. Detections and upper limits are both crucial to constrain theoretical models for the generation of radio halos. The upper limits are model dependent for radio interferometers and thus the process of obtaining these is tedious to perform manually. In this paper, we present a Python based tool to automate this process of estimating the upper limits. The tool allows users to create radio halos with defined parameters like physical size, redshift and brightness model. A family of radio halo models with a range of flux densities, decided based on the rms noise of the image, is then injected into the parent visibility file and imaged. The halo injected image and the original image are then compared to check for the radio halo detection using a threshold on the detected excess flux density. Injections separated by finer differences in the flux densities are carried out once the coarse range where the upper limit is likely to be located has been identified. The code recommends an upper limit and provides a range of images for manual inspection. The user may then decide on the upper limit. We discuss the advantages and limitations of this tool. A wider usage of this tool in the context of the ongoing and upcoming all sky surveys with the LOFAR and SKA is proposed with the aim of constraining the physics of radio halo formation. The tool is publicly available at .
C1 [George, Lijo T.; Kale, Ruta; Wadadekar, Yogesh] Tata Inst Fundamental Res, Natl Ctr Radio Astrophys, Post Bag 3, Pune 411007, Maharashtra, India.
RP George, LT (corresponding author), Tata Inst Fundamental Res, Natl Ctr Radio Astrophys, Post Bag 3, Pune 411007, Maharashtra, India.
EM ltg@ncra.tifr.res.in
FU Department of Atomic Energy, Government of IndiaDepartment of Atomic
Energy (DAE) [12-RD-TFR-5.02-0700]; DST-INSPIRE Faculty Award of the
Government of India
FX We acknowledge the support of the Department of Atomic Energy,
Government of India, under project no. 12-R&D-TFR-5.02-0700. We thank
the staff of the GMRT that made these observations possible. GMRT is run
by the National Centre for Radio Astrophysics of the Tata Institute of
Fundamental Research. RK acknowledges support from the DST-INSPIRE
Faculty Award of the Government of India. This research made use of
Astropy,3 a community-developed core Python package for Astronomy [1,
23]. This research made use of Astroquery, an astropy affiliated package
that contains a collection of tools to access online Astronomical data
[11].
CR Ade PAR, 2016, ASTRON ASTROPHYS, V594, DOI 10.1051/0004-6361/201525830
Bonafede A, 2017, MON NOT R ASTRON SOC, V470, P3465, DOI 10.1093/mnras/stx1475
Bonafede A., 2015, ADV ASTROPHYSICS SQU, P95
Brunetti G, 2007, ASTROPHYS J, V670, pL5, DOI 10.1086/524037
Brunetti G, 2016, MON NOT R ASTRON SOC, V458, P2584, DOI 10.1093/mnras/stw496
Condon JJ, 1998, ASTRON J, V115, P1693, DOI 10.1086/300337
Deo DK, 2017, EXP ASTRON, V44, P165, DOI 10.1007/s10686-017-9557-y
Feretti L, 2001, ASTRON ASTROPHYS, V373, P106, DOI 10.1051/0004-6361:20010581
Feretti L, 2012, ASTRON ASTROPHYS REV, V20, DOI 10.1007/s00159-012-0054-z
Ginsburg A, 2019, ASTRON J, V157, DOI 10.3847/1538-3881/aafc33
Giovannini G, 1999, NEW ASTRON, V4, P141, DOI 10.1016/S1384-1076(99)00018-4
Intema HT, 2009, ASTRON ASTROPHYS, V501, P1185, DOI 10.1051/0004-6361/200811094
Intema H.T, 2014, ARXIV14024889
Johnston-Hollitt M., 2017, ARXIV170604930
Johnston-Hollitt M., 2015, ADV ASTROPHYSICS SQU, P92
Kale R, 2015, ASTRON ASTROPHYS, V579, DOI 10.1051/0004-6361/201525695
Kale R, 2013, ASTRON ASTROPHYS, V557, DOI 10.1051/0004-6361/201321515
McMullin J. P., 2007, CASA ARCHITECTURE AP, P127
Murgia M, 2009, ASTRON ASTROPHYS, V499, P679, DOI 10.1051/0004-6361/200911659
Nayana AJ, 2017, MON NOT R ASTRON SOC, V467, P155, DOI 10.1093/mnras/stx044
Price-Whelan AM, 2018, ASTRON J, V156, DOI 10.3847/1538-3881/aabc4f
Shimwell TW, 2017, ASTRON ASTROPHYS, V598, DOI 10.1051/0004-6361/201629313
Thompson AR, 2017, ASTRON ASTROPHYS LIB, P1, DOI 10.1007/978-3-319-44431-4
van Weeren RJ, 2019, SPACE SCI REV, V215, DOI 10.1007/s11214-019-0584-z
Venturi T, 2008, ASTRON ASTROPHYS, V484, P327, DOI 10.1051/0004-6361:200809622
Venturi T, 2007, ASTRON ASTROPHYS, V463, P937, DOI 10.1051/0004-6361:20065961
NR 26
TC 0
Z9 0
U1 0
U2 0
PU SPRINGER
PI DORDRECHT
PA VAN GODEWIJCKSTRAAT 30, 3311 GZ DORDRECHT, NETHERLANDS
SN 0922-6435
EI 1572-9508
J9 EXP ASTRON
JI Exp. Astron.
DI 10.1007/s10686-020-09692-7
PG 14
WC Astronomy & Astrophysics
SC Astronomy & Astrophysics
GA QA6ZJ
UT WOS:000613591200002
DA 2021-04-21
ER
PT J
AU Grattarola, D
Alippi, C
AF Grattarola, Daniele
Alippi, Cesare
TI Graph Neural Networks in TensorFlow and Keras with Spektral
SO IEEE COMPUTATIONAL INTELLIGENCE MAGAZINE
LA English
DT Article
AB Graph neural networks have enabled the application of deep learning to problems that can be described by graphs, which are found throughout the different fields of science, from physics to biology, natural language processing, telecommunications or medicine. In this paper we present Spektral, an open-source Python library for building graph neural networks with TensorFlow and the Keras application programming interface. Spektral implements a large set of methods for deep learning on graphs, including message-passing and pooling operators, as well as utilities for processing graphs and loading popular benchmark datasets. The purpose of this library is to provide the essential building blocks for creating graph neural networks, focusing on the guiding principles of user-fr iendliness and quick prototyping on which Keras is based. Spektral is, therefore, suitable for absolute beginners and expert deep learning practitioners alike. In this work, we present an overview of Spektral's features and report the performance of the methods implemented by the library in scenarios of node classification, graph classification, and graph regression.
C1 [Grattarola, Daniele; Alippi, Cesare] Univ Svizzera Italiana, Lugano, Switzerland.
[Alippi, Cesare] Politecn Milan, Milan, Italy.
RP Grattarola, D (corresponding author), Univ Svizzera Italiana, Lugano, Switzerland.
EM daniele.grattarola@usi.ch
CR Abadi M, 2016, PROCEEDINGS OF OSDI'16: 12TH USENIX SYMPOSIUM ON OPERATING SYSTEMS DESIGN AND IMPLEMENTATION, P265
Allamanis M., 2017, ARXIV171100740
Battaglia P., 2016, ADV NEURAL INFORM PR, P4502
Battaglia P.W., 2018, ARXIV180601261
Berg R. v. d., 2017, ARXIV1706
Bianchi F.M., 2019, ARXIV190101343
Bianchi F. M., 2020, P 37 INT C MACH LEAR
Cangea C., 2018, ARXIV181101287
Chollet F., 2015, KERAS
De Cao N., 2018, ARXIV180809920
Defferrard M., 2016, ADV NEURAL INFORM PR, P3844
Do K, 2019, KDD'19: PROCEEDINGS OF THE 25TH ACM SIGKDD INTERNATIONAL CONFERENCCE ON KNOWLEDGE DISCOVERY AND DATA MINING, P750, DOI 10.1145/3292500.3330958
Du Jian, 2017, ARXIV171010370
Farrell S., 2018, NOVEL DEEP LEARNING
Fernandes P., 2018, ARXIV181101824
Fey M., 2019, ARXIV190302428
Gainza P, 2020, NAT METHODS, V17, P184, DOI 10.1038/s41592-019-0666-6
Gao H, 2019, GRAPH U NETS
Gilmer J., 2017, ARXIV170401212
Hamilton W., 2017, ADV NEURAL INF PROCE, P1024
Hamrick J.B., 2018, ARXIV180601203
Ivanov S., 2019, UNDERSTANDING ISOMOR
Keras, 2019, WHY USE KERAS
Kersting K., 2016, BENCHMARK DATA SETS
Kipf T., 2018, ARXIV180204687
Kipf T. N., 2016, INT C LEARN REPR ICL
Klicpera J., 2019, IINT C LEARN REPR IC
Klicpera J., 2020, ARXIV200303123
Lee J., 2019, SELF ATTENTION GRAPH
Li Y., 2015, ARXIV151105493
Li Y., 2017, ARXIV170701926
Ramakrishnan R, 2014, SCI DATA, V1, DOI 10.1038/sdata.2014.22
Sanchez-Gonzalez A., 2018, ARXIV180601242
Santoro A., 2017, ADV NEURAL INFORM PR, V30, P4967
Scarselli F, 2009, IEEE T NEURAL NETWOR, V20, P61, DOI 10.1109/TNN.2008.2005605
Schlichtkrull Michael, 2018, The Semantic Web. 15th International Conference, ESWC 2018. Proceedings: LNCS 10843, P593, DOI 10.1007/978-3-319-93417-4_38
Sen P, 2008, AI MAG, V29, P93, DOI 10.1609/aimag.v29i3.2157
Shang JY, 2019, THIRTY-THIRD AAAI CONFERENCE ON ARTIFICIAL INTELLIGENCE / THIRTY-FIRST INNOVATIVE APPLICATIONS OF ARTIFICIAL INTELLIGENCE CONFERENCE / NINTH AAAI SYMPOSIUM ON EDUCATIONAL ADVANCES IN ARTIFICIAL INTELLIGENCE, P1126
Shchur Oleksandr, 2018, ARXIV181105868
Simonovsky Martin, 2017, P IEEE C COMP VIS PA
Stark C, 2006, NUCLEIC ACIDS RES, V34, pD535, DOI 10.1093/nar/gkj109
Thekumparampil Kiran K, 2018, ARXIV180303735
Velickovc P., 2017, ARXIV171010903
Wang M., 2019, ARXIV190901315
Wang Y., 2018, ARXIV180107829
Xie T, 2018, PHYS REV LETT, V120, DOI 10.1103/PhysRevLett.120.145301
Xu K., 2019, INT C LEARN REPR ICL
Ying R., 2018, ARXIV180608804
Ying R, 2018, KDD'18: PROCEEDINGS OF THE 24TH ACM SIGKDD INTERNATIONAL CONFERENCE ON KNOWLEDGE DISCOVERY & DATA MINING, P974, DOI 10.1145/3219819.3219890
You J, 2018, ARXIV180208773
Zambaldi V., 2018, ARXIV180601830
Zhang M., 2018, P AAAI C ART INT
Zitnik M, 2017, BIOINFORMATICS, V33, pI190, DOI 10.1093/bioinformatics/btx252
NR 53
TC 0
Z9 0
U1 4
U2 4
PU IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
PI PISCATAWAY
PA 445 HOES LANE, PISCATAWAY, NJ 08855-4141 USA
SN 1556-603X
EI 1556-6048
J9 IEEE COMPUT INTELL M
JI IEEE Comput. Intell. Mag.
PD FEB
PY 2021
VL 16
IS 1
BP 99
EP 106
DI 10.1109/MCI.2020.3039072
PG 8
WC Computer Science, Artificial Intelligence
SC Computer Science
GA PS2ZO
UT WOS:000607796100011
OA Bronze
DA 2021-04-21
ER
PT J
AU Kuhbach, M
Bajaj, P
Zhao, H
Celik, MH
Jagle, EA
Gault, B
AF Kuehbach, Markus
Bajaj, Priyanshu
Zhao, Huan
Celik, Murat H.
Jaegle, Eric A.
Gault, Baptiste
TI On strong-scaling and open-source tools for analyzing atom probe
tomography data
SO NPJ COMPUTATIONAL MATERIALS
LA English
DT Article
AB The development of strong-scaling computational tools for high-throughput methods with an open-source code and transparent metadata standards has successfully transformed many computational materials science communities. While such tools are mature already in the condensed-matter physics community, the situation is still very different for many experimentalists. Atom probe tomography (APT) is one example. This microscopy and microanalysis technique has matured into a versatile nano-analytical characterization tool with applications that range from materials science to geology and possibly beyond. Here, data science tools are required for extracting chemo-structural spatial correlations from the reconstructed point cloud. For APT and other high-end analysis techniques, post-processing is mostly executed with proprietary software tools, which are opaque in their execution and have often limited performance. Software development by members of the scientific community has improved the situation but compared to the sophistication in the field of computational materials science several gaps remain. This is particularly the case for open-source tools that support scientific computing hardware, tools which enable high-throughput workflows, and open well-documented metadata standards to align experimental research better with the fair data stewardship principles. To this end, we introduce paraprobe, an open-source tool for scientific computing and high-throughput studying of point cloud data, here exemplified with APT. We show how to quantify uncertainties while applying several computational geometry, spatial statistics, and clustering tasks for post-processing APT datasets as large as two billion ions. These tools work well in concert with Python and HDF5 to enable several orders of magnitude performance gain, automation, and reproducibility.
C1 [Kuehbach, Markus; Bajaj, Priyanshu; Zhao, Huan; Jaegle, Eric A.; Gault, Baptiste] Max Planck Inst Eisenforsch GmbH MPIE, Dusseldorf, Germany.
[Bajaj, Priyanshu] M4p Mat Solut GmbH, Magdeburg, Germany.
[Celik, Murat H.] Julich Supercomp Ctr JSC, Inst Adv Simulat IAS, Julich, Germany.
[Jaegle, Eric A.] Univ Bundeswehr Munchen, Neubiberg, Germany.
[Gault, Baptiste] Imperial Coll London, Royal Sch Mines, Dept Mat, London, England.
RP Kuhbach, M (corresponding author), Max Planck Inst Eisenforsch GmbH MPIE, Dusseldorf, Germany.
EM m.kuehbach@mpie.de
OI Gault, Baptiste/0000-0002-4934-0458; Zhao, Huan/0000-0001-8840-084X
FU Max-Planck-Society's Research Network on Big-Data-Driven Materials
Science; German Research FoundationGerman Research Foundation (DFG) [RO
2342/8-1]
FX M.K. gratefully acknowledges the funding and computing time grants
through BiGmax, the Max-Planck-Society's Research Network on
Big-Data-Driven Materials Science and the funding from the German
Research Foundation through project RO 2342/8-1. The authors appreciate
computer administration advice from Berthold Beckschafer and Achim Kuhl.
The work catalyzed from scientific discussions with Andrew Breen,
Baptiste Gault, Leigh Stephenson, Jan Janssen, and Franz Roters on how
to professionalize tools for APT.
CR Amdahl G. M, 1967, AFIPS C P, P483, DOI DOI 10.1145/1465482.1465560
ASTM International, 2015, ISOASTM5290015
Balasubramanian L., 2012, INT J COMPUT APPL, V42, P35, DOI DOI 10.5120/5819-8132
Barnes JP, 2018, SCRIPTA MATER, V148, P91, DOI 10.1016/j.scriptamat.2017.05.012
Bokeh, 2020, BOK PYTH LIB INT VIS
Boll T, 2007, ULTRAMICROSCOPY, V107, P796, DOI 10.1016/j.ultramic.2007.02.011
Breen A, 2013, ULTRAMICROSCOPY, V132, P92, DOI 10.1016/j.ultramic.2013.02.014
Brinkhoff T, 1996, PROC INT CONF DATA, P258, DOI 10.1109/ICDE.1996.492114
Cameron ME, 2004, MATH VISUAL, P3
Cecen A, 2018, ACTA MATER, V158, P53, DOI 10.1016/j.actamat.2018.07.056
Ceguerra AV, 2013, CURR OPIN SOLID ST M, V17, P224, DOI 10.1016/j.cossms.2013.09.006
Chandra Robit, 2001, PARALLEL PROGRAMMING
Chang YH, 2019, NAT COMMUN, V10, DOI 10.1038/s41467-019-08752-7
Cojocaru-Miredin O, 2018, SCRIPTA MATER, V148, P106, DOI 10.1016/j.scriptamat.2017.03.034
Cressie NAC., 1991, STAT SPATIAL DATA
Curtarolo S, 2013, NAT MATER, V12, P191, DOI [10.1038/nmat3568, 10.1038/NMAT3568]
Day AC., 2019, MICROSC MICROANAL, V25, P338, DOI [10.1017/S1431927619002423, DOI 10.1017/S1431927619002423]
De Geuser F, 2011, MICROSC RES TECHNIQ, V74, P257, DOI 10.1002/jemt.20899
De Geuser F, 2020, ACTA MATER, V188, P406, DOI 10.1016/j.actamat.2020.02.023
Draxl C, 2020, HDB MAT MODELING
EDELSBRUNNER H, 1994, ACM T GRAPHIC, V13, P43, DOI 10.1145/174462.156635
Ester M., 1996, KDD-96 Proceedings. Second International Conference on Knowledge Discovery and Data Mining, P226
Felfer P, 2015, ULTRAMICROSCOPY, V150, P30, DOI 10.1016/j.ultramic.2014.11.015
Felfer P, 2016, ULTRAMICROSCOPY, V169, P62, DOI 10.1016/j.ultramic.2016.07.008
Felfer P, 2015, ULTRAMICROSCOPY, V159, P438, DOI 10.1016/j.ultramic.2015.06.002
Felfer P, 2013, ULTRAMICROSCOPY, V132, P100, DOI 10.1016/j.ultramic.2013.03.004
Gault B, 2011, ULTRAMICROSCOPY, V111, P448, DOI 10.1016/j.ultramic.2010.11.016
Gault B., 2012, ATOM PROBE MICROSCOP
Geiser BP, 2007, MICROSC MICROANAL, V13, P437, DOI 10.1017/S1431927607070948
Ghamarian I, 2019, ULTRAMICROSCOPY, V200, P28, DOI 10.1016/j.ultramic.2019.01.011
Giddings AD, 2018, SCRIPTA MATER, V148, P82, DOI 10.1016/j.scriptamat.2017.09.004
Gin S, 2017, GEOCHIM COSMOCHIM AC, V202, P57, DOI 10.1016/j.gca.2016.12.029
Gotz M., 2015, P WORKSH MACH LEARN, P1
Gwalani H, 2019, MODEL SIMUL MATER SC, V27, DOI 10.1088/1361-651X/ab4b3d
Haley D, 2015, ULTRAMICROSCOPY, V159, P338, DOI 10.1016/j.ultramic.2015.03.005
Haley D, 2009, PHILOS MAG, V89, P925, DOI 10.1080/14786430902821610
Haley D, 2020, 3DEPICT VISUALISATIO
Haley D, 2020, APTTOOLS
Hellman OC, 2000, MICROSC MICROANAL, V6, P437, DOI 10.1007/s100050010051
Hennessy J. L., 2012, COMPUTER ARCHITECTUR
Herbig M, 2018, SCRIPTA MATER, V148, P98, DOI 10.1016/j.scriptamat.2017.03.017
Himanen L, 2019, ADV SCI, V6, DOI 10.1002/advs.201900808
Hono K, 1999, ACTA MATER, V47, P3127, DOI 10.1016/S1359-6454(99)00175-5
Hudson D, 2011, ULTRAMICROSCOPY, V111, P480, DOI 10.1016/j.ultramic.2010.11.007
Hyde JM, 2000, P MRS FALL M 2000 S, V650, P6, DOI DOI 0.1557/PROC-650-R6.6
Jagle EA, 2014, MICROSC MICROANAL, V20, P1662, DOI 10.1017/S1431927614013294
Janssen J, 2019, COMP MATER SCI, V163, P24, DOI 10.1016/j.commatsci.2018.07.043
Katnagallu S, 2018, MATER CHARACT, V146, P307, DOI 10.1016/j.matchar.2018.02.040
Keutgen J., 2020, MICROSC MICROANAL, P1
Kirchmayer A, 2020, ADV ENG MATER, V22, DOI 10.1002/adem.202000149
Kontis P, 2018, SCRIPTA MATER, V145, P76, DOI 10.1016/j.scriptamat.2017.10.005
Kuhbach M, 2020, MODEL SIMUL MATER SC, V28, DOI 10.1088/1361-651X/ab7f8c
Kuhbach M, 2019, MICROSC MICROANAL, V25, P320, DOI 10.1017/S1431927618016252
Kuhbach M, 2020, OPEN STRONG SCALING
Kuhbach M, 2020, **DROPPED REF**
Kuzmina M, 2015, SCIENCE, V349, P1080, DOI 10.1126/science.aab2633
Larson D.J., 2013, LOCAL ELECTRODE ATOM
Lefebvre W., 2016, ATOM PROBE TOMOGRAPH
Li T, 2018, NAT CATAL, V1, P300, DOI 10.1038/s41929-018-0043-3
Lu H, 2018, INT PARALL DISTRIB P, P54, DOI 10.1109/IPDPS.2018.00016
Marquis E. A., 2002, MICROSTRUCTURAL EVOL
McCarroll IE, 2020, MATER TODAY ADV, V7, DOI 10.1016/j.mtadv.2020.100090
Miller M.K., 1996, ATOM PROBE FIELD ION
Montoya JH, 2017, NPJ COMPUT MATER, V3, DOI 10.1038/s41524-017-0017-z
Moody MP, 2008, MICROSC RES TECHNIQ, V71, P542, DOI 10.1002/jemt.20582
Moody MP, 2009, ULTRAMICROSCOPY, V109, P815, DOI 10.1016/j.ultramic.2009.03.016
Morozov D, 2016, SC '16: PROCEEDINGS OF THE INTERNATIONAL CONFERENCE FOR HIGH PERFORMANCE COMPUTING, NETWORKING, STORAGE AND ANALYSIS, P728, DOI 10.1109/SC.2016.61
Okabe A., 2000, SPATIAL TESSELLATION
Patwary MMA, 2016, INT PARALL DISTRIB P, P494, DOI 10.1109/IPDPS.2016.57
Perea DE, 2016, SCI REP-UK, V6, DOI 10.1038/srep22321
Philippe T, 2010, ULTRAMICROSCOPY, V110, P862, DOI 10.1016/j.ultramic.2010.03.004
Piazolo S, 2016, NAT COMMUN, V7, DOI 10.1038/ncomms10490
Pizzi G, 2016, COMP MATER SCI, V111, P218, DOI 10.1016/j.commatsci.2015.09.013
Prabhat, 2014, HIGH PERFORMANCE PAR, V1
Reinhard DA., 2019, MICROSC MICROANAL, V25, P302, DOI [10.1017/S1431927619002241, DOI 10.1017/S1431927619002241]
Ringer SP, 2020, ATOM PROBE WORKBENCH
Robson JD, 2004, ACTA MATER, V52, P4669, DOI 10.1016/j.actamat.2004.06.024
Rusitzka KAK, 2018, SCI REP-UK, V8, DOI 10.1038/s41598-018-36110-y
Saxey DW, 2018, SCRIPTA MATER, V148, P115, DOI 10.1016/j.scriptamat.2017.11.014
Saxey DW, 2011, ULTRAMICROSCOPY, V111, P473, DOI 10.1016/j.ultramic.2010.11.021
Schreiber DK, 2018, ULTRAMICROSCOPY, V194, P89, DOI 10.1016/j.ultramic.2018.07.010
Seal Sudip, 2008, 2008 37th International Conference on Parallel Processing (ICPP), P338, DOI 10.1109/ICPP.2008.73
Seal SK, 2014, PROCEEDINGS OF 2014 IEEE INTERNATIONAL PARALLEL & DISTRIBUTED PROCESSING SYMPOSIUM WORKSHOPS (IPDPSW), P1180, DOI 10.1109/IPDPSW.2014.133
Sepehri-Amin H, 2017, ACTA MATER, V126, P1, DOI 10.1016/j.actamat.2016.12.050
Snir M., 1998, MPI THE COMPLETE REF, V1
Stephenson LT, 2007, MICROSC MICROANAL, V13, P448, DOI 10.1017/S1431927607070900
Sudbrack CK, 2006, PHYS REV B, V73, DOI 10.1103/PhysRevB.73.212101
The CGAL Project, 2018, CGAL USER REFERENCE, V4
Ulfig RM., 2017, MICROSC MICROANAL, V23, P40, DOI [10.1017/S1431927617000885, DOI 10.1017/S1431927617000885]
Valley JW, 2014, NAT GEOSCI, V7, P219, DOI [10.1038/ngeo2075, 10.1038/NGEO2075]
Voyles PM, 2002, NATURE, V416, P826, DOI 10.1038/416826a
Wei Y, 2020, MACHINE LEARNING ENH
White LF, 2017, NAT COMMUN, V8, DOI 10.1038/ncomms15597
Wilkinson MD, 2016, SCI DATA, V3, DOI 10.1038/sdata.2016.18
Yao L, 2010, PHIL MAG LETT, V90, P121, DOI 10.1080/09500830903472997
Zelenty J, 2017, MICROSC MICROANAL, V23, P269, DOI 10.1017/S1431927617000320
Zhao H, 2018, ACTA MATER, V156, P318, DOI 10.1016/j.actamat.2018.07.003
Zhao H, 2018, SCRIPTA MATER, V154, P106, DOI 10.1016/j.scriptamat.2018.05.024
NR 98
TC 0
Z9 0
U1 0
U2 0
PU NATURE RESEARCH
PI BERLIN
PA HEIDELBERGER PLATZ 3, BERLIN, 14197, GERMANY
EI 2057-3960
J9 NPJ COMPUT MATER
JI npj Comput. Mater.
PD JAN 29
PY 2021
VL 7
IS 1
AR 21
DI 10.1038/s41524-020-00486-1
PG 10
WC Chemistry, Physical; Materials Science, Multidisciplinary
SC Chemistry; Materials Science
GA QE7QY
UT WOS:000616403300005
OA DOAJ Gold
DA 2021-04-21
ER
PT J
AU Bishnoi, S
Ravinder, R
Grover, HS
Kodamana, H
Krishnan, NMA
AF Bishnoi, Suresh
Ravinder, R.
Grover, Hargun Singh
Kodamana, Hariprasad
Krishnan, N. M. Anoop
TI Scalable Gaussian processes for predicting the optical, physical,
thermal, and mechanical properties of inorganic glasses with large
datasets
SO MATERIALS ADVANCES
LA English
DT Article
ID DESIGN; DISSOLUTION; MODEL
AB Among machine learning approaches, Gaussian process regression (GPR) is an extremely useful technique to predict composition-property relationships in glasses. The GPR's main advantage over other machine learning methods is its inherent ability to provide the standard deviation of the predictions. However, the method remains restricted to small datasets due to the substantial computational cost associated with it. Herein, using a scalable GPR algorithm, namely, kernel interpolation for scalable structured Gaussian processes (KISS-GP) along with massively scalable GP (MSGP), we develop composition-property models for inorganic glasses. The models are based on a large dataset with more than 100 000 glass compositions, 37 components, and nine crucial properties: density, Young's, shear, bulk moduli, thermal expansion coefficient, Vickers' hardness, refractive index, glass transition temperature, and liquidus temperature. We show that the models developed here are superior to the state-of-the-art machine learning models. We also demonstrate that the GPR models can reasonably capture the underlying composition-dependent physics, even in the regions where there are very few training data. Finally, to accelerate glass design, the models developed here are shared publicly as part of a package, namely, Python for Glass Genomics (PyGGi, see: http://pyggi.iitd.ac.in).
C1 [Bishnoi, Suresh; Ravinder, R.; Grover, Hargun Singh; Krishnan, N. M. Anoop] Indian Inst Technol Delhi, Dept Civil Engn, New Delhi 110016, India.
[Kodamana, Hariprasad] Indian Inst Technol Delhi, Dept Chem Engn, New Delhi 110016, India.
[Krishnan, N. M. Anoop] Indian Inst Technol Delhi, Dept Mat Sci & Engn, New Delhi 110016, India.
RP Krishnan, NMA (corresponding author), Indian Inst Technol Delhi, Dept Civil Engn, New Delhi 110016, India.; Kodamana, H (corresponding author), Indian Inst Technol Delhi, Dept Chem Engn, New Delhi 110016, India.; Krishnan, NMA (corresponding author), Indian Inst Technol Delhi, Dept Mat Sci & Engn, New Delhi 110016, India.
EM kadamana@iitd.ac.in; krishnan@iitd.ac.in
RI Mana, Anoop Krishnan Naduvath/AAI-6494-2020
OI Mana, Anoop Krishnan Naduvath/0000-0003-1500-4947
FU Department of Science and Technology, India, under the INSPIRE faculty
schemeDepartment of Science & Technology (India)Department of Science &
Technology (DOST), Philippines [DST/INSPIRE/04/2016/002774]; DST SERB
Early Career Award [ECR/2018/002228]
FX NMAK acknowledges the financial support for this research provided by
the Department of Science and Technology, India, under the INSPIRE
faculty scheme (DST/INSPIRE/04/2016/002774) and DST SERB Early Career
Award (ECR/2018/002228). The authors thank the IIT Delhi HPC facility
for providing the computational and storage resources.
CR Alcobaca E, 2020, ACTA MATER, V188, P92, DOI 10.1016/j.actamat.2020.01.047
Bassman L, 2018, NPJ COMPUT MATER, V4, DOI 10.1038/s41524-018-0129-0
Bishnoi S, 2019, J NON-CRYST SOLIDS, V524, DOI 10.1016/j.jnoncrysol.2019.119643
Brauer DS, 2007, J NON-CRYST SOLIDS, V353, P263, DOI 10.1016/j.jnoncrysol.2006.12.005
Cassar DR, 2018, ACTA MATER, V159, P249, DOI 10.1016/j.actamat.2018.08.022
Gardner J., 2018, ADV NEURAL INFORM PR, P7576
Gopakumar AM, 2018, SCI REP-UK, V8, DOI 10.1038/s41598-018-21936-3
Gu GX, 2018, EXTREME MECH LETT, V18, P19, DOI 10.1016/j.eml.2017.10.001
Han TH, 2020, ACTA BIOMATER, V107, P286, DOI 10.1016/j.actbio.2020.02.037
Hauseux P, 2018, APPL MATH MODEL, V62, P86, DOI 10.1016/j.apm.2018.04.021
Hu YJ, 2020, NPJ COMPUT MATER, V6, DOI 10.1038/s41524-020-0291-z
Kasimuthumaniyan S, 2020, J NON-CRYST SOLIDS, V534, DOI 10.1016/j.jnoncrysol.2020.119955
Krishnan NMA, 2018, J NON-CRYST SOLIDS, V487, P37, DOI 10.1016/j.jnoncrysol.2018.02.023
Lillington JNP, 2020, J NON-CRYST SOLIDS, V546, DOI 10.1016/j.jnoncrysol.2020.120276
Liu H, 2019, NPJ MAT DEGRAD, V3, DOI 10.1038/s41529-019-0094-1
Lu X., 2020, SOLIDS, P120490
Lu XN, 2019, J PHYS CHEM B, V123, P1412, DOI 10.1021/acs.jpcb.8b11108
Makishima A., 1973, Journal of Non-Crystalline Solids, V12, P35, DOI 10.1016/0022-3093(73)90053-7
Mauro JC, 2016, CHEM MATER, V28, P4267, DOI 10.1021/acs.chemmater.6b01054
Montazerian M, 2020, INT MATER REV, V65, P297, DOI 10.1080/09506608.2019.1694779
Pleiss G., 2018, ARXIV180306058
Rappel H, 2019, EUR J MECH A-SOLID, V75, P169, DOI 10.1016/j.euromechsol.2019.01.001
Rappel H, 2019, PROBABILIST ENG MECH, V55, P28, DOI 10.1016/j.probengmech.2018.08.004
Rappel H, 2018, MECH TIME-DEPEND MAT, V22, P221, DOI 10.1007/s11043-017-9361-0
Rasmussen CE, 2004, LECT NOTES ARTIF INT, V3176, P63, DOI 10.1007/978-3-540-28650-9_4
Ravinder R, 2020, MATER HORIZ, V7, P1819, DOI 10.1039/d0mh00162g
SILVERMAN BW, 1985, J R STAT SOC B, V47, P1
Smedskjaer MM, 2011, J PHYS CHEM B, V115, P12930, DOI 10.1021/jp208796b
Snelson E, 2006, P ADV NEURAL INF PRO, P1257
Stevensson B, 2018, PHYS CHEM CHEM PHYS, V20, P8192, DOI 10.1039/c7cp08593a
Tewari A., 2020, DATA CENTRIC ENG, V1, pe8
Varshneya A., 2013, FUNDAMENTALS INORGAN, V2nd
Wang MY, 2018, J NON-CRYST SOLIDS, V498, P294, DOI 10.1016/j.jnoncrysol.2018.04.063
Wilkinson Collin J., 2019, Journal of Non-Crystalline Solids: X, V2, P5, DOI 10.1016/j.nocx.2019.100019
Wilson A, 2015, INT C MACH LEARN, P1775
Wilson A. G., 2005, ARXIV151101870
Yang K, 2019, SCI REP-UK, V9, DOI 10.1038/s41598-019-45344-3
Yu YT, 2018, J NON-CRYST SOLIDS, V489, P16, DOI 10.1016/j.jnoncrysol.2018.03.015
NR 38
TC 1
Z9 1
U1 0
U2 0
PU ROYAL SOC CHEMISTRY
PI CAMBRIDGE
PA THOMAS GRAHAM HOUSE, SCIENCE PARK, MILTON RD, CAMBRIDGE CB4 0WF, CAMBS,
ENGLAND
EI 2633-5409
J9 MATER ADV
JI Mater. Adv.
PD JAN 7
PY 2021
VL 2
IS 1
BP 477
EP 487
DI 10.1039/d0ma00764a
PG 11
WC Materials Science, Multidisciplinary
SC Materials Science
GA PY5ML
UT WOS:000612088000032
OA DOAJ Gold
DA 2021-04-21
ER
PT J
AU Bessagnet, B
Menut, L
Beauchamp, M
AF Bessagnet, Bertrand
Menut, Laurent
Beauchamp, Maxime
TI An N-dimensional Fortran interpolation programme (NterGeo.v2020a) for
geophysics sciences - application to a back-trajectory programme
(Backplumes.v2020r1) using CHIMERE or WRF outputs
SO GEOSCIENTIFIC MODEL DEVELOPMENT
LA English
DT Article
ID MODEL; TRANSPORT; URBAN
AB An interpolation programme coded in Fortran for irregular N-dimensional cases is presented and freely available. The need for interpolation procedures over irregular meshes or matrixes with interdependent input data dimensions is frequent in geophysical models. Also, these models often embed look-up tables of physics or chemistry modules. Fortran is a fast and powerful language and is highly portable. It is easy to interface models written in Fortran with each other. Our programme does not need any libraries; it is written in standard Fortran and tested with two usual compilers. The programme is fast and competitive compared to current Python libraries. A normalization option parameter is provided when considering different types of units on each dimension. Some tests and examples are provided and available in the code package. Moreover, a geophysical application embedding this interpolation programme is provided and discussed; it consists in determining back trajectories using chemistry-transport or mesoscale meteorological model outputs, respectively, from the widely used CHIMERE and Weather Research and Forecasting (WRF) models.
C1 [Bessagnet, Bertrand; Menut, Laurent] Sorbonne Univ, PSL Univ, CNRS, LMD IPSL,Ecole Polytech,Inst Polytech Paris,ENS, F-91128 Palaiseau, France.
[Bessagnet, Bertrand] Citepa, Tech Reference Ctr Air Pollut & Climate Change, 42 Rue Paradis, F-75010 Paris, France.
[Beauchamp, Maxime] Lab STICC UMR CNRS, IMT Atlantique, 655 Ave TechnopOle, F-29280 Plouzane, France.
RP Bessagnet, B (corresponding author), Sorbonne Univ, PSL Univ, CNRS, LMD IPSL,Ecole Polytech,Inst Polytech Paris,ENS, F-91128 Palaiseau, France.; Bessagnet, B (corresponding author), Citepa, Tech Reference Ctr Air Pollut & Climate Change, 42 Rue Paradis, F-75010 Paris, France.
EM bertrand.bessagnet@lmd.polytechnique.fr
RI Bessagnet, Bertrand/AAB-9241-2019; MENUT, Laurent/O-2296-2016
OI Bessagnet, Bertrand/0000-0003-2062-4681; MENUT,
Laurent/0000-0001-9776-0812
FU DGA (French Directorate General of Armaments) [2018 60 0074]
FX This research was funded by the DGA (French Directorate General of
Armaments; grant no. 2018 60 0074) in the framework of the NETDESA
project.
CR Donner LJ, 2011, J CLIMATE, V24, P3484, DOI 10.1175/2011JCLI3955.1
Flamant C, 2018, ATMOS CHEM PHYS, V18, P12363, DOI 10.5194/acp-18-12363-2018
HARDY RL, 1971, J GEOPHYS RES, V76, P1905, DOI 10.1029/JB076i008p01905
HARDY RL, 1990, COMPUT MATH APPL, V19, P163, DOI 10.1016/0898-1221(90)90272-L
HOFSTRA N, 2008, J GEOPHYS RES-ATMOS, V113, DOI DOI 10.1029/2008JD010100
Hong SY, 2006, MON WEATHER REV, V134, P2318, DOI 10.1175/MWR3199.1
Kouatchou J., 2018, NASA MODELING GURU B
Lin JC, 2003, J GEOPHYS RES-ATMOS, V108, DOI 10.1029/2002JD003161
Mailler S, 2016, ATMOS CHEM PHYS, V16, P1219, DOI 10.5194/acp-16-1219-2016
Mailler S, 2017, GEOSCI MODEL DEV, V10, P2397, DOI 10.5194/gmd-10-2397-2017
Menut L, 2015, ATMOS CHEM PHYS, V15, P7897, DOI 10.5194/acp-15-7897-2015
Menut L., 2020, BACKPLUMES PROGRAM V
Nehrkorn T, 2010, METEOROL ATMOS PHYS, V107, P51, DOI 10.1007/s00703-010-0068-x
Nenes A, 1998, AQUAT GEOCHEM, V4, P123, DOI 10.1023/A:1009604003981
Nenes A, 1999, ATMOS ENVIRON, V33, P1553, DOI 10.1016/S1352-2310(98)00352-5
Pielke R.A., 1984, MESOSCALE METEOROLOG
Pisso I, 2019, GEOSCI MODEL DEV, V12, P4955, DOI 10.5194/gmd-12-4955-2019
Rap A, 2009, J ATMOS SCI, V66, P105, DOI 10.1175/2008JAS2626.1
Scipy C., 2014, INTERPOLATE UNSTRUCT
Shepard D, 1968, P517, DOI [10.1145/800186.810616, DOI 10.1145/800186.810616]
Skamarock W. C., 2008, 1125 NCAR
Stein AF, 2015, B AM METEOROL SOC, V96, P2059, DOI 10.1175/BAMS-D-14-00110.1
Stull R. B., 1988, INTRO BOUNDARY LAYER
Sun T, 2019, GEOSCI MODEL DEV, V12, P2781, DOI 10.5194/gmd-12-2781-2019
TROEN I, 1986, BOUND-LAY METEOROL, V37, P129, DOI 10.1007/BF00122760
Wessel P, 2019, GEOCHEM GEOPHY GEOSY, V20, P5556, DOI 10.1029/2019GC008515
Zender CS, 2008, ENVIRON MODELL SOFTW, V23, P1338, DOI 10.1016/j.envsoft.2008.03.004
NR 27
TC 0
Z9 0
U1 0
U2 0
PU COPERNICUS GESELLSCHAFT MBH
PI GOTTINGEN
PA BAHNHOFSALLEE 1E, GOTTINGEN, 37081, GERMANY
SN 1991-959X
EI 1991-9603
J9 GEOSCI MODEL DEV
JI Geosci. Model Dev.
PD JAN 7
PY 2021
VL 14
IS 1
BP 91
EP 106
DI 10.5194/gmd-14-91-2021
PG 16
WC Geosciences, Multidisciplinary
SC Geology
GA PS0VM
UT WOS:000607650100002
OA DOAJ Gold
DA 2021-04-21
ER
PT J
AU Mandanici, A
Sara, SA
Fiumara, G
Mandaglio, G
AF Mandanici, Andrea
Sara, Salvatore Alessandro
Fiumara, Giacomo
Mandaglio, Giuseppe
TI Studying Physics, Getting to Know Python: RC Circuit, Simple
Experiments, Coding, and Data Analysis With Raspberry Pi
SO COMPUTING IN SCIENCE & ENGINEERING
LA English
DT Article
AB Raspberry Pi (RPi) is a well-known single-board computer natively equipped with a Linux-based operating system, Raspbian, and a powerful programming language, Python. In this article, we propose an integrated project on physics and computer science exploiting RPi and Python: a set of lab activities, coding, and discussion related to the study of charging and discharging phases of a capacitor in an RC circuit. In our simple experiments, entirely computer-controlled, the RPi and Python scripts are used to: (i) apply a known constant voltage to the circuit at a desired time; (ii) measure the voltage on selected circuit elements as a function of time; (iii) evaluate and analyze experimental data. This approach is based on inexpensive hardware and open source software. It allows a hands-on experience with electric circuits and with dedicated examples of Python coding. The codes involve Python modules such as Numpy, Scipy, and Matplotlib that prove to be easy to use and efficient for our goals, supporting the choice of Python language for further study or research tasks.
C1 [Mandanici, Andrea; Sara, Salvatore Alessandro; Fiumara, Giacomo; Mandaglio, Giuseppe] Univ Messina, Dept MIFT, I-98166 Messina, Italy.
RP Mandanici, A (corresponding author), Univ Messina, Dept MIFT, I-98166 Messina, Italy.
EM andrea.mandanici@unime.it; alessandro.sara93@gmail.com;
giacomo.fiumara@unime.it; giuseppe.mandaglio@unime.it
RI Mandanici, Andrea/K-4349-2015
OI Mandanici, Andrea/0000-0002-3238-4948
CR Colbry D, 2020, AUTHOREA, DOI [10.22541/ au.159309337.74459966, DOI 10.22541/AU.159309337.74459966]
Galeriu C, 2015, PHYS TEACH, V53, P285, DOI 10.1119/1.4917435
Mada Sanjaya W. S., 2018, Journal of Physics: Conference Series, V1090, DOI 10.1088/1742-6596/1090/1/012015
Malthe-Sorenssen A, 2015, ELEMENTARY MECH USIN, DOI [10.1007/ 978-3-319-19596-4, DOI 10.1007/978-3-319-19596-4]
Mandanici Andrea, 2020, Physics Education, V55, DOI 10.1088/1361-6552/ab73d2
Mandanici A, 2018, EUR J PHYS, V39, DOI 10.1088/1361-6404/aad16a
Pereira N. S. A., 2016, Physics Education, V51, DOI 10.1088/0031-9120/51/6/065007
Pine D. J, 2019, INTRO PYTHON SCI ENG, DOI [10.1201/9780429506413, DOI 10.1201/9780429506413]
Robinson Ian, 2018, Physics Education, V53, DOI 10.1088/1361-6552/aaacb2
Sherwood B. A., 2015, MATTER INTERACTIONS
Singh P., 2015, Physics Education, V50, P317, DOI 10.1088/0031-9120/50/3/317
NR 11
TC 0
Z9 0
U1 0
U2 0
PU IEEE COMPUTER SOC
PI LOS ALAMITOS
PA 10662 LOS VAQUEROS CIRCLE, PO BOX 3014, LOS ALAMITOS, CA 90720-1314 USA
SN 1521-9615
EI 1558-366X
J9 COMPUT SCI ENG
JI Comput. Sci. Eng.
PD JAN
PY 2021
VL 23
IS 1
BP 93
EP 96
DI 10.1109/MCSE.2020.3037002
PG 4
WC Computer Science, Interdisciplinary Applications
SC Computer Science
GA QO8XG
UT WOS:000623419900011
DA 2021-04-21
ER
PT J
AU Giese, F
Konstandin, T
Schmitz, K
van de Vis, J
AF Giese, Felix
Konstandin, Thomas
Schmitz, Kai
van de Vis, Jorinde
TI Model-independent energy budget for LISA
SO JOURNAL OF COSMOLOGY AND ASTROPARTICLE PHYSICS
LA English
DT Article
DE cosmological phase transitions; gravitational waves / sources; particle
physics cosmology connection
AB We provide an easy method to obtain the kinetic energy fraction in gravitational waves, generated during a cosmological first-order phase transition, as a function of only the wall velocity and quantities that can be determined from the particle physics model at the nucleation temperature. This generalizes recent work that achieved this goal for detonations. Here we present the corresponding results for deflagrations and hybrids. Unlike for detonations, the sound speed in the symmetric phase also enters the analysis. We perform a detailed comparison between our model-independent approach and other approaches in the literature. We provide a Python code snippet to determine the kinetic energy fraction K as a function of the wall velocity, the two speeds of sound and the strength parameter of the phase transition. We also assess how realistic sizable deviations in speed of sound are close to the phase transition temperature in a specific model.
C1 [Giese, Felix; Konstandin, Thomas; van de Vis, Jorinde] DESY, Notkestr 85, D-22607 Hamburg, Germany.
[Schmitz, Kai] CERN, Theoret Phys Dept, CH-1211 Geneva 23, Switzerland.
RP Giese, F (corresponding author), DESY, Notkestr 85, D-22607 Hamburg, Germany.
EM felix.giese@desy.de; thomas.konstandin@desy.de; kai.schmitz@cern.ch;
jorinde.van.de.vis@desy.de
FU Deutsche Forschungsgemeinschaft under Germany's Excellence Strategy -
EXC 2121 "Quantum Universe" [390833306]; European UnionEuropean
Commission [796961]
FX This project has been supported by the Deutsche Forschungsgemeinschaft
under Germany's Excellence Strategy - EXC 2121 "Quantum Universe" -
390833306 (F. G., T. K. and J. v.d.V) and the European Union's Horizon
2020 Research and Innovation Programme under grant agreement number
796961, "AxiBAU" (K. S.).
CR Azatov A., ARXIV201002590
Baker J., ARXIV190706482
Bodeker D, 2017, J COSMOL ASTROPART P, DOI 10.1088/1475-7516/2017/05/025
Caprini C, 2020, J COSMOL ASTROPART P, DOI 10.1088/1475-7516/2020/03/024
Caprini C, 2018, CLASSICAL QUANT GRAV, V35, DOI 10.1088/1361-6382/aac608
Caprini C, 2016, J COSMOL ASTROPART P, DOI 10.1088/1475-7516/2016/04/001
Christensen N, 2019, REP PROG PHYS, V82, DOI 10.1088/1361-6633/aae6b5
D'Onofrio M, 2016, PHYS REV D, V93, DOI 10.1103/PhysRevD.93.025003
Delaunay C, 2008, J HIGH ENERGY PHYS, DOI 10.1088/1126-6708/2008/04/029
Ellis J, 2020, J COSMOL ASTROPART P, DOI 10.1088/1475-7516/2020/11/020
Ellis J, 2020, J COSMOL ASTROPART P, DOI 10.1088/1475-7516/2020/07/050
Ellis J, 2019, J COSMOL ASTROPART P, DOI 10.1088/1475-7516/2019/06/024
Ellis J, 2019, J COSMOL ASTROPART P, DOI 10.1088/1475-7516/2019/04/003
Espinosa JR, 2012, NUCL PHYS B, V854, P592, DOI 10.1016/j.nuclphysb.2011.09.010
Espinosa JR, 2010, J COSMOL ASTROPART P, DOI 10.1088/1475-7516/2010/06/028
Giese F, 2020, J COSMOL ASTROPART P, DOI 10.1088/1475-7516/2020/07/057
Guada V, 2020, COMPUT PHYS COMMUN, V256, DOI 10.1016/j.cpc.2020.107480
Guada V, 2019, PHYS REV D, V99, DOI 10.1103/PhysRevD.99.056020
Guo HK, 2021, J COSMOL ASTROPART P, DOI 10.1088/1475-7516/2021/01/001
Hindmarsh M, 2020, PHYS REV D, V101, DOI 10.1103/PhysRevD.101.089902
Hindmarsh M, 2019, J COSMOL ASTROPART P, DOI 10.1088/1475-7516/2019/12/062
Hindmarsh M, 2018, PHYS REV LETT, V120, DOI 10.1103/PhysRevLett.120.071301
Hindmarsh M, 2017, PHYS REV D, V96, DOI 10.1103/PhysRevD.96.103520
Hindmarsh M, 2015, PHYS REV D, V92, DOI 10.1103/PhysRevD.92.123009
Hindmarsh M, 2014, PHYS REV LETT, V112, DOI 10.1103/PhysRevLett.112.041301
Hoche S., ARXIV200710343
Jinno R., ARXIV201000971
KAMIONKOWSKI M, 1994, PHYS REV D, V49, P2837, DOI 10.1103/PhysRevD.49.2837
Kozaczuk J, 2015, J HIGH ENERGY PHYS, DOI 10.1007/JHEP10(2015)135
KURKISUONIO H, 1995, PHYS REV D, V51, P5431, DOI 10.1103/PhysRevD.51.5431
Kurup G, 2017, PHYS REV D, V96, DOI 10.1103/PhysRevD.96.015036
Landau L., 1989, FLUID MECH
Leitao L, 2015, NUCL PHYS B, V891, P159, DOI 10.1016/j.nuclphysb.2014.12.008
LISA collaboration, ARXIV170200786
Maggiore M., 2000, Physics Reports, V331, P283, DOI 10.1016/S0370-1573(99)00102-7
Schmitz K, 2020, SYMMETRY-BASEL, V12, DOI 10.3390/sym12091477
Weir DJ, 2018, PHILOS T R SOC A, V376, DOI 10.1098/rsta.2017.0126
NR 37
TC 0
Z9 0
U1 0
U2 0
PU IOP PUBLISHING LTD
PI BRISTOL
PA TEMPLE CIRCUS, TEMPLE WAY, BRISTOL BS1 6BE, ENGLAND
SN 1475-7516
J9 J COSMOL ASTROPART P
JI J. Cosmol. Astropart. Phys.
PD JAN
PY 2021
IS 1
AR 072
DI 10.1088/1475-7516/2021/01/072
PG 26
WC Astronomy & Astrophysics; Physics, Particles & Fields
SC Astronomy & Astrophysics; Physics
GA QK9CC
UT WOS:000620675000072
OA Green Published
DA 2021-04-21
ER
PT J
AU Haghighat, E
Juanes, R
AF Haghighat, Ehsan
Juanes, Ruben
TI SciANN: A Keras/TensorFlow wrapper for scientific computations and
physics-informed deep learning using artificial neural networks
SO COMPUTER METHODS IN APPLIED MECHANICS AND ENGINEERING
LA English
DT Article
DE SciANN; Deep neural networks; Scientific computations; PINN; vPINN
AB In this paper, we introduce SciANN, a Python package for scientific computing and physics-informed deep learning using artificial neural networks. SciANN uses the widely used deep-learning packages TensorFlow and Keras to build deep neural networks and optimization models, thus inheriting many of Keras's functionalities, such as batch optimization and model reuse for transfer learning. SciANN is designed to abstract neural network construction for scientific computations and solution and discovery of partial differential equations (PDE) using the physics-informed neural networks (PINN) architecture, therefore providing the flexibility to set up complex functional forms. We illustrate, in a series of examples, how the framework can be used for curve fitting on discrete data, and for solution and discovery of PDEs in strong and weak forms. We summarize the features currently available in SciANN, and also outline ongoing and future developments. (C) 2020 Elsevier B.V. All rights reserved.
C1 [Haghighat, Ehsan; Juanes, Ruben] MIT, 77 Massachusetts Ave, Cambridge, MA 02139 USA.
RP Haghighat, E (corresponding author), MIT, 77 Massachusetts Ave, Cambridge, MA 02139 USA.
EM ehsanh@mit.edu
OI Haghighat, Ehsan/0000-0003-2659-0507; /0000-0002-7370-2332
FU KFUPM-MIT, United States, collaborative agreement 'Multiscale Reservoir
Science'
FX This work was funded by the KFUPM-MIT, United States, collaborative
agreement `Multiscale Reservoir Science'.
CR Abadi M, 2016, PROCEEDINGS OF OSDI'16: 12TH USENIX SYMPOSIUM ON OPERATING SYSTEMS DESIGN AND IMPLEMENTATION, P265
Baydin A., 2018, J MACHINE LEARNING R, V18, P1
Berg J, 2018, NEUROCOMPUTING, V317, P28, DOI 10.1016/j.neucom.2018.06.056
Bergen KJ, 2019, SCIENCE, V363, P1299, DOI 10.1126/science.aau0323
Bergstra J., 2010, P PYTH SCI COMP C SC, V4
Bishop CM., 2006, PATTERN RECOGN
Bojarski M., 2016, ARXIV160407316
Brenner MP, 2019, PHYS REV FLUIDS, V4, DOI 10.1103/PhysRevFluids.4.100501
Brunton SL, 2020, ANNU REV FLUID MECH, V52, P477, DOI 10.1146/annurev-fluid-010719-060214
Chen T., 2015, ARXIV151201274
Chollet F., 2017, DEEP LEARNING PYTHON
COMSOL, 2020, COMSOL MULT US GUID
Cybenko G., 1989, Mathematics of Control, Signals, and Systems, V2, P303, DOI 10.1007/BF02551274
Dafermos CM, 2000, HYPERBOLIC CONSERVAT
Dana S., 2020, ARXIV200311372
Felfernig A, 2011, RECOMMENDER SYSTEMS HANDBOOK, P187, DOI 10.1007/978-0-387-85820-3_6
Goodfellow I, 2016, ADAPT COMPUT MACH LE, P1
Graves A, 2013, INT CONF ACOUST SPEE, P6645, DOI 10.1109/ICASSP.2013.6638947
Haghighat E., 2020, ARXIV200302751
HORNIK K, 1991, NEURAL NETWORKS, V4, P251, DOI 10.1016/0893-6080(91)90009-T
HORNIK K, 1989, NEURAL NETWORKS, V2, P359, DOI 10.1016/0893-6080(89)90020-8
Kharazmi E., 2019, ARXIV191200873, P1
Kong QK, 2019, SEISMOL RES LETT, V90, P3, DOI 10.1785/0220180259
Krizhevsky Alex, 2012, ADV NEURAL INFORM PR, P1097, DOI DOI 10.1145/3065386
LeCun Y, 2015, NATURE, V521, P436, DOI 10.1038/nature14539
Miotto R, 2018, BRIEF BIOINFORM, V19, P1236, DOI 10.1093/bib/bbx044
Raissi M, 2019, J COMPUT PHYS, V378, P686, DOI 10.1016/j.jcp.2018.10.045
Raissi M, 2020, SCIENCE, V367, P1026, DOI 10.1126/science.aaw4741
Raissi M, 2018, J MACH LEARN RES, V19
Ross ZE, 2019, SCIENCE, V364, P767, DOI 10.1126/science.aaw6888
Rudy S, 2019, SIAM J APPL DYN SYST, V18, P643, DOI 10.1137/18M1191944
RUMELHART DE, 1986, NATURE, V323, P533, DOI 10.1038/323533a0
Simo J., 1998, INTERD APPL, V7
Tartakovsky A.M., 2018, ARXIV180803398
Weinan E, 2018, COMMUN MATH STAT, V6, P1, DOI 10.1007/s40304-018-0127-z
Xu K., 2020, ARXIV200400265, P1
Zhang SA, 2019, ACM COMPUT SURV, V52, DOI 10.1145/3285029
Zienkiewicz O. C., 1969, International Journal for Numerical Methods in Engineering, V1, P75, DOI 10.1002/nme.1620010107
NR 38
TC 0
Z9 0
U1 8
U2 8
PU ELSEVIER SCIENCE SA
PI LAUSANNE
PA PO BOX 564, 1001 LAUSANNE, SWITZERLAND
SN 0045-7825
EI 1879-2138
J9 COMPUT METHOD APPL M
JI Comput. Meth. Appl. Mech. Eng.
PD JAN 1
PY 2021
VL 373
AR 113552
DI 10.1016/j.cma.2020.113552
PG 17
WC Engineering, Multidisciplinary; Mathematics, Interdisciplinary
Applications; Mechanics
SC Engineering; Mathematics; Mechanics
GA PH2ZF
UT WOS:000600286800014
OA Bronze
DA 2021-04-21
ER
PT J
AU Ilten, P
AF Ilten, Philip
TI CIMBA: Fast Monte Carlo generation using cubic interpolation
SO COMPUTER PHYSICS COMMUNICATIONS
LA English
DT Article
DE Interpolation; Monte Carlo; Event generators; Phase space and event
simulation
AB Monte Carlo generation of high energy particle collisions is a critical tool for both theoretical and experimental particle physics, connecting perturbative calculations to phenomenological models, and theory predictions to full detector simulation. The generation of minimum bias events can be particularly computationally expensive, where non-perturbative effects play an important role and specific processes and fiducial regions can no longer be well defined. In particular scenarios, particle guns can be used to quickly sample kinematics for single particles produced in minimum bias events. CIMBA (Cubic Interpolation for Minimum Bias Approximation) provides a comprehensive package to smoothly sample predefined kinematic grids, from any general purpose Monte Carlo generator, for all particles produced in minimum bias events. These grids are provided for a number of beam configurations including those of the Large Hadron Collider.
Program summary
Program title: CIMBA (Cubic Interpolation for Minimum Bias Approximation)
CPC Library link to program files: http://dx.doi.org/10.17632/49m44md4ph.1
Licensing provisions: GPL version 2 or later
Programming language: Python, C++
Nature of problem: generation of simulated events in high energy particle physics is quickly becoming a bottleneck in analysis development for collaborations on the Large Hadron Collider (LHC). With the expected long-term continuation of the high luminosity LHC, this problem must be solved in the near future. Significant progress has been made in designing new ways to perform detector simulation, including parametric detector models and machine learning techniques, e.g. calorimeter shower evolution with generative adversarial networks. Consequently, the efficiency of generating physics events using general purpose Monte Carlo event generators, rather than just detector simulation, needs to be improved.
Solution method: in many cases, single particle generation from pre-sampled phase-space distributions can be used as a fast alternative to full event generation. Phase-space distributions sampled in particle pseudorapidity and transverse momentum are sampled from large, once-off, minimum bias samples generated with PYTHIA 8. A novel smooth sampling of these distributions is performed using piecewise cubic Hermite interpolating polynomials. Distributions are created for all generated particles, as well as particles produced directly from hadronisation. Interpolation grid libraries are provided for a number of common collider configurations, and code is provided which can produce custom interpolation grid libraries.
Restrictions: Single particle generation (C) 2020 Elsevier B.V. All rights reserved.
C1 [Ilten, Philip] Univ Birmingham, Sch Phys & Astron, Birmingham, W Midlands, England.
RP Ilten, P (corresponding author), Univ Birmingham, Sch Phys & Astron, Birmingham, W Midlands, England.
EM philten@cern.ch
OI Ilten, Philip/0000-0001-5534-1732
FU Birmingham Fellowship
FX We thank Stephen Farry, Jonathan Plews, Yotam Soreq, and Nigel Watson
for providing useful feedback and testing. PI is supported by a
Birmingham Fellowship.
CR Aaij R, 2018, PHYS REV LETT, V120, DOI 10.1103/PhysRevLett.120.061801
Bellm J, 2016, EUR PHYS J C, V76, DOI 10.1140/epjc/s10052-016-4018-8
Belyaev I, 2011, J PHYS CONF SER, V331, DOI 10.1088/1742-6596/331/3/032047
Bothmann E., 2019, ARXIV190509127
Brun R, 1997, NUCL INSTRUM METH A, V389, P81, DOI 10.1016/S0168-9002(97)00048-X
Buckley A, 2011, PHYS REP, V504, P145, DOI 10.1016/j.physrep.2011.03.005
Faessler A, 2000, PHYS REV C, V61, DOI 10.1103/PhysRevC.61.035206
FRITSCH FN, 1980, SIAM J NUMER ANAL, V17, P238, DOI 10.1137/0717021
Herbison-Evans D., 1995, SOLVING QUARTICS CUB, P3, DOI [10.1016/B978-0-12-543457-7.50009-7, DOI 10.1016/B978-0-12-543457-7.50009-7]
Horn R.A., 2012, MATRIX ANAL
Ilten P, 2018, J HIGH ENERGY PHYS, DOI 10.1007/JHEP06(2018)004
Ilten P, 2016, PHYS REV LETT, V116, DOI 10.1103/PhysRevLett.116.251803
Press W., 2002, NUMERICAL RECIPES C
Runge C., 1901, Z MATH PHYS, V46, P20
Shmakov S. L., 2011, INT J PURE APPL MATH, V71, P251
Sjostrand T, 2015, COMPUT PHYS COMMUN, V191, P159, DOI 10.1016/j.cpc.2015.01.024
Thompson J.R., 1990, NONPARAMETRIC FUNCTI, V21
Vidal X. Cid, 2019, ARXIV190408458
NR 18
TC 0
Z9 0
U1 2
U2 2
PU ELSEVIER
PI AMSTERDAM
PA RADARWEG 29, 1043 NX AMSTERDAM, NETHERLANDS
SN 0010-4655
EI 1879-2944
J9 COMPUT PHYS COMMUN
JI Comput. Phys. Commun.
PD JAN
PY 2021
VL 258
AR 107622
DI 10.1016/j.cpc.2020.107622
PG 8
WC Computer Science, Interdisciplinary Applications; Physics, Mathematical
SC Computer Science; Physics
GA OO4OM
UT WOS:000587360000046
DA 2021-04-21
ER
PT J
AU Richardson, AS
Gordon, DF
Swanekamp, SB
Rittersdorf, IM
Adamson, PE
Grannis, OS
Morgan, GT
Ostenfeld, A
Phlips, KL
Sun, CG
Tang, G
Watkins, DJ
AF Richardson, A. S.
Gordon, D. F.
Swanekamp, S. B.
Rittersdorf, I. M.
Adamson, P. E.
Grannis, O. S.
Morgan, G. T.
Ostenfeld, A.
Phlips, K. L.
Sun, C. G.
Tang, G.
Watkins, D. J.
TI TurboPy: A lightweight python framework for computational physics
SO COMPUTER PHYSICS COMMUNICATIONS
LA English
DT Article
DE Framework; Physics; Computational physics; Python; Dynamic factory
pattern; Resource sharing
AB Computational physics problems often have a common set of aspects to them that any particular numerical code will have to address. Because these aspects are common to many problems, having a framework already designed and ready to use will not only speed the development of new codes, but also enhance compatibility between codes.
Some of the most common aspects of computational physics problems are: a grid, a clock which tracks the flow of the simulation, and a set of models describing the dynamics of various quantities on the grid. Having a framework that could deal with these basic aspects of the simulation in a common way could provide great value to computational scientists by solving various numerical and class design issues that routinely arise.
This paper describes the newly developed computational framework that we have built for rapidly prototyping new physics codes. This framework, called turboPy, is a lightweight physics modeling framework based on the design of the particle-in-cell code turboWAVE. It implements a class (called Simulation) which drives the simulation and manages communication between physics modules, a class (called PhysicsModule) which handles the details of the dynamics of the various parts of the problem, and some additional classes such as a Grid class and a Diagnostic class to handle various ancillary issues that commonly arise.
Program summary
Program Title: TurboPy
CPC Library link to program files: http:fidx.dm.org/10.17632/rznn6s5myw. 1
Developer's repository link: https: igithub.com/NRL-Plasma-Physics-Division/turbopy
Licensing provisions: CC0 1.0
Programming language: Python
Nature of problem: Many computation physics problems have a common set of aspects to them that are often addressed in a custom way in every different code, which leads to lengthy and redundant development and testing, as well as introducing roadblocks to interoperability.
Solution method: Implement a set of python classes as a lightweight framework that deals with these common problems, so that development time on new computational physics codes is reduced, and interoperability and reusability are increased.
References: A.S. Richardson et al., TurboPy: A lightweight computational physics framework. NRL-Plasma-Physics-Divisioniturbopy (v2020.08.05). doi: 10.5281/zenodo.3973693 Published by Elsevier B.V.
C1 [Richardson, A. S.; Gordon, D. F.; Swanekamp, S. B.; Rittersdorf, I. M.; Adamson, P. E.] US Naval Res Lab, Plasma Phys Div, Washington, DC 20375 USA.
[Grannis, O. S.; Morgan, G. T.; Ostenfeld, A.; Phlips, K. L.; Sun, C. G.; Tang, G.; Watkins, D. J.] Syntek Technol, Fairfax, VA USA.
RP Richardson, AS (corresponding author), US Naval Res Lab, Plasma Phys Div, Washington, DC 20375 USA.
EM steve.richardson@nrl.navy.mil
FU U.S. Naval Research Laboratory base program
FX This work was supported by the U.S. Naval Research Laboratory base
program.
CR Freeman E., 2004, HEAD 1 DESIGN PATTER
Gamma E., 1994, ADDISON WESLEY PROFE
Gordon DF, 2000, IEEE T PLASMA SCI, V28, P1224, DOI 10.1109/27.893300
Jackson J.D., 1998, CLASSICAL ELECTRODYN
Oliphant T.E., 2006, A GUIDE TO NUMPY, VVolume 1
PrestonWerner T., TOML TOMS OBVIOUS MI
van der Walt S, 2011, COMPUT SCI ENG, V13, P22, DOI 10.1109/MCSE.2011.37
Wilson G, 2014, PLOS BIOL, V12, DOI 10.1371/journal.pbio.1001745
NR 8
TC 0
Z9 0
U1 4
U2 4
PU ELSEVIER
PI AMSTERDAM
PA RADARWEG 29, 1043 NX AMSTERDAM, NETHERLANDS
SN 0010-4655
EI 1879-2944
J9 COMPUT PHYS COMMUN
JI Comput. Phys. Commun.
PD JAN
PY 2021
VL 258
AR 107607
DI 10.1016/j.cpc.2020.107607
PG 11
WC Computer Science, Interdisciplinary Applications; Physics, Mathematical
SC Computer Science; Physics
GA OO4OM
UT WOS:000587360000037
DA 2021-04-21
ER
PT J
AU Hosseinzadeh, G
Dauphin, F
Villar, VA
Berger, E
Jones, DO
Challis, P
Chornock, R
Drout, MR
Foley, RJ
Kirshner, RP
Lunnan, R
Margutti, R
Milisavljevic, D
Pan, YC
Rest, A
Scolnic, DM
Magnier, E
Metcalfe, N
Wainscoat, R
Waters, C
AF Hosseinzadeh, Griffin
Dauphin, Frederick
Villar, V. Ashley
Berger, Edo
Jones, David O.
Challis, Peter
Chornock, Ryan
Drout, Maria R.
Foley, Ryan J.
Kirshner, Robert P.
Lunnan, Ragnhild
Margutti, Raffaella
Milisavljevic, Dan
Pan, Yen-Chen
Rest, Armin
Scolnic, Daniel M.
Magnier, Eugene
Metcalfe, Nigel
Wainscoat, Richard
Waters, Christopher
TI Photometric Classification of 2315 Pan-STARRS1 Supernovae with Superphot
SO ASTROPHYSICAL JOURNAL
LA English
DT Article
DE Supernovae; Astrostatistics; Light curve classification
ID ACTIVE GALACTIC NUCLEI; IA SUPERNOVAE; GALAXIES; SPECTROSCOPY;
REDSHIFTS; IDENTIFICATION; TELESCOPE; CLUSTERS; CATALOG
AB The classification of supernovae (SNe) and its impact on our understanding of explosion physics and progenitors have traditionally been based on the presence or absence of certain spectral features. However, current and upcoming wide-field time-domain surveys have increased the transient discovery rate far beyond our capacity to obtain even a single spectrum of each new event. We must therefore rely heavily on photometric classification-connecting SN light curves back to their spectroscopically defined classes. Here, we present Superphot, an open-source Python implementation of the machine-learning classification algorithm of Villar et al., and apply it to 2315 previously unclassified transients from the Pan-STARRS1 Medium Deep Survey for which we obtained spectroscopic host-galaxy redshifts. Our classifier achieves an overall accuracy of 82%, with completenesses and purities of >80% for the best classes (SNe Ia and superluminous SNe). For the worst performing SN class (SNe Ibc), the completeness and purity fall to 37% and 21%, respectively. Our classifier provides 1257 newly classified SNe Ia, 521 SNe II, 298 SNe Ibc, 181 SNe IIn, and 58 SLSNe. These are among the largest uniformly observed samples of SNe available in the literature and will enable a wide range of statistical studies of each class.
C1 [Hosseinzadeh, Griffin; Dauphin, Frederick; Villar, V. Ashley; Berger, Edo; Challis, Peter; Kirshner, Robert P.] Ctr Astrophys Harvard & Smithsonian, 60 Garden St, Cambridge, MA 02138 USA.
[Dauphin, Frederick] Carnegie Mellon Univ, Dept Phys, 5000 Forbes Ave, Pittsburgh, PA 15213 USA.
[Dauphin, Frederick; Rest, Armin] Space Telescope Sci Inst, 3700 San Martin Dr, Baltimore, MD 21218 USA.
[Villar, V. Ashley] Columbia Univ, Dept Astron, New York, NY 10027 USA.
[Jones, David O.; Foley, Ryan J.] Univ Calif Santa Cruz, Dept Astron & Astrophys, Santa Cruz, CA 95064 USA.
[Chornock, Ryan; Margutti, Raffaella] Northwestern Univ, Ctr Interdisciplinary Explorat & Res Astrophys, 2145 Sheridan Rd, Evanston, IL 60208 USA.
[Chornock, Ryan; Margutti, Raffaella] Northwestern Univ, Dept Phys & Astron, 2145 Sheridan Rd, Evanston, IL 60208 USA.
[Drout, Maria R.] Univ Toronto, David A Dunlap Dept Astron & Astrophys, 50 St George St, Toronto, ON M5S 3H4, Canada.
[Drout, Maria R.] Observ Carnegie Inst Sci, 813 Santa Barbara St, Pasadena, CA 91101 USA.
[Kirshner, Robert P.] Gordon & Betty Moore Fdn, 1661 Page Mill Rd, Palo Alto, CA 94304 USA.
[Lunnan, Ragnhild] Stockholm Univ, Dept Astron, Oskar Klein Ctr, Albanova Univ Ctr, SE-10691 Stockholm, Sweden.
[Milisavljevic, Dan] Purdue Univ, Dept Phys & Astron, 525 Northwestern Ave, W Lafayette, IN 47907 USA.
[Pan, Yen-Chen] Natl Cent Univ, Grad Inst Astron, 300 Jhongda Rd, Taoyuan 32001, Taiwan.
[Rest, Armin] Johns Hopkins Univ, Dept Phys & Astron, 3400 North Charles St, Baltimore, MD 21218 USA.
[Scolnic, Daniel M.] Duke Univ, Dept Phys, Campus Box 90305, Durham, NC 27708 USA.
[Magnier, Eugene; Wainscoat, Richard] Univ Hawaii, Inst Astron, 2680 Woodlawn Dr, Honolulu, HI 96822 USA.
[Metcalfe, Nigel] Univ Durham, Dept Phys, South Rd, Durham DH1 3LE, England.
[Waters, Christopher] Princeton Univ, Dept Astrophys Sci, 4 Ivy Lane, Princeton, NJ 08540 USA.
RP Hosseinzadeh, G (corresponding author), Ctr Astrophys Harvard & Smithsonian, 60 Garden St, Cambridge, MA 02138 USA.
EM griffin.hosseinzadeh@cfa.harvard.edu
OI Lunnan, Ragnhild/0000-0001-9454-4639; Metcalfe,
Nigel/0000-0001-9034-4402; Kirshner, Robert/0000-0002-1966-3942;
Chornock, Ryan/0000-0002-7706-5668; Foley, Ryan/0000-0002-2445-5275;
Scolnic, Daniel/0000-0002-4934-5849
FU NSFNational Science Foundation (NSF) [PHY-1914448, AST-2037297];
NASANational Aeronautics & Space Administration (NASA) [NAS 5-26555,
NNX15AE50G]; Harvard Data Science Initiative; LSSTC Data Science
Fellowship Program - LSSTC, NSF [1829740]; Brinson Foundation; Moore
FoundationGordon and Betty Moore Foundation; SAO REU program; National
Science Foundation REUNational Science Foundation (NSF); Department of
Defense ASSURE programs under NSF [AST-1852268]; Smithsonian
InstitutionSmithsonian Institution; Ford Foundation through a
Dissertation Fellowship; Simons Foundation through a Simons Junior
Fellowship [718240]; Gordon and Betty Moore Foundation postdoctoral
fellowship at the University of California, Santa Cruz; Space Telescope
Science InstituteSpace Telescope Science Institute; Gordon & Betty Moore
FoundationGordon and Betty Moore Foundation; Heising-Simons Foundation;
Alfred P. Sloan FoundationAlfred P. Sloan Foundation; David and Lucile
Packard FoundationThe David & Lucile Packard Foundation; Marie
Sklodowska-Curie Individual Fellowship within the Horizon 2020 European
Union (EU) Framework Programme for Research and Innovation
[H2020-MSCA-IF-2017-794467]; Max-Planck SocietyMax Planck
SocietyFoundation CELLEX; Max Planck Institute for Extraterrestrial
Physics; National Aeronautics and Space AdministrationNational
Aeronautics & Space Administration (NASA) [NNX08AR22G]; NASA Science
Mission DirectorateNational Aeronautics & Space Administration (NASA);
National Science FoundationNational Science Foundation (NSF)
[AST-1238877]; University of Maryland, Eotvos Lorand University (ELTE),
Los Alamos National Laboratory; Gordon and Betty Moore FoundationGordon
and Betty Moore Foundation
FX We thank Jessica Mink and Brian Hsu for assisting with the host-galaxy
redshifts. We also thank the authors of the "Scientific Python
Cookiecutter" tutorial for advice on how to document, package, and
release the Superphot package. The Berger TimeDomain Group is supported
in part by NSF grant AST-1714498 and NASA grant NNX15AE50G. We
acknowledge partial funding support from the Harvard Data Science
Initiative. G.H. thanks the LSSTC Data Science Fellowship Program, which
is funded by LSSTC, NSF Cybertraining grant #1829740, the Brinson
Foundation, and the Moore Foundation; his participation in the program
has benefited this work. F.D. thanks the SAO REU program, funded in part
by the National Science Foundation REU and Department of Defense ASSURE
programs under NSF grant No. AST-1852268 and by the Smithsonian
Institution. V.A.V. acknowledges support by the Ford Foundation through
a Dissertation Fellowship and the Simons Foundation through a Simons
Junior Fellowship (#718240). D.O.J. is supported by a Gordon and Betty
Moore Foundation postdoctoral fellowship at the University of
California, Santa Cruz. The UCSC team is supported in part by NASA
grants 14-WPS14-0048, NNG16PJ34C, NNG17PX03C; NSF grants AST-1518052 and
AST-1815935; NASA through grant No. AR-14296 from the Space Telescope
Science Institute, which is operated by AURA, Inc., under NASA contract
NAS 5-26555; the Gordon & Betty Moore Foundation; the Heising-Simons
Foundation; and by fellowships from the Alfred P. Sloan Foundation and
the David and Lucile Packard Foundation to R.J.F. R.L. is supported by a
Marie Sklodowska-Curie Individual Fellowship within the Horizon 2020
European Union (EU) Framework Programme for Research and Innovation
(H2020-MSCA-IF-2017-794467). D. M. acknowledges NSF support from grants
PHY-1914448 and AST-2037297.; The Pan-STARRS1 Surveys (PS1) and the PS1
public science archive have been made possible through contributions by
the Institute for Astronomy, the University of Hawaii, the Pan-STARRS
Project Office, the Max-Planck Society and its participating institutes,
the Max Planck Institute for Astronomy, Heidelberg, and the Max Planck
Institute for Extraterrestrial Physics, Garching, Johns Hopkins
University, Durham University, the University of Edinburgh, Queen's
University Belfast, the Center for Astrophysics | Harvard & Smithsonian,
Las Cumbres Observatory, the National Central University of Taiwan, the
Space Telescope Science Institute, the National Aeronautics and Space
Administration under grant No. NNX08AR22G issued through the Planetary
Science Division of the NASA Science Mission Directorate, the National
Science Foundation grant No. AST-1238877, the University of Maryland,
Eotvos Lorand University (ELTE), Los Alamos National Laboratory, and the
Gordon and Betty Moore Foundation.
CR Ade PAR, 2014, ASTRON ASTROPHYS, V571, DOI 10.1051/0004-6361/201321591
Ahumada R, 2020, ASTROPHYS J SUPPL S, V249, DOI 10.3847/1538-4365/ab929e
ALGEO J, 1977, AM SPEECH, V52, P47, DOI 10.2307/454719
Baldeschi A, 2020, ASTROPHYS J, V902, DOI 10.3847/1538-4357/abb1c0
Balestra I, 2010, ASTRON ASTROPHYS, V512, DOI 10.1051/0004-6361/200913626
Bayes M, 1763, PHILOS T, V53, P370, DOI DOI 10.1098/RSTL.1763.0053
Bellm EC, 2019, PUBL ASTRON SOC PAC, V131, DOI 10.1088/1538-3873/aaecbe
Bianco FB, 2014, ASTROPHYS J SUPPL S, V213, DOI 10.1088/0067-0049/213/2/19
Boone K, 2019, ASTRON J, V158, DOI 10.3847/1538-3881/ab5182
Breiman L, 2001, MACH LEARN, V45, P5, DOI 10.1023/A:1010933404324
Bronder TJ, 2008, ASTRON ASTROPHYS, V477, P716, DOI 10.1051/0004-6361:20077655
Cannon R, 2006, MON NOT R ASTRON SOC, V372, P425, DOI 10.1111/j.1365-2966.2006.10875.x
Cappellaro E., 2012, CBET, V3274, P1
Chambers K.C., 2016, ARXIV161205560
Charnock T, 2017, ASTROPHYS J LETT, V837, DOI 10.3847/2041-8213/aa603d
Chawla NV, 2002, J ARTIF INTELL RES, V16, P321, DOI 10.1613/jair.953
Chornock R, 2014, ASTROPHYS J, V780, DOI 10.1088/0004-637X/780/1/44
Colless M., 2003, ARXIVASTROPH0306581
Cowie LL, 2010, ASTROPHYS J, V711, P928, DOI 10.1088/0004-637X/711/2/928
da Costa-Luis C. O., 2019, J OPEN SOURCE SOFTWA, V4, P1277, DOI DOI 10.21105/joss.01277
DRESSLER A, 1992, ASTROPHYS J SUPPL S, V78, P1, DOI 10.1086/191620
Drinkwater MJ, 2010, MON NOT R ASTRON SOC, V401, P1429, DOI 10.1111/j.1365-2966.2009.15754.x
Drout MR, 2014, ASTROPHYS J, V794, DOI 10.1088/0004-637X/794/1/23
ELIAS JH, 1985, ASTROPHYS J, V296, P379, DOI 10.1086/163456
Finkelstein SL, 2009, ASTROPHYS J LETT, V703, pL162, DOI 10.1088/0004-637X/703/2/L162
Fitzpatrick EL, 1999, PUBL ASTRON SOC PAC, V111, P63, DOI 10.1086/316293
Foley RJ, 2013, ASTROPHYS J, V778, DOI 10.1088/0004-637X/778/2/167
Fremling U. C., 2019, ARXIV191012973
Gal-Yam A., 2016, HDB SUPERNOVAE
Gal-Yam A, 2019, ANNU REV ASTRON ASTR, V57, P305, DOI 10.1146/annurev-astro-081817-051819
Gal-Yam A, 2012, SCIENCE, V337, P927, DOI 10.1126/science.1203601
Garcet O, 2007, ASTRON ASTROPHYS, V474, P473, DOI 10.1051/0004-6361:20077778
Gelman A., 1992, STAT SCI, V7, P457, DOI DOI 10.1214/SS/1177011136
Gezari S, 2012, NATURE, V485, P217, DOI 10.1038/nature10990
Gomez S, 2020, ASTROPHYS J, V904, DOI 10.3847/1538-4357/abbf49
Graham ML, 2018, ASTRON J, V155, DOI 10.3847/1538-3881/aa99d4
Graur O, 2017, ASTROPHYS J, V837, DOI 10.3847/1538-4357/aa5eb8
Graur O, 2017, ASTROPHYS J, V837, DOI 10.3847/1538-4357/aa5eb7
Hasinger G, 2018, ASTROPHYS J, V858, DOI 10.3847/1538-4357/aabacf
HASTINGS WK, 1970, BIOMETRIKA, V57, P97, DOI 10.2307/2334940
Hewett PC, 2010, MON NOT R ASTRON SOC, V405, P2302, DOI 10.1111/j.1365-2966.2010.16648.x
Holoien TWS, 2019, MON NOT R ASTRON SOC, V484, P1899, DOI 10.1093/mnras/stz073
Hosseinzadeh G., 2020, ZENODO, DOI [10.5281/zenodo.374789, DOI 10.5281/ZENODO.374789]
Howell DA, 2006, NATURE, V443, P308, DOI 10.1038/nature05103
Hunter JD, 2007, COMPUT SCI ENG, V9, P90, DOI 10.1109/MCSE.2007.55
Im MS, 2001, ASTRON J, V122, P750, DOI 10.1086/322081
Ishida EEO, 2019, MON NOT R ASTRON SOC, V483, P2, DOI 10.1093/mnras/sty3015
Ivezic Z, 2019, ASTROPHYS J, V873, DOI 10.3847/1538-4357/ab042c
Jha S, 2007, ASTROPHYS J, V659, P122, DOI 10.1086/512054
Jones DH, 2009, MON NOT R ASTRON SOC, V399, P683, DOI 10.1111/j.1365-2966.2009.15338.x
Jones DO, 2018, ASTROPHYS J, V857, DOI 10.3847/1538-4357/aab6b1
Jones DO, 2017, ASTROPHYS J, V843, DOI 10.3847/1538-4357/aa767b
Karhunen K, 2014, MON NOT R ASTRON SOC, V441, P1802, DOI 10.1093/mnras/stu688
Kessler R, 2009, PUBL ASTRON SOC PAC, V121, P1028, DOI 10.1086/605984
Kimura A, 2017, IEEE INT CON DIS, P354, DOI 10.1109/ICDCSW.2017.47
Kumar R., 2019, J OPEN SOURCE SOFT, V4, P1143, DOI [10.21105/joss.01143, DOI 10.21105/joss.01143]
Kurtz MJ, 1998, PUBL ASTRON SOC PAC, V110, P934, DOI 10.1086/316207
Lamareille F, 2009, ASTRON ASTROPHYS, V495, P53, DOI 10.1051/0004-6361:200810397
Le Fevre O, 2005, ASTRON ASTROPHYS, V439, P845, DOI 10.1051/0004-6361:20041960
Lemaitre G, 2017, J MACH LEARN RES, V18
Lidman C, 2020, MON NOT R ASTRON SOC, V496, P19, DOI 10.1093/mnras/staa1341
Lilly SJ, 2007, ASTROPHYS J SUPPL S, V172, P70, DOI 10.1086/516589
Louppe G., 2015, THESIS
Lunnan R, 2018, ASTROPHYS J, V852, DOI 10.3847/1538-4357/aa9f1a
Lunnan R, 2014, ASTROPHYS J, V787, DOI 10.1088/0004-637X/787/2/138
Magnier EA, 2020, ASTROPHYS J SUPPL S, V251, DOI 10.3847/1538-4365/abb82c
Magnier EA, 2020, ASTROPHYS J SUPPL S, V251, DOI 10.3847/1538-4365/abb829
Magnier EA, 2020, ASTROPHYS J SUPPL S, V251, DOI 10.3847/1538-4365/abb82a
Masters DC, 2019, ASTROPHYS J, V877, DOI 10.3847/1538-4357/ab184d
METROPOLIS N, 1953, J CHEM PHYS, V21, P1087, DOI 10.1063/1.1699114
Minkowski R., 1941, PASP, V53, P224
Moller A, 2016, J COSMOL ASTROPART P, DOI 10.1088/1475-7516/2016/12/008
Muthukrishna D, 2019, PUBL ASTRON SOC PAC, V131, DOI 10.1088/1538-3873/ab1609
Narayan G, 2011, ASTROPHYS J LETT, V731, DOI 10.1088/2041-8205/731/1/L11
Newman JA, 2013, ASTROPHYS J SUPPL S, V208, DOI 10.1088/0067-0049/208/1/5
Norris RP, 2006, ASTRON J, V132, P2409, DOI 10.1086/508275
Oliphant T.E., 2006, A GUIDE TO NUMPY, VVolume 1
Owen FN, 2009, ASTROPHYS J SUPPL S, V182, P625, DOI 10.1088/0067-0049/182/2/625
Pedregosa F, 2011, J MACH LEARN RES, V12, P2825
Perez F, 2007, COMPUT SCI ENG, V9, P21, DOI 10.1109/MCSE.2007.53
Price-Whelan AM, 2018, ASTRON J, V156, DOI 10.3847/1538-3881/aabc4f
Quimby RM, 2011, NATURE, V474, P487, DOI 10.1038/nature10095
Quimby RM, 2018, ASTROPHYS J, V855, DOI 10.3847/1538-4357/aaac2f
Quimby RM, 2013, ASTROPHYS J LETT, V768, DOI 10.1088/2041-8205/768/1/L20
Rest A, 2005, ASTROPHYS J, V634, P1103, DOI 10.1086/497060
Rest A, 2014, ASTROPHYS J, V795, DOI 10.1088/0004-637X/795/1/44
Richards JW, 2012, MON NOT R ASTRON SOC, V419, P1121, DOI 10.1111/j.1365-2966.2011.19768.x
Riess AG, 2004, ASTROPHYS J, V600, pL163, DOI 10.1086/378311
Riess AG, 2004, ASTROPHYS J, V607, P665, DOI 10.1086/383612
Ross NP, 2008, MON NOT R ASTRON SOC, V387, P1323, DOI 10.1111/j.1365-2966.2008.13332.x
Rovilos E, 2011, ASTRON ASTROPHYS, V529, DOI 10.1051/0004-6361/201015763
Sako M, 2011, ASTROPHYS J, V738, DOI 10.1088/0004-637X/738/2/162
Salvatier J, 2016, PEERJ COMPUT SCI, DOI 10.7717/peerj-cs.55
Sanders NE, 2015, ASTROPHYS J, V799, DOI 10.1088/0004-637X/799/2/208
Sanders NE, 2013, ASTROPHYS J, V769, DOI 10.1088/0004-637X/769/1/39
Scarlata C, 2009, ASTROPHYS J LETT, V704, pL98, DOI 10.1088/0004-637X/704/2/L98
Schlafly EF, 2011, ASTROPHYS J, V737, DOI 10.1088/0004-637X/737/2/103
SCHLEGEL EM, 1990, MON NOT R ASTRON SOC, V244, P269
Smith AG, 2012, MON NOT R ASTRON SOC, V422, P25, DOI 10.1111/j.1365-2966.2012.20400.x
Spearman C, 1904, AM J PSYCHOL, V15, P72, DOI 10.2307/1412159
Sravan N, 2020, ASTROPHYS J, V893, DOI 10.3847/1538-4357/ab8128
Stalin CS, 2010, MON NOT R ASTRON SOC, V401, P294, DOI 10.1111/j.1365-2966.2009.15636.x
Stritzinger MD, 2018, ASTRON ASTROPHYS, V609, DOI 10.1051/0004-6361/201730842
Szokoly GP, 2004, ASTROPHYS J SUPPL S, V155, P271, DOI 10.1086/424707
Taddia F, 2018, ASTRON ASTROPHYS, V609, DOI 10.1051/0004-6361/201730844
Taddia F, 2015, ASTRON ASTROPHYS, V574, DOI 10.1051/0004-6361/201423915
Tajer M, 2007, ASTRON ASTROPHYS, V467, P73, DOI 10.1051/0004-6361:20066667
Theano Development Team, 2016, ARXIV160502688 THEAN
TONRY J, 1979, ASTRON J, V84, P1511, DOI 10.1086/112569
Trump JR, 2009, ASTROPHYS J, V696, P1195, DOI 10.1088/0004-637X/696/2/1195
UOMOTO A, 1985, ASTRON ASTROPHYS, V149, pL7
Valenti S, 2016, MON NOT R ASTRON SOC, V459, P3939, DOI 10.1093/mnras/stw870
Villar VA, 2019, ASTROPHYS J, V884, DOI 10.3847/1538-4357/ab418c
Villar VA, 2020, ASTROPHYS J, V905, DOI 10.3847/1538-4357/abc6fd
Virtanen P, 2020, NAT METHODS, V17, P261, DOI 10.1038/s41592-019-0686-2
Waters CZ, 2020, ASTROPHYS J SUPPL S, V251, DOI 10.3847/1538-4365/abb82b
Wen ZL, 2015, ASTROPHYS J, V807, DOI 10.1088/0004-637X/807/2/178
Wheeler J. C., 1986, GALAXY DISTANCES DEV
WHEELER JC, 1985, ASTROPHYS J, V294, pL17, DOI 10.1086/184500
NR 119
TC 2
Z9 2
U1 0
U2 0
PU IOP PUBLISHING LTD
PI BRISTOL
PA TEMPLE CIRCUS, TEMPLE WAY, BRISTOL BS1 6BE, ENGLAND
SN 0004-637X
EI 1538-4357
J9 ASTROPHYS J
JI Astrophys. J.
PD DEC
PY 2020
VL 905
IS 2
AR 93
DI 10.3847/1538-4357/abc42b
PG 20
WC Astronomy & Astrophysics
SC Astronomy & Astrophysics
GA PG5SZ
UT WOS:000599795900001
DA 2021-04-21
ER
PT J
AU Honeywell, S
Quackenbush, S
Reina, L
Reuschle, C
AF Honeywell, Steve
Quackenbush, Seth
Reina, Laura
Reuschle, Christian
TI NLOX, a one-loop provider for Standard Model processes
SO COMPUTER PHYSICS COMMUNICATIONS
LA English
DT Article
DE NLO QCD and EW automation; One-loop provider; Higher-order calculations
ID ELECTROWEAK RADIATIVE-CORRECTIONS; HEAVY-QUARK PRODUCTION; MONTE-CARLO
TOOLS; PAIR PRODUCTION; QCD CORRECTIONS; BOSON PRODUCTION; AMPLITUDES;
RENORMALIZATION; INTERFACE; UNITARITY
AB NLOX is a computer program for calculations in high-energy particle physics. It provides fully renormalized scattering matrix elements in the Standard Model of particle physics, up to one-loop accuracy for all possible coupling-power combinations in the strong and electroweak couplings, and for processes with up to six external particles.
Program summary
Program Title: NLOX
Program Files doi: http://dx.doi.org/10.17632/y7jth5hznv.1
Licensing provisions: CC BY NC 3.0
Programming language: C++. Fortran interface available, and Fortran compiler required for dependencies.
Nature of problem: The computation of higher-order terms in the coupling expansion of Standard Model scattering amplitudes is required for precision studies in collider experiments. Techniques for computing the first corrections are well-known, and are now suited to automation. We wish to provide code that calculates virtual (one-loop) quantum chromodynamics and electroweak corrections for desired amplitudes using a package that automates the production of this code.
Solution method: We use Python scripts and a computer algebra system, FORM, to reduce virtual amplitudes to C++ code and data based on Feynman rules of the Standard Model. The scripts perform a tensor decomposition of the one loop integral to reduce the amplitude to dependence on tensor integral coefficients. These coefficients are called at runtime by the provided library TRed, which performs tensor reduction into base (scalar) coefficients at runtime. The scripts identify repeated structures to be calculated once in the produced code for efficiency. The tensor reduction code is designed such that needed tensor coefficients need to be computed only once per evaluation of the desired amplitude, and are built recursively from other needed coefficients.
Additional comments including restrictions and unusual features: The code-producing scripts are not provided in this release, only fixed libraries such as TRed and required interface code for pre-generated processes. Some processes are provided with this release, with others available upon request. (C) 2020 Elsevier B.V. All rights reserved.
C1 [Honeywell, Steve; Quackenbush, Seth; Reina, Laura; Reuschle, Christian] Florida State Univ, Phys Dept, Tallahassee, FL 32306 USA.
[Reuschle, Christian] Lund Univ, Dept Astron & Theoret Phys, SE-22362 Lund, Sweden.
RP Reuschle, C (corresponding author), Lund Univ, Dept Astron & Theoret Phys, SE-22362 Lund, Sweden.
EM sjh07@hep.fsu.edu; squackenbush@hep.fsu.edu; reina@hep.fsu.edu;
creuschle@hep.fsu.edu
RI Reuschle, Christian/AAB-1841-2020
OI Reuschle, Christian/0000-0002-4732-3400
FU U.S. Department of EnergyUnited States Department of Energy (DOE)
[DE-SC0010102]; European Union's Horizon 2020 research and innovative
programme [668679]; National Science FoundationNational Science
Foundation (NSF) [NSF PHY-1748958, NSF PHY-1607611]
FX We would like to thank T. Schutzmeier for the initial working design of
NLOX, and D. Wackeroth for sharing her expertise and knowledge of QCD
and EW one-loop calculations. This work has been supported by the U.S.
Department of Energy under grant DE-SC0010102. C.R. acknowledges current
support by the European Union's Horizon 2020 research and innovative
programme, under grant agreement No. 668679. S.H., L.R., and C.R. are
grateful for the hospitality of the Kavli Institute for Theoretical
Physics (KITP) during the workshop on LHC Run II and the Precision
Frontier where part of this work was being developed. Their research at
the KITP was supported in part by the National Science Foundation under
grant NSF PHY-1748958. L. R. would like to also thank the Aspen Center
for Physics for the hospitality offered while parts of this work were
being completed. The work performed at the Aspen Center for Physics is
supported in part by the National Science Foundation under grant NSF
PHY-1607611.
CR Actis S, 2013, J HIGH ENERGY PHYS, DOI 10.1007/JHEP04(2013)037
Actis S, 2017, COMPUT PHYS COMMUN, V214, P140, DOI 10.1016/j.cpc.2017.01.004
Alioli S, 2014, COMPUT PHYS COMMUN, V185, P560, DOI 10.1016/j.cpc.2013.10.020
Alioli S, 2010, J HIGH ENERGY PHYS, DOI 10.1007/JHEP06(2010)043
ALTARELLI G, 1979, NUCL PHYS B, V157, P461, DOI 10.1016/0550-3213(79)90116-0
Alwall J, 2014, J HIGH ENERGY PHYS, DOI 10.1007/JHEP07(2014)079
Andersen J.R., 2017, 10 HOUCH WORKSH PHYS
Andersen J.R., 2014, ARXIV14051067
Arnold K, 2009, COMPUT PHYS COMMUN, V180, P1661, DOI 10.1016/j.cpc.2009.03.006
Badger S, 2013, COMPUT PHYS COMMUN, V184, P1981, DOI 10.1016/j.cpc.2013.03.018
Baglio J., 2014, ARXIV14043940
Baglio J., 2011, ARXIV11074038
BANERJEE P, 2002, PHYS REV D, V65
Baur U, 1998, PHYS REV D, V57, P199, DOI 10.1103/PhysRevD.57.199
Becker S, 2012, PHYS REV D, V86, DOI 10.1103/PhysRevD.86.074009
Becker S, 2012, J HIGH ENERGY PHYS, DOI 10.1007/JHEP07(2012)090
Becker S, 2010, J HIGH ENERGY PHYS, DOI 10.1007/JHEP12(2010)013
BEENAKKER W, 1994, NUCL PHYS B, V411, P343, DOI 10.1016/0550-3213(94)90454-5
BEENAKKER W, 1991, NUCL PHYS B, V351, P507, DOI 10.1016/S0550-3213(05)80032-X
BEENAKKER W, 1989, PHYS REV D, V40, P54, DOI 10.1103/PhysRevD.40.54
Bellm J., 2017, ARXIV170506919
Bellm J, 2016, EUR PHYS J C, V76, DOI 10.1140/epjc/s10052-016-4018-8
Berger CF, 2008, PHYS REV D, V78, DOI 10.1103/PhysRevD.78.036003
BERN Z, 1995, NUCL PHYS B, V435, P59, DOI 10.1016/0550-3213(94)00488-Z
BERN Z, 1994, NUCL PHYS B, V425, P217, DOI 10.1016/0550-3213(94)90179-1
Bernreuther W., 2007, PHYS LETT B, V644, P386
Bernreuther W, 2006, PHYS REV D, V74, DOI 10.1103/PhysRevD.74.113005
Bernreuther W, 2008, PHYS REV D, V78, DOI 10.1103/PhysRevD.78.017503
Bevilacqua G, 2013, COMPUT PHYS COMMUN, V184, P986, DOI 10.1016/j.cpc.2012.10.033
Biedermann B, 2017, EUR PHYS J C, V77, DOI 10.1140/epjc/s10052-017-5054-8
Biedermann B, 2017, J HIGH ENERGY PHYS, DOI 10.1007/JHEP01(2017)033
Binoth T, 2010, COMPUT PHYS COMMUN, V181, P1612, DOI 10.1016/j.cpc.2010.05.016
Bredenstein A, 2008, J HIGH ENERGY PHYS, DOI 10.1088/1126-6708/2008/08/108
Britto R, 2005, NUCL PHYS B, V725, P275, DOI 10.1016/j.nuclphysb.2005.07.014
Campbell J, 2004, PHYS REV D, V69, DOI 10.1103/PhysRevD.69.074021
Campbell J.M., 2015, MCFM V 8 0
Campbell JM, 2016, PHYS REV D, V94, DOI 10.1103/PhysRevD.94.093009
Campbell JM, 2012, J HIGH ENERGY PHYS, DOI 10.1007/JHEP07(2012)052
Carrazza S, 2016, COMPUT PHYS COMMUN, V209, P134, DOI 10.1016/j.cpc.2016.07.033
Carter J, 2011, COMPUT PHYS COMMUN, V182, P1566, DOI 10.1016/j.cpc.2011.03.026
Cascioli F, 2012, PHYS REV LETT, V108, DOI 10.1103/PhysRevLett.108.111601
Chiesa M, 2017, J HIGH ENERGY PHYS, DOI 10.1007/JHEP10(2017)181
Chiesa M, 2016, J PHYS G NUCL PARTIC, V43, DOI 10.1088/0954-3899/43/1/013002
COLLINS J, 1978, PHYS REV D, V18, P242, DOI 10.1103/PhysRevD.18.242
Cullen G, 2014, EUR PHYS J C, V74, DOI 10.1140/epjc/s10052-014-3001-5
Cullen G, 2012, EUR PHYS J C, V72, DOI 10.1140/epjc/s10052-012-1889-1
Dawson S, 1999, PHYS REV D, V59, DOI 10.1103/PhysRevD.59.054012
Denner A, 2004, NUCL PHYS B, V680, P85, DOI 10.1016/j.nuclphysb.2003.12.028
Denner A, 2012, NUCL PHYS B, V854, P504, DOI 10.1016/j.nuclphysb.2011.09.001
Denner A, 2003, PHYS LETT B, V575, P290, DOI 10.1016/j.physletb.2003.09.069
DENNER A, 1993, FORTSCHR PHYS, V41, P307, DOI 10.1002/prop.2190410402
Denner A, 2006, NUCL PHYS B, V734, P62, DOI 10.1016/j.nuclphysb.2005.11.007
Denner A, 2017, J HIGH ENERGY PHYS, DOI 10.1007/JHEP02(2017)053
Denner A, 2017, COMPUT PHYS COMMUN, V212, P220, DOI 10.1016/j.cpc.2016.10.013
Dittmaier S, 1998, PHYS LETT B, V441, P383, DOI 10.1016/S0370-2693(98)01192-7
Dittmaier S, 2002, PHYS REV D, V65, DOI 10.1103/PhysRevD.65.073007
Ellis RK, 2008, J HIGH ENERGY PHYS, DOI 10.1088/1126-6708/2008/02/002
Ellis RK, 2009, NUCL PHYS B, V822, P270, DOI 10.1016/j.nuclphysb.2009.07.023
ELLIS RK, 1986, NUCL PHYS B, V269, P445, DOI 10.1016/0550-3213(86)90232-4
ELLIS RK, 2008, J HIGH ENERGY PHYS
Figueroa D, 2018, PHYS REV D, V98, DOI 10.1103/PhysRevD.98.093002
Figueroa D., 2018, POS LL2018, V2018, DOI 10.22323/1.303. 0082.
Frederix R, 2018, J HIGH ENERGY PHYS, DOI 10.1007/JHEP02(2018)031
Frixione S, 2015, J HIGH ENERGY PHYS, DOI 10.1007/JHEP06(2015)184
Garzelli MV, 2012, J HIGH ENERGY PHYS, DOI 10.1007/JHEP11(2012)056
Garzelli MV, 2012, PHYS REV D, V85, DOI 10.1103/PhysRevD.85.074022
Giele WT, 2008, J HIGH ENERGY PHYS, DOI 10.1088/1126-6708/2008/04/049
Gleisberg T, 2009, J HIGH ENERGY PHYS, DOI 10.1088/1126-6708/2009/02/007
Gong W, 2009, PHYS REV D, V79, DOI 10.1103/PhysRevD.79.033005
Greiner N, 2018, J HIGH ENERGY PHYS, DOI 10.1007/JHEP01(2018)079
Gutschow C, 2018, EUR PHYS J C, V78, DOI 10.1140/epjc/s10052-018-5804-2
Hahn T, 1999, COMPUT PHYS COMMUN, V118, P153, DOI 10.1016/S0010-4655(98)00173-8
Hirschi V, 2011, J HIGH ENERGY PHYS, DOI 10.1007/JHEP05(2011)044
Hollik W, 2008, PHYS REV D, V77, DOI 10.1103/PhysRevD.77.014008
HOLLIK WFL, 1990, FORTSCHR PHYS, V38, P165, DOI 10.1002/prop.2190380302
Honeywell S., 2017, THESIS
Kallweit S, 2017, J HIGH ENERGY PHYS, DOI 10.1007/JHEP11(2017)120
Kallweit S, 2016, J HIGH ENERGY PHYS, DOI 10.1007/JHEP04(2016)021
Kallweit S, 2015, J HIGH ENERGY PHYS, DOI 10.1007/JHEP04(2015)012
Kardos A, 2012, PHYS REV D, V85, DOI 10.1103/PhysRevD.85.054015
Kreimer D., 1993, ARXIVHEPPH9401354
Kuehn JH, 2007, EUR PHYS J C, V51, P37, DOI 10.1140/epjc/s10052-007-0275-x
Kuhn JH, 2006, EUR PHYS J C, V45, P139, DOI 10.1140/epjc/s2005-02423-6
Kuipers J, 2013, COMPUT PHYS COMMUN, V184, P1453, DOI 10.1016/j.cpc.2012.12.028
Lazopoulos A, 2008, PHYS LETT B, V666, P62, DOI 10.1016/j.physletb.2008.06.073
Maltoni F, 2016, J HIGH ENERGY PHYS, DOI 10.1007/JHEP02(2016)113
Moretti S, 2008, PHYS LETT B, V660, P607, DOI 10.1016/j.physletb.2007.10.039
Moretti S, 2006, NUCL PHYS B, V759, P50, DOI 10.1016/j.nuclphysb.2006.09.028
NAGY Z, 2003, J HIGH ENERGY PHYS
Nagy Z, 2006, PHYS REV D, V74, DOI 10.1103/PhysRevD.74.093006
NASON P, 1990, NUCL PHYS B, V335, P260
NASON P, 1988, NUCL PHYS B, V303, P607, DOI 10.1016/0550-3213(88)90422-1
NOGUEIRA P, 1993, J COMPUT PHYS, V105, P279, DOI 10.1006/jcph.1993.1074
Ossola G, 2007, NUCL PHYS B, V763, P147, DOI 10.1016/j.nuclphysb.2006.11.012
PASSARINO G, 1979, NUCL PHYS B, V160, P151, DOI 10.1016/0550-3213(79)90234-7
Reina L., 2012, POS LL2012
Reina L, 2012, J HIGH ENERGY PHYS, DOI 10.1007/JHEP09(2012)119
Reuschle C, 2013, PHYS REV D, V88, DOI 10.1103/PhysRevD.88.105020
Riemann TDT, 2009, PHYS REV D, V80, DOI 10.1103/PhysRevD.80.036003
Schonherr M, 2018, J HIGH ENERGY PHYS, DOI 10.1007/JHEP07(2018)076
SIRLIN A, 1980, PHYS REV D, V22, P971, DOI 10.1103/PhysRevD.22.971
THOOFT G, 1974, ANN I H POINCARE A, V20, P69
van Hameren A, 2011, COMPUT PHYS COMMUN, V182, P2427, DOI 10.1016/j.cpc.2011.06.011
van Hameren A, 2009, J HIGH ENERGY PHYS, DOI 10.1088/1126-6708/2009/09/106
van Hameren A, 2009, J HIGH ENERGY PHYS, DOI 10.1088/1126-6708/2009/07/088
Vermaseren J A M, 2000, ARXIVMATHPH0010025
Weinzierl S, 2006, EUR PHYS J C, V45, P745, DOI 10.1140/epjc/s2005-02467-6
You Y, 2003, PHYS LETT B, V571, P85, DOI 10.1016/j.physletb.2003.07.064
NR 108
TC 0
Z9 0
U1 2
U2 2
PU ELSEVIER
PI AMSTERDAM
PA RADARWEG 29, 1043 NX AMSTERDAM, NETHERLANDS
SN 0010-4655
EI 1879-2944
J9 COMPUT PHYS COMMUN
JI Comput. Phys. Commun.
PD DEC
PY 2020
VL 257
AR 107284
DI 10.1016/j.cpc.2020.107284
PG 17
WC Computer Science, Interdisciplinary Applications; Physics, Mathematical
SC Computer Science; Physics
GA OD3WI
UT WOS:000579783600005
DA 2021-04-21
ER
PT J
AU Schuler, M
Golez, D
Murakami, Y
Bittner, N
Herrmann, A
Strand, HUR
Werner, P
Eckstein, M
AF Schuler, Michael
Golez, Denis
Murakami, Yuta
Bittner, Nikolaj
Herrmann, Andreas
Strand, Hugo U. R.
Werner, Philipp
Eckstein, Martin
TI NESSi: The Non-Equilibrium Systems Simulation package
SO COMPUTER PHYSICS COMMUNICATIONS
LA English
DT Article
DE Numerical simulations; Nonequilibrium dynamics of quantum many-body
problems; Keldysh formalism; Kadanoff-Baym equations
ID MEAN-FIELD THEORY; ANDERSON MODEL; FERMIONS
AB The nonequilibrium dynamics of correlated many-particle systems is of interest in connection with pump-probe experiments on molecular systems and solids, as well as theoretical investigations of transport properties and relaxation processes. Nonequilibrium Green's functions are a powerful tool to study interaction effects in quantum many-particle systems out of equilibrium, and to extract physically relevant information for the interpretation of experiments. We present the open-source software package NESSi (The Non-Equilibrium Systems Simulation package) which allows to perform many-body dynamics simulations based on Green's functions on the L-shaped Kadanoff-Baym contour. NESSi contains the library libcntr which implements tools for basic operations on these nonequilibrium Green's functions, for constructing Feynman diagrams, and for the solution of integral and integro-differential equations involving contour Green's functions. The library employs a discretization of the Kadanoff-Baym contour into time N points and a high-order implementation of integration routines. The total integrated error scales up to O(N-7), which is important since the numerical effort increases at least cubically with the simulation time. A distributed-memory parallelization over reciprocal space allows large-scale simulations of lattice systems. We provide a collection of example programs ranging from dynamics in simple two-level systems to problems relevant in contemporary condensed matter physics, including Hubbard clusters and Hubbard or Holstein lattice models. The libcntr library is the basis of a follow-up software package for nonequilibrium dynamical mean-field theory calculations based on strong-coupling perturbative impurity solvers.
Program summary
Program Title: NESSi
CPC Library link to program files: http://dx.doi.org/10.17632/973crf9hgd.1
Licensing provisions: MPL v2.0
Programming language: C++, python
External routines/libraries: cmake, eigen3, hdf5 (optional), mpi (optional), omp (optional)
Nature of problem: Solves equations of motion of time-dependent Green's functions on the Kadanoff-Baym contour.
Solution method: Higher-order solution methods of integral and integro-differential equations on the Kadanoff-Baym contour. (c) 2020 Published by Elsevier B.V.
C1 [Schuler, Michael; Golez, Denis; Murakami, Yuta; Bittner, Nikolaj; Herrmann, Andreas; Werner, Philipp] Univ Fribourg, Dept Phys, CH-1700 Fribourg, Switzerland.
[Schuler, Michael] SLAC, Stanford Inst Mat & Energy Sci, Stanford, CA 94025 USA.
[Schuler, Michael] Stanford Univ, Stanford, CA 94025 USA.
[Golez, Denis; Strand, Hugo U. R.] Flatiron Inst, Ctr Computat Quantum Phys, 162 Fifth Ave, New York, NY 10010 USA.
[Murakami, Yuta] Tokyo Inst Technol, Dept Phys, Meguro Ku, Tokyo 1528551, Japan.
[Strand, Hugo U. R.] Chalmers Univ Technol, Dept Phys, SE-41296 Gothenburg, Sweden.
[Eckstein, Martin] Univ Erlangen Nurnberg, Dept Phys, D-91058 Erlangen, Germany.
RP Eckstein, M (corresponding author), Univ Erlangen Nurnberg, Dept Phys, D-91058 Erlangen, Germany.
EM martin.eckstein@fau.de
RI Golez, Denis/AAK-7279-2021; Werner, Philipp/C-7247-2009
OI Strand, Hugo/0000-0002-7263-4403; Schuler, Michael/0000-0001-7322-6367
FU Swiss National Science FoundationSwiss National Science Foundation
(SNSF)European Commission [PP0022-118866, 200021-140648, 200021-165539];
NCCR MARVEL; European Research CouncilEuropean Research Council
(ERC)European Commission [278023, 716648, 724103]; Alexander von
Humboldt Foundation, GermanyAlexander von Humboldt Foundation
FX We thank Marcus Kollar, Naoto Tsuji, Jiajun Li, and Nagamalleswararao
Dasari, for important feedback while using the library, and for
collaborations on early stages of the library. The development of this
library has been supported by the Swiss National Science Foundation
through SNF Professorship PP0022-118866 (ME,PW), Grants 200021-140648
and 200021-165539 (DG), and NCCR MARVEL (MS,YM), as well as the European
Research Council through ERC Starting Grant Nos. 278023 (AH,HS,PW) and
716648 (ME), and ERC Consolidator Grant No. 724103 (MS,NB,PW,YM). MS
thanks the Alexander von Humboldt Foundation, Germany for its support
with a Feodor Lynen scholarship. The Flatiro Institute as a division of
the Simons Foundation.
CR Abrikosov A, 1975, METHODS QUANTUM FIEL
Alvermann A, 2011, J COMPUT PHYS, V230, P5930, DOI 10.1016/j.jcp.2011.04.006
[Anonymous], 2019, MODERN C NATIVE HEAD
Aoki H, 2014, REV MOD PHYS, V86, P779, DOI 10.1103/RevModPhys.86.779
Aryasetiawan F, 1998, REP PROG PHYS, V61, P237, DOI 10.1088/0034-4885/61/3/002
Balzer K, 2012, NONEQUILIBRIUM GREEN
Brunner H., 1986, NUMERICAL SOLUTION V
Daley AJ, 2004, J STAT MECH-THEORY E, DOI 10.1088/1742-5468/2004/04/P04005
Eckstein M, 2010, PHYS REV B, V82, DOI 10.1103/PhysRevB.82.115115
Georges A, 1996, REV MOD PHYS, V68, P13, DOI 10.1103/RevModPhys.68.13
Giamarchi T, 2003, QUANTUM PHYS ONE DIM, V121
Golez D, 2016, PHYS REV B, V94, DOI 10.1103/PhysRevB.94.035121
Gubernatis J. E, 2016, QUANTUM MONTE CARLO
Hedin L, 1999, J PHYS-CONDENS MAT, V11, pR489, DOI 10.1088/0953-8984/11/42/201
Ido K, 2015, PHYS REV B, V92, DOI 10.1103/PhysRevB.92.245106
Kadanoff L., 1962, QUANTUM STAT MECH
Kamenev A., 2011, FIELD THEORY NONEQUI
KEITER H, 1971, J APPL PHYS, V42, P1460, DOI 10.1063/1.1660293
KELDYSH LV, 1965, SOV PHYS JETP-USSR, V20, P1018
Kemper AF, 2014, PHYS REV B, V90, DOI 10.1103/PhysRevB.90.075126
LUTTINGER JM, 1951, PHYS REV, V84, P814, DOI 10.1103/PhysRev.84.814
Mahan Gerald D., 1990, MANY PARTICLE PHYS
METZNER W, 1989, PHYS REV LETT, V62, P324, DOI 10.1103/PhysRevLett.62.324
Murakami Y, 2016, PHYS REV B, V93, DOI 10.1103/PhysRevB.93.094509
Murakami Y, 2015, PHYS REV B, V91, DOI 10.1103/PhysRevB.91.045128
Peierls R, 1933, Z PHYS, V80, P763, DOI 10.1007/BF01342591
Press W. H., 2007, NUMERICAL RECIPES AR
PRUSCHKE T, 1989, Z PHYS B CON MAT, V74, P439, DOI 10.1007/BF01311391
Schlunzen N, 2017, PHYS REV B, V95, DOI 10.1103/PhysRevB.95.165139
Schlunzen N, 2016, CONTRIB PLASM PHYS, V56, P5, DOI 10.1002/ctpp.201610003
Schuler M, 2016, PHYS REV B, V93, DOI 10.1103/PhysRevB.93.054303
Sentef MA, 2016, PHYS REV B, V93, DOI 10.1103/PhysRevB.93.144506
Stan A, 2009, J CHEM PHYS, V130, DOI 10.1063/1.3127247
Stefanucci G., 2013, NONEQUILIBRIUM MANY
STEINBERG J, 1972, NUMER MATH, V19, P212, DOI 10.1007/BF01404691
Tsuji N, 2013, PHYS REV B, V88, DOI 10.1103/PhysRevB.88.165115
von Friesen MP, 2009, PHYS REV LETT, V103, DOI 10.1103/PhysRevLett.103.176404
von Friesen MP, 2010, PHYS REV B, V82, DOI 10.1103/PhysRevB.82.155108
White SR, 2004, PHYS REV LETT, V93, DOI 10.1103/PhysRevLett.93.076401
NR 39
TC 4
Z9 4
U1 5
U2 5
PU ELSEVIER
PI AMSTERDAM
PA RADARWEG 29, 1043 NX AMSTERDAM, NETHERLANDS
SN 0010-4655
EI 1879-2944
J9 COMPUT PHYS COMMUN
JI Comput. Phys. Commun.
PD DEC
PY 2020
VL 257
AR 107484
DI 10.1016/j.cpc.2020.107484
PG 48
WC Computer Science, Interdisciplinary Applications; Physics, Mathematical
SC Computer Science; Physics
GA OD3WI
UT WOS:000579783600004
DA 2021-04-21
ER
PT J
AU Reuber, GS
Simons, FJ
AF Reuber, Georg S.
Simons, Frederik J.
TI Multi-physics adjoint modeling of Earth structure: combining
gravimetric, seismic, and geodynamic inversions
SO GEM-INTERNATIONAL JOURNAL ON GEOMATHEMATICS
LA English
DT Article
DE Gravitational potential; Wave equation; Stokes equation; Adjoint-state
method; Multi-physics inversion
ID WAVE-FORM INVERSION; MANTLE-CIRCULATION MODELS; STOCHASTIC NEWTON MCMC;
GRAVITY-ANOMALIES; DATA ASSIMILATION; FLOW; CONVECTION; REFLECTION;
SENSITIVITY; KERNELS
AB We discuss the resolving power of three geophysical imaging and inversion techniques, and their combination, for the reconstruction of material parameters in the Earth's subsurface. The governing equations are those of Newton and Poisson for gravitational problems, the acoustic wave equation under Hookean elasticity for seismology, and the geodynamics equations of Stokes for incompressible steady-state flow in the mantle. The observables are the gravitational potential, the seismic displacement, and the surface velocity, all measured at the surface. The inversion parameters of interest are the mass density, the acoustic wave speed, and the viscosity. These systems of partial differential equations and their adjoints were implemented in a single Python code using the finite-element library FeNICS. To investigate the shape of the cost functions, we present a grid search in the parameter space for three end-member geological settings: a falling block, a subduction zone, and a mantle plume. The performance of a gradient-based inversion for each single observable separately, and in combination, is presented. We furthermore investigate the performance of a shape-optimizing inverse method, when the material is known, and an inversion that inverts for the material parameters of an anomaly with known shape.
C1 [Reuber, Georg S.] Johannes Gutenberg Univ Mainz, Inst Geosci, D-55128 Mainz, Germany.
[Reuber, Georg S.] Max Planck Grad Ctr, Mainz, Germany.
[Reuber, Georg S.] Johannes Gutenberg Univ Mainz, Mainz Inst Multiscale Modelling M3ODEL, D-55128 Mainz, Germany.
[Simons, Frederik J.] Princeton Univ, Dept Geosci, Princeton, NJ 08544 USA.
[Simons, Frederik J.] Princeton Univ, Program Appl & Computat Math, Princeton, NJ 08544 USA.
RP Reuber, GS (corresponding author), Johannes Gutenberg Univ Mainz, Inst Geosci, D-55128 Mainz, Germany.; Reuber, GS (corresponding author), Max Planck Grad Ctr, Mainz, Germany.; Reuber, GS (corresponding author), Johannes Gutenberg Univ Mainz, Mainz Inst Multiscale Modelling M3ODEL, D-55128 Mainz, Germany.
EM reuber@uni-mainz.de
RI Simons, Frederik J/A-3427-2008
OI Simons, Frederik J/0000-0003-2021-6645
FU Projekt DEAL
FX Open Access funding enabled and organized by Projekt DEAL.
CR Aghasi A, 2011, SIAM J IMAGING SCI, V4, P618, DOI 10.1137/100800208
Akcelik V., 2002, P IEEE ACM SC2002 C, DOI [10.1109/SC.2002.10002, DOI 10.1109/SC.2002.10002]
Aki K, 1980, QUANTITATIVE SEISMOL
Alnaes M. S., 2015, ARCH NUMER SOFTW, V3, P9, DOI [10.11588/ans.2015.100.20553, DOI 10.11588/ANS.2015.100.20553]
Askan A, 2007, B SEISMOL SOC AM, V97, P1990, DOI 10.1785/0120070079
Askan A, 2010, CR MECANIQUE, V338, P364, DOI 10.1016/j.crme.2010.07.002
Aster RC, 2019, PARAMETER ESTIMATION AND INVERSE PROBLEMS, 3RD EDITION
Balay S., 2019, TECHNICAL REPORT
Berkel P, 2010, MATH GEOSCI, V42, P795, DOI 10.1007/s11004-010-9297-2
Blakely R.J., 1995, POTENTIAL THEORY GRA
Brown JR, 2017, FDN SEISMOLOGY BRING
Bunge HP, 2003, GEOPHYS J INT, V152, P280, DOI 10.1046/j.1365-246X.2003.01823.x
Bunge HP, 2002, PHILOS T R SOC A, V360, P2545, DOI 10.1098/rsta.2002.1080
BUNKS C, 1995, GEOPHYSICS, V60, P1457, DOI 10.1190/1.1443880
Burger M, 2001, INVERSE PROBL, V17, P1327, DOI 10.1088/0266-5611/17/5/307
Chao BF, 2005, J GEODYN, V39, P223, DOI 10.1016/j.jog.2004.11.001
Claerbout JF., 1992, EARTH SOUNDINGS ANAL
Colli L, 2018, GONDWANA RES, V53, P252, DOI 10.1016/j.gr.2017.04.027
Conrad CP, 2013, NATURE, V498, P479, DOI 10.1038/nature12203
Conrad CP, 1997, GEOPHYS J INT, V129, P95, DOI 10.1111/j.1365-246X.1997.tb00939.x
Crestel B, 2019, INVERSE PROBL, V35, DOI 10.1088/1361-6420/aaf129
Dahlen F., 1998, THEORETICAL GLOBAL S
de Hoop MV, 2009, INVERSE PROBL, V25, DOI 10.1088/0266-5611/25/2/025005
Domenzain D., 2018, 88 ANN INT M SEG, P4763, DOI [10.1190/segam2018-2997794.1, DOI 10.1190/SEGAM2018-2997794.1]
DORMAN LM, 1970, J GEOPHYS RES, V75, P3357, DOI 10.1029/JB075i017p03357
Dorn O, 2015, HDB MATH METHODS IMA
Elkins-Tanton LT, 2007, J GEOPHYS RES-SOL EA, V112, DOI 10.1029/2005JB004072
Elkins-Tanton LT, 2005, GEOL SOC AM SPEC PAP, V388, DOI 10.1130/2005.2388(27)
Fichtner A, 2006, PHYS EARTH PLANET IN, V157, P105, DOI 10.1016/j.pepi.2006.03.018
Fichtner A, 2006, PHYS EARTH PLANET IN, V157, P86, DOI 10.1016/j.pepi.2006.03.016
Fichtner A, 2018, J GEOPHYS RES-SOL EA, V123, P2984, DOI 10.1002/2017JB015249
Fichtner A, 2011, GEOPHYS J INT, V185, P775, DOI 10.1111/j.1365-246X.2011.04966.x
Fischer D, 2012, INVERSE PROBL, V28, DOI 10.1088/0266-5611/28/6/065012
Forte AM, 2001, NATURE, V410, P1049, DOI 10.1038/35074000
Freeden W, 2015, HDB GEOMATHEMATICS, P3
Freeden W, 2018, GEM INT J GEOMATHEMA, V9, P199, DOI 10.1007/s13137-018-0103-5
GAUTHIER O, 1986, GEOPHYSICS, V51, P1387, DOI 10.1190/1.1442188
Geng Y, 2020, COMMUN MATH PHYS, V28, P228, DOI [10.4208/cicp.OA-2018-0087, DOI 10.4208/CICP.OA-2018-0087]
Gerya T. V., 2019, INTRO NUMERICAL GEOD
Ghelichkhan S, 2016, GEM INT J GEOMATHEMA, V7, P1, DOI 10.1007/s13137-016-0080-5
Glatzmaier GA, 2014, INTRO MODELING CONVE
Gouveia WP, 1998, J GEOPHYS RES-SOL EA, V103, P2759, DOI 10.1029/97JB02933
Harig C, 2010, GEOCHEM GEOPHY GEOSY, V11, DOI 10.1029/2010GC003038
Hofmann-Wellenhof B., 2006, PHYS GEODESY
Horbach A, 2014, GEM INT J GEOMATHEMA, V5, P163, DOI 10.1007/s13137-014-0061-5
Kellogg O. D., 1967, FDN POTENTIAL THEORY
Kennett BLN, 2008, GEOPHYSICAL CONTINUA
Laurain A, 2018, STRUCT MULTIDISCIP O, V58, P1311, DOI 10.1007/s00158-018-1950-2
Lewis KW, 2012, GEOPHYS RES LETT, V39, DOI 10.1029/2012GL052708
Lithgow-Bertelloni C, 1998, REV GEOPHYS, V36, P27, DOI 10.1029/97RG02282
Liu Q, 2008, GEOPHYS J INT, V174, P265, DOI 10.1111/j.1365-246X.2008.03798.x
Liu QY, 2006, B SEISMOL SOC AM, V96, P2383, DOI 10.1785/0120060041
Logg A, 2012, AUTOMATED SOLUTION D
Ma Y, 2012, GEOPHYSICS, V77, pR189, DOI [10.1190/GEO2011-0395.1, 10.1190/geo2011-0395.1]
MALEVSKY AV, 1992, GEOPHYS ASTRO FLUID, V65, P149, DOI 10.1080/03091929208225244
Malvern L.E., 1969, INTRO MECH CONTINUOU
MARTIN D, 1989, J PETROL, V30, P1471, DOI 10.1093/petrology/30.6.1471
MARTIN D, 1988, NATURE, V332, P534, DOI 10.1038/332534a0
Martin J, 2012, SIAM J SCI COMPUT, V34, pA1460, DOI 10.1137/110845598
Mead JL, 2020, J INVERSE ILL-POSE P, V28, P105, DOI 10.1515/jiip-2019-0068
MELOSH HJ, 1980, GEOPHYS J ROY ASTR S, V60, P333, DOI 10.1111/j.1365-246X.1980.tb04812.x
Michel V, 2005, INVERSE PROBL, V21, P997, DOI 10.1088/0266-5611/21/3/013
Michel V, 2008, INVERSE PROBL, V24, DOI 10.1088/0266-5611/24/4/045019
Michel V, 2015, HDB GEOMATHEMATICS, P2087
Michel V, 2017, INVERSE PROBL, V33, DOI 10.1088/1361-6420/aa9909
Michel V, 2016, J INVERSE ILL-POSE P, V24, P687, DOI 10.1515/jiip-2015-0026
MORA P, 1989, GEOPHYSICS, V54, P1575, DOI 10.1190/1.1442625
MORA P, 1988, GEOPHYSICS, V53, P750, DOI 10.1190/1.1442510
MORGAN WJ, 1971, NATURE, V230, P42, DOI 10.1038/230042a0
MORGAN WJ, 1965, J GEOPHYS RES, V70, P6175, DOI 10.1029/JZ070i024p06175
Nolet G, 2008, BREVIARY OF SEISMIC TOMOGRAPHY: IMAGING THE INTERIOR OF THE EARTH AND SUN, P1, DOI 10.1017/CBO9780511984709
Nolet G, 2015, HDB GEOMATHEMATICS, P1887
OLDENBURG DW, 1974, GEOPHYSICS, V39, P526, DOI 10.1190/1.1440444
Pan WY, 2020, GEOPHYS J INT, V221, P1292, DOI 10.1093/gji/ggaa089
PARKER RL, 1973, GEOPHYS J ROY ASTR S, V31, P447, DOI 10.1111/j.1365-246X.1973.tb06513.x
PARMENTIER EM, 1976, J GEOPHYS RES, V81, P1839, DOI 10.1029/JB081i011p01839
PELTIER WR, 1985, ANNU REV FLUID MECH, V17, P561
Peng DP, 1999, J COMPUT PHYS, V155, P410, DOI 10.1006/jcph.1999.6345
Petra N, 2014, SIAM J SCI COMPUT, V36, pA1525, DOI 10.1137/130934805
Petra N, 2012, J GLACIOL, V58, P889, DOI 10.3189/2012JoG11J182
Plattner A., 2020, LEAD EDGE, V39, P332, DOI [10.1190/tle39050332.1, DOI 10.1190/TLE39050332.1]
Plessix RE, 2006, GEOPHYS J INT, V167, P495, DOI 10.1111/j.1365-246X.2006.02978.x
Ranalli G., 1995, RHEOLOGY EARTH
Ratnaswamy V, 2015, GEOPHYS J INT, V202, P768, DOI 10.1093/gji/ggv166
Reuber GS, 2020, GEOPHYS J INT, V223, P851, DOI 10.1093/gji/ggaa344
Reuber GS, 2018, FRONT EARTH SC-SWITZ, V6, DOI 10.3389/feart.2018.00117
Robbins AR, 2018, J APPL GEOPHYS, V151, P66, DOI 10.1016/j.jappgeo.2018.01.027
Scherzer O, 2000, J MATH IMAGING VIS, V12, P43, DOI 10.1023/A:1008344608808
Schubert G, 2001, MANTLE CONVECTION EA
Schuster G T., 2017, SEISMIC INVERSION
Sheriff R. E., 1995, EXPLORATION SEISMOLO
SLEEP NH, 1975, GEOPHYS J ROY ASTR S, V42, P827, DOI 10.1111/j.1365-246X.1975.tb06454.x
SLEEP NH, 1990, J GEOPHYS RES-SOLID, V95, P6715, DOI 10.1029/JB095iB05p06715
Stefanov P, 2019, ACTA MATH SIN, V35, P1085, DOI 10.1007/s10114-019-8338-0
Stern RJ, 2002, REV GEOPHYS, V40, DOI 10.1029/2001RG000108
Symes WW, 2009, INVERSE PROBL, V25, DOI 10.1088/0266-5611/25/12/123008
TARANTOLA A, 1988, PURE APPL GEOPHYS, V128, P365, DOI 10.1007/BF01772605
TARANTOLA A, 1984, GEOPHYS PROSPECT, V32, P998, DOI 10.1111/j.1365-2478.1984.tb00751.x
TARANTOLA A, 1986, GEOPHYSICS, V51, P1893, DOI 10.1190/1.1442046
TARANTOLA A, 1984, GEOPHYSICS, V49, P1259, DOI 10.1190/1.1441754
Tikhonov A.N., 1977, SOLUTION ILL POSED P
Tomlinson KY, 2001, GEOL S AM S, P341
Troltzsch F., 2010, OPTIMAL CONTROL PART
Tromp J, 2005, GEOPHYS J INT, V160, P195, DOI 10.1111/J.1365-246X.2004.02453.X
Tromp J., 2020, NAT REV EARTH ENV, V1, P40, DOI [10.1038/s43017-019-0003-8, DOI 10.1038/S43017-019-0003-8]
VANDENBERG AP, 1993, GEOPHYS J INT, V115, P62
Villa U., 2019, ARXIV190903948
Virieux J, 2009, GEOPHYSICS, V74, pWCC1, DOI 10.1190/1.3238367
WILSON JT, 1973, TECTONOPHYSICS, V19, P149, DOI 10.1016/0040-1951(73)90037-1
WOODWARD MJ, 1992, GEOPHYSICS, V57, P15, DOI 10.1190/1.1443179
XU PL, 1992, GEOPHYS J INT, V110, P321, DOI 10.1111/j.1365-246X.1992.tb00877.x
XU PL, 1992, GEOPHYS J INT, V111, P170, DOI 10.1111/j.1365-246X.1992.tb00563.x
Yilmaz O., 2001, SEISMIC DATA ANAL
Yuan YO, 2014, GEOPHYSICS, V79, pWA79, DOI 10.1190/GEO2013-0383.1
Zienkiewicz OC, 1977, FINITE ELEMENT METHO
NR 115
TC 0
Z9 0
U1 1
U2 1
PU SPRINGER HEIDELBERG
PI HEIDELBERG
PA TIERGARTENSTRASSE 17, D-69121 HEIDELBERG, GERMANY
SN 1869-2672
EI 1869-2680
J9 GEM INT J GEOMATHEMA
JI GEM Int. J. Geomathematics
PD NOV 10
PY 2020
VL 11
IS 1
AR 30
DI 10.1007/s13137-020-00166-8
PG 38
WC Mathematics, Interdisciplinary Applications
SC Mathematics
GA OP9HC
UT WOS:000588400200001
OA Other Gold
DA 2021-04-21
ER
PT J
AU Ryu, T
Krolik, J
Piran, T
AF Ryu, Taeho
Krolik, Julian
Piran, Tsvi
TI Measuring Stellar and Black Hole Masses of Tidal Disruption Events
SO ASTROPHYSICAL JOURNAL
LA English
DT Article
DE Black hole physics; Supermassive black holes; Gravitation; Stellar
physics; Tidal disruption; Galaxy nuclei
ID CANDIDATE; RICH
AB The flare produced when a star is tidally disrupted by a supermassive black hole holds potential as a diagnostic of both the black hole mass and the star mass. We propose a new method to realize this potential based upon a physical model of optical/UV light production in which shocks near the apocenters of debris orbits dissipate orbital energy, which is then radiated from that region. Measurement of the optical/UV luminosity and color temperature at the peak of the flare leads directly to the two masses. The black hole mass depends mostly on the temperature observed at peak luminosity, while the mass of the disrupted star depends mostly on the peak luminosity. We introduce TDEmass, a method to infer the black hole and stellar masses given these two input quantities. Using TDEmass, we find, for 21 well-measured events, black hole masses between 5 x 10(5) and 10(7) M-circle dot and disrupted stars with initial masses between 0.6 and 13 M-circle dot. An open-source python-based tool for TDEmass is available at https://github.com/taehoryu/TDEmass.git..
C1 [Ryu, Taeho; Krolik, Julian] Johns Hopkins Univ, Phys & Astron Dept, Baltimore, MD 21218 USA.
[Piran, Tsvi] Hebrew Univ Jerusalem, Racah Inst Phys, IL-91904 Jerusalem, Israel.
RP Ryu, T (corresponding author), Johns Hopkins Univ, Phys & Astron Dept, Baltimore, MD 21218 USA.
OI Piran, Tsvi/0000-0002-7964-5420
FU ERCEuropean Research Council (ERC)European Commission; NSFNational
Science Foundation (NSF) [AST-1715032]
FX We thank Iair Arcavi and Nicholas Stone for helpful comments. We are
grateful to the anonymous referee for some useful comments. This
research was partially supported by an advanced ERC grant TReX and by
NSF grant AST-1715032.
CR Arcavi I, 2014, ASTROPHYS J, V793, DOI 10.1088/0004-637X/793/1/38
Baldassare VF, 2020, ASTROPHYS J LETT, V898, DOI 10.3847/2041-8213/aba0c1
Blagorodnova N, 2019, ASTROPHYS J, V873, DOI 10.3847/1538-4357/ab04b0
Blagorodnova N, 2017, ASTROPHYS J, V844, DOI 10.3847/1538-4357/aa7579
Chornock R, 2014, ASTROPHYS J, V780, DOI 10.1088/0004-637X/780/1/44
Dai LX, 2015, ASTROPHYS J LETT, V812, DOI 10.1088/2041-8205/812/2/L39
Ferrarese L, 2005, SPACE SCI REV, V116, P523, DOI 10.1007/s11214-005-3947-6
French KD, 2016, ASTROPHYS J LETT, V818, DOI 10.3847/2041-8205/818/1/L21
Gafton E, 2019, MON NOT R ASTRON SOC, V487, P4790, DOI 10.1093/mnras/stz1530
Gezari S, 2012, NATURE, V485, P217, DOI 10.1038/nature10990
Goicovic FG, 2019, MON NOT R ASTRON SOC, V487, P981, DOI 10.1093/mnras/stz1368
Graur O, 2018, ASTROPHYS J, V853, DOI 10.3847/1538-4357/aaa3fd
Gultekin K, 2009, ASTROPHYS J, V698, P198, DOI 10.1088/0004-637X/698/1/198
Guillochon J, 2013, ASTROPHYS J, V767, DOI 10.1088/0004-637X/767/1/25
Haring N, 2004, ASTROPHYS J, V604, pL89, DOI 10.1086/383567
Hinkle JT, 2021, MON NOT R ASTRON SOC, V500, P1673, DOI 10.1093/mnras/staa3170
Hinkle JT, 2020, ASTROPHYS J LETT, V894, DOI 10.3847/2041-8213/ab89a2
Holoien TWS, 2016, MON NOT R ASTRON SOC, V455, P2918, DOI 10.1093/mnras/stv2486
Holoien TWS, 2014, MON NOT R ASTRON SOC, V445, P3263, DOI 10.1093/mnras/stu1922
Holoien TWS, 2019, ASTROPHYS J, V880, DOI 10.3847/1538-4357/ab2ae1
Holoien TWS, 2020, ASTROPHYS J, V898, DOI 10.3847/1538-4357/ab9f3d
Holoien TWS, 2019, ASTROPHYS J, V883, DOI 10.3847/1538-4357/ab3c66
Hung T, 2017, ASTROPHYS J, V842, DOI 10.3847/1538-4357/aa7337
Jiang YF, 2016, ASTROPHYS J, V830, DOI 10.3847/0004-637X/830/2/125
Kormendy J, 2013, ANNU REV ASTRON ASTR, V51, P511, DOI 10.1146/annurev-astro-082708-101811
Krolik J., 2020, ARXIV200103234
Krolik J, 2016, ASTROPHYS J, V827, DOI 10.3847/0004-637X/827/2/127
Law-Smith J, 2019, ASTROPHYS J LETT, V882, DOI 10.3847/2041-8213/ab379a
Law-Smith J, 2017, ASTROPHYS J, V850, DOI 10.3847/1538-4357/aa94c7
Leloudas G, 2019, ASTROPHYS J, V887, DOI 10.3847/1538-4357/ab5792
Liu FK, 2017, MON NOT R ASTRON SOC, V472, pL99, DOI 10.1093/mnrasl/slx147
McConnell NJ, 2013, ASTROPHYS J, V764, DOI 10.1088/0004-637X/764/2/184
Miller JM, 2015, NATURE, V526, P542, DOI 10.1038/nature15708
Mockler B, 2019, ASTROPHYS J, V872, DOI 10.3847/1538-4357/ab010f
Nicholl M, 2020, MON NOT R ASTRON SOC, V499, P482, DOI 10.1093/mnras/staa2824
Nicholl M, 2019, MON NOT R ASTRON SOC, V488, P1878, DOI 10.1093/mnras/stz1837
Noble SC, 2009, ASTROPHYS J, V692, P411, DOI 10.1088/0004-637X/692/1/411
Paxton B, 2011, ASTROPHYS J SUPPL S, V192, DOI 10.1088/0067-0049/192/1/3
PHINNEY ES, 1989, IAU SYMP, P543
Piran T, 2015, ASTROPHYS J, V806, DOI 10.1088/0004-637X/806/2/164
Ryu T., 2020, APJ
Shiokawa H, 2015, ASTROPHYS J, V804, DOI 10.1088/0004-637X/804/2/85
Tejeda E, 2017, MON NOT R ASTRON SOC, V469, P4483, DOI 10.1093/mnras/stx1089
van Velzen S., 2019, ATEL, V12568, P1
van Velzen S., 2020, ARXIV200101409
Wen SX, 2020, ASTROPHYS J, V897, DOI 10.3847/1538-4357/ab9817
Wevers T, 2019, MON NOT R ASTRON SOC, V488, P4816, DOI 10.1093/mnras/stz1976
Wevers T, 2017, MON NOT R ASTRON SOC, V471, P1694, DOI 10.1093/mnras/stx1703
Xiao T, 2011, ASTROPHYS J, V739, DOI 10.1088/0004-637X/739/1/28
Zhou Z. Q., 2020, ARXIV200202267
NR 50
TC 3
Z9 3
U1 0
U2 0
PU IOP PUBLISHING LTD
PI BRISTOL
PA TEMPLE CIRCUS, TEMPLE WAY, BRISTOL BS1 6BE, ENGLAND
SN 0004-637X
EI 1538-4357
J9 ASTROPHYS J
JI Astrophys. J.
PD NOV
PY 2020
VL 904
IS 1
AR 73
DI 10.3847/1538-4357/abbf4d
PG 11
WC Astronomy & Astrophysics
SC Astronomy & Astrophysics
GA OU8TP
UT WOS:000591796800001
DA 2021-04-21
ER
PT J
AU Fontes, D
Romao, JC
AF Fontes, Duarte
Romao, Jorge C.
TI FeynMaster: A plethora of Feynman tools
SO COMPUTER PHYSICS COMMUNICATIONS
LA English
DT Article
DE FeynCalc; QGRAF; FeynRules; Feynman rules; Feynman diagrams; Loop
calculations; Renormalization
ID LOOP CALCULATIONS; GENERATION; AMPLITUDES; DIAGRAMS; PHYSICS; LEVEL
AB FeynMaster is a multi-tasking software for particle physics studies. By making use of already existing programs (FeynRules, QGRAF, FeynCalc), FeynMaster automatically generates Feynman rules, generates and draws Feynman diagrams, generates amplitudes, performs both loop and algebraic calculations, and fully renormalizes models. In parallel with this automatic character, FeynMaster allows the user to manipulate the generated results in Mathematica notebooks in a flexible and consistent way. It can be downloaded in https://porthos.tecnico.ulisboa.pt/FeynMaster/.
Program summary
Program Title: FeynMaster
Program Files doi: http://dx.doi.org/10.17632/f6yrbk4cm3.1
Licensing provisions: CC0 1.0
Programming language: Python, Wolfram Mathematica
Nature of problem: Although different softwares exist that separately handle Feynman rules, Feynman diagrams, loop calculations and renormalization, there seems to be missing a single software to address all those topics in a flexible and consistent way. Indeed, despite the undisputed quality of some of the existing softwares, they usually do not combine an automatic character with the possibility of manipulating the final analytical expressions in a practical way. And although interfaces between different softwares exist, they tend not to be free of constraints, since the notation changes between softwares and a conversion is not totally automatic.
Solution method: Using both Python and Wolfram Mathematica to combine FeynRules [1, 2], QGRAF [3] and FeynCalc [4, 5], FeynMaster performs all the above listed tasks, and at the same time allows the user to handle the final results in Mathematica notebooks.
Additional comments including restrictions and unusual features: Besides computing the results automatically, FeynMaster generates notebooks that allow the user to act upon them. It also includes a thorough interaction with numerical calculations, as it converts the expressions to LoopTools [6].
Algebraic computations only guaranteed up to 2 particles in both the initial and final states. Complex problems may require much computational time. The drawing of Feynman diagrams is only guaranteed to properly work with the diagrams up to 1-loop; moreover, diagrams with more than two particles in the initial or final states, as well as some reducible diagrams, are also not warranted. Due to some problems in LATEX breqn package, some lines in PDF outputs may go out of the screen.
C1 [Fontes, Duarte] Univ Lisbon, Inst Super Tecn, Dept Fis, Ave Rovisco Pais 1, P-1049001 Lisbon, Portugal.
Univ Lisbon, Inst Super Tecn, CFTP, Ave Rovisco Pais 1, P-1049001 Lisbon, Portugal.
RP Fontes, D (corresponding author), Univ Lisbon, Inst Super Tecn, Dept Fis, Ave Rovisco Pais 1, P-1049001 Lisbon, Portugal.
EM duartefontes@tecnico.ulisboa.pt; jorge.romao@tecnico.ulisboa.pt
RI romao, jorge/C-5991-2009
OI romao, jorge/0000-0002-9683-4055
FU project CFTP-FCT Unit 777 through POCTI Portugal (FEDER)
[UID/FIS/00777/2013, UID/FIS/00777/2019]; project CFTP-FCT Unit 777
through COMPETE Portugal [UID/FIS/00777/2013, UID/FIS/00777/2019];
project CFTP-FCT Unit 777 through QREN Portugal [UID/FIS/00777/2013,
UID/FIS/00777/2019]; project CFTP-FCT Unit 777 through EU
[UID/FIS/00777/2013, UID/FIS/00777/2019]; POCTI Portugal (FEDER)
[PTDC/FIS-PAR/29436/2017]; COMPETE Portugal [PTDC/FIS-PAR/29436/2017];
QREN Portugal [PTDC/FIS-PAR/29436/2017]; EUEuropean Commission
[PTDC/FIS-PAR/29436/2017]; Portuguese Fundacao para a Ciencia e
TecnologiaPortuguese Foundation for Science and TechnologyEuropean
Commission [SFRH/BD/135698/2018]
FX Both authors are very grateful to Antonio P. Lacerda, who kicked off the
entire program. We also thank Vladyslav Shtabovenko, Paulo Nogueira and
Augusto Barroso for useful discussions concerning FeynCalc, QGRAF and
renormalization, respectively; Maximilian Loschner for bringing the
feynmf package to our attention; Miguel P. Bento and Patrick Blackstone
for testing the program; Darius Jurciukonis for a careful reading of the
manuscript; Joao P. Silva for the suggestion of the name `FeynMaster',
as well as for a careful reading of the manuscript. D.F. is also
grateful to Isabel Fonseca for many useful suggestions concerning Python
and to Sofia Gomes for a suggestion regarding the printing of the
Feynman rules. Both authors are supported by projects CFTP-FCT Unit 777
(UID/FIS/00777/2013 and UID/FIS/00777/2019), and
PTDC/FIS-PAR/29436/2017, which are partially funded through POCTI
Portugal (FEDER), COMPETE Portugal, QREN Portugal and EU. D.F. is also
supported by the Portuguese Fundacao para a Ciencia e Tecnologia under
the project SFRH/BD/135698/2018.
CR AKYEAMPONG DA, 1973, NUOVO CIMENTO A, VA 17, P578, DOI 10.1007/BF02786835
Alloul A, 2014, COMPUT PHYS COMMUN, V185, P2250, DOI 10.1016/j.cpc.2014.04.012
Alwall J, 2014, J HIGH ENERGY PHYS, DOI 10.1007/JHEP07(2014)079
BARROSO A, 1991, PHYS LETT B, V261, P123, DOI 10.1016/0370-2693(91)91336-T
Belanger G, 2006, PHYS REP, V430, P117, DOI 10.1016/j.physrep.2006.02.001
CHANOWITZ M, 1979, NUCL PHYS B, V159, P225, DOI 10.1016/0550-3213(79)90333-X
Christensen ND, 2009, COMPUT PHYS COMMUN, V180, P1614, DOI 10.1016/j.cpc.2009.02.018
Cullen G, 2014, EUR PHYS J C, V74, DOI 10.1140/epjc/s10052-014-3001-5
Cullen G, 2012, EUR PHYS J C, V72, DOI 10.1140/epjc/s10052-012-1889-1
Degrande C, 2015, COMPUT PHYS COMMUN, V197, P239, DOI 10.1016/j.cpc.2015.08.015
DENNER A, 1993, FORTSCHR PHYS, V41, P307, DOI 10.1002/prop.2190410402
Denner A, 2006, NUCL PHYS B, V734, P62, DOI 10.1016/j.nuclphysb.2005.11.007
Hahn T, 1999, COMPUT PHYS COMMUN, V118, P153, DOI 10.1016/S0010-4655(98)00173-8
Hahn T, 2001, COMPUT PHYS COMMUN, V140, P418, DOI 10.1016/S0010-4655(01)00290-9
Harlander R, 1999, PROG PART NUCL PHYS, V43, P167, DOI 10.1016/S0146-6410(99)00095-2
Jegerlehner F, 2001, EUR PHYS J C, V18, P673, DOI 10.1007/s100520100573
Kreimer D., HEPPH9401354
KUBLBECK J, 1990, COMPUT PHYS COMMUN, V60, P165, DOI 10.1016/0010-4655(90)90001-H
Kuipers J, 2013, COMPUT PHYS COMMUN, V184, P1453, DOI 10.1016/j.cpc.2012.12.028
Lorca A, 2006, COMPUT PHYS COMMUN, V174, P71, DOI 10.1016/j.cpc.2005.09.003
MERTIG R, 1991, COMPUT PHYS COMMUN, V64, P345, DOI 10.1016/0010-4655(91)90130-D
NOGUEIRA P, 1993, J COMPUT PHYS, V105, P279, DOI 10.1006/jcph.1993.1074
OHL T, 1995, COMPUT PHYS COMMUN, V90, P340, DOI 10.1016/0010-4655(95)90137-S
Pukhov A., HEPPH9908288 COMPHEP
Romao J.C., 2019, ADV QUANTUM FIELD TH
Romao JC, 2012, INT J MOD PHYS A, V27, DOI 10.1142/S0217751X12300256
Semenov A, 1998, COMPUT PHYS COMMUN, V115, P124, DOI 10.1016/S0010-4655(98)00143-X
Semenov A.V., HEPPH9608488 LANHEP
Shtabovenko V, 2016, COMPUT PHYS COMMUN, V207, P432, DOI 10.1016/j.cpc.2016.06.008
Tanabashi M, 2018, PHYS REV D, V98, DOI 10.1103/PhysRevD.98.030001
Tentyukov M, 2000, COMPUT PHYS COMMUN, V132, P124, DOI 10.1016/S0010-4655(00)00147-8
Wang JX, 2004, NUCL INSTRUM METH A, V534, P241, DOI 10.1016/j.nima.2004.07.094
Zerf N., ARXIV191106345
NR 33
TC 0
Z9 0
U1 0
U2 0
PU ELSEVIER
PI AMSTERDAM
PA RADARWEG 29, 1043 NX AMSTERDAM, NETHERLANDS
SN 0010-4655
EI 1879-2944
J9 COMPUT PHYS COMMUN
JI Comput. Phys. Commun.
PD NOV
PY 2020
VL 256
AR 107311
DI 10.1016/j.cpc.2020.107311
PG 20
WC Computer Science, Interdisciplinary Applications; Physics, Mathematical
SC Computer Science; Physics
GA OS6DF
UT WOS:000590251400003
DA 2021-04-21
ER
PT J
AU Nascimento, RG
Fricke, K
Viana, FAC
AF Nascimento, Renato G.
Fricke, Kajetan
Viana, Felipe A. C.
TI A tutorial on solving ordinary differential equations using Python and
hybrid physics-informed neural network
SO ENGINEERING APPLICATIONS OF ARTIFICIAL INTELLIGENCE
LA English
DT Article
DE Physics-informed neural network; Scientific machine learning;
Uncertainty quantification; Hybrid model python implementation
AB We present a tutorial on how to directly implement integration of ordinary differential equations through recurrent neural networks using Python. In order to simplify the implementation, we leveraged modern machine learning frameworks such as TensorFlow and Keras. Besides, offering implementation of basic models (such as multilayer perceptrons and recurrent neural networks) and optimization methods, these frameworks offer powerful automatic differentiation. With all that, the main advantage of our approach is that one can implement hybrid models combining physics-informed and data-driven kernels, where data-driven kernels are used to reduce the gap between predictions and observations. Alternatively, we can also perform model parameter identification. In order to illustrate our approach, we used two case studies. The first one consisted of performing fatigue crack growth integration through Euler's forward method using a hybrid model combining a data-driven stress intensity range model with a physics-based crack length increment model. The second case study consisted of performing model parameter identification of a dynamic two-degree-of-freedom system through Runge-Kutta integration. The examples presented here as well as source codes are all open-source under the GitHub repository https://github.com/PML- UCF/pinn_code_tutorial.
C1 [Nascimento, Renato G.; Fricke, Kajetan; Viana, Felipe A. C.] Univ Cent Florida, Dept Mech & Aerosp Engn, Orlando, FL 32816 USA.
RP Viana, FAC (corresponding author), Univ Cent Florida, Dept Mech & Aerosp Engn, Orlando, FL 32816 USA.
EM viana@ucf.edu
OI Viana, Felipe/0000-0002-2196-7603
CR Abadi M, 2016, PROCEEDINGS OF OSDI'16: 12TH USENIX SYMPOSIUM ON OPERATING SYSTEMS DESIGN AND IMPLEMENTATION, P265
Altan A, 2020, MECH SYST SIGNAL PR, V138, DOI 10.1016/j.ymssp.2019.106548
Altan Aytac, 2018, 2018 6 INT C CONTR E, P1
Baydin AG, 2018, J MACH LEARN RES, V18
Butcher JC, 1996, APPL NUMER MATH, V22, P113, DOI 10.1016/S0168-9274(96)00048-7
Chen T. Q., 2018, ADV NEURAL INFORM PR, P6572
Cheng Y, 2018, ENG APPL ARTIF INTEL, V74, P303, DOI 10.1016/j.engappai.2018.07.003
Cho K., 2014, ARXIV 14061078, P1724
Chollet F., 2015, KERAS
Collins J., 1993, FAILURE MAT MECH DES
CONNOR JT, 1994, IEEE T NEURAL NETWOR, V5, P240, DOI 10.1109/72.279188
Dourado A, 2020, J COMPUT INF SCI ENG, V20, DOI 10.1115/1.4047173
Dowling N., 2012, MECH BEHAV MAT ENG M
Elsken T, 2019, J MACH LEARN RES, V20
Goodfellow I, 2016, DEEP LEARNING
Graves A, 2013, INT CONF ACOUST SPEE, P6645, DOI 10.1109/ICASSP.2013.6638947
Hochreiter S, 1997, NEURAL COMPUT, V9, P1735, DOI 10.1162/neco.1997.9.8.1735
Kandasamy K., 2018, P ADV NEUR INF PROC, P2016
Karpatne A, 2017, IEEE T KNOWL DATA EN, V29, P2318, DOI 10.1109/TKDE.2017.2720168
Liu C., 2018, EUR C COMP VIS ECCV
Nascimento RG, 2020, AIAA J, V58, P5459, DOI 10.2514/1.J059250
Pan SW, 2020, SIAM J APPL DYN SYST, V19, P480, DOI 10.1137/19M1267246
Pang G., 2020, NONLINEAR SYSTEMS CO, P323, DOI [10.1007/978-3- 030- 44992-6_14, DOI 10.1007/978-3-030-44992-6_14.]
Paris P., 1963, T ASME J BASIC ENG, V85, P528, DOI DOI 10.1115/1.3656900
Press WH, 2007, NUMERICAL RECIPES AR
Raissi M, 2019, J COMPUT PHYS, V378, P686, DOI 10.1016/j.jcp.2018.10.045
Raissi M, 2018, J COMPUT PHYS, V357, P125, DOI 10.1016/j.jcp.2017.11.039
Sak H, 2014, INTERSPEECH, P338
Shen CQ, 2018, ENG APPL ARTIF INTEL, V76, P170, DOI 10.1016/j.engappai.2018.09.010
Singh Shubhendu Kumar, 2019, 2019 18th IEEE International Conference On Machine Learning And Applications (ICMLA), P34, DOI 10.1109/ICMLA.2019.00015
Srivastava N, 2014, J MACH LEARN RES, V15, P1929
Sutskever I., 2011, P1017
TensorFlow Contributors, 2020, CREAT OP
Viana F.A.C., 2019, PHYS INFORMED NEURAL, DOI [10.5281/zenodo.3356877, DOI 10.5281/ZENODO.3356877]
Yucesan Y.A., 2020, INT J PROGNOST HLTH, V11, P27
NR 35
TC 0
Z9 0
U1 17
U2 17
PU PERGAMON-ELSEVIER SCIENCE LTD
PI OXFORD
PA THE BOULEVARD, LANGFORD LANE, KIDLINGTON, OXFORD OX5 1GB, ENGLAND
SN 0952-1976
EI 1873-6769
J9 ENG APPL ARTIF INTEL
JI Eng. Appl. Artif. Intell.
PD NOV
PY 2020
VL 96
AR 103996
DI 10.1016/j.engappai.2020.103996
PG 11
WC Automation & Control Systems; Computer Science, Artificial Intelligence;
Engineering, Multidisciplinary; Engineering, Electrical & Electronic
SC Automation & Control Systems; Computer Science; Engineering
GA OH6ND
UT WOS:000582708400036
DA 2021-04-21
ER
PT J
AU Romero, J
Bisson, M
Fatica, M
Bernaschi, M
AF Romero, Joshua
Bisson, Mauro
Fatica, Massimiliano
Bernaschi, Massimo
TI High performance implementations of the 2D Ising model on GPUs
SO COMPUTER PHYSICS COMMUNICATIONS
LA English
DT Article
DE 6,5 software including parallel algorithms; 23 statistical physics and
thermodynamics; Ising model; GPU programming
ID MONTE-CARLO SIMULATIONS
AB We present and make available novel implementations of the two-dimensional Ising model that is used as a benchmark to show the computational capabilities of modern Graphic Processing Units (GPUs). The rich programming environment now available on GPUs and flexible hardware capabilities allowed us to quickly experiment with several implementation ideas: a simple stencil-based algorithm, recasting the stencil operations into matrix multiplies to take advantage of Tensor Cores available on NVIDIA GPUs, and a highly optimized multi-spin coding approach. Using the managed memory API available in CUDA allows for simple and efficient distribution of these implementations across a multi-GPU NVIDIA DGX-2 server. We show that even a basic GPU implementation can outperform current results published on TPUs (Yang et al., 2019) and that the optimized multi-GPU implementation can simulate very large lattices faster than custom FPGA solutions (Ortega-Zamorano et al., 2016).
Program summary
Program title: cuIsing (optimized).
CPC Library link to program files: http://dx.doi.org/10.17632/xrb9xtkbcp.1
Licensing provisions: MIT license.
Programming languages: CUDA C, Python.
Nature of problem: Two dimensional Ising model for spin systems.
Solution method: Checkerboard Metropolis algorithm. (c) 2020 Elsevier B.V. All rights reserved.
C1 [Romero, Joshua] NVIDIA Corp, Santa Clara, CA 95050 USA.
Natl Res Council Italy, Ist Applicaz Calcolo, I-00185 Rome, Italy.
RP Romero, J (corresponding author), NVIDIA Corp, Santa Clara, CA 95050 USA.
EM joshr@nvidia.com
OI Romero, Joshua/0000-0003-1358-5565
CR Baity-Jesi M, 2014, COMPUT PHYS COMMUN, V185, P550, DOI 10.1016/j.cpc.2013.10.019
Bernaschi M., 2012, COMPUT PHYS COMM, V183
BINDER K, 1981, PHYS REV LETT, V47, P693, DOI 10.1103/PhysRevLett.47.693
Block B, 2010, COMPUT PHYS COMMUN, V181, P1549, DOI 10.1016/j.cpc.2010.05.005
Ising E, 1925, Z PHYS, V31, P253, DOI 10.1007/BF02980577
JACOBS L, 1981, J COMPUT PHYS, V41, P203, DOI 10.1016/0021-9991(81)90089-9
Lam S.K., 2015, P 2 WORKSH LLVM COMP, P1, DOI [DOI 10.1145/2833157.2833162DOI:10.1145/2833157.2833162, 10.1145/2833157.2833162]
METROPOLIS N, 1953, J CHEM PHYS, V21, P1087, DOI 10.1063/1.1699114
Okuta R., 2017, 31 ANN C NEUR INF PR
Onsager L, 1944, PHYS REV, V65, P117, DOI 10.1103/PhysRev.65.117
Ortega-Zamorano F, 2016, IEEE T PARALL DISTR, V27, P2618, DOI 10.1109/TPDS.2015.2505725
Preis T, 2009, J COMPUT PHYS, V228, P4468, DOI 10.1016/j.jcp.2009.03.018
Weigel M, 2012, J COMPUT PHYS, V231, P3064, DOI 10.1016/j.jcp.2011.12.008
WOLFF U, 1989, PHYS REV LETT, V62, P361, DOI 10.1103/PhysRevLett.62.361
Yang K, 2019, PROCEEDINGS OF SC19: THE INTERNATIONAL CONFERENCE FOR HIGH PERFORMANCE COMPUTING, NETWORKING, STORAGE AND ANALYSIS, DOI 10.1145/3295500.3356149
NR 15
TC 0
Z9 0
U1 2
U2 5
PU ELSEVIER
PI AMSTERDAM
PA RADARWEG 29, 1043 NX AMSTERDAM, NETHERLANDS
SN 0010-4655
EI 1879-2944
J9 COMPUT PHYS COMMUN
JI Comput. Phys. Commun.
PD NOV
PY 2020
VL 256
AR 107473
DI 10.1016/j.cpc.2020.107473
PG 9
WC Computer Science, Interdisciplinary Applications; Physics, Mathematical
SC Computer Science; Physics
GA NH2BZ
UT WOS:000564482200006
DA 2021-04-21
ER
PT J
AU Valiulin, VE
Mikheyenkov, AV
Chtchelkatchev, NM
Kugel, KI
AF Valiulin, V. E.
Mikheyenkov, A., V
Chtchelkatchev, N. M.
Kugel, K., I
TI Quantum entanglement, local indicators, and the effect of external
fields in the Kugel-Khomskii model
SO PHYSICAL REVIEW B
LA English
DT Article
ID ELEMENTARY EXCITATIONS; PYTHON FRAMEWORK; ORBITAL PHYSICS; DYNAMICS;
QUTIP
AB Using the exact diagonalization technique, we determine the energy spectrum and wave functions for finite chains described by the two-spin (Kugel-Khomskii) model with different types of intersubsystem exchange terms. The obtained solutions provide the possibility to address the problem of quantum entanglement inherent in this class of models. We put the main emphasis on the calculations of the concurrence treated as an adequate numerical measure of the entanglement. We also analyze the behavior of two-site correlation functions considered a local indicator of entanglement. We construct the phase diagrams of the models involving the regions of nonzero entanglement. The pronounced effect of external fields, conjugated to both spin variables in the regions with entanglement, could both enhance and weaken the entanglement depending on the parameters of the models.
C1 [Valiulin, V. E.; Mikheyenkov, A., V; Chtchelkatchev, N. M.] Natl Res Univ, Moscow Inst Phys & Technol, Dolgoprudnyi 141701, Russia.
[Valiulin, V. E.; Mikheyenkov, A., V; Chtchelkatchev, N. M.] Russian Acad Sci, Inst High Pressure Phys, Troitsk 108840, Russia.
[Kugel, K., I] Russian Acad Sci, Inst Theoret & Appl Electrodynam, Moscow 125412, Russia.
[Kugel, K., I] Natl Res Univ, Higher Sch Econ, Moscow 101000, Russia.
RP Valiulin, VE (corresponding author), Natl Res Univ, Moscow Inst Phys & Technol, Dolgoprudnyi 141701, Russia.; Valiulin, VE (corresponding author), Russian Acad Sci, Inst High Pressure Phys, Troitsk 108840, Russia.
EM valiulin@phystech.edu
RI Valiulin, Valeriy E./U-8031-2019
OI Valiulin, Valeriy E./0000-0001-6643-6526
FU Russian Foundation for Basic ResearchRussian Foundation for Basic
Research (RFBR) [19-02-00509, 20-02-00015]; Russian Science
FoundationRussian Science Foundation (RSF) [20-62-46047, 18-12-00438]
FX This work was supported by the Russian Foundation for Basic Research,
Projects No. 19-02-00509 and No. 20-02-00015. K.I.K. acknowledges the
support from the Russian Science Foundation, Project No. 20-62-46047.
The work K.I.K. was partially performed during his stay at the Mikheev
Institute of Metal Physics, Ural Branch, Russian Academy of Sciences,
Ekaterinburg 620990, Russia. N.M.C. acknowledges the support from the
Russian Science Foundation, Project No. 18-12-00438. The computations
were carried out on MVS-10P at the Joint Supercomputer Center of the
Russian Academy of Sciences (JSCC RAS). This work has been carried out
also using computing resources of the federal collective usage center
Complex for Simulation and Data Processing for Mega-Science Facilities
at NRC "Kurchatov Institute", [57].
CR Ashkin J, 1943, PHYS REV, V64, P178, DOI 10.1103/PhysRev.64.178
Baldini E, 2020, NAT PHYS, V16, P541, DOI 10.1038/s41567-020-0823-y
Beenakker C. W. J., 2006, P INT SCH PHYS E, VCLXII, P307
Belemuk AM, 2018, NEW J PHYS, V20, DOI 10.1088/1367-2630/aacbba
Belemuk AM, 2017, PHYS REV B, V96, DOI 10.1103/PhysRevB.96.094435
Benckiser E, 2008, NEW J PHYS, V10, DOI 10.1088/1367-2630/10/5/053027
Bengtsson I., 2006, GEOMETRY QUANTUM STA
Brzezicki W, 2014, PHYS REV LETT, V112, DOI 10.1103/PhysRevLett.112.117204
Brzezicki W, 2013, PHYS REV B, V87, DOI 10.1103/PhysRevB.87.064407
Brzezicki W, 2007, PHYS REV B, V75, DOI 10.1103/PhysRevB.75.134415
Chen MC, 2020, PHYS REV LETT, V124, DOI 10.1103/PhysRevLett.124.080502
Chen Y, 2007, PHYS REV B, V75, DOI 10.1103/PhysRevB.75.195113
Chtchelkatchev NM, 2002, PHYS REV B, V66, DOI 10.1103/PhysRevB.66.161320
Cuffaro ME, 2013, PHILOS SCI, V80, P1125, DOI 10.1086/673733
Eriksson E, 2009, PHYS REV B, V79, DOI 10.1103/PhysRevB.79.224424
Fumagalli R, 2020, PHYS REV B, V101, DOI 10.1103/PhysRevB.101.205117
Gale EPG, 2020, PHYS REV A, V101, DOI 10.1103/PhysRevA.101.052328
Georgescu I, 2020, NAT REV PHYS, V2, P278, DOI 10.1038/s42254-020-0189-1
Gotfryd D., 2020, PHYS REV RES, V2
Grimsmo AL, 2020, PHYS REV X, V10, DOI 10.1103/PhysRevX.10.011058
Gross C, 2017, SCIENCE, V357, P995, DOI 10.1126/science.aal3837
Horodecki R, 2009, REV MOD PHYS, V81, P865, DOI 10.1103/RevModPhys.81.865
Hyllus P, 2005, PHYS REV A, V72, DOI 10.1103/PhysRevA.72.012321
Ishihara S, 2005, NEW J PHYS, V7, DOI 10.1088/1367-2630/7/1/119
Jackeli G, 2009, PHYS REV LETT, V102, DOI 10.1103/PhysRevLett.102.017205
Johansson JR, 2013, COMPUT PHYS COMMUN, V184, P1234, DOI 10.1016/j.cpc.2012.11.019
Johansson JR, 2012, COMPUT PHYS COMMUN, V183, P1760, DOI 10.1016/j.cpc.2012.02.021
Josefsson M, 2020, PHYS REV B, V101, DOI 10.1103/PhysRevB.101.081408
Jozsa R, 2003, P ROY SOC A-MATH PHY, V459, P2011, DOI 10.1098/rspa.2002.1097
Juraschek DM, 2020, PHYS REV LETT, V124, DOI 10.1103/PhysRevLett.124.117401
Kovaleva NN, 2013, J PHYS-CONDENS MAT, V25, DOI 10.1088/0953-8984/25/15/155602
Kugel K. I., 1982, Soviet Physics - Uspekhi, V25, P231, DOI 10.1070/PU1982v025n04ABEH004537
Kugel K. I., 1980, SOV J LOW TEMP PHYS, V6, P99
Luscher A, 2009, PHYS REV B, V79, DOI 10.1103/PhysRevB.79.195102
McArdle S, 2020, REV MOD PHYS, V92, DOI 10.1103/RevModPhys.92.015003
Medvedeva D, 2017, PHYS REV B, V96, DOI 10.1103/PhysRevB.96.235149
Meier QN, 2020, PHYS REV B, V102, DOI 10.1103/PhysRevB.102.014102
Mishra KC, 2007, PHYS REV B, V76, DOI 10.1103/PhysRevB.76.035127
Oles AM, 2012, J PHYS-CONDENS MAT, V24, DOI 10.1088/0953-8984/24/31/313201
Roggero A, 2020, PHYS REV D, V101, DOI 10.1103/PhysRevD.101.074038
Saitoh E, 2001, NATURE, V410, P180, DOI 10.1038/35065547
Schiffer S, 2019, PHYS REV A, V100, DOI 10.1103/PhysRevA.100.063619
Streltsov SV, 2017, PHYS-USP+, V60, P1121, DOI 10.3367/UFNe.2017.08.038196
Tanaka A, 2019, PHYS REV B, V99, DOI 10.1103/PhysRevB.99.205133
Tanaka Y, 2004, NEW J PHYS, V6, DOI 10.1088/1367-2630/6/1/161
Tokura Y, 2000, SCIENCE, V288, P462, DOI 10.1126/science.288.5465.462
Vahulin VE, 2019, JETP LETT+, V109, P546, DOI 10.1134/S0021364019080125
van den Brink J, 2001, PHYS REV LETT, V87, DOI 10.1103/PhysRevLett.87.217202
van den Brink J, 1998, PHYS REV B, V58, P10276, DOI 10.1103/PhysRevB.58.10276
Vidal G, 2003, PHYS REV LETT, V90, DOI 10.1103/PhysRevLett.90.227902
Wang Y, 2019, PHYS REV B, V100, DOI 10.1103/PhysRevB.100.155133
Wu B.-H., 2020, PHYS REV RES, V2
You WL, 2015, PHYS REV B, V92, DOI 10.1103/PhysRevB.92.054423
You WL, 2012, PHYS REV B, V86, DOI 10.1103/PhysRevB.86.094412
Zanaty E, 2018, APPL MATH INF SCI, V12, P265, DOI [10.18576/amis/120127, DOI 10.18576/AMIS/120127]
NR 55
TC 0
Z9 0
U1 2
U2 2
PU AMER PHYSICAL SOC
PI COLLEGE PK
PA ONE PHYSICS ELLIPSE, COLLEGE PK, MD 20740-3844 USA
SN 2469-9950
EI 2469-9969
J9 PHYS REV B
JI Phys. Rev. B
PD OCT 19
PY 2020
VL 102
IS 15
AR 155125
DI 10.1103/PhysRevB.102.155125
PG 10
WC Materials Science, Multidisciplinary; Physics, Applied; Physics,
Condensed Matter
SC Materials Science; Physics
GA OC7JY
UT WOS:000579334000002
DA 2021-04-21
ER
PT J
AU Conroy, KE
Kochoska, A
Hey, D
Pablo, H
Hambleton, KM
Jones, D
Giammarco, J
Abdul-Masih, M
Prsa, A
AF Conroy, Kyle E.
Kochoska, Angela
Hey, Daniel
Pablo, Herbert
Hambleton, Kelly M.
Jones, David
Giammarco, Joseph
Abdul-Masih, Michael
Prsa, Andrej
TI Physics of Eclipsing Binaries. V. General Framework for Solving the
Inverse Problem
SO ASTROPHYSICAL JOURNAL SUPPLEMENT SERIES
LA English
DT Article
ID LIGHT; STARS
AB PHOEBE 2 is a Python package for modeling the observables of eclipsing star systems, but until now it has focused entirely on the forward model-that is, generating a synthetic model given fixed values of a large number of parameters describing the system and the observations. The inverse problem, obtaining orbital and stellar parameters given observational data, is more complicated and computationally expensive as it requires generating a large set of forward models to determine which set of parameters and uncertainties best represents the available observational data. The process of determining the best solution and also of obtaining reliable and robust uncertainties on those parameters often requires the use of multiple algorithms, including both optimizers and samplers. Furthermore, the forward model of PHOEBE has been designed to be as physically robust as possible, but it is computationally expensive compared to other codes. It is useful, therefore, to use whichever code is most efficient given the reasonable assumptions for a specific system, but learning the intricacies of multiple codes presents a barrier to doing this in practice. Here we present release 2.3 of PHOEBE (publicly available from), which introduces a general framework for defining and handling distributions on parameters and utilizing multiple different estimation, optimization, and sampling algorithms. The presented framework supports multiple forward models, including the robust model built into PHOEBE itself.
C1 [Conroy, Kyle E.; Kochoska, Angela; Hambleton, Kelly M.; Prsa, Andrej] Villanova Univ, Dept Astrophys & Planetary Sci, 800 East Lancaster Ave, Villanova, PA 19085 USA.
[Hey, Daniel] Univ Sydney, Sch Phys, Sydney Inst Astron SIfA, Sydney, NSW 2006, Australia.
[Hey, Daniel] Aarhus Univ, Stellar Astrophys Ctr, Dept Phys & Astron, DK-8000 Aarhus C, Denmark.
[Pablo, Herbert] Amer Assoc Variable Star Observers, 49 Bay State Rd, Cambridge, MA 02138 USA.
[Jones, David] Inst Astrofis Canarias, E-38205 Tenerife, Spain.
[Jones, David] Univ La Laguna, Dept Astrofis, E-38206 Tenerife, Spain.
[Giammarco, Joseph] Eastern Univ, Dept Astron & Phys, 1300 Eagle Rd, St Davids, PA 19087 USA.
[Abdul-Masih, Michael] Katholieke Univ Leuven, Inst Astron, Celestijnenlaan 200 D, B-3001 Leuven, Belgium.
RP Conroy, KE (corresponding author), Villanova Univ, Dept Astrophys & Planetary Sci, 800 East Lancaster Ave, Villanova, PA 19085 USA.
EM kyle.conroy@villanova.edu
RI Jones, David/G-8109-2014
OI Jones, David/0000-0003-3947-5946; Prsa, Andrej/0000-0002-1913-0281;
Abdul-Masih, Michael/0000-0001-6566-7568; Conroy,
Kyle/0000-0002-5442-8550; Hey, Daniel/0000-0003-3244-5357
FU NSF AAG grantsNational Science Foundation (NSF)NSF - Directorate for
Mathematical & Physical Sciences (MPS) [1517474, 1909109]; NASANational
Aeronautics & Space Administration (NASA) [17-ADAP17-68]; State Research
Agency (AEI) of the Spanish Ministry of Science, Innovation and
Universities (MCIU); European Regional Development Fund (FEDER)European
Commission [AYA2017-83383-P]; Spanish Ministry of Science, Innovation
and Universities [P/308614]; General Budgets of the Autonomous Community
of the Canary Islands by the Ministry of Economy, Industry, Trade and
Knowledge [P/308614]; NASA ADAP grant [18-ADAP18-228]; Australian
Government Research Training ProgramAustralian Government; FWO-Odysseus
program [G0F8H6N]
FX The development of PHOEBE is possible through the NSF AAG grants 1517474
and 1909109 and NASA 17-ADAP17-68, which we gratefully acknowledge.;
D.J. acknowledges support from the State Research Agency (AEI) of the
Spanish Ministry of Science, Innovation and Universities (MCIU) and the
European Regional Development Fund (FEDER) under grant AYA2017-83383-P.
D.J. also acknowledges support under grant P/308614 financed by funds
transferred from the Spanish Ministry of Science, Innovation and
Universities, charged to the General State Budgets and with funds
transferred from the General Budgets of the Autonomous Community of the
Canary Islands by the Ministry of Economy, Industry, Trade and
Knowledge.; K.H. gratefully acknowledges support from NASA ADAP grant
18-ADAP18-228.; D.R.H. gratefully acknowledges the support of the
Australian Government Research Training Program.; M.A. acknowledges
support from the FWO-Odysseus program under project G0F8H6N.
CR Foreman-Mackey D., 2016, J OPEN SOURCE SOFTWA, V24, P1, DOI [10.21105/joss.00024, DOI 10.21105/joss.00024]
Foreman-Mackey D., 2019, JOSS, V4, P1864, DOI 10.21105/joss.01864
Foreman-Mackey D, 2017, ASTRON J, V154, DOI 10.3847/1538-3881/aa9332
Foreman-Mackey D, 2013, PUBL ASTRON SOC PAC, V125, P306, DOI 10.1086/670067
Gao FC, 2012, COMPUT OPTIM APPL, V51, P259, DOI 10.1007/s10589-010-9329-3
Goodman J, 2010, COMM APP MATH COM SC, V5, P65, DOI 10.2140/camcos.2010.5.65
Harmanec P, 2002, ASTRON ASTROPHYS, V387, P580, DOI 10.1051/0004-6361:20020453
Hogg D.W., 2010, ARXIV10084686
Horvat M, 2018, ASTROPHYS J SUPPL S, V237, DOI 10.3847/1538-4365/aacd0f
Hunter JD, 2007, COMPUT SCI ENG, V9, P90, DOI 10.1109/MCSE.2007.55
Jenkins J. M., 2017, KSCI19081002 STSCI
Jones D, 2020, ASTROPHYS J SUPPL S, V247, DOI 10.3847/1538-4365/ab7927
Maxted PFL, 2016, ASTRON ASTROPHYS, V591, DOI 10.1051/0004-6361/201628579
Mowlavi N, 2017, ASTRON ASTROPHYS, V606, DOI 10.1051/0004-6361/201730613
Oliphant Travis E., 2006, A GUIDE TO NUMPY, V1
Pra A., 2005, APJ, V628, P426, DOI DOI 10.1086/430591
Pra A., 2018, MODELING ANAL ECLIPS
Pra A., 2019, 1908018 ASCL
Price-Whelan A.M., 2017, JOSS, V2, P357, DOI [10.21105/joss.00357, DOI 10.21105/JOSS.00357]
Prsa A, 2008, ASTROPHYS J, V687, P542, DOI 10.1086/591783
Prsa A, 2016, ASTROPHYS J SUPPL S, V227, DOI 10.3847/1538-4365/227/2/29
Robitaille TP, 2013, ASTRON ASTROPHYS, V558, DOI 10.1051/0004-6361/201322068
SCHWARZ G, 1978, ANN STAT, V6, P461, DOI 10.1214/aos/1176344136
Shenar T, 2018, ASTRON ASTROPHYS, V616, DOI 10.1051/0004-6361/201833006
Southworth J, 2007, ASTRON ASTROPHYS, V467, P1215, DOI 10.1051/0004-6361:20077184
Southworth J, 2004, MON NOT R ASTRON SOC, V351, P1277, DOI 10.1111/j.1365-2966.2004.07871.x
Southworth J, 2011, MON NOT R ASTRON SOC, V417, P2166, DOI 10.1111/j.1365-2966.2011.19399.x
Southworth J, 2009, ASTROPHYS J, V707, P167, DOI 10.1088/0004-637X/707/1/167
Speagle JS, 2020, MON NOT R ASTRON SOC, V493, P3132, DOI 10.1093/mnras/staa278
Torres G, 2010, ASTRON ASTROPHYS REV, V18, P67, DOI 10.1007/s00159-009-0025-1
UNDERHILL AB, 1994, ASTROPHYS J, V432, P770, DOI 10.1086/174615
Virtanen P, 2020, NAT METHODS, V17, P261, DOI 10.1038/s41592-019-0686-2
Wilson RE, 2008, ASTROPHYS J, V672, P575, DOI 10.1086/523634
Wilson RE, 2014, ASTROPHYS J, V780, DOI 10.1088/0004-637X/780/2/151
WILSON RE, 1971, ASTROPHYS J, V166, P605, DOI 10.1086/150986
WILSON RE, 1979, ASTROPHYS J, V234, P1054, DOI 10.1086/157588
NR 36
TC 1
Z9 1
U1 0
U2 0
PU IOP PUBLISHING LTD
PI BRISTOL
PA TEMPLE CIRCUS, TEMPLE WAY, BRISTOL BS1 6BE, ENGLAND
SN 0067-0049
EI 1538-4365
J9 ASTROPHYS J SUPPL S
JI Astrophys. J. Suppl. Ser.
PD OCT
PY 2020
VL 250
IS 2
AR 34
DI 10.3847/1538-4365/abb4e2
PG 17
WC Astronomy & Astrophysics
SC Astronomy & Astrophysics
GA NY7FO
UT WOS:000576550600001
DA 2021-04-21
ER
PT J
AU Cid-Fuentes, JA
Alvarez, P
Amela, R
Ishii, K
Morizawa, RK
Badia, RM
AF Alvarez Cid-Fuentes, Javier
Alvarez, Pol
Amela, Ramon
Ishii, Kuninori
Morizawa, Rafael K.
Badia, Rosa M.
TI Efficient development of high performance data analytics in Python
SO FUTURE GENERATION COMPUTER SYSTEMS-THE INTERNATIONAL JOURNAL OF ESCIENCE
LA English
DT Article
ID BIG DATA; COMPLEXITY
AB Our society is generating an increasing amount of data at an unprecedented scale, variety, and speed. This also applies to numerous research areas, such as genomics, high energy physics, and astronomy, for which large-scale data processing has become crucial. However, there is still a gap between the traditional scientific computing ecosystem and big data analytics tools and frameworks. On the one hand, high performance computing (HPC) programming models lack productivity, and do not provide means for processing large amounts of data in a simple manner. On the other hand, existing big data processing tools have performance issues in HPC environments, and are not general-purpose. In this paper, we propose and evaluate PyCOMPSs, a task-based programming model for Python, as an excellent solution for distributed big data processing in HPC infrastructures. Among other useful features, PyCOMPSs offers a highly productive general-purpose programming model, is infrastructure-agnostic, and provides transparent data management with support for distributed storage systems. We show how two machine learning algorithms (Cascade SVM and K-means) can be developed with PyCOMPSs, and evaluate PyCOMPSs' productivity based on these algorithms. Additionally, we evaluate PyCOMPSs performance on an HPC cluster using up to 1,536 cores and 320 million input vectors. Our results show that PyCOMPSs achieves similar performance and scalability to MPI in HPC infrastructures, while providing a much more productive interface that allows the easy development of data analytics algorithms. (C) 2019 The Authors. Published by Elsevier B.V.
C1 [Alvarez Cid-Fuentes, Javier; Alvarez, Pol; Amela, Ramon; Badia, Rosa M.] Barcelona Supercomp Ctr BSC, Barcelona, Spain.
[Ishii, Kuninori; Morizawa, Rafael K.] Fujitsu Ltd, Tokyo, Japan.
[Badia, Rosa M.] CSIC, Artificial Intelligence Res Inst IIIA, Madrid, Spain.
RP Cid-Fuentes, JA (corresponding author), Barcelona Supercomp Ctr BSC, Barcelona, Spain.
EM javier.alvarez@bsc.es
OI Alvarez Cid-Fuentes, Javier/0000-0001-7153-4649
FU European UnionEuropean Commission [H2020-MSCA-COFUND-2016-754433];
Spanish GovernmentSpanish GovernmentEuropean Commission [SEV2015-0493];
Spanish Ministry of Science and InnovationSpanish Government
[TIN2015-65316-P]; Generalitat de Catalunya, SpainGeneralitat de
Catalunya [2014-SGR-1051]; Fujitsu; BSC (Script Language Platform)
FX This work has received funding from the European Union's Horizon 2020
research and innovation programme under the Marie Sklodowska-Curie grant
agreement H2020-MSCA-COFUND-2016-754433. This work has been supported by
the Spanish Government (SEV2015-0493), by the Spanish Ministry of
Science and Innovation (contract TIN2015-65316-P), by Generalitat de
Catalunya, Spain (contract 2014-SGR-1051). The research leading to these
results has also received funding from the collaboration between Fujitsu
and BSC (Script Language Platform).
CR Ahrens JP, 2011, COMPUT SCI ENG, V13, P14, DOI 10.1109/MCSE.2011.77
Amancio DR, 2014, PLOS ONE, V9, DOI 10.1371/journal.pone.0094137
Amela R., 2017, P 7 WORKSH PYTH HIGH
Boehm Barry, 2000, SOFTWARE COST ESTIMA
BROOKS FP, 1987, COMPUTER, V20, P10
Brumfiel G, 2011, NATURE, V469, P282, DOI 10.1038/469282a
Chen CLP, 2014, INFORM SCIENCES, V275, P314, DOI 10.1016/j.ins.2014.01.015
Chen M, 2014, MOBILE NETW APPL, V19, P171, DOI 10.1007/s11036-013-0489-0
Conejero J, 2018, INT J HIGH PERFORM C, V32, P45, DOI 10.1177/1094342017701278
CORTES C, 1995, MACH LEARN, V20, P273, DOI 10.1023/A:1022627411411
Dagum L, 1998, IEEE COMPUT SCI ENG, V5, P46, DOI 10.1109/99.660313
Dalcin LD, 2011, ADV WATER RESOUR, V34, P1124, DOI 10.1016/j.advwatres.2011.04.013
Dean J, 2004, USENIX ASSOCIATION PROCEEDINGS OF THE SIXTH SYMPOSIUM ON OPERATING SYSTEMS DESIGN AND IMPLEMENTATION (OSDE '04), P137
Dubey R, 2016, INT J ADV MANUF TECH, V84, P631, DOI 10.1007/s00170-015-7674-1
Glock P., 2015, THESIS
Gotz M., 2015, P WORKSH MACH LEARN
Graf H.P., 2004, ADV NEURAL INF PROCE, V17, P521
Gropp W, 1996, PARALLEL COMPUT, V22, P789, DOI 10.1016/0167-8191(96)00024-5
Hsieh CJ, 2017, KDD'17: PROCEEDINGS OF THE 23RD ACM SIGKDD INTERNATIONAL CONFERENCE ON KNOWLEDGE DISCOVERY AND DATA MINING, P245, DOI 10.1145/3097983.3098080
Inoubli W, 2018, FUTURE GENER COMP SY, V86, P546, DOI 10.1016/j.future.2018.04.032
Jain AK, 2010, PATTERN RECOGN LETT, V31, P651, DOI 10.1016/j.patrec.2009.09.011
Kitchin R, 2014, GEOJOURNAL, V79, P1, DOI 10.1007/s10708-013-9516-8
LLOYD SP, 1982, IEEE T INFORM THEORY, V28, P129, DOI 10.1109/tit.1982.1056489
Marozzo F, 2015, CONCURR COMP-PRACT E, V27, P5214, DOI 10.1002/cpe.3563
Marti Jonathan, 2013, 2013 IEEE Ninth World Congress on Services (SERVICES), P349, DOI 10.1109/SERVICES.2013.59
Marx V, 2013, NATURE, V496, P253, DOI 10.1038/496253a
McCabe T. J., 1976, IEEE Transactions on Software Engineering, VSE-2, P308, DOI 10.1109/TSE.1976.233837
McKinney W., 2012, PYTHON DATA ANAL DAT
Millman KJ, 2011, COMPUT SCI ENG, V13, P9, DOI 10.1109/MCSE.2011.36
Misale C, 2018, FUTURE GENER COMP SY, V87, P392, DOI 10.1016/j.future.2018.05.030
NEJMEH BA, 1988, COMMUN ACM, V31, P188, DOI 10.1145/42372.42379
Nguyen V., 2007, 22 ANN FOR COCOMO SY
Pedregosa F, 2011, J MACH LEARN RES, V12, P2825
PILLET V, 1995, TRANSPUT OCCAM ENG S, V44, P17
Raghupathi W, 2014, HEALTH INF SCI SYST, V2, DOI 10.1186/2047-2501-2-3
Reed DA, 2015, COMMUN ACM, V58, P56, DOI 10.1145/2699414
Reyes-Ortiz JL, 2015, PROCEDIA COMPUT SCI, V53, P121, DOI 10.1016/j.procs.2015.07.286
SHEPPERD M, 1988, SOFTWARE ENG J, V3, P30, DOI 10.1049/sej.1988.0003
Shvachko K., 2010, SYMPOSIUM, P1, DOI DOI 10.1109/MSST.2010.5496972
Szalay AS, 2011, COMPUT SCI ENG, V13, P34, DOI 10.1109/MCSE.2011.74
Tejedor E, 2017, INT J HIGH PERFORM C, V31, P66, DOI 10.1177/1094342015594678
Totoni E., 2017, P INT C SUP
Tous R, 2015, PROCEEDINGS 2015 IEEE INTERNATIONAL CONFERENCE ON BIG DATA, P299, DOI 10.1109/BigData.2015.7363768
Turner V., 2014, DIGITAL UNIVERSE OPP
Witten I.H, 2016, DATA MINING PRACTICA
Wozniak JM, 2013, IEEE ACM INT SYMP, P95, DOI 10.1109/CCGrid.2013.99
Zaharia Matei, 2012, P 9 USENIX C NETW SS, P2, DOI DOI 10.1111/J.1095-8649.2005.00662.X
Zhang K., 2012, INT C ART INT STAT, V22, P1425
NR 48
TC 0
Z9 0
U1 0
U2 9
PU ELSEVIER
PI AMSTERDAM
PA RADARWEG 29, 1043 NX AMSTERDAM, NETHERLANDS
SN 0167-739X
EI 1872-7115
J9 FUTURE GENER COMP SY
JI Futur. Gener. Comp. Syst.
PD OCT
PY 2020
VL 111
BP 570
EP 581
DI 10.1016/j.future.2019.09.051
PG 12
WC Computer Science, Theory & Methods
SC Computer Science
GA LZ3VB
UT WOS:000541155100041
OA Other Gold, Green Published
DA 2021-04-21
ER
PT J
AU Canton, L
Fontana, A
AF Canton, Luciano
Fontana, Andrea
TI Nuclear physics applied to the production of innovative
radiopharmaceuticals
SO EUROPEAN PHYSICAL JOURNAL PLUS
LA English
DT Article
ID CODE
AB In this work, we review the theory of nuclear reactions induced by charged particles at cyclotrons and we show in a pedagogical way how to perform a reaction yield calculation in a realistic irradiation case. The topic is currently of great interest in the field of radioisotope production for medical applications, in which an international effort is underway to find efficient production routes of novel radiopharmaceuticals that could be used in theranostics or for multimodal imaging: Particular interest is devoted to the reaction channels that allow the production of a given isotope with high yield and high purity. In part I, the nuclear reaction theory is reviewed, with a discussion on the main reaction mechanisms that are important for the calculation of the cross sections: direct reactions, compound nucleus formation and decay and pre-equilibrium emission. The role of modern nuclear reaction codes, such as Talys, for the calculation of nuclear cross sections is also shown with examples. In part II, a tutorial demonstrates how calculate the production yield of the isotope Mn-52g starting from the Talys cross section for the specific reaction Cr-52(p,n)Mn-52g: This is a real and up-to-date research case motivated by the search of a beta(+) emitting radioisotope with paramagnetic properties that could be used for a combined PET-MRI imaging. In the exercise, all the steps to calculate the yield and activities of the reaction are shown in great detail and the result is compared with the reference values. The calculation is performed both in Excel and in Python, and the input files are provided as supplementary material.
C1 [Canton, Luciano] INFN, Sez Padova, Padua, Italy.
[Fontana, Andrea] INFN, Sez Pavia, Pavia, Italy.
RP Canton, L (corresponding author), INFN, Sez Padova, Padua, Italy.
EM luciano.canton@pd.infn.it
RI Canton, Luciano/G-3180-2015
OI Canton, Luciano/0000-0002-8922-7660; Fontana, Andrea/0000-0003-4718-5711
CR Amos K, 2003, NUCL PHYS A, V728, P65, DOI 10.1016/j.nuclphysa.2003.08.019
ARRONAX, 2018, RAD YIELD CALC
CLINE CK, 1971, NUCL PHYS A, VA172, P225, DOI 10.1016/0375-9474(71)90713-5
Gadioli E., 1992, PREEQUILIBRIUM NUCL
Goriely S, 2008, ASTRON ASTROPHYS, V487, P767, DOI 10.1051/0004-6361:20078825
HAUSER W, 1952, PHYS REV, V87, P366, DOI 10.1103/PhysRev.87.366
Hilaire S, 2003, ANN PHYS-NEW YORK, V306, P209, DOI 10.1016/S0003-4916(03)00076-9
Hilarie S., 2014, JOINT ICTP IAEA WORK
IAEA, 2015, MED PORT MED RAD PRO
IAEA, DEC PORT 2017
IAEA, 2016, CRP THER RAD LAB NEW, P2016
Iliadis C., 2015, NUCL PHYS STARS
Karahancer S, 2019, AIRFIELD AND HIGHWAY PAVEMENTS 2019: TESTING AND CHARACTERIZATION OF PAVEMENT MATERIALS, P127
Koning AJ, 2012, NUCL DATA SHEETS, V113, P2841, DOI 10.1016/j.nds.2012.11.002
Koning AJ, 2008, INTERNATIONAL CONFERENCE ON NUCLEAR DATA FOR SCIENCE AND TECHNOLOGY, VOL 1, PROCEEDINGS, P211, DOI 10.1051/ndata:07767
Leo W.R., 1994, TECHNIQUES NUCL PART
LINDER B, 1959, PHYS REV, V114, P322, DOI 10.1103/PhysRev.114.322
MOLDAUER PA, 1976, PHYS REV C, V14, P764, DOI 10.1103/PhysRevC.14.764
Newman M., 2012, COMPUTATIONAL PHYS
Otuka N, 2015, RADIOCHIM ACTA, V103, P1, DOI 10.1515/ract-2013-2234
Pupillo G, 2019, J RADIOANAL NUCL CH, V322, P1711, DOI 10.1007/s10967-019-06844-8
Satchler G.R., 1983, DIRECT NUCL REACTION
Syed M, 2020, QAIM MED RADIONUCLID
VERBAARSCHOT JJM, 1985, PHYS REP, V129, P367, DOI 10.1016/0370-1573(85)90070-5
WEST HI, 1987, PHYS REV C, V35, P2067, DOI 10.1103/PhysRevC.35.2067
WESTIN GD, 1971, PHYS REV C, V4, P363, DOI 10.1103/PhysRevC.4.363
NR 26
TC 0
Z9 0
U1 1
U2 1
PU SPRINGER HEIDELBERG
PI HEIDELBERG
PA TIERGARTENSTRASSE 17, D-69121 HEIDELBERG, GERMANY
SN 2190-5444
J9 EUR PHYS J PLUS
JI Eur. Phys. J. Plus
PD SEP 29
PY 2020
VL 135
IS 9
AR 770
DI 10.1140/epjp/s13360-020-00730-z
PG 21
WC Physics, Multidisciplinary
SC Physics
GA NY1NN
UT WOS:000576165200002
DA 2021-04-21
ER
PT J
AU Mushtaq, A
Noreen, A
Olaussen, K
AF Mushtaq, Asif
Noreen, Amna
Olaussen, Kare
TI Numerical Solutions of Quantum Mechanical Eigenvalue Problems
SO FRONTIERS IN PHYSICS
LA English
DT Article
DE numpy array; FFT (fast fourier transform); quantum mechanics; python
classes; eigenvalue problems; sparse SciPy routines; Schrodinger
equations
AB A large class of problems in quantum physics involve solution of the time independent Schrodinger equation in one or more space dimensions. These are boundary value problems, which in many cases only have solutions for specific (quantized) values of the total energy. In this article we describe a Python package that "automagically" transforms an analytically formulated Quantum Mechanical eigenvalue problem to a numerical form which can be handled by existing (or novel) numerical solvers. We illustrate some uses of this package. The problem is specified in terms of a small set of parameters and selectors (all provided with default values) that are easy to modify, and should be straightforward to interpret. From this the numerical details required by the solver is generated by the package, and the selected numerical solver is executed. In all cases the spatial continuum is replaced by a finite rectangular lattice. We compare common stensil discretizations of the Laplace operator with formulations involving Fast Fourier (and related trigonometric) Transforms. The numerical solutions are based on the NumPy and SciPy packages for Python 3, in particular routines from thescipy.linalg,scipy.sparse.linalg, andscipy.fftpacklibraries. These, like most Python resources, are freely available for Linux, MacOS, and MSWindows. We demonstrate that some interesting problems, like the lowest eigenvalues of anharmonic oscillators, can be solved quite accurately in up to three space dimensions on a modern laptop-with some patience in the 3-dimensional case. We demonstrate that a reduction in the lattice distance, for a fixed the spatial volume, does not necessarily lead to more accurate results: A smaller lattice length increases the spectral width of the lattice Laplace operator, which in turn leads to an enhanced amplification of the numerical noise generated by round-off errors.
C1 [Mushtaq, Asif] Nord Univ, Seksjon Matemat, Bodo, Norway.
[Noreen, Amna] Nordland Fylkeskommune, Bodin Videregaende Skole, Bodo, Norway.
[Olaussen, Kare] MJAU, Trondheim, Norway.
RP Mushtaq, A (corresponding author), Nord Univ, Seksjon Matemat, Bodo, Norway.
EM asif.mushtaq@nord.no
FU Mathematics Teaching and Learning Research Group within The department
of mathematics, Bodo, Nord University
FX AM would like to thank Mathematics Teaching and Learning Research Group
within The department of mathematics, Bodo, Nord University for partial
support.
CR BENDER CM, 1971, PHYS REV LETT, V27, P461, DOI 10.1103/PhysRevLett.27.461
BENDER CM, 1973, PHYS REV D, V7, P1620, DOI 10.1103/PhysRevD.7.1620
BENDER CM, 1968, PHYS REV LETT, V21, P406, DOI 10.1103/PhysRevLett.21.406
BENDER CM, 1969, PHYS REV, V184, P1231, DOI 10.1103/PhysRev.184.1231
BENDER CM, 1977, PHYS REV D, V16, P1740, DOI 10.1103/PhysRevD.16.1740
Dirac P.A.M., 1925, Proceedings of the Royal Society of London, V109, P642, DOI 10.1098/rspa.1925.0150
Heisenberg W, 1925, Z PHYS, V33, P879, DOI 10.1007/BF01328377
JANKE W, 1995, PHYS REV LETT, V75, P2787, DOI 10.1103/PhysRevLett.75.2787
Jones E., 2014, SCIPY OPEN SOURCE SC
Mushtaq A, 2020, PYTHON PACKAGE NUMER, DOI [10.6084/m9.figshare.127655, DOI 10.6084/M9.FIGSHARE.127655]
Mushtaq A, 2011, COMPUT PHYS COMMUN, V182, P1810, DOI 10.1016/j.cpc.2010.12.046
Noreen A, 2015, P INT MULTICONFERENC, P206
Noreen A, 2013, PHYS REV LETT, V111, DOI 10.1103/PhysRevLett.111.040402
Noreen A, 2012, COMPUT PHYS COMMUN, V183, P2291, DOI 10.1016/j.cpc.2012.05.015
Oliphant TE, 2007, COMPUT SCI ENG, V9, P10, DOI 10.1109/MCSE.2007.58
Pauli W, 1926, Z PHYS, V36, P336, DOI 10.1007/BF01450175
Schrodinger E, 1926, PHYS REV, V28, P1049, DOI 10.1103/PhysRev.28.1049
van der Walt S, 2011, COMPUT SCI ENG, V13, P22, DOI 10.1109/MCSE.2011.37
Zinn-Justin J, 2004, ANN PHYS-NEW YORK, V313, P269, DOI 10.1016/j.aop.2004.04.003
NR 19
TC 0
Z9 0
U1 0
U2 0
PU FRONTIERS MEDIA SA
PI LAUSANNE
PA AVENUE DU TRIBUNAL FEDERAL 34, LAUSANNE, CH-1015, SWITZERLAND
SN 2296-424X
J9 FRONT PHYS-LAUSANNE
JI Front. Physics
PD SEP 28
PY 2020
VL 8
AR 390
DI 10.3389/fphy.2020.00390
PG 10
WC Physics, Multidisciplinary
SC Physics
GA NY9SJ
UT WOS:000576724300001
OA DOAJ Gold, Green Published
DA 2021-04-21
ER
PT J
AU Harris, CR
Millman, KJ
van der Walt, SJ
Gommers, R
Virtanen, P
Cournapeau, D
Wieser, E
Taylor, J
Berg, S
Smith, NJ
Kern, R
Picus, M
Hoyer, S
van Kerkwijk, MH
Brett, M
Haldane, A
del Rio, JF
Wiebe, M
Peterson, P
Gerard-Marchant, P
Sheppard, K
Reddy, T
Weckesser, W
Abbasi, H
Gohlke, C
Oliphant, TE
AF Harris, Charles R.
Millman, K. Jarrod
van der Walt, Stefan J.
Gommers, Ralf
Virtanen, Pauli
Cournapeau, David
Wieser, Eric
Taylor, Julian
Berg, Sebastian
Smith, Nathaniel J.
Kern, Robert
Picus, Matti
Hoyer, Stephan
van Kerkwijk, Marten H.
Brett, Matthew
Haldane, Allan
del Rio, Jaime Fernandez
Wiebe, Mark
Peterson, Pearu
Gerard-Marchant, Pierre
Sheppard, Kevin
Reddy, Tyler
Weckesser, Warren
Abbasi, Hameer
Gohlke, Christoph
Oliphant, Travis E.
TI Array programming with NumPy
SO NATURE
LA English
DT Review
ID PYTHON; SCIENTISTS
AB Array programming provides a powerful, compact and expressive syntax for accessing, manipulating and operating on data in vectors, matrices and higher-dimensional arrays. NumPy is the primary array programming library for the Python language. It has an essential role in research analysis pipelines in fields as diverse as physics, chemistry, astronomy, geoscience, biology, psychology, materials science, engineering, finance and economics. For example, in astronomy, NumPy was an important part of the software stack used in the discovery of gravitational waves(1)and in the first imaging of a black hole(2). Here we review how a few fundamental array concepts lead to a simple and powerful programming paradigm for organizing, exploring and analysing scientific data. NumPy is the foundation upon which the scientific Python ecosystem is constructed. It is so pervasive that several projects, targeting audiences with specialized needs, have developed their own NumPy-like interfaces and array objects. Owing to its central position in the ecosystem, NumPy increasingly acts as an interoperability layer between such array computation libraries and, together with its application programming interface (API), provides a flexible framework to support the next decade of scientific and industrial analysis.
NumPy is the primary array programming library for Python; here its fundamental concepts are reviewed and its evolution into a flexible interoperability layer between increasingly specialized computational libraries is discussed.
C1 [Millman, K. Jarrod; van der Walt, Stefan J.; Brett, Matthew] Univ Calif Berkeley, Brain Imaging Ctr, Berkeley, CA 94720 USA.
[Millman, K. Jarrod] Univ Calif Berkeley, Div Biostat, Berkeley, CA 94720 USA.
[Millman, K. Jarrod; van der Walt, Stefan J.; Berg, Sebastian; Picus, Matti; Weckesser, Warren] Univ Calif Berkeley, Berkeley Inst Data Sci, Berkeley, CA 94720 USA.
[van der Walt, Stefan J.] Stellenbosch Univ, Appl Math, Stellenbosch, South Africa.
[Gommers, Ralf; Abbasi, Hameer; Oliphant, Travis E.] Quansight, Austin, TX USA.
[Virtanen, Pauli] Univ Jyvaskyla, Dept Phys, Jyvaskyla, Finland.
[Virtanen, Pauli] Univ Jyvaskyla, Nanosci Ctr, Jyvaskyla, Finland.
[Cournapeau, David] Mercari JP, Tokyo, Japan.
[Wieser, Eric] Univ Cambridge, Dept Engn, Cambridge, England.
[Kern, Robert] Enthought, Austin, TX USA.
[Hoyer, Stephan] Google Res, Mountain View, CA USA.
[van Kerkwijk, Marten H.] Univ Toronto, Dept Astron & Astrophys, Toronto, ON, Canada.
[Brett, Matthew] Univ Birmingham, Sch Psychol, Birmingham, W Midlands, England.
[Haldane, Allan] Temple Univ, Dept Phys, Philadelphia, PA 19122 USA.
[del Rio, Jaime Fernandez] Google, Zurich, Switzerland.
[Wiebe, Mark] Univ British Columbia, Dept Phys & Astron, Vancouver, BC, Canada.
[Peterson, Pearu] Tallinn Univ Technol, Inst Cybernet, Dept Mech & Appl Math, Tallinn, Estonia.
[Gerard-Marchant, Pierre] Univ Georgia, Dept Biol & Agr Engn, Athens, GA 30602 USA.
[Gerard-Marchant, Pierre] France IX Serv, Paris, France.
[Sheppard, Kevin] Univ Oxford, Dept Econ, Oxford, England.
[Reddy, Tyler] Los Alamos Natl Lab, CCS 7, Los Alamos, NM USA.
[Gohlke, Christoph] Univ Calif Irvine, Biomed Engn Dept, Lab Fluorescence Dynam, Irvine, CA USA.
RP Millman, KJ; van der Walt, SJ (corresponding author), Univ Calif Berkeley, Brain Imaging Ctr, Berkeley, CA 94720 USA.; Millman, KJ (corresponding author), Univ Calif Berkeley, Div Biostat, Berkeley, CA 94720 USA.; Millman, KJ; van der Walt, SJ (corresponding author), Univ Calif Berkeley, Berkeley Inst Data Sci, Berkeley, CA 94720 USA.; van der Walt, SJ (corresponding author), Stellenbosch Univ, Appl Math, Stellenbosch, South Africa.; Gommers, R (corresponding author), Quansight, Austin, TX USA.
EM millman@berkeley.edu; stefanv@berkeley.edu; ralf.gommers@gmail.com
RI Virtanen, Pauli/D-9518-2012
OI Virtanen, Pauli/0000-0001-9957-1257; van der Walt,
Stefan/0000-0001-9276-1891; Millman, Kenneth/0000-0002-5263-5070
CR Abadi M, 2016, PROCEEDINGS OF OSDI'16: 12TH USENIX SYMPOSIUM ON OPERATING SYSTEMS DESIGN AND IMPLEMENTATION, P265
Abbott BP, 2016, PHYS REV LETT, V116, DOI 10.1103/PhysRevLett.116.061102
[Anonymous], 2020, REP RAS A AW COMM 20
[Anonymous], 2015, J OPER OCEANOGR, V8, ps80, DOI [10.1080/1755876X.2015.1022329, DOI 10.1088/1749-4699/8/1/014009]
Ascher D., 2001, OPEN SOURCE PROJECT
Barrett K. A., 1995, UCRLMA118543 LAWR LI, V1
Behnel S, 2011, COMPUT SCI ENG, V13, P31, DOI 10.1109/MCSE.2010.118
Bezanson J, 2017, SIAM REV, V59, P65, DOI 10.1137/141000671
Chael AA, 2016, ASTROPHYS J, V829, DOI 10.3847/0004-637X/829/1/11
Chen T., 2015, MXNET FLEXIBLE EFFIC
Chiu Y. H., 1995, UCRLMA118543 LAWR LI, V4
Chiu Y. H., 1995, UCRLMA118543 LAWR LI, V3
Cock PJA, 2009, BIOINFORMATICS, V25, P1422, DOI 10.1093/bioinformatics/btp163
Dongarra J, 2008, IEEE ANN HIST COMPUT, V30, P30, DOI 10.1109/MAHC.2008.29
Dubois P. F., 1996, Computers in Physics, V10, P262
Dubois P. F., 1995, UCRLMA118543 LAWR LI, V2
Dubois PF, 2007, COMPUT SCI ENG, V9, P7, DOI 10.1109/MCSE.2007.51
Entschev P, 2019, EUROPYTHON 2019
Greenfield P., 2003, PYCON DC 2003
Guelton Serge, 2015, Computational Science and Discovery, V8, DOI 10.1088/1749-4680/8/1/014001
Hagberg A, 2008, 7 PYTH SCI C SCIPY20, V7, P11, DOI DOI 10.1016/J.JELECTROCARD.2010.09.003
Hamman J., 2018, EGU GEN ASSEMBLY C
Hannay JE, 2009, 2009 ICSE WORKSHOP ON SOFTWARE ENGINEERING FOR COMPUTATIONAL SCIENCE AND ENGINEERING, P1, DOI 10.1109/SECSE.2009.5069155
Harrington J., 2008, P 7 PYTH SCI C, P33
Harrington J., 2009, P 8 PYTH SCI C, P84
Hoyer S., 2017, J OPEN RES SOFTWARE, V5, P10, DOI [10.5334/jors.148, DOI 10.5334/J0RS.148]
Hunter JD, 2007, COMPUT SCI ENG, V9, P90, DOI 10.1109/MCSE.2007.55
Ihaka R., 1996, J COMPUT GRAPH STAT, V5, P299, DOI DOI 10.1080/10618600.1996.10474713
Iverson K. E., 1962, P MAY 1 3 1962 SPRIN, P345, DOI DOI 10.1145/1460833.1460872
Jenness T, 2018, PROC SPIE, V10707, DOI 10.1117/12.2312157
Kluyver T, 2016, POSITIONING AND POWER IN ACADEMIC PUBLISHING: PLAYERS, AGENTS AND AGENDAS, P87, DOI 10.3233/978-1-61499-649-1-87
Lam S.K., 2015, P 2 WORKSH LLVM COMP, P1, DOI [DOI 10.1145/2833157.2833162DOI:10.1145/2833157.2833162, 10.1145/2833157.2833162]
Lattner C, 2004, INT SYM CODE GENER, P75, DOI 10.1109/CGO.2004.1281665
MATSAKIS ND, 2014, P 2014 ACM SIGADA AN, V34, P103, DOI DOI 10.1145/2692956.2663188
Millman J., 2010, P56, DOI DOI 10.1016/S0168-0102(02)00204-3
Millman K. J., 2014, IMPLEMENTING REPROD, P149
Millman KJ, 2007, COMPUT SCI ENG, V9, P52, DOI 10.1109/MCSE.2007.46
Millman KJ, 2018, FRONT NEUROSCI-SWITZ, V12, DOI 10.3389/fnins.2018.00727
Millman KJ, 2011, COMPUT SCI ENG, V13, P9, DOI 10.1109/MCSE.2011.36
Munro D. H., 1995, Computers in Physics, V9, P609
Oliphant T.E., 2006, A GUIDE TO NUMPY, VVolume 1
Oliphant TE, 2007, COMPUT SCI ENG, V9, P10, DOI 10.1109/MCSE.2007.58
Paszke A., 2019, ADV NEURAL INFORM PR, V32, P8024
Pedregosa F, 2011, J MACH LEARN RES, V12, P2825
Perez F, 2007, COMPUT SCI ENG, V9, P21, DOI 10.1109/MCSE.2007.53
Perez F, 2011, COMPUT SCI ENG, V13, P13, DOI 10.1109/MCSE.2010.119
Price-Whelan AM, 2018, ASTRON J, V156, DOI 10.3847/1538-3881/aac387
Robitaille TP, 2013, ASTRON ASTROPHYS, V558, DOI 10.1051/0004-6361/201322068
van der Walt S., 2008, P 7 PYTH SCI C, P27
van der Walt S, 2014, PEERJ, V2, DOI 10.7717/peerj.453
van der Walt S, 2011, COMPUT SCI ENG, V13, P22, DOI 10.1109/MCSE.2011.37
Virtanen P, 2020, NAT METHODS, V17, P261, DOI 10.1038/s41592-019-0686-2
Wang Q., 2013, P INT C HIGH PERF CO, DOI DOI 10.1145/2503210.2503219
Wilson G, 2006, COMPUT SCI ENG, V8, P66, DOI 10.1109/MCSE.2006.122
Xianyi Z., 2012, 2012 IEEE 18 INT C P, P684, DOI DOI 10.1109/ICPADS.2012.97
Yang T.-Y., 1997, P TOOLS US 97 INT C, P112
NR 56
TC 203
Z9 201
U1 19
U2 25
PU NATURE RESEARCH
PI BERLIN
PA HEIDELBERGER PLATZ 3, BERLIN, 14197, GERMANY
SN 0028-0836
EI 1476-4687
J9 NATURE
JI Nature
PD SEP 17
PY 2020
VL 585
IS 7825
BP 357
EP 362
DI 10.1038/s41586-020-2649-2
PG 6
WC Multidisciplinary Sciences
SC Science & Technology - Other Topics
GA NR0WV
UT WOS:000571285200009
PM 32939066
OA Green Published, Other Gold
DA 2021-04-21
ER
PT J
AU Liu, GR
AF Liu, G. R.
TI A Neural Element Method
SO INTERNATIONAL JOURNAL OF COMPUTATIONAL METHODS
LA English
DT Article
DE Artificial intelligence; artificial neural network; neural element;
physic-law-based method; computational mechanics; computational method
ID INVERSE IDENTIFICATION; ELASTIC-WAVES; MATERIAL CONSTANTS; TISSUE
AB Methods of artificial neural networks (ANNs) have been applied to solve various science and engineering problems. TrumpetNets and TubeNets were recently proposed by the author for creating two-way deepnets using the standard finite element method (FEM) and smoothed FEM (S-FEM) as trainers. The significance of these specially configured ANNs is that the solutions to inverse problems have been, for the first time, analytically derived in explicit formulae.
This paper presents a novel neural element method (NEM) with a focus on mechanics problems. The key idea is to use artificial neurons to form elemental units called neural-pulse-units (NPUs), using which the shape functions can then be constructed, and used in the standard weak and weakened-weak (W2) formulations to establish discrete stiffness matrices, similar to the standard FEM and S-FEM. Theory, formulation and codes in Python are presented in detail. Numerical examples are then used to demonstrate this novel NEM. For the first time, we have made a clear connection in theory, formulations and coding, between ANN methods and physical-law-based computational methods. We believe that this novel NEM fundamentally changes the way of approaching mechanics problems, and opens a window of opportunity for a range of applications. It offers a new direction of research on unconventional computational methods. It may also have an impact on how the well-established weak and W2 formulations can be introduced to machine learning processes, for example, creating well-behaved loss functions with preferable convexity.
C1 [Liu, G. R.] Univ Cincinnati, Dept Aerosp Engn & Engn Mech, Cincinnati, OH 45221 USA.
RP Liu, GR (corresponding author), Univ Cincinnati, Dept Aerosp Engn & Engn Mech, Cincinnati, OH 45221 USA.
CR Cybenko G., 1989, Mathematics of Control, Signals, and Systems, V2, P303, DOI 10.1007/BF02551274
Dai KY, 2007, FINITE ELEM ANAL DES, V43, P847, DOI 10.1016/j.finel.2007.05.009
Deng B, 2009, INVERSE PROBL SCI EN, V17, P1073, DOI 10.1080/17415970903063151
Han X, 2003, NEUROCOMPUTING, V51, P341, DOI 10.1016/S0925-2312(02)00578-7
Han X, 2002, INVERSE PROBL ENG, V10, P309, DOI 10.1080/10682760290024436
Hoang KC, 2013, INVERSE PROBL SCI EN, V21, P1310, DOI 10.1080/17415977.2012.757315
Jiang C, 2008, EXP MECH, V48, P539, DOI 10.1007/s11340-007-9081-5
Liu GR, 2008, COMPUT METHOD APPL M, V197, P3898, DOI 10.1016/j.cma.2008.03.012
Liu GR, 2007, INT J NUMER METH ENG, V71, P902, DOI 10.1002/nme.1968
Liu GR, 2007, COMPUT MECH, V39, P859, DOI 10.1007/s00466-006-0075-4
Liu GR, 2019, INT J COMP METH-SING, V16, DOI 10.1142/S0219876219500452
Liu G.R., 2003, FINITE ELEMENT METHO
Liu G. R., 2019, INT J COMPUT METHODS
Liu G R, 2010, SMOOTHED FINITE ELEM
Liu G.R., 2003, SMOOTHED PARTICLE HY
Liu G. R., 2020, LECT NOTES COMPUTATI
Liu G.R., 2003, COMPUTATIONAL INVERS
Liu G.R., 2009, MESHFREE METHODS MOV
Liu GR, 2005, COMPUT METHOD APPL M, V194, P3090, DOI 10.1016/j.cma.2004.08.003
Liu GR, 2002, J SOUND VIB, V254, P823, DOI 10.1006/jsvi.2001.4126
Liu GR, 2002, J SOUND VIB, V252, P239, DOI 10.1006/jsvi.2001.3814
Liu GR, 2001, COMPUT METHOD APPL M, V191, P989, DOI 10.1016/S0045-7825(01)00314-0
Liu GR, 2001, J COMPOS MATER, V35, P954, DOI 10.1106/86AQ-JY72-5VKT-K1NV
Liu GR, 2001, COMPOS SCI TECHNOL, V61, P1401, DOI 10.1016/S0266-3538(01)00033-1
Liu GR, 2002, COMPUT METHOD APPL M, V191, P3543, DOI 10.1016/S0045-7825(02)00292-X
Matsugu M, 2003, NEURAL NETWORKS, V16, P555, DOI 10.1016/S0893-6080(03)00115-1
RUMELHART DE, 1986, NATURE, V323, P533, DOI 10.1038/323533a0
Yang ZL, 2003, INVERSE PROBL ENG, V11, P243, DOI 10.1080/1068276031000135890
NR 28
TC 0
Z9 0
U1 4
U2 4
PU WORLD SCIENTIFIC PUBL CO PTE LTD
PI SINGAPORE
PA 5 TOH TUCK LINK, SINGAPORE 596224, SINGAPORE
SN 0219-8762
EI 1793-6969
J9 INT J COMP METH-SING
JI Int. J. Comput. Methods
PD SEP
PY 2020
VL 17
IS 7
AR 2050021
DI 10.1142/S0219876220500218
PG 30
WC Engineering, Multidisciplinary; Mathematics, Interdisciplinary
Applications
SC Engineering; Mathematics
GA NE9YF
UT WOS:000562959100015
DA 2021-04-21
ER
PT J
AU Al Atoum, B
Biagi, SF
Gonzalez-Diaz, D
Jones, BJP
McDonald, AD
AF Al Atoum, B.
Biagi, S. F.
Gonzalez-Diaz, D.
Jones, B. J. P.
McDonald, A. D.
TI Electron transport in gaseous detectors with a Python-based Monte Carlo
simulation code
SO COMPUTER PHYSICS COMMUNICATIONS
LA English
DT Article
DE Detector design and simulation; Gases and fluids; Electron scattering
ID DIFFUSION; COEFFICIENTS; MIXTURES; XENON; DRIFT
AB Understanding electron drift and diffusion in gases and gas mixtures is a topic of central importance for the development of modern particle detection instrumentation. The industry-standard MagBoltz code has become an invaluable tool during its 20 years of development, providing capability to solve for electron transport ('swarm') properties based on a growing encyclopedia of built-in collision cross sections. We have made a refactorization of this code from FORTRAN into Cython, and studied a range of gas mixtures of interest in high energy and nuclear physics. The results from the new open source PyBoltz package match the outputs from the original MagBoltz code, with comparable simulation speed. An extension to the capabilities of the original code is demonstrated, in implementation of a new Modified Effective Range Theory interface. We hope that the versatility afforded by the new Python code-base will encourage continued use and development of the MagBoltz tools by the particle physics community. (C) 2020 Elsevier B.V. All rights reserved.
C1 [Al Atoum, B.; Jones, B. J. P.; McDonald, A. D.] Univ Texas Arlington, Dept Phys, Arlington, TX 76019 USA.
[Biagi, S. F.] Univ Liverpool, Phys Dept, Liverpool L69 7ZE, Merseyside, England.
[Gonzalez-Diaz, D.] Univ Santiago de Compostela, Inst Galego Fis Altas Enerxias, Campus Sur,Rua Xose Maria Suarez Nunez S-N, E-15782 Santiago De Compostela, Spain.
RP Al Atoum, B (corresponding author), Univ Texas Arlington, Dept Phys, Arlington, TX 76019 USA.
EM bashar.atoum@mavs.uta.edu
RI ; Gonzalez Diaz, Diego/K-7265-2014
OI Jones, Benjamin/0000-0003-3400-8986; Gonzalez Diaz,
Diego/0000-0002-6809-5996
FU Department of Energy, USAUnited States Department of Energy (DOE)
[DE-SC0019054, DE-SC0019223]; Ramon y Cajal program (Spain)Spanish
Government [RYC-2015-18820]
FX We would like to thank Roxanne Guenette and the Harvard FAS Research
Computing center for the use of the Odyssey cluster, and members of the
NEXT collaboration including Neus Lopez March and Ryan Felkai for
illuminating conversations. The UTA group acknowledges support from the
Department of Energy, USA under Early Career Award number DE-SC0019054,
and by Department of Energy, USA Award DE-SC0019223. DGD acknowledges
the Ramon y Cajal program (Spain) under contract number RYC-2015-18820.
CR Al Atoum B., PYBOLTZ
AMENDOLIA SR, 1986, NUCL INSTRUM METH A, V244, P516, DOI 10.1016/0168-9002(86)91077-6
Azevedo CDR, 2016, J INSTRUM, V11, DOI 10.1088/1748-0221/11/02/C02007
Azevedo CDR, 2015, PHYS LETT B, V741, P272, DOI 10.1016/j.physletb.2014.12.054
Behnel S, 2011, COMPUT SCI ENG, V13, P31, DOI 10.1109/MCSE.2010.118
BIAGI SF, 1989, NUCL INSTRUM METH A, V283, P716, DOI 10.1016/0168-9002(89)91446-0
BIAGI SF, 1988, NUCL INSTRUM METH A, V273, P533, DOI 10.1016/0168-9002(88)90050-2
Biagi SF, 1999, NUCL INSTRUM METH A, V421, P234, DOI 10.1016/S0168-9002(98)01233-9
Brockmann R., 1994, DEV TIME PROJECTION
Burns J, 2017, J INSTRUM, V12, DOI 10.1088/1748-0221/12/10/T10006
Capitelli M, 2000, J THERMOPHYS HEAT TR, V14, P259, DOI 10.2514/2.6517
CHANIN LM, 1962, PHYS REV, V128, P219, DOI 10.1103/PhysRev.128.219
CROMPTON RW, 1967, J APPL PHYS, V38, P4093, DOI 10.1063/1.1709082
DANIEL TN, 1970, J PHYS PT B ATOM M P, V3, P363, DOI 10.1088/0022-3700/3/3/007
Felkai R, 2018, NUCL INSTRUM METH A, V905, P82, DOI 10.1016/j.nima.2018.07.013
Fernandes A.F.M., 2019, ARXIV190603984
FRASER GW, 1986, NUCL INSTRUM METH A, V247, P544, DOI 10.1016/0168-9002(86)90417-1
Gonzalez-Diaz D, 2018, NUCL INSTRUM METH A, V878, P200, DOI 10.1016/j.nima.2017.09.024
Henriques CAO, 2019, J HIGH ENERGY PHYS, DOI 10.1007/JHEP01(2019)027
Henriques CAO, 2017, PHYS LETT B, V773, P663, DOI 10.1016/j.physletb.2017.09.017
HUNTER SR, 1988, PHYS REV A, V38, P5539, DOI 10.1103/PhysRevA.38.5539
Jeon B.-H., 1998, IEEJ T FUNDAM MAT, V118, P874
Jones B.J.P., AL ATOUM ARGON XENON
Jones B.J.P., 2016, XEPA PROJECT XENON P
Kurokawa M, 2011, PHYS REV A, V84, DOI 10.1103/PhysRevA.84.062717
Lima IB, 2012, NUCL INSTRUM METH A, V670, P55, DOI 10.1016/j.nima.2011.12.060
Lopez-March N., LIDINE 2019 MANCH UK
Martin-Albo J, 2017, J PHYS CONF SER, V888, DOI 10.1088/1742-6596/888/1/012154
MARX JN, 1978, PHYS TODAY, V31, P46, DOI 10.1063/1.2994775
McDonald AD, 2019, J INSTRUM, V14, DOI 10.1088/1748-0221/14/08/P08009
Okhrimovskyy A, 2002, PHYS REV E, V65, DOI 10.1103/PhysRevE.65.037402
OMALLY TF, 1963, PHYS REV, V130, P1020, DOI 10.1103/PhysRev.130.1020
PACK JL, 1992, J APPL PHYS, V71, P5363, DOI 10.1063/1.350555
Raju G G, 2005, GASEOUS ELECT THEORY
Riegler W, 2003, NUCL INSTRUM METH A, V500, P144, DOI 10.1016/S0168-9002(03)00337-1
Ruiz-Choliz E, 2015, NUCL INSTRUM METH A, V799, P137, DOI 10.1016/j.nima.2015.07.062
Sahin O, 2018, J INSTRUM, V13, DOI 10.1088/1748-0221/13/10/P10032
Sahin O, 2010, J INSTRUM, V5, DOI 10.1088/1748-0221/5/05/P05002
Sauli F., 2014, GASEOUS RAD DETECTOR
Schindler H., GARFIELD
Simon A, 2018, J INSTRUM, V13, DOI 10.1088/1748-0221/13/07/P07013
SKULLERUD HR, 1968, J PHYS D APPL PHYS, V1, P1567, DOI 10.1088/0022-3727/1/11/423
Veenho R, 2009, J INSTRUM, V4, DOI 10.1088/1748-0221/4/12/P12017
Yousfi M, 2009, IEEE T PLASMA SCI, V37, P764, DOI 10.1109/TPS.2009.2017538
NR 44
TC 0
Z9 0
U1 1
U2 8
PU ELSEVIER
PI AMSTERDAM
PA RADARWEG 29, 1043 NX AMSTERDAM, NETHERLANDS
SN 0010-4655
EI 1879-2944
J9 COMPUT PHYS COMMUN
JI Comput. Phys. Commun.
PD SEP
PY 2020
VL 254
AR 107357
DI 10.1016/j.cpc.2020.107357
PG 9
WC Computer Science, Interdisciplinary Applications; Physics, Mathematical
SC Computer Science; Physics
GA LZ5GA
UT WOS:000541251200026
DA 2021-04-21
ER
PT J
AU Carrazza, S
Cruz-Martinez, JM
AF Carrazza, Stefano
Cruz-Martinez, Juan M.
TI VegasFlow: Accelerating Monte Carlo simulation across multiple hardware
platforms
SO COMPUTER PHYSICS COMMUNICATIONS
LA English
DT Article
DE Monte Carlo; Graphs; Integration; Machine learning; Hardware
acceleration
AB We present VegasFlow, a new software for fast evaluation of high dimensional integrals based on Monte Carlo integration techniques designed for platforms with hardware accelerators. The growing complexity of calculations and simulations in many areas of science have been accompanied by advances in the computational tools which have helped their developments. VegasFlow enables developers to delegate all complicated aspects of hardware or platform implementation to the library so they can focus on the problem at hand. This software is inspired on the Vegas algorithm, ubiquitous in the particle physics community as the driver of cross section integration, and based on Google's powerful TensorFlow library. We benchmark the performance of this library on many different consumer and professional grade GPUs and CPUs.
Program summary
Program Title: VegasFlow
CPC Library link to program files: http://dx.doi.org/10.17632/rpgcbzzhdt.1
Developer's repository link: https://github.com/N3PDF/vegasflow
Licensing provisions: GPLv3
Programming language: Python
Nature of problem: The solution of high dimensional integrals requires the implementation of Monte Carlo algorithms such as Vegas. Monte Carlo algorithms are known to require long computation times.
Solution method: Implementation of the Vegas algorithm using the dataflow graph infrastructure provided by the TensorFlow framework. Extension of the algorithm to take advantage of multi-threading CPU and multi-GPU setups. (C) 2020 Elsevier B.V. All rights reserved.
C1 [Carrazza, Stefano] Univ Milan, TIF Lab, Dipartimento Fis, Via Celoria 16, I-20133 Milan, Italy.
INFN, Sez Milano, Via Celoria 16, I-20133 Milan, Italy.
RP Carrazza, S (corresponding author), Univ Milan, TIF Lab, Dipartimento Fis, Via Celoria 16, I-20133 Milan, Italy.
EM stefano.carrazza@unimi.it
RI ; Carrazza, Stefano/D-8412-2017
OI Cruz-Martinez, Juan M/0000-0002-8061-1965; Carrazza,
Stefano/0000-0002-0079-6753
FU European Research Council under the European UnionEuropean Research
Council (ERC) [740006]; UNIMI, Italy
FX We thank Stefano Forte for a careful reading of the manuscript. We thank
Durham University's IPPP for the access to the P100 and V100 32 GB GPUs
used in order to benchmark this code. We also acknowledge the NVIDIA
Corporation for the donation of a Titan V GPU used for this research.
This project is supported by the European Research Council under the
European Unions Horizon 2020 research and innovation Programme (grant
agreement number 740006) and by the UNIMI, Italy Linea2A project "New
hardware for HEP".
CR Alwall J, 2014, J HIGH ENERGY PHYS, DOI 10.1007/JHEP07(2014)079
Bothmann E., ARXIV200105478
Brucherseifer M, 2014, PHYS LETT B, V736, P58, DOI 10.1016/j.physletb.2014.06.075
Buckley A., 2019, 19 INT WORKSH ADV CO
Campbell J, 2019, J HIGH ENERGY PHYS, DOI 10.1007/JHEP12(2019)034
Campbell JM, 2015, EUR PHYS J C, V75, DOI 10.1140/epjc/s10052-015-3461-2
Gao C., ARXIV200105486
Gao C., ARXIV200110028
Gehrmann T., 2017, POS RADCOR, V2018, DOI 10.22323/1.290.0074
Genz, 1984, P INT C TOOLS METH L, P81
Gleisberg T, 2009, J HIGH ENERGY PHYS, DOI 10.1088/1126-6708/2009/02/007
Lepage G. P., 1980, VEGAS ADAPTIVE MULTI
LEPAGE GP, 1978, J COMPUT PHYS, V27, P192
Martin Abadi, 2015, TENSORFLOW LARGE SCA
Muller T., CORR
Nickolls John, 2008, ACM Queue, V6, DOI 10.1145/1365490.1365500
NR 16
TC 0
Z9 0
U1 1
U2 6
PU ELSEVIER
PI AMSTERDAM
PA RADARWEG 29, 1043 NX AMSTERDAM, NETHERLANDS
SN 0010-4655
EI 1879-2944
J9 COMPUT PHYS COMMUN
JI Comput. Phys. Commun.
PD SEP
PY 2020
VL 254
AR 107376
DI 10.1016/j.cpc.2020.107376
PG 5
WC Computer Science, Interdisciplinary Applications; Physics, Mathematical
SC Computer Science; Physics
GA LZ5GA
UT WOS:000541251200029
DA 2021-04-21
ER
PT J
AU Reinarz, A
Charrier, DE
Bader, M
Bovard, L
Dumbser, M
Duru, K
Fambri, F
Gabriel, AA
Gallard, JM
Koppel, S
Krenz, L
Rannabauer, L
Rezzolla, L
Samfass, P
Tavelli, M
Weinzierl, T
AF Reinarz, Anne
Charrier, Dominic E.
Bader, Michael
Bovard, Luke
Dumbser, Michael
Duru, Kenneth
Fambri, Francesco
Gabriel, Alice-Agnes
Gallard, Jean-Matthieu
Koeppel, Sven
Krenz, Lukas
Rannabauer, Leonhard
Rezzolla, Luciano
Samfass, Philipp
Tavelli, Maurizio
Weinzierl, Tobias
TI ExaHyPE: An engine for parallel dynamically adaptive simulations of wave
problems
SO COMPUTER PHYSICS COMMUNICATIONS
LA English
DT Article
DE Hyperbolic; PDE; ADER-DG; Finite volumes; AMR; MPI; TBB; MPI plus X
ID FINITE-ELEMENT-METHOD; DISCONTINUOUS GALERKIN METHOD; DIFFUSE INTERFACE
METHOD; CONSERVATION-LAWS; HIGH-ORDER; VOLUME SCHEMES; EQUATIONS; ENERGY
AB ExaHyPE ("An Exascale Hyperbolic PDE Engine") is a software engine for solving systems of first-order hyperbolic partial differential equations (PDEs). Hyperbolic PDEs are typically derived from the conservation laws of physics and are useful in a wide range of application areas. Applications powered by ExaHyPE can be run on a student's laptop, but are also able to exploit thousands of processor cores on state-of-the-art supercomputers. The engine is able to dynamically increase the accuracy of the simulation using adaptive mesh refinement where required. Due to the robustness and shock capturing abilities of ExaHyPE's numerical methods, users of the engine can simulate linear and non-linear hyperbolic PDEs with very high accuracy. Users can tailor the engine to their particular PDE by specifying evolved quantities, fluxes, and source terms. A complete simulation code for a new hyperbolic PDE can often be realised within a few hours - a task that, traditionally, can take weeks, months, often years for researchers starting from scratch. In this paper, we showcase ExaHyPE's workflow and capabilities through real-world scenarios from our two main application areas: seismology and astrophysics.
Program summary
Program title: ExaHyPE-Engine
Program Files doi: http://dx.doi.org/10.17632/6sz8hnpz.1
Licensing provisions: BSD 3-clause
Programming languages: C++, Python, Fortran
Nature of Problem: The ExaHyPE PDE engine offers robust algorithms to solve linear and non-linear hyperbolic systems of PDEs written in first order form. The systems may contain both conservative and non-conservative terms.
Solution method: ExaHyPE employs the discontinuous Galerkin (DG) method combined with explicit one-step ADER (arbitrary high-order derivative) time-stepping. An a-posteriori limiting approach is applied to the ADER-DG solution, whereby spurious solutions are discarded and recomputed with a robust, patch-based finite volume scheme. ExaHyPE uses dynamical adaptive mesh refinement to enhance the accuracy of the solution around shock waves, complex geometries, and interesting features. (C) 2020 The Authors. Published by Elsevier B.V.
C1 [Reinarz, Anne; Bader, Michael; Gallard, Jean-Matthieu; Krenz, Lukas; Rannabauer, Leonhard; Samfass, Philipp] Tech Univ Munich, Dept Informat, Boltzmannstr 3, D-85748 Garching, Germany.
[Charrier, Dominic E.; Weinzierl, Tobias] Univ Durham, Dept Comp Sci, South Rd, Durham DH1 3LE, England.
[Dumbser, Michael; Fambri, Francesco; Tavelli, Maurizio] Univ Trento, Lab Appl Math, Via Messiano 77, I-38123 Trento, Italy.
[Bovard, Luke; Koeppel, Sven; Rezzolla, Luciano] Goethe Univ, Inst Theoret Phys, Max von Laue Str 1, D-60438 Frankfurt, Germany.
[Duru, Kenneth] Australian Natl Univ, Math Sci Inst, Canberra, ACT, Australia.
[Fambri, Francesco] Max Planck Inst Plasma Phys, Boltzmannstr 2, D-85748 Garching, Germany.
[Duru, Kenneth; Gabriel, Alice-Agnes] Ludwig Maximilians Univ Munchen, Dept Earth & Environm Sci, Theresienstr 41, D-80333 Munich, Germany.
RP Reinarz, A (corresponding author), Tech Univ Munich, Dept Informat, Boltzmannstr 3, D-85748 Garching, Germany.
EM reinarz@in.tum.de
RI Fambri, Francesco/A-7455-2018; Dumbser, Michael/F-2740-2010; Gabriel,
Alice-Agnes/AAC-4066-2020
OI Fambri, Francesco/0000-0002-6070-8372; Gabriel,
Alice-Agnes/0000-0003-0112-8412; Dumbser, Michael/0000-0002-8201-8372;
Duru, Kenneth/0000-0002-5260-7942; Reinarz, Anne/0000-0003-1787-7637;
Koppel, Sven/0000-0003-2303-7765
FU European UnionEuropean Commission [671698, 823844]; Leibniz
Supercomputing Centre, Germany [pr48ma, pr63qo]
FX This project has received funding from the European Union's Horizon 2020
research and innovation programme under grant agreement No 671698,
www.exahype.eu.; The ExaHyPE team acknowledges additional support by the
European Union's Horizon 2020 research and innovation program (ChEESE,
grant no. 823844).; The authors gratefully acknowledge the support by
the Leibniz Supercomputing Centre, Germany (www.lrz.de), which also
provided the computing resources on SuperMUC (Grant No. pr48ma and Grant
No. pr63qo)
CR ABGRALL R, 1994, J COMPUT PHYS, V114, P45, DOI 10.1006/jcph.1994.1148
BAER MR, 1986, INT J MULTIPHAS FLOW, V12, P861, DOI 10.1016/0301-9322(86)90033-9
Bishop NT, 2016, LIVING REV RELATIV, V19, DOI 10.1007/s41114-016-0001-9
Charrier D., 2020, SIAM J SCI COMPUTING
Charrier DE, 2019, INT J HIGH PERFORM C, V33, P973, DOI 10.1177/1094342019842645
Charrier DE, 2018, LECT NOTES COMPUT SC, V10778, P3, DOI 10.1007/978-3-319-78054-2_1
Clain S, 2011, J COMPUT PHYS, V230, P4028, DOI 10.1016/j.jcp.2011.02.026
COCKBURN B, 1991, ESAIM-MATH MODEL NUM, V25, P337, DOI 10.1051/m2an/1991250303371
Cockburn B, 1998, J COMPUT PHYS, V141, P199, DOI 10.1006/jcph.1998.5892
COCKBURN B, 1989, MATH COMPUT, V52, P411, DOI 10.2307/2008474
COCKBURN B, 1990, MATH COMPUT, V54, P545, DOI 10.2307/2008501
COCKBURN B, 1989, J COMPUT PHYS, V84, P90, DOI 10.1016/0021-9991(89)90183-6
Day SM, 2001, B SEISMOL SOC AM, V91, P520, DOI 10.1785/0120000103
Diot S, 2013, INT J NUMER METH FL, V73, P362, DOI 10.1002/fld.3804
Dumbser M, 2008, J COMPUT PHYS, V227, P3971, DOI 10.1016/j.jcp.2007.12.005
Dumbser M, 2018, AXIOMS, V7, DOI 10.3390/axioms7030063
Dumbser M, 2018, PHYS REV D, V97, DOI 10.1103/PhysRevD.97.084053
Dumbser M, 2016, J COMPUT PHYS, V304, P275, DOI 10.1016/j.jcp.2015.10.014
Dumbser M, 2014, J COMPUT PHYS, V278, P47, DOI 10.1016/j.jcp.2014.08.009
Dumbser M, 2014, COMPUT METHOD APPL M, V268, P359, DOI 10.1016/j.cma.2013.09.022
Dumbser M, 2013, COMPUT METHOD APPL M, V257, P47, DOI 10.1016/j.cma.2013.01.006
Dumbser M, 2011, COMPUT METHOD APPL M, V200, P1204, DOI 10.1016/j.cma.2010.10.011
Duru K., 2017, ARXIV180206380
Duru K., STABLE DISCONTINUOUS
Duru K, 2019, COMPUT METHOD APPL M, V350, P898, DOI 10.1016/j.cma.2019.02.036
Eckhardt W., 2009, 8 INT C PPAM 2009 1, P567, DOI [10.1007/978-3-642-14390-8_59, DOI 10.1007/978-3-642-14390-8_59]
Fambri F, 2018, MON NOT R ASTRON SOC, V477, P4543, DOI 10.1093/mnras/sty734
Fambri F., COMPUT PHYS COMMUN, V219
Gaburro E, 2018, COMPUT FLUIDS, V175, P180, DOI 10.1016/j.compfluid.2018.08.013
Gassner G, 2008, J SCI COMPUT, V34, P260, DOI 10.1007/s10915-007-9169-1
Gassner G, 2011, J COMPUT PHYS, V230, P4232, DOI 10.1016/j.jcp.2010.10.024
Hartmann R, 2002, J COMPUT PHYS, V183, P508, DOI 10.1006/jcph.2002.7206
Heinecke A, 2016, SC '16: PROCEEDINGS OF THE INTERNATIONAL CONFERENCE FOR HIGH PERFORMANCE COMPUTING, NETWORKING, STORAGE AND ANALYSIS, P981, DOI 10.1109/SC.2016.83
Koppel S, 2018, J PHYS CONF SER, V1031, DOI 10.1088/1742-6596/1031/1/012017
Kolgan VP, 2011, J COMPUT PHYS, V230, P2384, DOI 10.1016/j.jcp.2010.12.033
Krenz Lukas, 2020, LECT NOTES COMPUTER, P2020
Loubere R, 2014, COMMUN COMPUT PHYS, V16, P718, DOI 10.4208/cicp.181113.140314a
MICHEL FC, 1972, ASTROPHYS SPACE SCI, V15, P153, DOI 10.1007/BF00649949
Moran J., 2003, DOVER BOOKS AERONAUT
Muller A., 2010, 5 EUR C COMP FLUID D
Persson P.-O., 44 AIAA AER SCI M EX, P112
Qiu JX, 2005, SIAM J SCI COMPUT, V26, P907, DOI 10.1137/S1064827503425298
Qiu JX, 2004, J COMPUT PHYS, V193, P115, DOI 10.1016/j.jcp.2003.07.026
Radice D, 2011, PHYS REV D, V84, DOI 10.1103/PhysRevD.84.024010
Rannabauer L., 2018, ADJ P ENV
Rannabauer L, 2018, COMPUT FLUIDS, V173, P299, DOI 10.1016/j.compfluid.2018.01.031
Reed W., TRIANGULAR MESH METH
Reinders James, 2007, INTEL THREADING BUIL
Reps B, 2017, ACM T MATH SOFTWARE, V44, DOI 10.1145/3054946
Rezzolla L, 2013, RELATIVISTIC HYDRODY
RUGGIERI A, 2016, COMPUTATIONAL ASTROP, V3, DOI DOI 10.3389/FMOLL.2016.00063
Schreiber M., 2018, HIPEAC 2018 3 COSH W
Tavelli M, 2019, J COMPUT PHYS, V386, P158, DOI 10.1016/j.jcp.2019.02.004
Tavelli M, 2016, J COMPUT PHYS, V319, P294, DOI 10.1016/j.jcp.2016.05.009
Titarev VA, 2002, J SCI COMPUT, V17, P609, DOI 10.1023/A:1015126814947
Toro EF, 2001, GODUNOV METHODS: THEORY AND APPLICATIONS, P907
Ulrich T, 2019, NAT COMMUN, V10, DOI 10.1038/s41467-019-09125-w
Uphoff C, 2017, SC'17: PROCEEDINGS OF THE INTERNATIONAL CONFERENCE FOR HIGH PERFORMANCE COMPUTING, NETWORKING, STORAGE AND ANALYSIS, DOI 10.1145/3126908.3126948
Van Leer B, 1997, J COMPUT PHYS, V135, P229, DOI 10.1006/jcph.1997.5704
Weinzierl M, 2018, ACM T MATH SOFTWARE, V44, DOI 10.1145/3165280
Weinzierl T, 2016, PARALLEL COMPUT, V52, P42, DOI 10.1016/j.parco.2015.12.007
Weinzierl T., 2020, ACM T MATH SOFTWARE
Weinzierl T, 2019, ACM T MATH SOFTWARE, V45, DOI 10.1145/3319797
Weinzierl T, 2011, SIAM J SCI COMPUT, V33, P2732, DOI 10.1137/100799071
Wollherr S, 2019, J GEOPHYS RES-SOL EA, V124, P6666, DOI 10.1029/2018JB016355
Zanotti O, 2015, COMPUT FLUIDS, V118, P204, DOI 10.1016/j.compfluid.2015.06.020
NR 66
TC 5
Z9 5
U1 0
U2 2
PU ELSEVIER
PI AMSTERDAM
PA RADARWEG 29, 1043 NX AMSTERDAM, NETHERLANDS
SN 0010-4655
EI 1879-2944
J9 COMPUT PHYS COMMUN
JI Comput. Phys. Commun.
PD SEP
PY 2020
VL 254
AR 107251
DI 10.1016/j.cpc.2020.107251
PG 16
WC Computer Science, Interdisciplinary Applications; Physics, Mathematical
SC Computer Science; Physics
GA LZ5GA
UT WOS:000541251200009
OA Green Published, Green Accepted, Other Gold
DA 2021-04-21
ER
PT J
AU Ott, J
Pritchard, M
Best, N
Linstead, E
Curcic, M
Baldi, P
AF Ott, Jordan
Pritchard, Mike
Best, Natalie
Linstead, Erik
Curcic, Milan
Baldi, Pierre
TI A Fortran-Keras Deep Learning Bridge for Scientific Computing
SO SCIENTIFIC PROGRAMMING
LA English
DT Article
ID NEURAL-NETWORKS; PARAMETERIZATION; MODEL; FRAMEWORK; DROPOUT
AB Implementing artificial neural networks is commonly achieved via high-level programming languages such as Python and easy-to-use deep learning libraries such as Keras. These software libraries come preloaded with a variety of network architectures, provide autodifferentiation, and support GPUs for fast and efficient computation. As a result, a deep learning practitioner will favor training a neural network model in Python, where these tools are readily available. However, many large-scale scientific computation projects are written in Fortran, making it difficult to integrate with modern deep learning methods. To alleviate this problem, we introduce a software library, the Fortran-Keras Bridge (FKB). This two-way bridge connects environments where deep learning resources are plentiful with those where they are scarce. The paper describes several unique features offered by FKB, such as customizable layers, loss functions, and network ensembles. The paper concludes with a case study that applies FKB to address open questions about the robustness of an experimental approach to global climate simulation, in which subgrid physics are outsourced to deep neural network emulators. In this context, FKB enables a hyperparameter search of one hundred plus candidate models of subgrid cloud and radiation physics, initially implemented in Keras, to be transferred and used in Fortran. Such a process allows the model's emergent behavior to be assessed, i.e., when fit imperfections are coupled to explicit planetary-scale fluid dynamics. The results reveal a previously unrecognized strong relationship between offline validation error and online performance, in which the choice of the optimizer proves unexpectedly critical. This in turn reveals many new neural network architectures that produce considerable improvements in climate model stability including some with reduced error, for an especially challenging training dataset.
C1 [Ott, Jordan; Baldi, Pierre] Univ Calif Irvine, Dept Comp Sci, Irvine, CA 92697 USA.
[Pritchard, Mike] Univ Calif Irvine, Dept Earth Syst Sci, Irvine, CA USA.
[Best, Natalie; Linstead, Erik] Chapman Univ, Fowler Sch Engn, Orange, CA USA.
[Curcic, Milan] Univ Miami, Dept Ocean Sci, Coral Gables, FL 33124 USA.
RP Baldi, P (corresponding author), Univ Calif Irvine, Dept Comp Sci, Irvine, CA 92697 USA.
EM jott1@uci.edu; mspritch@uci.edu; best120@mail.chapman.edu;
linstead@chapman.edu; m.curcic@umiami.edu; pfbaldi@ics.uci.edu
FU NSF NRTNational Science Foundation (NSF)NSF - Office of the Director
(OD) [1633631]; NSFNational Science Foundation (NSF) [OAC-1835863,
AGS-1734164]; National Science FoundationNational Science Foundation
(NSF) [ACI-1548562, TG-ATM190002]
FX Th work of JO and PB is supported by NSF NRT (Grant 1633631). MP
acknowledges NSF funding from OAC-1835863 and AGS-1734164. This research
also used HPC resources of the Extreme Science and Engineering Discovery
Environment (XSEDE), which was supported by the National Science
Foundation under Grant no. ACI-1548562 [67] and allocation number
TG-ATM190002.
CR Abadi M, 2016, PROCEEDINGS OF OSDI'16: 12TH USENIX SYMPOSIUM ON OPERATING SYSTEMS DESIGN AND IMPLEMENTATION, P265
Adam Paszke, 2017, AUTOMATIC DIFFERENTI
Agostinelli F, 2019, NAT MACH INTELL, V1, P356, DOI 10.1038/s42256-019-0070-z
[Anonymous], 2019, KAGGLE STATE DATA SC
[Anonymous], FORTRAN
Archambeau F., 2004, INT J FINITE VOLUMES, V1, P1
Baldi P, 2014, ARTIF INTELL, V210, P78, DOI 10.1016/j.artint.2014.02.004
Bar-Sinai Y, 2019, P NATL ACAD SCI USA, V116, P15344, DOI 10.1073/pnas.1814058116
Bergstra J., 2013, P 12 PYTH SCI C, P13, DOI DOI 10.1088/1749-4699/8/1/014008
Bernal J., 2015, 8037 NAT I STAND TEC, DOI [10.6028/NIST.IR.8037, DOI 10.6028/NIST.IR.8037]
Bernal J, 2015, J RES NATL INST STAN, V120, DOI 10.6028/jres.120.009
Beucler T., 2020, ENFORCING ANAL CONST
Borgesson L., 1996, DEV GEOTECHNICAL ENG, V79, P565
Brenowitz ND, 2018, GEOPHYS RES LETT, V45, P6289, DOI 10.1029/2018GL078510
Brenowitz N. D., 2020, INTERPRETING STABILI
Brierley P., FORTRAN90 MLP BACKPR
BROOKS BR, 1983, J COMPUT CHEM, V4, P187, DOI 10.1002/jcc.540040211
Chollet F., 2018, DEEP LEARNING PYTHON
Chollet F., 2015, KERAS
Curcic Milan, 2018, ACM SIGPLAN Fortran Forum, V38, P4, DOI 10.1145/3323057.3323059
Donelan MA, 2012, J GEOPHYS RES-OCEANS, V117, DOI 10.1029/2011JC007787
Ferrari A., 2005, TECH REP, DOI [10.2172/877507, DOI 10.2172/877507]
Fischer J., 2008, NEK5000 WEB PAGE
Gagne DJ, 2020, J ADV MODEL EARTH SY, V12, DOI 10.1029/2019MS001896
Gentine P, 2018, GEOPHYS RES LETT, V45, P5742, DOI 10.1029/2018GL078202
Golaz JC, 2019, J ADV MODEL EARTH SY, V11, P2089, DOI 10.1029/2018MS001603
Grabowski WW, 2001, J ATMOS SCI, V58, P978, DOI 10.1175/1520-0469(2001)058<0978:CCPWTL>2.0.CO;2
Held IM, 2019, J ADV MODEL EARTH SY, V11, P3691, DOI 10.1029/2019MS001829
Hertel L., 2020, SOFTWAREX
Hurrell JW, 2013, B AM METEOROL SOC, V94, P1339, DOI 10.1175/BAMS-D-12-00121.1
Jiang GQ, 2018, GEOPHYS RES LETT, V45, P3706, DOI 10.1002/2018GL077004
John Gagne D., 2019, P AGUS FALL M SAN FR
John Gagne D., 2020, P 100 AM METH SOC AN
Khairoutdinov M, 2005, J ATMOS SCI, V62, P2136, DOI 10.1175/JAS3453.1
Khairoutdinov M, 2008, J CLIMATE, V21, P413, DOI 10.1175/2007JCLI1630.1
Komatitsch D., 2012, SPECFEM3D CARTESIAN
Krizhevsky Alex, 2012, ADV NEURAL INFORM PR, P1097, DOI DOI 10.1145/3065386
LaHaye N, 2019, IEEE J-STARS, V12, P3056, DOI 10.1109/JSTARS.2019.2920234
Lary DJ, 2004, ATMOS CHEM PHYS, V4, P143, DOI 10.5194/acp-4-143-2004
Ling J, 2016, J FLUID MECH, V807, P155, DOI 10.1017/jfm.2016.615
Madenci E., 2015, FINITE ELEMENT METHO
Murray Y. D., 2007, TECH REP
Nissen S, 2003, IMPLEMENTATION FAST
Ott J, 2018, INT C PROGRAM COMPRE, P336, DOI 10.1145/3196321.3196359
Ott J, 2019, J BIG DATA-GER, V6, DOI 10.1186/s40537-019-0198-z
Ott J, 2018, IEEE WORK CONF MIN S, P376, DOI 10.1145/3196398.3196402
Powers JG, 2017, B AM METEOROL SOC, V98, P1717, DOI 10.1175/BAMS-D-15-00308.1
Pritchard MS, 2014, J ADV MODEL EARTH SY, V6, P723, DOI 10.1002/2014MS000340
Raissi M, 2019, J COMPUT PHYS, V378, P686, DOI 10.1016/j.jcp.2018.10.045
Rasp S., 2019, ONLINE LEARNING WAY
Rasp S, 2018, P NATL ACAD SCI USA, V115, P9684, DOI 10.1073/pnas.1810286115
Rudy SH, 2017, SCI ADV, V3, DOI 10.1126/sciadv.1602614
Schmidhuber J, 2015, NEURAL NETWORKS, V61, P85, DOI 10.1016/j.neunet.2014.09.003
Silver D, 2016, NATURE, V529, P484, DOI 10.1038/nature16961
Snoek J., 2012, ADV NEURAL INFORM PR, P2951, DOI DOI 10.1094/PDIS-11-11-0999-PDN
Srivastava N, 2014, J MACH LEARN RES, V15, P1929
Szegedy C., 2015, BATCH NORMALIZATION
Thayer-Calder K, 2009, J ATMOS SCI, V66, P3297, DOI 10.1175/2009JAS3081.1
Tompson J, 2014, ACM T GRAPHIC, V33, DOI 10.1145/2629500
Towns J, 2014, COMPUT SCI ENG, V16, P62, DOI 10.1109/MCSE.2014.80
Urban G, 2018, GASTROENTEROLOGY, V155, P1069, DOI 10.1053/j.gastro.2018.06.037
Valiev M, 2010, COMPUT PHYS COMMUN, V181, P1477, DOI 10.1016/j.cpc.2010.04.018
Vega-Carrillo HR, 2006, RADIAT PROT DOSIM, V118, P251, DOI 10.1093/rpd/nci354
Wallcraft AJ, 2007, PROCEEDINGS OF THE HPCMP USERS GROUP CONFERENCE 2007, P259
Zhu XX, 2017, IEEE GEOSC REM SEN M, V5, P8, DOI 10.1109/MGRS.2017.2762307
NR 65
TC 0
Z9 0
U1 2
U2 2
PU HINDAWI LTD
PI LONDON
PA ADAM HOUSE, 3RD FLR, 1 FITZROY SQ, LONDON, W1T 5HF, ENGLAND
SN 1058-9244
EI 1875-919X
J9 SCI PROGRAMMING-NETH
JI Sci. Program.
PD AUG 28
PY 2020
VL 2020
AR 8888811
DI 10.1155/2020/8888811
PG 13
WC Computer Science, Software Engineering
SC Computer Science
GA NQ5HK
UT WOS:000570897500001
OA DOAJ Gold, Green Published
DA 2021-04-21
ER
PT J
AU Lin, GL
Lin, YH
AF Lin, Guey-Lin
Lin, Yen-Hsun
TI Analysis on the black hole formations inside old neutron stars by
isospin-violating dark matter with self-interaction
SO JOURNAL OF COSMOLOGY AND ASTROPARTICLE PHYSICS
LA English
DT Article
DE dark matter theory; dark matter detectors; neutron stars
ID CAPTURE; MILKY
AB Fermionic dark matter (DM) with attractive self-interaction is possible to form black holes (BH) inside the Gyr-old neutron stars (NS). Therefore by observing such NS corresponding to their adjacent DM environments can place bounds on DM properties, eg. DMbaryon cross section sigma(xb), DM mass m(x), dark coupling alpha(x) and mediator mass m(phi). In case of isospin violation, DM couples to neutron and proton in different strengths. Even NS is composed of protons roughly one to two percent of the total baryons, the contribution from protons to the DM capture rate could be drastically changed in the presence of isospin violation. We demonstrate that this effect can be important in certain cases. On the other hand, DM-forming BH inside the star is subject to many criteria and the underlying dynamics is rich with interesting features. We also systematically review the relevant physics based on the virial equation. Moreover, an accompanied python package dm2nsbh to realize the mechanism is also released on the github for other relevant research.
C1 [Lin, Guey-Lin] Natl Chiao Tung Univ, Inst Phys, Hsinchu 300, Taiwan.
[Lin, Yen-Hsun] Acad Sinica, Inst Phys, Taipei 115, Taiwan.
RP Lin, GL (corresponding author), Natl Chiao Tung Univ, Inst Phys, Hsinchu 300, Taiwan.
EM glin@cc.nctu.edu.tw; yenhsun@gate.sinica.edu.tw
OI Lin, Yen-Hsun/0000-0001-7911-7591
FU Academia Sinica, TaiwanAcademia Sinica - Taiwan; Ministry of Science and
Technology, TaiwanMinistry of Science and Technology, Taiwan
[107-2119-M-009-017-MY3]
FX YHL thanks the authors of ref. [41] for providing the data plot of
proton capture rate as well as the kind support by the Academia Sinica,
Taiwan. GLL is supported by the Ministry of Science and Technology,
Taiwan under Grant No. 107-2119-M-009-017-MY3. YHL thanks Gang Guo and
Meng-Ru Wu for useful discussions.
CR Aad G, 2015, EUR PHYS J C, V75, DOI 10.1140/epjc/s10052-015-3517-3
Aalbers J, 2016, J COSMOL ASTROPART P, DOI 10.1088/1475-7516/2016/11/017
Aartsen MG, 2017, EUR PHYS J C, V77, DOI 10.1140/epjc/s10052-017-4689-9
Abdallah J, 2015, PHYS DARK UNIVERSE, V9-10, P8, DOI 10.1016/j.dark.2015.08.001
Acevedo JF, 2020, J COSMOL ASTROPART P, DOI 10.1088/1475-7516/2020/03/038
Ackermann M, 2017, ASTROPHYS J, V840, DOI 10.3847/1538-4357/aa6cab
Aguilar M, 2015, PHYS REV LETT, V115, DOI 10.1103/PhysRevLett.115.211101
Akerib DS, 2017, PHYS REV LETT, V118, DOI 10.1103/PhysRevLett.118.021303
Ambrosi G, 2017, NATURE, V552, P63, DOI 10.1038/nature24475
Amole C, 2017, PHYS REV LETT, V118, DOI 10.1103/PhysRevLett.118.251301
Aprile E, 2018, PHYS REV LETT, V121, DOI 10.1103/PhysRevLett.121.111302
Aprile E, 2017, PHYS REV LETT, V119, DOI 10.1103/PhysRevLett.119.181301
Baryakhtar M, 2017, PHYS REV LETT, V119, DOI 10.1103/PhysRevLett.119.131801
Bell NF, 2018, J COSMOL ASTROPART P, DOI 10.1088/1475-7516/2018/09/018
Bell NF, 2013, PHYS REV D, V87, DOI 10.1103/PhysRevD.87.123507
Bertoni B, 2013, PHYS REV D, V88, DOI 10.1103/PhysRevD.88.123505
Bohmer CG, 2007, J COSMOL ASTROPART P, DOI 10.1088/1475-7516/2007/06/025
Boylan-Kolchin M, 2012, MON NOT R ASTRON SOC, V422, P1203, DOI 10.1111/j.1365-2966.2012.20695.x
Boylan-Kolchin M, 2011, MON NOT R ASTRON SOC, V415, pL40, DOI 10.1111/j.1745-3933.2011.01074.x
Bramante J, 2017, PHYS REV D, V96, DOI 10.1103/PhysRevD.96.063002
Bramante J, 2014, PHYS REV D, V89, DOI 10.1103/PhysRevD.89.015010
Buckley MR, 2010, PHYS REV D, V81, DOI 10.1103/PhysRevD.81.083522
Bullock JS, 2017, ANNU REV ASTRON ASTR, V55, P343, DOI 10.1146/annurev-astro-091916-055313
Busoni G, 2013, J COSMOL ASTROPART P, DOI 10.1088/1475-7516/2013/07/010
Catena R, 2016, J COSMOL ASTROPART P, DOI 10.1088/1475-7516/2016/12/016
Chen CS, 2018, J HIGH ENERGY PHYS, DOI 10.1007/JHEP08(2018)069
Chen CS, 2018, J HIGH ENERGY PHYS, DOI 10.1007/JHEP04(2018)074
Chen CS, 2016, PHYS DARK UNIVERSE, V14, P35, DOI 10.1016/j.dark.2016.09.001
Chen CS, 2016, J COSMOL ASTROPART P, DOI 10.1088/1475-7516/2016/01/013
Chen CS, 2014, J COSMOL ASTROPART P, DOI 10.1088/1475-7516/2014/10/049
Chen J, 2015, J COSMOL ASTROPART P, DOI 10.1088/1475-7516/2015/12/021
Choi K, 2015, PHYS REV LETT, V114, DOI 10.1103/PhysRevLett.114.141301
COLPI M, 1986, PHYS REV LETT, V57, P2485, DOI 10.1103/PhysRevLett.57.2485
Dasgupta B, 2019, J COSMOL ASTROPART P, DOI 10.1088/1475-7516/2019/08/018
Davoudiasl H, 2012, PHYS REV D, V85, DOI 10.1103/PhysRevD.85.115019
de Lavallaz A, 2010, PHYS REV D, V81, DOI 10.1103/PhysRevD.81.123521
Eby J, 2016, J HIGH ENERGY PHYS, DOI 10.1007/JHEP02(2016)028
Elbert OD, 2018, ASTROPHYS J, V853, DOI 10.3847/1538-4357/aa9710
Elbert OD, 2015, MON NOT R ASTRON SOC, V453, P29, DOI 10.1093/mnras/stv1470
Ellis J, 2018, PHYS REV D, V97, DOI 10.1103/PhysRevD.97.123007
Ellis J, 2018, PHYS LETT B, V781, P607, DOI 10.1016/j.physletb.2018.04.048
Feng JL, 2011, PHYS LETT B, V703, P124, DOI 10.1016/j.physletb.2011.07.083
Fornengo N, 2017, J COSMOL ASTROPART P, DOI 10.1088/1475-7516/2017/12/012
Gaidau C, 2019, J COSMOL ASTROPART P, DOI 10.1088/1475-7516/2019/06/022
Garani R, 2019, J COSMOL ASTROPART P, DOI 10.1088/1475-7516/2019/05/035
Garani R, 2017, J COSMOL ASTROPART P, DOI 10.1088/1475-7516/2017/05/007
GOULD A, 1987, ASTROPHYS J, V321, P560, DOI 10.1086/165652
GOULD A, 1988, ASTROPHYS J, V328, P919, DOI 10.1086/166347
GOULD A, 1987, ASTROPHYS J, V321, P571, DOI 10.1086/165653
Gresham MI, 2018, PHYS REV D, V98, DOI 10.1103/PhysRevD.98.096001
Gresham MI, 2018, PHYS REV D, V97, DOI 10.1103/PhysRevD.97.036003
Gresham MI, 2017, PHYS REV D, V96, DOI 10.1103/PhysRevD.96.096012
Guver T, 2014, J COSMOL ASTROPART P, DOI 10.1088/1475-7516/2014/05/013
Hamaguchi K, 2019, PHYS LETT B, V795, P484, DOI 10.1016/j.physletb.2019.06.060
IceCube collaboration, 2017, EUR PHYS J C, V77, P214
IceCube PINGU collaboration, ARXIV14012046
Joglekar A., ARXIV191113293
Jungman G, 1996, PHYS REP, V267, P195, DOI 10.1016/0370-1573(95)00058-5
Kamada A, 2017, PHYS REV LETT, V119, DOI 10.1103/PhysRevLett.119.111102
Kaplan DE, 2009, PHYS REV D, V79, DOI 10.1103/PhysRevD.79.115016
Kaplinghat M, 2014, PHYS REV D, V89, DOI 10.1103/PhysRevD.89.035009
Kong K, 2015, PHYS LETT B, V743, P256, DOI 10.1016/j.physletb.2015.02.057
Kouvaris C, 2015, PHYS REV D, V92, DOI 10.1103/PhysRevD.92.063526
Kouvaris C, 2014, PHYS REV D, V90, DOI 10.1103/PhysRevD.90.043512
Kouvaris C, 2012, PHYS REV LETT, V108, DOI 10.1103/PhysRevLett.108.191301
Kouvaris C, 2011, PHYS REV D, V83, DOI 10.1103/PhysRevD.83.083512
Kouvaris C, 2010, PHYS REV D, V82, DOI 10.1103/PhysRevD.82.063531
Kouvaris C, 2008, PHYS REV D, V77, DOI 10.1103/PhysRevD.77.023006
Leung SC, 2011, PHYS REV D, V84, DOI 10.1103/PhysRevD.84.107301
Lin GL, 2015, PHYS REV D, V91, DOI 10.1103/PhysRevD.91.033002
LUX collaboration, 2017, PHYS REV LETT, V118, P251302
McDermott SD, 2012, PHYS REV D, V85, DOI 10.1103/PhysRevD.85.023519
Oman KA, 2015, MON NOT R ASTRON SOC, V452, P3650, DOI 10.1093/mnras/stv1504
Petraki K, 2013, INT J MOD PHYS A, V28, DOI 10.1142/S0217751X13300287
Raj N, 2018, PHYS REV D, V97, DOI 10.1103/PhysRevD.97.043006
Randall SW, 2008, ASTROPHYS J, V679, P1173, DOI 10.1086/587859
Robertson A, 2018, MON NOT R ASTRON SOC, V476, pL20, DOI 10.1093/mnrasl/sly024
Tolos L, 2015, PHYS REV D, V92, DOI 10.1103/PhysRevD.92.123002
Tulin S, 2018, PHYS REP, V730, P1, DOI 10.1016/j.physrep.2017.11.004
Tulin S, 2013, PHYS REV D, V87, DOI 10.1103/PhysRevD.87.115007
Walker MG, 2011, ASTROPHYS J, V742, DOI 10.1088/0004-637X/742/1/20
Wise MB, 2015, PHYS REV D, V91, DOI 10.1103/PhysRevD.91.039907
Wise MB, 2014, PHYS REV D, V90, DOI 10.1103/PhysRevD.90.055030
Zheng H, 2015, ASTROPHYS J, V800, DOI 10.1088/0004-637X/800/2/141
Zurek KM, 2014, PHYS REP, V537, P91, DOI 10.1016/j.physrep.2013.12.001
NR 85
TC 2
Z9 2
U1 1
U2 1
PU IOP PUBLISHING LTD
PI BRISTOL
PA TEMPLE CIRCUS, TEMPLE WAY, BRISTOL BS1 6BE, ENGLAND
SN 1475-7516
J9 J COSMOL ASTROPART P
JI J. Cosmol. Astropart. Phys.
PD AUG
PY 2020
IS 8
AR 022
DI 10.1088/1475-7516/2020/08/022
PG 24
WC Astronomy & Astrophysics; Physics, Particles & Fields
SC Astronomy & Astrophysics; Physics
GA OS4PK
UT WOS:000590146600006
DA 2021-04-21
ER
PT J
AU Kermode, JR
AF Kermode, James R.
TI f90wrap: an automated tool for constructing deep Python interfaces to
modern Fortran codes
SO JOURNAL OF PHYSICS-CONDENSED MATTER
LA English
DT Article
DE Fortran; Python; f2py; interoperability; interfacing; wrapping codes
ID EXCHANGE
AB f90wrap is a tool to automatically generate Python extension modules which interface to Fortran libraries that makes use of derived types. It builds on the capabilities of the popular f2py utility by generating a simpler Fortran 90 interface to the original Fortran code which is then suitable for wrapping with f2py, together with a higher-level Pythonic wrapper that makes the existance of an additional layer transparent to the final user. f90wrap has been used to wrap a number of large software packages of relevance to the condensed matter physics community, including the QUIP molecular dynamics code and the CASTEP density functional theory code.
C1 [Kermode, James R.] Univ Warwick, Sch Engn, Warwick Ctr Predict Modelling, Coventry CV4 7AL, W Midlands, England.
RP Kermode, JR (corresponding author), Univ Warwick, Sch Engn, Warwick Ctr Predict Modelling, Coventry CV4 7AL, W Midlands, England.
EM J.R.Kermode@warwick.ac.uk
RI Kermode, James R/O-6631-2014
OI Kermode, James R/0000-0001-6755-6271
FU CASTEP Developers Group; EPSRCUK Research & Innovation (UKRI)Engineering
& Physical Sciences Research Council (EPSRC) [EP/P002188/1,
EP/L014742/1, EP/J022055/1, EP/R043612/1]
FX I would like to thank the many contributors and users of the f90wrap
GitHub repository, as well as support and encouragement from mentors and
early users: Sandro De Vita, Gabor Csanyi and Noam Bernstein. In
particular, Tamas Stenzcel has made significant recent contributions to
f90wrap while working on the QUIP use case. The CasPyTep application has
benefited from support and encouragement from the CASTEP Developers
Group, in particular Phil Hasnip and Matt Probert. Greg Corbett carried
out the initial work on this use case, and Sebastian Potthoff added MPI
support to CasPyTep and optimised the performance of the nudged elastic
band algorithm. I acknowledge useful discussions with members of the UK
Car Parrinello Consortium, in particular David Bowler and Chris Skylaris
in addition to those listed above. This work was in part supported by
the EPSRC under grants EP/P002188/1, EP/L014742/1, EP/J022055/1 and
EP/R043612/1.
CR Abrahams D., 2003, C/C++ Users Journal, V21, P29
Aldegunde M, 2016, J COMPUT PHYS, V311, P173, DOI 10.1016/j.jcp.2016.01.034
ARCHER team 2019, 2019, ARCH APPL US
BADER RFW, 1985, ACCOUNTS CHEM RES, V18, P9, DOI 10.1021/ar00109a003
Bartok AP, 2010, PHYS REV LETT, V104, DOI 10.1103/PhysRevLett.104.136403
Beazley DM, 2003, FUTURE GENER COMP SY, V19, P599, DOI 10.1016/S0167-739X(02)00171-1
Bernstein N, 2009, REP PROG PHYS, V72, DOI 10.1088/0034-4885/72/2/026501
Bezanson J, 2017, SIAM REV, V59, P65, DOI 10.1137/141000671
Clark SJ, 2005, Z KRISTALLOGR, V220, P567, DOI 10.1524/zkri.220.5.567.65075
Corbett G, 2015, RUTHERFORD APPLETON
Csanyi G, 2007, IOP COMPUT PHYS NEWS
Ghiringhelli LM, 2017, NPJ COMPUT MATER, V3, DOI 10.1038/s41524-017-0048-5
Henkelman G, 2020, ORIGINAL VERSION BAD
Larsen AH, 2017, J PHYS-CONDENS MAT, V29, DOI 10.1088/1361-648X/aa680e
Hunter JD, 2007, COMPUT SCI ENG, V9, P90, DOI 10.1109/MCSE.2007.55
Jones E., 2001, SCIPY OPEN SOURCE SC
Kermode J, 2020, MODIFIED VERSION BAD
Kermode J, 2020, SOURCE CODE F90WRAP
Kermode J R, 2006, LIBATOMS QUIP GAP
Kluyver T, 2016, POSITIONING AND POWER IN ACADEMIC PUBLISHING: PLAYERS, AGENTS AND AGENDAS, P87, DOI 10.3233/978-1-61499-649-1-87
Lejaeghere K, 2016, SCIENCE, V351, DOI 10.1126/science.aad3000
libAtoms/QUIP collaboration, 2006, SOURC COD QUIP
Lu Y, 2019, J CHEM THEORY COMPUT, V15, P1317, DOI 10.1021/acs.jctc.8b01036
Mortensen JJ, 2005, PHYS REV B, V71, DOI 10.1103/PhysRevB.71.035109
Murray-Rust P, 2011, J CHEMINFORMATICS, V3, DOI 10.1186/1758-2946-3-44
Oliphant T.E., 2006, A GUIDE TO NUMPY
Oliphant TE, 2007, COMPUT SCI ENG, V9, P10, DOI 10.1109/MCSE.2007.58
Ousterhout JK, 1998, COMPUTER, V31, P23, DOI 10.1109/2.660187
Packwood D, 2016, J CHEM PHYS, V144, DOI 10.1063/1.4947024
Peterson P, 2009, INT J COMPUT SCI ENG, V4, P296, DOI 10.1504/IJCSE.2009.029165
Pletzer A, 2008, COMPUT SCI ENG, V10, P86, DOI 10.1109/MCSE.2008.94
Tang W, 2009, J PHYS-CONDENS MAT, V21, DOI 10.1088/0953-8984/21/8/084204
Togo A., 2018, ARXIV180801590
Togo A, 2015, SCRIPTA MATER, V108, P1, DOI 10.1016/j.scriptamat.2015.07.021
NR 34
TC 1
Z9 1
U1 1
U2 4
PU IOP PUBLISHING LTD
PI BRISTOL
PA TEMPLE CIRCUS, TEMPLE WAY, BRISTOL BS1 6BE, ENGLAND
SN 0953-8984
EI 1361-648X
J9 J PHYS-CONDENS MAT
JI J. Phys.-Condes. Matter
PD JUL 15
PY 2020
VL 32
IS 30
AR 305901
DI 10.1088/1361-648X/ab82d2
PG 6
WC Physics, Condensed Matter
SC Physics
GA LM5VE
UT WOS:000532316300001
PM 32209737
OA Other Gold, Green Published
DA 2021-04-21
ER
PT J
AU van den Oord, G
Jansson, F
Pelupessy, I
Chertova, M
Gronqvist, JH
Siebesma, P
Crommelin, D
AF van den Oord, Gijs
Jansson, Fredrik
Pelupessy, Inti
Chertova, Maria
Gronqvist, Johanna H.
Siebesma, Pier
Crommelin, Daan
TI A Python interface to the Dutch Atmospheric Large-Eddy Simulation
SO SOFTWAREX
LA English
DT Article
DE Large-eddy simulation; Atmospheric sciences
AB We present a Python interface for the Dutch Atmospheric Large Eddy Simulation (DALES), an existing Fortran code for high-resolution, turbulence-resolving simulation of atmospheric physics. The interface is based on an infrastructure for remote and parallel function calls and makes it possible to use and control the DALES weather simulations from a Python context. The interface is designed within the OMUSE framework, and allows the user to set up and control the simulation, apply perturbations and forcings, collect and analyse data in real time without exposing the user to the details of setting up and running the parallel Fortran DALES code. Another significant possibility is coupling the DALES simulation to other models, for example larger scale numerical weather prediction (NWP) models that can supply realistic lateral boundary conditions. Finally, the Python interface to DALES can serve as an educational tool for exploring weather dynamics, which we demonstrate with an example Jupyter notebook. (C) 2020 Netherlands eScience Center. Published by Elsevier B.V.
C1 [van den Oord, Gijs; Pelupessy, Inti; Chertova, Maria] Netherlands eSci Ctr, Sci Pk 140, NL-1098 XG Amsterdam, Netherlands.
[Jansson, Fredrik; Crommelin, Daan] Ctr Wiskunde & Informat, Sci Pk 123, NL-1098 XG Amsterdam, Netherlands.
[Gronqvist, Johanna H.] Abo Akad Univ, Fac Sci & Engn, Phys, Porthansgatan 3, Turku 20500, Finland.
[Siebesma, Pier] Delft Univ Appl Sci, Ctr Civil Engn & Geosci, Stevinweg 1, NL-2628 CN Delft, Netherlands.
[Siebesma, Pier] Koninklijk Nederlands Meteorol Inst, Utrechtseweg 297, NL-3731 GA De Bilt, Netherlands.
[Crommelin, Daan] Univ Amsterdam, Korteweg de Vries Inst Math, Sci Pk 105-107, NL-1098 XG Amsterdam, Netherlands.
RP van den Oord, G (corresponding author), Netherlands eSci Ctr, Sci Pk 140, NL-1098 XG Amsterdam, Netherlands.
EM g.vandenoord@esciencecenter.nl
FU Netherlands eScience Center (NLeSC) [027.015, G03]
FX This work was supported by the Netherlands eScience Center (NLeSC) under
grant no. 027.015.G03.
CR Behnel S, 2011, COMPUT SCI ENG, V13, P31, DOI 10.1109/MCSE.2010.118
Carver G, 2018, GMD UNPUB
Dubois P. F., 1996, Computers in Physics, V10, P262
Grabowski WW, 2001, J ATMOS SCI, V58, P978, DOI 10.1175/1520-0469(2001)058<0978:CCPWTL>2.0.CO;2
Heus T, 2010, GEOSCI MODEL DEV, V3, P415, DOI 10.5194/gmd-3-415-2010
Heus T, 2019, OVERVIEW ALL NAMOPTI
Jansson F, 2019, J ADV MODEL EARTH SY, V11, P2958, DOI 10.1029/2018MS001600
Kluyver T, 2016, POSITIONING AND POWER IN ACADEMIC PUBLISHING: PLAYERS, AGENTS AND AGENDAS, P87, DOI 10.3233/978-1-61499-649-1-87
Kurtzer GM, 2017, PLOS ONE, V12, DOI 10.1371/journal.pone.0177459
Larson J, 2005, INT J HIGH PERFORM C, V19, P277, DOI 10.1177/1094342005056115
Monteiro JM, 2018, GEOSCI MODEL DEV, V11, P3781, DOI 10.5194/gmd-11-3781-2018
Pelupessy I, 2019, LECT NOTES COMPUT SC, V11539, P379, DOI 10.1007/978-3-030-22747-0_29
Pelupessy I, 2017, GEOSCI MODEL DEV, V10, P3167, DOI 10.5194/gmd-10-3167-2017
Peterson P, 2009, INT J COMPUT SCI ENG, V4, P296, DOI 10.1504/IJCSE.2009.029165
Rose B. E. J., 2018, J OPEN SOURCE SOFTWA, V3, P659, DOI [10. 21105/joss. 00659, DOI 10.21105/JOSS.00659]
SCHMIDT H, 1989, J FLUID MECH, V200, P511, DOI 10.1017/S0022112089000753
Valcke S, 2013, GEOSCI MODEL DEV, V6, P373, DOI 10.5194/gmd-6-373-2013
Zwart SFP, 2013, COMPUT PHYS COMMUN, V184, P456, DOI 10.1016/j.cpc.2012.09.024
NR 18
TC 0
Z9 0
U1 0
U2 0
PU ELSEVIER
PI AMSTERDAM
PA RADARWEG 29, 1043 NX AMSTERDAM, NETHERLANDS
SN 2352-7110
J9 SOFTWAREX
JI SoftwareX
PD JUL-DEC
PY 2020
VL 12
AR 100608
DI 10.1016/j.softx.2020.100608
PG 6
WC Computer Science, Software Engineering
SC Computer Science
GA PH8TA
UT WOS:000600676600070
OA DOAJ Gold, Green Published
DA 2021-04-21
ER
PT J
AU Butenko, K
Bahls, C
Schroder, M
Kohling, R
van Rienenid, U
AF Butenko, Konstantin
Bahls, Christian
Schroeder, Max
Koehling, Ruediger
van Rienenid, Ursula
TI OSS-DBS: Open-source simulation platform for deep brain stimulation with
a comprehensive automated modeling
SO PLOS COMPUTATIONAL BIOLOGY
LA English
DT Article
ID PARKINSONS-DISEASE; FINITE-ELEMENT; TISSUE; VOLUME
AB In this study, we propose a new open-source simulation platform that comprises computer-aided design and computer-aided engineering tools for highly automated evaluation of electric field distribution and neural activation during Deep Brain Stimulation (DBS). It will be shown how a Volume Conductor Model (VCM) is constructed and examined using Python-controlled algorithms for generation, discretization and adaptive mesh refinement of the computational domain, as well as for incorporation of heterogeneous and anisotropic properties of the tissue and allocation of neuron models. The utilization of the platform is facilitated by a collection of predefined input setups and quick visualization routines. The accuracy of a VCM, created and optimized by the platform, was estimated by comparison with a commercial software. The results demonstrate no significant deviation between the models in the electric potential distribution. A qualitative estimation of different physics for the VCM shows an agreement with previous computational studies. The proposed computational platform is suitable for an accurate estimation of electric fields during DBS in scientific modeling studies. In future, we intend to acquire SDA and EMA approval. Successful incorporation of open-source software, controlled by in-house developed algorithms, provides a highly automated solution. The platform allows for optimization and uncertainty quantification (UQ) studies, while employment of the open-source software facilitates accessibility and reproducibility of simulations.
C1 [Butenko, Konstantin; Bahls, Christian; van Rienenid, Ursula] Univ Rostock, Inst Gen Elect Engn, Rostock, Germany.
[Schroeder, Max] Univ Rostock, Inst Commun Engn, Rostock, Germany.
[Koehling, Ruediger] Rostock Univ, Oscar Langendorff Inst Physiol, Med Ctr, Rostock, Germany.
[Koehling, Ruediger] Univ Rostock, Interdisciplinary Fac, Rostock, Germany.
[van Rienenid, Ursula] Univ Rostock, Dept Life Light & Matter, Rostock, Germany.
RP Butenko, K (corresponding author), Univ Rostock, Inst Gen Elect Engn, Rostock, Germany.
EM konstantin.butenko@uni-rostock.de
RI Kohling, Rudiger/K-8647-2013
OI Kohling, Rudiger/0000-0003-3330-4898; Schroder, Max/0000-0003-1522-494X;
van Rienen, Ursula/0000-0003-1042-2058; Bahls,
Christian/0000-0003-0511-0017
FU Deutsche Forschungsgemeinschaft (DFG, German Research Foundation)German
Research Foundation (DFG) [SFB 1270/1 - 299150580]
FX This work and the authors are funded by the Deutsche
Forschungsgemeinschaft (DFG, German Research Foundation) - SFB 1270/1 -
299150580. The funders had no role in study design, data collection and
analysis, decision to publish, or preparation of the manuscript.
CR Ahrens J, 2005, PARAVIEW END USER TO, V717
Alnaes M. S., 2015, ARCH NUMER SOFTW, V3, P9, DOI [10.11588/ans.2015.100.20553, DOI 10.11588/ANS.2015.100.20553]
Athawale TM, 2018, COMPUTER METHODS BIO, V7, P438
Bohme A, 2016, IEEE ENG MED BIO, P5821, DOI 10.1109/EMBC.2016.7592051
Butenko K, 2019, IEEE ENG MED BIO, P2136, DOI 10.1109/EMBC.2019.8857910
Butson CR, 2007, NEUROIMAGE, V34, P661, DOI 10.1016/j.neuroimage.2006.09.034
Butson CR, 2005, CLIN NEUROPHYSIOL, V116, P2490, DOI 10.1016/j.clinph.2005.06.023
Carnevale NT, 2006, NEURON BOOK
Felter W, 2015, INT SYM PERFORM ANAL, P171, DOI 10.1109/ISPASS.2015.7095802
Gabriel S, 1996, PHYS MED BIOL, V41, P2271, DOI 10.1088/0031-9155/41/11/003
Grant PF, 2010, IEEE T BIO-MED ENG, V57, P2386, DOI 10.1109/TBME.2010.2055054
Horn A, 2015, NEUROIMAGE, V107, P127, DOI 10.1016/j.neuroimage.2014.12.002
Howell B, 2017, BRAIN STIMUL, V10, P46, DOI 10.1016/j.brs.2016.09.001
Johnson MD, 2008, NEUROTHERAPEUTICS, V5, P294, DOI 10.1016/j.nurt.2008.01.010
Kom M, 2009, MED BIOL ENG COMPUT, V47, P21, DOI [10.1007/s11517-008-0411-2, DOI 10.1007/S11517-008-0411-2]
Krack P, 2019, MOVEMENT DISORD, V34, P1795, DOI 10.1002/mds.27860
Kuhn AA, 2008, J NEUROSCI, V28, P6165, DOI 10.1523/JNEUROSCI.0282-08.2008
MCADAMS ET, 1994, MED BIOL ENG COMPUT, V32, P126, DOI 10.1007/BF02518908
McIntyre CC, 2002, J NEUROPHYSIOL, V87, P995, DOI 10.1152/jn.00353.2001
Muller J, 2020, NEUROIMAGE-CLIN, V25, DOI 10.1016/j.nicl.2019.102135
Papp EA, 2014, NEUROIMAGE, V97, P374, DOI 10.1016/j.neuroimage.2014.04.001
Peterson EJ, 2011, J NEURAL ENG, V8, DOI 10.1088/1741-2560/8/4/046030
PLONSEY R, 1967, B MATH BIOPHYS, V29, P657, DOI 10.1007/BF02476917
Quinn EJ, 2015, MOVEMENT DISORD, V30, P1750, DOI 10.1002/mds.26376
Ribes A, 2007, P INT COMP SOFTW APP, P553
Rincon D, 2018, ADV ELECTROMAGN, V7, P46, DOI 10.7716/aem.v7i3.720
Rohlfing T, 2008, PROC SPIE, V6914, DOI 10.1117/12.770441
Schmidt C, 2018, IEEE T BIO-MED ENG, V65, P1828, DOI 10.1109/TBME.2017.2758324
Schmidt C, 2018, IEEE T NEUR SYS REH, V26, P281, DOI 10.1109/TNSRE.2016.2608925
Schmidt C, 2013, IEEE T BIO-MED ENG, V60, P1378, DOI 10.1109/TBME.2012.2235835
Schmidt C, 2012, IEEE T BIO-MED ENG, V59, P1583, DOI 10.1109/TBME.2012.2189885
Schoberl J., 1997, Computing and Visualization in Science, V1, P41, DOI 10.1007/s007910050004
Silberstein P, 2003, BRAIN, V126, P2597, DOI 10.1093/brain/awg267
Van Rienen U., 2001, NUMERICAL METHODS CO, P17
Vermaas M, 2020, NEUROINFORMATICS, V18, P569, DOI 10.1007/s12021-020-09458-8
NR 35
TC 2
Z9 2
U1 1
U2 2
PU PUBLIC LIBRARY SCIENCE
PI SAN FRANCISCO
PA 1160 BATTERY STREET, STE 100, SAN FRANCISCO, CA 94111 USA
SN 1553-734X
EI 1553-7358
J9 PLOS COMPUT BIOL
JI PLoS Comput. Biol.
PD JUL
PY 2020
VL 16
IS 7
AR e1008023
DI 10.1371/journal.pcbi.1008023
PG 18
WC Biochemical Research Methods; Mathematical & Computational Biology
SC Biochemistry & Molecular Biology; Mathematical & Computational Biology
GA MY0AE
UT WOS:000558078100038
PM 32628719
OA DOAJ Gold, Green Published
DA 2021-04-21
ER
PT J
AU Lucsanyi, D
Prod'homme, T
AF Lucsanyi, David
Prod'homme, Thibaut
TI Simulating Charge Deposition by Cosmic Rays Inside Astronomical Imaging
Detectors
SO IEEE TRANSACTIONS ON NUCLEAR SCIENCE
LA English
DT Article; Proceedings Paper
CT Conference on Radiation and its Effects on Components and Systems
(RADECS)
CY SEP 16-20, 2019
CL Montpellier, FRANCE
DE Cosmic rays; Charge coupled devices; Protons; Detectors; Radiation
effects; Monte Carlo methods; Silicon; Charge-coupled device (CCD);
charge deposition; cosmic ray (CR) imaging; Gaia; galactic CRs (GCRs);
Geant4 (G4); Monte Carlo methods; PLATO
ID GEANT4 PHYSICS PROCESSES; ENERGY ELECTROMAGNETIC MODELS
AB In the context of space astronomy missions, accurate and fast modeling of spurious images generated by solar and galactic cosmic rays (GCRs) in imaging detectors is critical in order to assess and mitigate their detrimental effects. Currently, it is only possible to accurately reproduce the CR images including energy loss straggling, with the use of complex Monte Carlo particle transport codes. However, these codes are difficult to use and cannot be easily included in instrument simulation pipelines. Presented here is CosmiX, a novel, open-source, and Python-based CR model, overcoming the above-mentioned limitations, yet still capable of accurately reproducing the CR images, paving the way for semianalytical Monte Carlo modeling of the CR tracks. These are demonstrated by using CosmiX to reproduce irradiation test data of a PLATO charge-coupled device (CCD) and in-orbit CR data of Gaia CCD detectors.
C1 [Lucsanyi, David; Prod'homme, Thibaut] European Space Agcy, Estec, NL-2201 AZ Noordwijk, Netherlands.
RP Prod'homme, T (corresponding author), European Space Agcy, Estec, NL-2201 AZ Noordwijk, Netherlands.
EM thibaut.prodhomme@esa.int
FU European Space Agency (ESA)European Space Agency
FX This work was supported by the European Space Agency (ESA).
CR Allison J, 2016, NUCL INSTRUM METH A, V835, P186, DOI 10.1016/j.nima.2016.06.125
Apostolakis J, 2015, J PHYS CONF SER, V664, DOI 10.1088/1742-6596/664/7/072021
Crowley C, 2016, ASTRON ASTROPHYS, V595, DOI 10.1051/0004-6361/201628990
de Bruijne JHJ, 2015, ASTRON ASTROPHYS, V576, DOI 10.1051/0004-6361/201424018
Fang JT, 2019, IEEE T NUCL SCI, V66, P444, DOI 10.1109/TNS.2018.2879593
Garcia L, 2018, PROC SPIE, V10709, DOI 10.1117/12.2314090
Geant4 Collaboration, 2018, GEANT4 DOC EL PHYS C
Giardino G, 2019, PUBL ASTRON SOC PAC, V131, DOI 10.1088/1538-3873/ab2fd6
Kirsch C. T., 2018, THESIS
Kohley R., 2015, GAIADETNESACRKO033
Kohley R, 2014, PROC SPIE, V9154, DOI 10.1117/12.2056420
Lucsanyi D, 2018, PROC SPIE, V10709, DOI 10.1117/12.2314047
Marcos-Arenal P, 2014, ASTRON ASTROPHYS, V566, DOI 10.1051/0004-6361/201323304
Meroli S, 2011, J INSTRUM, V6, DOI 10.1088/1748-0221/6/06/P06013
Niemi S.-M., 2015, EUCLID VISIBLE INSTR
Pierron J, 2017, IEEE T NUCL SCI, V64, P2340, DOI 10.1109/TNS.2017.2662220
Prod'homme T, 2019, IEEE T NUCL SCI, V66, P134, DOI 10.1109/TNS.2018.2886029
Prod'homme T, 2018, PROC SPIE, V10709, DOI 10.1117/12.2314077
Puig L, 2018, EXP ASTRON, V46, P211, DOI 10.1007/s10686-018-9604-3
Raine M, 2014, NUCL INSTRUM METH B, V325, P97, DOI 10.1016/j.nimb.2014.01.014
Santin G, 2005, IEEE T NUCL SCI, V52, P2294, DOI 10.1109/TNS.2005.860749
Tylka AJ, 1997, IEEE T NUCL SCI, V44, P2150, DOI 10.1109/23.659030
Valentin A, 2012, NUCL INSTRUM METH B, V288, P66, DOI 10.1016/j.nimb.2012.07.028
Valentin A, 2012, NUCL INSTRUM METH B, V287, P124, DOI 10.1016/j.nimb.2012.06.007
van Dokkum PG, 2001, PUBL ASTRON SOC PAC, V113, P1420, DOI 10.1086/323894
NR 25
TC 0
Z9 0
U1 1
U2 1
PU IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
PI PISCATAWAY
PA 445 HOES LANE, PISCATAWAY, NJ 08855-4141 USA
SN 0018-9499
EI 1558-1578
J9 IEEE T NUCL SCI
JI IEEE Trans. Nucl. Sci.
PD JUL
PY 2020
VL 67
IS 7
BP 1623
EP 1628
DI 10.1109/TNS.2020.2986285
PG 6
WC Engineering, Electrical & Electronic; Nuclear Science & Technology
SC Engineering; Nuclear Science & Technology
GA MN2IM
UT WOS:000550669800052
DA 2021-04-21
ER
PT J
AU Bujila, R
Omar, A
Poludniowski, G
AF Bujila, Robert
Omar, Artur
Poludniowski, Gavin
TI A validation of SpekPy: A software toolkit for modelling X-ray tube
spectra
SO PHYSICA MEDICA-EUROPEAN JOURNAL OF MEDICAL PHYSICS
LA English
DT Article
DE Radiology; X-ray; Production of X-rays; Radiation dosimetry
ID MONTE-CARLO-SIMULATION; COMPUTATIONAL TOOL
AB Purpose: To validate the SpekPy software toolkit that has been developed to estimate the spectra emitted from tungsten anode X-ray tubes. The model underlying the toolkit introduces improvements upon a well-known semi -empirical model of X-ray emission. Materials and methods: Using the same theoretical framework as the widely -used SpekCalc software, new elec- tron penetration data was simulated using the Monte Carlo (MC) method, alternative bremsstrahlung cross - sections were applied, L -line characteristic emissions were included, and improvements to numerical methods implemented. The SpekPy toolkit was developed with the Python programming language. The toolkit was va- lidated against other popular X-ray spectrum models (50 to 120 kVp), X-ray spectra estimated with MC (30 to 150 kVp) as well as reference half value layers (HVL) associated with numerous radiation qualities from standard laboratories (20 to 300 kVp). Results: T he toolkit can be used to estimate X-ray spectra that agree with other popular X-ray spectrum models for typical configurations in diagnostic radiology as well as with MC spectra over a wider range of conditions. The improvements over SpekCalc are most evident at lower incident electron energies for lightly and moderately filtered radiation qualities. Using the toolkit, estimations of the HVL over a large range of standard radiation qualities closely match reference values. Conclusions: A toolkit to estimate X-ray spectra has been developed and extensively validated for central -axis spectra. This toolkit can provide those working in Medical Physics and beyond with a powerful and user-friendly way of estimating spectra from X-ray tubes.
C1 [Bujila, Robert; Omar, Artur; Poludniowski, Gavin] Karolinska Univ Hosp, Med Radiat Phys & Nucl Med, Stockholm, Sweden.
[Bujila, Robert] Royal Inst Technol, Dept Phys, Stockholm, Sweden.
[Omar, Artur; Poludniowski, Gavin] Karolinska Inst, Dept Oncol Pathol, Stockholm, Sweden.
RP Bujila, R (corresponding author), Karolinska Univ Hosp, Med Radiat Phys & Nucl Med, Stockholm, Sweden.
EM work.robert.bujila@gmail.com
OI Omar, Artur/0000-0002-2643-7994
CR Ali ESM, 2008, MED PHYS, V35, P4149, DOI 10.1118/1.2966348
Ay MR, 2004, PHYS MED BIOL, V49, P4897, DOI 10.1088/0031-9155/49/21/004
Bazalova M, 2007, PHYS MED BIOL, V52, P5945, DOI 10.1088/0031-9155/52/19/015
Berger M.J., 2010, XCOM PHOTON CROSS SE
BIRCH R, 1979, PHYS MED BIOL, V24, P505, DOI 10.1088/0031-9155/24/3/002
Birch RM, 1979, REPORT SERIES, V30
Boone JM, 1997, MED PHYS, V24, P1661, DOI 10.1118/1.597953
Carlson T., 2013, PHOTOELECTRON AUGER
Cranley K, 1997, 78 IPEM
Deslattes RD, 2003, REV MOD PHYS, V75, P35, DOI 10.1103/RevModPhys.75.35
ESTAR N, 2009, EST STOPP POW RANG T
Hernandez AM, 2014, MED PHYS, V41, DOI 10.1118/1.4866216
Hernandez G, 2016, MED PHYS, V43, P4655, DOI 10.1118/1.4955120
ICRU, 2003, J ICRU
ICRU, 2012, J ICRU, V12
ICRU, 2009, J ICRU, V9
ICRU, 2005, J ICRU, V5
Kawrakow I, 2000, PIRS701 NRC
Landry G, 2013, FRONT PHYS, V1, DOI 10.3389/fphy.2013.00022
Ma CM, 1995, PIRS0509D NRC
Oden J, 2018, PHYS MEDICA, V47, P42, DOI 10.1016/j.ejmp.2018.02.016
Omar A, 2018, RADIAT PHYS CHEM, V148, P73, DOI 10.1016/j.radphyschem.2018.02.009
PEAPLE LHJ, 1969, PHYS MED BIOL, V14, P73, DOI 10.1088/0031-9155/14/1/005
Perkins S.T, 1991, TABLES GRAPHS ATOMIC
Persson M, 2016, MED PHYS, V43, P4398, DOI 10.1118/1.4954008
Poludniowski G, 2009, PHYS MED BIOL, V54, pN433, DOI 10.1088/0031-9155/54/19/N01
Poludniowski G., 2017, HDB XRAY IMAGING PHY, P185
Poludniowski GG, 2007, MED PHYS, V34, P2164, DOI 10.1118/1.2734725
Poludniowski GG, 2007, MED PHYS, V34, P2175, DOI 10.1118/1.2734726
Punnoose J, 2016, MED PHYS, V43, P4711, DOI 10.1118/1.4955438
Rogers D, 2009, NRC REPORT PIRS, V509, P12
Salvat F, 2006, PENELOPE 2008 CODE S, P7
SELTZER SM, 1985, NUCL INSTRUM METH B, V12, P95, DOI 10.1016/0168-583X(85)90707-4
Siewerdsen JH, 2004, MED PHYS, V31, P3057, DOI 10.1118/1.1758350
WAGGENER RG, 1972, RADIOLOGY, V105, P169, DOI 10.1148/105.1.169
NR 35
TC 5
Z9 5
U1 1
U2 3
PU ELSEVIER SCI LTD
PI OXFORD
PA THE BOULEVARD, LANGFORD LANE, KIDLINGTON, OXFORD OX5 1GB, OXON, ENGLAND
SN 1120-1797
EI 1724-191X
J9 PHYS MEDICA
JI Phys. Medica
PD JUL
PY 2020
VL 75
BP 44
EP 54
DI 10.1016/j.ejmp.2020.04.026
PG 11
WC Radiology, Nuclear Medicine & Medical Imaging
SC Radiology, Nuclear Medicine & Medical Imaging
GA MD6AH
UT WOS:000544053500007
PM 32512239
DA 2021-04-21
ER
PT J
AU Minganti, F
Miranowicz, A
Chhajlany, RW
Arkhipov, II
Nori, F
AF Minganti, Fabrizio
Miranowicz, Adam
Chhajlany, Ravindra W.
Arkhipov, Ievgen I.
Nori, Franco
TI Hybrid-Liouvillian formalism connecting exceptional points of
non-Hermitian Hamiltonians and Liouvillians via postselection of quantum
trajectories
SO PHYSICAL REVIEW A
LA English
DT Article
ID PYTHON FRAMEWORK; PHOTON; JUMPS; STATES; DYNAMICS; QUTIP
AB Exceptional points (EPs) are degeneracies of classical and quantum open systems, which are studied in many areas of physics including optics, optoelectronics, plasmonics, and condensed matter physics. In the semiclassical regime, open systems can be described by phenomenological effective non-Hermitian Hamiltonians (NHHs) capturing the effects of gain and loss in terms of imaginary fields. The EPs that characterize the spectra of such Hamiltonians (HEPs) describe the time evolution of a system without quantum jumps. It is well known that a full quantum treatment describing more generic dynamics must crucially take into account such quantum jumps. In a recent paper [F. Minganti et al., Phys. Rev. A 100, 062131 (2019)], we generalized the notion of EPs to the spectra of Liouvillian superoperators governing open system dynamics described by Lindblad master equations. Intriguingly, we found that in situations where a classical-to-quantum correspondence exists, the two types of dynamics can yield different EPs. In a recent experimental work [M. Naghiloo et al., Nat. Phys. 15, 1232 (2019)], it was shown that one can engineer a non-Hermitian Hamiltonian in the quantum limit by postselecting on certain quantum jump trajectories. This raises an interesting question concerning the relation between Hamiltonian and Lindbladian EPs, and quantum trajectories. We discuss these connections by introducing a hybrid-Liouvillian superoperator, capable of describing the passage from an NHH (when one postselects only those trajectories without quantum jumps) to a true Liouvillian including quantum jumps (without postselection). Beyond its fundamental interest, our approach allows to intuitively relate the effects of postselection and finite-efficiency detectors.
C1 [Minganti, Fabrizio; Miranowicz, Adam; Chhajlany, Ravindra W.; Nori, Franco] RIKEN Cluster Pioneering Res, Theoret Quantum Phys Lab, Wako, Saitama 3510198, Japan.
[Miranowicz, Adam; Chhajlany, Ravindra W.] Adam Mickiewicz Univ, Fac Phys, PL-61614 Poznan, Poland.
[Arkhipov, Ievgen I.] Palacky Univ, Joint Lab Opt, Olomouc 77146, Czech Republic.
[Arkhipov, Ievgen I.] Palacky Univ, Fac Sci, CAS, Inst Phys, Olomouc 77146, Czech Republic.
[Nori, Franco] Univ Michigan, Dept Phys, Ann Arbor, MI 48109 USA.
RP Minganti, F (corresponding author), RIKEN Cluster Pioneering Res, Theoret Quantum Phys Lab, Wako, Saitama 3510198, Japan.
EM fabrizio.minganti@riken.jp; adam@riken.jp; ravi@amu.edu.pl;
ievgen.arkhipov@upol.cz; fnori@riken.jp
RI ARKHIPOV, Ievgen I./A-9602-2017; Minganti, Fabrizio/AAX-4108-2020;
Miranowicz, Adam/C-1481-2009; Nori, Franco/B-1222-2009
OI ARKHIPOV, Ievgen I./0000-0001-6547-8855; Minganti,
Fabrizio/0000-0003-4850-1130; Chhajlany, Ravindra/0000-0003-1069-7924;
Nori, Franco/0000-0003-3682-7432; Miranowicz, Adam/0000-0002-8222-9268
FU FY2018 JSPS Postdoctoral Fellowship for Research in Japan; Polish
National Science Centre (NCN) under the Maestro Grant
[DEC-2019/34/A/ST2/00081]; Grant Agency of the Czech RepublicGrant
Agency of the Czech Republic [18-08874S]; Ministry of Education, Youth
and Sports of the Czech RepublicMinistry of Education, Youth & Sports -
Czech Republic [CZ.02.1.01/0.0/0.0/16_019/0000754]; NTT Research, Army
Research Office (ARO) [W911NF-18-1-0358]; Japan Science and Technology
Agency (JST) (CREST Grant) [JPMJCR1676]; Japan Society for the Promotion
of Science (JSPS) (KAKENHI)Ministry of Education, Culture, Sports,
Science and Technology, Japan (MEXT)Japan Society for the Promotion of
ScienceGrants-in-Aid for Scientific Research (KAKENHI) [JP20H00134];
Japan Society for the Promotion of Science (JSPS) (JSPS-RFBR
Grant)Ministry of Education, Culture, Sports, Science and Technology,
Japan (MEXT)Japan Society for the Promotion of Science
[JPJSBP120194828]; Japan Society for the Promotion of Science (JSPS)
(JSPS-FWO Grant)Ministry of Education, Culture, Sports, Science and
Technology, Japan (MEXT)Japan Society for the Promotion of Science
[VS.059.18N]; Foundational Questions Institute Fund (FQXi) of the
Silicon Valley Community Foundation [FQXi-IAF19-06]
FX The authors acknowledge a discussion with M. Schiro and comments from
Jan Wiersig. F.M. was supported by the FY2018 JSPS Postdoctoral
Fellowship for Research in Japan. A.M. and R.C. were supported by the
Polish National Science Centre (NCN) under the Maestro Grant No.
DEC-2019/34/A/ST2/00081. I.A. thanks the Grant Agency of the Czech
Republic (Project No. 18-08874S) and the Project No.
CZ.02.1.01/0.0/0.0/16_019/0000754 of the Ministry of Education, Youth
and Sports of the Czech Republic. F.N. is supported in part by NTT
Research, Army Research Office (ARO) (Grant No. W911NF-18-1-0358), Japan
Science and Technology Agency (JST) (via the CREST Grant No.
JPMJCR1676), Japan Society for the Promotion of Science (JSPS) (via the
KAKENHI Grant No. JP20H00134, JSPS-RFBR Grant No. JPJSBP120194828, and
JSPS-FWO Grant No. VS.059.18N), and Grant No. FQXi-IAF19-06 from the
Foundational Questions Institute Fund (FQXi), a donor advised fund of
the Silicon Valley Community Foundation.
CR Albert V. V., 2017, THESIS
Albert VV, 2014, PHYS REV A, V89, DOI 10.1103/PhysRevA.89.022118
Arkhipov II, 2020, PHYS REV A, V101, DOI 10.1103/PhysRevA.101.013812
Avila BJ, 2020, SCI REP-UK, V10, DOI 10.1038/s41598-020-58582-7
Barnett S. M., 2009, QUANTUM INFORM
Bartolo N, 2017, EUR PHYS J-SPEC TOP, V226, P2705, DOI 10.1140/epjst/e2016-60385-8
BASCHE T, 1995, NATURE, V373, P132, DOI 10.1038/373132a0
Bender CM, 1998, PHYS REV LETT, V80, P5243, DOI 10.1103/PhysRevLett.80.5243
BERGQUIST JC, 1986, PHYS REV LETT, V57, P1699, DOI 10.1103/PhysRevLett.57.1699
Breuer H.-P., 2007, THEORY OPEN QUANTUM
Carmichael H. J., 2007, STAT METHODS QUANTUM
CARMICHAEL HJ, 1993, PHYS REV LETT, V70, P2273, DOI 10.1103/PhysRevLett.70.2273
Carmichael HJ., 1999, STAT METHODS QUANTUM
Chen C, 2019, NEW J PHYS, V21, DOI 10.1088/1367-2630/ab32ab
Chen PY, 2018, NAT ELECTRON, V1, P297, DOI 10.1038/s41928-018-0072-6
Chen WJ, 2017, NATURE, V548, P192, DOI 10.1038/nature23281
Daley AJ, 2014, ADV PHYS, V63, P77, DOI 10.1080/00018732.2014.933502
DALIBARD J, 1992, PHYS REV LETT, V68, P580, DOI 10.1103/PhysRevLett.68.580
Deleglise S, 2008, NATURE, V455, P510, DOI 10.1038/nature07288
Gao T, 2015, NATURE, V526, P554, DOI 10.1038/nature15522
Gardiner C., 2004, QUANTUM NOISE HDB MA
Garrahan JP, 2009, J PHYS A-MATH THEOR, V42, DOI 10.1088/1751-8113/42/7/075007
Gleyzes S, 2007, NATURE, V446, P297, DOI 10.1038/nature05589
Gneiting C., ARXIV200108929
Guerlin C, 2007, NATURE, V448, P889, DOI 10.1038/nature06057
Haroche S., 2006, EXPLORING QUANTUM AT
Hatano N, 2019, MOL PHYS, V117, P2121, DOI 10.1080/00268976.2019.1593535
Hatridge M, 2013, SCIENCE, V339, P178, DOI 10.1126/science.1226897
Hodaei H, 2017, NATURE, V548, P187, DOI 10.1038/nature23280
Huang R., ARXIV200109492
Jelezko F, 2002, APPL PHYS LETT, V81, P2160, DOI 10.1063/1.1507838
Jin JS, 2018, PHYS REV B, V98, DOI 10.1103/PhysRevB.98.241108
Jing H, 2014, PHYS REV LETT, V113, DOI 10.1103/PhysRevLett.113.053604
Johansson JR, 2013, COMPUT PHYS COMMUN, V184, P1234, DOI 10.1016/j.cpc.2012.11.019
Johansson JR, 2012, COMPUT PHYS COMMUN, V183, P1760, DOI 10.1016/j.cpc.2012.02.021
Kuo PC, 2020, PHYS REV A, V101, DOI 10.1103/PhysRevA.101.013814
Landa H, 2020, PHYS REV LETT, V124, DOI 10.1103/PhysRevLett.124.043601
Langbein W, 2018, PHYS REV A, V98, DOI 10.1103/PhysRevA.98.023805
Lau HK, 2018, NAT COMMUN, V9, DOI 10.1038/s41467-018-06477-7
Lidar D. A., ARXIV190200967
Liu ZP, 2016, PHYS REV LETT, V117, DOI 10.1103/PhysRevLett.117.110802
Macieszczak K, 2016, PHYS REV LETT, V116, DOI 10.1103/PhysRevLett.116.240404
Macieszczak K, 2016, PHYS REV A, V93, DOI 10.1103/PhysRevA.93.022103
Mathisen T, 2018, ENTROPY-SWITZ, V20, DOI 10.3390/e20010020
Milburn G.J., 2010, QUANTUM MEASUREMENT
Minev ZK, 2019, NATURE, V570, P200, DOI 10.1038/s41586-019-1287-z
Minganti F, 2019, PHYS REV A, V100, DOI 10.1103/PhysRevA.100.062131
Minganti F, 2018, PHYS REV A, V98, DOI 10.1103/PhysRevA.98.042118
Miri MA, 2019, SCIENCE, V363, P42, DOI 10.1126/science.aar7709
MOLMER K, 1993, J OPT SOC AM B, V10, P524, DOI 10.1364/JOSAB.10.000524
Mortensen NA, 2018, OPTICA, V5, P1342, DOI 10.1364/OPTICA.5.001342
Naghiloo M, 2019, NAT PHYS, V15, P1232, DOI 10.1038/s41567-019-0652-z
NAGOURNEY W, 1986, PHYS REV LETT, V56, P2797, DOI 10.1103/PhysRevLett.56.2797
Neumann P, 2010, SCIENCE, V329, P542, DOI 10.1126/science.1189075
Ofek N, 2016, NATURE, V536, P441, DOI 10.1038/nature18949
Ozdemir SK, 2019, NAT MATER, V18, P783, DOI 10.1038/s41563-019-0304-9
Paris MGA, 2012, EUR PHYS J-SPEC TOP, V203, P61, DOI 10.1140/epjst/e2012-01535-1
Peil S, 1999, PHYS REV LETT, V83, P1287, DOI 10.1103/PhysRevLett.83.1287
Peng B, 2014, SCIENCE, V346, P328, DOI 10.1126/science.1258004
Peng B, 2014, NAT PHYS, V10, P394, DOI [10.1038/NPHYS2927, 10.1038/nphys2927]
Perina J, 2019, PHYS REV A, V100, DOI 10.1103/PhysRevA.100.053820
Prosen T, 2010, J STAT MECH-THEORY E, DOI 10.1088/1742-5468/2010/07/P07020
Ren J, 2017, OPT LETT, V42, P1556, DOI 10.1364/OL.42.001556
Robledo L, 2011, NATURE, V477, P574, DOI 10.1038/nature10401
Rose DC, 2016, PHYS REV E, V94, DOI 10.1103/PhysRevE.94.052132
Rota R, 2018, NEW J PHYS, V20, DOI 10.1088/1367-2630/aab703
Sarandy MS, 2005, PHYS REV A, V71, DOI 10.1103/PhysRevA.71.012331
SAUTER T, 1986, PHYS REV LETT, V57, P1696, DOI 10.1103/PhysRevLett.57.1696
Sayrin C, 2011, NATURE, V477, P73, DOI 10.1038/nature10376
Sun L, 2014, NATURE, V511, P444, DOI 10.1038/nature13436
van Caspel MT, 2019, SCIPOST PHYS, V6, DOI 10.21468/SciPostPhys.6.2.026
Vicentini F, 2018, PHYS REV A, V97, DOI 10.1103/PhysRevA.97.013853
Vijay R, 2011, PHYS REV LETT, V106, DOI 10.1103/PhysRevLett.106.110502
Wiersig J, 2020, PHYS REV A, V101, DOI 10.1103/PhysRevA.101.053846
Wiersig J, 2016, PHYS REV A, V93, DOI 10.1103/PhysRevA.93.033809
Wiersig J, 2014, PHYS REV LETT, V112, DOI 10.1103/PhysRevLett.112.203901
Wolff C, 2019, NANOPHOTONICS-BERLIN, V8, P1319, DOI 10.1515/nanoph-2019-0036
Yang, 2018, PARITY TIME SYMMETRY
Zhang J, 2018, NAT PHOTONICS, V12, P479, DOI 10.1038/s41566-018-0213-5
Zhang MZ, 2019, PHYS REV LETT, V123, DOI 10.1103/PhysRevLett.123.180501
Zhang N, 2015, SCI REP-UK, V5, DOI 10.1038/srep11912
Zurek WH, 2003, REV MOD PHYS, V75, P715, DOI 10.1103/RevModPhys.75.715
NR 82
TC 6
Z9 6
U1 6
U2 8
PU AMER PHYSICAL SOC
PI COLLEGE PK
PA ONE PHYSICS ELLIPSE, COLLEGE PK, MD 20740-3844 USA
SN 1050-2947
EI 1094-1622
J9 PHYS REV A
JI Phys. Rev. A
PD JUN 24
PY 2020
VL 101
IS 6
AR 062112
DI 10.1103/PhysRevA.101.062112
PG 14
WC Optics; Physics, Atomic, Molecular & Chemical
SC Optics; Physics
GA MJ5OR
UT WOS:000548140000007
DA 2021-04-21
ER
PT J
AU Philcox, OHE
Spergel, DN
Villaescusa-Navarro, F
AF Philcox, Oliver H. E.
Spergel, David N.
Villaescusa-Navarro, Francisco
TI Effective halo model: Creating a physical and accurate model of the
matter power spectrum and cluster counts
SO PHYSICAL REVIEW D
LA English
DT Article
ID LARGE-SCALE STRUCTURE; COVARIANCE-MATRIX; NEUTRAL HYDROGEN; ANALYTIC
MODEL; BIAS; SIMULATION; UNIVERSE; GALAXY
AB We introduce a physically motivated model of the matter power spectrum, based on the halo model and perturbation theory. This model achieves 1% accuracy on all k-scales between k = 0.02h Mpc(-1) to k = 1h Mpc(-1). Our key ansatz is that the number density of halos depends on the nonlinear density contrast filtered on some unknown scale R. Using the effective field theory of large scale structure to evaluate the two-halo term, we obtain a model for the power spectrum with only two fitting parameters: R and the effective "sound speed," which encapsulates small-scale physics. This is tested with two suites of cosmological simulations across a broad range of cosmologies and found to be highly accurate. Due to its physical motivation, the statistics can be easily extended beyond the power spectrum; we additionally derive the one-loop covariance matrices of cluster counts and their combination with the matter power spectrum. This yields a significantly better fit to simulations than previous models, and includes a new model for supersample effects, which is rigorously tested with separate universe simulations. At low redshift, we find a significant (similar to 10%) exclusion covariance from accounting for the finite size of halos which has not previously been modeled. Such power spectrum and covariance models will enable joint analysis of upcoming large-scale structure surveys, gravitational lensing surveys, and cosmic microwave background maps on scales down to the nonlinear scale. We provide a publicly released Python code.
C1 [Philcox, Oliver H. E.; Spergel, David N.; Villaescusa-Navarro, Francisco] Princeton Univ, Dept Astrophys Sci, Princeton, NJ 08544 USA.
[Spergel, David N.; Villaescusa-Navarro, Francisco] Flatiron Inst, Ctr Computat Astrophys, 162 Fifth Ave, New York, NY 10010 USA.
RP Philcox, OHE (corresponding author), Princeton Univ, Dept Astrophys Sci, Princeton, NJ 08544 USA.
EM ohep2@cantab.ac.uk
RI Philcox, Oliver/ABE-4244-2020
OI Philcox, Oliver/0000-0002-3033-9932
FU WFIRST program [NNG26PJ30C, NNN12AA01C]; Simons Foundation
FX We thank Jo Dunkley, Yin Li, Andrina Nicola, Fabian Schmidt, Marko
Simonovic, and Matias Zaldarriaga for useful discussions. We
additionally thank Colin Hill, Mikhail Ivanov, Leonardo Senatore,
Emmanuel Schaan, Marcel Schmittfull, Uros Seljak, Masahiro Takada, and
Ben Wandelt for comments on a draft of this paper. Furthermore, the
authors acknowledge insightful and detailed feedback from the anonymous
referee. O. H. E. P. and F. A. V.-N. acknowledge funding from the WFIRST
program through grants No. NNG26PJ30C and No. NNN12AA01C. The Flatiron
Institute is supported by the Simons Foundation.
CR Ade PAR, 2014, ASTRON ASTROPHYS, V571, DOI 10.1051/0004-6361/201321591
Angulo R, 2015, J COSMOL ASTROPART P, DOI 10.1088/1475-7516/2015/09/029
Assassi V, 2014, J COSMOL ASTROPART P, DOI 10.1088/1475-7516/2014/08/056
Baldauf T, 2016, J COSMOL ASTROPART P, DOI 10.1088/1475-7516/2016/03/007
Baldauf T, 2015, PHYS REV D, V92, DOI 10.1103/PhysRevD.92.123007
Baldauf T, 2015, PHYS REV D, V92, DOI 10.1103/PhysRevD.92.043514
Baldauf T, 2013, PHYS REV D, V88, DOI 10.1103/PhysRevD.88.083507
Baldauf T, 2012, PHYS REV D, V86, DOI 10.1103/PhysRevD.86.083540
Baldauf T, 2011, J COSMOL ASTROPART P, DOI 10.1088/1475-7516/2011/10/031
Baumann D, 2012, J COSMOL ASTROPART P, DOI 10.1088/1475-7516/2012/07/051
Bernardeau F, 2002, PHYS REP, V367, P1, DOI 10.1016/S0370-1573(02)00135-7
Bhattacharya S, 2011, ASTROPHYS J, V732, DOI 10.1088/0004-637X/732/2/122
Blas D, 2016, J COSMOL ASTROPART P, DOI 10.1088/1475-7516/2016/07/052
Blas D, 2016, J COSMOL ASTROPART P, DOI 10.1088/1475-7516/2016/07/028
Carrasco JJM, 2014, J COSMOL ASTROPART P, DOI 10.1088/1475-7516/2014/07/056
Carrasco JJM, 2014, J COSMOL ASTROPART P, DOI 10.1088/1475-7516/2014/07/057
Carrasco JJM, 2012, J HIGH ENERGY PHYS, DOI 10.1007/JHEP09(2012)082
Castorina E, 2017, MON NOT R ASTRON SOC, V471, P1788, DOI 10.1093/mnras/stx1599
Chen AY, 2020, PHYS REV D, V101, DOI 10.1103/PhysRevD.101.103522
Chiang CT, 2014, J COSMOL ASTROPART P, DOI 10.1088/1475-7516/2014/05/048
Chisari N.E., 2019, OPEN J ASTROPHYS, V2, P4, DOI DOI 10.21105/ASTRO.1905.06082
Chudaykin A, 2019, J COSMOL ASTROPART P, DOI 10.1088/1475-7516/2019/11/034
Cooray A, 2000, ASTROPHYS J, V535, pL9, DOI 10.1086/312696
Cooray A, 2002, PHYS REP, V372, P1, DOI 10.1016/S0370-1573(02)00276-4
Cooray A, 2001, ASTROPHYS J, V554, P56, DOI 10.1086/321376
Crocce M, 2010, MON NOT R ASTRON SOC, V403, P1353, DOI 10.1111/j.1365-2966.2009.16194.x
Desjacques V, 2018, PHYS REP, V733, P1, DOI 10.1016/j.physrep.2017.12.002
Duffy AR, 2010, MON NOT R ASTRON SOC, V405, P2161, DOI 10.1111/j.1365-2966.2010.16613.x
Fang WJ, 2012, PHYS REV D, V85, DOI 10.1103/PhysRevD.85.023007
FELDMAN HA, 1994, ASTROPHYS J, V426, P23, DOI 10.1086/174036
Feng C, 2017, ASTROPHYS J, V846, DOI 10.3847/1538-4357/aa7ff1
Foreman S, 2016, J COSMOL ASTROPART P, DOI 10.1088/1475-7516/2016/04/033
Fujita T, 2020, J COSMOL ASTROPART P, DOI 10.1088/1475-7516/2020/01/009
Garrison LH, 2019, MON NOT R ASTRON SOC, V485, P3370, DOI 10.1093/mnras/stz634
Garrison LH, 2018, ASTROPHYS J SUPPL S, V236, DOI 10.3847/1538-4365/aabfd3
Ginzburg D, 2017, PHYS REV D, V96, DOI 10.1103/PhysRevD.96.083528
Giocoli C, 2017, MON NOT R ASTRON SOC, V470, P3574, DOI 10.1093/mnras/stx1399
Hamann J, 2010, J COSMOL ASTROPART P, DOI 10.1088/1475-7516/2010/07/022
Hamaus N, 2014, PHYS REV LETT, V112, DOI 10.1103/PhysRevLett.112.041304
Hand N., 2019, NBODYKIT MASSIVELY P
Hand N, 2017, J COSMOL ASTROPART P, DOI 10.1088/1475-7516/2017/10/009
Hill JC, 2013, PHYS REV D, V88, DOI 10.1103/PhysRevD.88.063526
HUCHRA JP, 1982, ASTROPHYS J, V257, P423, DOI 10.1086/160000
Hurier G, 2017, ASTRON ASTROPHYS, V604, DOI 10.1051/0004-6361/201630041
Ivanov MM, 2018, J COSMOL ASTROPART P, DOI 10.1088/1475-7516/2018/07/053
Kainulainen K, 2011, PHYS REV D, V84, DOI 10.1103/PhysRevD.84.063004
Komatsu E, 2002, MON NOT R ASTRON SOC, V336, P1256, DOI 10.1046/j.1365-8711.2002.05889.x
Konstandin T, 2019, J COSMOL ASTROPART P, DOI 10.1088/1475-7516/2019/11/027
Lacasa F, 2018, ASTRON ASTROPHYS, V615, DOI 10.1051/0004-6361/201732343
Lacasa F, 2018, ASTRON ASTROPHYS, V611, DOI 10.1051/0004-6361/201630281
Lacasa F, 2016, J COSMOL ASTROPART P, DOI 10.1088/1475-7516/2016/08/005
Lawrence E., 2010, COSMICEMU COSMIC EMU
Lazeyras T, 2016, J COSMOL ASTROPART P, DOI 10.1088/1475-7516/2016/02/018
Lesgourgues J, 2011, J COSMOL ASTROPART P, DOI 10.1088/1475-7516/2011/09/032
Lewandowski M, 2020, J COSMOL ASTROPART P, DOI 10.1088/1475-7516/2020/03/018
Lewis A, 2011, CAMB CODE ANISOTROPI
Li Y, 2014, PHYS REV D, V89, DOI 10.1103/PhysRevD.89.083519
Lima M, 2004, PHYS REV D, V70, DOI 10.1103/PhysRevD.70.043504
Ma CP, 2000, ASTROPHYS J, V543, P503, DOI 10.1086/317146
Massara E, 2014, J COSMOL ASTROPART P, DOI 10.1088/1475-7516/2014/12/053
McEwen JE, 2016, J COSMOL ASTROPART P, DOI 10.1088/1475-7516/2016/09/015
Mead AJ, 2016, MON NOT R ASTRON SOC, V459, P1468, DOI 10.1093/mnras/stw681
Mead AJ, 2015, MON NOT R ASTRON SOC, V454, P1958, DOI 10.1093/mnras/stv2036
Mirbabayi M, 2015, J COSMOL ASTROPART P, DOI 10.1088/1475-7516/2015/07/030
Mohammed I, 2017, MON NOT R ASTRON SOC, V466, P780, DOI 10.1093/mnras/stw3196
Mohammed I, 2014, MON NOT R ASTRON SOC, V445, P3382, DOI 10.1093/mnras/stu1972
Navarro JF, 1996, ASTROPHYS J, V462, P563, DOI 10.1086/177173
NEYMAN J, 1952, ASTROPHYS J, V116, P144, DOI 10.1086/145599
Nishimichi T, 2019, ASTROPHYS J, V884, DOI 10.3847/1538-4357/ab3719
Padmanabhan H, 2017, MON NOT R ASTRON SOC, V469, P2323, DOI 10.1093/mnras/stx979
Pajer E, 2013, PHYS REV D, V88, DOI 10.1103/PhysRevD.88.083502
Peacock JA, 2000, MON NOT R ASTRON SOC, V318, P1144, DOI 10.1046/j.1365-8711.2000.03779.x
Peebles PJE., 1980, LARGE SCALE STRUCTUR
PRESS WH, 1974, ASTROPHYS J, V187, P425, DOI 10.1086/152650
Schaan E, 2014, PHYS REV D, V90, DOI 10.1103/PhysRevD.90.123523
SCHERRER RJ, 1991, ASTROPHYS J, V381, P349, DOI 10.1086/170658
Schmidt F, 2016, PHYS REV D, V93, DOI 10.1103/PhysRevD.93.063512
Schneider A, 2019, J COSMOL ASTROPART P, DOI 10.1088/1475-7516/2019/03/020
Seljak U, 2000, MON NOT R ASTRON SOC, V318, P203, DOI 10.1046/j.1365-8711.2000.03715.x
Seljak U, 2015, PHYS REV D, V91, DOI 10.1103/PhysRevD.91.123516
Senatore L., 2015, J COSMOL ASTROPART P, V05
Senatore L, 2018, J COSMOL ASTROPART P, DOI 10.1088/1475-7516/2018/05/019
Senatore L, 2015, J COSMOL ASTROPART P, DOI 10.1088/1475-7516/2015/11/007
Sheth RK, 1999, MON NOT R ASTRON SOC, V308, P119, DOI 10.1046/j.1365-8711.1999.02692.x
Sheth RK, 2002, MON NOT R ASTRON SOC, V329, P61, DOI 10.1046/j.1365-8711.2002.04950.x
Simonovic M, 2018, J COSMOL ASTROPART P, DOI 10.1088/1475-7516/2018/04/030
Smith RE, 2003, MON NOT R ASTRON SOC, V341, P1311, DOI 10.1046/j.1365-8711.2003.06503.x
Smith RE, 2007, PHYS REV D, V75, DOI 10.1103/PhysRevD.75.063512
Smith RE, 2011, MON NOT R ASTRON SOC, V418, P729, DOI 10.1111/j.1365-2966.2011.19525.x
Smith RE, 2011, PHYS REV D, V83, DOI 10.1103/PhysRevD.83.043526
Smith RE, 2008, PHYS REV D, V78, DOI 10.1103/PhysRevD.78.023523
Springel V, 2005, MON NOT R ASTRON SOC, V364, P1105, DOI 10.1111/j.1365-2966.2005.09655.x
Takada M, 2007, NEW J PHYS, V9, DOI 10.1088/1367-2630/9/12/446
Takada M, 2014, MON NOT R ASTRON SOC, V441, P2456, DOI 10.1093/mnras/stu759
Takada M, 2013, PHYS REV D, V87, DOI 10.1103/PhysRevD.87.123504
Takahashi R, 2012, ASTROPHYS J, V761, DOI 10.1088/0004-637X/761/2/152
Thiele L, 2019, PHYS REV D, V99, DOI 10.1103/PhysRevD.99.103511
Tinker JL, 2010, ASTROPHYS J, V724, P878, DOI 10.1088/0004-637X/724/2/878
Valageas P, 2011, ASTRON ASTROPHYS, V532, DOI 10.1051/0004-6361/201116638
Valageas P, 2011, ASTRON ASTROPHYS, V527, DOI 10.1051/0004-6361/201015685
van den Bosch FC, 2013, MON NOT R ASTRON SOC, V430, P725, DOI 10.1093/mnras/sts006
VillaescusaNavarro F., ARXIV190905273
Voivodic R., ARXIV200306411
Wang J., ARXIV191109720
Warren MS, 2006, ASTROPHYS J, V646, P881, DOI 10.1086/504962
Werner KF, 2020, MON NOT R ASTRON SOC, V492, P1614, DOI 10.1093/mnras/stz3469
NR 106
TC 4
Z9 4
U1 0
U2 0
PU AMER PHYSICAL SOC
PI COLLEGE PK
PA ONE PHYSICS ELLIPSE, COLLEGE PK, MD 20740-3844 USA
SN 1550-7998
EI 1550-2368
J9 PHYS REV D
JI Phys. Rev. D
PD JUN 18
PY 2020
VL 101
IS 12
AR 123520
DI 10.1103/PhysRevD.101.123520
PG 41
WC Astronomy & Astrophysics; Physics, Particles & Fields
SC Astronomy & Astrophysics; Physics
GA LZ0HO
UT WOS:000540912900005
DA 2021-04-21
ER
PT J
AU Davelaar, J
Philippov, AA
Bromberg, O
Singh, CB
AF Davelaar, Jordy
Philippov, Alexander A.
Bromberg, Omer
Singh, Chandra B.
TI Particle Acceleration in Kink-unstable Jets
SO ASTROPHYSICAL JOURNAL LETTERS
LA English
DT Article
DE Plasma astrophysics; Plasma physics; Plasma jets; Jets; High energy
astrophysics
ID RELATIVISTIC JETS; ASTROPHYSICAL JETS; INSTABILITIES; SIMULATIONS;
RECONNECTION; STABILITY; PYTHON
AB Magnetized jets in gamma-ray bursts and active galactic nuclei are thought to be efficient accelerators of particles; however, the process responsible for the acceleration is still a matter of active debate. In this work, we study the kink instability in non-rotating force-free jets using first-principle particle-in-cell simulations. We obtain similar overall evolution of the instability as found in magnetohydrodynamics simulations. The instability first generates large-scale current sheets, which at later times break up into small-scale turbulence. Reconnection in these sheets proceeds in the strong guide field regime, which results in a formation of steep power laws in the particle spectra. Later evolution shows heating of the plasma, which is driven by small-amplitude turbulence induced by the kink instability. These two processes energize particles due to a combination of ideal and non-ideal electric fields.
C1 [Davelaar, Jordy] Radboud Univ Nijmegen, Dept Astrophys IMAPP, POB 9010, NL-6500 GL Nijmegen, Netherlands.
[Davelaar, Jordy; Philippov, Alexander A.] Flatiron Inst, Ctr Computat Astrophys, 162 Fifth Ave, New York, NY 10010 USA.
[Philippov, Alexander A.] Moscow Inst Phys & Technol, Inst Sky Per 9, Dolgoprudnyi 141700, Moscow Region, Russia.
[Bromberg, Omer; Singh, Chandra B.] Tel Aviv Univ, Raymond & Beverly Sackler Sch Phys & Astron, IL-69978 Tel Aviv, Israel.
RP Davelaar, J (corresponding author), Radboud Univ Nijmegen, Dept Astrophys IMAPP, POB 9010, NL-6500 GL Nijmegen, Netherlands.; Davelaar, J (corresponding author), Flatiron Inst, Ctr Computat Astrophys, 162 Fifth Ave, New York, NY 10010 USA.
EM j.davelaar@astro.ru.nl
RI ; Philippov, Alexander/I-3162-2017
OI Davelaar, Jordy/0000-0002-2685-2434; Philippov,
Alexander/0000-0001-7801-0362
FU ERC Synergy Grant [610058]; ISFIsrael Science Foundation [1657/18]; ISF
(I-CORE) [1829/12]; BSFUS-Israel Binational Science Foundation
[2018312]; National Science FoundationNational Science Foundation (NSF)
[AST-1910248]; Simons Foundation
FX The authors thank A. Bhattacharjee, L. Comisso, H. Hakobyan, B.
Ripperda, L. Sironi, A. Spitkovsky, and A. Tchekhovskoy for insightful
comments over the course of this project. J.D. is funded by the ERC
Synergy Grant 610058 (Goddi et al. 2017). The authors thank the
anonymous referee for insightful comments. O.B. and C.S. were funded by
an ISF grant 1657/18 and by an ISF (I-CORE) grant 1829/12. O.B. and S.P.
were also supported by a BSF grant 2018312. S.P. acknowledges support by
the National Science Foundation under grant No. AST-1910248. The
Flatiron Institute is supported by the Simons Foundation.
CR Alves EP, 2018, PHYS REV LETT, V121, DOI 10.1103/PhysRevLett.121.245101
Appl S, 2000, ASTRON ASTROPHYS, V355, P818
Begelman MC, 1998, ASTROPHYS J, V493, P291, DOI 10.1086/305119
Bodo G, 2013, MON NOT R ASTRON SOC, V434, P3030, DOI 10.1093/mnras/stt1225
Bromberg O, 2019, ASTROPHYS J, V884, DOI 10.3847/1538-4357/ab3fa5
Bromberg O, 2016, MON NOT R ASTRON SOC, V456, P1739, DOI 10.1093/mnras/stv2591
Cerutti B, 2014, ASTROPHYS J, V782, DOI 10.1088/0004-637X/782/2/104
Cerutti B, 2015, MON NOT R ASTRON SOC, V448, P606, DOI 10.1093/mnras/stv042
Childs H., 2005, IEEEP, V2005, P190
Comisso L, 2018, PHYS REV LETT, V121, DOI 10.1103/PhysRevLett.121.255101
Das U, 2019, MON NOT R ASTRON SOC, V482, P2107, DOI 10.1093/mnras/sty2675
Drake JF, 2006, NATURE, V443, P553, DOI 10.1038/nature05116
Drenkhahn G, 2002, ASTRON ASTROPHYS, V391, P1141, DOI 10.1051/0004-6361:20020839
Duck RC, 1997, PLASMA PHYS CONTR F, V39, P715, DOI 10.1088/0741-3335/39/5/004
Giannios D, 2006, AIP CONF PROC, V848, P530, DOI 10.1063/1.2348028
Goddi C, 2017, INT J MOD PHYS D, V26, DOI 10.1142/S0218271817300014
Guo F, 2014, PHYS REV LETT, V113, DOI 10.1103/PhysRevLett.113.155005
Hunter JD, 2007, COMPUT SCI ENG, V9, P90, DOI 10.1109/MCSE.2007.55
Jones E., 2001, SCIPY OPEN SOURCE SC
Loureiro NF, 2007, PHYS PLASMAS, V14, DOI 10.1063/1.2783986
Lyubarskii YE, 1999, MON NOT R ASTRON SOC, V308, P1006, DOI 10.1046/j.1365-8711.1999.02763.x
McKinney JC, 2012, MON NOT R ASTRON SOC, V419, P573, DOI 10.1111/j.1365-2966.2011.19721.x
McKinney JC, 2009, MON NOT R ASTRON SOC, V394, pL126, DOI 10.1111/j.1745-3933.2009.00625.x
Millman KJ, 2011, COMPUT SCI ENG, V13, P9, DOI 10.1109/MCSE.2011.36
Mizuno Y, 2012, ASTROPHYS J, V757, DOI 10.1088/0004-637X/757/1/16
Mizuno Y, 2009, ASTROPHYS J, V700, P684, DOI 10.1088/0004-637X/700/1/684
O'Neill SM, 2012, MON NOT R ASTRON SOC, V422, P1436, DOI 10.1111/j.1365-2966.2012.20721.x
Oliphant TE, 2007, COMPUT SCI ENG, V9, P10, DOI 10.1109/MCSE.2007.58
Petropoulou M, 2018, MON NOT R ASTRON SOC, V481, P5687, DOI 10.1093/mnras/sty2702
Pudritz RE, 2012, SPACE SCI REV, V169, P27, DOI 10.1007/s11214-012-9895-z
Ripperda B, 2017, MON NOT R ASTRON SOC, V471, P3465, DOI 10.1093/mnras/stx1875
ROSENBLU.MN, 1973, PHYS FLUIDS, V16, P1894, DOI 10.1063/1.1694231
Sironi L, 2014, ASTROPHYS J LETT, V783, DOI 10.1088/2041-8205/783/1/L21
Spitkovsky A, 2005, AIP CONF PROC, V801, P345
Tchekhovskoy A, 2016, MON NOT R ASTRON SOC, V461, pL46, DOI 10.1093/mnrasl/slw064
van der Walt S, 2011, COMPUT SCI ENG, V13, P22, DOI 10.1109/MCSE.2011.37
Werner GR, 2016, ASTROPHYS J LETT, V816, DOI 10.3847/2041-8205/816/1/L8
Werner GR, 2017, ASTROPHYS J LETT, V843, DOI 10.3847/2041-8213/aa7892
Zenitani S, 2001, ASTROPHYS J, V562, pL63, DOI 10.1086/337972
Zhdankin V, 2017, PHYS REV LETT, V118, DOI 10.1103/PhysRevLett.118.055103
Zhdankin V, 2013, ASTROPHYS J, V771, DOI 10.1088/0004-637X/771/2/124
NR 41
TC 3
Z9 3
U1 0
U2 0
PU IOP PUBLISHING LTD
PI BRISTOL
PA TEMPLE CIRCUS, TEMPLE WAY, BRISTOL BS1 6BE, ENGLAND
SN 2041-8205
EI 2041-8213
J9 ASTROPHYS J LETT
JI Astrophys. J. Lett.
PD JUN
PY 2020
VL 896
IS 2
AR L31
DI 10.3847/2041-8213/ab95a2
PG 7
WC Astronomy & Astrophysics
SC Astronomy & Astrophysics
GA MH6UX
UT WOS:000546862700001
DA 2021-04-21
ER
PT J
AU Liao, JQ
Huang, JF
Tian, L
Kuang, LM
Sun, CP
AF Liao, Jie-Qiao
Huang, Jin-Feng
Tian, Lin
Kuang, Le-Man
Sun, Chang-Pu
TI Generalized ultrastrong optomechanical-like coupling
SO PHYSICAL REVIEW A
LA English
DT Article
ID PYTHON FRAMEWORK; QUANTUM; CAVITY; DYNAMICS; NUMBER; QUTIP
AB Ultrastrong optomechanical interaction is a significant element for the study of the fundamentals and applications of optomechanical physics, but its realization remains a big challenge in the field of optomechanics. In this work, we propose a reliable scheme to realize a generalized ultrastrong optomechanical-like coupling in a cross-Kerr-type coupled two-bosonic-mode system, in which one of the two bosonic modes is strongly driven. The generalized optomechanical-like interaction takes the form of a product of the excitation number operator of one mode and the rotated quadrature operator of the other mode. Here, both the coupling strength and the phase angle of the rotated quadrature operator are tunable via the driving field. The optomechanical-like coupling can be strongly enhanced to enter the ultrastrong-coupling regime, where the few-photon optomechanical effects such as photon blockade and macroscopic quantum superposition become accessible. The controllability of the quadrature phase angle provides a new degree of freedom for the manipulation of optomechanical systems and enables the implementation of geometric quantum operations. We also present some discussions on the experimental implementation of this scheme. This study will pave the way to the study of quantum physics and quantum technology at the few-photon level in optomechanical systems.
C1 [Liao, Jie-Qiao; Huang, Jin-Feng; Kuang, Le-Man] Hunan Normal Univ, Key Lab Low Dimens Quantum Struct & Quantum Contr, Minist Educ,Dept Phys, Key Lab Matter Microstruct & Funct Hunan Prov, Changsha 410081, Peoples R China.
[Liao, Jie-Qiao; Huang, Jin-Feng; Kuang, Le-Man] Hunan Normal Univ, Synerget Innovat Ctr Quantum Effects & Applicat, Changsha 410081, Peoples R China.
[Tian, Lin] Univ Calif Merced, Sch Nat Sci, Merced, CA 95343 USA.
[Sun, Chang-Pu] Beijing Computat Sci Res Ctr, Beijing 100193, Peoples R China.
[Sun, Chang-Pu] China Acad Engn Phys, Grad Sch, Beijing 100084, Peoples R China.
RP Liao, JQ (corresponding author), Hunan Normal Univ, Key Lab Low Dimens Quantum Struct & Quantum Contr, Minist Educ,Dept Phys, Key Lab Matter Microstruct & Funct Hunan Prov, Changsha 410081, Peoples R China.; Liao, JQ (corresponding author), Hunan Normal Univ, Synerget Innovat Ctr Quantum Effects & Applicat, Changsha 410081, Peoples R China.
EM jqliao@hunnu.edu.cn
FU NSFCNational Natural Science Foundation of China (NSFC) [11421063,
11534002, 11822501, 11774087, 11505055, 11935006, 11375060, 11434011,
11775075]; Natural Science Foundation of Hunan Province, ChinaNatural
Science Foundation of Hunan Province [2017JJ1021]; Hunan Science and
Technology Plan Project [2017XK2018]; Scientific Research Fund of Hunan
Provincial Education DepartmentHunan Provincial Education Department
[18A007]; National Science Foundation (USA)National Science Foundation
(NSF) [1720501, 2006076]; National Basic Research Program of
ChinaNational Basic Research Program of China [2014CB921403,
2016YFA0301201]; NSAF [U1530401]
FX J.-Q.L. is supported in part by NSFC Grants No. 11822501, No. 11774087,
and No. 11935006, Natural Science Foundation of Hunan Province, China
Grant No. 2017JJ1021, and Hunan Science and Technology Plan Project
Grant No. 2017XK2018. J.-F.H. is supported in part by the NSFC Grant No.
11505055, and Scientific Research Fund of Hunan Provincial Education
Department Grant No. 18A007. L.T. is supported by the National Science
Foundation (USA) under Awards No. 1720501 and No. 2006076. L.-M.K. is
supported by the NSFC Grants No. 11935006, No. 11375060, No. 11434011,
and No. 11775075. C.P.S. is supported by the National Basic Research
Program of China Grants No. 2014CB921403 and No. 2016YFA0301201, the
NSFC Grants No. 11421063 and No. 11534002, and the NSAF Grant No.
U1530401.
CR Aspelmeyer M, 2014, REV MOD PHYS, V86, P1391, DOI 10.1103/RevModPhys.86.1391
Aspelmeyer M, 2012, PHYS TODAY, V65, P29, DOI 10.1063/PT.3.1640
Barnett S.M., 1997, METHODS THEORETICAL
BIALYNIC Z, 1968, PHYS REV, V173, P1207, DOI 10.1103/PhysRev.173.1207
Bose S, 1999, PHYS REV A, V59, P3204, DOI 10.1103/PhysRevA.59.3204
Bose S, 1997, PHYS REV A, V56, P4175, DOI 10.1103/PhysRevA.56.4175
Bourassa J, 2012, PHYS REV A, V86, DOI 10.1103/PhysRevA.86.013814
DEOLIVEIRA FAM, 1990, PHYS REV A, V41, P2645, DOI 10.1103/PhysRevA.41.2645
Ding SQ, 2017, PHYS REV LETT, V119, DOI 10.1103/PhysRevLett.119.193602
Gong ZR, 2009, PHYS REV A, V80, DOI 10.1103/PhysRevA.80.065801
Grangier P, 1998, NATURE, V396, P537, DOI 10.1038/25059
Heikkila TT, 2014, PHYS REV LETT, V112, DOI 10.1103/PhysRevLett.112.203603
Hoi IC, 2013, PHYS REV LETT, V111, DOI 10.1103/PhysRevLett.111.053601
Holland ET, 2015, PHYS REV LETT, V115, DOI 10.1103/PhysRevLett.115.180501
Hong T, 2013, PHYS REV A, V88, DOI 10.1103/PhysRevA.88.023812
Hu D, 2015, PHYS REV A, V91, DOI 10.1103/PhysRevA.91.013812
Hu Y, 2011, PHYS REV A, V84, DOI 10.1103/PhysRevA.84.012329
IMOTO N, 1985, PHYS REV A, V32, P2287, DOI 10.1103/PhysRevA.32.2287
Johansson JR, 2013, COMPUT PHYS COMMUN, V184, P1234, DOI 10.1016/j.cpc.2012.11.019
Johansson JR, 2012, COMPUT PHYS COMMUN, V183, P1760, DOI 10.1016/j.cpc.2012.02.021
Kang HS, 2003, PHYS REV LETT, V91, DOI 10.1103/PhysRevLett.91.093601
Karuza M, 2013, J OPTICS-UK, V15, DOI 10.1088/2040-8978/15/2/025704
Khosla KE, 2013, NEW J PHYS, V15, DOI 10.1088/1367-2630/15/4/043025
Kimble HJ, 1998, PHYS SCRIPTA, VT76, P127, DOI 10.1238/Physica.Topical.076a00127
Kippenberg TJ, 2008, SCIENCE, V321, P1172, DOI 10.1126/science.1156032
Kok P, 2007, REV MOD PHYS, V79, P135, DOI 10.1103/RevModPhys.79.135
Kuang LM, 2007, PHYS REV A, V76, DOI 10.1103/PhysRevA.76.052324
Kuang LM, 2003, PHYS REV A, V68, DOI 10.1103/PhysRevA.68.043606
Lemonde MA, 2016, NAT COMMUN, V7, DOI 10.1038/ncomms11338
Li PB, 2016, SCI REP-UK, V6, DOI 10.1038/srep19065
Liao JQ, 2016, PHYS REV LETT, V116, DOI 10.1103/PhysRevLett.116.163602
Liao JQ, 2014, NEW J PHYS, V16, DOI 10.1088/1367-2630/16/7/072001
Liao JQ, 2013, PHYS REV A, V87, DOI 10.1103/PhysRevA.87.043809
Liao JQ, 2012, PHYS REV A, V85, DOI 10.1103/PhysRevA.85.025803
Liu T, 2017, QUANTUM INF PROCESS, V16, DOI 10.1007/s11128-017-1664-1
Lu XY, 2015, PHYS REV LETT, V114, DOI 10.1103/PhysRevLett.114.093602
Ludwig M, 2008, NEW J PHYS, V10, DOI 10.1088/1367-2630/10/9/095013
Majer J, 2007, NATURE, V449, P443, DOI 10.1038/nature06184
Mancini S, 1997, PHYS REV A, V55, P3042, DOI 10.1103/PhysRevA.55.3042
Marshall W, 2003, PHYS REV LETT, V91, DOI 10.1103/PhysRevLett.91.130401
Matsko AB, 2003, OPT LETT, V28, P96, DOI 10.1364/OL.28.000096
Maurer C, 2004, NEW J PHYS, V6, DOI 10.1088/1367-2630/6/1/094
MILBURN GJ, 1989, PHYS REV LETT, V62, P2124, DOI 10.1103/PhysRevLett.62.2124
Miranowicz A., 1990, Quantum Optics, V2, P253, DOI 10.1088/0954-8998/2/3/006
Nemoto K, 2004, PHYS REV LETT, V93, DOI 10.1103/PhysRevLett.93.250502
Nigg SE, 2012, PHYS REV LETT, V108, DOI 10.1103/PhysRevLett.108.240502
Nunnenkamp A, 2011, PHYS REV LETT, V107, DOI 10.1103/PhysRevLett.107.063602
Paternostro M, 2003, PHYS REV A, V67, DOI 10.1103/PhysRevA.67.023811
Pikovski I, 2012, NAT PHYS, V8, P393, DOI [10.1038/NPHYS2262, 10.1038/nphys2262]
Pirkkalainen JM, 2015, NAT COMMUN, V6, DOI 10.1038/ncomms7981
Qian J, 2012, PHYS REV LETT, V109, DOI 10.1103/PhysRevLett.109.253601
Rabl P, 2011, PHYS REV LETT, V107, DOI 10.1103/PhysRevLett.107.063601
Rebic S, 2009, PHYS REV LETT, V103, DOI 10.1103/PhysRevLett.103.150503
Rimberg AJ, 2014, NEW J PHYS, V16, DOI 10.1088/1367-2630/16/5/055008
Sankey JC, 2010, NAT PHYS, V6, P707, DOI 10.1038/NPHYS1707
Schmidt H, 1996, OPT LETT, V21, P1936, DOI 10.1364/OL.21.001936
Semiao FL, 2005, PHYS REV A, V72, DOI 10.1103/PhysRevA.72.064305
Sinclair GF, 2007, PHYS REV A, V76, DOI 10.1103/PhysRevA.76.033803
Sinclair GF, 2008, PHYS REV A, V77, DOI 10.1103/PhysRevA.77.033843
Tan SM, 1999, J OPT B-QUANTUM S O, V1, P424, DOI 10.1088/1464-4266/1/4/312
Thompson JD, 2008, NATURE, V452, P72, DOI 10.1038/nature06715
Vanner MR, 2011, P NATL ACAD SCI USA, V108, P16182, DOI 10.1073/pnas.1105098108
Vitali D, 2000, PHYS REV LETT, V85, P445, DOI 10.1103/PhysRevLett.85.445
Wang ZY, 2017, NAT COMMUN, V8, DOI 10.1038/ncomms15886
Xia KY, 2018, PHYS REV LETT, V121, DOI 10.1103/PhysRevLett.121.203602
Xu XW, 2013, PHYS REV A, V87, DOI 10.1103/PhysRevA.87.025803
Xuereb A, 2012, PHYS REV LETT, V109, DOI 10.1103/PhysRevLett.109.223601
Zhu SL, 2003, PHYS REV LETT, V91, DOI 10.1103/PhysRevLett.91.187902
NR 68
TC 2
Z9 2
U1 3
U2 13
PU AMER PHYSICAL SOC
PI COLLEGE PK
PA ONE PHYSICS ELLIPSE, COLLEGE PK, MD 20740-3844 USA
SN 1050-2947
EI 1094-1622
J9 PHYS REV A
JI Phys. Rev. A
PD JUN 1
PY 2020
VL 101
IS 6
AR 063802
DI 10.1103/PhysRevA.101.063802
PG 15
WC Optics; Physics, Atomic, Molecular & Chemical
SC Optics; Physics
GA LT5UV
UT WOS:000537136900021
DA 2021-04-21
ER
PT J
AU Huybrechts, D
Minganti, F
Nori, F
Wouters, M
Shammah, N
AF Huybrechts, Dolf
Minganti, Fabrizio
Nori, Franco
Wouters, Michiel
Shammah, Nathan
TI Validity of mean-field theory in a dissipative critical system:
Liouvillian gap, PT-symmetric antigap, and permutational symmetry in the
XYZ model
SO PHYSICAL REVIEW B
LA English
DT Article
ID QUANTUM PHASE-TRANSITION; PYTHON FRAMEWORK; RADIATION; DYNAMICS;
PHYSICS; CAVITY; SUPERFLUID; DRIVEN; ARRAYS; QUTIP
AB We study the all-to-all connected XYZ (anisotropic-Heisenberg) spin model with local and collective dissipations, comparing the results of mean-field (MF) theory with the solution of the Lindblad master equation. Exploiting the weak PT symmetry of the model (referred to as Liouvillian PT symmetry), we efficiently calculate the Liouvillian gap, introducing the idea of an antigap, and we demonstrate the presence of a paramagnetic-to-ferromagnetic phase transition. Leveraging the permutational symmetry of the model [N. Shammah et al., Phys Rev. A 98, 063815 (2018)], we characterize criticality, finding exactly (up to numerical precision) the steady state for N up to N = 95 spins. We demonstrate that the MF theory correctly predicts the results in the thermodynamic limit in all regimes of parameters, and quantitatively describes the finite-size behavior in the small anisotropy regime. However, for an intermediate number of spins and for large anisotropy, we find a significant discrepancy between the results of the MF theory and those of the full quantum simulation. We also study other more experimentally accessible witnesses of the transition, which can be used for finite-size studies, namely, the bimodality coefficient and the angular-averaged susceptibility. In contrast to the bimodality coefficient, the angular-averaged susceptibility fails to capture the onset of the transition, in striking difference with respect to lower-dimensional studies. We also analyze the competition between local dissipative processes (which disentangle the spin system) and collective dissipative ones (generating entanglement). The nature of the phase transition is almost unaffected by the presence of these terms. Our results mark a stark difference with the common intuition that an all-to-all connected system should fall onto the mean-field solution also for intermediate number of spins.
C1 [Huybrechts, Dolf; Wouters, Michiel] Univ Antwerp, Theory Quantum & Complex Syst, B-2610 Antwerp, Belgium.
[Minganti, Fabrizio; Nori, Franco; Shammah, Nathan] RIKEN, Cluster Pioneering Res, Theoret Quantum Phys Lab, Wako, Saitama 3510198, Japan.
[Nori, Franco] Univ Michigan, Dept Phys, Ann Arbor, MI 48109 USA.
[Shammah, Nathan] Univ Milan, Dipartimento Fis, Quantum Technol Lab, I-20133 Milan, Italy.
[Shammah, Nathan] Unitary Fund, Berkeley, CA USA.
RP Huybrechts, D (corresponding author), Univ Antwerp, Theory Quantum & Complex Syst, B-2610 Antwerp, Belgium.
EM dolf.huybrechts@uantwerpen.be; fabrizio.minganti@riken.jp;
fnori@riken.jp; michiel.wouters@uantwerpen.be; nathan.shammah@gmail.com
RI Minganti, Fabrizio/AAX-4108-2020; Nori, Franco/B-1222-2009
OI Minganti, Fabrizio/0000-0003-4850-1130; Huybrechts,
Dolf/0000-0002-5821-3493; Nori, Franco/0000-0003-3682-7432
FU FY2018 JSPS Postdoctoral Fellowship for Research in Japan; NTT Research,
Army Research Office (ARO) [W911NF-18-1-0358]; Japan Science and
Technology Agency (JST) (CREST Grant) [JPMJCR1676]; Japan Society for
the Promotion of Science (JSPS) (KAKENHI Grant)Ministry of Education,
Culture, Sports, Science and Technology, Japan (MEXT)Japan Society for
the Promotion of ScienceGrants-in-Aid for Scientific Research (KAKENHI)
[JP20H00134]; Japan Society for the Promotion of Science (JSPS)
(JSPS-RFBR Grant)Ministry of Education, Culture, Sports, Science and
Technology, Japan (MEXT)Japan Society for the Promotion of Science
[JPJSBP120194828]; Japan Society for the Promotion of Science (JSPS)
(JSPS-FWO Grant)Ministry of Education, Culture, Sports, Science and
Technology, Japan (MEXT)Japan Society for the Promotion of Science
[VS.059.18N]; Foundational Questions Institute Fund (FQXi)
[FQXi-IAF19-06]; Research Foundation-Flanders (FWO)FWO; Flemish
Government department EWI; Silicon Valley Community Foundation;
[UAntwerpen/DOCPRO/34878]
FX The authors acknowledge useful discussions with A. Biella, G. Piccitto,
R. Rota, M. Wauters, and W. Verstraelen. F.M. is supported by the FY2018
JSPS Postdoctoral Fellowship for Research in Japan. F.N. is supported in
part by: NTT Research, Army Research Office (ARO) (Grant No.
W911NF-18-1-0358), Japan Science and Technology Agency (JST) (via the
CREST Grant No. JPMJCR1676), Japan Society for the Promotion of Science
(JSPS) (via the KAKENHI Grant No. JP20H00134, JSPS-RFBR Grant No.
JPJSBP120194828, and JSPS-FWO Grant No. VS.059.18N), and the Grant No.
FQXi-IAF19-06 from the Foundational Questions Institute Fund (FQXi), a
donor advised fund of the Silicon Valley Community Foundation. D.H. is
supported by UAntwerpen/DOCPRO/34878. Part of the computational
resources and services used in this work were provided by the VSC
(Flemish Supercomputer Center), funded by the Research
Foundation-Flanders (FWO) and the Flemish Government department EWI.
CR Albert VV, 2014, PHYS REV A, V89, DOI 10.1103/PhysRevA.89.022118
Altman E, 2015, PHYS REV X, V5, DOI 10.1103/PhysRevX.5.011017
Angelakis DG, 2007, PHYS REV A, V76, DOI 10.1103/PhysRevA.76.031805
Angerer A, 2018, NAT PHYS, V14, P1168, DOI 10.1038/s41567-018-0269-7
Angerer A, 2017, SCI ADV, V3, DOI 10.1126/sciadv.1701626
Aspelmeyer M, 2014, REV MOD PHYS, V86, P1391, DOI 10.1103/RevModPhys.86.1391
Ballarini D, 2019, NANOPHOTONICS-BERLIN, V8, P641, DOI 10.1515/nanoph-2018-0188
Bartolo N, 2016, PHYS REV A, V94, DOI 10.1103/PhysRevA.94.033841
Baumann K, 2010, NATURE, V464, P1301, DOI 10.1038/nature09009
Baumgartner B, 2008, J PHYS A-MATH THEOR, V41, DOI 10.1088/1751-8113/41/39/395303
Benito M, 2016, PHYS REV A, V93, DOI 10.1103/PhysRevA.93.023846
Bermudez A, 2017, PHYS REV B, V95, DOI 10.1103/PhysRevB.95.024431
Bernien H, 2017, NATURE, V551, P579, DOI 10.1038/nature24622
Biella A, 2018, PHYS REV B, V97, DOI 10.1103/PhysRevB.97.035103
Biella A, 2017, PHYS REV A, V96, DOI 10.1103/PhysRevA.96.023839
BINDER K, 1981, PHYS REV LETT, V47, P693, DOI 10.1103/PhysRevLett.47.693
BINDER K, 1981, Z PHYS B CON MAT, V43, P119, DOI 10.1007/BF01293604
Biondi M, 2017, NEW J PHYS, V19, DOI 10.1088/1367-2630/aa99b2
Biondi M, 2017, PHYS REV A, V96, DOI 10.1103/PhysRevA.96.043809
Birnbaum KM, 2005, NATURE, V436, P87, DOI 10.1038/nature03804
Bloch I, 2008, REV MOD PHYS, V80, P885, DOI 10.1103/RevModPhys.80.885
Bohnet JG, 2012, NATURE, V484, P78, DOI 10.1038/nature10920
BONIFACIO R, 1970, PHYS REV A-GEN PHYS, V2, P336, DOI 10.1103/PhysRevA.2.336
BONIFACIO R, 1975, PHYS REV A, V11, P1507, DOI 10.1103/PhysRevA.11.1507
BONIFACIO R, 1971, PHYS REV A-GEN PHYS, V4, P302, DOI 10.1103/PhysRevA.4.302
Bradac C, 2017, NAT COMMUN, V8, DOI 10.1038/s41467-017-01397-4
Braggio C., ARXIV190900999
Breuer H.-P., 2007, THEORY OPEN QUANTUM
Briceno RA, 2020, PHYS REV D, V101, DOI 10.1103/PhysRevD.101.014509
Buca B., ARXIV191212185
Buca B, 2012, NEW J PHYS, V14, DOI 10.1088/1367-2630/14/7/073007
Buchhold M, 2013, PHYS REV A, V87, DOI 10.1103/PhysRevA.87.063622
Carmichael HJ, 2015, PHYS REV X, V5, DOI 10.1103/PhysRevX.5.031028
CARMICHAEL HJ, 1985, PHYS REV LETT, V55, P2790, DOI 10.1103/PhysRevLett.55.2790
Carmichael HJ., 1999, STAT METHODS QUANTUM
Carusotto I, 2013, REV MOD PHYS, V85, DOI 10.1103/RevModPhys.85.299
Casteels W, 2017, PHYS REV A, V95, DOI 10.1103/PhysRevA.95.012128
Casteels W, 2016, PHYS REV A, V93, DOI 10.1103/PhysRevA.93.033824
Casteels W, 2018, PHYS REV A, V97, DOI 10.1103/PhysRevA.97.062107
Casteels W, 2017, PHYS REV A, V95, DOI 10.1103/PhysRevA.95.013812
Chan CK, 2015, PHYS REV A, V91, DOI 10.1103/PhysRevA.91.051601
CHISSOM BS, 1970, AM STAT, V24, P19, DOI 10.2307/2681309
Chudnovsky EM, 2002, PHYS REV LETT, V89, DOI 10.1103/PhysRevLett.89.157201
Cirigliano V, 2019, PHYS REV LETT, V122, DOI 10.1103/PhysRevLett.122.221801
Cirio M, 2019, PHYS REV LETT, V122, DOI 10.1103/PhysRevLett.122.190403
Ciuti C, 2003, SEMICOND SCI TECH, V18, pS279, DOI 10.1088/0268-1242/18/10/301
Dalla Torre EG, 2016, PHYS REV A, V94, DOI 10.1103/PhysRevA.94.061802
Dalla Torre EG, 2012, PHYS REV B, V85, DOI 10.1103/PhysRevB.85.184302
Defenu N, 2018, PHYS REV LETT, V121, DOI 10.1103/PhysRevLett.121.240403
Deveaud B., 2007, PHYS SEMICONDUCTOR M
DICKE RH, 1954, PHYS REV, V93, P99, DOI 10.1103/PhysRev.93.99
Diehl S, 2008, NAT PHYS, V4, P878, DOI 10.1038/nphys1073
Diehl S, 2010, PHYS REV LETT, V105, DOI 10.1103/PhysRevLett.105.015702
Dimer F, 2007, PHYS REV A, V75, DOI 10.1103/PhysRevA.75.013804
El-Ganainy R, 2018, NAT PHYS, V14, P11, DOI [10.1038/NPHYS4323, 10.1038/nphys4323]
Fink JM, 2017, PHYS REV X, V7, DOI 10.1103/PhysRevX.7.011012
Fink T, 2018, NAT PHYS, V14, P365, DOI 10.1038/s41567-017-0020-9
Fitzpatrick M, 2017, PHYS REV X, V7, DOI 10.1103/PhysRevX.7.011016
Foss-Feig M, 2017, PHYS REV A, V95, DOI 10.1103/PhysRevA.95.043826
Garttner M, 2017, NAT PHYS, V13, P781, DOI [10.1038/NPHYS4119, 10.1038/nphys4119]
Gegg M, 2016, NEW J PHYS, V18, DOI 10.1088/1367-2630/18/4/043037
Gelhausen J, 2017, PHYS REV A, V95, DOI 10.1103/PhysRevA.95.063824
Gil-Santos E, 2017, PHYS REV LETT, V118, DOI 10.1103/PhysRevLett.118.063605
Glaetzle AW, 2015, PHYS REV LETT, V114, DOI 10.1103/PhysRevLett.114.173002
Greentree AD, 2006, NAT PHYS, V2, P856, DOI 10.1038/nphys466
Greiner M, 2002, NATURE, V415, P39, DOI 10.1038/415039a
Haroche S., 2006, EXPLORING QUANTUM AT
Hartmann MJ, 2006, NAT PHYS, V2, P849, DOI 10.1038/nphys462
Hartmann MJ, 2008, LASER PHOTONICS REV, V2, P527, DOI 10.1002/lpor.200810046
Hartmann MJ, 2007, PHYS REV LETT, V99, DOI 10.1103/PhysRevLett.99.160501
HOPFIELD JJ, 1958, PHYS REV, V112, P1555, DOI 10.1103/PhysRev.112.1555
Houck AA, 2012, NAT PHYS, V8, P292, DOI [10.1038/NPHYS2251, 10.1038/nphys2251]
Huybrechts D, 2019, PHYS REV A, V99, DOI 10.1103/PhysRevA.99.043841
Iemini F, 2018, PHYS REV LETT, V121, DOI 10.1103/PhysRevLett.121.035301
Iles-Smith J, 2014, PHYS REV A, V90, DOI 10.1103/PhysRevA.90.032114
Imamoglu A, 1997, PHYS REV LETT, V79, P1467, DOI 10.1103/PhysRevLett.79.1467
Jin JS, 2016, PHYS REV X, V6, DOI 10.1103/PhysRevX.6.031011
Johansson JR, 2013, COMPUT PHYS COMMUN, V184, P1234, DOI 10.1016/j.cpc.2012.11.019
Johansson JR, 2012, COMPUT PHYS COMMUN, V183, P1760, DOI 10.1016/j.cpc.2012.02.021
Joshi C, 2013, PHYS REV A, V88, DOI 10.1103/PhysRevA.88.063835
Kakuyanagi K, 2016, PHYS REV LETT, V117, DOI 10.1103/PhysRevLett.117.210503
Kasprzak J, 2006, NATURE, V443, P409, DOI 10.1038/nature05131
Kavokin A.V., 2007, MICROCAVITIES, V2nd ed.
Kay A, 2008, EPL-EUROPHYS LETT, V84, DOI 10.1209/0295-5075/84/20001
Kessler EM, 2012, PHYS REV A, V86, DOI 10.1103/PhysRevA.86.012116
Khasseh R, 2019, PHYS REV LETT, V123, DOI 10.1103/PhysRevLett.123.184301
Kirton P, 2019, ADV QUANTUM TECHNOL, V2, P1800043
Kirton P, 2018, NEW J PHYS, V20, DOI 10.1088/1367-2630/aaa11d
Kirton P, 2017, PHYS REV LETT, V118, DOI 10.1103/PhysRevLett.118.123602
Kowalewska-Kudlaszyk A, 2019, PHYS REV A, V100, DOI 10.1103/PhysRevA.100.053857
Kshetrimayum A, 2017, NAT COMMUN, V8, DOI 10.1038/s41467-017-01511-6
Lambert N, 2004, PHYS REV LETT, V92, DOI 10.1103/PhysRevLett.92.073602
Lambert N, 2016, PHYS REV B, V94, DOI 10.1103/PhysRevB.94.224510
Lambert N, 2009, PHYS REV B, V80, DOI 10.1103/PhysRevB.80.165308
Landa H, 2020, PHYS REV LETT, V124, DOI 10.1103/PhysRevLett.124.043601
Landau L. D., 2013, COURSE THEORETICAL P, V5
Lang C, 2011, PHYS REV LETT, V106, DOI 10.1103/PhysRevLett.106.243601
Lebreuilly J, 2017, PHYS REV A, V96, DOI 10.1103/PhysRevA.96.033828
Lee TE, 2014, PHYS REV A, V90, DOI 10.1103/PhysRevA.90.052109
Lee TE, 2013, PHYS REV LETT, V110, DOI 10.1103/PhysRevLett.110.257204
Lee TE, 2011, PHYS REV A, V84, DOI 10.1103/PhysRevA.84.031402
LEHMBERG RH, 1970, PHYS REV A-GEN PHYS, V2, P883, DOI 10.1103/PhysRevA.2.883
Maghrebi MF, 2016, PHYS REV B, V93, DOI 10.1103/PhysRevB.93.014307
Marino J, 2016, PHYS REV LETT, V116, DOI 10.1103/PhysRevLett.116.070407
Markovic D, 2018, PHYS REV LETT, V121, DOI 10.1103/PhysRevLett.121.040505
Meiser D, 2010, PHYS REV A, V81, DOI 10.1103/PhysRevA.81.033847
Mendoza-Arenas JJ, 2016, PHYS REV A, V93, DOI 10.1103/PhysRevA.93.023821
METZNER W, 1991, PHYS REV B, V43, P8549, DOI 10.1103/PhysRevB.43.8549
Minganti F, 2018, PHYS REV A, V98, DOI 10.1103/PhysRevA.98.042118
Miranowicz A, 2013, PHYS REV A, V87, DOI 10.1103/PhysRevA.87.023809
Miri MA, 2019, SCIENCE, V363, P42, DOI 10.1126/science.aar7709
Morrison S, 2008, PHYS REV A, V77, DOI 10.1103/PhysRevA.77.043810
Morrison S, 2008, PHYS REV LETT, V100, DOI 10.1103/PhysRevLett.100.040403
Muller M, 2012, ADV ATOM MOL OPT PHY, V61, P1, DOI 10.1016/B978-0-12-396482-3.00001-6
Nation PD, 2015, PHYS REV E, V91, DOI 10.1103/PhysRevE.91.013307
Nguyen TL, 2018, PHYS REV X, V8, DOI 10.1103/PhysRevX.8.011032
Niederle AE, 2016, PHYS REV A, V94, DOI 10.1103/PhysRevA.94.033607
NIU Q, 1989, PHYS REV B, V39, P2134, DOI 10.1103/PhysRevB.39.2134
Noe GT, 2012, NAT PHYS, V8, P219, DOI [10.1038/NPHYS2207, 10.1038/nphys2207]
Novo L, 2013, PHYS REV A, V88, DOI 10.1103/PhysRevA.88.012305
Olmos B, 2014, PHYS REV E, V90, DOI 10.1103/PhysRevE.90.042147
Overbeck VR, 2017, PHYS REV A, V95, DOI 10.1103/PhysRevA.95.042133
Ozdemir SK, 2019, NAT MATER, V18, P783, DOI 10.1038/s41563-019-0304-9
Pappalardi S, 2018, PHYS REV B, V98, DOI 10.1103/PhysRevB.98.134303
Pathria R., 2011, STAT MECH
Prosen T, 2012, PHYS REV A, V86, DOI 10.1103/PhysRevA.86.044103
Prosen T, 2012, PHYS REV LETT, V109, DOI 10.1103/PhysRevLett.109.090404
Puri S, 2017, NAT COMMUN, V8, DOI 10.1038/ncomms15785
Qian J, 2015, PHYS REV A, V92, DOI 10.1103/PhysRevA.92.063407
Qian J, 2012, PHYS REV A, V85, DOI 10.1103/PhysRevA.85.065401
Raino G, 2018, NATURE, V563, P671, DOI 10.1038/s41586-018-0683-0
Ramos A, 2019, EUR PHYS J D, V73, DOI 10.1140/epjd/e2019-100180-4
Rodriguez SRK, 2017, PHYS REV LETT, V118, DOI 10.1103/PhysRevLett.118.247402
Roscher D, 2018, PHYS REV A, V98, DOI 10.1103/PhysRevA.98.062117
Rota R, 2018, NEW J PHYS, V20, DOI 10.1088/1367-2630/aab703
Rota R, 2017, PHYS REV B, V95, DOI 10.1103/PhysRevB.95.134431
Russomanno A, 2017, PHYS REV B, V95, DOI 10.1103/PhysRevB.95.214307
Sachdev S, 2001, QUANTUM PHASE TRANSI
Munoz CS, 2018, PHYS REV LETT, V121, DOI 10.1103/PhysRevLett.121.123604
Savona V, 2017, PHYS REV A, V96, DOI 10.1103/PhysRevA.96.033826
Scheel S, 2018, EPL-EUROPHYS LETT, V122, DOI 10.1209/0295-5075/122/34001
Schmidt S, 2009, PHYS REV LETT, V103, DOI 10.1103/PhysRevLett.103.086403
Schoelkopf RJ, 2008, NATURE, V451, P664, DOI 10.1038/451664a
Schutz S, 2016, PHYS REV LETT, V117, DOI 10.1103/PhysRevLett.117.083001
Shammah N, 2018, PHYS REV A, V98, DOI 10.1103/PhysRevA.98.063815
Shammah N, 2017, PHYS REV A, V96, DOI 10.1103/PhysRevA.96.023863
Sieberer LM, 2014, PHYS REV B, V89, DOI 10.1103/PhysRevB.89.134310
Sieberer LM, 2013, PHYS REV LETT, V110, DOI 10.1103/PhysRevLett.110.195301
Tsomokos DI, 2008, NEW J PHYS, V10, DOI 10.1088/1367-2630/10/11/113020
van Caspel M, 2018, PHYS REV A, V97, DOI 10.1103/PhysRevA.97.052106
Verstraelen W, 2018, APPL SCI-BASEL, V8, DOI 10.3390/app8091427
Verstraete F, 2009, NAT PHYS, V5, P633, DOI 10.1038/NPHYS1342
Vicentini F, 2018, PHYS REV A, V97, DOI 10.1103/PhysRevA.97.013853
Viteau M, 2012, PHYS REV LETT, V109, DOI 10.1103/PhysRevLett.109.053002
Weimer H, 2015, PHYS REV LETT, V114, DOI 10.1103/PhysRevLett.114.040402
Wilson RM, 2016, PHYS REV A, V94, DOI 10.1103/PhysRevA.94.033801
You JQ, 2011, NATURE, V474, P589, DOI 10.1038/nature10122
Zhang J, 2017, NATURE, V551, P601, DOI 10.1038/nature24654
NR 158
TC 8
Z9 8
U1 6
U2 9
PU AMER PHYSICAL SOC
PI COLLEGE PK
PA ONE PHYSICS ELLIPSE, COLLEGE PK, MD 20740-3844 USA
SN 2469-9950
EI 2469-9969
J9 PHYS REV B
JI Phys. Rev. B
PD JUN 1
PY 2020
VL 101
IS 21
AR 214302
DI 10.1103/PhysRevB.101.214302
PG 21
WC Materials Science, Multidisciplinary; Physics, Applied; Physics,
Condensed Matter
SC Materials Science; Physics
GA LT5YH
UT WOS:000537145900004
DA 2021-04-21
ER
PT J
AU Ustun, TS
Hussain, SMS
AF Ustun, Taha Selim
Hussain, S. M. Suhail
TI An Improved Security Scheme for IEC 61850 MMS Messages in Intelligent
Substation Communication Networks
SO JOURNAL OF MODERN POWER SYSTEMS AND CLEAN ENERGY
LA English
DT Article
DE Public key; Substations; Authentication; IEC Standards; Communication
networks; Real-time systems; IEC 62351 standard; certificate authority;
certificate-based authentication; cybersecurity; smart grid
AB Advanced connectivity in substations brings along cybersecurity considerations. Especially, the use of standardized data objects and message structures stipulated by IEC 61850 makes them much more vulnerable to unauthorized access and manipulation. In order to tackle these vulnerabilities, different methods are investigated by researchers all over the world. An important aspect of such efforts is the real-time performance consideration since power systems are bound by the rules of physics and all control/communication tasks need to be completed in a certain time frame. Security schemes for substation communication have been proposed in the recent literature. However, they must be improved to ensure a full security solution. Recently published IEC 62351 standard aims to fill this gap. Node authentication is vital for substation communication networks based on IEC 61850 to mitigate a variety of attacks such as man-in-the-middle (MITM) attack. This short communication presents a node authentication mechanism based on transport layer security (TLS) with certificates to address this knowledge gap. It also investigates the real-time performance by implementing the proposed scheme with Python.
C1 [Ustun, Taha Selim; Hussain, S. M. Suhail] AIST FREA, Fukushima Renewable Energy Inst, Fukushima, Japan.
[Ustun, Taha Selim] Res Inst Energy Frontier, Tsukuba, Ibaraki, Japan.
RP Ustun, TS (corresponding author), AIST FREA, Fukushima Renewable Energy Inst, Fukushima, Japan.
EM selim.ustun@aist.go.jp; Suhail.hussain@aist.go.jp
RI Hussain, S. M. Suhail/O-3552-2016
OI Hussain, S. M. Suhail/0000-0002-7779-8140
CR [Anonymous], 2007, 623511 IEC
[Anonymous], 2013, 618505 IEC
Barrett M.P, 2018, FRAMEWORK IMPROVING
Farooq SM, 2019, IEEE ACCESS, V7, P32343, DOI 10.1109/ACCESS.2019.2902571
Farooq SM, 2018, ELECTRONICS-SWITZ, V7, DOI 10.3390/electronics7120370
Housley R., 2013, INTERNET X 509 PUBLI
Hussain SMS, 2019, IEEE ACCESS, V7, P80980, DOI 10.1109/ACCESS.2019.2923728
Lee A., 2014, GUIDELINES SMART GRI
Ustun TS, 2019, IEEE ACCESS, V7, P156044, DOI 10.1109/ACCESS.2019.2948117
Zhang J, 2019, J MOD POWER SYST CLE, V7, P948, DOI 10.1007/s40565-019-0498-5
NR 10
TC 0
Z9 0
U1 1
U2 2
PU IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
PI PISCATAWAY
PA 445 HOES LANE, PISCATAWAY, NJ 08855-4141 USA
SN 2196-5625
EI 2196-5420
J9 J MOD POWER SYST CLE
JI J. Mod. Power Syst. Clean Energy
PD MAY
PY 2020
VL 8
IS 3
BP 591
EP 595
DI 10.35833/mpce.2019.000104
PG 5
WC Engineering, Electrical & Electronic
SC Engineering
GA OZ0PG
UT WOS:000594638200021
OA DOAJ Gold
DA 2021-04-21
ER
PT J
AU Abenza, ME
AF Abenza, Miguel Escudero
TI Precision early universe thermodynamics made simple: N-eff and neutrino
decoupling in the Standard Model and beyond
SO JOURNAL OF COSMOLOGY AND ASTROPARTICLE PHYSICS
LA English
DT Article
DE cosmological neutrinos; cosmology of theories beyond the SM; particle
physics - cosmology connection; physics of the early universe
ID BIG-BANG NUCLEOSYNTHESIS; NONEQUILIBRIUM CORRECTIONS; MASSLESS
NEUTRINOS; STERILE NEUTRINOS; DARK-MATTER; TEMPERATURE; SPECTRA; BOUNDS
AB Precision measurements of the number of effective relativistic neutrino species and the primordial element abundances require accurate theoretical predictions for early Universe observables in the Standard Model and beyond. Given the complexity of accurately modelling the thermal history of the early Universe, in this work, we extend a previous method presented by the author in [1] to obtain simple, fast and accurate early Universe thermodynamics. The method is based upon the approximation that all relevant species can be described by thermal equilibrium distribution functions characterized by a temperature and a chemical potential. We apply the method to neutrino decoupling in the Standard Model and find Na-eff(SM) = 3.045 a result in excellent agreement with previous state-of-the-art calculations. We apply the method to study the thermal history of the Universe in the presence of a very light (1 eV < m(phi) < 1 MeV) and weakly coupled (lambda less than or similar to 10(-9)) neutrinophilic scalar. We find our results to be in excellent agreement with the solution to the exact Liouville equation. Finally, we release a code: NUDEC_BSM (available in both Mathematica and Python formats), with which neutrino decoupling can be accurately and efficiently solved in the Standard Model and beyond: https://github.com/MiguelEA/nudec_BSM.
C1 [Abenza, Miguel Escudero] Kings Coll London, Theoret Particle Phys & Cosmol Grp, Dept Phys, London WC2R 2LS, England.
RP Abenza, ME (corresponding author), Kings Coll London, Theoret Particle Phys & Cosmol Grp, Dept Phys, London WC2R 2LS, England.
EM miguel.escudero@kcl.ac.uk
OI Escudero, Miguel/0000-0002-4487-8742
FU European Research Council under the European Union's Horizon 2020
program (ERC Grant) [648680 DARKHORIZONS]
FX I am grateful to Sam Witte, Chris McCabe, Sergio Pastor, Stefano
Gariazzo and Pablo F. de Salas for their very helpful comments and
suggestions over a draft version of this paper, and to Toni Pich for
useful correspondence. This work is supported by the European Research
Council under the European Union's Horizon 2020 program (ERC Grant
Agreement No 648680 DARKHORIZONS).
CR Abazajian K., ARXIV190704473
Aitken K, 2017, PHYS REV D, V96, DOI 10.1103/PhysRevD.96.075009
Alvey J, 2020, EUR PHYS J C, V80, DOI 10.1140/epjc/s10052-020-7727-y
Arbey A, 2020, COMPUT PHYS COMMUN, V248, DOI 10.1016/j.cpc.2019.106982
Balantekin AB, 2018, ANNU REV NUCL PART S, V68, P313, DOI 10.1146/annurev-nucl-101916-123044
Bennett JJ, 2020, J COSMOL ASTROPART P, DOI 10.1088/1475-7516/2020/03/003
Berlin A, 2019, PHYS REV D, V100, DOI 10.1103/PhysRevD.100.015038
Bernstein J., 1988, KINETIC THEORY EXPAN
Binder T, 2016, J COSMOL ASTROPART P, DOI 10.1088/1475-7516/2016/11/043
Birrell J, 2015, NUCL PHYS B, V890, P481, DOI 10.1016/j.nuclphysb.2014.11.020
Boehm C, 2013, J COSMOL ASTROPART P, DOI 10.1088/1475-7516/2013/08/041
Bringmann T, 2007, J COSMOL ASTROPART P, DOI 10.1088/1745-7516/2007/04/016
Bringmann T, 2009, NEW J PHYS, V11, DOI 10.1088/1367-2630/11/10/105027
Brust C, 2013, J HIGH ENERGY PHYS, DOI 10.1007/JHEP12(2013)058
Buen-Abad MA, 2015, PHYS REV D, V92, DOI 10.1103/PhysRevD.92.023531
Cadamuro D, 2011, J COSMOL ASTROPART P, DOI 10.1088/1475-7516/2011/02/003
Chacko Z, 2004, PHYS REV D, V70, DOI 10.1103/PhysRevD.70.085008
Chacko Z, 2015, PHYS REV D, V92, DOI 10.1103/PhysRevD.92.055033
CHIKASHIGE Y, 1981, PHYS LETT B, V98, P265, DOI 10.1016/0370-2693(81)90011-3
Cicoli M, 2013, PHYS REV D, V87, DOI 10.1103/PhysRevD.87.043520
Consiglio R, 2018, COMPUT PHYS COMMUN, V233, P237, DOI 10.1016/j.cpc.2018.06.022
Cooke RJ, 2016, ASTROPHYS J, V830, DOI 10.3847/0004-637X/830/2/148
CORE collaboration, 2018, JCAP, V04
Cuoco A, 2005, PHYS REV D, V71, DOI 10.1103/PhysRevD.71.123501
de Salas PF, 2015, PHYS REV D, V92, DOI 10.1103/PhysRevD.92.123534
de Salas PF, 2016, J COSMOL ASTROPART P, DOI 10.1088/1475-7516/2016/07/051
Diacoumis JAD, 2019, J COSMOL ASTROPART P, DOI 10.1088/1475-7516/2019/01/001
DICUS DA, 1982, PHYS REV D, V26, P2694, DOI 10.1103/PhysRevD.26.2694
DODELSON S, 1994, PHYS REV LETT, V72, P17, DOI 10.1103/PhysRevLett.72.17
DODELSON S, 1992, PHYS REV D, V46, P3372, DOI 10.1103/PhysRevD.46.3372
Dodelson S., 2003, MODERN COSMOLOGY
Dolgov AD, 2000, NUCL PHYS B, V590, P562, DOI 10.1016/S0550-3213(00)00566-6
Dolgov AD, 1999, NUCL PHYS B, V548, P385, DOI 10.1016/S0550-3213(99)00127-3
Dolgov AD, 1999, NUCL PHYS B, V543, P269, DOI 10.1016/S0550-3213(98)00818-9
Dolgov AD, 2002, PHYS REP, V370, P333, DOI 10.1016/S0370-1573(02)00139-4
Dolgov AD, 2002, NUCL PHYS B, V632, P363, DOI 10.1016/S0550-3213(02)00274-2
Dolgov AD, 1997, NUCL PHYS B, V503, P426, DOI 10.1016/S0550-3213(97)00479-3
Dvorkin C, 2019, PHYS REV D, V99, DOI 10.1103/PhysRevD.99.115009
Edsjo J, 1997, PHYS REV D, V56, P1879, DOI 10.1103/PhysRevD.56.1879
Elor G, 2019, PHYS REV D, V99, DOI 10.1103/PhysRevD.99.035031
ENQVIST K, 1992, NUCL PHYS B, V374, P392, DOI 10.1016/0550-3213(92)90359-J
Erler J, 2013, PROG PART NUCL PHYS, V71, P119, DOI 10.1016/j.ppnp.2013.03.004
Escudero M, 2020, EUR PHYS J C, V80, DOI 10.1140/epjc/s10052-020-7854-5
Escudero M, 2019, PHYS REV D, V100, DOI 10.1103/PhysRevD.100.103531
Escudero M, 2019, J HIGH ENERGY PHYS, DOI 10.1007/JHEP03(2019)071
Escudero M, 2019, J COSMOL ASTROPART P, DOI 10.1088/1475-7516/2019/02/007
Esposito S, 2000, NUCL PHYS B, V590, P539, DOI 10.1016/S0550-3213(00)00554-X
Esposito S, 2003, NUCL PHYS B, V658, P217, DOI 10.1016/S0550-3213(03)00151-2
FIELDS BD, 1993, PHYS REV D, V47, P4309, DOI 10.1103/PhysRevD.47.4309
Fixsen DJ, 2009, ASTROPHYS J, V707, P916, DOI 10.1088/0004-637X/707/2/916
Fornengo N, 1997, PHYS REV D, V56, P5123, DOI 10.1103/PhysRevD.56.5123
Fradette A, 2019, PHYS REV D, V99, DOI 10.1103/PhysRevD.99.075004
Froustey J, 2020, PHYS REV D, V101, DOI 10.1103/PhysRevD.101.043524
Gariazzo S, 2019, J COSMOL ASTROPART P, DOI 10.1088/1475-7516/2019/07/014
GELMINI GB, 1981, PHYS LETT B, V99, P411, DOI 10.1016/0370-2693(81)90559-1
GEORGI HM, 1981, NUCL PHYS B, V193, P297, DOI 10.1016/0550-3213(81)90336-9
GONDOLO P, 1991, NUCL PHYS B, V360, P145, DOI 10.1016/0550-3213(91)90438-4
Grohs E, 2016, PHYS REV D, V93, DOI 10.1103/PhysRevD.93.083522
Grohs E.B., ARXIV190309187
Hall LJ, 2010, J HIGH ENERGY PHYS, DOI 10.1007/JHEP03(2010)080
HANNESTAD S, 1995, PHYS REV D, V52, P1764, DOI 10.1103/PhysRevD.52.1764
Hannestad S, 2002, PHYS REV D, V65, DOI 10.1103/PhysRevD.65.083006
Hasegawa T, 2019, J COSMOL ASTROPART P, DOI 10.1088/1475-7516/2019/12/012
HECKLER AF, 1994, PHYS REV D, V49, P611, DOI 10.1103/PhysRevD.49.611
Hu W.T., 1995, THESIS
Ibe M, 2020, J HIGH ENERGY PHYS, DOI 10.1007/JHEP04(2020)009
Iocco F, 2009, PHYS REP, V472, P1, DOI 10.1016/j.physrep.2009.02.002
Kane G, 2020, J COSMOL ASTROPART P, DOI 10.1088/1475-7516/2020/02/019
Kawasaki M, 2000, PHYS REV D, V62, DOI 10.1103/PhysRevD.62.023506
KAWASAKI M, 1993, NUCL PHYS B, V403, P671, DOI 10.1016/0550-3213(93)90366-W
Kawasaki M, 2018, PHYS REV D, V97, DOI 10.1103/PhysRevD.97.023502
Kolb EW., 1990, FRONT PHYS-BEIJING, V69, P1
Kreisch C.D., ARXIV190200534
Landau L.D., 1980, STAT PHYS 1, V5
MA CP, 1995, ASTROPHYS J, V455, P7, DOI 10.1086/176550
Mangano G, 2005, NUCL PHYS B, V729, P221, DOI 10.1016/j.nuclphysb.2005.09.041
Mangano G, 2002, PHYS LETT B, V534, P8, DOI 10.1016/S0370-2693(02)01622-2
NASA PICO collaboration, ARXIV190210541 NASA
Nelson AE, 2019, PHYS REV D, V100, DOI 10.1103/PhysRevD.100.075002
PEEBLES PJE, 1968, ASTROPHYS J, V153, P1, DOI 10.1086/149628
Pisanti O, 2008, COMPUT PHYS COMMUN, V178, P956, DOI 10.1016/j.cpc.2008.02.015
Pitrou C, 2018, PHYS REP, V754, P1, DOI 10.1016/j.physrep.2018.04.005
Planck Collaboration, ARXIV180706209 PLANC
Planck Collaboration, ARXIV180706205 PLANC
Pospelov M, 2010, ANNU REV NUCL PART S, V60, P539, DOI 10.1146/annurev.nucl.012809.104521
Sabti N, 2020, J COSMOL ASTROPART P, DOI 10.1088/1475-7516/2020/01/004
Sarkar S, 1996, REP PROG PHYS, V59, P1493, DOI 10.1088/0034-4885/59/12/001
SCHECHTER J, 1982, PHYS REV D, V25, P774, DOI 10.1103/PhysRevD.25.774
Sehgal N., ARXIV190610134
Serpico PD, 2004, J COSMOL ASTROPART P, DOI 10.1088/1475-7516/2004/12/010
Shi XD, 1999, PHYS REV LETT, V82, P2832, DOI 10.1103/PhysRevLett.82.2832
Simons Observatory collaboration, 2019, JCAP, V2
SPT-3G collaboration, 2014, P SPIE INT SOC OPT E, V9153
STARKMAN GD, 1994, ASTROPHYS J, V434, P12, DOI 10.1086/174700
Tanabashi M, 2018, PHYS REV D, V98, DOI 10.1103/PhysRevD.98.030001
Venumadhav T, 2016, PHYS REV D, V94, DOI 10.1103/PhysRevD.94.043515
Vogel H, 2014, J COSMOL ASTROPART P, DOI 10.1088/1475-7516/2014/02/029
Weinberg S, 2013, PHYS REV LETT, V110, DOI 10.1103/PhysRevLett.110.241301
WEYMANN R, 1965, PHYS FLUIDS, V8, P2112, DOI 10.1063/1.1761165
Wilkinson RJ, 2016, PHYS REV D, V94, DOI 10.1103/PhysRevD.94.103525
NR 100
TC 16
Z9 16
U1 0
U2 1
PU IOP PUBLISHING LTD
PI BRISTOL
PA TEMPLE CIRCUS, TEMPLE WAY, BRISTOL BS1 6BE, ENGLAND
SN 1475-7516
J9 J COSMOL ASTROPART P
JI J. Cosmol. Astropart. Phys.
PD MAY
PY 2020
IS 5
AR 048
DI 10.1088/1475-7516/2020/05/048
PG 49
WC Astronomy & Astrophysics; Physics, Particles & Fields
SC Astronomy & Astrophysics; Physics
GA LR0MS
UT WOS:000535391600049
DA 2021-04-21
ER
PT J
AU Vavilala, VS
AF Vavilala, Vaibhav S.
TI Combining high-performance hardware, cloud computing, and deep learning
frameworks to accelerate physical simulations: probing the Hopfield
network
SO EUROPEAN JOURNAL OF PHYSICS
LA English
DT Article
DE Hopfield network; computational physics; graphics processing unit; cloud
computing; high-performance computing; deep learning; python
ID NEURAL-NETWORKS
AB The synthesis of high-performance computing (particularly graphics processing units), cloud computing services (like Google Colab), and high-level deep learning frameworks (such as PyTorch) has powered the burgeoning field of artificial intelligence. While these technologies are popular in the computer science discipline, the physics community is less aware of how such innovations, freely available online, can improve research and education. In this tutorial, we take the Hopfield network as an example to show how the confluence of these fields can dramatically accelerate physics-based computer simulations and remove technical barriers in implementing such programs, thereby making physics experimentation and education faster and more accessible. To do so, we introduce the cloud, the GPU, and AI frameworks that can be easily repurposed for physics simulation. We then introduce the Hopfield network and explain how to produce large-scale simulations and visualizations for free in the cloud with very little code (fully self-contained in the text). Finally, we suggest programming exercises throughout the paper, geared towards advanced undergraduate students studying physics, biophysics, or computer science.
C1 [Vavilala, Vaibhav S.] Columbia Univ, Dept Comp Sci, New York, NY 10027 USA.
RP Vavilala, VS (corresponding author), Columbia Univ, Dept Comp Sci, New York, NY 10027 USA.
EM vsv2109@columbia.edu
FU DJ Angus Foundation Summer Research Program; NSFNational Science
Foundation (NSF) [DMR-1054020]
FX The author thanks Professor Yogesh N Joglekar for helpful conversations,
comments, and revisions. This work was supported by the DJ Angus
Foundation Summer Research Program and NSF Grant No. DMR-1054020.
CR Abadi M., 2016, ARXIV160304467
Akenine-Moller T., 2008, REAL TIME RENDERING
Amit D. J., 1989, MODELING BRAIN FUNCT
AMIT DJ, 1987, ANN PHYS-NEW YORK, V173, P30, DOI 10.1016/0003-4916(87)90092-3
Baldi P, 2014, NAT COMMUN, V5, DOI 10.1038/ncomms5308
Bar-Yam Y., 1997, DYNAMICS COMPLEX SYS
Bharitkar S, 2000, IEEE T NEURAL NETWOR, V11, P879, DOI 10.1109/72.857769
Caballero MD, 2014, AM J PHYS, V82, P231, DOI 10.1119/1.4837437
Carneiro T, 2018, IEEE ACCESS, V6, P61677, DOI 10.1109/ACCESS.2018.2874767
Coates Adam, 2013, P 30 INT C MACH LEAR, P1337
Goodfellow I, 2016, DEEP LEARNING
Gopalsamy K, 2007, NONLINEAR ANAL-REAL, V8, P375, DOI 10.1016/j.nonrwa.2005.11.010
Hertz J., 1991, INTRO THEORY NEURAL
HOPFIELD JJ, 1982, P NATL ACAD SCI-BIOL, V79, P2554, DOI 10.1073/pnas.79.8.2554
Jouppi NP, 2018, IEEE MICRO, V38, P10, DOI 10.1109/MM.2018.032271057
Kavan L, 2011, ACM T GRAPHIC, V30, DOI 10.1145/1964921.1964988
Kazemi F, 2008, EL COMP ENG 2008 CCE, P001855
Kim B, 2019, COMPUT GRAPH FORUM, V38, P59, DOI 10.1111/cgf.13619
Li Y, 2004, SICE 2004 ANNUAL CONFERENCE, VOLS 1-3, P999
Li Y, 2005, IEEE T CIRCUITS-I, V52, P200, DOI 10.1109/TCSI.2004.838146
MCCLELLAND JL, 1988, BEHAV RES METH INSTR, V20, P263, DOI 10.3758/BF03203842
MCELIECE RJ, 1987, IEEE T INFORM THEORY, V33, P461, DOI 10.1109/TIT.1987.1057328
Mehta P, 2019, PHYS REP, V810, P1, DOI 10.1016/j.physrep.2019.03.001
Okuta R., 2017, WORKSH MACH LEARN SY
Paszke A., 2019, ADV NEURAL INFORM PR, V32, P8024
RUMELHART DE, 1986, NATURE, V323, P533, DOI 10.1038/323533a0
Schutt KT, 2019, J CHEM THEORY COMPUT, V15, P448, DOI 10.1021/acs.jctc.8b00908
Singh MP, 2001, PHYS REV E, V64, DOI 10.1103/PhysRevE.64.051912
Tariq S, 2008, SIGGRAPH 08
Tsuboshita Y, 2010, J PHYS SOC JPN, V79, DOI 10.1143/JPSJ.79.024002
Widodo W., 2018, IOP Conference Series: Materials Science and Engineering, V434, DOI 10.1088/1757-899X/434/1/012034
NR 31
TC 0
Z9 0
U1 1
U2 23
PU IOP PUBLISHING LTD
PI BRISTOL
PA TEMPLE CIRCUS, TEMPLE WAY, BRISTOL BS1 6BE, ENGLAND
SN 0143-0807
EI 1361-6404
J9 EUR J PHYS
JI Eur. J. Phys.
PD MAY
PY 2020
VL 41
IS 3
AR 035802
DI 10.1088/1361-6404/ab7027
PG 15
WC Education, Scientific Disciplines; Physics, Multidisciplinary
SC Education & Educational Research; Physics
GA KW7IS
UT WOS:000521358100001
DA 2021-04-21
ER
PT J
AU van der Gucht, J
Davelaar, J
Hendriks, L
Porth, O
Olivares, H
Mizuno, Y
Fromm, CM
Falcke, H
AF van der Gucht, Jeffrey
Davelaar, Jordy
Hendriks, Luc
Porth, Oliver
Olivares, Hector
Mizuno, Yosuke
Fromm, Christian M.
Falcke, Heino
TI Deep Horizon: A machine learning network that recovers accreting black
hole parameters
SO ASTRONOMY & ASTROPHYSICS
LA English
DT Article
DE accretion; accretion disks; black hole physics; radiative transfer;
methods; data analysis
ID STRONG GRAVITATIONAL LENSES; SCATTER-BROADENED IMAGE; GRMHD SIMULATIONS;
CLASSIFICATION; FIELD; STAR; PYTHON; ARRAY; SHAPE; JETS
AB Context. The Event Horizon Telescope recently observed the first shadow of a black hole. Images like this can potentially be used to test or constrain theories of gravity and deepen the understanding in plasma physics at event horizon scales, which requires accurate parameter estimations.
Aims. In this work, we present Deep Horizon, two convolutional deep neural networks that recover the physical parameters from images of black hole shadows. We investigate the effects of a limited telescope resolution and observations at higher frequencies.
Methods. We trained two convolutional deep neural networks on a large image library of simulated mock data. The first network is a Bayesian deep neural regression network and is used to recover the viewing angle i, and position angle, mass accretion rate & x1e40;, electron heating prescription R-high and the black hole mass M-BH. The second network is a classification network that recovers the black hole spin a.
Results. We find that with the current resolution of the Event Horizon Telescope, it is only possible to accurately recover a limited number of parameters of a static image, namely the mass and mass accretion rate. Since potential future space-based observing missions will operate at frequencies above 230 GHz, we also investigated the applicability of our network at a frequency of 690 GHz. The expected resolution of space-based missions is higher than the current resolution of the Event Horizon Telescope, and we show that Deep Horizon can accurately recover the parameters of simulated observations with a comparable resolution to such missions.
C1 [van der Gucht, Jeffrey; Davelaar, Jordy; Hendriks, Luc; Falcke, Heino] Radboud Univ Nijmegen, Dept Astrophys IMAPP, POB 9010, NL-6500 GL Nijmegen, Netherlands.
[Davelaar, Jordy] Flatiron Inst, Ctr Computat Astrophys, 162 Fifth Ave, New York, NY 10010 USA.
[Porth, Oliver; Olivares, Hector; Mizuno, Yosuke; Fromm, Christian M.] Inst Theoret Phys, Max von Laue Str 1, D-60438 Frankfurt, Germany.
[Porth, Oliver] Univ Amsterdam, Anton Pannekoek Inst, POB 94249, NL-1090 GE Amsterdam, Netherlands.
[Fromm, Christian M.] Max Planck Inst Radio Astron, Huegel 69, D-53115 Bonn, Germany.
RP Davelaar, J (corresponding author), Radboud Univ Nijmegen, Dept Astrophys IMAPP, POB 9010, NL-6500 GL Nijmegen, Netherlands.; Davelaar, J (corresponding author), Flatiron Inst, Ctr Computat Astrophys, 162 Fifth Ave, New York, NY 10010 USA.
EM j.davelaar@astro.ru.nl
RI Mizuno, Yosuke/D-5656-2017
OI Mizuno, Yosuke/0000-0002-8131-6730; Falcke, Heino/0000-0002-2526-6724;
Davelaar, Jordy/0000-0002-2685-2434
FU ERC Synergy Grant "BlackHoleCam-Imaging the Event Horizon of Black
Holes" [610058]; Simons Foundation
FX The authors thank S. Caron, B. Stienen, C.F. Gammie, J. Lin, M. Johnson,
and L. Rezzolla for valuable discussions and feedback during the
project, and the two anonymous referees for their constructive comments
on our manuscript. This work was funded by the ERC Synergy Grant
"BlackHoleCam-Imaging the Event Horizon of Black Holes" (Grant 610058,
Goddi et al. 2017). The Simons Foundation supports the Flatiron
Institute. The GRMHD simulations were performed on the LOEWE cluster in
CSC in Frankfurt, and the ray-tracing simulations on COMA in Nijmegen.
This research has made use of NASA's Astrophysics Data System. The
results and analyses presented in this manuscript were done with the use
of the following software: python (Oliphant 2007; Millman & Aivazis
2011), scipy (Jones et al. 2001), numpy (van derWalt et al. 2011), and
matplotlib (Hunter 2007).
CR Akiyama K, 2019, ASTROPHYS J LETT, V875, DOI 10.3847/2041-8213/ab1141
Akiyama K, 2015, ASTROPHYS J, V807, DOI 10.1088/0004-637X/807/2/150
[Anonymous], 2019, ASTROPHYS J LETT, V875, pL1, DOI DOI 10.3847/2041-8213/AB0F43
[Anonymous], 2019, ASTROPHYS J LETT, V875, pL1, DOI DOI 10.3847/2041-8213/AB0C57
[Anonymous], 2019, ASTROPHYS J LETT, V875, pL1, DOI DOI 10.3847/2041-8213/AB0EC7
[Anonymous], 2019, ASTROPHYS J LETT, V875, pL1, DOI [10.3847/2041-8213/ab0c96, DOI 10.3847/2041-8213/AB0C96]
[Anonymous], 2019, ASTROPHYS J LETT, V875, pL1, DOI DOI 10.3847/2041-8213/AB0E85
Ball NM, 2006, ASTROPHYS J, V650, P497, DOI 10.1086/507440
Bellinger EP, 2016, ASTROPHYS J, V830, DOI 10.3847/0004-637X/830/1/31
Bird S, 2010, ASTRON ASTROPHYS, V524, DOI 10.1051/0004-6361/201014876
BISNOVATYIKOGAN GS, 1976, ASTROPHYS SPACE SCI, V42, P401, DOI 10.1007/BF01225967
Bower GC, 2006, ASTROPHYS J, V648, pL127, DOI 10.1086/508019
Broderick A. E., 2020, APJ UNPUB
Bronzwaer T, 2018, ASTRON ASTROPHYS, V613, DOI 10.1051/0004-6361/201732149
Cantiello M, 2018, ASTROPHYS J, V856, DOI 10.3847/1538-4357/aab043
Chael A. A., 2019, ASCL1904004
Chael A, 2019, MON NOT R ASTRON SOC, V486, P2873, DOI 10.1093/mnras/stz988
Chandra M, 2015, ASTROPHYS J, V810, DOI 10.1088/0004-637X/810/2/162
Chollet F., 2015, KERAS
Davelaar J, 2018, ASTRON ASTROPHYS, V612, DOI 10.1051/0004-6361/201732025
Davelaar J, 2019, ASTRON ASTROPHYS, V632, DOI 10.1051/0004-6361/201936150
Dexter J, 2012, MON NOT R ASTRON SOC, V421, P1517, DOI 10.1111/j.1365-2966.2012.20409.x
Fadely R, 2012, ASTROPHYS J, V760, DOI 10.1088/0004-637X/760/1/15
Falcke H, 2000, ASTROPHYS J, V528, pL13, DOI 10.1086/312423
Fan XL, 2019, SCI CHINA PHYS MECH, V62, DOI 10.1007/s11433-018-9321-7
Fish VL, 2020, ADV SPACE RES, V65, P821, DOI 10.1016/j.asr.2019.03.029
FISHBONE LG, 1976, ASTROPHYS J, V207, P962, DOI 10.1086/154565
Fromm CM, 2019, ASTRON ASTROPHYS, V629, DOI 10.1051/0004-6361/201834724
Gal Y., 2015, ARXIV150602158
Gal Y, 2016, THESIS
Gal Y., 2015, ARXIV150602142
Gebhardt K, 2011, ASTROPHYS J, V729, DOI 10.1088/0004-637X/729/2/119
George D, 2018, PHYS LETT B, V778, P64, DOI 10.1016/j.physletb.2017.12.053
Goddi C, 2017, INT J MOD PHYS D, V26, DOI 10.1142/S0218271817300014
GOODMAN J, 1989, MON NOT R ASTRON SOC, V238, P995, DOI 10.1093/mnras/238.3.995
Gralla SE, 2019, PHYS REV D, V100, DOI 10.1103/PhysRevD.100.024018
Hastie T., 2001, SPRINGER SERIES STAT
He K., 2015, DEEP RESIDUAL LEARNI
Hendriks L, 2019, PUBL ASTRON SOC PAC, V131, DOI 10.1088/1538-3873/aaeeec
Hezaveh YD, 2017, NATURE, V548, P555, DOI 10.1038/nature23463
Hon M, 2017, MON NOT R ASTRON SOC, V469, P4578, DOI 10.1093/mnras/stx1174
Howes GG, 2010, MON NOT R ASTRON SOC, V409, pL104, DOI 10.1111/j.1745-3933.2010.00958.x
Hunter JD, 2007, COMPUT SCI ENG, V9, P90, DOI 10.1109/MCSE.2007.55
Jacobs C, 2017, MON NOT R ASTRON SOC, V471, P167, DOI 10.1093/mnras/stx1492
Johannsen T, 2010, ASTROPHYS J, V718, P446, DOI 10.1088/0004-637X/718/1/446
Johnson MD, 2020, SCI ADV, V6, DOI 10.1126/sciadv.aaz1310
Johnson MD, 2015, ASTROPHYS J, V805, DOI 10.1088/0004-637X/805/2/180
Jones E., 2001, SCIPY OPEN SOURCE SC
Kendall A., 2017, ADV NEURAL INFORM PR, P5574
KERR RP, 1963, PHYS REV LETT, V11, P237, DOI 10.1103/PhysRevLett.11.237
Kim EJ, 2017, MON NOT R ASTRON SOC, V464, P4463, DOI 10.1093/mnras/stw2672
Kim EJ, 2015, MON NOT R ASTRON SOC, V453, P507, DOI 10.1093/mnras/stv1608
Kingma D.P., 2014, ARXIV 14126980, DOI DOI 10.1145/1830483.1830503
Kiureghian AD, 2009, STRUCT SAF, V31, P105, DOI 10.1016/j.strusafe.2008.06.020
Krizhevsky Alex, 2012, ADV NEURAL INFORM PR, P1097, DOI DOI 10.1145/3065386
Lecun Y, 1998, P IEEE, V86, P2278, DOI 10.1109/5.726791
Levasseur LP, 2017, ASTROPHYS J LETT, V850, DOI 10.3847/2041-8213/aa9704
Lukic V., 2017, IAU S, V325, P217
MACKAY DJC, 1992, NEURAL COMPUT, V4, P415, DOI 10.1162/neco.1992.4.3.415
Martin Abadi, 2015, TENSORFLOW LARGE SCA
Millman KJ, 2011, COMPUT SCI ENG, V13, P9, DOI 10.1109/MCSE.2011.36
Mizuno Y, 2018, NAT ASTRON, V2, P585, DOI 10.1038/s41550-018-0449-5
Moscibrodzka M, 2017, MON NOT R ASTRON SOC, V468, P2214, DOI 10.1093/mnras/stx587
Moscibrodzka M, 2016, ASTRON ASTROPHYS, V586, DOI 10.1051/0004-6361/201526630
Nair V, 2010, ICML, V27, P807, DOI DOI 10.0RG/PAPERS/432.PDF
NARAYAN R, 1989, MON NOT R ASTRON SOC, V238, P963, DOI 10.1093/mnras/238.3.963
Narayan R, 2003, PUBL ASTRON SOC JPN, V55, pL69, DOI 10.1093/pasj/55.6.L69
Narayan R, 2019, ASTROPHYS J LETT, V885, DOI 10.3847/2041-8213/ab518c
Narayan R, 2012, MON NOT R ASTRON SOC, V426, P3241, DOI 10.1111/j.1365-2966.2012.22002.x
ODEWAHN SC, 1992, ASTROPHYS SPACE SC L, V174, P215
Oliphant TE, 2007, COMPUT SCI ENG, V9, P10, DOI 10.1109/MCSE.2007.58
Olivares H, 2019, ASTRON ASTROPHYS, V629, DOI 10.1051/0004-6361/201935559
Palumbo D., 2018, AM ASTR SOC M, V231, P347
Petrillo CE, 2017, MON NOT R ASTRON SOC, V472, P1129, DOI 10.1093/mnras/stx2052
Porth Oliver, 2017, Computational Astrophysics and Cosmology, V4, DOI 10.1186/s40668-017-0020-2
Porth O, 2019, ASTROPHYS J SUPPL S, V243, DOI 10.3847/1538-4365/ab29fd
Psaltis D, 2015, ASTROPHYS J, V814, DOI 10.1088/0004-637X/814/2/115
Ressler SM, 2015, MON NOT R ASTRON SOC, V454, P1848, DOI 10.1093/mnras/stv2084
Roelofs F, 2019, ASTRON ASTROPHYS, V625, DOI 10.1051/0004-6361/201732423
Rowan ME, 2017, ASTROPHYS J, V850, DOI 10.3847/1538-4357/aa9380
Ryan BR, 2018, ASTROPHYS J, V864, DOI 10.3847/1538-4357/aad73a
Schwarkschid K, 1916, SITZBER K PREUSS AKA, P189
Sevilla-Noarbe I, 2015, ASTRON COMPUT, V11, P64, DOI 10.1016/j.ascom.2015.03.010
Shen H., 2019, ARXIV190301998
Simonyan K., 2014, arXiv: 1409.1556, DOI DOI 10.1109/CVPR.2015.7298594
Srivastava N, 2014, J MACH LEARN RES, V15, P1929
Suchkov AA, 2005, ASTRON J, V130, P2439, DOI 10.1086/497363
Szegedy C, 2015, PROC CVPR IEEE, P1, DOI 10.1109/CVPR.2015.7298594
Tchekhovskoy A, 2011, MON NOT R ASTRON SOC, V418, pL79, DOI 10.1111/j.1745-3933.2011.01147.x
van der Walt S, 2011, COMPUT SCI ENG, V13, P22, DOI 10.1109/MCSE.2011.37
Vasconcellos EC, 2011, ASTRON J, V141, DOI 10.1088/0004-6256/141/6/189
Walker RC, 2018, ASTROPHYS J, V855, DOI 10.3847/1538-4357/aaafcc
Walsh JL, 2013, ASTROPHYS J, V770, DOI 10.1088/0004-637X/770/2/86
Weir N, 1995, PUBL ASTRON SOC PAC, V107, P1243, DOI 10.1086/133683
Zeiler MD, 2014, LECT NOTES COMPUT SC, V8689, P818, DOI 10.1007/978-3-319-10590-1_53
NR 95
TC 2
Z9 2
U1 0
U2 0
PU EDP SCIENCES S A
PI LES ULIS CEDEX A
PA 17, AVE DU HOGGAR, PA COURTABOEUF, BP 112, F-91944 LES ULIS CEDEX A,
FRANCE
SN 0004-6361
EI 1432-0746
J9 ASTRON ASTROPHYS
JI Astron. Astrophys.
PD APR 24
PY 2020
VL 636
AR A94
DI 10.1051/0004-6361/201937014
PG 12
WC Astronomy & Astrophysics
SC Astronomy & Astrophysics
GA LJ4BM
UT WOS:000530111800010
OA Bronze
DA 2021-04-21
ER
PT J
AU Bobra, MG
Mumford, SJ
Hewett, RJ
Christe, SD
Reardon, K
Savage, S
Ireland, J
Pereira, TMD
Chen, B
Perez-Suarez, D
AF Bobra, Monica G.
Mumford, Stuart J.
Hewett, Russell J.
Christe, Steven D.
Reardon, Kevin
Savage, Sabrina
Ireland, Jack
Pereira, Tiago M. D.
Chen, Bin
Perez-Suarez, David
TI A Survey of Computational Tools in Solar Physics
SO SOLAR PHYSICS
LA English
DT Article
DE Instrumentation and data management
AB The SunPy Project developed a 13-question survey to understand the software and hardware usage of the solar-physics community. Of the solar-physics community, 364 members across 35 countries responded to our survey. We found that 99 +/- 0.5 of respondents use software in their research and 66% use the Python scientific-software stack. Students are twice as likely as faculty, staff scientists, and researchers to use Python rather than Interactive Data Language (IDL). In this respect, the astrophysics and solar-physics communities differ widely: 78% of solar-physics faculty, staff scientists, and researchers in our sample uses IDL, compared with 44% of astrophysics faculty and scientists sampled by Momcheva and Tollerud (2015). 63 +/- 4 of respondents have not taken any computer-science courses at an undergraduate or graduate level. We also found that most respondents use consumer hardware to run software for solar-physics research. Although 82% of respondents work with data from space-based or ground-based missions, some of which (e.g. the Solar Dynamics Observatory and Daniel K. Inouye Solar Telescope) produce terabytes of data a day, 14% use a regional or national cluster, 5% use a commercial cloud provider, and 29% use exclusively a laptop or desktop. Finally, we found that 73 +/- 4 of respondents cite scientific software in their research, although only 42 +/- 3 do so routinely.
C1 [Bobra, Monica G.] Stanford Univ, WW Hansen Expt Phys Lab, Stanford, CA 94305 USA.
[Mumford, Stuart J.] Univ Sheffield, Sch Math & Stat, SP2RC, Sheffield, SP, England.
[Mumford, Stuart J.] Aperio Software Ltd, Headingley Enterprise & Arts Ctr, Bennett Rd, Leeds, W Yorkshire, England.
[Hewett, Russell J.] Virginia Polytech Inst & State Univ, Dept Math, Blacksburg, VA 24061 USA.
[Christe, Steven D.; Ireland, Jack] NASA, Goddard Space Flight Ctr, Greenbelt, MD 20771 USA.
[Reardon, Kevin] Natl Solar Observ, Boulder, CO 80303 USA.
[Savage, Sabrina] NASA, Marshall Space Flight Ctr, Huntsville, AL 35812 USA.
[Pereira, Tiago M. D.] Univ Oslo, Inst Theoret Astrophys, Oslo, Norway.
[Pereira, Tiago M. D.] Univ Oslo, Rosseland Ctr Solar Phys, Oslo, Norway.
[Chen, Bin] New Jersey Inst Technol, Ctr Solar Terr Res, Newark, NJ 07102 USA.
[Perez-Suarez, David] UCL, London, England.
RP Mumford, SJ (corresponding author), Univ Sheffield, Sch Math & Stat, SP2RC, Sheffield, SP, England.; Mumford, SJ (corresponding author), Aperio Software Ltd, Headingley Enterprise & Arts Ctr, Bennett Rd, Leeds, W Yorkshire, England.
EM stuart.mumford@sheffield.ac.uk
RI Chen, Bin/W-4943-2017
OI Chen, Bin/0000-0002-0660-3350; Bobra, Monica/0000-0002-5662-9604;
Savage, Sabrina/0000-0002-6172-0517; Perez-Suarez,
David/0000-0003-0784-6909
CR [Anonymous], 2018, OPEN SOURCE SOFTWARE
[Anonymous], 2020, PROGR IMPLEMENTATION
Barnes WT, 2020, ASTROPHYS J, V890, DOI 10.3847/1538-4357/ab4f7a
Bauer A.E., 2019, PETABYTES SCI
Bobra M., 2020, SUNPY SURVEY SURVEY
Buckheit J. B., 1995, WAVELAB REPRODUCIBLE
Caswell Thomas A, 2020, MATPLOTLIB MATPLOTLI
Claerbout J.F., 1992, TECH PROGRAM EXPANDE, P601
Eghbal N, 2016, ROADS BRIDGES UNSEEN
Freeland SL, 1998, SOL PHYS, V182, P497, DOI 10.1023/A:1005038224881
Guo P., 2014, PYTHON IS NOW MOST P
Hunter JD, 2007, COMPUT SCI ENG, V9, P90, DOI 10.1109/MCSE.2007.55
Millman J., 2010, P56, DOI DOI 10.1016/S0168-0102(02)00204-3
Momcheva I., 2015, SOFTWARE USE ASTRONO
Mumford S.J., 2020, SUNPY
Pedregosa F, 2011, J MACH LEARN RES, V12, P2825
Price-Whelan AM, 2018, ASTRON J, V156, DOI 10.3847/1538-3881/aabc4f
Reback J., 2020, PANDAS DEV PANDAS PA, DOI 10.5281/zenodo.3509134
Rocklin M., 2015, P 14 PYTH SCI C, P126, DOI 10.25080/Majora-7b98e3ed-013
Ruede U, 2018, SIAM REV, V60, P707, DOI 10.1137/16M1096840
Taylor J.R., 1997, INTRO ERROR ANAL STU
Tollerud E., 2019, B AM ASTRON SOC, P180
van der Walt S, 2011, COMPUT SCI ENG, V13, P22, DOI 10.1109/MCSE.2011.37
VanderPlas J., 2012, 2012 Conference on Intelligent Data Understanding (CIDU 2012), P47, DOI 10.1109/CIDU.2012.6382200
Virtanen P, 2020, NAT METHODS, V17, P261, DOI 10.1038/s41592-019-0686-2
Waskom M., 2020, MWASKOM SEABORN V0 1, DOI 10.5281/zenodo.592845
2019, REPRODUCIBILITY REPL
NR 27
TC 0
Z9 0
U1 0
U2 0
PU SPRINGER
PI DORDRECHT
PA VAN GODEWIJCKSTRAAT 30, 3311 GZ DORDRECHT, NETHERLANDS
SN 0038-0938
EI 1573-093X
J9 SOL PHYS
JI Sol. Phys.
PD APR 20
PY 2020
VL 295
IS 4
AR 57
DI 10.1007/s11207-020-01622-2
PG 15
WC Astronomy & Astrophysics
SC Astronomy & Astrophysics
GA LI2CV
UT WOS:000529291400002
OA Green Published, Other Gold
DA 2021-04-21
ER
PT J
AU Raaijmakers, G
Greif, SK
Riley, TE
Hinderer, T
Hebeler, K
Schwenk, A
Watts, AL
Nissanke, S
Guillot, S
Lattimer, JM
Ludlam, RM
AF Raaijmakers, G.
Greif, S. K.
Riley, T. E.
Hinderer, T.
Hebeler, K.
Schwenk, A.
Watts, A. L.
Nissanke, S.
Guillot, S.
Lattimer, J. M.
Ludlam, R. M.
TI Constraining the Dense Matter Equation of State with Joint Analysis of
NICER and LIGO/Virgo Measurements
SO ASTROPHYSICAL JOURNAL LETTERS
LA English
DT Article
DE Neutron star cores; Gravitational waves; Millisecond pulsars; Rotation
powered pulsars; Bayesian statistics; Nuclear physics; Nuclear
astrophysics; X-ray astronomy
ID NEUTRON-STAR; EFFICIENT; PYTHON
AB The Neutron Star Interior Composition Explorer collaboration recently published a joint estimate of the mass and the radius of PSR J0030+0451, derived via X-ray pulse-profile modeling. Raaijmakers et al. explored the implications of this measurement for the dense matter equation of state (EOS) using two parameterizations of the high-density EOS: a piecewise-polytropic model, and a model based on the speed of sound in neutron stars (NSs). In this work we obtain further constraints on the EOS following this approach, but we also include information about the tidal deformability of NSs from the gravitational wave signal of the compact binary merger GW170817. We compare the constraints on the EOS to those set by the recent measurement of a 2.14 M pulsar, included as a likelihood function approximated by a Gaussian, and find a small increase in information gain. To show the flexibility of our method, we also explore the possibility that GW170817 was a NS-black hole merger, which yields weaker constraints on the EOS.
C1 [Raaijmakers, G.; Hinderer, T.; Nissanke, S.] Univ Amsterdam, Astron Inst Anton Pannekoek, GRAPPA, Sci Pk 904, NL-1098 XH Amsterdam, Netherlands.
[Raaijmakers, G.; Hinderer, T.; Guillot, S.] Univ Amsterdam, Inst High Energy Phys, Sci Pk 904, NL-1098 XH Amsterdam, Netherlands.
[Greif, S. K.; Hebeler, K.; Schwenk, A.] Tech Univ Darmstadt, Inst Kernphys, D-64289 Darmstadt, Germany.
[Greif, S. K.; Hebeler, K.; Schwenk, A.] GSI Helmholtzzentrum Schwerionenforsch, ExtreMe Matter Inst EMMI, D-64291 Darmstadt, Germany.
[Riley, T. E.; Watts, A. L.] Univ Amsterdam, Astron Inst Anton Pannekoek, Sci Pk 904, NL-1090 GE Amsterdam, Netherlands.
[Hinderer, T.] Delta Inst Theoret Phys, Sci Pk 904, NL-1090 GL Amsterdam, Netherlands.
[Schwenk, A.] Max Planck Inst Kernphys, Saupfercheckweg 1, D-69117 Heidelberg, Germany.
[Nissanke, S.] Nikhef, Sci Pk 105, NL-1098 XG Amsterdam, Netherlands.
[Guillot, S.] CNRS, IRAP, 9 Ave Colonel Roche,BP 44346, F-31028 Toulouse 4, France.
[Guillot, S.] Univ Toulouse, CNES, UPS OMP, F-31028 Toulouse, France.
[Lattimer, J. M.] SUNY Stony Brook, Dept Phys & Astron, Stony Brook, NY 11794 USA.
[Ludlam, R. M.] CALTECH, Cahill Ctr Astron & Astrophys, Pasadena, CA 91125 USA.
RP Raaijmakers, G (corresponding author), Univ Amsterdam, Astron Inst Anton Pannekoek, GRAPPA, Sci Pk 904, NL-1098 XH Amsterdam, Netherlands.; Raaijmakers, G (corresponding author), Univ Amsterdam, Inst High Energy Phys, Sci Pk 904, NL-1098 XH Amsterdam, Netherlands.
EM G.Raaijmakers@uva.nl
OI Greif, Svenja/0000-0001-8641-2062; Schwenk, Achim/0000-0001-8027-4076
FU NASA through the NICER mission and the Astrophysics Explorers Program;
Nederlandse Organisatie voor Wetenschappelijk Onderzoek (NWO) through
the VIDI; ERCEuropean Research Council (ERC)European Commission
[639217]; NWO Exact and Natural Sciences; SURF Cooperative; Deutsche
Forschungsgemeinschaft (DFG, German Research Foundation)German Research
Foundation (DFG) [279384907-SFB 1245]; CNESCentre National D'etudes
Spatiales; NASANational Aeronautics & Space Administration (NASA)
[HST-HF2-51440.001]; NASA through the NICER mission [80NSSC17K0554];
U.S. DOEUnited States Department of Energy (DOE) [DE-FG02-87ER40317]
FX We thank the anonymous referee for comments that helped to improve this
work. This work was supported in part by NASA through the NICER mission
and the Astrophysics Explorers Program. G.R., T.H., and S.N. are
grateful for support from the Nederlandse Organisatie voor
Wetenschappelijk Onderzoek (NWO) through the VIDI and Projectruimte
grants (PI Nissanke). T.E.R. and A.L.W. acknowledge support from ERC
Starting grant No. 639217 CSINEUTRONSTAR (PI Watts). This work was
sponsored by NWO Exact and Natural Sciences for the use of supercomputer
facilities, and was carried out on the Dutch national e-infrastructure
with the support of SURF Cooperative. S.K.G., K.H., and A.S. acknowledge
support from the Deutsche Forschungsgemeinschaft (DFG, German Research
Foundation)-Project-ID 279384907-SFB 1245. S.G. acknowledges the support
of the CNES. R.M.L. acknowledges the support of NASA through Hubble
Fellowship Program grant HST-HF2-51440.001. J.M.L. acknowledges support
by NASA through the NICER mission with Grant 80NSSC17K0554 and by the
U.S. DOE through Grant DE-FG02-87ER40317. This research has made
extensive use of NASA's Astrophysics Data System Bibliographic Services
(ADS) and the arXiv. We thank Jocelyn Read, Wynn Ho, and Cole Miller for
comments on a draft manuscript.
CR Abbott BP, 2020, ASTROPHYS J LETT, V892, DOI 10.3847/2041-8213/ab75f5
Abbott BP, 2019, PHYS REV X, V9, DOI 10.1103/PhysRevX.9.031040
Abbott BP, 2019, PHYS REV X, V9, DOI 10.1103/PhysRevX.9.011001
Abbott BP, 2018, PHYS REV LETT, V121, DOI 10.1103/PhysRevLett.121.161101
Abbott BP, 2017, PHYS REV LETT, V119, DOI 10.1103/PhysRevLett.119.161101
Alsing J, 2018, MON NOT R ASTRON SOC, V478, P1377, DOI 10.1093/mnras/sty1065
Alvarez-Castillo D, 2016, EUR PHYS J A, V52, DOI 10.1140/epja/i2016-16069-2
Annala E, 2018, PHYS REV LETT, V120, DOI 10.1103/PhysRevLett.120.172703
Antoniadis J, 2013, SCIENCE, V340, DOI 10.1126/science.1233232
Arzoumanian Z, 2018, ASTROPHYS J SUPPL S, V235, DOI 10.3847/1538-4365/aab5b0
Ascenzi S, 2019, ASTROPHYS J, V877, DOI 10.3847/1538-4357/ab1b15
Bauswein A, 2017, ASTROPHYS J LETT, V850, DOI 10.3847/2041-8213/aa9994
BAYM G, 1971, ASTROPHYS J, V170, P299, DOI 10.1086/151216
Baym G, 2018, REP PROG PHYS, V81, DOI 10.1088/1361-6633/aaae14
Behnel S, 2011, COMPUT SCI ENG, V13, P31, DOI 10.1109/MCSE.2010.118
Buchner J, 2014, ASTRON ASTROPHYS, V564, DOI 10.1051/0004-6361/201322971
Capano CD, 2020, NAT ASTRON, V4, P625, DOI 10.1038/s41550-020-1014-6
Coughlin MW, 2019, PHYS REV D, V100, DOI 10.1103/PhysRevD.100.043011
Coughlin MW, 2018, MON NOT R ASTRON SOC, V480, P3871, DOI 10.1093/mnras/sty2174
Cromartie HT, 2020, NAT ASTRON, V4, P72, DOI 10.1038/s41550-019-0880-2
Dalcin L, 2008, J PARALLEL DISTR COM, V68, P655, DOI 10.1016/j.jpdc.2007.09.005
De S, 2018, PHYS REV LETT, V121, DOI 10.1103/PhysRevLett.121.091102
Demorest PB, 2010, NATURE, V467, P1081, DOI 10.1038/nature09466
Douchin F, 2001, ASTRON ASTROPHYS, V380, P151, DOI 10.1051/0004-6361:20011402
Essick R, 2020, PHYS REV D, V101, DOI 10.1103/PhysRevD.101.063007
Feroz F, 2008, MON NOT R ASTRON SOC, V384, P449, DOI 10.1111/j.1365-2966.2007.12353.x
Feroz F, 2009, MON NOT R ASTRON SOC, V398, P1601, DOI 10.1111/j.1365-2966.2009.14548.x
Feroz F., 2013, ARXIV13062144
Fonseca E, 2016, ASTROPHYS J, V832, DOI 10.3847/0004-637X/832/2/167
Forum MP, 1994, MPI MESS PASS INT ST
Foucart F, 2018, PHYS REV D, V98, DOI 10.1103/PhysRevD.98.081501
Fraga ES, 2014, ASTROPHYS J LETT, V781, DOI 10.1088/2041-8205/781/2/L25
Gao H, 2017, ASTROPHYS J LETT, V851, DOI 10.3847/2041-8213/aaa0c6
GarciaBellido J, 1996, PHYS REV D, V54, P6040, DOI 10.1103/PhysRevD.54.6040
Gough B., 2009, GNU SCI LIB REFERENC
Greif SK, 2019, MON NOT R ASTRON SOC, V485, P5363, DOI 10.1093/mnras/stz654
Hebeler K, 2015, ANNU REV NUCL PART S, V65, P457, DOI 10.1146/annurev-nucl-102313-025446
Hebeler K, 2013, ASTROPHYS J, V773, DOI 10.1088/0004-637X/773/1/11
Hebeler K, 2010, PHYS REV C, V82, DOI 10.1103/PhysRevC.82.014314
Hinderer T, 2019, PHYS REV D, V100, DOI 10.1103/PhysRevD.100.063021
Hunter JD, 2007, COMPUT SCI ENG, V9, P90, DOI 10.1109/MCSE.2007.55
Jones E., 2001, SCIPY OPEN SOURCE SC
Kasliwal MM, 2017, SCIENCE, V358, P1559, DOI 10.1126/science.aap9455
KASS RE, 1995, J AM STAT ASSOC, V90, P773, DOI 10.1080/01621459.1995.10476572
Kastaun W, 2019, PHYS REV D, V100, DOI 10.1103/PhysRevD.100.103023
Kluyver T, 2016, POSITIONING AND POWER IN ACADEMIC PUBLISHING: PLAYERS, AGENTS AND AGENDAS, P87, DOI 10.3233/978-1-61499-649-1-87
Kulkarni S. R., 2005, ARXIVASTROPH0510256
KULLBACK S, 1951, ANN MATH STAT, V22, P79, DOI 10.1214/aoms/1177729694
Lattimer JM, 2016, PHYS REP, V621, P127, DOI 10.1016/j.physrep.2015.12.005
LATTIMER JM, 1976, ASTROPHYS J, V210, P549, DOI 10.1086/154860
Li LX, 1998, ASTROPHYS J, V507, pL59, DOI 10.1086/311680
Lindblom L, 2018, PHYS REV D, V97, DOI 10.1103/PhysRevD.97.123019
Lindblom L, 2010, PHYS REV D, V82, DOI 10.1103/PhysRevD.82.103011
Margalit B, 2019, ASTROPHYS J LETT, V880, DOI 10.3847/2041-8213/ab2ae2
Margalit B, 2017, ASTROPHYS J LETT, V850, DOI 10.3847/2041-8213/aa991c
Metzger BD, 2010, MON NOT R ASTRON SOC, V406, P2650, DOI 10.1111/j.1365-2966.2010.16864.x
Metzger BD, 2017, LIVING REV RELATIV, V20, DOI 10.1007/s41114-017-0006-z
Miller MC, 2019, ASTROPHYS J LETT, V887, DOI 10.3847/2041-8213/ab50c5
Miller MC, 2020, ASTROPHYS J, V888, DOI 10.3847/1538-4357/ab4ef9
Most ER, 2018, PHYS REV LETT, V120, DOI 10.1103/PhysRevLett.120.261103
Oertel M, 2017, REV MOD PHYS, V89, DOI 10.1103/RevModPhys.89.015007
Oliphant TE, 2007, COMPUT SCI ENG, V9, P10, DOI 10.1109/MCSE.2007.58
Perez F, 2007, COMPUT SCI ENG, V9, P21, DOI 10.1109/MCSE.2007.53
Raaijmakers G, 2019, ASTROPHYS J LETT, V887, DOI 10.3847/2041-8213/ab451a
Radice D, 2019, EUR PHYS J A, V55, DOI 10.1140/epja/i2019-12716-4
Rezzolla L, 2018, ASTROPHYS J LETT, V852, DOI 10.3847/2041-8213/aaa401
Riley TE, 2019, ASTROPHYS J LETT, V887, DOI 10.3847/2041-8213/ab481c
Riley TE, 2018, MON NOT R ASTRON SOC, V478, P1093, DOI 10.1093/mnras/sty1051
Rosswog S, 1999, ASTRON ASTROPHYS, V341, P499
Ruiz M, 2018, PHYS REV D, V97, DOI 10.1103/PhysRevD.97.021501
Shibata M, 2019, PHYS REV D, V100, DOI 10.1103/PhysRevD.100.023015
Shibata M, 2017, PHYS REV D, V96, DOI 10.1103/PhysRevD.96.123012
Tews I, 2020, ASTROPHYS J, V892, DOI 10.3847/1538-4357/ab7232
Tews I, 2018, PHYS REV C, V98, DOI 10.1103/PhysRevC.98.045804
The LIGO Scientific Collaboration & The Virgo Collaboration, 2018, GWTC 1 PUBL DAT REL
van der Walt S, 2011, COMPUT SCI ENG, V13, P22, DOI 10.1109/MCSE.2011.37
Watts AL, 2019, AIP CONF PROC, V2127, DOI 10.1063/1.5117798
Yang H, 2018, ASTROPHYS J, V856, DOI 10.3847/1538-4357/aab2b0
NR 78
TC 25
Z9 25
U1 3
U2 5
PU IOP PUBLISHING LTD
PI BRISTOL
PA TEMPLE CIRCUS, TEMPLE WAY, BRISTOL BS1 6BE, ENGLAND
SN 2041-8205
EI 2041-8213
J9 ASTROPHYS J LETT
JI Astrophys. J. Lett.
PD APR 10
PY 2020
VL 893
IS 1
AR L21
DI 10.3847/2041-8213/ab822f
PG 13
WC Astronomy & Astrophysics
SC Astronomy & Astrophysics
GA LE7OV
UT WOS:000526913600001
OA Green Accepted
DA 2021-04-21
ER
PT J
AU Teodorescu, E
Echim, MM
AF Teodorescu, E.
Echim, M. M.
TI Open-Source Software Analysis Tool to Investigate Space Plasma
Turbulence and Nonlinear DYNamics (ODYN)
SO EARTH AND SPACE SCIENCE
LA English
DT Article
DE open-source software data analysis tool based on Python; portfolio of
methods to analyze turbulence and nonlinear dynamics; the software
includes visualisation tools; the software can ingest and process large
collections of spacecraft data; user-friendly parametrisation of
analysis
ID SOLAR-WIND; MULTIFRACTAL ANALYSIS; INTERMITTENCY; COMPLEXITY; ULYSSES;
FLUCTUATIONS; INFORMATION; FRACTALS; SPECTRUM; DENSITY
AB We have designed and built a versatile modularized software library-ODYN-that wraps a comprehensive set of advanced data analysis methods meant to facilitate the study of turbulence, nonlinear dynamics, and complexity in space plasmas. The Python programming language is used for the algorithmic implementation of models and methods devised to understand fundamental phenomena of space plasma physics like elements of spectral analysis, probability distribution functions and their moments, multifractal analysis, or information theory. ODYN is an open-source software analysis tool and freely available to any user interested in turbulence and nonlinear dynamics analysis and provides a tool to perform automatic analysis on large collections of space measurements, in situ or simulations, a feature that distinguishes ODYN from other similar software. A user-friendly configurator is provided, which allows customization of key parameters of the analysis methods, most useful for nonprogrammers.
C1 [Teodorescu, E.; Echim, M. M.] Inst Space Sci, Magurele, Romania.
[Echim, M. M.] Royal Belgian Inst Space Aeron BIRA IASB, Brussels, Belgium.
RP Teodorescu, E (corresponding author), Inst Space Sci, Magurele, Romania.
EM eliteo@spacescience.ro; marius.echim@oma.be
RI ; Echim, Marius/F-1813-2010
OI Teodorescu, Eliza/0000-0002-5294-0075; Echim, Marius/0000-0001-7038-9494
FU European Community's Seventh Framework Programme (FP7/2007-2013)European
Commission [313038/STORM]; Romanian Space Agency (ROSA) via Research
Program for Space Technology Development and Innovation and Advanced
Research (STAR Project) [122, 182/2017]; Romanian Ministry of Research
through UEFISCDI PCCDI grant VESSConsiliul National al Cercetarii
Stiintifice (CNCS)Unitatea Executiva pentru Finantarea Invatamantului
Superior, a Cercetarii, Dezvoltarii si Inovarii (UEFISCDI) [18/2018];
Romanian Ministry of Research through Program Nucleu; Belgian Solar
Terrestrial Center of Excellence (STCE)
FX This research was supported by the European Community's Seventh
Framework Programme (FP7/2007-2013) under grant agreement no
313038/STORM; Romanian Space Agency (ROSA) via grants from the Research
Program for Space Technology Development and Innovation and Advanced
Research (STAR Project nos. 122 and 182/2017); and the Romanian Ministry
of Research through UEFISCDI PCCDI grant VESS (contract no. 18/2018) and
Program Nucleu. M. E. acknowledges support from the Belgian Solar
Terrestrial Center of Excellence (STCE). The latest version of ODYN
together with the data analyzed in this report and other test data sets
is available at: http://www.spacescience.ro/projects/odyn/results.html.A
comprehensive database specifically tailored for turbulence and
nonlinear dynamics analysis is available at:
http://www.storm-fp7.eu/index.php/targeted-databases. Satellite data
downloaded from official archives of Cluster, Venus Express, and Ulysses
can be processed in ODYN. E. T. would like to thank Profs. Tom Chang and
Jay Johnson and Dr. Gabriel Voitcu for their patience in having lengthy
and fruitful discussions on theoretical and technical aspects on the
analysis methods implemented in ODYN.
CR Alexandrova O, 2013, SPACE SCI REV, V178, P101, DOI 10.1007/s11214-013-0004-8
Barnes A., 1979, SOLAR SYSTEM PLASMA, V1, P257
BARTLETT MS, 1948, NATURE, V161, P686, DOI 10.1038/161686a0
BENZI R, 1993, PHYS REV E, V48, pR29, DOI 10.1103/PhysRevE.48.R29
Blum D, 2015, ELIFE, V4, DOI 10.7554/eLife.04024
Boldyrev S, 2012, ASTROPHYS J LETT, V758, DOI 10.1088/2041-8205/758/2/L44
Boyd T. J. M., 2003, PHYS PLASMAS, DOI [10.1017/CBO9780511755750, DOI 10.1017/CB09780511755750]
Bruno R, 2003, J GEOPHYS RES-SPACE, V108, DOI 10.1029/2002JA009615
Bruno R, 2001, PLANET SPACE SCI, V49, P1201, DOI 10.1016/S0032-0633(01)00061-7
Bruno R., 2005, LIVING REV SOL PHYS, V2, P4, DOI [10.12942/lrsp-2005-4, DOI 10.12942/LRSP-2005-4]
Burlaga L. F., 1991, J GEOPHYS RES-SPACE, V96, DOI [10.1029/91JA00087, DOI 10.1029/91JA00087]
Chang T, 2004, PHYS PLASMAS, V11, P1287, DOI 10.1063/1.1667496
CHANG T, 1992, IEEE T PLASMA SCI, V20, P691, DOI 10.1109/27.199515
Chang T, 2010, NONLINEAR PROC GEOPH, V17, P545, DOI 10.5194/npg-17-545-2010
Chang T., 2015, INTRO SPACE PLASMA C, DOI [10.1017/CBO9780511980251, DOI 10.1017/CB09780511980251]
Chang T, 2008, PHYS REV E, V77, DOI 10.1103/PhysRevE.77.045401
Chang T, 2008, AIP CONF PROC, V1039, P75, DOI 10.1063/1.2982488
Chen CHK, 2017, ASTROPHYS J, V842, DOI 10.3847/1538-4357/aa74e0
Chen CHK, 2014, ASTROPHYS J LETT, V789, DOI 10.1088/2041-8205/789/1/L8
CHHABRA A, 1989, PHYS REV LETT, V62, P1327, DOI 10.1103/PhysRevLett.62.1327
Consolini G, 2005, ASTROPHYS SPACE SC L, V321, P51
Consolini G, 2011, NONLINEAR PROC GEOPH, V18, P277, DOI 10.5194/npg-18-277-2011
Cover Thomas M., 2006, ELEMENTS INFORM THEO
Darbellay GA, 1999, IEEE T INFORM THEORY, V45, P1315, DOI 10.1109/18.761290
de Wit TD, 1996, NONLINEAR PROC GEOPH, V3, P262, DOI 10.5194/npg-3-262-1996
Echim MM, 2007, NONLINEAR PROC GEOPH, V14, P525, DOI 10.5194/npg-14-525-2007
Escoubet CP, 1997, SPACE SCI REV, V79, P1
Frisch U., 1995, TURBULENCE LEGACY
GOLDSTEIN BE, 1995, GEOPHYS RES LETT, V22, P3393, DOI 10.1029/95GL03183
GRASSBERGER P, 1983, PHYSICA D, V9, P189, DOI 10.1016/0167-2789(83)90298-1
HALSEY TC, 1986, PHYS REV A, V33, P1141, DOI 10.1103/PhysRevA.33.1141
HENTSCHEL HGE, 1983, PHYSICA D, V8, P435, DOI 10.1016/0167-2789(83)90235-X
Hnat B, 2005, PHYS REV LETT, V94, DOI 10.1103/PhysRevLett.94.204502
Hnat B, 2002, GEOPHYS RES LETT, V29, DOI 10.1029/2001GL014587
Howes GG, 2008, J GEOPHYS RES-SPACE, V113, DOI 10.1029/2007JA012665
Howes GG, 2012, ASTROPHYS J LETT, V753, DOI 10.1088/2041-8205/753/1/L19
Huang SY, 2017, ASTROPHYS J LETT, V836, DOI 10.3847/2041-8213/836/1/L10
Iroshnikov P.S., 1964, SVA, V7, P566
Johnson JR, 2005, J GEOPHYS RES-SPACE, V110, DOI 10.1029/2004JA010638
Kiyani K, 2006, PHYS REV E, V74, DOI 10.1103/PhysRevE.74.051122
Kiyani KH, 2009, PHYS REV LETT, V103, DOI 10.1103/PhysRevLett.103.075006
Kolmogoroff A, 1941, CR ACAD SCI URSS, V30, P301
KRAICHNAN RH, 1965, PHYS FLUIDS, V8, P1385, DOI 10.1063/1.1761412
Kugiumtzis D, 2000, PHYS REV E, V62, pR25, DOI 10.1103/PhysRevE.62.R25
Lamy H., 2008, D31001708 COSPAR, P1686
Lewis LM, 2018, ELIFE, V7, DOI 10.7554/eLife.30274
LI WT, 1990, J STAT PHYS, V60, P823, DOI 10.1007/BF01025996
Lin CY, 2012, CELL, V151, P56, DOI 10.1016/j.cell.2012.08.026
Lion S, 2016, ASTROPHYS J, V824, DOI 10.3847/0004-637X/824/1/47
Macek WM, 2008, GEOPHYS RES LETT, V35, DOI 10.1029/2007GL032263
Macek WM, 2009, J GEOPHYS RES-SPACE, V114, DOI 10.1029/2008JA013795
MANDELBROT BB, 1989, PURE APPL GEOPHYS, V131, P5, DOI 10.1007/BF00874478
MARSCH E, 1990, J GEOPHYS RES-SPACE, V95, P11945, DOI 10.1029/JA095iA08p11945
MARSCH E, 1994, ANN GEOPHYS-ATM HYDR, V12, P1127, DOI 10.1007/s00585-994-1127-8
Matthaeus WH, 2015, PHILOS T R SOC A, V373, DOI 10.1098/rsta.2014.0154
MATTHAEUS WH, 1991, J GEOPHYS RES, V96, P5421, DOI 10.1029/90JA02609
MENEVEAU C, 1987, PHYS REV LETT, V59, P1424, DOI 10.1103/PhysRevLett.59.1424
Morley S. K., 2011, P 9 PYTH SCI C SCIPY
Munteanu C, 2016, ANN GEOPHYS-GERMANY, V34, P437, DOI 10.5194/angeo-34-437-2016
Ng CS, 2010, J GEOPHYS RES-SPACE, V115, DOI 10.1029/2009JA014377
Ott E., 1993, CHAOS DYNAMICAL SYST
Pagel C, 2001, NONLINEAR PROC GEOPH, V8, P313, DOI 10.5194/npg-8-313-2001
Pedregosa F, 2011, J MACH LEARN RES, V12, P2825
Roberts OW, 2017, ASTROPHYS J, V850, DOI 10.3847/1538-4357/aa93e5
Sorriso-Valvo L, 2001, PLANET SPACE SCI, V49, P1193, DOI 10.1016/S0032-0633(01)00060-5
Sorriso-Valvo L, 2017, ADV SPACE RES, V59, P1642, DOI 10.1016/j.asr.2016.12.024
SREENIVASAN KR, 1991, ANNU REV FLUID MECH, V23, P539
Stoneback RA, 2018, J GEOPHYS RES-SPACE, V123, P5271, DOI 10.1029/2018JA025297
Strehl A., 2003, Journal of Machine Learning Research, V3, P583, DOI 10.1162/153244303321897735
Svedhem H., 2007, PLANET SPACE SCI, V43, P185, DOI [10.1134/S0038094609030010, DOI 10.1134/S0038094609030010]
Szczepaniak A, 2008, NONLINEAR PROC GEOPH, V15, P615, DOI 10.5194/npg-15-615-2008
Tam SWY, 2011, NONLINEAR PROC GEOPH, V18, P405, DOI 10.5194/npg-18-405-2011
Tam SWY, 2010, PHYS REV E, V81, DOI 10.1103/PhysRevE.81.036414
Taylor GI, 1938, PROC R SOC LON SER-A, V164, P0476, DOI 10.1098/rspa.1938.0032
Teodorescu E, 2015, ASTROPHYS J LETT, V804, DOI 10.1088/2041-8205/804/2/L41
THEILER J, 1992, PHYSICA D, V58, P77, DOI 10.1016/0167-2789(92)90102-S
TU CY, 1995, SPACE SCI REV, V73, P1, DOI 10.1007/BF00748891
VanderPlas J., 2012, 2012 Conference on Intelligent Data Understanding (CIDU 2012), P47, DOI 10.1109/CIDU.2012.6382200
Voros Z, 2003, ANN GEOPHYS-GERMANY, V21, P1955
Wawrzaszek A, 2015, ASTROPHYS J LETT, V814, DOI 10.1088/2041-8205/814/2/L19
Wawrzaszek A, 2010, J GEOPHYS RES-SPACE, V115, DOI 10.1029/2009JA015176
WELCH PD, 1967, IEEE T ACOUST SPEECH, VAU15, P70, DOI 10.1109/TAU.1967.1161901
WENZEL KP, 1992, ASTRON ASTROPHYS SUP, V92, P207
Weygand JM, 2005, J GEOPHYS RES-SPACE, V110, DOI 10.1029/2004JA010581
Yordanova E, 2005, NONLINEAR PROC GEOPH, V12, P817, DOI 10.5194/npg-12-817-2005
NR 85
TC 2
Z9 2
U1 0
U2 0
PU AMER GEOPHYSICAL UNION
PI WASHINGTON
PA 2000 FLORIDA AVE NW, WASHINGTON, DC 20009 USA
EI 2333-5084
J9 EARTH SPACE SCI
JI Earth Space Sci.
PD APR
PY 2020
VL 7
IS 4
AR UNSP e2019EA001004
DI 10.1029/2019EA001004
PG 16
WC Astronomy & Astrophysics; Geosciences, Multidisciplinary
SC Astronomy & Astrophysics; Geology
GA LP8XI
UT WOS:000534600100010
PM 32715025
OA DOAJ Gold, Green Published
DA 2021-04-21
ER
PT J
AU Aebischer, J
Kuhr, T
Lieret, K
AF Aebischer, Jason
Kuhr, Thomas
Lieret, Kilian
TI Clustering of B -> D(*)tau- nu(tau) kinematic distributions with
ClusterKinG
SO JOURNAL OF HIGH ENERGY PHYSICS
LA English
DT Article
DE B physics; Beyond Standard Model; e(+)-e(-) Experiments; Flavor physics
ID PROGRAM; SPHENO
AB New Physics can manifest itself in kinematic distributions of particle decays. The parameter space defining the shape of such distributions can be large which is chalenging for both theoretical and experimental studies. Using clustering algorithms, the parameter space can however be dissected into subsets (clusters) which correspond to similar kinematic distributions. Clusters can then be represented by benchmark points, which allow for less involved studies and a concise presentation of the results. We demonstrate this concept using the Python package ClusterKinG, an easy to use framework for the clustering of distributions that particularly aims to make these techniques more accessible in a High Energy Physics context. As an example we consider B over bar -> D) (tau-nu over bar tau distributions and discuss various clustering methods and possible implications for future experimental analyses.
C1 [Aebischer, Jason] Excellence Cluster Universe, Garching, Germany.
[Kuhr, Thomas; Lieret, Kilian] Excellence Cluster Origins, Garching, Germany.
[Kuhr, Thomas; Lieret, Kilian] Ludwig Maximilians Univ Munchen, Munich, Germany.
RP Aebischer, J (corresponding author), Excellence Cluster Universe, Garching, Germany.
EM jason.aebischer@tum.de; thomas.kuhr@lmu.de; kilian.lieret@lmu.de
FU DFG cluster of excellence "Origin and Structure of the Universe"German
Research Foundation (DFG); DFG cluster of excellence "ORIGINS: from the
Origin of the Universe to the First Building Blocks of Life"
FX We thank Alejandro Celis and David Straub for useful discussions. The
work of J. A., T. K. and K. L. is supported by the DFG clusters of
excellence "Origin and Structure of the Universe"and "ORIGINS: from the
Origin of the Universe to the First Building Blocks of Life".
CR Aebischer J., 2019, ARXIV190310434
Aebischer J, 2016, J HIGH ENERGY PHYS, DOI 10.1007/JHEP05(2016)037
Aebischer J, 2019, EUR PHYS J C, V79, DOI 10.1140/epjc/s10052-019-6977-z
Aebischer J, 2018, EUR PHYS J C, V78, DOI 10.1140/epjc/s10052-018-6492-7
Aebischer J, 2018, COMPUT PHYS COMMUN, V232, P71, DOI 10.1016/j.cpc.2018.05.022
Aebischer J, 2017, J HIGH ENERGY PHYS, DOI 10.1007/JHEP09(2017)158
Alguero M, 2019, EUR PHYS J C, V79, DOI 10.1140/epjc/s10052-019-7216-3
Alonso R, 2016, PHYS REV D, V94, DOI 10.1103/PhysRevD.94.094021
Alonso R, 2014, J HIGH ENERGY PHYS, DOI 10.1007/JHEP04(2014)159
Altmannshofer W, 2017, PHYS REV D, V96, DOI 10.1103/PhysRevD.96.095010
BaBar collaboration, 2012, PHYS REV LETT, V109
Becirevic D, 2019, NUCL PHYS B, V946, DOI 10.1016/j.nuclphysb.2019.114707
Becirevic D, 2018, PHYS REV D, V98, DOI 10.1103/PhysRevD.98.055003
Beirevi D., ARXIV190702257
Belle Belle-II collaboration, 2019, 10 INT WORKSH CKM U
Bernlochner F.U., ARXIV200200020
Bhattacharya B, 2019, J HIGH ENERGY PHYS, DOI 10.1007/JHEP05(2019)191
Bhattacharya S, 2019, EUR PHYS J C, V79, DOI 10.1140/epjc/s10052-019-6767-7
Blanke M, 2019, PHYS REV D, V99, DOI 10.1103/PhysRevD.99.075006
Brivio I., COMPUTING TOOLS SMEF
Brivio I, 2017, J HIGH ENERGY PHYS, DOI 10.1007/JHEP12(2017)070
Criado J, 2019, EUR PHYS J C, V79, DOI 10.1140/epjc/s10052-019-6769-5
Carvalho A., ARXIV171008261
Carvalho A., ARXIV160806578
Carvalho A, 2016, J HIGH ENERGY PHYS, DOI 10.1007/JHEP04(2016)126
Celis A, 2017, EUR PHYS J C, V77, DOI 10.1140/epjc/s10052-017-4967-6
Celis A, 2017, PHYS LETT B, V771, P168, DOI 10.1016/j.physletb.2017.05.037
Ciuchini M, 2019, EUR PHYS J C, V79, DOI 10.1140/epjc/s10052-019-7210-9
Criado JC, 2018, COMPUT PHYS COMMUN, V227, P42, DOI 10.1016/j.cpc.2018.02.016
Datta A, 2019, PHYS LETT B, V797, DOI 10.1016/j.physletb.2019.134858
Dedes A, 2020, COMPUT PHYS COMMUN, V247, DOI 10.1016/j.cpc.2019.106931
Dekens W, 2019, J HIGH ENERGY PHYS, DOI 10.1007/JHEP10(2019)197
Duell S, POS ICHEP2016 1074
Evans J.A., ARXIV160600003
Gomez JD, 2019, PHYS REV D, V100, DOI 10.1103/PhysRevD.100.093003
Gratrex J, 2016, PHYS REV D, V93, DOI 10.1103/PhysRevD.93.054008
Gripaios B, 2019, J HIGH ENERGY PHYS, DOI 10.1007/JHEP01(2019)128
Grzadkowski B, 2010, J HIGH ENERGY PHYS, DOI [10.1007/JHEP10(2010)85, 10.1007/JHEP10(2010)085]
Hartigan J. A., 1975, WILEY SERIES PROBABI
Jenkins EE, 2018, J HIGH ENERGY PHYS, DOI 10.1007/JHEP03(2018)016
Jenkins EE, 2018, J HIGH ENERGY PHYS, DOI 10.1007/JHEP01(2018)084
Jenkins EE, 2014, J HIGH ENERGY PHYS, DOI 10.1007/JHEP01(2014)035
Jenkins EE, 2013, J HIGH ENERGY PHYS, DOI 10.1007/JHEP10(2013)087
Jones E., 2001, SCIPY OPEN SOURCE SC
Jung M, 2019, J HIGH ENERGY PHYS, DOI 10.1007/JHEP01(2019)009
Kaufman L, 1990, FINDING GROUPS DATA
Khachatryan V, 2016, PHYS REV D, V94, DOI 10.1103/PhysRevD.94.052012
Macqueen J., 1965, P 5 BERK S MATH STAT, P281, DOI DOI 10.1007/S11665-016-2173-6
Mahmoudi F, 2008, COMPUT PHYS COMMUN, V178, P745, DOI 10.1016/j.cpc.2007.12.006
MASSEY FJ, 1951, J AM STAT ASSOC, V46, P68, DOI 10.2307/2280095
Millman J., 2010, P56, DOI DOI 10.1016/S0168-0102(02)00204-3
Murgui C, 2019, J HIGH ENERGY PHYS, DOI 10.1007/JHEP09(2019)103
PETTITT AN, 1976, BIOMETRIKA, V63, P161, DOI 10.1093/biomet/63.1.161
Porod W, 2003, COMPUT PHYS COMMUN, V153, P275, DOI 10.1016/S0010-4655(03)00222-4
Porod W, 2012, COMPUT PHYS COMMUN, V183, P2458, DOI 10.1016/j.cpc.2012.05.021
Porod W, 2014, EUR PHYS J C, V74, P1, DOI 10.1140/epjc/s10052-014-2992-2
Shi RX, 2019, J HIGH ENERGY PHYS, DOI 10.1007/JHEP12(2019)065
Straub D.M, ARXIV181008132
Wilks SS, 1938, ANN MATH STAT, V9, P60, DOI 10.1214/aoms/1177732360
NR 59
TC 4
Z9 4
U1 1
U2 3
PU SPRINGER
PI NEW YORK
PA ONE NEW YORK PLAZA, SUITE 4600, NEW YORK, NY, UNITED STATES
SN 1029-8479
J9 J HIGH ENERGY PHYS
JI J. High Energy Phys.
PD APR 1
PY 2020
IS 4
AR 007
DI 10.1007/JHEP04(2020)007
PG 21
WC Physics, Particles & Fields
SC Physics
GA LB3CY
UT WOS:000524516700006
OA DOAJ Gold
DA 2021-04-21
ER
PT J
AU Koerner, LJ
Caswell, TA
Allan, DB
Campbell, SI
AF Koerner, Lucas J.
Caswell, Thomas A.
Allan, Daniel B.
Campbell, Stuart I.
TI A Python Instrument Control and Data Acquisition Suite for Reproducible
Research
SO IEEE TRANSACTIONS ON INSTRUMENTATION AND MEASUREMENT
LA English
DT Article
DE Computerized instrumentation; demodulation
AB Tools that standardize and automate experimental data collection are needed for greater confidence in research results. The National Synchrotron Light Source-II (NSLS-II) has generated an open-source Python data acquisition, management, and analysis software suite that automates X-ray experiments and collects an experimental record that facilitates complete reproducibility. Here, we show that the NSLS-II tools are not only useful for X-ray science at large-scale facilities by presenting an add-on package that adapts these tools for use in a small laboratory with common physics and electrical engineering instruments. The composite software suite eases and automates the execution of experiments, records extensive metadata, stores data in portable containers, and speeds up the analysis through tools for comprehensive searches. In total, this software suite increases the reproducibility of laboratory experiments. We demonstrate the software via the evaluation of two lock-in amplifiers-the miniature ADA2200 and the ubiquitous Stanford Research Systems (SRS) SR810. The frequency resolution, signal-to-noise ratio, and dynamic reserve of the lock-in amplifiers are measured and presented. The usage of the software suite is described throughout these measurements so that the reader can implement the tools in their lab.
C1 [Koerner, Lucas J.] Univ St Thomas, Dept Elect & Comp Engn, St Paul, MN 55105 USA.
[Caswell, Thomas A.; Allan, Daniel B.; Campbell, Stuart I.] Brookhaven Natl Lab, Natl Synchrotron Light Source 2, Upton, NY 11973 USA.
RP Koerner, LJ (corresponding author), Univ St Thomas, Dept Elect & Comp Engn, St Paul, MN 55105 USA.
EM koerner.lucas@stthomas.edu
RI Campbell, Stuart I/A-8485-2010; Campbell, Stuart/ABA-6344-2020
OI Campbell, Stuart I/0000-0001-7079-0878; Campbell,
Stuart/0000-0001-7079-0878; Koerner, Lucas/0000-0002-7236-7202
FU National Synchrotron Light Source II, U.S. Department of Energy (DOE)
Office of Science User FacilityUnited States Department of Energy (DOE);
DOE Office of Science, Brookhaven National Laboratory [DE-SC0012704]
FX This work was supported by the National Synchrotron Light Source II,
U.S. Department of Energy (DOE) Office of Science User Facility operated
for the DOE Office of Science, Brookhaven National Laboratory under
Contract DE-SC0012704.
CR [Anonymous], 2018, PYVISA DOCUMENTATION
[Anonymous], 1999, STANDARD COMMANDS PR, V1
Arkilic A., 2017, Synchrotron Radiation News, V30, P44, DOI 10.1080/08940886.2017.1289810
Arkilic A., 2015, P 15 INT C ACC LARG
Baumer B., 2014, R MARKDOWN INTEGRATI
Bengtsson LE, 2012, REV SCI INSTRUM, V83, DOI 10.1063/1.4731683
Dalesio L., 1999, P INT C ACC LARG EXP, P1
Ellis J. M., 2001, U.S. Patent, Patent No. [6 324 485 B1, 6324485]
Hayden EC, 2014, NATURE, V516, P131, DOI 10.1038/516131a
Herr AE, 2005, ANAL CHEM, V77, P585, DOI 10.1021/ac0489768
Johnson JL, 2015, JALA-J LAB AUTOM, V20, P10, DOI 10.1177/2211068214553022
Koerner L., 2019, INSTRBUILDER GITHUB
Kozubal A. J., 1989, P INT C ACC LARG EXP, P288
Li G, 2011, REV SCI INSTRUM, V82, DOI 10.1063/1.3633943
Liechti C., 2017, PYSERIAL DOCUMENTATI
MEADE ML, 1982, J PHYS E SCI INSTRUM, V15, P395, DOI 10.1088/0022-3735/15/4/001
Millman J., 2010, P56, DOI DOI 10.1016/S0168-0102(02)00204-3
Myers FB, 2008, LAB CHIP, V8, P2015, DOI 10.1039/b812343h
Newville M., 2016, PYEPICS PYTHON EPICS
NSLS, NSLS 2 SOFTW DOC
Oliphant T, 2006, NUMPY GUIDE NUMPY
Orozco L., 2014, ANALOG DIALOGUE, V48, P1
Ortega-Robles E, 2018, REV SCI INSTRUM, V89, DOI 10.1063/1.4997455
Pastell M., 2016, PWEAVE
Pernstich KP, 2012, J RES NATL INST STAN, V117, P176, DOI 10.6028/jres.117.010
Ragan-Kelley M., 2014, P AGU FALL M DEC
Saar BG, 2010, SCIENCE, V330, P1368, DOI 10.1126/science.1197236
Stodden V., 2013, SIAM NEWS, V46, P4
Sushynskyi O, 2016, 2016 13TH INTERNATIONAL CONFERENCE ON MODERN PROBLEMS OF RADIO ENGINEERING, TELECOMMUNICATIONS AND COMPUTER SCIENCE (TCSET), P418, DOI 10.1109/TCSET.2016.7452075
Xie Y, 2014, IMPLEMENT REPROD, V1, P20
NR 30
TC 3
Z9 3
U1 2
U2 8
PU IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
PI PISCATAWAY
PA 445 HOES LANE, PISCATAWAY, NJ 08855-4141 USA
SN 0018-9456
EI 1557-9662
J9 IEEE T INSTRUM MEAS
JI IEEE Trans. Instrum. Meas.
PD APR
PY 2020
VL 69
IS 4
BP 1698
EP 1707
DI 10.1109/TIM.2019.2914711
PN 2
PG 10
WC Engineering, Electrical & Electronic; Instruments & Instrumentation
SC Engineering; Instruments & Instrumentation
GA KW4VO
UT WOS:000521164300028
DA 2021-04-21
ER
PT J
AU Owens, M
Lang, MH
Barnard, L
Riley, P
Ben-Nun, M
Scott, CJ
Lockwood, M
Reiss, MA
Arge, CN
Gonzi, S
AF Owens, Mathew
Lang, Matthew
Barnard, Luke
Riley, Pete
Ben-Nun, Michal
Scott, Chris J.
Lockwood, Mike
Reiss, Martin A.
Arge, Charles N.
Gonzi, Siegfried
TI A Computationally Efficient, Time-Dependent Model of the Solar Wind for
Use as a Surrogate to Three-Dimensional Numerical Magnetohydrodynamic
Simulations
SO SOLAR PHYSICS
LA English
DT Article
ID DATA ASSIMILATION; ARRIVAL-TIME; PLASMA BETA; WSA-ENLIL; CORONA; INPUT;
SUN; VALIDATION; PREDICTION; PARAMETERS
AB Near-Earth solar-wind conditions, including disturbances generated by coronal mass ejections (CMEs), are routinely forecast using three-dimensional, numerical magnetohydrodynamic (MHD) models of the heliosphere. The resulting forecast errors are largely the result of uncertainty in the near-Sun boundary conditions, rather than heliospheric model physics or numerics. Thus ensembles of heliospheric model runs with perturbed initial conditions are used to estimate forecast uncertainty. MHD heliospheric models are relatively cheap in computational terms, requiring tens of minutes to an hour to simulate CME propagation from the Sun to Earth. Thus such ensembles can be run operationally. However, ensemble size is typically limited to 101 members, which may be inadequate to sample the relevant high-dimensional parameter space. Here, we describe a simplified solar-wind model that can estimate CME arrival time in approximately 0.01 seconds on a modest desktop computer and thus enables significantly larger ensembles. It is a one-dimensional, incompressible, hydrodynamic model, which has previously been used for the steady-state solar wind, but it is here used in time-dependent form. This approach is shown to adequately emulate the MHD solutions to the same boundary conditions for both steady-state solar wind and CME-like disturbances. We suggest it could serve as a "surrogate" model for the full three-dimensional MHD models. For example, ensembles of 105members can be used to identify regions of parameter space for more detailed investigation by the MHD models. Similarly, the simplicity of the model means it can be rewritten as an adjoint model, enabling variational data assimilation with MHD models without the need to alter their code. The model code is available as an Open Source download in the Python language.
C1 [Owens, Mathew; Lang, Matthew; Barnard, Luke; Scott, Chris J.; Lockwood, Mike] Univ Reading, Dept Meteorol, Earley Gate,POB 243, Reading RG6 6BB, Berks, England.
[Riley, Pete; Ben-Nun, Michal] Predict Sci Inc, 9990 Mesa Rim Rd,Suite 170, San Diego, CA 92121 USA.
[Reiss, Martin A.; Arge, Charles N.] NASA, Goddard Space Flight Ctr, Heliophys Sci Div, Greenbelt, MD 20771 USA.
[Reiss, Martin A.] Austrian Acad Sci, Space Res Inst, A-8042 Graz, Austria.
[Gonzi, Siegfried] Met Off, Exeter, Devon, England.
RP Owens, M (corresponding author), Univ Reading, Dept Meteorol, Earley Gate,POB 243, Reading RG6 6BB, Berks, England.
EM m.j.owens@reading.ac.uk
RI Lockwood, Mike/G-1030-2011; Scott, Christopher J/H-8664-2012; Lang,
Matthew/J-7510-2017; Barnard, Luke/L-2930-2015
OI Lockwood, Mike/0000-0002-7397-2172; Lang, Matthew/0000-0002-1904-3700;
Barnard, Luke/0000-0001-9876-4612; Owens, Mathew/0000-0003-2061-2453;
Reiss, Martin/0000-0002-6362-5054
FU Science and Technology Facilities Council (STFC)UK Research & Innovation
(UKRI)Science & Technology Facilities Council (STFC)Science and
Technology Development Fund (STDF) [ST/R000921/1]; Natural Environment
Research Council (NERC)UK Research & Innovation (UKRI)Natural
Environment Research Council (NERC) [NE/P016928/1]
FX Work was part-funded by Science and Technology Facilities Council (STFC)
grant number ST/R000921/1 and Natural Environment Research Council
(NERC) grant number NE/P016928/1. This work was aided by useful
discussions at the Lorentz Center workshop on Ensemble Forecasts in
Space Weather, organized by E. Doornbos, J. Guerra, and S. Murray.
CR Arge CN, 2013, AIP CONF PROC, V1539, P11, DOI 10.1063/1.4810977
Arge CN, 2004, J ATMOS SOL-TERR PHY, V66, P1295, DOI 10.1016/j.jastp.2004.03.018
Arge CN, 2003, AIP CONF PROC, V679, P190
BLANNING RW, 1975, SIMULATION, V24, P177, DOI 10.1177/003754977502400606
Cannon P., 2013, EXTREME SPACE WEATHE
Cargill PJ, 2004, SOL PHYS, V221, P135, DOI 10.1023/B:SOLA.0000033366.10725.a2
Case AW, 2008, GEOPHYS RES LETT, V35, DOI 10.1029/2008GL034493
Cash MD, 2015, SPACE WEATHER, V13, P611, DOI 10.1002/2015SW001232
Dumbovic M, 2018, ASTROPHYS J, V854, DOI 10.3847/1538-4357/aaaa66
Emmons D, 2013, SPACE WEATHER, V11, P95, DOI 10.1002/swe.20019
Gary GA, 2001, SOL PHYS, V203, P71, DOI 10.1023/A:1012722021820
Hapgood MA, 2011, ADV SPACE RES, V47, P2059, DOI 10.1016/j.asr.2010.02.007
Hickmann KS, 2015, SOL PHYS, V290, P1105, DOI 10.1007/s11207-015-0666-3
Jian LK, 2016, SPACE WEATHER, V14, P592, DOI 10.1002/2016SW001435
Jian LK, 2015, SPACE WEATHER, V13, P316, DOI 10.1002/2015SW001174
Johnson C, 2009, MON WEATHER REV, V137, P1717, DOI 10.1175/2009MWR2715.1
Kay C, 2018, J GEOPHYS RES-SPACE, V123, P7220, DOI 10.1029/2018JA025780
Kay C, 2015, ASTROPHYS J, V805, DOI 10.1088/0004-637X/805/2/168
Lang M, 2019, SPACE WEATHER, V17, P59, DOI 10.1029/2018SW001857
Lang M, 2017, SPACE WEATHER, V15, P1490, DOI 10.1002/2017SW001681
Lee CO, 2015, SOL PHYS, V290, P1207, DOI 10.1007/s11207-015-0667-2
Lee CO, 2013, SOL PHYS, V285, P349, DOI 10.1007/s11207-012-9980-1
Linker JA, 1999, J GEOPHYS RES-SPACE, V104, P9809, DOI 10.1029/1998JA900159
Lu D, 2019, GEOSCI MODEL DEV, V12, P1791, DOI 10.5194/gmd-12-1791-2019
MacNeice P, 2018, SPACE WEATHER, V16, P1644, DOI 10.1029/2018SW002040
Mays ML, 2015, SOL PHYS, V290, P1775, DOI 10.1007/s11207-015-0692-1
Merkin VG, 2016, J GEOPHYS RES-SPACE, V121, P2866, DOI 10.1002/2015JA022200
Millward G, 2013, SPACE WEATHER, V11, P57, DOI 10.1002/swe.20024
Murray SA, 2018, SPACE WEATHER, V16, P777, DOI 10.1029/2018SW001861
Odstrcil D, 2003, ADV SPACE RES, V32, P497, DOI 10.1016/S0273-1177(03)00332-6
Odstrcil D, 2005, J GEOPHYS RES-SPACE, V110, DOI 10.1029/2003JA010135
Owens M, 2004, ANN GEOPHYS-GERMANY, V22, P4397, DOI 10.5194/angeo-22-4397-2004
Owens MJ, 2017, SCI REP-UK, V7, DOI 10.1038/s41598-017-04546-3
Owens MJ, 2017, SCI REP-UK, V7, DOI 10.1038/srep41548
Owens MJ, 2008, SPACE WEATHER, V6, DOI 10.1029/2007SW000380
Owens MJ, 2017, SPACE WEATHER, V15, P1461, DOI 10.1002/2017SW001679
Paruolo P, 2013, J R STAT SOC A STAT, V176, P609, DOI 10.1111/j.1467-985X.2012.01059.x
PIZZO V, 1978, J GEOPHYS RES-SPACE, V83, P5563, DOI 10.1029/JA083iA12p05563
Pizzo VJ, 2015, SPACE WEATHER, V13, P676, DOI 10.1002/2015SW001221
Pomoell J, 2018, J SPACE WEATHER SPAC, V8, DOI 10.1051/swsc/2018020
Press W.H., 1989, NUMERICAL RECIPES
Reiss MA, 2019, ASTROPHYS J SUPPL S, V240, DOI 10.3847/1538-4365/aaf8b3
Reiss MA, 2016, SPACE WEATHER, V14, P495, DOI 10.1002/2016SW001390
Richardson IG, 2002, J GEOPHYS RES-SPACE, V107, DOI 10.1029/2001JA000504
Riley P, 2011, SOL PHYS, V270, P575, DOI 10.1007/s11207-011-9766-x
Riley P, 2001, J GEOPHYS RES-SPACE, V106, P15889, DOI 10.1029/2000JA000121
Riley P, 2018, SPACE WEATHER, V16, P1245, DOI 10.1029/2018SW001962
Riley P, 2012, J ATMOS SOL-TERR PHY, V83, P1, DOI 10.1016/j.jastp.2011.12.013
Saltelli A, 2002, J AM STAT ASSOC, V97, P702, DOI 10.1198/016214502388618447
Schwenn R., 1990, PHYS INNER HELIOSPHE, VVol. 1, P99
Shen F, 2018, ASTROPHYS J, V866, DOI 10.3847/1538-4357/aad806
Torok T, 2018, ASTROPHYS J, V856, DOI 10.3847/1538-4357/aab36d
Toth G, 2005, J GEOPHYS RES-SPACE, V110, DOI 10.1029/2005JA011126
Tu CY, 2004, J GEOPHYS RES-SPACE, V109, DOI 10.1029/2004JA010391
Vrsnak B, 2002, J GEOPHYS RES-SPACE, V107, DOI 10.1029/2001JA000120
Zhao XP, 2002, J GEOPHYS RES-SPACE, V107, DOI 10.1029/2001JA009143
NR 56
TC 4
Z9 4
U1 0
U2 0
PU SPRINGER
PI DORDRECHT
PA VAN GODEWIJCKSTRAAT 30, 3311 GZ DORDRECHT, NETHERLANDS
SN 0038-0938
EI 1573-093X
J9 SOL PHYS
JI Sol. Phys.
PD MAR 19
PY 2020
VL 295
IS 3
AR 43
DI 10.1007/s11207-020-01605-3
PG 17
WC Astronomy & Astrophysics
SC Astronomy & Astrophysics
GA KX8PP
UT WOS:000522137100001
OA Other Gold, Green Accepted
DA 2021-04-21
ER
PT J
AU Li, JC
Liu, J
Kooi, K
AF Li, Jianchen
Liu, Jing
Kooi, Kyler
TI HPGe detector field calculation methods demonstrated with an educational
program, GeFiCa
SO EUROPEAN PHYSICAL JOURNAL C
LA English
DT Article
ID DRIFT VELOCITY; ELECTRON
AB A review of tools and methods to calculate electrostatic potentials and fields inside high-purity germanium detectors in various configurations is given. The methods are illustrated concretely with a new educational program named GeFiCa - Germanium detector Field Calculator. Demonstrated in GeFiCa are generic numerical calculations based on the successive over-relaxation method as well as analytic ones whenever simplification is possible due to highly symmetric detector geometries. GeFiCa is written in C++and provided as an extension to the CERN ROOT libraries widely used in the particle physics community. Calculation codes for individual detectors, provided as ROOT macros and python scripts, are distributed along with the GeFiCa core library, serving as both examples showing the usage of GeFiCa and starting points for customized calculations. They can be run without compilation in a ROOT interactive session or directly from a Linux shell. The numerical results are saved in a ROOT tree, making full use of the I/O optimization and plotting functionalities in ROOT. The speed and precision of the calculation are comparable to other commonly used packages, which qualifies GeFiCa as a scientific research tool. However, the main focus of GeFiCa is to clearly explain and demonstrate the analytic and numeric methods to solve Poisson's equation, practical coding considerations and visualization methods, with intensive documentation and example macros. It serves as a one-stop resource for people who want to understand the operating mechanism of such a package under the hood.
C1 [Li, Jianchen; Liu, Jing; Kooi, Kyler] Univ South Dakota, Dept Phys, 414 East Clark St, Vermillion, SD 57069 USA.
RP Liu, J (corresponding author), Univ South Dakota, Dept Phys, 414 East Clark St, Vermillion, SD 57069 USA.
EM Jing.Liu@usd.edu
RI Liu, Jing/A-4365-2011
OI Liu, Jing/0000-0003-1869-2407
FU NSFNational Science Foundation (NSF) [OIA-1738695, OISE-1743790,
OAC-1626516]; Office of Research at the University of South Dakota
FX The authors thank David Radford at the Oak Ridge National Laboratory for
his patient instruction in various aspects of the field calculation,
Oliver Schulz at the Max-Planck-Institut fur Physik for his introduction
of the Julia language and the SSD package, Christopher Haufe and Anna
Reine at the University of North Carolina at Chapel Hill for their
instruction on how to use FEniCS to calculate fields in a point-contact
detector. This work is supported by NSF award OIA-1738695 and
OISE-1743790, and the Office of Research at the University of South
Dakota. Computations supporting this project were performed on High
Performance Computing systems at the University of South Dakota, funded
by NSF award OAC-1626516.
CR Aalseth CE, 2011, PHYS REV LETT, V106, DOI 10.1103/PhysRevLett.106.131301
Abgrall N, 2014, ADV HIGH ENERGY PHYS, V2014, DOI 10.1155/2014/365432
Abt I, 2007, NUCL INSTRUM METH A, V583, P332, DOI 10.1016/j.nima.2007.09.017
Abt I, 2010, EUR PHYS J C, V68, P609, DOI 10.1140/epjc/s10052-010-1364-9
Agostinelli S, 2003, NUCL INSTRUM METH A, V506, P250, DOI 10.1016/S0168-9002(03)01368-8
Agostini M., 2016, NUCL PART PHYS P, V1876, P273
Allison J, 2006, IEEE T NUCL SCI, V53, P270, DOI 10.1109/TNS.2006.869826
Allison J, 2016, NUCL INSTRUM METH A, V835, P186, DOI 10.1016/j.nima.2016.06.125
Amman M., 2018, ARXIV180903046
[Anonymous], 2019, SOLID STATE DETECTOR
Barrett R, 1994, TEMPLATES SOLUTION L, P2
Boswell M, 2011, IEEE T NUCL SCI, V58, P1212, DOI 10.1109/TNS.2011.2144619
Bruyneel B, 2016, EUR PHYS J A, V52, DOI 10.1140/epja/i2016-16070-9
Bruyneel B, 2006, NUCL INSTRUM METH A, V569, P764, DOI 10.1016/j.nima.2006.08.130
GATTI E, 1982, NUCL INSTRUM METHODS, V193, P651, DOI 10.1016/0029-554X(82)90265-8
Giovanetti G., 2015, THESIS
Hansen W.L., 1982, MRS ONLINE P LIB ARC, V16, P1982
He Z, 2001, NUCL INSTRUM METH A, V463, P250, DOI 10.1016/S0168-9002(01)00223-6
Kerman S, 2016, PHYS REV D, V93, DOI 10.1103/PhysRevD.93.113006
Lenz D., 2010, THESIS
Liu J., 2009, THESIS
LUKE PN, 1989, IEEE T NUCL SCI, V36, P926, DOI 10.1109/23.34577
Mihailescu L, 2000, NUCL INSTRUM METH A, V447, P350, DOI 10.1016/S0168-9002(99)01286-3
NASHASHIBI T, 1990, IEEE T NUCL SCI, V37, P452, DOI 10.1109/23.106661
NASHASHIBI T, 1992, NUCL INSTRUM METH A, V322, P551, DOI 10.1016/0168-9002(92)91230-7
RADEKA V, 1988, ANNU REV NUCL PART S, V38, P217, DOI 10.1146/annurev.ns.38.120188.001245
Radford D., 2017, LARGE INVERTED COAXI
Radford D., 2015, MAJORANA SIGGEN
Radford D., ICPC SIGGEN
REGGIANI L, 1978, PHYS REV B, V17, P2800, DOI 10.1103/PhysRevB.17.2800
Salathe M, 2017, NUCL INSTRUM METH A, V868, P19, DOI 10.1016/j.nima.2017.06.036
Salathe M., 2015, THESIS
Takahashi Y, 2019, EPJ WEB CONF, V214, DOI 10.1051/epjconf/201921402011
Wang GJ, 2015, MAT SCI SEMICON PROC, V39, P54, DOI 10.1016/j.mssp.2015.04.044
Weisshaar D., 2016, NUCL INSTRUM METHODS
Yue Q, 2016, J PHYS CONF SER, V718, DOI 10.1088/1742-6596/718/4/042066
NR 36
TC 0
Z9 0
U1 0
U2 2
PU SPRINGER
PI NEW YORK
PA ONE NEW YORK PLAZA, SUITE 4600, NEW YORK, NY, UNITED STATES
SN 1434-6044
EI 1434-6052
J9 EUR PHYS J C
JI Eur. Phys. J. C
PD MAR 11
PY 2020
VL 80
IS 3
AR 230
DI 10.1140/epjc/s10052-020-7786-0
PG 25
WC Physics, Particles & Fields
SC Physics
GA KV4TF
UT WOS:000520474100002
OA DOAJ Gold
DA 2021-04-21
ER
PT J
AU Vogt, J
Blagau, A
Pick, L
AF Vogt, J.
Blagau, A.
Pick, L.
TI Robust Adaptive Spacecraft Array Derivative Analysis
SO EARTH AND SPACE SCIENCE
LA English
DT Article
ID GRADIENT CALCULATION; SWARM CONSTELLATION
AB Multispacecraft missions such as Cluster, Themis, Swarm, and MMS contribute to the exploration of geospace with their capability to produce gradient and curl estimates from sets of spatially distributed in situ measurements. This paper combines all existing estimators of the reciprocal vector family for spatial derivatives and their errors. The resulting framework proves to be robust and adaptive in the sense that it works reliably for arrays with arbitrary numbers of spacecraft and possibly degenerate geometries. The analysis procedure is illustrated using synthetic data as well as magnetic measurements from the Cluster and Swarm missions. An implementation of the core algorithm in Python is shown to be compact and computationally efficient so that it can be easily integrated in the various free and open source packages for the Space Physics and Heliophysics community.
C1 [Vogt, J.; Blagau, A.; Pick, L.] Jacobs Univ, Dept Phys & Earth Sci, Bremen, Germany.
[Blagau, A.] Inst Space Sci, Bucharest, Romania.
[Pick, L.] Deutsch GeoForschungsZentrum, Potsdam, Germany.
RP Vogt, J (corresponding author), Jacobs Univ, Dept Phys & Earth Sci, Bremen, Germany.
EM j.vogt@jacobs-university.de
OI Pick, Leonie/0000-0002-5266-9764
FU Deutsche ForschungsgemeinschaftGerman Research Foundation (DFG) [SPP
1788, VO 855/4-1]; ESA project MAGICS, PRODEX [4000127660]
FX Financial support from the Deutsche Forschungsgemeinschaft in the
context of the SPP 1788 Dynamic Earth through Grant VO 855/4-1 is
acknowledged. The work in Romania was supported by the ESA project
MAGICS, PRODEX Contract 4000127660. Swarm Level 1b and Level 2 data are
available from ESA (https://www.esa.int/Swarm).Cluster FGM data are
provided by the Cluster Science Archive
(https://www.cosmos.esa.int/web/csa).
CR Blagau A, 2019, J GEOPHYS RES-SPACE, V124, P6869, DOI 10.1029/2018JA026439
Burrell AG, 2018, J GEOPHYS RES-SPACE, V123, P10384, DOI 10.1029/2018JA025877
Chanteur G, 2000, ESA SP PUBL, V449, P265
Chanteur G., 1998, ISSI SCI REP SER, V1, P371
De Keyser J, 2007, ANN GEOPHYS-GERMANY, V25, P971, DOI 10.5194/angeo-25-971-2007
De Keyser J, 2008, ANN GEOPHYS-GERMANY, V26, P3295, DOI 10.5194/angeo-26-3295-2008
Dunlop MW, 2015, J GEOPHYS RES-SPACE, V120, P8307, DOI 10.1002/2015JA021707
Dunlop MW, 2005, ANN GEOPHYS-GERMANY, V23, P901, DOI 10.5194/angeo-23-901-2005
Dunlop MW, 1988, ADV SPACE RES, V8, P9, DOI [10.1016/0273-1177(88)90141-X, DOI 10.1016/0273-1177(88)90141-X]
Gil Y, 2016, EARTH SPACE SCI, V3, P388, DOI 10.1002/2015EA000136
Hamrin M, 2008, ANN GEOPHYS-GERMANY, V26, P3491, DOI 10.5194/angeo-26-3491-2008
Harvey C.C., 1998, ISSI SCI RES SERIES, P307
Paschmann G., 2008, MULTISPACECRAFT ANAL
Paschmann G., 1998, SR001 ISSIESA
Ritter P, 2006, EARTH PLANETS SPACE, V58, P463, DOI 10.1186/BF03351942
Ritter P, 2013, EARTH PLANETS SPACE, V65, P1285, DOI 10.5047/eps.2013.09.006
Robert P., 1998, ISSI SCI REPORTS SER, V1, P395
Robert P., 1998, ISSI SCI REP SER, P323
Shen C, 2012, J GEOPHYS RES-SPACE, V117, DOI 10.1029/2012JA018075
Stansby D., 2019, HELIOPYTHON HELIOPY
Stoneback RA, 2018, J GEOPHYS RES-SPACE, V123, P5271, DOI 10.1029/2018JA025297
Vogt J, 2008, ANN GEOPHYS-GERMANY, V26, P1699, DOI 10.5194/angeo-26-1699-2008
Vogt J, 2011, ANN GEOPHYS-GERMANY, V29, P2239, DOI 10.5194/angeo-29-2239-2011
Vogt J, 2009, ANN GEOPHYS-GERMANY, V27, P3249, DOI 10.5194/angeo-27-3249-2009
Vogt J., 2014, HDB GEOMATHEMATICS, P1
Vogt J., 2019, LOCAL LEAST SQUARES
Vogt J., 1998, KEPLERLAAN, V1, P419
NR 27
TC 0
Z9 0
U1 0
U2 1
PU AMER GEOPHYSICAL UNION
PI WASHINGTON
PA 2000 FLORIDA AVE NW, WASHINGTON, DC 20009 USA
EI 2333-5084
J9 EARTH SPACE SCI
JI Earth Space Sci.
PD MAR
PY 2020
VL 7
IS 3
AR UNSP e2019EA000953
DI 10.1029/2019EA000953
PG 8
WC Astronomy & Astrophysics; Geosciences, Multidisciplinary
SC Astronomy & Astrophysics; Geology
GA LH9XG
UT WOS:000529137300002
OA DOAJ Gold, Green Published
DA 2021-04-21
ER
PT J
AU Mathieu, D
AF Mathieu, Didier
TI Modeling Sensitivities of Energetic Materials using the Python Language
and Libraries
SO PROPELLANTS EXPLOSIVES PYROTECHNICS
LA English
DT Article
DE Sensitivity; Explosive; Molecular modeling; Computer program; QSPR
ID PREDICTING IMPACT SENSITIVITIES; SOFTWARE; KINETICS; DESIGN
AB Assessing the value of new compounds as components of energetic materials requires the determination of a significant amount of data, including sensitivities to various stimuli. Unfortunately, the dependence of these properties on molecular structure is still poorly understood. In view of estimating their values for putative high energy molecules, standard quantitative structure-property relationship (QSPR) methodologies are widely used. In doing so, a special focus is put on standard descriptors and formalisms. To foster further progress through consideration of alternative approaches, this article emphasizes how the Python language and associated libraries make it straightforward to implement arbitrary models, including schemes a la Keshavarz based on the occurrences of highly specific molecular fragments as well as the non-linear expressions naturally arising from physics-based approaches to sensitivities. Two previously published models are implemented for illustrative purposes. The first one is a simple fragment-based equation for electric spark sensitivity of nitroarenes. The second one is a model for impact sensitivity of general molecular energetic materials. In each case a Python implementation is provided as supporting information and may be used as is or serve as a template to implement alternative schemes.
C1 [Mathieu, Didier] CEA, DAM, F-37260 Le Ripault, Monts, France.
RP Mathieu, D (corresponding author), CEA, DAM, F-37260 Le Ripault, Monts, France.
EM didier.mathieu@cea.fr
FU CEAFrench Atomic Energy Commission
FX The research reported in this publication was supported by CEA. The
author also acknowledges stimulating discussions with Julien Glorian
(Institut Saint-Louis, France).
CR Asay BW, 2010, SHOCK WAVE SCI TECHN, V5, P45, DOI 10.1007/978-3-540-87953-4_3
Bondarchuk SV, 2018, J PHYS CHEM A, V122, P5455, DOI 10.1021/acs.jpca.8b01743
Bondarchuk SV, 2017, INT J QUANTUM CHEM, V117, DOI 10.1002/qua.25430
BRILL TB, 1993, CHEM REV, V93, P2667, DOI 10.1021/cr00024a005
Burns L., 2019, 257 NAT M AM CHEM SO
KAMLET MJ, 1979, PROPELLANTS EXPLOS, V4, P30, DOI 10.1002/prep.19790040204
Keshavarz MH, 2017, PROPELL EXPLOS PYROT, V42, P854, DOI 10.1002/prep.201700144
Keshavarz MH, 2013, PROPELL EXPLOS PYROT, V38, P754, DOI 10.1002/prep.201200128
Keshavarz MH, 2019, CENT EUR J ENERG MAT, V16, P65, DOI 10.22211/cejem/104389
Keshavarz MH, 2015, CENT EUR J ENERG MAT, V12, P215
Kuklja MM, 2003, APPL PHYS A-MATER, V76, P359, DOI 10.1007/s00339-002-1821-x
Lu T, 2012, J COMPUT CHEM, V33, P580, DOI 10.1002/jcc.22885
Mathieu D, 2005, PHYS SCRIPTA, VT118, P171, DOI 10.1238/Physica.Topical.118a00171
Mathieu D, 2012, J PHYS CHEM A, V116, P1794, DOI 10.1021/jp209730a
Mathieu D, 2017, IND ENG CHEM RES, V56, P8191, DOI 10.1021/acs.iecr.7b02021
Mathieu D, 2016, IND ENG CHEM RES, V55, P7569, DOI 10.1021/acs.iecr.6b01536
Mathieu D, 2015, J MOL GRAPH MODEL, V62, P81, DOI 10.1016/j.jmgm.2015.09.001
Mathieu D, 2014, J PHYS CHEM A, V118, P9720, DOI 10.1021/jp507057r
Mathieu D, 2013, J PHYS CHEM A, V117, P2253, DOI 10.1021/jp311677s
Mathieu D, 2010, J HAZARD MATER, V176, P313, DOI 10.1016/j.jhazmat.2009.11.030
Mauri A, 2006, MATCH-COMMUN MATH CO, V56, P237
Mereau R, 2004, PHYS REV B, V69, DOI 10.1103/PhysRevB.69.104101
Moriwaki H, 2018, J CHEMINFORMATICS, V10, DOI 10.1186/s13321-018-0258-y
Morrill JA, 2008, J MOL GRAPH MODEL, V27, P349, DOI 10.1016/j.jmgm.2008.06.003
Murray JS, 1998, MOL PHYS, V93, P187, DOI 10.1080/00268979809482203
Pospisil M, 2010, J MOL MODEL, V16, P895, DOI 10.1007/s00894-009-0587-x
Ren G., 2016, STAT THEORY INITIATI
Rice BM, 2002, J MOL STRUC-THEOCHEM, V583, P69, DOI 10.1016/S0166-1280(01)00782-5
Sahigara F, 2012, MOLECULES, V17, P4791, DOI 10.3390/molecules17054791
STORM CB, 1990, NATO ADV SCI I C-MAT, V309, P605
Zhang H, 2013, ASIAN J CHEM, V25, P5670, DOI 10.14233/ajchem.2013.OH58
NR 31
TC 1
Z9 1
U1 7
U2 13
PU WILEY-V C H VERLAG GMBH
PI WEINHEIM
PA POSTFACH 101161, 69451 WEINHEIM, GERMANY
SN 0721-3115
EI 1521-4087
J9 PROPELL EXPLOS PYROT
JI Propellants Explos. Pyrotech.
PD JUN
PY 2020
VL 45
IS 6
BP 966
EP 973
DI 10.1002/prep.201900377
PG 8
WC Chemistry, Applied; Engineering, Chemical
SC Chemistry; Engineering
GA LT6QU
UT WOS:000514741600001
DA 2021-04-21
ER
PT J
AU Haber, A
Verhaegen, M
AF Haber, Aleksandar
Verhaegen, Michel
TI Modeling and state-space identification of deformable mirrors
SO OPTICS EXPRESS
LA English
DT Article
ID ADAPTIVE SECONDARY MIRROR; PREDICTIVE CONTROL; SHAPE CONTROL; DESIGN;
CONTROLLER
AB To develop high-performance controllers for adaptive optics (AO) systems, it is essential to first derive sufficiently accurate state-space models of deformable mirrors (DMs). However, it is often challenging to develop realistic large-scale finite element (FE) state-space models that take into account the system damping, actuator dynamics, boundary conditions, and multi-physics phenomena affecting the system dynamics. Furthermore, it is challenging to establish a modeling framework capable of the automated and quick derivation of state-space models for different actuator configurations and system geometries. On the other hand, for accurate model-based control and system monitoring, it is often necessary to estimate state-space models from the experimental data. However, this is a challenging problem since the DM dynamics is inherently infinite-dimensional and it is characterized by a large number of eigenmodes and eigenfrequencies. In this paper, we provide modeling and estimation frameworks that address these challenges. We develop an FE state-space model of a faceplate DM that incorporates damping and actuator dynamics. We investigate the frequency and time domain responses for different model parameters. The state-space modeling process is completely automated using the LiveLink for MATLAB toolbox that is incorporated into the COMSOL Multiphysics software package. The developed state-space model is used to generate the estimation data. This data, together with a subspace identification algorithm, is used to estimate reduced-order DM models. We address the model-order selection and model validation problems. The results of this paper provide essential modeling and estimation tools to broad AO and mechatronics scientific communities. The developed Python, MATLAB, and COMSOL Multiphysics codes are available online. (C) 2020 Optical Society of America under the terms of the OSA Open Access Publishing Agreement
C1 [Haber, Aleksandar] CUNY Coll Staten Isl, Dept Engn & Environm Sci, 2800 Victory Blvd, Staten Isl, NY 10314 USA.
[Verhaegen, Michel] Delft Univ Technol, Delft Ctr Syst & Control, Mekelweg 5, NL-2628 CD Delft, Netherlands.
RP Haber, A (corresponding author), CUNY Coll Staten Isl, Dept Engn & Environm Sci, 2800 Victory Blvd, Staten Isl, NY 10314 USA.
EM aleksandar.haber@csi.cuny.edu
FU H2020 European Research Council [ERC 339681]; City University of New
York (PSC-CUNY Award A) [61303-00 49, 62267-00 50]
FX H2020 European Research Council (ERC 339681); City University of New
York (PSC-CUNY Award A (61303-00 49), PSC-CUNY Award A (62267-00 50)).
CR Agapito G, 2011, EUR J CONTROL, V17, P273, DOI 10.3166/EJC.17.273-289
Andersen T, 2006, OPT ENG, V45, DOI 10.1117/1.2227014
Bathe K.-J., 2014, FINITE ELEMENT PROCE
Bohm M, 2015, IEEE INTL CONF CONTR, P418, DOI 10.1109/CCA.2015.7320665
Brusa G, 1999, P SOC PHOTO-OPT INS, V3762, P38, DOI 10.1117/12.363599
Brusa G, 1998, APPL OPTICS, V37, P4656, DOI 10.1364/AO.37.004656
Brusa G, 1998, P SOC PHOTO-OPT INS, V3353, P764, DOI 10.1117/12.321693
Brusa G., 2000, P BACK WORKSH EXTR L, V57, P181
Cayrel M, 2012, PROC SPIE, V8444, DOI 10.1117/12.925175
Chiuso A, 2010, IEEE T CONTR SYST T, V18, P705, DOI 10.1109/TCST.2009.2023914
Ellenbroek R, 2006, PROC SPIE, V6272, pU1191, DOI 10.1117/12.671688
Fraanje R, 2010, INT J OPTOMECHATRONI, V4, P269, DOI 10.1080/15599612.2010.512380
Haber A, 2013, OPT EXPRESS, V21, P21530, DOI 10.1364/OE.21.021530
Haber A., 2019, ARXIV190802379
Haber A., 2019, 2019 COMSOL MULT C B
Haber A., 2019, P ASME 2019 DYN SYST
Haber A., 2019, MACHINE LEARNING SYS
Haber A, 2018, IEEE T CONTROL NETW, V5, P694, DOI 10.1109/TCNS.2017.2728201
Haber A, 2018, COMPUT METHOD APPL M, V335, P610, DOI 10.1016/j.cma.2018.01.034
Haber A, 2016, OPT LETT, V41, P5162, DOI 10.1364/OL.41.005162
Haber A, 2014, IEEE T AUTOMAT CONTR, V59, P2754, DOI 10.1109/TAC.2014.2310375
Haber A, 2013, OPT LETT, V38, P3061, DOI 10.1364/OL.38.003061
Hamelinck R. F. M. M., 2010, THESIS
Hamelinck R, 2008, PROC SPIE, V7015, DOI 10.1117/12.787755
Heimsten R, 2012, OPT ENG, V51, DOI 10.1117/1.OE.51.2.026601
Heimsten R, 2012, APPL OPTICS, V51, P515, DOI 10.1364/AO.51.000515
Hippler S., 2018, ARXIV180802693
Houtzager I., 2009, P 15 IFAC S SYST ID, P96
Houtzager I, 2009, IEEE DECIS CONTR P, P3370, DOI 10.1109/CDC.2009.5400695
Iserles, 2009, 1 COURSE NUMERICAL A
Jenkins DR, 2018, MON NOT R ASTRON SOC, V478, P3149, DOI 10.1093/mnras/sty1310
Kuiper S, 2018, PROC SPIE, V10706, DOI 10.1117/12.2311981
Kuiper S, 2016, PROC SPIE, V9912, DOI 10.1117/12.2230891
Kulcsar C, 2012, AUTOMATICA, V48, P1939, DOI 10.1016/j.automatica.2012.03.030
MacMartin DG, 2003, PROC SPIE, V5054, P275, DOI 10.1117/12.484661
MacMynowski DG, 2011, EUR J CONTROL, V17, P249, DOI 10.3166/EJC.17.249-260
Manetti M, 2016, J VIB CONTROL, V22, P1288, DOI 10.1177/1077546314536426
Manetti M, 2014, PROC SPIE, V9148, DOI 10.1117/12.2055919
Manetti M, 2014, IEEE T CONTR SYST T, V22, P838, DOI 10.1109/TCST.2013.2267814
Manetti M, 2012, CONTROL ENG PRACT, V20, P783, DOI 10.1016/j.conengprac.2012.03.018
Manetti M, 2010, CONTROL ENG PRACT, V18, P1386, DOI 10.1016/j.conengprac.2010.07.002
Massioni P, 2011, J OPT SOC AM A, V28, P2298, DOI 10.1364/JOSAA.28.002298
Preumont A., 2018, VIBRATION CONTROL AC, V4th
Ravensbergen SK, 2013, PRECIS ENG, V37, P353, DOI 10.1016/j.precisioneng.2012.10.004
Ravensbergen S. K., 2009, P SOC PHOTO-OPT INS, V7466
Ravensbergen S. K., 2012, THESIS
Ruppel T, 2013, IEEE T CONTR SYST T, V21, P579, DOI 10.1109/TCST.2012.2186813
Sedghi B, 2012, PROC SPIE, V8444, DOI 10.1117/12.925123
Sinquin B, 2018, J OPT SOC AM A, V35, P1612, DOI 10.1364/JOSAA.35.001612
Sinquin JC, 2008, P SOC PHOTO-OPT INS, V7015, DOI 10.1117/12.787400
Song H, 2011, EUR J CONTROL, V17, P290, DOI 10.3166/EJC.17.290-301
Stephens D. G., 1965, TECH REP
Timoshenko S., 1959, THEORY PLATES SHELLS
Tyson R, 1999, ADAPTIVE OPTICS ENG
Tyson RK, 2014, PRINCIPLES AND APPLICATIONS OF FOURIER OPTICS, P1, DOI 10.1088/978-0-750-31056-7
Verhaegen M., 2007, FILTERING SYSTEM IDE
Vogel CR, 2006, J OPT SOC AM A, V23, P1074, DOI 10.1364/JOSAA.23.001074
Yu CP, 2018, IEEE T CONTR SYST T, V26, P664, DOI 10.1109/TCST.2017.2692738
Zienkiewicz OC, 2005, FINITE ELEMENT METHOD FOR FLUID DYNAMICS, 6TH EDITION, P1
NR 59
TC 3
Z9 3
U1 1
U2 3
PU OPTICAL SOC AMER
PI WASHINGTON
PA 2010 MASSACHUSETTS AVE NW, WASHINGTON, DC 20036 USA
SN 1094-4087
J9 OPT EXPRESS
JI Opt. Express
PD FEB 17
PY 2020
VL 28
IS 4
BP 4726
EP 4740
DI 10.1364/OE.382880
PG 15
WC Optics
SC Optics
GA KN1BS
UT WOS:000514575500032
PM 32121705
OA DOAJ Gold, Green Published
DA 2021-04-21
ER
PT J
AU Barnes, WT
Bobra, MG
Christe, SD
Freij, N
Hayes, LA
Ireland, J
Mumford, S
Perez-Suarez, D
Ryan, DF
Shih, ABY
Chanda, P
Glogowski, K
Hewett, R
Hughitt, VK
Hill, A
Hiware, K
Inglis, A
Kirk, MSF
Konge, S
Mason, JP
Maloney, SA
Murray, SA
Panda, A
Park, J
Pereira, TMD
Reardon, K
Savage, S
Sipocz, BM
Stansby, D
Jain, Y
Taylor, G
Yadav, T
Rajul
Dang, TK
AF Barnes, Will T.
Bobra, Monica G.
Christe, Steven D.
Freij, Nabil
Hayes, Laura A.
Ireland, Jack
Mumford, Stuart
Perez-Suarez, David
Ryan, Daniel F.
Shih, Albert Y.
Chanda, Prateek
Glogowski, Kolja
Hewett, Russell
Hughitt, V. Keith
Hill, Andrew
Hiware, Kaustubh
Inglis, Andrew
Kirk, Michael S. F.
Konge, Sudarshan
Mason, James Paul
Maloney, Shane Anthony
Murray, Sophie A.
Panda, Asish
Park, Jongyeob
Pereira, Tiago M. D.
Reardon, Kevin
Savage, Sabrina
Sipocz, Brigitta M.
Stansby, David
Jain, Yash
Taylor, Garrison
Yadav, Tannmay
Rajul
Dang, Trung Kien
CA The SunPy Community
TI The SunPy Project: Open Source Development and Status of the Version 1.0
Core Package
SO ASTROPHYSICAL JOURNAL
LA English
DT Article
DE The Sun
ID SOLAR; TELESCOPE; REGION
AB The goal of the SunPy project is to facilitate and promote the use and development of community-led, free, and open source data analysis software for solar physics based on the scientific Python environment. The project achieves this goal by developing and maintaining the sunpy core package and supporting an ecosystem of affiliated packages. This paper describes the first official stable release (version 1.0) of the core package, as well as the project organization and infrastructure. This paper concludes with a discussion of the future of the SunPy project.
C1 [Barnes, Will T.] Lockheed Martin Solar & Astrophys Lab, Palo Alto, CA 94304 USA.
[Barnes, Will T.] Bay Area Environm Res Inst, Moffett Field, CA 94952 USA.
[Bobra, Monica G.] Stanford Univ, WW Hansen Expt Phys Lab, Stanford, CA 94305 USA.
[Christe, Steven D.; Hayes, Laura A.; Ireland, Jack; Ryan, Daniel F.; Shih, Albert Y.; Inglis, Andrew; Kirk, Michael S. F.; Mason, James Paul] NASA, Goddard Space Flight Ctr, Greenbelt, MD 20771 USA.
[Mumford, Stuart] Univ Sheffield, Sch Math & Stat, SP2RC, Sheffield, S Yorkshire, England.
[Mumford, Stuart] Aperio Software, Leeds LS6 3HN, W Yorkshire, England.
[Perez-Suarez, David] UCL, Gower St, London, England.
[Kirk, Michael S. F.] Catholic Univ Amer, Washington, DC 20664 USA.
[Chanda, Prateek] Indian Inst Engn Sci & Technol Shibpur, Sibpur, India.
[Glogowski, Kolja] Kiepenheuer Inst Sonnenphys, Freiburg, Germany.
[Glogowski, Kolja] Univ Freiburg, Ctr Comp, ESci Dept, Freiburg, Germany.
[Hewett, Russell] Virginia Polytech Inst & State Univ, Dept Math, Blacksburg, VA 24061 USA.
[Hughitt, V. Keith] NCI, Ctr Canc Res, Bethesda, MD 20892 USA.
[Hill, Andrew] Oklahoma Baptist Univ, Shawnee, OK 74804 USA.
[Hiware, Kaustubh; Jain, Yash; Yadav, Tannmay; Rajul] Indian Inst Technol Kharagpur, Kharagpur, W Bengal, India.
[Konge, Sudarshan] Microsoft India Dev Ctr, Bangalore, Karnataka, India.
[Maloney, Shane Anthony; Murray, Sophie A.] Trinity Coll Dublin, Sch Phys, Astrophys & Space Phys, Dublin, Ireland.
[Maloney, Shane Anthony; Murray, Sophie A.] Dublin Inst Adv Studies, Cosm Phys, Astron & Astrophys, Dublin, Ireland.
[Panda, Asish] Google, Mountain View, CA USA.
[Park, Jongyeob] Korea Astron & Space Sci Inst, Div Space Sci, Daejeon 34055, South Korea.
[Pereira, Tiago M. D.] Univ Oslo, Inst Theoret Astrophys, Oslo, Norway.
[Pereira, Tiago M. D.] Univ Oslo, Rosseland Ctr Solar Phys, Oslo, Norway.
[Reardon, Kevin] Natl Solar Observ, Boulder, CO 80303 USA.
[Savage, Sabrina] NASA, Marshall Space Flight Ctr, Huntsville, AL 35812 USA.
[Sipocz, Brigitta M.] Univ Washington, Dept Astron, DIRAC Inst, Seattle, WA 98195 USA.
[Stansby, David] Univ England, Mullard Space Sci Lab, Holmbury Hill Rd, Dorking RH5 6NT, Surrey, England.
[Taylor, Garrison] Harvard Smithsonian Ctr Astrophys, 60 Garden St, Cambridge, MA 02138 USA.
[Freij, Nabil] Univ Reading, Inst Environm Analyt, Reading RG6 6BX, Berks, England.
[Ryan, Daniel F.] Amer Univ, Washington, DC 20016 USA.
[Dang, Trung Kien] Natl Univ Singapore, Natl Univ Hlth Syst, Saw Swee Hock Sch Publ Hlth, Singapore, Singapore.
RP Christe, SD (corresponding author), NASA, Goddard Space Flight Ctr, Greenbelt, MD 20771 USA.
EM steven.christe@nasa.gov; mail@kien.ai
RI Stansby, David/AAT-8717-2020
OI Dang, Trung Kien/0000-0001-7562-6495; Shih, Albert/0000-0001-6874-2594;
Freij, Nabil/0000-0002-6253-082X; Barnes, Will/0000-0001-9642-6089;
Bobra, Monica/0000-0002-5662-9604; Murray, Sophie/0000-0002-9378-5315;
Savage, Sabrina/0000-0002-6172-0517; Perez-Suarez,
David/0000-0003-0784-6909
FU NSFNational Science Foundation (NSF) [AST-1715122]; DIRAC Institute in
the Department of Astronomy at the University of Washington; STFC
studentshipUK Research & Innovation (UKRI)Science & Technology
Facilities Council (STFC) [ST/N504336/1]; STFC grantUK Research &
Innovation (UKRI)Science & Technology Facilities Council (STFC)
[ST/N000692/1]; Google; NumFocus; Solar Physics Division of the American
Astronomical Society; Space program
FX The following individuals recognize support for their personal
contributions. B.M.S. is supported by the NSF grant AST-1715122 and
acknowledges support from the DIRAC Institute in the Department of
Astronomy at the University of Washington. The DIRAC Institute is
supported through generous gifts from the Charles and Lisa Simonyi Fund
for Arts and Sciences, and the Washington Research Foundation. D.S. was
supported by STFC studentship ST/N504336/1 and STFC grant ST/N000692/1.;
We acknowledge financial contributions from Google as part of the Google
Summer of Code program and from the Space program. We acknowledge
financial contributions from NumFocus for improving the usability of
SunPy's Data Downloader. Additionally, we acknowledge current and future
funding from the Solar Physics Division of the American Astronomical
Society for SunPy workshops and tutorials at annual meetings.; This work
has made use of data from the European Space Agency (ESA) mission
Gaia,80 processed by the Gaia Data Processing and Analysis Consortium
(DPAC).81 Funding for the DPAC has been provided by national
institutions, in particular, the institutions participating in the Gaia
Multilateral Agreement.
CR Abbott BP, 2016, PHYS REV LETT, V116, DOI 10.1103/PhysRevLett.116.061102
Akiyama K, 2019, ASTROPHYS J LETT, V875, DOI 10.3847/2041-8213/ab0ec7
Annex A., 2018, PYTHON HELIOPHYSICS, DOI [10.5281/zenodo.2529131, DOI 10.5281/ZENODO.2529131]
Bangerth Wolfgang, 2013, COMPUTATIONAL SCI DI, V6, P1, DOI 10.1088/1749-4699/6/1/015010
Beck JG, 2000, SOL PHYS, V191, P47, DOI 10.1023/A:1005226402796
Brown AGA, 2018, ASTRON ASTROPHYS, V616, DOI 10.1051/0004-6361/201833051
Brueckner GE, 1995, SOL PHYS, V162, P357, DOI 10.1007/BF00733434
Burrell AG, 2018, J GEOPHYS RES-SPACE, V123, P10384, DOI 10.1029/2018JA025877
Caswell T. A., 2019, MATPLOTLIB MATPLOTLI, DOI [10.5281/zenodo.2893252, DOI 10.5281/ZENODO.2893252]
Christe S., 2014, SUNPY PROPOSAL ENHAN, DOI [10.5281/zenodo.3261707, DOI 10.5281/ZENODO.3261707]
Christe S., 2019, RECOMMENDATION COMPL, DOI [10.5281/zenodo.3362439, DOI 10.5281/ZENODO.3362439]
Christe S., 2014, SUNPY PROPOSAL ENHAN, DOI [10.5281/zenodo.3261403, DOI 10.5281/ZENODO.3261403]
Christe S., 2018, SUNPY PROPOSAL ENHAN, DOI [10.5281/zenodo.3261663, DOI 10.5281/ZENODO.3261663]
Damevski K, 2009, P 2009 WORKSH COMP B, V13, DOI 10.1145/1687774.1687787
De Pontieu B, 2014, SOL PHYS, V289, P2733, DOI 10.1007/s11207-014-0485-y
de Wijn AG, 2012, PROC SPIE, V8444, DOI 10.1117/12.926511
Delaboudiniere J.-P., 1995, SOL PHYS, P291, DOI 10.1007/978-94-009-0191-9_8
Dominique M, 2013, SOL PHYS, V286, P21, DOI 10.1007/s11207-013-0252-5
Freeland SL, 1998, SOL PHYS, V182, P497, DOI 10.1023/A:1005038224881
GARCIA HA, 1994, SOL PHYS, V154, P275, DOI 10.1007/BF00681100
Ginsburg A, 2019, ASTRON J, V157, DOI 10.3847/1538-3881/aafc33
Glogowski K., 2019, JOSS, V4, P1614, DOI [10.21105/joss.01614, DOI 10.21105/JOSS.01614]
Golub L., 2008, HINODE MISSION, P27, DOI [10.1007/978-0-387-88739-5_5, DOI 10.1007/978-0-387-88739-5_5]
Greenfield P., 2013, ASTROPY PROPOSAL ENH, DOI [10.5281/zenodo.1043886, DOI 10.5281/ZENODO.1043886]
Greisen EW, 2002, ASTRON ASTROPHYS, V395, P1061, DOI 10.1051/0004-6361:20021326
Guo P., 2014, PYTHON IS NOW MOST P
Handy BN, 1999, SOL PHYS, V187, P229, DOI 10.1023/A:1005166902804
Hanser FA, 1996, P SOC PHOTO-OPT INS, V2812, P344, DOI 10.1117/12.254082
Hill F, 2009, EARTH MOON PLANETS, V104, P315, DOI 10.1007/s11038-008-9274-7
Howard RA, 2008, SPACE SCI REV, V136, P67, DOI 10.1007/s11214-008-9341-4
Hunter JD, 2007, COMPUT SCI ENG, V9, P90, DOI 10.1109/MCSE.2007.55
Hurlburt N, 2012, SOL PHYS, V275, P67, DOI 10.1007/s11207-010-9624-2
Jones E., 2001, SCIPY OPEN SOURCE SC
Lemen JR, 2012, SOL PHYS, V275, P17, DOI 10.1007/s11207-011-9776-8
Lin RP, 2002, SOL PHYS, V210, P3, DOI 10.1023/A:1022428818870
Meegan C, 2009, ASTROPHYS J, V702, P791, DOI 10.1088/0004-637X/702/1/791
Millman J., 2010, P56, DOI DOI 10.1016/S0168-0102(02)00204-3
Mumford S., 2014, SUNPY PROPOSAL ENHAN, DOI [10.5281/zenodo.3261752, DOI 10.5281/ZENODO.3261752]
Mumford S., 2019, SUNPY PROPOSAL ENHAN, DOI [10.5281/zenodo.3261800, DOI 10.5281/ZENODO.3261800]
Mumford S., 2018, SUNPY PROPOSAL ENHAN, DOI [10.5281/zenodo.3261794, DOI 10.5281/ZENODO.3261794]
Mumford Stuart J., 2015, Computational Science and Discovery, V8, DOI 10.1088/1749-4699/8/1/014009
Mumford S. J., 2020, JOSS, DOI [10.21105/joss.01832, DOI 10.21105/JOSS.01832]
Muna D., 2016, ARXIV161003159
NAKAJIMA H, 1994, P IEEE, V82, P705, DOI 10.1109/5.284737
National Academies of Sciences E, 2019, REPRODUCIBILITY REPL, DOI [10.17226/25303, DOI 10.17226/25303, 10.17226/25303.]
National Academies of Sciences Engineering and Medicine, 2018, OP SOURC SOFTW POL O, DOI [10.17226/25217, DOI 10.17226/25217]
Pedregosa F, 2011, J MACH LEARN RES, V12, P2825
Pence WD, 2010, ASTRON ASTROPHYS, V524, DOI 10.1051/0004-6361/201015362
Price-Whelan AM, 2018, ASTRON J, V156, DOI 10.3847/1538-3881/aac387
Prusti T, 2016, ASTRON ASTROPHYS, V595, DOI 10.1051/0004-6361/201629272
Robitaille TP, 2013, ASTRON ASTROPHYS, V558, DOI 10.1051/0004-6361/201322068
Rocklin M., 2015, P 14 PYTH SCI C, P126, DOI 10.25080/Majora-7b98e3ed-013
Scherrer PH, 1995, SOL PHYS, V162, P129, DOI 10.1007/BF00733429
Schou J, 2012, SOL PHYS, V275, P229, DOI 10.1007/s11207-011-9842-2
Seaton DB, 2013, SOL PHYS, V286, P43, DOI 10.1007/s11207-012-0114-6
Seidelmann PK, 2007, CELEST MECH DYN ASTR, V98, P155, DOI 10.1007/s10569-007-9072-y
The Mars Climate Orbiter Mishap Investigation Board, 1999, MARS CLIMATE ORBITER
Thompson WT, 2006, ASTRON ASTROPHYS, V449, P791, DOI 10.1051/0004-6361:20054262
TSUNETA S, 1991, SOL PHYS, V136, P37, DOI 10.1007/BF00151694
van der Walt S, 2011, COMPUT SCI ENG, V13, P22, DOI 10.1109/MCSE.2011.37
Verbeeck C, 2014, ASTRON ASTROPHYS, V561, DOI 10.1051/0004-6361/201321243
Ware A., 2019, PYTHON HELIOPHYSICS, DOI [10.5281/zenodo.2537188, DOI 10.5281/ZENODO.2537188]
Wilson G, 2014, PLOS BIOL, V12, DOI 10.1371/journal.pbio.1001745
Woods TN, 2012, SOL PHYS, V275, P115, DOI 10.1007/s11207-009-9487-6
NR 64
TC 24
Z9 24
U1 2
U2 2
PU IOP PUBLISHING LTD
PI BRISTOL
PA TEMPLE CIRCUS, TEMPLE WAY, BRISTOL BS1 6BE, ENGLAND
SN 0004-637X
EI 1538-4357
J9 ASTROPHYS J
JI Astrophys. J.
PD FEB 10
PY 2020
VL 890
IS 1
AR 68
DI 10.3847/1538-4357/ab4f7a
PG 12
WC Astronomy & Astrophysics
SC Astronomy & Astrophysics
GA KT0DZ
UT WOS:000518682400001
OA Green Published, Other Gold
DA 2021-04-21
ER
PT J
AU Barnes, R
Luger, R
Deitrick, R
Driscoll, P
Quinn, TR
Fleming, DP
Smotherman, H
McDonald, DV
Wilhelm, C
Garcia, R
Barth, P
Guyer, B
Meadows, VS
Bitz, CM
Gupta, P
Domagal-Goldman, SD
Armstrong, J
AF Barnes, Rory
Luger, Rodrigo
Deitrick, Russell
Driscoll, Peter
Quinn, Thomas R.
Fleming, David P.
Smotherman, Hayden
McDonald, Diego V.
Wilhelm, Caitlyn
Garcia, Rodolfo
Barth, Patrick
Guyer, Benjamin
Meadows, Victoria S.
Bitz, Cecilia M.
Gupta, Pramod
Domagal-Goldman, Shawn D.
Armstrong, John
TI VPLanet: The Virtual Planet Simulator
SO PUBLICATIONS OF THE ASTRONOMICAL SOCIETY OF THE PACIFIC
LA English
DT Article
DE binaries (including multiple); close; methods; numerical; planets and
satellites; atmospheres; planets and satellites; dynamical evolution and
stability; planets and satellites; interiors; planets and satellites;
magnetic fields; planets and satellites; physical evolution; stars;
kinematics and dynamics; stars; pre-main sequence
ID CLOSE BINARY STARS; TIDAL DISSIPATION; OORT CLOUD; HYDRODYNAMIC ESCAPE;
HABITABLE ZONES; SUPER-EARTHS; ORBITAL CIRCULARIZATION; TERRESTRIAL
EXOPLANETS; INSOLATION QUANTITIES; DYNAMICAL EVOLUTION
AB We describe a software package called VPLanet that simulates fundamental aspects of planetary system evolution over Gyr timescales, with a focus on investigating habitable worlds. In this initial release, eleven physics modules are included that model internal, atmospheric, rotational, orbital, stellar, and galactic processes. Many of these modules can be coupled to simultaneously simulate the evolution of terrestrial planets, gaseous planets, and stars. The code is validated by reproducing a selection of observations and past results. VPLanet is written in C and designed so that the user can choose the physics modules to apply to an individual object at runtime without recompiling, i.e., a single executable can simulate the diverse phenomena that are relevant to a wide range of planetary and stellar systems. This feature is enabled by matrices and vectors of function pointers that are dynamically allocated and populated based on user input. The speed and modularity of VPLanet enables large parameter sweeps and the versatility to add/remove physical phenomena to assess their importance. VPLanet is publicly available from a repository that contains extensive documentation, numerous examples, Python scripts for plotting and data management, and infrastructure for community input and future development.
C1 [Barnes, Rory; Quinn, Thomas R.; Fleming, David P.; Smotherman, Hayden; McDonald, Diego V.; Wilhelm, Caitlyn; Garcia, Rodolfo; Guyer, Benjamin; Meadows, Victoria S.; Gupta, Pramod] Univ Washington, Dept Astron, Box 951580, Seattle, WA 98195 USA.
[Barnes, Rory; Luger, Rodrigo; Deitrick, Russell; Driscoll, Peter; Quinn, Thomas R.; Fleming, David P.; Smotherman, Hayden; McDonald, Diego V.; Wilhelm, Caitlyn; Garcia, Rodolfo; Meadows, Victoria S.; Bitz, Cecilia M.; Gupta, Pramod; Domagal-Goldman, Shawn D.; Armstrong, John] NASA, Virtual Planetary Lab, Lead Team, Washington, DC 20546 USA.
[Luger, Rodrigo] Flatiron Inst, Ctr Computat Astrophys, New York, NY 10010 USA.
[Deitrick, Russell] Univ Bern, Ctr Space & Habitabil, Gesellschaftsstr 6, CH-3012 Bern, Switzerland.
[Driscoll, Peter] Carnegie Inst Sci, Dept Terr Magnetism, 5241 Broad Branch Rd, Washington, DC 20015 USA.
[Barth, Patrick] Max Planck Inst Astron, Konigstuhl 17, D-69117 Heidelberg, Germany.
[Bitz, Cecilia M.] Univ Washington, Dept Atmospher Sci, Box 951640, Seattle, WA 98195 USA.
[Domagal-Goldman, Shawn D.] NASA, Planetary Environm Lab, Goddard Space Flight Ctr, 8800 Greenbelt Rd, Greenbelt, MD 20771 USA.
[Armstrong, John] Weber State Univ, Dept Phys, Ogden, UT 84408 USA.
RP Barnes, R (corresponding author), Univ Washington, Dept Astron, Box 951580, Seattle, WA 98195 USA.; Barnes, R (corresponding author), NASA, Virtual Planetary Lab, Lead Team, Washington, DC 20546 USA.
EM rory@astro.washington.edu
RI Driscoll, Peter/X-7034-2019
OI Driscoll, Peter/0000-0001-6241-3925; Barth, Patrick/0000-0002-5418-0882;
Deitrick, Russell/0000-0001-9423-8121; Quinn, Thomas/0000-0001-5510-2803
FU NASA Astrobiology Institute's Virtual Planetary Laboratory [NNA13AA93A];
NASANational Aeronautics & Space Administration (NASA) [NNX15AN35G,
13-13NAI7_0024]; NASA Headquarters under the NASA Earth and Space
Science Fellowship [80NSSC17K0482]
FX This work was supported by the NASA Astrobiology Institute's Virtual
Planetary Laboratory under Cooperative Agreement number NNA13AA93A.
Additional support was provided by NASA grants NNX15AN35G, and
13-13NAI7_0024. D.P.F. is supported by NASA Headquarters under the NASA
Earth and Space Science Fellowship Program-grant 80NSSC17K0482. This
work also benefited from participation in the NASA Nexus for Exoplanet
Systems Science (NExSS) research coordination network. We thank an
anonymous referee whose comments greatly improved the quality of this
manuscript. We are also grateful for stimulating conversations with
Brian Jackson, Hector Martinez-Rodriguez, Terry Hurford, Ludmila Carone,
Juliette Becker, John Ahlers, Quadry Chance, and Nathan Kaib.
CR Abe-Ouchi A, 2013, NATURE, V500, P190, DOI 10.1038/nature12374
Allart R, 2019, ASTRON ASTROPHYS, V623, DOI 10.1051/0004-6361/201834917
Andrault D, 2011, EARTH PLANET SC LETT, V304, P251, DOI 10.1016/j.epsl.2011.02.006
Anglada-Escude G, 2016, NATURE, V536, P437, DOI 10.1038/nature19106
Araki T, 2005, NATURE, V436, P499, DOI 10.1038/nature03980
Arevalo R, 2013, GEOCHEM GEOPHY GEOSY, V14, P2265, DOI 10.1002/ggge.20152
Armstrong JC, 2014, ASTROBIOLOGY, V14, P277, DOI 10.1089/ast.2013.1129
Armstrong JC, 2004, ICARUS, V171, P255, DOI 10.1016/j.icarus.2004.05.007
Baland RM, 2016, ICARUS, V268, P12, DOI 10.1016/j.icarus.2015.11.039
Baraffe I, 2015, ASTRON ASTROPHYS, V577, DOI 10.1051/0004-6361/201425481
BARKER BM, 1970, PHYS REV D, V2, P1428, DOI 10.1103/PhysRevD.2.1428
BARKSTROM BR, 1990, EOS, V71, P297, DOI DOI 10.1029/E0071I009P00297
Barnes R, 2017, CELEST MECH DYN ASTR, V129, P509, DOI 10.1007/s10569-017-9783-7
Barnes R, 2015, ASTROPHYS J, V801, DOI 10.1088/0004-637X/801/2/101
Barnes R, 2013, ASTROBIOLOGY, V13, P225, DOI 10.1089/ast.2012.0851
BERGER A, 1991, QUATERNARY SCI REV, V10, P297, DOI 10.1016/0277-3791(91)90033-Q
BERGER AL, 1978, J ATMOS SCI, V35, P2362, DOI 10.1175/1520-0469(1978)035<2362:LTVODI>2.0.CO;2
Berta-Thompson ZK, 2015, NATURE, V527, P204, DOI 10.1038/nature15762
Bills BG, 2005, ICARUS, V175, P233, DOI 10.1016/j.icarus.2004.10.028
Bills BG, 2000, J GEOPHYS RES-PLANET, V105, P29277, DOI 10.1029/2000JE001248
Bolmont E, 2017, MON NOT R ASTRON SOC, V464, P3728, DOI 10.1093/mnras/stw2578
Bolmont E, 2012, ASTRON ASTROPHYS, V544, DOI 10.1051/0004-6361/201219645
Braithwaite RJ, 2000, J GLACIOL, V46, P7, DOI 10.3189/172756500781833511
Brasser R, 2014, MON NOT R ASTRON SOC, V440, P3685, DOI 10.1093/mnras/stu555
Chambers JE, 1999, MON NOT R ASTRON SOC, V304, P793, DOI 10.1046/j.1365-8711.1999.02379.x
Chassefiere E, 1996, ICARUS, V124, P537, DOI 10.1006/icar.1996.0229
Chassefiere E, 2004, PLANET SPACE SCI, V52, P1039, DOI 10.1016/j.pss.2004.07.002
Chassefiere E, 1996, J GEOPHYS RES-PLANET, V101, P26039, DOI 10.1029/96JE01951
Clark PU, 1998, PALEOCEANOGRAPHY, V13, P1, DOI 10.1029/97PA02660
Cogne JP, 2004, EARTH PLANET SC LETT, V227, P427, DOI 10.1016/j.epsl.2004.09.002
Collins BF, 2010, ASTRON J, V140, P1306, DOI 10.1088/0004-6256/140/5/1306
COLOMBO G, 1966, ASTROPHYS J, V145, P296, DOI 10.1086/148762
Cook A. H., 1980, INTERIORS PLANETS
Costa A, 2009, GEOCHEM GEOPHY GEOSY, V10, DOI 10.1029/2008GC002138
Cox A.N., 2000, ALLENS ASTROPHYSICAL
CRANK J, 1947, P CAMB PHILOS SOC, V43, P50, DOI 10.1017/S0305004100023197
Cranmer SR, 2011, ASTROPHYS J, V741, DOI 10.1088/0004-637X/741/1/54
Darwin G. H., 1880, RSPT, V171, P713, DOI [10.1098/rstl.1880.0020, DOI 10.1098/RSTL.1880.0020]
Deitrick R, 2018, ASTRON J, V155, DOI 10.3847/1538-3881/aac214
Deitrick R, 2018, ASTRON J, V155, DOI 10.3847/1538-3881/aaa301
DONAHUE TM, 1982, SCIENCE, V216, P630, DOI 10.1126/science.216.4546.630
Doyle LR, 2011, SCIENCE, V333, P1602, DOI 10.1126/science.1210923
Driscoll P, 2014, PHYS EARTH PLANET IN, V236, P36, DOI 10.1016/j.pepi.2014.08.004
Driscoll PE, 2015, ASTROBIOLOGY, V15, P739, DOI 10.1089/ast.2015.1325
Driscoll P, 2011, ICARUS, V213, P12, DOI 10.1016/j.icarus.2011.02.010
DUNCAN M, 1987, ASTRON J, V94, P1330, DOI 10.1086/114571
DZIEWONSKI AM, 1981, PHYS EARTH PLANET IN, V25, P297, DOI 10.1016/0031-9201(81)90046-7
Efroimsky M, 2009, CELEST MECH DYN ASTR, V104, P257, DOI 10.1007/s10569-009-9204-7
Elkins-Tanton LT, 2008, EARTH PLANET SC LETT, V271, P181, DOI 10.1016/j.epsl.2008.03.062
Elkins-Tanton LT, 2012, ANNU REV EARTH PL SC, V40, P113, DOI 10.1146/annurev-earth-042711-105503
Ellis KM, 2000, ICARUS, V147, P129, DOI 10.1006/icar.2000.6399
Erkaev NV, 2007, ASTRON ASTROPHYS, V472, P329, DOI 10.1051/0004-6361:20066929
Fabrycky DC, 2007, ASTROPHYS J, V665, P754, DOI 10.1086/519075
Ferraz-Mello S, 2008, CELEST MECH DYN ASTR, V101, P171, DOI 10.1007/s10569-008-9133-x
Fleming DP, 2019, ASTROPHYS J, V881, DOI 10.3847/1538-4357/ab2ed2
Fleming DP, 2018, ASTROPHYS J, V858, DOI 10.3847/1538-4357/aabd38
Garcia-Sanchez J, 2001, ASTRON ASTROPHYS, V379, P634, DOI 10.1051/0004-6361:20011330
Gillmann C, 2009, EARTH PLANET SC LETT, V286, P503, DOI 10.1016/j.epsl.2009.07.016
Gillon M, 2017, NATURE, V542, P456, DOI 10.1038/nature21360
Gillon M, 2016, NATURE, V533, P221, DOI 10.1038/nature17448
Gladman B, 1996, ICARUS, V122, P166, DOI 10.1006/icar.1996.0117
GLEN JW, 1958, NATURE, V182, P1560, DOI 10.1038/1821560a0
GOLDREIC.P, 1966, ASTRON J, V71, P1, DOI 10.1086/109844
GOLDREICH P, 1966, ICARUS, V5, P375, DOI 10.1016/0019-1035(66)90051-0
Gomi H, 2013, PHYS EARTH PLANET IN, V224, P88, DOI 10.1016/j.pepi.2013.07.010
Goni MFS, 2019, EARTH PLANET SC LETT, V511, P117, DOI 10.1016/j.epsl.2019.01.032
Greenberg R, 2009, ASTROPHYS J LETT, V698, pL42, DOI 10.1088/0004-637X/698/1/L42
HEISLER J, 1986, ICARUS, V65, P13, DOI 10.1016/0019-1035(86)90060-6
HEISLER J, 1987, ICARUS, V70, P269, DOI 10.1016/0019-1035(87)90135-7
Heller R, 2011, ASTRON ASTROPHYS, V528, DOI 10.1051/0004-6361/201015809
Henning WG, 2009, ASTROPHYS J, V707, P1000, DOI 10.1088/0004-637X/707/2/1000
Hirschmann MM, 2000, GEOCHEM GEOPHY GEOSY, V1, DOI 10.1029/2000GC000070
Howard L. N., 1966, APPL MECH, P1109, DOI DOI 10.1007/978-3-662-29364-5_147
Hubbard W. B., 1984, PLANETARY INTERIORS
HUBBARD WB, 1978, ICARUS, V33, P336, DOI 10.1016/0019-1035(78)90153-7
HUNTEN DM, 1987, ICARUS, V69, P532, DOI 10.1016/0019-1035(87)90022-4
HUNTEN DM, 1973, J ATMOS SCI, V30, P1481, DOI 10.1175/1520-0469(1973)030<1481:TEOLGF>2.0.CO;2
HUT P, 1981, ASTRON ASTROPHYS, V99, P126
Huybers P, 2008, PALEOCEANOGRAPHY, V23, DOI 10.1029/2007PA001463
Jackson AP, 2012, MON NOT R ASTRON SOC, V422, P2024, DOI 10.1111/j.1365-2966.2012.20657.x
Jackson B, 2009, ASTROPHYS J, V698, P1357, DOI 10.1088/0004-637X/698/2/1357
Jackson I, 2004, J GEOPHYS RES-SOL EA, V109, DOI 10.1029/2003JB002406
JANKOWSKI DG, 1989, ICARUS, V80, P211, DOI 10.1016/0019-1035(89)90169-3
Jaupart C., 2015, TREATISE GEOPHYS, V7, P253
Jenson JW, 1996, J GEOPHYS RES-SOL EA, V101, P8717, DOI 10.1029/96JB00169
Johnson RE, 2013, ASTROPHYS J LETT, V768, DOI 10.1088/2041-8205/768/1/L4
Kaib NA, 2013, NATURE, V493, P381, DOI 10.1038/nature11780
KASTING JF, 1983, ICARUS, V53, P479, DOI 10.1016/0019-1035(83)90212-9
KASTING JF, 1984, ICARUS, V57, P335, DOI 10.1016/0019-1035(84)90122-2
KASTING JF, 1988, ICARUS, V74, P472, DOI 10.1016/0019-1035(88)90116-9
KASTING JF, 1993, ICARUS, V101, P108, DOI 10.1006/icar.1993.1010
Kenyon SJ, 2004, NATURE, V432, P598, DOI 10.1038/nature03136
KINOSHITA H, 1977, CELESTIAL MECH, V15, P277, DOI 10.1007/BF01228425
Kinoshita H., 1975, SAOSR, V364, P1
Kopparapu RK, 2013, ASTROPHYS J, V765, DOI 10.1088/0004-637X/765/2/131
Kordopatis G, 2015, MON NOT R ASTRON SOC, V447, P3526, DOI 10.1093/mnras/stu2726
Labrosse S, 2001, EARTH PLANET SC LETT, V190, P111, DOI 10.1016/S0012-821X(01)00387-9
LAMBECK K, 1977, PHILOS T R SOC A, V287, P545, DOI 10.1098/rsta.1977.0159
Lammer H, 2013, MON NOT R ASTRON SOC, V430, P1247, DOI 10.1093/mnras/sts705
Laskar J, 2004, ICARUS, V170, P343, DOI 10.1016/j.icarus.2004.04.005
LASKAR J, 1993, ASTRON ASTROPHYS, V270, P522
LASKAR J, 1986, ASTRON ASTROPHYS, V157, P59
Leconte J, 2010, ASTRON ASTROPHYS, V516, DOI 10.1051/0004-6361/201014337
Lee MH, 2006, ICARUS, V184, P573, DOI 10.1016/j.icarus.2006.04.017
Lefebre F, 2002, ANN GLACIOL-SER, V35, P391, DOI 10.3189/172756402781816889
Lehmer OR, 2017, ASTROPHYS J, V845, DOI 10.3847/1538-4357/aa8137
Leung GCK, 2013, ASTROPHYS J, V763, DOI 10.1088/0004-637X/763/2/107
Levrard B, 2007, ASTRON ASTROPHYS, V462, pL5, DOI 10.1051/0004-6361:20066487
Lincowski AP, 2018, ASTROPHYS J, V867, DOI 10.3847/1538-4357/aae36a
Line MR, 2014, ASTROPHYS J, V783, DOI 10.1088/0004-637X/783/2/70
Lissauer JJ, 2007, ASTROPHYS J, V660, pL149, DOI 10.1086/518121
Lopez ED, 2018, MON NOT R ASTRON SOC, V479, P5303, DOI 10.1093/mnras/sty1707
Lopez ED, 2014, ASTROPHYS J, V792, DOI 10.1088/0004-637X/792/1/1
Lopez ED, 2013, ASTROPHYS J, V776, DOI 10.1088/0004-637X/776/1/2
Lopez ED, 2012, ASTROPHYS J, V761, DOI 10.1088/0004-637X/761/1/59
Lovett E. O, 1895, AJ, V15, P113
Luger R, 2015, ASTROBIOLOGY, V15, P119, DOI 10.1089/ast.2014.1231
Luger R, 2015, ASTROBIOLOGY, V15, P57, DOI 10.1089/ast.2014.1215
Lurie JC, 2017, ASTRON J, V154, DOI 10.3847/1538-3881/aa974d
Lynch CR, 2018, MON NOT R ASTRON SOC, V478, P1763, DOI 10.1093/mnras/sty1138
Ma B, 2018, MON NOT R ASTRON SOC, V480, P2411, DOI 10.1093/mnras/sty1933
MacDonald RJ, 2019, MON NOT R ASTRON SOC, V486, P1292, DOI 10.1093/mnras/stz789
Mardling RA, 2002, ASTROPHYS J, V573, P829, DOI 10.1086/340752
Matt SP, 2019, ASTROPHYS J LETT, V870, DOI 10.3847/2041-8213/aafa1b
Matt SP, 2015, ASTROPHYS J LETT, V799, DOI 10.1088/2041-8205/799/2/L23
MCKENZIE D, 1984, J PETROL, V25, P713, DOI 10.1093/petrology/25.3.713
MCKENZIE D, 1988, J PETROL, V29, P625, DOI 10.1093/petrology/29.3.625
McQuillan A, 2014, ASTROPHYS J SUPPL S, V211, DOI 10.1088/0067-0049/211/2/24
Meadows V. S., 2018, FACTORS AFFECTING EX, P57
Meadows VS, 2018, ASTROBIOLOGY, V18, P133, DOI 10.1089/ast.2016.1589
Meibom S, 2005, ASTROPHYS J, V620, P970, DOI 10.1086/427082
Minchev I, 2012, ASTRON ASTROPHYS, V548, DOI 10.1051/0004-6361/201219714
Moore WB, 2003, J GEOPHYS RES-PLANET, V108, DOI 10.1029/2002JE001943
Morbidelli A, 2018, SPACE SCI REV, V214, DOI 10.1007/s11214-018-0545-y
Murray C.D., 1999, SOLAR SYSTEM DYNAMIC
Murray-Clay RA, 2009, ASTROPHYS J, V693, P23, DOI 10.1088/0004-637X/693/1/23
NORTH GR, 1979, J ATMOS SCI, V36, P1189, DOI 10.1175/1520-0469(1979)036<1189:DBSAMA>2.0.CO;2
Nortmann L, 2018, SCIENCE, V362, P1388, DOI 10.1126/science.aat5348
Odert P, 2019, ARXIV190310772
Olson P, 2006, EARTH PLANET SC LETT, V250, P561, DOI 10.1016/j.epsl.2006.08.008
OREILLY TC, 1981, GEOPHYS RES LETT, V8, P313, DOI 10.1029/GL008i004p00313
Owen J. E., 2013, ARXIV13033899
Owen JE, 2017, ASTROPHYS J, V847, DOI 10.3847/1538-4357/aa890a
Owen JE, 2016, MON NOT R ASTRON SOC, V459, P4088, DOI 10.1093/mnras/stw959
PARKER EN, 1964, ASTROPHYS J, V139, P72, DOI 10.1086/147740
Paterson WSB., 1994, PHYS GLACIERS, V3
PEALE SJ, 1969, ASTRON J, V74, P483, DOI 10.1086/110825
Penev K, 2018, ASTRON J, V155, DOI 10.3847/1538-3881/aaaf71
Pozzo M, 2012, NATURE, V485, P355, DOI 10.1038/nature11031
Press W. H., 1987, AM J PHYS, V55, P90, DOI [DOI 10.1119/1.14981, 10.1119/1.14981]
Prsa A, 2016, ASTRON J, V152, DOI 10.3847/0004-6256/152/2/41
Rauch K P, 2002, BAAS, V34, P938
Reid IN, 2002, ASTRON J, V124, P2721, DOI 10.1086/343777
Reiners A, 2012, ASTROPHYS J, V746, DOI 10.1088/0004-637X/746/1/43
REMY F, 1985, ICARUS, V63, P1, DOI 10.1016/0019-1035(85)90016-8
Repetto S, 2014, MON NOT R ASTRON SOC, V444, P542, DOI 10.1093/mnras/stu1454
Ribas I, 2005, ASTROPHYS J, V622, P680, DOI 10.1086/427977
Rickman H, 2008, CELEST MECH DYN ASTR, V102, P111, DOI 10.1007/s10569-008-9140-y
Rickman H, 2005, EARTH MOON PLANETS, V97, P411, DOI 10.1007/s11038-006-9113-7
Robertson P, 2014, SCIENCE, V345, P440, DOI [10.1126/science.1253253, 10.1126/science.1255525]
Rodriguez A, 2011, MON NOT R ASTRON SOC, V415, P2349, DOI 10.1111/j.1365-2966.2011.18861.x
Rokar R., 2008, APJ, V684, pL79, DOI [10.1086/592231, DOI 10.1086/592231]
Rokar R., 2012, MNRAS, V426, P2089, DOI DOI 10.1111/J.1365-2966.2012.21860.X
Scalo J, 2007, ASTROBIOLOGY, V7, P85, DOI 10.1089/ast.2006.0000
Schaefer L, 2016, ASTROPHYS J, V829, DOI 10.3847/0004-637X/829/2/63
Schubert G., 2002, GEODYNAMICS
SEGATZ M, 1988, ICARUS, V75, P187, DOI 10.1016/0019-1035(88)90001-2
SKUMANICH A, 1972, ASTROPHYS J, V171, P565, DOI 10.1086/151310
SOLOMATOV VS, 1995, PHYS FLUIDS, V7, P266, DOI 10.1063/1.868624
Sotin C, 2007, ICARUS, V191, P337, DOI 10.1016/j.icarus.2007.04.006
Sotin C, 1999, PHYS EARTH PLANET IN, V112, P171, DOI 10.1016/S0031-9201(99)00004-7
Tian F, 2015, EARTH PLANET SC LETT, V432, P126, DOI 10.1016/j.epsl.2015.09.051
TOUMA J, 1993, SCIENCE, V259, P1294, DOI 10.1126/science.259.5099.1294
Udry S, 2007, ASTRON ASTROPHYS, V469, pL43, DOI 10.1051/0004-6361:20077612
van Dishoeck E. F., 2014, PROTOSTARS PLANETS, VVI, P835, DOI [10.2458/azu_uapress_9780816531240-ch036, DOI 10.2458/AZU_UAPRESS_9780816531240-CH036]
VEEDER GJ, 1994, J GEOPHYS RES-PLANET, V99, P17095, DOI 10.1029/94JE00637
Veeder GJ, 2012, ICARUS, V219, P701, DOI 10.1016/j.icarus.2012.04.004
Volkov AN, 2013, ASTROPHYS J, V765, DOI 10.1088/0004-637X/765/2/90
Ward WR, 2004, ASTRON J, V128, P2501, DOI 10.1086/424533
WARD WR, 1991, ICARUS, V94, P160, DOI 10.1016/0019-1035(91)90146-K
Ward WR, 1992, LONG TERM ORBITAL SP, P298
WARREN SG, 1979, J ATMOS SCI, V36, P1377, DOI 10.1175/1520-0469(1979)036<1377:SSAATF>2.0.CO;2
WATSON AJ, 1981, ICARUS, V48, P150, DOI 10.1016/0019-1035(81)90101-9
WILLIAMS JG, 1978, GEOPHYS RES LETT, V5, P943, DOI 10.1029/GL005i011p00943
Winn JN, 2005, ASTROPHYS J, V628, pL159, DOI 10.1086/432834
Wisdom J, 2008, ICARUS, V193, P637, DOI 10.1016/j.icarus.2007.09.002
Wright NJ, 2011, ASTROPHYS J, V743, DOI 10.1088/0004-637X/743/1/48
Wu YQ, 2002, ASTROPHYS J, V564, P1024, DOI 10.1086/324193
Yoder C.F., 1995, GLOBAL EARTH PHYS HD
ZAHN JP, 1989, ASTRON ASTROPHYS, V220, P112
ZAHN JP, 1989, ASTRON ASTROPHYS, V223, P112
ZAHNLE KJ, 1986, ICARUS, V68, P462, DOI 10.1016/0019-1035(86)90051-5
ZAHNLE KJ, 1988, ICARUS, V74, P62, DOI 10.1016/0019-1035(88)90031-0
Zhang K, 2008, ICARUS, V193, P267, DOI 10.1016/j.icarus.2007.08.024
NR 194
TC 4
Z9 4
U1 1
U2 5
PU IOP PUBLISHING LTD
PI BRISTOL
PA TEMPLE CIRCUS, TEMPLE WAY, BRISTOL BS1 6BE, ENGLAND
SN 0004-6280
EI 1538-3873
J9 PUBL ASTRON SOC PAC
JI Publ. Astron. Soc. Pac.
PD FEB
PY 2020
VL 132
IS 1008
AR 024502
DI 10.1088/1538-3873/ab3ce8
PG 61
WC Astronomy & Astrophysics
SC Astronomy & Astrophysics
GA KT4TT
UT WOS:000519008100001
DA 2021-04-21
ER
PT J
AU Glaser, C
Garcia-Fernandez, D
Nelles, A
Alvarez-Muniz, J
Barwick, SW
Besson, DZ
Clark, BA
Connolly, A
Deaconu, C
de Vries, KD
Hanson, JC
Hokanson-Fasig, B
Lahmann, R
Latif, U
Kleinfelder, SA
Persichilli, C
Pan, Y
Pfendner, C
Plaisier, I
Seckel, D
Torres, J
Toscano, S
van Eijndhoven, N
Vieregg, A
Welling, C
Winchen, T
Wissel, SA
AF Glaser, C.
Garcia-Fernandez, D.
Nelles, A.
Alvarez-Muniz, J.
Barwick, S. W.
Besson, D. Z.
Clark, B. A.
Connolly, A.
Deaconu, C.
de Vries, K. D.
Hanson, J. C.
Hokanson-Fasig, B.
Lahmann, R.
Latif, U.
Kleinfelder, S. A.
Persichilli, C.
Pan, Y.
Pfendner, C.
Plaisier, I
Seckel, D.
Torres, J.
Toscano, S.
van Eijndhoven, N.
Vieregg, A.
Welling, C.
Winchen, T.
Wissel, S. A.
TI NuRadioMC: simulating the radio emission of neutrinos from interaction
to detector
SO EUROPEAN PHYSICAL JOURNAL C
LA English
DT Article
ID CHERENKOV RADIATION; AIR-SHOWERS; PULSES; ICE; SCALE; MEDIA; ARRAY
AB NuRadioMC is a Monte Carlo framework designed to simulate ultra-high energy neutrino detectors that rely on the radio detection method. This method exploits the radio emission generated in the electromagnetic component of a particle shower following a neutrino interaction. NuRadioMC simulates everything from the neutrino interaction in a medium, the subsequent Askaryan radio emission, the propagation of the radio signal to the detector and finally the detector response. NuRadioMC is designed as a modern, modular Python-based framework, combining flexibility in detector design with user-friendliness. It includes a state-of-the-art event generator, an improved modelling of the radio emission, a revisited approach to signal propagation and increased flexibility and precision in the detector simulation. This paper focuses on the implemented physics processes and their implications for detector design. A variety of models and parameterizations for the radio emission of neutrino-induced showers are compared and reviewed. Comprehensive examples are used to discuss the capabilities of the code and different aspects of instrumental design decisions.
C1 [Glaser, C.; Barwick, S. W.; Persichilli, C.] Univ Calif Irvine, Dept Phys & Astron, Irvine, CA 92697 USA.
[Garcia-Fernandez, D.; Nelles, A.; Plaisier, I; Welling, C.] DESY, Platanenallee 6, D-15738 Zeuthen, Germany.
[Garcia-Fernandez, D.; Nelles, A.; Plaisier, I; Welling, C.] Friedrich Alexander Univ Erlangen Nurnberg, Erlangen Ctr Astroparticle Phys, D-91058 Erlangen, Germany.
[Alvarez-Muniz, J.] Univ Santiago de Compostela, Dept Fis Particulas, IGFAE, Santiago De Compostela, Spain.
[Besson, D. Z.; Latif, U.] Univ Kansas, Dept Phys & Astron, Lawrence, KS 66045 USA.
[Clark, B. A.; Connolly, A.; Torres, J.] Ohio State Univ, Dept Phys, 174 W 18th Ave, Columbus, OH 43210 USA.
[Clark, B. A.; Connolly, A.; Torres, J.] Ohio State Univ, Ctr Cosmol & Astroparticle Phys, Columbus, OH 43210 USA.
[Deaconu, C.; Vieregg, A.] Univ Chicago, Kavli Inst Cosmol Phys, Chicago, IL 60637 USA.
[de Vries, K. D.; van Eijndhoven, N.; Winchen, T.] Vrije Univ Brussels, Brussels, Belgium.
[Hanson, J. C.] Whittier Coll, Dept Phys, Whittier, CA USA.
[Hokanson-Fasig, B.] Univ Wisconsin, Dept Phys, 1150 Univ Ave, Madison, WI 53706 USA.
[Hokanson-Fasig, B.] Univ Wisconsin, Wisconsin IceCube Particle Astrophys Ctr, Madison, WI USA.
[Kleinfelder, S. A.] Univ Calif Irvine, Dept Elect Engn & Comp Sci, Irvine, CA 92697 USA.
[Pan, Y.; Seckel, D.] Univ Delaware, Dept Phys, Newark, DE 19716 USA.
[Pan, Y.; Seckel, D.] Univ Delaware, Bartol Res Inst, Newark, DE 19716 USA.
[Lahmann, R.; Pfendner, C.] Otterbein Univ, Westerville, OH USA.
[Toscano, S.] Univ Libre, Brussels, Belgium.
[Winchen, T.] Max Planck Inst Radio Astron, Bonn, Germany.
[Wissel, S. A.] Calif Polytech State Univ San Luis Obispo, Dept Phys, San Luis Obispo, CA 93407 USA.
RP Glaser, C (corresponding author), Univ Calif Irvine, Dept Phys & Astron, Irvine, CA 92697 USA.
EM christian.glaser@uci.edu; daniel.garcia@desy.de; anna.nelles@desy.de
RI Nelles, Anna/AAU-1193-2020; Lahmann, Robert/L-7461-2015
OI Nelles, Anna/0000-0002-1720-6350; Wissel, Stephanie/0000-0003-0569-6978;
Glaser, Christian/0000-0001-5998-2553; Clark, Brian/0000-0003-4089-2245;
Deaconu, Cosmin/0000-0002-4953-6397; Latif, Uzair/0000-0002-7609-1266
FU German research foundation (DFG)German Research Foundation (DFG) [GL
914/1-1, NE 2031/2-1]; Ministerio de Economia, Industria y
Competitividad [FPA 2017-85114-P]; Xunta deGaliciaXunta de
GaliciaEuropean Commission [ED431C 2017/07]; Feder FundsEuropean
Commission; RENATA Red Nacional Tematica de Astroparticulas
[FPA2015-68783-REDT]; Maria de Maeztu Unit of Excellence
[MDM-2016-0692]; U.S. National Science Foundation-Physics
DivisionNational Science Foundation (NSF) [NSF-1607719]; National
Science FoundationNational Science Foundation (NSF) [DGE-1343012]; NSF
CAREER awardNational Science Foundation (NSF)NSF - Office of the
Director (OD) [28820]; NSFNational Science Foundation (NSF) [49285];
ERC-StG of the European Research Council [805486]
FX This article and NuRadioMC itself would not exist without the
constructive spirit of the InIceMC working group of
theARAandARIANNAcollaborations. We acknowledge funding from the German
research foundation (DFG) under grant GL 914/1-1 (CG) and grant NE
2031/2-1 (DGF, AN, IP, and CW). JAM is supported by Ministerio de
Economia, Industria y Competitividad (FPA 2017-85114-P), Xunta deGalicia
(ED431C 2017/07), Feder Funds, RENATA Red Nacional Tematica de
Astroparticulas (FPA2015-68783-REDT) and Maria de Maeztu Unit of
Excellence (MDM-2016-0692). We are grateful to the U.S. National Science
Foundation-Office of Polar Programs, the U.S. National Science
Foundation-Physics Division (grant NSF-1607719) and the U.S. Department
of Energy(SWBandCP). BAC thanks the National Science Foundation for
support through the Graduate Research Fellowship Program Award
DGE-1343012. AC acknowledges funding from the NSF CAREER award 28820 and
NSF award 49285. We acknowledge Belgian Funds for Scientific Research
(FRS-FNRS and FWO) (ST and NvE), the FWO programme for International
Research Infrastructure, and funds from the ERC-StG (No. 805486) of the
European Research Council (KDdV).
CR Aab A, 2016, PHYS REV LETT, V116, DOI 10.1103/PhysRevLett.116.241101
Aab A, 2015, PHYS REV D, V91, DOI 10.1103/PhysRevD.91.092008
Aartsen MG, 2018, SCIENCE, V361, DOI 10.1126/science.aat1378
Aartsen MG, 2017, J INSTRUM, V12, DOI 10.1088/1748-0221/12/03/P03012
Aartsen MG, 2015, ASTROPHYS J, V809, DOI 10.1088/0004-637X/809/1/98
Aartsen MG, 2013, NUCL INSTRUM METH A, V711, P73, DOI 10.1016/j.nima.2013.01.054
Abreu P, 2011, NUCL INSTRUM METH A, V635, P92, DOI 10.1016/j.nima.2011.01.049
Ackermann M., 2019, ARXIV190304333
Ackermann M., 2019, ARXIV190304334
Alley R, 1988, ANN GLACIOL, V10, P1
Allison P, 2019, NUCL INSTRUM METH A, V930, P112, DOI 10.1016/j.nima.2019.01.067
Allison P, 2016, PHYS REV D, V93, DOI 10.1103/PhysRevD.93.082003
Allison P, 2015, ASTROPART PHYS, V70, P62, DOI 10.1016/j.astropartphys.2015.04.006
Alvarez-Muniz J, 2009, ASTROPART PHYS, V32, P100, DOI 10.1016/j.astropartphys.2009.06.005
Alvarez-Muniz J, 2000, PHYS REV D, V62, DOI 10.1103/PhysRevD.62.063001
Alvarez-Muniz J, 1998, PHYS LETT B, V434, P396, DOI 10.1016/S0370-2693(98)00905-8
Alvarez-Muniz J, 2006, PHYS REV D, V74, DOI 10.1103/PhysRevD.74.023007
Alvarez-Muniz J, 2019, PHYS REV D, V99, DOI 10.1103/PhysRevD.99.069902
Alvarez-Muniz J, 2018, PHYS REV D, V97, DOI 10.1103/PhysRevD.97.023021
Alvarez-Muniz J, 2012, ASTROPART PHYS, V35, P325, DOI 10.1016/j.astropartphys.2011.10.005
Alvarez-Muniz J, 2011, PHYS REV D, V84, DOI 10.1103/PhysRevD.84.103003
Alvarez-Muniz J, 2010, PHYS REV D, V81, DOI 10.1103/PhysRevD.81.123009
AlvarezMuniz J, 1997, PHYS LETT B, V411, P218, DOI 10.1016/S0370-2693(97)01009-5
Amsler C., 2018, PHYS LETT B, V667
ANITA collaboration, ARXIV190311043 ANITA
ANITA collaboration, 2018, PHYS REV D, V98
Anker A, 2019, ADV SPACE RES, V64, P2595, DOI 10.1016/j.asr.2019.06.016
[Anonymous], TUTORIAL
[Anonymous], ONL DOC
[Anonymous], SIGN PROP BAS CLASS
[Anonymous], CLUST DOC
[Anonymous], EV GEN SKEL
[Anonymous], EX D N R AN
[Anonymous], EX PULS CAL MEAS
[Anonymous], EX MULT COINC
ARA collaboration, 2019, ASTROPART PHYS, V108, P63, DOI [10.1016/j.astropartphys.2019.01.004, DOI 10.1016/J.ASTROPARTPHYS.2019.01.004]
ARA Project, ATT MOD
Argiro S, 2007, NUCL INSTRUM METH A, V580, P1485, DOI 10.1016/j.nima.2007.07.010
ASKARYAN GA, 1965, SOV PHYS JETP-USSR, V21, P658
Barwick S, 2005, J GLACIOL, V51, P231, DOI 10.3189/172756505781829467
Barwick SW, 2018, J COSMOL ASTROPART P, DOI 10.1088/1475-7516/2018/07/055
Barwick SW, 2015, IEEE T NUCL SCI, V62, P2202, DOI 10.1109/TNS.2015.2468182
Barwick SW, 2015, ASTROPART PHYS, V70, P12, DOI 10.1016/j.astropartphys.2015.04.002
Batista RA, 2016, J COSMOL ASTROPART P, DOI 10.1088/1475-7516/2016/05/038
Bellm J., ARXIV170506919
BERESINSKY VS, 1969, PHYS LETT B, VB 28, P423, DOI 10.1016/0370-2693(69)90341-4
Brent R.P., 1973, ALGORITHMS MINIMIZAT
Buitink S., 2018, P ARENA 2018 CAT SIC
Buniy RV, 2002, PHYS REV D, V65, DOI 10.1103/PhysRevD.65.016003
Connolly A, 2011, PHYS REV D, V83, DOI 10.1103/PhysRevD.83.113009
Cooper-Sarkar A, 2011, J HIGH ENERGY PHYS, DOI 10.1007/JHEP08(2011)042
Deaconu C, 2018, PHYS REV D, V98, DOI 10.1103/PhysRevD.98.043010
DeYoung T, 2007, ASTROPART PHYS, V27, P238, DOI 10.1016/j.astropartphys.2006.11.003
Dookayka K., 2011, THESIS
Dunsch M., ARXIV180907740
Dutta SI, 2001, PHYS REV D, V63, DOI 10.1103/PhysRevD.63.094020
Engel Ralph, 2019, Computing and Software for Big Science, V3, DOI 10.1007/s41781-018-0013-0
Essig R., 2013, FERMILABCONF13653
Frezza F, 2015, J OPT SOC AM A, V32, P1485, DOI 10.1364/JOSAA.32.001485
Galassi M, GNU SCI LIB REFERENC
Gandhi R, 1996, ASTROPART PHYS, V5, P81, DOI 10.1016/0927-6505(96)00008-4
Gandhi R, 1998, PHYS REV D, V58, P58
Garcia-Fernandez D, 2019, PHYS REV D, V99, DOI 10.1103/PhysRevD.99.063009
Garcia-Fernandez D, 2013, PHYS REV D, V87, DOI 10.1103/PhysRevD.87.023003
Gerhardt L, 2010, PHYS REV D, V82, DOI 10.1103/PhysRevD.82.074017
Glaser C, 2019, EUR PHYS J C, V79, DOI 10.1140/epjc/s10052-019-6971-5
Glaser C, 2016, J COSMOL ASTROPART P, DOI 10.1088/1475-7516/2016/09/024
Gottowik M, 2018, ASTROPART PHYS, V103, P87, DOI 10.1016/j.astropartphys.2018.07.004
GRAND collaboration, ARXIV181009994 GRAND
GREISEN K, 1966, PHYS REV LETT, V16, P748, DOI 10.1103/PhysRevLett.16.748
Hanson JC, 2017, ASTROPART PHYS, V91, P75, DOI 10.1016/j.astropartphys.2017.03.008
Hawley RL, 2008, J GLACIOL, V54, P839, DOI 10.3189/002214308787779951
Heinze J., ARXIV190103338
Hu CY, 2012, ASTROPART PHYS, V35, P421, DOI 10.1016/j.astropartphys.2011.11.008
Huege T, 2013, AIP CONF PROC, V1535, P128, DOI 10.1063/1.4807534
IceCube Collaboration, 2017, P SCI ICRC2017, V35, P981, DOI DOI 10.22323/1.301.0981
IceCube collaboration, 2018, POS ICRC2017, V1005
James CW, 2017, EPJ WEB CONF, V135, DOI 10.1051/epjconf/201713504001
Jones E., 2001, SCIPY OPEN SOURCE SC
Karle A., 2019, P 18 INT WORKSH NEUT
Kleinfelder S.A., ARXIV150802460
Koehne JH, 2013, COMPUT PHYS COMMUN, V184, P2070, DOI 10.1016/j.cpc.2013.04.001
Kolundzija B, 2011, 2011 5th European Conference on Antennas and Propagation (EuCAP), P2844
Kunz K. S., 1993, FINITE DIFFERENCE TI
LANDAU L, 1953, DOKL AKAD NAUK SSSR+, V92, P535
MIGDAL AB, 1956, PHYS REV, V103, P1811, DOI 10.1103/PhysRev.103.1811
Persichilli C., 2018, THESIS
Schellart P, 2013, ASTRON ASTROPHYS, V560, DOI 10.1051/0004-6361/201322683
Tanabashi M, 2018, PHYS REV D, V98, DOI 10.1103/PhysRevD.98.030001
The HDF Group, HIER DAT FORM VERS 5
van der Walt S, 2011, COMPUT SCI ENG, V13, P22, DOI 10.1109/MCSE.2011.37
van Vliet A., ARXIV190101899
Werner K, 2012, ASTROPART PHYS, V37, P5, DOI 10.1016/j.astropartphys.2012.07.007
Winchen T., 2017, Journal of Physics: Conference Series, V898, DOI 10.1088/1742-6596/898/3/032004
Winchen T., 2018, P ARENA 2018 CAT SIC
Wissel S., 2018, P ARENA 2018 CAT SIC
ZAS E, 1992, PHYS REV D, V45, P362, DOI 10.1103/PhysRevD.45.362
ZATSEPIN GT, 1966, JETP LETT-USSR, V4, P78
NR 98
TC 3
Z9 3
U1 1
U2 1
PU SPRINGER
PI NEW YORK
PA ONE NEW YORK PLAZA, SUITE 4600, NEW YORK, NY, UNITED STATES
SN 1434-6044
EI 1434-6052
J9 EUR PHYS J C
JI Eur. Phys. J. C
PD JAN 31
PY 2020
VL 80
IS 2
AR 77
DI 10.1140/epjc/s10052-020-7612-8
PG 35
WC Physics, Particles & Fields
SC Physics
GA KL2CB
UT WOS:000513235800002
OA DOAJ Gold, Green Published
DA 2021-04-21
ER
PT S
AU Rieger, M
AF Rieger, Marcel
GP IOP Publishing
TI Design Pattern for Analysis Automation on Distributed Resources using
Luigi Analysis Workflows
SO 19TH INTERNATIONAL WORKSHOP ON ADVANCED COMPUTING AND ANALYSIS
TECHNIQUES IN PHYSICS RESEARCH
SE Journal of Physics Conference Series
LA English
DT Proceedings Paper
CT 19th International Workshop on Advanced Computing and Analysis
Techniques in Physics Research (ACAT)
CY MAR 11-15, 2019
CL Saas Fee, SWITZERLAND
AB In particle physics, workflow management systems are primarily used as tailored solutions in dedicated areas such as Monte Carlo event generation. However, physicists performing data analyses are usually required to steer their individual workflows manually, which is time-consuming and often leads to undocumented relations between particular workloads. We present the Luigi Analysis Workflows (Law) Python package, which is based on the open-source pipelining tool Luigi, originally developed by Spotify. It establishes a generic design pattern for analyses of arbitrary scale and complexity, and shifts the focus from executing to defining the analysis logic. Law provides the building blocks to seamlessly integrate interchangeable remote resources without, however, limiting itself to a specific choice of infrastructure. In particular, it encourages and enables the separation of analysis algorithms on the one hand, and run locations, storage locations, and software environments on the other hand. To cope with the sophisticated demands of end-to-end HEP analyses, Law supports job execution on WLCG infrastructure (ARC, gLite) as well as on local computing clusters (HTCondor, LSF), remote file access via most common protocols through the GFAL2 library, and an environment sandboxing mechanism with support for Docker and Singularity containers. Moreover, the novel approach ultimately aims for analysis preservation out-of-the-box. Law is entirely experiment independent and developed open-source.
C1 [Rieger, Marcel] CERN, Geneva, Switzerland.
RP Rieger, M (corresponding author), CERN, Geneva, Switzerland.
EM marcel.rieger@cern.ch
CR Andreetto P, 2008, Journal of Physics: Conference Series, DOI 10.1088/1742-6596/119/6/062007
Ayllon A. A., 2017, J PHYS C SER, V898
Boettiger Carl, 2015, ACM SIGOPS Operating Systems Review, V49, P71
CMS Collaboration, 2019, J HIGH ENERGY PHYS, V2019, P26, DOI [10.1007/JHEP03(2019)026, DOI 10.1007/JHEP03(2019)026]
Cowton J, 2015, J PHYS CONF SER, V664, DOI 10.1088/1742-6596/664/3/032030
Ellert M, 2007, FUTURE GENER COMP SY, V23, P219, DOI 10.1016/j.future.2006.05.008
Free Software Foundation, 2016, GNU MAK
Kurtzer GM, 2017, PLOS ONE, V12, DOI 10.1371/journal.pone.0177459
Lumb I., 2004, GRID RESOURCE MANAGE
Rieger M., 2018, LUIGI ANAL WORKFLOWS
Tannenbaum T., 2002, BEOWULF CLUSTER COMP
Thain D, 2005, CONCURR COMP-PRACT E, V17, P323, DOI 10.1002/cpe.938
The ATLAS Collaboration, 2005, ATLAS COMPUTING TECH
The CMS Collaboration, 2005, LHC COMP GRID TECHN
The CMS Collaboration, 2005, CMS COMP TECHN DES R
The DPHEP Collaboration, 2012, ARXIV12054667
The Luigi Authors, 2018, LUIG DOC
NR 17
TC 0
Z9 0
U1 0
U2 0
PU IOP PUBLISHING LTD
PI BRISTOL
PA DIRAC HOUSE, TEMPLE BACK, BRISTOL BS1 6BE, ENGLAND
SN 1742-6588
EI 1742-6596
J9 J PHYS CONF SER
PY 2020
VL 1525
AR 012035
DI 10.1088/1742-6596/1525/1/012035
PG 6
WC Computer Science, Interdisciplinary Applications; Physics,
Multidisciplinary
SC Computer Science; Physics
GA BQ7XS
UT WOS:000618973400035
OA Green Published, Bronze
DA 2021-04-21
ER
PT J
AU Sadovyi, MI
Riezina, OV
Tryfonova, OM
AF Sadovyi, Mykola, I
Riezina, Olga, V
Tryfonova, Olena M.
TI THE USE OF COMPUTER GRAPHICS IN TEACHING PHYSICS AND TECHNICAL
DISCIPLINES AT PEDAGOGICAL UNIVERSITIES
SO INFORMATION TECHNOLOGIES AND LEARNING TOOLS
LA Russian
DT Article
DE educational process; teaching physics and technical subjects;
digitization; process modeling; scientific graphics; Python programming
language
AB The article deals with the issue on using scientific graphics during teaching physics and technical subjects in terms of digitization of the educational process in higher education institutions. We have analyzed the literature, regulatory documents disclosing the problem of digitization of Ukrainian society and European trends in digitization and emphasized the need to modernize approaches and learning tools in the modern context. The need to distinguish scientific and presentation visual aids is emphasized. In the 21st century it became a time requirement. Presentational visual aids perform the functions of direct identification of phenomena and processes. The possibilities of their research are limited. Perspectives are provided by scientific visual aids. It provides avenues for the pursuit of research and digital competences in the study of physics and technical subjects. The article substantiates the use of scientific graphics in the educational process of physics and technical subjects. The advantages of Python programming language are outlined. It is considered as a means of creating scientific graphics. Python is a popular language among the scientific community. It is clear and concise. This provides an advantage for laboratory programming, which is done by a researcher, not a professional programmer. This language is supported by all major operating systems, is free, has a simple syntax that makes it easy to learn and read programs. The article discusses several tasks that we consider appropriate to offer students to perform computer simulations using the Python programming language and its NumPy and Matplotlib modules. The detailed progress of the solution of the problem is given, code snippets are offered. The ability to change one or more parameters is emphasized. This improves not only the programming skills but also the understanding of the physical and technical content of the solution of the task.
The implementation of the developed methodology has shown its effectiveness during the educational process in physics and technical subjects. This is confirmed by the positive dynamics of the quality of students' knowledge.
C1 [Sadovyi, Mykola, I] Volodymyr Vynnychenko Cent Ukrainian State Pedag, Dept Theory & Methods Technol Preparat Labour & H, Kropyvnytskyi, Ukraine.
[Riezina, Olga, V] Volodymyr Vynnychenko Cent Ukrainian State Pedag, Dept Comp Studies & Technol, Kropyvnytskyi, Ukraine.
[Tryfonova, Olena M.] Volodymyr Vynnychenko Cent Ukrainian State Pedag, Dept Nat Sci & Their Teaching Methods, Kropyvnytskyi, Ukraine.
RP Sadovyi, MI (corresponding author), Volodymyr Vynnychenko Cent Ukrainian State Pedag, Dept Theory & Methods Technol Preparat Labour & H, Kropyvnytskyi, Ukraine.
EM smikdpu@i.ua; olga.riezina@gmail.com; olenatrifonova82@gmail.com
RI Riezina, Olga/AAI-8785-2021
CR Averbukh V.L., SCI VISUALIZATION, V11, P1, DOI [10.26583/sv.11.3.01, DOI 10.26583/SV.11.3.01]
Ayer VM, 2014, POWDER DIFFR, V29, pS48, DOI 10.1017/S0885715614000931
Binder JM, 2017, SOFTWAREX, V6, P85, DOI 10.1016/j.softx.2017.02.001
Blakeney C., 2017, B AM PHYS SOC, V62
Bykov VY, 2019, INF TECHNOL LEARN TO, V74, P1
Kalapusha L.R., 2007, COMPUTER SIMULATION
Khomutenko MV, 2015, INF TECHNOL LEARN TO, V45, P78, DOI 10.33407/itlt.v45i1.1191
Kinder J. M., 2016, STUDENTS GUIDE PYTHO
Kulikova N.V., NAUCHNAYA VIZUALIZAT, V10, P102, DOI [10.26583/sv.10.5.07, DOI 10.26583/SV.10.5.07]
Pilyugin V.V., SCI VISUALIZATION, V11, P46, DOI [10.26583/sv.11.5.05, DOI 10.26583/SV.11.5.05]
Plaskura P, 2019, INF TECHNOL LEARN TO, V71, P1
Ramskyy Yu.C., 2008, NAUKOVYY CHASOPYS 2, P93
Sadovyi M.I., 2019, CLOUD ORIENTED ED EN
Scopatz A., EFFECTIVE COMPUTATIO
Shabanov P.A., SCI GRAPHICS PYTHON
Somenko D.V., 2015, THESIS
Teplytskyy I.O., 2004, TEORIYA TA METODYKA, V4, P414
Tryfonova O.M., 2009, THESIS
Vovkotrub V.P., 2011, SELECTED PROBLEMS PH
Zhaldak M. I., 2013, PROBLEMS INFORM ED P, V3, P8
NR 20
TC 0
Z9 0
U1 2
U2 2
PU NATL ACAD PEDAGOGICAL SCIENCES UKRAINE, INST INFO TECHNOL & LEARNING
TOOLS
PI KYIV
PA VUL M BERLYNSKOHO 9, KYIV, 04060, UKRAINE
SN 2076-8184
J9 INF TECHNOL LEARN TO
JI Inf. Technol. Learn. Tools
PY 2020
VL 80
IS 6
BP 188
EP 206
DI 10.33407/itlt.v80i6.3740
PG 19
WC Education & Educational Research
SC Education & Educational Research
GA PO2LE
UT WOS:000605000400012
OA DOAJ Gold
DA 2021-04-21
ER
PT J
AU Sun, TX
Meng, XY
Cao, JF
Wang, Y
Guo, Z
Wang, ZJ
Liu, HG
Zhang, XZ
Tai, RZ
AF Sun, Tianxiao
Meng, Xiangyu
Cao, Jiefeng
Wang, Yong
Guo, Zhi
Wang, Zhijun
Liu, Haigang
Zhang, Xiangzhi
Tai, Renzhong
TI A portable data-collection system for soft x-ray absorption spectroscopy
in the Shanghai Synchrotron Radiation Facility
SO REVIEW OF SCIENTIFIC INSTRUMENTS
LA English
DT Article
ID XAFS; TEMPERATURE
AB Based on the Experimental Physics and Industrial Control System, a portable data-collection system for soft x-ray absorption spectroscopy has been developed at the BL02B and BL08U beamlines of the Shanghai Synchrotron Radiation Facility. The data-collection system can be used to carry out total electron yield (TEY) and total fluorescence yield (TFY) experiments simultaneously. The hardware consists of current preamplifiers, voltage-to-frequency converters, and a multi-channel counter, which are aimed at improving the signal-to-noise ratio. The control logic is developed using Python and Java. The novelty of this control system is its designed portability while being extensible and readable and having low noise and high real-time capabilities. The oxygen K-edge absorption spectra of SrTiO3 were obtained using the TEY and TFY technology at the BL02B beamline. Furthermore, the TEY and TFY spectra of the relaxor ferroelectric single-crystal of lead magnesium niobate-lead titanate measured by the present data-collection system have lower peak-to-peak noise amplitude than the ones measured by using a picoammeter. The experimental results show that the spectral signal-to-noise ratio recorded by the present system is 5.7-12.4 dB higher than that with the picoammeter detector.
C1 [Sun, Tianxiao; Meng, Xiangyu; Cao, Jiefeng; Wang, Yong; Guo, Zhi; Wang, Zhijun; Liu, Haigang; Zhang, Xiangzhi; Tai, Renzhong] Chinese Acad Sci, Shanghai Inst Appl Phys, Shanghai 201800, Peoples R China.
[Sun, Tianxiao; Meng, Xiangyu; Cao, Jiefeng; Wang, Yong; Guo, Zhi; Wang, Zhijun; Liu, Haigang; Zhang, Xiangzhi; Tai, Renzhong] Chinese Acad Sci, Shanghai Adv Res Inst, Shanghai Synchrotron Radiat Facil, Shanghai 201210, Peoples R China.
[Sun, Tianxiao] Univ Chinese Acad Sci, Beijing 200049, Peoples R China.
RP Meng, XY (corresponding author), Chinese Acad Sci, Shanghai Inst Appl Phys, Shanghai 201800, Peoples R China.; Meng, XY (corresponding author), Chinese Acad Sci, Shanghai Adv Res Inst, Shanghai Synchrotron Radiat Facil, Shanghai 201210, Peoples R China.
EM mengxiangyu@zjlab.org.cn; caojiefeng@zjlab.org.cn;
zhangxiangzhi@zjlab.org.cn
FU National Key Research and Development Program [2016YFB0700402,
2017YFA0403401]; National Natural Science Foundation of ChinaNational
Natural Science Foundation of China (NSFC) [11705272, 11875314,
U1632268, 51571208]
FX This work was financially supported by the National Key Research and
Development Program (Grant Nos. 2016YFB0700402 and 2017YFA0403401) and
the National Natural Science Foundation of China (Grant Nos. 11705272,
11875314, U1632268, and 51571208).
CR Abe H, 2018, J SYNCHROTRON RADIAT, V25, P972, DOI 10.1107/S1600577518006021
Akiyama D, 2019, J NUCL MATER, V520, P27, DOI 10.1016/j.jnucmat.2019.03.055
Annamaria K., 2019, ENVIRON SCI TECHNOL, V53, P6877, DOI [10.1021/acs.est.8b06952, DOI 10.1021/ACS.EST.8B06952]
Elena F., 2018, J SYNCHROTRON RADIAT, V25, P232, DOI [10.1107/s1600577517016253, DOI 10.1107/S1600577517016253]
Gursoy D, 2014, J SYNCHROTRON RADIAT, V21, P1188, DOI 10.1107/S1600577514013939
Guo Z, 2017, J SYNCHROTRON RADIAT, V24, P877, DOI 10.1107/S1600577517006087
Himpsel FJ, 2011, PHYS STATUS SOLIDI B, V248, P292, DOI 10.1002/pssb.201046212
Isomura N, 2019, J SYNCHROTRON RADIAT, V26, P462, DOI 10.1107/S1600577519001504
KRAUSE MO, 1979, J PHYS CHEM REF DATA, V8, P307, DOI 10.1063/1.555594
Liu HG, 2019, REV SCI INSTRUM, V90, DOI 10.1063/1.5080760
Liu XS, 2014, ADV MATER, V26, P7710, DOI 10.1002/adma.201304676
Lu RY, 2016, NUCL SCI TECH, V27, DOI 10.1007/s41365-016-0084-8
Luo JJ, 2019, J HAZARD MATER, V376, P21, DOI 10.1016/j.jhazmat.2019.05.012
Mangold S, 2018, J SYNCHROTRON RADIAT, V25, P960, DOI 10.1107/S1600577518007518
Martensson N, 2013, J PHYS CONF SER, V430, DOI 10.1088/1742-6596/430/1/012131
Meng XY, 2019, J SYNCHROTRON RADIAT, V26, P543, DOI 10.1107/S1600577518018179
Mooney TM, 2000, AIP CONF PROC, V521, P322
Niibe M, 2013, J PHYS CONF SER, V425, DOI 10.1088/1742-6596/425/13/132008
Tomson NC, 2015, CHEM SCI, V6, P2474, DOI 10.1039/c4sc03294b
Xiao QF, 2017, J SYNCHROTRON RADIAT, V24, P333, DOI 10.1107/S1600577516017604
Zhang LJ, 2015, NUCL SCI TECH, V26
Zheng L, 2014, SPECTROCHIM ACTA B, V101, P1, DOI 10.1016/j.sab.2014.07.006
NR 22
TC 0
Z9 1
U1 2
U2 2
PU AMER INST PHYSICS
PI MELVILLE
PA 1305 WALT WHITMAN RD, STE 300, MELVILLE, NY 11747-4501 USA
SN 0034-6748
EI 1089-7623
J9 REV SCI INSTRUM
JI Rev. Sci. Instrum.
PD JAN 1
PY 2020
VL 91
IS 1
AR 014709
DI 10.1063/1.5128054
PG 6
WC Instruments & Instrumentation; Physics, Applied
SC Instruments & Instrumentation; Physics
GA PK6QL
UT WOS:000602566800002
PM 32012623
DA 2021-04-21
ER
PT S
AU Nayak, AK
Hagishima, A
AF Nayak, Ajaya Ketan
Hagishima, Aya
BE Kurnitski, J
Kalamees, T
TI Modification of building energy simulation tool TRNSYS for modelling
nonlinear heat and moisture transfer phenomena by TRNSYS/MATLAB
integration
SO 12TH NORDIC SYMPOSIUM ON BUILDING PHYSICS (NSB 2020)
SE E3S Web of Conferences
LA English
DT Proceedings Paper
CT 12th Nordic Symposium on Building Physics (NSB)
CY SEP 06-09, 2020
CL Tallinn, ESTONIA
ID EVAPORATION; PERFORMANCE; ROOF
AB Software for numerical simulation of various types of energy used in buildings, i.e. building energy simulation (BES), have become an essential tool for recent research pertaining to building physics. TRNSYS is a well-known BES used in both academia and the construction industry for a wide range of simulations, such as the design and performance evaluation of buildings and related facilities for heating, cooling, and ventilation. TRNSYS has a modular structure comprising various components, and each component is interconnected and compiled through a common interface using a FORTRAN compiler. Its modular structure enables interactions with various external numerical simulation tools, such as MATLAB, Python, and ESP-r. For ordinary simulations of building energy load using TRNSYS, the generic module Type 56 is usually recommended, which provides detailed physics modelling of building thermal behaviours based on unsteady energy conservation equations and Fourier's law for each building material. However, Type 56 explicitly depends on the transfer function method to discretise the original differential equations; therefore, it cannot model nonlinear phenomena, such as latent heat and moisture transfer between a building surface and ambient air. In other words, the current TRNSYS cannot be used to estimate the effectiveness of evaporation during cooling, which is a typical passive design method. Hence, the authors developed a MATLAB/TRNSYS integration scheme, in which TRNSYS was modified to model simultaneous heat and moisture transfer from the wet roof surface of a building. This scheme enabled TRNSYS to calculate the rate of evaporative heat and moisture transfer dynamically from the roof surface, assuming a control volume approximation of the roof surface. Finally, the effect of evaporative cooling on the thermal performance of an Indian building was estimated using the modified model.
C1 [Nayak, Ajaya Ketan; Hagishima, Aya] Kyushu Univ, Interdisciplinary Grad Sch Engn Sci, Dept Energy & Environm Engn, 6-1 Kasuga Koen, Kasuga, Fukuoka 8168580, Japan.
[Nayak, Ajaya Ketan] Kalinga Inst Ind Technol, Sch Mech Engn, Bhubaneswar 751024, Odisha, India.
RP Nayak, AK (corresponding author), Kyushu Univ, Interdisciplinary Grad Sch Engn Sci, Dept Energy & Environm Engn, 6-1 Kasuga Koen, Kasuga, Fukuoka 8168580, Japan.; Nayak, AK (corresponding author), Kalinga Inst Ind Technol, Sch Mech Engn, Bhubaneswar 751024, Odisha, India.
EM ajaya.ketana@gmail.com
CR [Anonymous], 2004, TRNSYS 18 DOCUMENTAT
Ben Cheikh H, 2004, RENEW ENERG, V29, P1877, DOI 10.1016/j.renene.2003.12.021
Chen W, 2011, ENERG CONVERS MANAGE, V52, P2217, DOI 10.1016/j.enconman.2010.12.029
Crawley DB, 2008, BUILD ENVIRON, V43, P661, DOI 10.1016/j.buildenv.2006.10.027
Delcroix1 B., 2012, CONDUCTION TRANSFER
dos Santos GH, 2013, APPL THERM ENG, V51, P25, DOI 10.1016/j.applthermaleng.2012.08.046
Fermanel F, 1999, APPL THERM ENG, V19, P1107, DOI 10.1016/S1359-4311(98)00110-0
Hagishima A, 2003, BUILD ENVIRON, V38, P873, DOI 10.1016/S0360-1323(03)00033-7
Hendel M, 2015, APPL THERM ENG, V78, P658, DOI 10.1016/j.applthermaleng.2014.11.060
Ibanez M, 2005, APPL THERM ENG, V25, P1796, DOI 10.1016/j.applthermaleng.2004.11.001
Kas O., 2008, ENERGY COMPARISON EX, V33, P1816
Klein S.A., 2004, TRNSYS, V16
Lu X, 2006, APPL THERM ENG, V26, P1901, DOI 10.1016/j.applthermaleng.2006.01.017
Nayak AK, 2020, APPL THERM ENG, V165, DOI 10.1016/j.applthermaleng.2019.114514
Purohit I, 2006, INT J AMBIENT ENERGY, V27, P193, DOI 10.1080/01430750.2006.9675398
Shah MM, 2002, HVAC&R RES, V8, P125, DOI 10.1080/10789669.2002.10391292
Spanaki A, 2014, APPL ENERG, V123, P273, DOI 10.1016/j.apenergy.2014.02.040
Stephenson D.G., 1971, ASHRAE T 2, V77, P117
TIWARI GN, 1982, ENERG CONVERS MANAGE, V22, P143, DOI 10.1016/0196-8904(82)90036-X
Zingre KT, 2015, ENERGY, V82, P813, DOI 10.1016/j.energy.2015.01.092
NR 20
TC 0
Z9 0
U1 2
U2 2
PU E D P SCIENCES
PI CEDEX A
PA 17 AVE DU HOGGAR PARC D ACTIVITES COUTABOEUF BP 112, F-91944 CEDEX A,
FRANCE
SN 2267-1242
J9 E3S WEB CONF
PY 2020
VL 172
AR 25009
DI 10.1051/e3sconf/202017225009
PG 8
WC Architecture; Construction & Building Technology; Green & Sustainable
Science & Technology; Engineering, Civil; Physics, Applied
SC Architecture; Construction & Building Technology; Science & Technology -
Other Topics; Engineering; Physics
GA BQ4RB
UT WOS:000594033400252
OA DOAJ Gold
DA 2021-04-21
ER
PT S
AU Hillairet, J
AF Hillairet, Julien
BE Bonoli, P
Pinsker, R
Wang, X
TI RF Network Analysis of the WEST ICRH Antenna with the Open-Source Python
scikit-rf Package
SO 23RD TOPICAL CONFERENCE ON RADIOFREQUENCY POWER IN PLASMAS
SE AIP Conference Proceedings
LA English
DT Proceedings Paper
CT 23rd Topical Conference on Radiofrequency Power in Plasmas
CY MAY 14-17, 2019
CL Hefei, PEOPLES R CHINA
SP Chinese Acad Sci, Inst Plasma Phys, Spinner Telecommunicat Devices Co Ltd
AB Scikit-rf is an open-source Python package developed for RF/Microwave engineering. The package provides a modern, object-oriented library for network analysis and calibration which is both flexible and scalable. Besides offering standard microwave network physics and operations, it is also capable of advanced operations such as interpolating between an individual set of networks or deriving network statistical properties. The package also allows direct plotting of rectangular plots, Smith Charts or automated uncertainty bounds. In this paper, the scikit-rf package is used to simulate the WEST ICRH antennas. The antenna is modelled by connecting the various elements that compose it, separately full-wave modelled. Tunable elements, such as the matching capacitors, can be either created from ideal lump components or from interpolating full-wave calculations performed at various capacitance configurations. Finally, a numerical antenna model of a WEST ICRH antenna can be used offline or online by using operating-system-level virtualization services.
C1 [Hillairet, Julien] CEA, IRFM, F-13108 St Paul Les Durance, France.
RP Hillairet, J (corresponding author), CEA, IRFM, F-13108 St Paul Les Durance, France.
EM julien.hillairet@cea.fr
CR Bussonnier Matthias, 2018, P 17 PYTH SCI C, DOI [10.25080/majora-4af1f417-011, DOI 10.25080/MAJORA-4AF1F417-011]
Durodie F, 2015, AIP CONF PROC, V1689, DOI 10.1063/1.4936520
Hagberg A, 2008, 7 PYTH SCI C SCIPY20, V7, P11, DOI DOI 10.1016/J.JELECTROCARD.2010.09.003
Hallbjorner P, 2003, MICROW OPT TECHN LET, V38, P99, DOI 10.1002/mop.10983
Helou W., 2018, THESIS
Helou W, 2015, AIP CONF PROC, V1689, DOI 10.1063/1.4936511
Hillairet J., 2019, JUPYTER NOTEBOOKS GI
Hillairet J, 2015, AIP CONF PROC, V1689, DOI 10.1063/1.4936512
Ince DC, 2012, NATURE, V482, P485, DOI 10.1038/nature10836
Kluyver T, 2016, POSITIONING AND POWER IN ACADEMIC PUBLISHING: PLAYERS, AGENTS AND AGENDAS, P87, DOI 10.3233/978-1-61499-649-1-87
Millman KJ, 2011, COMPUT SCI ENG, V13, P9, DOI 10.1109/MCSE.2011.36
Murphy N. A., 2018, PLASMAPY COMMUNITY, DOI [10.5281/zenodo.1238132., DOI 10.5281/ZENODO.1238132]
Pedregosa F, 2011, J MACH LEARN RES, V12, P2825
NR 13
TC 0
Z9 0
U1 0
U2 0
PU AMER INST PHYSICS
PI MELVILLE
PA 2 HUNTINGTON QUADRANGLE, STE 1NO1, MELVILLE, NY 11747-4501 USA
SN 0094-243X
BN 978-0-7354-2013-7
J9 AIP CONF PROC
PY 2020
VL 2254
AR 070010
DI 10.1063/5.0013523
PG 4
WC Physics, Fluids & Plasmas
SC Physics
GA BQ5CL
UT WOS:000598537500004
OA Bronze
DA 2021-04-21
ER
PT J
AU Saenz, J
Gurtubay, IG
Izaola, Z
Lopez, GA
AF Saenz, Jon
Gurtubay, Idoia G.
Izaola, Zunbeltz
Lopez, Gabriel A.
TI pygiftgenerator: a python module designed to prepare Moodle-based
quizzes
SO EUROPEAN JOURNAL OF PHYSICS
LA English
DT Article
DE STEM; physics; active learning; Moodle; formative evaluation
ID PHYSICS; STUDENTS
AB We present pygiftgenerator, a python module for systematically preparing a large number of numerical and multiple-choice questions for Moodle-based quizzes oriented to students' formative evaluation. The use of the module is illustrated by means of examples provided with the code and drawn from different topics, such as mechanics, electromagnetism, thermodynamics and modern physics. The fact that pygiftgenerator relies on a well-established computer language, which allows functions to be combined and reused in order to solve complex problems, makes it a very robust tool. Simply by changing the input parameters, a large question bank with solutions to complex physical problems, can be generated. Thus, it is a powerful alternative to the calculated and multiple-choice questions which can be written directly in the Moodle platform. The module writes questions to be imported into Moodle and produces simple and human-readable ASCII output using the GIFT format, which enables html definitions for URLs for importing figures, or for simple text formatting (sub/superindices or Greek letters) for equations and units. This format also allows LaTeX and MathJax typing for complex equations.
C1 [Saenz, Jon; Lopez, Gabriel A.] Univ Basque Country, UPV EHU, Dept Appl Phys 2, Leioa, Spain.
[Gurtubay, Idoia G.] Univ Basque Country, UPV EHU, Dept Condensed Matter Phys, Leioa 48940, Spain.
[Izaola, Zunbeltz] Inst Maquina Herramienta IMH, Escuela Univ Ingn Dual, Azkue Auzoa 1, Elgoibar, Spain.
RP Saenz, J (corresponding author), Univ Basque Country, UPV EHU, Dept Appl Phys 2, Leioa, Spain.
EM jon.saenz@ehu.eus
RI Gurtubay, Idoia/C-3899-2012; Saenz, Jon/A-7500-2011; Lopez, Gabriel
A./M-7414-2013
OI Gurtubay, Idoia/0000-0002-7060-5174; Saenz, Jon/0000-0002-5920-7570;
Lopez, Gabriel A./0000-0002-5429-7490
FU Spanish Government's MINECO; ERDFEuropean Commission [CGL2016-76561-R];
UPV/EHUUniversity of Basque Country [GIU17/02]; University of the Basque
Country UPV/EHUUniversity of Basque Country [GIU18/138]
FX JS acknowledges support by the Spanish Government's MINECO grant and
ERDF (Grant No. CGL2016-76561-R) and the UPV/EHU (Grant No. GIU17/02).
IG acknowledges the University of the Basque Country UPV/EHU (Grant No.
GIU18/138) for financial support.
CR [Anonymous], 2020, GIFT FORMAT MOODLEDO
[Anonymous], 2015, ECTS US GUID
[Anonymous], 2018, MOODLE PROJECT MOODL
[Anonymous], 2020, MOODLE PROJECT
Borondo J, 2014, EUR J ENG EDUC, V39, P496, DOI 10.1080/03043797.2013.874980
Broadbent J, 2018, ETR&D-EDUC TECH RES, V66, P1435, DOI 10.1007/s11423-018-9595-9
CLARK RE, 1994, ETR&D-EDUC TECH RES, V42, P21, DOI 10.1007/BF02299088
Corrigan-Gibbs H, 2015, ACM T COMPUT-HUM INT, V22, DOI 10.1145/2810239
European Commission/EACEA/Eurydice, 2015, EUR HIGH ED AR BOL P
Freeman S, 2014, P NATL ACAD SCI USA, V111, P8410, DOI 10.1073/pnas.1319030111
Gamage SHPW, 2019, INT J STEM EDUC, V6, DOI 10.1186/s40594-019-0181-4
Gurtubay IG, 2018, EDULEARN PROC, P5742
Gurtubay IG, 2018, INTED PROC, P5703
Gurtubay I G, 2019, 3 EUROSOTL C EXPL NE, P206
KOZMA RB, 1994, ETR&D-EDUC TECH RES, V42, P7, DOI 10.1007/BF02299087
Liagkou V, 2018, INT C DEP COMPL SYST, P338
Lopez GA, 2016, J SCI EDUC TECHNOL, V25, P575, DOI 10.1007/s10956-016-9614-8
Means B, 2010, TECHNICAL REPORT
Mir-Torres A, 2009, IADIS INT C, P47
Petty G.W., 2008, 1 COURSE ATMOSPHERIC
Saenz J, 2017, ICERI PROC, P5469
van Rossum G, 1995, PYTHON TUTORIAL
Wallace J. M., 2006, ATMOSPHERIC SCI INTR
NR 23
TC 0
Z9 0
U1 1
U2 1
PU IOP PUBLISHING LTD
PI BRISTOL
PA TEMPLE CIRCUS, TEMPLE WAY, BRISTOL BS1 6BE, ENGLAND
SN 0143-0807
EI 1361-6404
J9 EUR J PHYS
JI Eur. J. Phys.
PD JAN
PY 2020
VL 42
IS 1
AR 015702
DI 10.1088/1361-6404/abb114
PG 13
WC Education, Scientific Disciplines; Physics, Multidisciplinary
SC Education & Educational Research; Physics
GA OT3AK
UT WOS:000590722200001
DA 2021-04-21
ER
PT B
AU Park, H
DeNio, J
Choi, J
Lee, H
AF Park, Heecheon
DeNio, Joshus
Choi, Jeongyun
Lee, Hanku
GP IEEE
TI mpiPython: A Robust Python MPI Binding
SO 2020 3RD INTERNATIONAL CONFERENCE ON INFORMATION AND COMPUTER
TECHNOLOGIES (ICICT 2020)
LA English
DT Proceedings Paper
CT 3rd International Conference on Information and Computer Technologies
(ICICT)
CY MAR 09-12, 2020
CL San Jose, CA
DE Python; Message-Passing; High-Performance; Parallel Computing;
Performance
AB For the last two decades, Python has become one of the most popular programming languages and been used to develop and analyze data-intensive scientific and engineering applications and in the areas such as Bigdata Analytics, Social Media, Data Science, Physics, Psychology, Healthcare, Political Science, etc. Moreover, demand of supporting Python data-parallel applications for those areas is rapidly growing. There have been international efforts to produce a message passing interface for Python bindings to support parallel computing, but specific challenges still remain to improve Python bindings. The main purpose of this paper is to introduce our MPI Python binding, called mpiPython, with the MPI standard communication API. In this paper, we first will discuss the design issues of the mpiPython API, associated with its development. In the second part of the paper, we will discuss node/parallel performance to compare mpiPython to other MPI bindings on a Linux cluster and can expect mpiPython achieves quite acceptable performance.
C1 [Park, Heecheon; DeNio, Joshus; Choi, Jeongyun; Lee, Hanku] Minnesota State Univ Moorhead, Comp Sci & Informat Syst, Moorhead, MN 56563 USA.
RP Park, H (corresponding author), Minnesota State Univ Moorhead, Comp Sci & Informat Syst, Moorhead, MN 56563 USA.
EM heecheon92@gmail.com; joshua.denio@go.mnstate.ed;
jeongyun.choi@go.mnstate.edu; hanku.lee@mnstate.edu
CR Baker M., 1999, INT WORKSH JAV PAR D
Carpenter B., 2000, CONCURRENCY PRACTICE, V12
Carpenter B., 2001, PARALLEL PROGRAMMING
Lee H.-K., 2003, P INT C PAR DISTR PR
Lee H.-K., 2002, 6 WORKSH LANG COMP R
Lim SB, 2008, J SUPERCOMPUT, V43, P165, DOI 10.1007/s11227-007-0125-5
NR 6
TC 1
Z9 1
U1 0
U2 0
PU IEEE COMPUTER SOC
PI LOS ALAMITOS
PA 10662 LOS VAQUEROS CIRCLE, PO BOX 3014, LOS ALAMITOS, CA 90720-1264 USA
BN 978-1-7281-7283-5
PY 2020
BP 96
EP 101
DI 10.1109/ICICT50521.2020.00023
PG 6
WC Computer Science, Theory & Methods; Engineering, Electrical & Electronic
SC Computer Science; Engineering
GA BQ2XL
UT WOS:000582696300016
DA 2021-04-21
ER
PT S
AU Bhattarai, B
Maharjan, M
Hanif, S
Cai, MM
Pratt, R
AF Bhattarai, Bishnu
Maharjan, Manisha
Hanif, Sarmad
Cai, Mengmeng
Pratt, Robert
GP IEEE
TI Transactive Electric Water Heater Agent: Design and Performance
Evaluation
SO 2020 IEEE POWER & ENERGY SOCIETY INNOVATIVE SMART GRID TECHNOLOGIES
CONFERENCE (ISGT)
SE Innovative Smart Grid Technologies
LA English
DT Proceedings Paper
CT IEEE-Power-and-Energy-Society Innovative Smart Grid Technologies
Conference (ISGT)
CY FEB 17-20, 2020
CL Washington, DC
SP IEEE Power & Energy Soc
DE Demand flexibility; distribution system; electric water heater;
transactive energy system
ID STRATIFICATION
AB Electric water heaters (EWHs) are usually equipped with inbuilt thermostats to measure water temperature near the installed positions. Most of the existing EWHs have only two thermostats and often do not have water flow sensor installed. Estimation of state of heat energy (SOHE) inside the hot water tank, which is crucial for efficient control and operation of the EWH, under such imperfect system conditions is very challenging. This paper, therefore, designs a transactive EWH agent (TEWHA) to better estimate SOHE in presence of measurement errors and imperfect system knowledge. The TEWHA, built-in Python, approximates the EWH physics using limited measurements obtained from the ground truth GridLAB-D model. The performance of the TEWHA is validated against ground truth system for three scenarios: a) ideal condition where TEWHA has complete knowledge of the system, b) imperfect condition where TEWHA has incomplete system information, and c) noised condition where TEWHA measurements contain random errors. Finally, an optimization problem is formulated and solved to demonstrate how TEWHA could use the estimated and measured parameters for a transactive control of EWH.
C1 [Bhattarai, Bishnu; Maharjan, Manisha; Hanif, Sarmad; Pratt, Robert] Pacific Northwest Natl Lab, Elect Infrasruct Grp, Richland, WA 99354 USA.
[Cai, Mengmeng] Virginia Tech, Blacksburg, VA USA.
RP Bhattarai, B (corresponding author), Pacific Northwest Natl Lab, Elect Infrasruct Grp, Richland, WA 99354 USA.
FU PNNL; U.S. Department of EnergyUnited States Department of Energy (DOE)
[DE-AC05-76RL01830]
FX This research is supported by PNNL with funding from the U.S. Department
of Energy under contract No. DE-AC05-76RL01830.
CR Bhattarai B. P., 2016, IEEE PES GEN M, P1
Han YM, 2009, RENEW SUST ENERG REV, V13, P1014, DOI 10.1016/j.rser.2008.03.001
Hao H, 2018, IEEE T SMART GRID, V9, P4335, DOI 10.1109/TSG.2017.2655083
Hawlader M. N. A., 1988, International Journal of Solar Energy, V6, P119, DOI 10.1080/01425918808914224
Healy WM, 2008, ASHRAE TRAN, V114, P85
Koch S., 2012, TECH REP
Kondoh J., 2011, POW EN SOC GEN M 201, P1, DOI DOI 10.1109/PES.2011.6039149
Nel P., 2016, IEEE T SMART GRID, V9, P48
Recktenwald G., 2011, TECH REP
Svalstedt C., 2016, LOAD FLEXIBILITY HOU
Xu ZJ, 2014, IEEE T SMART GRID, V5, P2203, DOI 10.1109/TSG.2014.2317149
NR 11
TC 0
Z9 0
U1 0
U2 0
PU IEEE
PI NEW YORK
PA 345 E 47TH ST, NEW YORK, NY 10017 USA
SN 2167-9665
BN 978-1-7281-3103-0
J9 INNOV SMART GRID TEC
PY 2020
PG 5
WC Computer Science, Artificial Intelligence; Energy & Fuels; Engineering,
Electrical & Electronic
SC Computer Science; Energy & Fuels; Engineering
GA BQ1WX
UT WOS:000578005500006
DA 2021-04-21
ER
PT S
AU Ferigo, D
Traversaro, S
Metta, G
Pucci, D
AF Ferigo, Diego
Traversaro, Silvio
Metta, Giorgio
Pucci, Daniele
GP IEEE
TI Gym-Ignition: Reproducible Robotic Simulations for Reinforcement
Learning
SO 2020 IEEE/SICE INTERNATIONAL SYMPOSIUM ON SYSTEM INTEGRATION (SII)
SE IEEE/SICE International Symposium on System Integration
LA English
DT Proceedings Paper
CT IEEE/SICE International Symposium on System Integration (SII)
CY JAN 12-15, 2020
CL Honolulu, HI
SP IEEE, SICE
AB This paper presents Gym-Ignition, a new framework to create reproducible robotic environments for reinforcement learning research. It interfaces with the new generation of Gazebo, part of the Ignition Robotics suite, which provides three main improvements for reinforcement learning applications compared to the alternatives: 1) the modular architecture enables using the simulator as a 0, library, simplifying the interconnection with external software; 2) multiple physics and rendering engines are supported as plugins, simplifying their selection during the execution; 3) the new distributed simulation capability allows simulating complex scenarios while sharing the load on multiple workers and machines. The core of Gym-Ignition is a component that contains the Ignition Gazebo simulator and exposes a simple interface for its configuration and execution. We provide a Python package that allows developers to create robotic environments simulated in Ignition Gazebo. Environments expose the common OpenAI Gym interface, making them compatible out-of-the-box with third-party frameworks containing reinforcement learning algorithms. Simulations can be executed in both headless and GUI mode, the physics engine can run in accelerated mode, and instances can be parallelized. Furthermore, the Gym-Ignition software architecture provides abstraction of the Robot and the Task, making environments agnostic on the specific runtime. This abstraction allows their execution also in a real-time setting on actual robotic platforms, even if driven by different middlewares.
C1 [Ferigo, Diego; Traversaro, Silvio; Metta, Giorgio; Pucci, Daniele] Ist Italiano Tecnol, Dynam Interact Control, I-16163 Genoa, Italy.
[Ferigo, Diego] Univ Manchester, Machine Learning & Optimisat, Manchester M13 9PL, Lancs, England.
RP Ferigo, D (corresponding author), Ist Italiano Tecnol, Dynam Interact Control, I-16163 Genoa, Italy.; Ferigo, D (corresponding author), Univ Manchester, Machine Learning & Optimisat, Manchester M13 9PL, Lancs, England.
EM diego.ferigo@iit.it; silvio.traversaro@iit.it; giorgio.metta@iit.it;
daniele.pucci@iit.it
RI Traversaro, Silvio/M-6862-2019
OI Traversaro, Silvio/0000-0002-9283-6133
FU European UnionEuropean Commission [731540]
FX This project has received funding from the European Union's Horizon 2020
research and innovation programme under grant agreement No. 731540
(An.Dy).
CR Andrychowicz M., 2018, ARXIV180800177
Beattie C, 2016, ARXIV161203801
Beazley D. M., 1996, TCL TK WORKSH
Bellemare M. G., 2013, J ARTIFICIAL INTELLI
Brockman G., 2016, ARXIV160601540
Chebotar Y., 2018, ARXIV181005687
Christiano P., 2016, ARXIV161003518
Coumans E., 2016, PYBULLET PYTHON MODU
Delhaisse B., 2019, PYROBOLEARN PYTHON F, P11
Dulac-Arnold G., 2019, ARXIV190412901
Erez T., 2015, SIMULATION TOOLS MOD
Hwangbo J, 2019, SCI ROBOT, V4, DOI 10.1126/scirobotics.aau5872
Hwangbo J, 2018, IEEE ROBOT AUTOM LET, V3, P895, DOI 10.1109/LRA.2018.2792536
Ivaldi S., 2014, IEEE RAS INT C HUM R
Jain D., 2019, ARXIV190508926
Jeong R., 2019, ARXIV191009471
Juliani A., 2018, UNITY GEN PLATFORM I
Koenig N., 2004, DESIGN USE PARADIGMS
Krammer M., 2019, 13 INT MOD C FEB
Lee Jeongseok, 2018, J OPEN SOURCE SOFTWA
Li T., 2019, ICRA
Liang J., 2018, ARXIV181005762
Lopez NG, 2019, GYM GAZEBO2 TOOLKIT
Muratore F., 2019, ARXIV190704685
Parker S. G., 2010, OPTIX GEN PURPOSE RA
Peng XB, 2018, IEEE INT CONF ROBOT, P3803, DOI 10.1109/ICCABS.2018.8541936
Ramos F., 2019, ARXIV190601728
Tan J., 2018, ARXIV180410332
Tassa Y, 2018, ARXIV180100690
Todorov E., 2012, MUJOCO PHYS ENGINE M
Xia F., 2018, IEEE CVF C COMP VIS
Xie Z., 2019, ITERATIVE REINFORCEM
NR 32
TC 0
Z9 0
U1 0
U2 0
PU IEEE
PI NEW YORK
PA 345 E 47TH ST, NEW YORK, NY 10017 USA
SN 2474-2317
BN 978-1-7281-6667-4
J9 IEEE/SICE I S SYS IN
PY 2020
BP 885
EP 890
PG 6
WC Computer Science, Interdisciplinary Applications; Engineering,
Electrical & Electronic
SC Computer Science; Engineering
GA BP8HN
UT WOS:000565648500155
DA 2021-04-21
ER
PT S
AU Baltanas, SF
Ruiz-Sarmiento, JR
Gonzalez-Jimenez, J
AF Baltanas, Samuel-Felipe
Ruiz-Sarmiento, Jose-Raul
Gonzalez-Jimenez, Javier
BE Chova, LG
Martinez, AL
Torres, IC
TI EMPOWERING MOBILE ROBOTICS UNDERGRADUATE COURSES BY USING JUPYTER
NOTEBOOKS
SO 14TH INTERNATIONAL TECHNOLOGY, EDUCATION AND DEVELOPMENT CONFERENCE
(INTED2020)
SE INTED Proceedings
LA English
DT Proceedings Paper
CT 14th International Technology, Education and Development Conference
(INTED)
CY MAR 02-04, 2020
CL Valencia, SPAIN
DE Mobile robotics courses; undergraduate courses; Jupyter Notebook;
pedagogical material
ID HOME; LOCALIZATION
AB Mobile robotics has surged in popularity with the emergence of applications for commercial and industrial use such as autonomous cars, drones, or warehouse robots. Accordingly, new practitioners versed both in the theoretical and the practical aspects of the field are on high demand. Supporting material is key for the training of such stakeholders, especially in a multidisciplinary field such as robotics, which includes numerous and heterogeneous concepts from mathematics, statistics, physics, etc. In this regard, these complex concepts are better understood when presented in the context in which they are applied, where the environment, goals, and actions can be clearly visualized. This fact calls for modern and quality material using methodologies and tools applied to real use cases, seamlessly introducing theoretical concepts and their implementation and moving away from the traditional theory/exercise pedagogical approach.
This paper presents a collection of educational Jupyter Notebooks for use in undergraduate robotics courses, which have been built from the ground up to meet these issues. First, they make use of the Python programming language, praised both for its ease of use and the breadth of its library support. It has gained particular relevancy in Computer and Data Science applications, which can be of use in a field increasingly reliant on machine learning approaches as mobile robotics. Second, they are implemented using the Jupyter Notebook technology, widely resorted to in well-known online learning platforms such as Coursera or Udacity. Jupyter Notebooks permit us to combine at the same place theoretical explanations through text, images, mathematical equations, videos and/or links to additional resources, as well as executable code, this way producing comprehensive and contextualized material that incorporates the interactivity, dynamic visualizations and possibilities of an application. The notebooks provide a broad view of the robotics field, with a particular emphasis on mobile robotics, as they cover aspects like: probability bases, robot motion, sensing, localization, mapping, or motion planning.
The developed notebooks are meant to be an engaging tool for mobile robotics lecturers, students and practitioners seeking to enhance their knowledge basis. Currently they are being used in undergraduate courses at the University of Malaga (Spain) with promising results. The student version (without solutions) of the presented notebooks is publicly available at https://github.com/jotaraul/jupyter-notebooks-for-robotics-courses, while the complete version can be requested individually by any interested lecturer. This learning tool welcome any contribution from the mobile robotics community.
C1 [Baltanas, Samuel-Felipe; Ruiz-Sarmiento, Jose-Raul; Gonzalez-Jimenez, Javier] Univ Malaga, Machine Percept & Intelligent Robot Grp, Syst Engn & Auto Dept, Inst Invest Biomed Malaga IBIMA, Malaga, Spain.
RP Baltanas, SF (corresponding author), Univ Malaga, Machine Percept & Intelligent Robot Grp, Syst Engn & Auto Dept, Inst Invest Biomed Malaga IBIMA, Malaga, Spain.
RI Gonzalez-Jimenez, Javier/D-5774-2011
OI Gonzalez-Jimenez, Javier/0000-0003-3845-3497
FU WISER project [DPI2014-55826-R]; Spanish Ministry of Economy, Industry
and Competitiveness; I-PPIT-UMA program; University of Malaga;
Innovative Education Project [PIE19-165]
FX Work partially funded by the WISER project ([DPI2014-55826-R]), financed
by the Spanish Ministry of Economy, Industry and Competitiveness, by a
postdoc contract from the I-PPIT-UMA program, financed by the University
of Malaga, and by the Innovative Education Project PIE19-165 financed by
the same university.
CR Bogue R, 2016, IND ROBOT, V43, P583, DOI 10.1108/IR-07-2016-0194
Cadena C, 2016, IEEE T ROBOT, V32, P1309, DOI 10.1109/TRO.2016.2624754
Fernandez-Madrigal J.-A., 2012, IGI GLOBAL
Gonzalez-Jimenez J., 2012, 2012 IEEE RO MAN 21
Gunther M, 2018, ROBOT AUTON SYST, V110, P12, DOI 10.1016/j.robot.2018.08.016
Kluyver T, 2016, POSITIONING AND POWER IN ACADEMIC PUBLISHING: PLAYERS, AGENTS AND AGENDAS, P87, DOI 10.3233/978-1-61499-649-1-87
LaValle S. M, 2006, PLANNING ALGORITHMS
Orlandini A, 2016, PRESENCE-TELEOP VIRT, V25, P204, DOI 10.1162/PRES_a_00262
Perez F., 2015, PROJECT JUPYTER COMP, V11, P108
Python Software Foundation, 2018, PYTH LANG REF VERS 3
Quigley M, 2009, IEEE INT CONF ROBOT, P3604
Restas A., 2015, WORLD J ENG TECHNOLO, V3, P316, DOI [10.4236/wjet.2015.33C047, DOI 10.4236/WJET.2015.33C047]
Ruiz-Sarmiento JR, 2019, INTED PROC, P3321
Ruiz-Sarmiento JR, 2017, INTED PROC, P3803
Ruiz-Sarmiento JR, 2017, INT J ROBOT RES, V36, P131, DOI 10.1177/0278364917695640
Ruiz-Sarmiento JR, 2017, KNOWL-BASED SYST, V119, P257, DOI 10.1016/j.knosys.2016.12.016
Song GM, 2009, IEEE T CONSUM ELECTR, V55, P2034, DOI 10.1109/TCE.2009.5373766
Thrun S, 2002, COMMUN ACM, V45, P52
TIOBE Index, 2019, TIOB SOFTW QUAL CO
Vaussard F, 2014, ROBOT AUTON SYST, V62, P376, DOI 10.1016/j.robot.2013.09.014
Vazquez-Fernandez E, 2013, IET BIOMETRICS, V2, P10, DOI 10.1049/iet-bmt.2011.0006
Younes G, 2017, ROBOT AUTON SYST, V98, P67, DOI 10.1016/j.robot.2017.09.010
Zuniga-Noel D, 2019, IEEE ROBOT AUTOM LET, V4, P2862, DOI 10.1109/LRA.2019.2922618
NR 23
TC 0
Z9 0
U1 0
U2 0
PU IATED-INT ASSOC TECHNOLOGY EDUCATION & DEVELOPMENT
PI VALENICA
PA LAURI VOLPI 6, VALENICA, BURJASSOT 46100, SPAIN
SN 2340-1079
BN 978-84-09-17939-8
J9 INTED PROC
PY 2020
BP 5859
EP 5868
PG 10
WC Education & Educational Research
SC Education & Educational Research
GA BP5RO
UT WOS:000558088805147
DA 2021-04-21
ER
PT J
AU Stansbury, C
Lanzara, A
AF Stansbury, Conrad
Lanzara, Alessandra
TI PyARPES: An analysis framework for multimodal angle-resolved
photoemission spectroscopies
SO SOFTWAREX
LA English
DT Article
DE ARPES; NanoARPES; Pump-probe ARPES; Photoemission; Python; Qt; Jupyter
ID ELECTRON; SOFTWARE; SURFACE
AB The advent of higher resolution and throughput photoemission spectroscopy experiments has made angle-resolved photoemission spectroscopy (ARPES) a critical tool for the study of quantum materials. The simultaneous development of novel ARPES techniques, including nano/mu-ARPES, spin-resolved ARPES, and pump-probe ARPES mirrors the expansion in scanning modes for scanning tunneling microscopy, which made scanning probe methods the gold standard for driving insights into surface physics. In this paper, we introduce PyARPES, an open source and modular data analysis framework for angle-resolved photoemission spectroscopies. We highlight PyARPES' current capabilities for the analysis of the large photoemission datasets created by nano-ARPES and pump-probe ARPES experiments, show how PyARPES fulfills current data analysis needs for ARPES analysis, and discuss prospects for the future of analysis techniques enabled by PyARPES. (C) 2020 Published by Elsevier B.V.
C1 Lawrence Berkeley Natl Lab, Mat Sci Div, Berkeley, CA 94720 USA.
[Stansbury, Conrad] Univ Calif Berkeley, Dept Phys, Berkeley, CA 94720 USA.
RP Stansbury, C (corresponding author), Univ Calif Berkeley, Dept Phys, Berkeley, CA 94720 USA.
EM chstan@berkeley.edu
FU Office of Science, Office of Basic Energy Sciences, Materials Sciences
and Engineering Division, of the U.S. Department of Energy, as part of
the Ultrafast Materials Science ProgramUnited States Department of
Energy (DOE) [DE-AC02-05CH11231, KC2203]
FX This work was supported by the Director, Office of Science, Office of
Basic Energy Sciences, Materials Sciences and Engineering Division, of
the U.S. Department of Energy, under Contract No. DE-AC02-05CH11231, as
part of the Ultrafast Materials Science Program (KC2203).
CR Arango YC, 2016, SCI REP-UK, V6, DOI 10.1038/srep29493
Basham M, 2015, J SYNCHROTRON RADIAT, V22, P853, DOI 10.1107/S1600577515002283
Boschini F, 2018, NAT MATER, V17, P416, DOI 10.1038/s41563-018-0045-1
Bostwick A, 2007, NAT PHYS, V3, P36, DOI 10.1038/nphys477
Chen CY, 2015, NAT COMMUN, V6, DOI 10.1038/ncomms9585
Cilento F, 2018, SCI ADV, V4, DOI 10.1126/sciadv.aar1998
Devereaux TP, 2004, PHYS REV LETT, V93, DOI 10.1103/PhysRevLett.93.117004
Graf J, 2011, NAT PHYS, V7, P805, DOI 10.1038/nphys2027
Graf J, 2010, J APPL PHYS, V107, DOI 10.1063/1.3273487
He Y, 2017, REV SCI INSTRUM, V88, DOI 10.1063/1.4993919
Larsen AH, 2017, J PHYS-CONDENS MAT, V29, DOI 10.1088/1361-648X/aa680e
Horcas I, 2007, REV SCI INSTRUM, V78, DOI 10.1063/1.2432410
Hoyer S., 2017, J OPEN RES SOFTWARE, V5, P10, DOI [10.5334/jors.148, DOI 10.5334/J0RS.148]
Jozwiak C, 2016, NAT COMMUN, V7, DOI 10.1038/ncomms13143
Jozwiak C, 2013, NAT PHYS, V9, P293, DOI 10.1038/nphys2572
Kaminski A, 2005, NEW J PHYS, V7, DOI 10.1088/1367-2630/7/1/098
Konnecke M, 2015, J APPL CRYSTALLOGR, V48, P301, DOI 10.1107/S1600576714027575
Kordyuk AA, 2003, PHYS REV B, V67, DOI 10.1103/PhysRevB.67.064504
Laine RF, 2019, J PHYS D APPL PHYS, V52, DOI 10.1088/1361-6463/ab0261
Lanzara A, 2001, NATURE, V412, P510, DOI 10.1038/35087518
Lollobrigida V, 2015, J ELECTRON SPECTROSC, V205, P98, DOI 10.1016/j.elspec.2015.09.005
Lu DH, 2012, ANNU REV CONDEN MA P, V3, P129, DOI 10.1146/annurev-conmatphys-020911-125027
Necas D, 2012, CENT EUR J PHYS, V10, P181, DOI 10.2478/s11534-011-0096-2
Ong SP, 2013, COMP MATER SCI, V68, P314, DOI 10.1016/j.commatsci.2012.10.028
Reber TJ, 2014, REV SCI INSTRUM, V85, DOI 10.1063/1.4870283
Rotenberg E, 2014, J SYNCHROTRON RADIAT, V21, P1048, DOI 10.1107/S1600577514015409
Santander-Syro AF, 2014, NAT MATER, V13, P1085, DOI [10.1038/nmat4107, 10.1038/NMAT4107]
Smallwood CL, 2016, EPL-EUROPHYS LETT, V115, DOI 10.1209/0295-5075/115/27001
Sprinkle M, 2009, PHYS REV LETT, V103, DOI 10.1103/PhysRevLett.103.226803
van der Walt S, 2014, PEERJ, V2, DOI 10.7717/peerj.453
van der Walt S, 2011, COMPUT SCI ENG, V13, P22, DOI 10.1109/MCSE.2011.37
Verna A, 2016, J ELECTRON SPECTROSC, V209, P14, DOI 10.1016/j.elspec.2016.03.001
Wang Z, 2016, NAT MATER, V15, P835, DOI [10.1038/nmat4623, 10.1038/NMAT4623]
Zahl P, 2003, REV SCI INSTRUM, V74, P1222, DOI 10.1063/1.1540718
Zhou SY, 2007, NAT MATER, V6, P770, DOI 10.1038/nmat2003
NR 35
TC 0
Z9 0
U1 0
U2 2
PU ELSEVIER
PI AMSTERDAM
PA RADARWEG 29, 1043 NX AMSTERDAM, NETHERLANDS
SN 2352-7110
J9 SOFTWAREX
JI SoftwareX
PD JAN-JUN
PY 2020
VL 11
AR 100472
DI 10.1016/j.softx.2020.100472
PG 8
WC Computer Science, Software Engineering
SC Computer Science
GA MM1SA
UT WOS:000549937400025
OA DOAJ Gold, Green Published
DA 2021-04-21
ER
PT J
AU Eschle, J
Navarro, AP
Coutinho, RS
Serra, N
AF Eschle, Jonas
Navarro, Albert Puig
Coutinho, Rafael Silva
Serra, Nicola
TI zfit: Scalable pythonic fitting
SO SOFTWAREX
LA English
DT Article
DE Model fitting; Data analysis; Statistical inference; Python
AB Statistical modeling is a key element in many scientific fields and especially in High-Energy Physics (HEP) analysis. The standard framework to perform this task in HEP is the C++ ROOT/RooFit toolkit; with Python bindings that are only loosely integrated into the scientific Python ecosystem. In this paper, zfit, a new alternative to RooFit written in pure Python, is presented. Most of all, zfit provides a well defined high-level API and workflow for advanced model building and fitting, together with an implementation on top of TensorFlow, allowing a transparent usage of CPUs and GPUs. It is designed to be extendable in a very simple fashion, allowing the usage of cutting-edge developments from the scientific Python ecosystem in a transparent way. The main features of zfit are introduced, and its extension to data analysis, especially in the context of HEP experiments, is discussed. (C) 2020 The Authors. Published by Elsevier B.V.
C1 [Eschle, Jonas; Navarro, Albert Puig; Coutinho, Rafael Silva; Serra, Nicola] Univ Zurich, Phys Inst, Zurich, Switzerland.
RP Eschle, J (corresponding author), Univ Zurich, Phys Inst, Zurich, Switzerland.
EM Jonas.Eschle@cern.ch; albert.puig.navarro@gmail.com;
rafael.silva.coutinho@cern.ch; nicola.serra@cern.ch
OI Eschle, Jonas/0000-0002-7312-3699; Silva Coutinho,
Rafael/0000-0002-1545-959X
FU Swiss National Science Foundation (SNF)Swiss National Science Foundation
(SNSF) [168169, 174182, 182622]
FX We are grateful to Anton Poluektov, Chris Burr and Igor Babuschkin for
demonstrating the potential of unbinned model fitting within the context
of TensorFlow, which inspired this work. We also thank the Zurich LHCb
Group, Matthieu Marinangeli, Josh Bendavid, Lukas Heinrich and the HSF
community, especially Scikit-HEP project members, for useful
discussions. A. Puig, R. Silva Coutinho, J.Eschle and N. Serra
gratefully acknowledge the support by the Swiss National Science
Foundation (SNF) under contracts 168169, 174182 and 182622.
CR Babuschkin I, 2019, TENSORPROB
Brun R, 1997, NUCL INSTRUM METH A, V389, P81, DOI 10.1016/S0168-9002(97)00048-X
Dillon J. V., 2017, ARXIV171110604
JAMES F, 1975, COMPUT PHYS COMMUN, V10, P343, DOI 10.1016/0010-4655(75)90039-9
Jones E., 2001, SCIPY OPEN SOURCE SC
Kingma DP, 2015, 3 INT C LEARN REPR S
Martin Abadi, 2015, TENSORFLOW LARGE SCA
Millman KJ, 2011, COMPUT SCI ENG, V13, P9, DOI 10.1109/MCSE.2011.36
Oliphant TE, 2007, COMPUT SCI ENG, V9, P10, DOI 10.1109/MCSE.2007.58
Poluektov A, 2018, USING TENSORFLOW AMP, DOI [10.5281/zenodo.1415412, DOI 10.5281/ZENODO.1415412]
Verkerke W, 2003, ECONF C
NR 11
TC 0
Z9 0
U1 0
U2 0
PU ELSEVIER
PI AMSTERDAM
PA RADARWEG 29, 1043 NX AMSTERDAM, NETHERLANDS
SN 2352-7110
J9 SOFTWAREX
JI SoftwareX
PD JAN-JUN
PY 2020
VL 11
AR 100508
DI 10.1016/j.softx.2020.100508
PG 6
WC Computer Science, Software Engineering
SC Computer Science
GA MM1TE
UT WOS:000549940500003
OA DOAJ Gold, Green Accepted
DA 2021-04-21
ER
PT J
AU Guzel, O
Ozdarcan, O
AF Guzel, O.
Ozdarcan, O.
TI PyWD2015-A new GUI for the Wilson-Devinney code
SO CONTRIBUTIONS OF THE ASTRONOMICAL OBSERVATORY SKALNATE PLESO
LA English
DT Article
DE binaries: eclipsing; methods: data analysis
AB A new, modern graphical user interface (GUI) for the 2015 version of the Wilson-Devinney (WD) code is developed. PyWD2015 is written in Python 2:7 and uses the Qt4 interface framework. At its core, the GUI generates lcin and dcin files from user inputs and sends them to WD, then reads and visualises the output in a user friendly way. It also includes some useful tools for the user, which makes technical aspects of the modelling process significantly easier. While multiple sky surveys and space missions generate, reduce and categorize large amounts of observational data, it's up to dedicated studies to analyse peculiar or anomalous systems and make further progress in the field of physics of eclipsing binaries. We believe PyWD2015 will be a great dedicated study" suite for such systems.
C1 [Guzel, O.; Ozdarcan, O.] Ege Univ, Fac Sci, Astron & Space Sci Dept, TR-35100 Izmir, Turkey.
RP Guzel, O (corresponding author), Ege Univ, Fac Sci, Astron & Space Sci Dept, TR-35100 Izmir, Turkey.
EM ozanguzel35@outlook.com
RI Ozdarcan, Orkun/AAG-4552-2021
OI Ozdarcan, Orkun/0000-0003-4820-3950
CR Drilling J. S., 2000, ALLENS ASTROPHYSICAL, P381
Flower PJ, 1996, ASTROPHYS J, V469, P355, DOI 10.1086/177785
Gray DJ, 2005, PLANT DEVELOPMENT AND BIOTECHNOLOGY, P3
Hunter JD, 2007, COMPUT SCI ENG, V9, P90, DOI 10.1109/MCSE.2007.55
Kallrath J, 2009, ASTRON ASTROPHYS LIB, P3, DOI 10.1007/978-1-4419-0699-1_1
Kopal Z., 1959, CLOSE BINARY SYSTEMS
Oliphant T.E., 2015, GUIDE NUMPY
POPPER DM, 1980, ANNU REV ASTRON ASTR, V18, P115, DOI 10.1146/annurev.aa.18.090180.000555
Prsa A, 2005, ASTROPHYS J, V628, P426, DOI 10.1086/430591
Tokunaga A. T., 2000, ALLENS ASTROPHYSICAL, P143
Wilson RE, 2014, ASTROPHYS J, V780, DOI 10.1088/0004-637X/780/2/151
WILSON RE, 1971, ASTROPHYS J, V166, P605, DOI 10.1086/150986
NR 12
TC 1
Z9 1
U1 0
U2 0
PU SLOVAK ACADEMY SCIENCES ASTRONOMICAL INST
PI TATRANSKA LOMINICA
PA SLOVAK ACADEMY SCIENCES ASTRONOMICAL INST, TATRANSKA LOMINICA, SK-059
60, SLOVAKIA
SN 1335-1842
EI 1336-0337
J9 CONTRIB ASTRON OBS S
JI Contrib. Astron. Obs. S.
PY 2020
VL 50
IS 2
BP 535
EP 538
DI 10.31577/caosp.2020.50.2.535
PG 4
WC Astronomy & Astrophysics
SC Astronomy & Astrophysics
GA KZ9JA
UT WOS:000523574300033
OA Bronze
DA 2021-04-21
ER
PT J
AU Becchetti, FD
Damron, N
Torres-Isea, RO
AF Becchetti, F. D.
Damron, N.
Torres-Isea, R. O.
TI Applications of high-speed digital pulse acquisition and
software-defined electronics (SDE) in advanced nuclear teaching
laboratories
SO AMERICAN JOURNAL OF PHYSICS
LA English
DT Article
ID OF-FLIGHT SETUP; NEUTRON; SCINTILLATORS; SPECTROSCOPY
AB There is a new generation of high-speed programmable pulse digitizers available now from several vendors at modest cost. These digitizers in tandem with on-board or post-processing software combine to produce a Software-Defined Electronics (SDE) system that can be effectively used in several advanced physics teaching lab experiments. In particular, as we will demonstrate, they are particularly well suited for nuclear-physics related experiments, often replacing many analog electronics modules. Appropriate on-board SDE can generate full or partial integrals of the pulses, pulse-shape characterization (PSD) data, coincidence signal indication, fast timing, or other information. Likewise, external PC-based SDE post-processing software can readily be developed and applied by undergraduate students or instructors using one of several different software languages available: matlab, python, LabVIEW, root, basic, etc. As demonstrated here, an SDE-based system is a cost-effective substitute for many dedicated NIM or CAMAC electronics modules as this requires only a single digitizer module and a computer. A single digitizer with SDE is easily adapted for use in many different experiments. Applications of various high- and low-speed digitizers with SDE for many other types of physics teaching lab experiments will also be discussed.
C1 [Becchetti, F. D.; Damron, N.; Torres-Isea, R. O.] Univ Michigan, Dept Phys, Ann Arbor, MI 48109 USA.
RP Becchetti, FD (corresponding author), Univ Michigan, Dept Phys, Ann Arbor, MI 48109 USA.
FU REU [PHY 14-01242]
FX This work was supported in part by REU supplement to NSF grant PHY
14-01242. This paper is based mostly on the work presented by one of us
(N.D.) at the October 2018 Fall APS meeting (Ref. 42).
CR [Anonymous], RX1200 DIG SPECTR
[Anonymous], 1987, EXPT NUCL SCI
[Anonymous], 2018, ADV LAB NOT
[Anonymous], 37830 LLC TN
[Anonymous], 2017, LAB MAN NUCL SCI EXP
Becchetti FD, 2017, NUCL INSTRUM METH A, V874, P72, DOI 10.1016/j.nima.2017.08.034
Becchetti FD, 2016, AM J PHYS, V84, P883, DOI 10.1119/1.4964109
Becchetti FD, 2014, AM J PHYS, V82, P706, DOI 10.1119/1.4876218
Becchetti FD, 2013, AM J PHYS, V81, P112, DOI 10.1119/1.4769032
Bryan Jeff C., 2018, INTRO NUCL SCI
Damron Nathan, 2018, B AM PHYS SOC, V63, P12
Das Ashok, 2003, INTRO NUCL PARTICLE
DEVOIGT MJA, 1983, REV MOD PHYS, V55, P949, DOI 10.1103/RevModPhys.55.949
Di Fulvio A., 2019, COMMUNICATION
Duggan Jerome L., 1988, LAB INVESTIGATIONS N
Engbrecht J, 2018, AM J PHYS, V86, P549, DOI 10.1119/1.5038672
Essick John, 2019, HANDS ON INTRO LABVI
FROHNER FH, 1990, NUCL SCI ENG, V106, P345
Hammer J. W., 2008, ADV PHYS LAB MANUAL
Henley E. M., 2007, SUBATOMIC PHYS
Katz Sidney A., 2011, EXPT NUCL SCI
KNOLL G.F., 2011, RAD DETECTION MEASUR
Kolumban G, 2015, ICT EXPRESS, V1, P44, DOI 10.1016/S2405-9595(15)30021-7
Krane K. S., 1988, INTRO NUCL PHYS
Lavelle CM, 2018, AM J PHYS, V86, P384, DOI 10.1119/1.5026595
Leo W.R., 1994, TECHNIQUES NUCL PART
Melissinos Adrian C., 2013, EXPT MODERN PHYS
Peterson Randolph S., 1996, EXPT RAY SPECTROSCOP
Saha GB, 2010, BASICS OF PET IMAGING: PHYSICS, CHEMISTRY AND REGULATIONS, SECOND EDITION, P1, DOI 10.1007/978-1-4419-0805-6_1
Tsoulfanidis N, 2011, MEASUREMENT DETECTIO
Ward D, 2001, ADV NUCL PHYS, V26, P167
Weisshaar D, 2017, NUCL INSTRUM METH A, V847, P187, DOI 10.1016/j.nima.2016.12.001
Zaitseva NP, 2018, NUCL INSTRUM METH A, V889, P97, DOI 10.1016/j.nima.2018.01.093
NR 33
TC 0
Z9 0
U1 0
U2 3
PU AMER ASSN PHYSICS TEACHERS
PI COLLEGE PK
PA 5110 ROANOKE PLACE SUITE 101, COLLEGE PK, MD 20740 USA
SN 0002-9505
EI 1943-2909
J9 AM J PHYS
JI Am. J. Phys.
PD JAN
PY 2020
VL 88
IS 1
BP 70
EP 80
DI 10.1119/1.5125128
PG 11
WC Education, Scientific Disciplines; Physics, Multidisciplinary
SC Education & Educational Research; Physics
GA KA1FE
UT WOS:000505543900010
DA 2021-04-21
ER
PT J
AU Schneider, H
AF Schneider, Howard
TI The meaningful-based cognitive architecture model of schizophrenia
SO COGNITIVE SYSTEMS RESEARCH
LA English
DT Article
DE Cognitive architecture; Artificial general intelligence; Cortical
minicolumns; Psychosis; Schizophrenia
ID TOOL-USE; BEHAVIOR; LEVEL; SOLVE
AB In subsymbolic operation of the Meaningful-Based Cognitive Architecture (MBCA) the input sensory vector is propagated through a hierarchy of Hopfield-like Network (HLN) functional groups, is recognized and may associatively trigger in the instinctual core goals module as well as in groups of HLNs arranged as pre-causal and pattern memory, vectors propagated to the output motor group of HLNs which produce an output signal. In full causal symbolic operation, the processed sensory input vector is also propagated to the logic/working memory groups of HLNs, where it can be compared to other vectors in the logic/working memory, and produce various outputs in response. The processed sensory input vector can trigger in the instinctual core goals module intuitive logic, intuitive physics, intuitive psychology and intuitive planning procedural vectors, as well as trigger in the causal group of HLNs learned logic, physics, psychology and planning procedural vectors which are also sent to the logic/working memory groups of HLNs. These circuits can allow the MBCA to act causally on information it has never seen before. An example is given of a Python simulation where the MBCA which is controlling a legged robot causally determines that a shallow whitewater river will cause water damage to itself, while if the MBCA is acting associatively only and never having seen whitewater before and normally crossing shallow rivers, will cross the whitewater river and become damaged. While the MBCA does not attempt to replicate biological systems at the neuronal spiking level, its HLNs and the organization of its HLNs are indeed inspired by biological mammalian minicolumns and mammalian brains. The MBCA model leads to the hypothesis that in the course of hominin evolution, HLNs became co-opted into groups of HLNs providing more extensive working memories with causal abilities, unlike non-hominins. While such co-option of the minicolumns can allow advantageous causal symbolic processing integrated with subsymbolic processing, the order of magnitude of increased complexity required for such organization and operation, created a vulnerability in the human brain to psychosis, which does not occur with significant prevalence in non-humans. (C) 2019 Elsevier B.V. All rights reserved.
C1 [Schneider, Howard] Sheppard Clin North, Toronto, ON M2M3X4, Canada.
RP Schneider, H (corresponding author), Sheppard Clin North, Toronto, ON M2M3X4, Canada.
EM howard.schneider@gmail.com
CR Aase I, 2018, FRONT PSYCHOL, V9, DOI 10.3389/fpsyg.2018.00608
Albantakis L, 2014, PLOS COMPUT BIOL, V10, DOI 10.1371/journal.pcbi.1003966
Anderson JR, 2004, PSYCHOL REV, V111, P1036, DOI 10.1037/0033-295x.111.4.1036
Anttila V, 2018, SCIENCE, V360, P1313, DOI 10.1126/science.aap8757
Bach J, 2008, FR ART INT, V171, P63
Bechdolf A, 2012, BRIT J PSYCHIAT, V200, P22, DOI 10.1192/bjp.bp.109.066357
Benitez-Burraco A, 2017, BRAIN BEHAV EVOLUT, V89, P162, DOI 10.1159/000468506
Buxhoeveden DP, 2002, BRAIN, V125, P935, DOI 10.1093/brain/awf110
COHEN JD, 1992, PSYCHOL REV, V99, P45, DOI 10.1037/0033-295X.99.1.45
Collier M, 2018, 27 INT C ART NEUR NE
Crow TJ, 2000, BRAIN RES REV, V31, P118, DOI 10.1016/S0165-0173(99)00029-6
Eliasmith C, 2014, CURR OPIN NEUROBIOL, V25, P1, DOI 10.1016/j.conb.2013.09.009
Goodfellow I, 2016, DEEP LEARNING
Graves A, 2016, NATURE, V538, P471, DOI 10.1038/nature20101
Hawkins J., 2004, INTELLIGENCE
Jones CA, 2011, BRIT J PHARMACOL, V164, P1162, DOI 10.1111/j.1476-5381.2011.01386.x
Kilicay-Ergin N. H., 2012, IEEE Transactions on Systems, Man and Cybernetics, Part C (Applications and Reviews), V42, P1231, DOI 10.1109/TSMCC.2012.2201469
KURZWEIL R, 2012, HOW TO CREATE A MIND
Lake BM, 2017, BEHAV BRAIN SCI, V40, DOI 10.1017/S0140525X16001837
Langley P, 2017, P 31 AAAI C ART INT
Lazaro-Gredilla M., 2017, ARXIV161102252V2
Li Shen, 2018, ARXIV180810326
Liu CX, 2019, FRONT GENET, V10, DOI 10.3389/fgene.2019.00389
Mnih V, 2015, NATURE, V518, P529, DOI 10.1038/nature14236
Mountcastle VB, 1997, BRAIN, V120, P701, DOI 10.1093/brain/120.4.701
Neilands PD, 2016, PLOS ONE, V11, DOI 10.1371/journal.pone.0167419
Nissani M, 2006, J EXP PSYCHOL ANIM B, V32, P91, DOI 10.1037/0097-7403.32.1.91
Pearlson GD, 2008, SCHIZOPHRENIA BULL, V34, P722, DOI 10.1093/schbul/sbm130
Polimeni J, 2003, CAN J PSYCHIAT, V48, P34
Reich D, 2010, NATURE, V468, P1053, DOI 10.1038/nature09710
Rosenbloom Paul S., 2016, Journal of Artificial General Intelligence, V7, P1, DOI 10.1515/jagi-2016-0001
Russell Stuart J, 2009, ARTIFICIAL INTELLIGE
Sabaroedin K., 2018, BIOL PSYCHIAT
Samsonovich AV, 2010, FRONT ARTIF INTEL AP, V221, P195, DOI 10.3233/978-1-60750-661-4-195
Sawa K, 2009, JPN PSYCHOL RES, V51, P222, DOI 10.1111/j.1468-5884.2009.00396.x
Schneider H., ADV INTELLIGENT SYST, V948, DOI [10.1007/978-3-030-25719-4_61, DOI 10.1007/978-3-030-25719-4_61]
Schneider H, 2018, PROCEDIA COMPUT SCI, V145, P471, DOI 10.1016/j.procs.2018.11.109
Schwalger T, 2017, PLOS COMPUT BIOL, V13, DOI 10.1371/journal.pcbi.1005507
Seed AM, 2009, J EXP PSYCHOL ANIM B, V35, P23, DOI 10.1037/a0012925
Simon H., 1996, SCI ARTIFICIAL
Taylor AH, 2012, P ROY SOC B-BIOL SCI, V279, P4977, DOI 10.1098/rspb.2012.1998
Ullman S, 2019, SCIENCE, V363, P692, DOI 10.1126/science.aau6595
van Os J, 2001, ARCH GEN PSYCHIAT, V58, P663, DOI 10.1001/archpsyc.58.7.663
VISALBERGHI E, 1994, J COMP PSYCHOL, V108, P15, DOI 10.1037/0735-7036.108.1.15
Waismeyer A, 2015, DEVELOPMENTAL SCI, V18, P175, DOI 10.1111/desc.12208
Wente AO, 2019, CHILD DEV, V90, P859, DOI 10.1111/cdev.12943
Zhang RB, 2016, SCHIZOPHRENIA BULL, V42, P1068, DOI 10.1093/schbul/sbv221
NR 47
TC 0
Z9 0
U1 3
U2 14
PU ELSEVIER
PI AMSTERDAM
PA RADARWEG 29, 1043 NX AMSTERDAM, NETHERLANDS
SN 1389-0417
J9 COGN SYST RES
JI Cogn. Syst. Res.
PD JAN
PY 2020
VL 59
BP 73
EP 90
DI 10.1016/j.cogsys.2019.09.019
PG 18
WC Computer Science, Artificial Intelligence; Neurosciences; Psychology,
Experimental
SC Computer Science; Neurosciences & Neurology; Psychology
GA JQ3OW
UT WOS:000498859500006
DA 2021-04-21
ER
PT J
AU Cai, XX
Kittelmann, T
AF Cai, X-X
Kittelmann, T.
TI NCrystal: A library for thermal neutron transport
SO COMPUTER PHYSICS COMMUNICATIONS
LA English
DT Article
DE Thermal neutron scattering; Simulations; Monte Carlo; Crystals; Bragg
diffraction
ID CRYSTAL X-RAY; CROSS-SECTIONS; SCATTERING; RADIATION; OPTIMIZATION;
SIMULATIONS; PROGRAM; VERSION
AB An open source software package for modelling thermal neutron transport is presented. The code facilitates Monte Carlo-based transport simulations and focuses in the initial release on interactions in both mosaic single crystals as well as polycrystalline materials and powders. Both coherent elastic (Bragg diffraction) and incoherent or inelastic (phonon) scattering are modelled, using basic parameters of the crystal unit cell as input.
Included is a data library of validated crystal definitions, standalone tools and interfaces for C++, C and Python programming languages. Interfaces for two popular simulation packages, Geant4 and McStas, are provided, enabling highly realistic simulations of typical components at neutron scattering instruments, including beam filters, monochromators, analysers, samples, and detectors. All interfaces are presented in detail, along with the end-user configuration procedure which is deliberately kept user-friendly and consistent across all interfaces.
An overview of the relevant neutron scattering theory is provided, and the physics modelling capabilities of the software are discussed. Particular attention is given here to the ability to load crystal structures and form factors from various sources of input, and the results are benchmarked and validated against experimental data and existing crystallographic software. Good agreements are observed.
Program summary
Program Title: NCrystal
Program Files doi: http://dx.doi.org/10.17632/s3rpb5d9j3.1
Licensing provisions: Apache License, Version 2.0 (for core NCrystal).
Programming language: C++, C and Python
External routines/libraries: Geant4, McStas
Nature of problem: Thermal neutron transport in structured materials is inadequately supported in popular Monte Carlo transport applications, preventing simulations of a range of otherwise interesting setups.
Solution method: Provide models for thermal neutron transport in flexible open source library, to be used standalone or as backend in existing Monte Carlo packages. Facilitate validation and work sharing by making it possible to share material configurations between supported applications. (C) 2019 The Author(s). Published by Elsevier B.V.
C1 [Cai, X-X; Kittelmann, T.] European Spallat Source ERIC, Lund, Sweden.
[Cai, X-X] Tech Univ Denmark, DTU Nutech, Lyngby, Denmark.
RP Kittelmann, T (corresponding author), European Spallat Source ERIC, Lund, Sweden.
EM thomas.kittelmann@esss.se
RI Cai, Xiao-Xiao/O-3766-2018
OI Cai, Xiao-Xiao/0000-0002-9075-7052; Kittelmann,
Thomas/0000-0002-7396-4922
FU European UnionEuropean Commission [676548]
FX This work was supported in part by the European Union's Horizon 2020
research and innovation programme under grant agreement No 676548 (the
BrightnESS project). The authors would like to thank the following
colleagues for valuable contributions, testing, feedback, ideas or other
support: E. Dian, G. Galgoczi, R. Hall-Wilton, K. Kanaki, M. Klausz, E.
Klinkby, E. B. Knudsen, J.I. Marquez Damian, V. Maulerova, A. Morozov,
V. Santoro, and P. Willendrup.
CR Adib M., 2007, 6 C NUCL PART PHYS L, V642, P3954
Agostinelli S, 2003, NUCL INSTRUM METH A, V506, P250, DOI 10.1016/S0168-9002(03)01368-8
Allison J, 2006, IEEE T NUCL SCI, V53, P270, DOI 10.1109/TNS.2006.869826
Allison J, 2016, NUCL INSTRUM METH A, V835, P186, DOI 10.1016/j.nima.2016.06.125
[Anonymous], 1963, CRYSTAL STRUCTURES, Vvol. 1
Antao SM, 2009, CAN MINERAL, V47, P1245, DOI 10.3749/canmin.47.5.1245
Antao SM, 2008, CAN MINERAL, V46, P1501, DOI 10.3749/canmin.46.5.1501
Apache Software Foundation, 2004, APACHE LICENSE VERSI
Bethe HA, 1935, PHYS REV, V47, P0747, DOI 10.1103/PhysRev.47.747
BINDER K, 1970, PHYS STATUS SOLIDI, V41, P767, DOI 10.1002/pssb.19700410233
Blackman D., 2018, SCRAMBLED LINEAR PSE
Bohlen TT, 2014, NUCL DATA SHEETS, V120, P211, DOI 10.1016/j.nds.2014.07.049
Boin M, 2012, J PHYS CONF SER, V340, DOI 10.1088/1742-6596/340/1/012022
Boin M, 2011, J APPL CRYSTALLOGR, V44, P1040, DOI 10.1107/S0021889811025970
Boin M, 2012, J APPL CRYSTALLOGR, V45, P603, DOI 10.1107/S0021889812016056
Breit G, 1936, PHYS REV, V49, P0519, DOI 10.1103/PhysRev.49.519
Brown DA, 2018, NUCL DATA SHEETS, V148, P1, DOI 10.1016/j.nds.2018.02.001
Cai XX, 2019, J COMPUT PHYS, V380, P400, DOI 10.1016/j.jcp.2018.11.043
CASSELS JM, 1950, PROG NUCL PHYS, V1, P185
Cherkashyna N, 2014, J PHYS CONF SER, V528, DOI 10.1088/1742-6596/528/1/012013
Debye P, 1912, ANN PHYS-BERLIN, V39, P789
Dian E, 2019, J INSTRUM, V14, DOI 10.1088/1748-0221/14/01/P01021
DIAN M, 2017, ELSEV ASIA STUD SER, P1, DOI DOI 10.1109/NSSMIC.2017.8532930
Downs RT, 2003, AM MINERAL, V88, P247
Farhi E, 2014, J NEUTRON RES, V17, P5, DOI 10.3233/JNR-130001
Fermi E., 1936, Ricerca Scientifica, V7, P13
FREUND AK, 1983, NUCL INSTRUM METHODS, V213, P495, DOI 10.1016/0167-5087(83)90447-7
Galgoczi G, 2018, J INSTRUM, V13, DOI 10.1088/1748-0221/13/12/P12031
Garoby R, 2018, PHYS SCRIPTA, V93, DOI 10.1088/1402-4896/aa9bff
Ghalsasi P, 2011, INORG CHEM, V50, P86, DOI 10.1021/ic101248g
GLAUBER RJ, 1955, PHYS REV, V98, P1692, DOI 10.1103/PhysRev.98.1692
Goorley J.T., 2013, LAUR1322934 LOS AL N
Grazulis S, 2009, J APPL CRYSTALLOGR, V42, P726, DOI 10.1107/S0021889809016690
Grosse-Kunstleve R.W., 1998, SGLNFO COMPREHENSIVE
Gustafsson T, 2001, ACTA CRYSTALLOGR C, V57, P668, DOI 10.1107/S0108270101005352
Hahn T., 2002, INT TABLES CRYSTALLO
HALL SR, 1991, ACTA CRYSTALLOGR A, V47, P655, DOI 10.1107/S010876739101067X
HAZEN RM, 1976, AM MINERAL, V61, P266
Larsen AH, 2017, J PHYS-CONDENS MAT, V29, DOI 10.1088/1361-648X/aa680e
Hunter JD, 2007, COMPUT SCI ENG, V9, P90, DOI 10.1109/MCSE.2007.55
Jericha E., 2001, NEUTRON SCATTERING L
Kanaki K, 2018, J INSTRUM, V13, DOI 10.1088/1748-0221/13/07/P07016
Kanaki K, 2018, PHYSICA B, V551, P386, DOI 10.1016/j.physb.2018.03.025
KIRFEL A, 1990, ACTA CRYSTALLOGR A, V46, P271, DOI 10.1107/S0108767389012596
Kittel C, 2004, INTRO SOLID STATE PH
Kittelmann T, 2015, COMPUT PHYS COMMUN, V189, P114, DOI 10.1016/j.cpc.2014.11.009
Kittelmann T, 2014, J PHYS CONF SER, V513, DOI 10.1088/1742-6596/513/2/022017
KOESTER L, 1991, ATOM DATA NUCL DATA, V49, P65, DOI 10.1016/0092-640X(91)90012-S
Kropff F., 1977, UNPUB
Lefmann K., 1999, NEUTRON NEWS, V10, P20, DOI DOI 10.1080/10448639908233684
Leppanen J, 2015, ANN NUCL ENERGY, V82, P142, DOI 10.1016/j.anucene.2014.08.024
Lieutenant K, 2004, PROC SPIE, V5536, P134, DOI 10.1117/12.562814
MacFarlane R.E., 2012, LAUR1227079 LOS AL N
Damian JIM, 2014, ANN NUCL ENERGY, V65, P280, DOI 10.1016/j.anucene.2013.11.014
Marshall W., 1971, THEORY THERMAL NEUTR
Martin K, 2015, MASTERING CMAKE CROS
Mauri G, 2018, J INSTRUM, V13, DOI 10.1088/1748-0221/13/03/P03004
Messi F., 2017, NEUTRON TAGGING FACI
Metropolis N., 1987, LOS ALAMOS SCI, P125, DOI DOI 10.1128/JCM.05092-11
Morozov A, 2016, J INSTRUM, V11, DOI 10.1088/1748-0221/11/04/P04022
Newhauser WD, 2011, NAT REV CANCER, V11, P438, DOI 10.1038/nrc3069
Oliphant T, 2006, NUMPY GUIDE NUMPY
Otuka N, 2014, NUCL DATA SHEETS, V120, P272, DOI 10.1016/j.nds.2014.07.065
Park WB, 2011, J MATER CHEM, V21, P5780, DOI 10.1039/c0jm03538f
Peggs S., 2012, 2012001 ESS
Peggs S., 2013, 2013001 ESS
Peng LM, 1996, ACTA CRYSTALLOGR A, V52, P456, DOI 10.1107/S010876739600089X
PLACZEK G, 1952, PHYS REV, V86, P377, DOI 10.1103/PhysRev.86.377
Rauch H., 2000, 6 NEUTRON CCATTERING, P1, DOI [10.1007/10499706_6, DOI 10.1007/10499706_6]
RODRIGUEZCARVAJAL J, 1993, PHYSICA B, V192, P55, DOI 10.1016/0921-4526(93)90108-I
Romano PK, 2015, ANN NUCL ENERGY, V82, P90, DOI 10.1016/j.anucene.2014.07.048
Sands D., 1993, DOVER BOOKS CHEM SER
Santoro V, 2015, J NEUTRON RES, V18, P135, DOI 10.3233/JNR-160034
Saroun J, 1997, PHYSICA B, V234, P1102, DOI 10.1016/S0921-4526(97)00037-9
Sato T, 2018, J NUCL SCI TECHNOL, V55, P684, DOI 10.1080/00223131.2017.1419890
Schober H, 2014, J NEUTRON RES, V17, P109, DOI 10.3233/JNR-140016
SEARS VF, 1986, PHYS REP, V141, P281, DOI 10.1016/0370-1573(86)90129-8
SEARS VF, 1984, AECL8490 CHALK RIV N
Seeger P.A., 2002, NEUTRON NEWS, V13, P20, DOI DOI 10.1080/10448630208218490
Siegrist T, 1997, J APPL CRYSTALLOGR, V30, P418, DOI 10.1107/S0021889897003026
SJOLANDER A, 1958, ARK FYS, V14, P315
Squires G.L., 2012, INTRO THEORY THERMAL, DOI [10.1017/CB09781139107808, DOI 10.1017/CB09781139107808]
Toby BH, 2013, J APPL CRYSTALLOGR, V46, P544, DOI 10.1107/S0021889813003531
TRUCANO P, 1975, NATURE, V258, P136, DOI 10.1038/258136a0
van Rossum Jr G, 2011, PYTHON LANGUAGE REFE
van Sluijs R, 2015, J RADIOANAL NUCL CH, V306, P579, DOI 10.1007/s10967-015-4134-1
Vogel S., 2000, THESIS CRISTIAN ALBR
Wang BT, 2013, PHYS REV B, V88, DOI 10.1103/PhysRevB.88.104107
Waseda Y, 2011, XRAY DIFFRACTION CRY
Waters LS, 2007, AIP CONF PROC, V896, P81, DOI 10.1063/1.2720459
Willendrup P, 2004, PHYSICA B, V350, pE735, DOI 10.1016/j.physb.2004.03.193
Wyckoff R.W.G., 1922, ANAL EXPRESSION RESU
X-5 Monte Carlo Team, 2003, LACP030245 X5 MONT C, VII
Xu XG, 2008, PHYS MED BIOL, V53, pR193, DOI 10.1088/0031-9155/53/13/R01
YVON K, 1977, J APPL CRYSTALLOGR, V10, P73, DOI 10.1107/S0021889877012898
Zsigmond G., 2002, NEUTRON NEWS, V13, P11, DOI [10.1080/10448630208218488, DOI 10.1080/10448630208218488]
NR 96
TC 8
Z9 8
U1 1
U2 9
PU ELSEVIER
PI AMSTERDAM
PA RADARWEG 29, 1043 NX AMSTERDAM, NETHERLANDS
SN 0010-4655
EI 1879-2944
J9 COMPUT PHYS COMMUN
JI Comput. Phys. Commun.
PD JAN
PY 2020
VL 246
AR 106851
DI 10.1016/j.cpc.2019.07.015
PG 30
WC Computer Science, Interdisciplinary Applications; Physics, Mathematical
SC Computer Science; Physics
GA JP5UR
UT WOS:000498329900009
OA Other Gold, Green Published
DA 2021-04-21
ER
PT J
AU Gardenier, DW
van Leeuwen, J
Connor, L
Petroff, E
AF Gardenier, D. W.
van Leeuwen, J.
Connor, L.
Petroff, E.
TI Synthesising the intrinsic FRB population using frbpoppy
SO ASTRONOMY & ASTROPHYSICS
LA English
DT Article
DE radio continuum; general; methods; statistical
ID FAST RADIO-BURSTS; EVENT RATE COUNTS; PULSAR SURVEY; GALACTIC
POPULATION; DISTRIBUTIONS; SEARCHES; ENVIRONMENT; EVOLUTION; REDSHIFT;
FLUENCE
AB Context. Fast radio bursts (FRBs) are radio transients of an unknown origin whose nature we wish to determine. The number of detected FRBs is large enough for a statistical approach to parts of this challenge to be feasible. Aims. Our goal is to determine the current best-fit FRB population model. Our secondary aim is to provide an easy-to-use tool for simulating and understanding FRB detections. This tool can compare surveys, or provide information about the intrinsic FRB population. Methods. To understand the crucial link between detected FRBs and the underlying FRB source classes, we performed an FRB population synthesis to determine how the underlying population behaves. The Python package we developed for this synthesis, frbpoppy, is open source and freely available. frbpoppy simulates intrinsic FRB populations and the surveys that find them with the aim to produce virtual observed populations. These populations can then be compared with real data, which allows constraints to be placed on the underlying physics and selection effects. Results. We are able to replicate real Parkes and ASKAP FRB surveys in terms of detection rates and observed distributions. We also show the effect of beam patterns on the observed dispersion measure distributions. We compare four types of source models. The "complex" model, featuring a range of luminosities, pulse widths, and spectral indices, reproduces current detections best. Conclusions. Using frbpoppy, an open-source FRB population synthesis package, we explain current FRB detections and offer a first glimpse of what the true population must be.
C1 [Gardenier, D. W.; van Leeuwen, J.] ASTRON Netherlands Inst Radio Astron, Oude Hoogeveensedijk 4, NL-7991 PD Dwingeloo, Netherlands.
[Gardenier, D. W.; van Leeuwen, J.; Connor, L.; Petroff, E.] Univ Amsterdam, Anton Pannekoek Inst Astron, Sci Pk 904, NL-1098 XH Amsterdam, Netherlands.
RP Gardenier, DW (corresponding author), ASTRON Netherlands Inst Radio Astron, Oude Hoogeveensedijk 4, NL-7991 PD Dwingeloo, Netherlands.; Gardenier, DW (corresponding author), Univ Amsterdam, Anton Pannekoek Inst Astron, Sci Pk 904, NL-1098 XH Amsterdam, Netherlands.
EM gardenier@astron.nl
OI Gardenier, David/0000-0002-4760-152X
FU European Research Council under the European Union's Seventh Framework
Programme (FP/2007-2013)/ERC Grant [617199]; Vici research programme
"ARGO" - Netherlands Organisation for Scientific Research
(NWO)Netherlands Organization for Scientific Research (NWO)
[639.043.815]; Netherlands Research School for Astronomy (NOVA4-ARTS);
NWO Veni FellowshipNetherlands Organization for Scientific Research
(NWO)
FX We thank Chris Flynn for carefully reading this manuscript and for
providing valuable comment and suggestions. We also thank the
participants of the 2017 2019 FRB meetings for the useful discussions.
Thanks additionally go to Sarah Burke-Spolaor for initial survey
parameter data. DWG acknowledges travel support from the Research
Corporation for Scientific Advancement to attend the Aspen FRB meeting
in 2017. The research leading to these results has received funding from
the European Research Council under the European Union's Seventh
Framework Programme (FP/2007-2013)/ERC Grant Agreement n. 617199
("ALERT"); from Vici research programme "ARGO" with project number
639.043.815, financed by the Netherlands Organisation for Scientific
Research (NWO); and from the Netherlands Research School for Astronomy
(NOVA4-ARTS). EP further acknowledges funding from an NWO Veni
Fellowship. This research has made use of numpy (van derWalt et al.
2011), scipy (Oliphant 2007), astropy (Astropy Collaboration 2018),
matplotlib (Hunter 2007), bokeh (Bokeh Development Team 2018) and NASA's
Astrophysics Data System.
CR Adams EAK, 2019, NAT ASTRON, V3, P188, DOI 10.1038/s41550-019-0692-4
Ade PAR, 2016, ASTRON ASTROPHYS, V594, DOI 10.1051/0004-6361/201525830
Amiri M, 2019, NATURE, V566, P235, DOI 10.1038/s41586-018-0864-x
AMIRI M, 2017, APJ, V844, DOI DOI 10.3847/1538-4357/AA713F
AMIRI M, 2018, APJ, V863, DOI DOI 10.3847/1538-4357/AAD188
Bailes M, 2017, PUBL ASTRON SOC AUST, V34, DOI 10.1017/pasa.2017.39
Bannister KW, 2019, SCIENCE, V365, P565, DOI 10.1126/science.aaw5903
Bannister KW, 2017, ASTROPHYS J LETT, V841, DOI 10.3847/2041-8213/aa71ff
Bates SD, 2014, MON NOT R ASTRON SOC, V439, P2893, DOI 10.1093/mnras/stu157
Bates SD, 2013, MON NOT R ASTRON SOC, V431, P1352, DOI 10.1093/mnras/stt257
Batten A., 2019, J OPEN SOURCE SOFTW, V4, P1399
Beloborodov A. M, 2019, APJ UNPUB
Bhat NDR, 2004, ASTROPHYS J, V605, P759, DOI 10.1086/382680
BHATTACHARYA D, 1992, ASTRON ASTROPHYS, V254, P198
Bhattacharya M., 2019, MNRAS UNPUB
Bokeh Development Team, 2018, BOK PYTH LIB INT VIS
Caleb M, 2019, MON NOT R ASTRON SOC, V484, P5500, DOI 10.1093/mnras/stz386
Caleb M, 2016, MON NOT R ASTRON SOC, V458, P708, DOI 10.1093/mnras/stw175
Caleb M, 2016, MON NOT R ASTRON SOC, V458, P718, DOI 10.1093/mnras/stw109
Champion DJ, 2016, MON NOT R ASTRON SOC, V460, pL30, DOI 10.1093/mnrasl/slw069
CHAWLA P, 2017, APJ, V844, DOI DOI 10.3847/1538-4357/AA7D57
Chippendale AP, 2015, PROCEEDINGS OF THE 2015 INTERNATIONAL CONFERENCE ON ELECTROMAGNETICS IN ADVANCED APPLICATIONS (ICEAA), P541, DOI 10.1109/ICEAA.2015.7297174
Connor L, 2019, MON NOT R ASTRON SOC, V487, P5753, DOI 10.1093/mnras/stz1666
Connor L, 2016, MON NOT R ASTRON SOC, V460, P1054, DOI 10.1093/mnras/stw907
Connor L, 2016, MON NOT R ASTRON SOC, V458, pL19, DOI 10.1093/mnrasl/slv124
Cordes JM, 2016, MON NOT R ASTRON SOC, V457, P232, DOI 10.1093/mnras/stv2948
Cordes J.M., 2002, ARXIVASTROPH0207156
Cordes J. M., 2003, ARXIVASTROPH0301598
Cordes JM, 2006, ASTROPHYS J, V637, P446, DOI 10.1086/498335
Cordes JM, 2003, ASTROPHYS J, V596, P1142, DOI 10.1086/378231
Farah W, 2019, MON NOT R ASTRON SOC, V488, P2989, DOI 10.1093/mnras/stz1748
Farah W, 2018, MON NOT R ASTRON SOC, V478, P1209, DOI 10.1093/mnras/sty1122
Faucher-Giguere CA, 2006, ASTROPHYS J, V643, P332, DOI 10.1086/501516
FIALKOV A, 2018, APJ, V863, DOI DOI 10.3847/1538-4357/AAD196
Ghirlanda G, 2013, MON NOT R ASTRON SOC, V428, P1410, DOI 10.1093/mnras/sts128
GOURDJI K, 2019, APJ, V877, DOI DOI 10.3847/2041-8213/AB1F8A
GUNN JE, 1970, ASTROPHYS J, V160, P979, DOI 10.1086/150487
Hessels JWT, 2019, ASTROPHYS J LETT, V876, DOI 10.3847/2041-8213/ab13ae
HEWISH A, 1968, NATURE, V217, P709, DOI 10.1038/217709a0
Hogg D.W., 1999, ARXIVASTROPH9905116
Hunter JD, 2007, COMPUT SCI ENG, V9, P90, DOI 10.1109/MCSE.2007.55
Inoue S, 2004, MON NOT R ASTRON SOC, V348, P999, DOI 10.1111/j.1365-2966.2004.07359.x
Ioka K, 2003, ASTROPHYS J, V598, pL79, DOI 10.1086/380598
Izzard R.G., 2018, ARXIV180806883
Johnson NL., 1994, CONTINUOUS UNIVARIAT
Katz JI, 2014, PHYS REV D, V89, DOI 10.1103/PhysRevD.89.103009
Keane EF, 2018, NAT ASTRON, V2, P865, DOI 10.1038/s41550-018-0603-0
Keane EF, 2015, MON NOT R ASTRON SOC, V447, P2852, DOI 10.1093/mnras/stu2650
Keith MJ, 2010, MON NOT R ASTRON SOC, V409, P619, DOI 10.1111/j.1365-2966.2010.17325.x
Lazarus P, 2015, ASTROPHYS J, V812, DOI 10.1088/0004-637X/812/1/81
Lorimer DR, 2007, SCIENCE, V318, P777, DOI 10.1126/science.1147532
Lorimer DR, 2006, MON NOT R ASTRON SOC, V372, P777, DOI 10.1111/j.1365-2966.2006.10887.x
Lorimer DR, 2013, MON NOT R ASTRON SOC, V436, pL5, DOI 10.1093/mnrasl/slt098
Lorimer D.R., 2012, HDB PULSAR ASTRONOMY
Lu WB, 2018, MON NOT R ASTRON SOC, V477, P2457, DOI 10.1093/mnras/sty716
Luo R, 2018, MON NOT R ASTRON SOC, V481, P2320, DOI 10.1093/mnras/sty2364
LYNE AG, 1985, MON NOT R ASTRON SOC, V213, P613, DOI 10.1093/mnras/213.3.613
Maan Y., 2017, ARXIV170906104
Macquart JP, 2018, MON NOT R ASTRON SOC, V480, P4211, DOI 10.1093/mnras/sty2083
Macquart JP, 2018, MON NOT R ASTRON SOC, V474, P1900, DOI 10.1093/mnras/stx2825
MACQUART JP, 2013, APJ, V776, DOI DOI 10.1088/0004-637X/776/2/125
MACQUART JP, 2019, APJ, V872, DOI DOI 10.3847/2041-8213/AB03D6
Madau P, 2014, ANNU REV ASTRON ASTR, V52, P415, DOI 10.1146/annurev-astro-081811-125615
Masui K, 2015, NATURE, V528, P523, DOI 10.1038/nature15769
Masui KW, 2015, PHYS REV LETT, V115, DOI 10.1103/PhysRevLett.115.121301
McQuinn M, 2014, ASTROPHYS J LETT, V780, DOI 10.1088/2041-8205/780/2/L33
Metzger BD, 2019, MON NOT R ASTRON SOC, V485, P4091, DOI 10.1093/mnras/stz700
Michilli D, 2018, NATURE, V553, P182, DOI 10.1038/nature25149
NARAYAN R, 1990, ASTROPHYS J, V352, P222, DOI 10.1086/168529
NIINO Y, 2018, APJ, V858, DOI DOI 10.3847/1538-4357/AAB9A9
Oliphant TE, 2007, COMPUT SCI ENG, V9, P10, DOI 10.1109/MCSE.2007.58
Oosterloo T., 2009, WIDE FIELD ASTRONOMY, P70
Oppermann N, 2016, MON NOT R ASTRON SOC, V461, P984, DOI 10.1093/mnras/stw1401
PATEL C, 2018, APJ, V869, DOI DOI 10.3847/1538-4357/AAEE65
Petroff E, 2019, ASTRON ASTROPHYS REV, V27, DOI 10.1007/s00159-019-0116-6
Petroff E, 2016, PUBL ASTRON SOC AUST, V33, DOI 10.1017/pasa.2016.35
Petroff E, 2015, MON NOT R ASTRON SOC, V454, P457, DOI 10.1093/mnras/stv1953
Platts E, 2019, PHYS REP, V821, P1, DOI 10.1016/j.physrep.2019.06.003
Pol N., 2019, APJ UNPUB
Price-Whelan AM, 2018, ASTRON J, V156, DOI 10.3847/1538-3881/aabc4f
Ravi V, 2019, NATURE, V572, P352, DOI 10.1038/s41586-019-1389-7
Ravi V, 2016, SCIENCE, V354, P1249, DOI 10.1126/science.aaf6807
Ravi V, 2019, BAAS, V51, P420
RAVI V, 2019, APJ, V874, DOI DOI 10.3847/1538-4357/AB0748
Ravi V, 2019, NAT ASTRON, V3, P928, DOI 10.1038/s41550-019-0831-y
Remazeilles M, 2015, MON NOT R ASTRON SOC, V451, P4311, DOI 10.1093/mnras/stv1274
Romero GE, 2016, PHYS REV D, V93, DOI 10.1103/PhysRevD.93.023001
Sanidas S, 2019, ASTRON ASTROPHYS, V626, DOI 10.1051/0004-6361/201935609
Scholz P, 2016, ASTROPHYS J, V833, DOI 10.3847/1538-4357/833/2/177
Shannon RM, 2018, NATURE, V562, P386, DOI 10.1038/s41586-018-0588-y
Smits R, 2009, ASTRON ASTROPHYS, V493, P1161, DOI 10.1051/0004-6361:200810383
Spitler LG, 2016, NATURE, V531, P202, DOI 10.1038/nature17168
Spitler LG, 2014, ASTROPHYS J, V790, DOI 10.1088/0004-637X/790/2/101
SZARY A, 2014, APJ, V784, DOI DOI 10.1088/0004-637X/784/1/59
TAYLOR JH, 1977, ASTROPHYS J, V215, P885, DOI 10.1086/155426
Tendulkar SP, 2017, ASTROPHYS J LETT, V834, DOI 10.3847/2041-8213/834/2/L7
Thomas A, 2017, ROUTL AFR STUD, V23, P3
Thornton D, 2013, SCIENCE, V341, P53, DOI 10.1126/science.1236789
van der Walt S, 2011, COMPUT SCI ENG, V13, P22, DOI 10.1109/MCSE.2011.37
van Leeuwen J, 2010, ASTRON ASTROPHYS, V509, DOI 10.1051/0004-6361/200913121
van Leeuwen J., 2004, IAU S, V218, P41
Vedantham HK, 2016, ASTROPHYS J, V830, DOI 10.3847/0004-637X/830/2/75
Vieyro FL, 2017, ASTRON ASTROPHYS, V602, DOI 10.1051/0004-6361/201730556
Walker C. R. H., 2018, A A UNPUB
Wright EL, 2006, PUBL ASTRON SOC PAC, V118, P1711, DOI 10.1086/510102
XU SY, 2016, APJ, V832, DOI DOI 10.3847/0004-637X/832/2/199
YANG YP, 2017, APJ, V839, DOI DOI 10.3847/2041-8213/AA6C2E
Yao JM, 2017, ASTROPHYS J, V835, DOI 10.3847/1538-4357/835/1/29
ZHANG B, 2018, APJ, V867, DOI DOI 10.3847/2041-8213/AAE8E3
NR 109
TC 5
Z9 5
U1 0
U2 0
PU EDP SCIENCES S A
PI LES ULIS CEDEX A
PA 17, AVE DU HOGGAR, PA COURTABOEUF, BP 112, F-91944 LES ULIS CEDEX A,
FRANCE
SN 0004-6361
EI 1432-0746
J9 ASTRON ASTROPHYS
JI Astron. Astrophys.
PD DEC 16
PY 2019
VL 632
AR A125
DI 10.1051/0004-6361/201936404
PG 17
WC Astronomy & Astrophysics
SC Astronomy & Astrophysics
GA JW3ZY
UT WOS:000502994500006
OA Bronze
DA 2021-04-21
ER
PT J
AU Raaijmakers, G
Riley, TE
Watts, AL
Greif, SK
Morsink, SM
Hebeler, K
Schwenk, A
Hinderer, T
Nissanke, S
Guillot, S
Arzoumanian, Z
Bogdanov, S
Chakrabarty, D
Gendreau, KC
Ho, WCG
Lattimer, JM
Ludlam, RM
Wolff, MT
AF Raaijmakers, G.
Riley, T. E.
Watts, A. L.
Greif, S. K.
Morsink, S. M.
Hebeler, K.
Schwenk, A.
Hinderer, T.
Nissanke, S.
Guillot, S.
Arzoumanian, Z.
Bogdanov, S.
Chakrabarty, D.
Gendreau, K. C.
Ho, W. C. G.
Lattimer, J. M.
Ludlam, R. M.
Wolff, M. T.
TI A NICER View of PSR J0030+0451: Implications for the Dense Matter
Equation of State
SO ASTROPHYSICAL JOURNAL LETTERS
LA English
DT Article
ID ROTATING RELATIVISTIC STARS; NEUTRON-STAR; EFFICIENT; PYTHON; FORCES;
RADIUS; MODELS; MASS
AB Both the mass and radius of the millisecond pulsar PSR J0030+0451 have been inferred via pulse-profile modeling of X-ray data obtained by NASA's Neutron Star Interior Composition Explorer (NICER) mission. In this Letter we study the implications of the mass-radius inference reported for this source by Riley et al. for the dense matter equation of state (EoS), in the context of prior information from nuclear physics at low densities. Using a Bayesian framework we infer central densities and EoS properties for two choices of high-density extensions: a piecewise-polytropic model and a model based on assumptions of the speed of sound in dense matter. Around nuclear saturation density these extensions are matched to an EoS uncertainty band obtained from calculations based on chiral effective field theory interactions, which provide a realistic description of atomic nuclei as well as empirical nuclear matter properties within uncertainties. We further constrain EoS expectations with input from the current highest measured pulsar mass; together, these constraints offer a narrow Bayesian prior informed by theory as well as laboratory and astrophysical measurements. The NICER mass-radius likelihood function derived by Riley et al. using pulse-profile modeling is consistent with the highest-density region of this prior. The present relatively large uncertainties on mass and radius for PSR J0030+0451 offer, however, only a weak posterior information gain over the prior. We explore the sensitivity to the inferred geometry of the heated regions that give rise to the pulsed emission, and find a small increase in posterior gain for an alternative (but less preferred) model. Lastly, we investigate the hypothetical scenario of increasing the NICER exposure time for PSR J0030+0451.
C1 [Raaijmakers, G.; Riley, T. E.; Watts, A. L.; Hinderer, T.; Nissanke, S.] Univ Amsterdam, Astron Inst Anton Pannekoek, Sci Pk 904, NL-1090 GE Amsterdam, Netherlands.
[Greif, S. K.; Hebeler, K.; Schwenk, A.] Tech Univ Darmstadt, Inst Kernphys, D-64289 Darmstadt, Germany.
[Greif, S. K.; Hebeler, K.; Schwenk, A.] GSI Helmholtzzentrum Schwerionenforsch GmbH, ExtreMe Matter Inst EMMI, D-64291 Darmstadt, Germany.
[Morsink, S. M.] Univ Alberta, Dept Phys, 4-183 CCIS, Edmonton, AB T6G 2E1, Canada.
[Schwenk, A.] Max Planck Inst Kernphys, Saupfercheckweg 1, D-69117 Heidelberg, Germany.
[Hinderer, T.; Nissanke, S.] Univ Amsterdam, GRAPPA Inst High Energy Phys, Sci Pk 904, NL-1098 XH Amsterdam, Netherlands.
[Guillot, S.] CNRS, IRAP, 9 Ave Colonel Roche,BP 44346, F-31028 Toulouse 4, France.
[Guillot, S.] Univ Toulouse, CNES, UPS, OMP, F-31028 Toulouse, France.
[Arzoumanian, Z.; Gendreau, K. C.] NASA, Xray Astrophys Lab, Goddard Space Flight Ctr, Code 662, Greenbelt, MD 20771 USA.
[Bogdanov, S.] Columbia Univ, Columbia Astrophys Lab, 550 West 120th St, New York, NY 10027 USA.
[Chakrabarty, D.] MIT, MIT Kavli Inst Astrophys & Space Res, Cambridge, MA 02139 USA.
[Ho, W. C. G.] Haverford Coll, Dept Phys & Astron, 370 Lancaster Ave, Haverford, PA 19041 USA.
[Ho, W. C. G.] Univ Southampton, Math Sci Phys & Astron, Southampton SO17 1BJ, Hants, England.
[Ho, W. C. G.] Univ Southampton, STAG Res Ctr, Southampton SO17 1BJ, Hants, England.
[Lattimer, J. M.] SUNY Stony Brook, Dept Phys & Astron, Stony Brook, NY 11794 USA.
[Ludlam, R. M.] CALTECH, Cahill Ctr Astron & Astrophys, Pasadena, CA 91125 USA.
[Wolff, M. T.] US Naval Res Lab, Space Sci Div, Washington, DC 20375 USA.
RP Raaijmakers, G (corresponding author), Univ Amsterdam, Astron Inst Anton Pannekoek, Sci Pk 904, NL-1090 GE Amsterdam, Netherlands.
EM G.Raaijmakers@uva.nl
OI Ho, Wynn/0000-0002-6089-6836; Morsink, Sharon/0000-0003-4357-0575;
Schwenk, Achim/0000-0001-8027-4076; Greif, Svenja/0000-0001-8641-2062
FU NASA through the NICER mission; ERCEuropean Research Council
(ERC)European Commission [639217 CSINEUTRONSTAR]; NWO Exact and Natural
Sciences; DFGGerman Research Foundation (DFG)European Commission [SFB
1245]; Nederlandse Organisatie voor Wetenschappelijk Onderzoek (NWO)
through the VIDI grant; Nederlandse Organisatie voor Wetenschappelijk
Onderzoek (NWO) through Projectruimte grant; CNESCentre National
D'etudes Spatiales; NSERCNatural Sciences and Engineering Research
Council of Canada (NSERC); NASANational Aeronautics & Space
Administration (NASA) [80NSSC17K0554, HST-HF2-51440.001]; U.S. DOEUnited
States Department of Energy (DOE) [DE-FG02-87ER40317]; NASA through the
Astrophysics Explorers Program; SURF Cooperative
FX This work was supported in part by NASA through the NICER mission and
the Astrophysics Explorers Program. T.E.R. and A.L.W. acknowledge
support from ERC Starting Grant No..639217 CSINEUTRONSTAR (PI: Watts).
A.L.W. would like to thank Andrew Steiner for useful discussions on the
role of priors in previously published results. The authors would also
like to thank Kent Wood for helpful comments. This work was sponsored by
NWO Exact and Natural Sciences for the use of supercomputer facilities,
and was carried out on the Dutch national e-infrastructure with the
support of SURF Cooperative. S.K.G., K.H., and A.S. acknowledge support
from the DFG through SFB 1245. G.R., T.H., and S.N. are grateful for
support from the Nederlandse Organisatie voor Wetenschappelijk Onderzoek
(NWO) through the VIDI and Projectruimte grants (PI: Nissanke). S.G.
acknowledges the support of the CNES. S.M.M. thanks NSERC for support.
J.M.L. acknowledges support from NASA through Grant 80NSSC17K0554 and
the U.S. DOE from Grant DE-FG02-87ER40317. R.M.L. acknowledges the
support of NASA through Hubble Fellowship Program grant
HST-HF2-51440.001. This research has made extensive use of NASA's
Astrophysics Data System Bibliographic Services (ADS) and the arXiv.
CR Abbott BP, 2019, PHYS REV X, V9, DOI 10.1103/PhysRevX.9.011001
Abbott BP, 2018, PHYS REV LETT, V121, DOI 10.1103/PhysRevLett.121.161101
Abbott B, 2018, LIVING REV RELATIV, V21, DOI 10.1007/s41114-018-0012-9
Abbott BP, 2017, PHYS REV LETT, V119, DOI 10.1103/PhysRevLett.119.161101
Akmal A, 1998, PHYS REV C, V58, P1804, DOI 10.1103/PhysRevC.58.1804
Alford MG, 2016, EUR PHYS J A, V52, DOI 10.1140/epja/i2016-16062-9
AlGendy M, 2014, ASTROPHYS J, V791, DOI 10.1088/0004-637X/791/2/78
Annala E, 2018, PHYS REV LETT, V120, DOI 10.1103/PhysRevLett.120.172703
Antoniadis J, 2013, SCIENCE, V340, DOI 10.1126/science.1233232
Baillot dEtivaux N., 2019, ARXIV190501081
Baldo M, 1997, ASTRON ASTROPHYS, V328, P274
Baym G, 2018, REP PROG PHYS, V81, DOI 10.1088/1361-6633/aaae14
Behnel S, 2011, COMPUT SCI ENG, V13, P31, DOI 10.1109/MCSE.2010.118
Bilous AV, 2019, ASTROPHYS J LETT, V887, DOI 10.3847/2041-8213/ab53e7
Bogdanov S, 2019, ASTROPHYS J LETT, V887, DOI 10.3847/2041-8213/ab53eb
Bogdanov S, 2016, ASTROPHYS J, V831, DOI 10.3847/0004-637X/831/2/184
Bogdanov S, 2016, EUR PHYS J A, V52, DOI 10.1140/epja/i2016-16037-x
Buchner J, 2014, ASTRON ASTROPHYS, V564, DOI 10.1051/0004-6361/201322971
Chirenti C, 2019, ASTROPHYS J LETT, V884, DOI 10.3847/2041-8213/ab43e0
Cromartie HT, 2020, NAT ASTRON, V4, P72, DOI 10.1038/s41550-019-0880-2
Dalcin L, 2008, J PARALLEL DISTR COM, V68, P655, DOI 10.1016/j.jpdc.2007.09.005
De S, 2018, PHYS REV LETT, V121, DOI 10.1103/PhysRevLett.121.091102
Drischler C, 2019, PHYS REV LETT, V122, DOI 10.1103/PhysRevLett.122.042501
Feroz F, 2008, MON NOT R ASTRON SOC, V384, P449, DOI 10.1111/j.1365-2966.2007.12353.x
Feroz F, 2009, MON NOT R ASTRON SOC, V398, P1601, DOI 10.1111/j.1365-2966.2009.14548.x
Feroz F., 2013, ARXIV13062144
Flanagan EE, 2008, PHYS REV D, V77, DOI 10.1103/PhysRevD.77.021502
Fraga ES, 2014, ASTROPHYS J LETT, V781, DOI 10.1088/2041-8205/781/2/L25
Gendreau KC, 2016, PROC SPIE, V9905, DOI 10.1117/12.2231304
Gough B., 2009, GNU SCI LIB REFERENC
Greif SK, 2019, MON NOT R ASTRON SOC, V485, P5363, DOI 10.1093/mnras/stz654
Harding AK, 2002, ASTROPHYS J, V568, P862, DOI 10.1086/338985
HARTLE JB, 1968, ASTROPHYS J, V153, P807, DOI 10.1086/149707
HARTLE JB, 1967, ASTROPHYS J, V150, P1005, DOI 10.1086/149400
Hebeler K, 2015, ANNU REV NUCL PART S, V65, P457, DOI 10.1146/annurev-nucl-102313-025446
Hebeler K, 2013, ASTROPHYS J, V773, DOI 10.1088/0004-637X/773/1/11
Hebeler K, 2010, PHYS REV C, V82, DOI 10.1103/PhysRevC.82.014314
Higson E, 2018, JOSS, V3, P916, DOI [10.21105/joss.00916, DOI 10.21105/JOSS.00916]
Higson E, 2019, MON NOT R ASTRON SOC, V483, P2044, DOI 10.1093/mnras/sty3090
Higson E, 2018, BAYESIAN ANAL, V13, P873, DOI 10.1214/17-BA1075
Hunter JD, 2007, COMPUT SCI ENG, V9, P90, DOI 10.1109/MCSE.2007.55
Jones E., 2001, SCIPY OPEN SOURCE SC
KASS RE, 1995, J AM STAT ASSOC, V90, P773, DOI 10.1080/01621459.1995.10476572
Kluyver T, 2016, POSITIONING AND POWER IN ACADEMIC PUBLISHING: PLAYERS, AGENTS AND AGENDAS, P87, DOI 10.3233/978-1-61499-649-1-87
KULLBACK S, 1951, ANN MATH STAT, V22, P79, DOI 10.1214/aoms/1177729694
Lattimer JM, 2014, ASTROPHYS J, V784, DOI 10.1088/0004-637X/784/2/123
Lim Y, 2018, PHYS REV LETT, V121, DOI 10.1103/PhysRevLett.121.062701
Lo KH, 2013, ASTROPHYS J, V776, DOI 10.1088/0004-637X/776/1/19
Lynn JE, 2016, PHYS REV LETT, V116, DOI 10.1103/PhysRevLett.116.062501
Margueron J, 2018, PHYS REV C, V97, DOI 10.1103/PhysRevC.97.025806
Margueron J, 2018, PHYS REV C, V97, DOI 10.1103/PhysRevC.97.025805
Miller M. C., 2019, APJ
Miller MC, 2015, ASTROPHYS J, V808, DOI 10.1088/0004-637X/808/1/31
Most ER, 2018, PHYS REV LETT, V120, DOI 10.1103/PhysRevLett.120.261103
MPI Forum, 1994, MPI MESSAGE PASSING
Oertel M, 2017, REV MOD PHYS, V89, DOI 10.1103/RevModPhys.89.015007
Ozel F, 2016, ASTROPHYS J, V820, DOI 10.3847/0004-637X/820/1/28
Oliphant TE, 2007, COMPUT SCI ENG, V9, P10, DOI 10.1109/MCSE.2007.58
PANDHARIPANDE VR, 1975, NUCL PHYS A, VA237, P507, DOI 10.1016/0375-9474(75)90415-7
Paschalidis V, 2017, LIVING REV RELATIV, V20, DOI 10.1007/s41114-017-0008-x
Perez F, 2007, COMPUT SCI ENG, V9, P21, DOI 10.1109/MCSE.2007.53
Psaltis D, 2014, ASTROPHYS J, V787, DOI 10.1088/0004-637X/787/2/136
Raaijmakers G, 2018, MON NOT R ASTRON SOC, V478, P2177, DOI 10.1093/mnras/sty1052
Raithel CA, 2018, ASTROPHYS J LETT, V857, DOI 10.3847/2041-8213/aabcbf
Riley TE, 2019, ASTROPHYS J LETT, V887, DOI 10.3847/2041-8213/ab481c
Riley TE, 2018, MON NOT R ASTRON SOC, V478, P1093, DOI 10.1093/mnras/sty1051
Shoemaker D., 2019, ARXIV190403187
Skilling J, 2006, BAYESIAN ANAL, V1, P833, DOI 10.1214/06-BA127
Steiner AW, 2018, MON NOT R ASTRON SOC, V476, P421, DOI 10.1093/mnras/sty215
Steiner AW, 2013, ASTROPHYS J LETT, V765, DOI 10.1088/2041-8205/765/1/L5
Steiner AW, 2010, ASTROPHYS J, V722, P33, DOI 10.1088/0004-637X/722/1/33
STERGIOULAS N, 1995, ASTROPHYS J, V444, P306, DOI 10.1086/175605
Tews I, 2018, PHYS REV C, V98, DOI 10.1103/PhysRevC.98.045804
TEWS I, 2018, APJ, V860, DOI DOI 10.3847/1538-4357/AAC267
van der Walt S, 2011, COMPUT SCI ENG, V13, P22, DOI 10.1109/MCSE.2011.37
Watts A. L., 2019, ARXIV190407012
Watts AL, 2016, REV MOD PHYS, V88, DOI 10.1103/RevModPhys.88.021001
NR 77
TC 54
Z9 54
U1 2
U2 5
PU IOP PUBLISHING LTD
PI BRISTOL
PA TEMPLE CIRCUS, TEMPLE WAY, BRISTOL BS1 6BE, ENGLAND
SN 2041-8205
EI 2041-8213
J9 ASTROPHYS J LETT
JI Astrophys. J. Lett.
PD DEC 10
PY 2019
VL 887
IS 1
AR L22
DI 10.3847/2041-8213/ab451a
PG 13
WC Astronomy & Astrophysics
SC Astronomy & Astrophysics
GA KS5YZ
UT WOS:000518385600022
OA Green Published, Green Accepted
DA 2021-04-21
ER
PT J
AU Syben, C
Michen, M
Stimpel, B
Seitz, S
Ploner, S
Maier, AK
AF Syben, Christopher
Michen, Markus
Stimpel, Bernhard
Seitz, Stephan
Ploner, Stefan
Maier, Andreas K.
TI Technical Note: PYRO-NN: Python reconstruction operators in neural
networks
SO MEDICAL PHYSICS
LA English
DT Article
DE inverse problems; known operator learning; machine learning; open
source; reconstruction
ID IMAGE-RECONSTRUCTION; DEEP; FRAMEWORK; DOMAIN
AB PurposeRecently, several attempts were conducted to transfer deep learning to medical image reconstruction. An increasingly number of publications follow the concept of embedding the computed tomography (CT) reconstruction as a known operator into a neural network. However, most of the approaches presented lack an efficient CT reconstruction framework fully integrated into deep learning environments. As a result, many approaches use workarounds for mathematically unambiguously solvable problems.
MethodsPYRO-NN is a generalized framework to embed known operators into the prevalent deep learning framework Tensorflow. The current status includes state-of-the-art parallel-, fan-, and cone-beam projectors, and back-projectors accelerated with CUDA provided as Tensorflow layers. On top, the framework provides a high-level Python API to conduct FBP and iterative reconstruction experiments with data from real CT systems.
ResultsThe framework provides all necessary algorithms and tools to design end-to-end neural network pipelines with integrated CT reconstruction algorithms. The high-level Python API allows a simple use of the layers as known from Tensorflow. All algorithms and tools are referenced to a scientific publication and are compared to existing non-deep learning reconstruction frameworks. To demonstrate the capabilities of the layers, the framework comes with baseline experiments, which are described in the supplementary material. The framework is available as open-source software under the Apache 2.0 licence at .
ConclusionsPYRO-NN comes with the prevalent deep learning framework Tensorflow and allows to setup end-to-end trainable neural networks in the medical image reconstruction context. We believe that the framework will be a step toward reproducible research and give the medical physics community a toolkit to elevate medical image reconstruction with new deep learning techniques.
C1 [Syben, Christopher; Michen, Markus; Stimpel, Bernhard; Seitz, Stephan; Ploner, Stefan; Maier, Andreas K.] Friedrich Alexander Univ Erlangen Nurnberg, Pattern Recognit Lab, D-91058 Erlangen, Germany.
RP Syben, C (corresponding author), Friedrich Alexander Univ Erlangen Nurnberg, Pattern Recognit Lab, D-91058 Erlangen, Germany.
EM christopher.syben@fau.de
FU European Research CouncilEuropean Research Council (ERC)European
Commission [810316] Funding Source: Medline; Friedrich-Alexander
University Erlangen-Nurnberg Funding Source: Medline
CR Abadi M, 2016, PROCEEDINGS OF OSDI'16: 12TH USENIX SYMPOSIUM ON OPERATING SYSTEMS DESIGN AND IMPLEMENTATION, P265
Adler J, 2018, IEEE T MED IMAGING, V37, P1322, DOI 10.1109/TMI.2018.2799231
Antun V, 2019, ARXIV190205300
Chen H, 2018, IEEE T MED IMAGING, V37, P1333, DOI 10.1109/TMI.2018.2805692
FELDKAMP LA, 1984, J OPT SOC AM A, V1, P612, DOI 10.1364/JOSAA.1.000612
Galigekere RR, 2003, IEEE T MED IMAGING, V22, P1202, DOI 10.1109/TMI.2003.817787
Greenspan H, 2016, IEEE T MED IMAGING, V35, P1153, DOI 10.1109/TMI.2016.2553401
Gursoy D, 2014, J SYNCHROTRON RADIAT, V21, P1188, DOI 10.1107/S1600577514013939
Hammernik K., 2017, BILDVERARBEITUNG MED, V2017, P92, DOI DOI 10.1007/978-3-662-54345-0_25
Jin KH, 2017, IEEE T IMAGE PROCESS, V26, P4509, DOI 10.1109/TIP.2017.2713099
Kak A.C., 1988, PRINCIPLES COMPUTERI
Kofler A, 2018, LECT NOTES COMPUT SC, V11074, P91, DOI 10.1007/978-3-030-00129-2_11
Kohr H, 2017, OPERATOR DISCRETIZAT
Krizhevsky Alex, 2012, ADV NEURAL INFORM PR, P1097, DOI DOI 10.1145/3065386
LeCun Y, 2015, NATURE, V521, P436, DOI 10.1038/nature14539
Maier A, 2018, ARXIV181005401
Maier A, 2018, INT C PATT RECOG, P183, DOI 10.1109/ICPR.2018.8545553
Maier A, 2013, MED PHYS, V40, DOI 10.1118/1.4824926
Ronneberger O, 2015, LECT NOTES COMPUT SC, V9351, P234, DOI 10.1007/978-3-319-24574-4_28
Scherl H, 2007, IEEE NUCL SCI CONF R, P4464, DOI 10.1109/NSSMIC.2007.4437102
SHEPP LA, 1974, IEEE T NUCL SCI, VNS21, P21, DOI 10.1109/TNS.1974.6499235
Stimpel B, 2017, ARXIV171007498
Syben C, 2017, PYCONRAD
Syben C, 2018, PATTERN RECOGNITION
Syben C., 2018, P 5 INT C IM FORM XR, P386
van Aarle W, 2016, OPT EXPRESS, V24, P25129, DOI 10.1364/OE.24.025129
van den Oord A., 2016, ABS160903499 CORR
Wang G, 2018, IEEE T MED IMAGING, V37, P1289, DOI 10.1109/TMI.2018.2833635
Wurfl T, 2018, IEEE T MED IMAGING, V37, P1454, DOI 10.1109/TMI.2018.2833499
Wurfl Tobias, 2016, Medical Image Computing and Computer-Assisted Intervention - MICCAI 2016. 19th International Conference. Proceedings: LNCS 9902, P432, DOI 10.1007/978-3-319-46726-9_50
Ye JC, 2018, SIAM J IMAGING SCI, V11, P991, DOI 10.1137/17M1141771
Zeng GSL, 2000, IEEE T MED IMAGING, V19, P548, DOI 10.1109/42.870265
Zhu B, 2018, NATURE, V555, P487, DOI 10.1038/nature25988
NR 33
TC 4
Z9 4
U1 0
U2 4
PU WILEY
PI HOBOKEN
PA 111 RIVER ST, HOBOKEN 07030-5774, NJ USA
SN 0094-2405
EI 2473-4209
J9 MED PHYS
JI Med. Phys.
PD NOV
PY 2019
VL 46
IS 11
BP 5110
EP 5115
DI 10.1002/mp.13753]
PG 6
WC Radiology, Nuclear Medicine & Medical Imaging
SC Radiology, Nuclear Medicine & Medical Imaging
GA JK5PF
UT WOS:000494894600037
PM 31389023
DA 2021-04-21
ER
PT J
AU Kraml, S
Loc, TQ
Nhung, DT
Ninh, LD
AF Kraml, Sabine
Tran Quang Loc
Dao Thi Nhung
Le Duc Ninh
TI Constraining new physics from Higgs measurements with Lilith: update to
LHC Run 2 results
SO SCIPOST PHYSICS
LA English
DT Article
ID VECTOR-BOSON FUSION; PP COLLISIONS; ROOT-S=13 TEV; SEARCH
AB Lilith is a public Python library for constraining new physics from Higgs signal strength measurements. We here present version 2.0 of Lilith together with an updated XML database which includes the current ATLAS and CMS Run 2 Higgs results for 36 fb(-1). Both the code and the database were extended from the ordinary Gaussian approximation employed in Lilith-1.1 to using variable Gaussian and Poisson likelihoods. Moreover, Lilith can now make use of correlation matrices of arbitrary dimension. We provide detailed validations of the implemented experimental results as well as a status of global fits for reduced Higgs couplings, Two-Higgs-doublet models of Type I and Type II, and invisible Higgs decays. Lilith-2.0 is available on GitHub and ready to be used to constrain a wide class of new physics scenarios.
C1 [Kraml, Sabine] Univ Grenoble Alpes, CNRS, IN2P3, Lab Phys Subat & Cosmol, 53 Ave Martyrs, F-38026 Grenoble, France.
[Tran Quang Loc; Dao Thi Nhung; Le Duc Ninh] ICISE, Inst Interdisciplinary Res Sci & Educ, Quy Nhon 590000, Vietnam.
[Tran Quang Loc] VNUHCM Univ Sci, 227 Nguyen Van Cu,Dist 5, Ho Chi Minh City, Vietnam.
RP Kraml, S (corresponding author), Univ Grenoble Alpes, CNRS, IN2P3, Lab Phys Subat & Cosmol, 53 Ave Martyrs, F-38026 Grenoble, France.
EM sabine.kraml@lpsc.in2p3.fr
FU Vietnam National Foundation for Science and Technology Development
(NAFOSTED)National Foundation for Science & Technology Development
(NAFOSTED) [103.01-2017.78]; LPSC Grenoble; IN2P3 theory project
"LHC-itools: methods and tools for the interpretation of the LHC Run 2
results for new physics"; ICISE Quy Nhon
FX S.K. thanks R. Schofbeck, W. Waltenberger and N. Wardle for helpful
discussions. This work was supported in part by the IN2P3 theory project
"LHC-itools: methods and tools for the interpretation of the LHC Run 2
results for new physics". D.T.N and L.D.N. are funded by the Vietnam
National Foundation for Science and Technology Development (NAFOSTED)
under grant number 103.01-2017.78. D.T.N. thanks the LPSC Grenoble for
hospitality and financial support for a research visit within the
LHC-itools project. T.Q.L. acknowledges the hospitality and financial
support of the ICISE Quy Nhon.
CR Aaboud M, 2019, PHYS REV LETT, V122, DOI 10.1103/PhysRevLett.122.231801
Aaboud M, 2019, PHYS REV D, V99, DOI 10.1103/PhysRevD.99.072001
Aaboud M, 2019, PHYS LETT B, V789, P508, DOI 10.1016/j.physletb.2018.11.064
Aaboud M, 2018, PHYS REV D, V98, DOI 10.1103/PhysRevD.98.052005
Aaboud M, 2018, PHYS REV D, V98, DOI 10.1103/PhysRevD.98.052003
Aaboud M, 2018, PHYS REV D, V97, DOI 10.1103/PhysRevD.97.072016
Aaboud M, 2018, PHYS REV D, V97, DOI 10.1103/PhysRevD.97.072003
Aaboud M, 2018, J HIGH ENERGY PHYS, DOI 10.1007/JHEP03(2018)095
Aaboud M, 2018, PHYS LETT B, V776, P318, DOI 10.1016/j.physletb.2017.11.049
Aaboud M, 2017, J HIGH ENERGY PHYS, DOI 10.1007/JHEP12(2017)024
Aaboud M, 2017, PHYS REV LETT, V119, DOI 10.1103/PhysRevLett.119.051802
Aad G., 2019, Physics Letters B, V798, P368, DOI 10.1016/j.physletb.2019.134949
Aad G, 2016, J HIGH ENERGY PHYS, DOI 10.1007/JHEP08(2016)045
Alwall J, 2014, J HIGH ENERGY PHYS, DOI 10.1007/JHEP07(2014)079
Badger S., 2016, 9 HOUCH WORKSH PHYS
Ball RD, 2015, J HIGH ENERGY PHYS, DOI 10.1007/JHEP04(2015)040
Barducci D, 2018, COMPUT PHYS COMMUN, V222, P327, DOI 10.1016/j.cpc.2017.08.028
Barlow R., 2004, STAT PROBLEMS PARTIC
Bechtle P, 2014, EUR PHYS J C, V74, DOI 10.1140/epjc/s10052-013-2711-4
Belanger G, 2013, PHYS REV D, V88, DOI 10.1103/PhysRevD.88.075008
Belanger G, 2013, PHYS LETT B, V723, P340, DOI 10.1016/j.physletb.2013.05.024
Belanger G, 2013, J HIGH ENERGY PHYS, DOI 10.1007/JHEP02(2013)053
Berkhout P, 2004, STAT NEERL, V58, P349, DOI 10.1111/j.1467-9574.2004.00126.x
Bernon J, 2016, PHYS REV D, V93, DOI 10.1103/PhysRevD.93.035027
Bernon J, 2015, PHYS REV D, V92, DOI 10.1103/PhysRevD.92.075004
Bernon J, 2015, EUR PHYS J C, V75, DOI 10.1140/epjc/s10052-015-3645-9
Bernon J, 2014, PHYS REV D, V90, DOI 10.1103/PhysRevD.90.071301
Biekotter A, 2019, SCIPOST PHYS, V6, DOI 10.21468/SciPostPhys.6.6.064
Boudjema F., 2013, WORKSH LIK LHC SEARC
Chowdhury D, 2018, J HIGH ENERGY PHYS, DOI 10.1007/JHEP05(2018)161
de Florian D., 2016, HDB LHC HIGGS CROSS, DOI [10.23731/CYRM-2017-002, DOI 10.23731/CYRM-2017-002]
Demartin F, 2017, EUR PHYS J C, V77, DOI 10.1140/epjc/s10052-017-4601-7
Ellis J, 2018, J HIGH ENERGY PHYS, DOI 10.1007/JHEP06(2018)146
Ferreira PM, 2014, PHYS REV D, V89, DOI 10.1103/PhysRevD.89.115003
LHC Higgs Cross Section Working Group, 2013, CERN2013004, DOI [10.5170/CERN-2013-004, DOI 10.5170/CERN-2013-004]
Sanz V, 2018, ADV HIGH ENERGY PHYS, V2018, DOI 10.1155/2018/7168480
Sirunyan AM, 2018, J HIGH ENERGY PHYS, DOI 10.1007/JHEP08(2018)066
Sirunyan AM, 2019, EUR PHYS J C, V79, DOI 10.1140/epjc/s10052-019-6909-y
Sirunyan AM, 2019, J HIGH ENERGY PHYS, DOI 10.1007/JHEP06(2019)093
Sirunyan AM, 2019, PHYS LETT B, V793, P520, DOI 10.1016/j.physletb.2019.04.025
Sirunyan AM, 2019, PHYS LETT B, V791, P96, DOI 10.1016/j.physletb.2018.12.073
Sirunyan AM, 2019, J HIGH ENERGY PHYS, DOI 10.1007/JHEP03(2019)026
Sirunyan AM, 2019, PHYS REV LETT, V122, DOI 10.1103/PhysRevLett.122.021801
Sirunyan AM, 2018, J HIGH ENERGY PHYS, DOI 10.1007/JHEP11(2018)185
Sirunyan AM, 2018, J HIGH ENERGY PHYS, DOI 10.1007/JHEP06(2018)101
Sirunyan AM, 2018, PHYS LETT B, V780, P501, DOI 10.1016/j.physletb.2018.02.050
Sirunyan AM, 2018, PHYS LETT B, V779, P283, DOI 10.1016/j.physletb.2018.02.004
Sirunyan AM, 2018, PHYS REV LETT, V120, DOI 10.1103/PhysRevLett.120.071802
Sirunyan AM, 2017, J HIGH ENERGY PHYS, DOI 10.1007/JHEP11(2017)047
NR 49
TC 14
Z9 14
U1 2
U2 7
PU SCIPOST FOUNDATION
PI AMSTERDAM
PA C/O J S CAUX, INST PHYSICS, UNIV AMSTERDAM, AMSTERDAM, 1098 XH,
NETHERLANDS
SN 2542-4653
J9 SCIPOST PHYS
JI SciPost Phys.
PD OCT
PY 2019
VL 7
IS 4
AR 052
DI 10.21468/SciPostPhys.7.4.052
PG 27
WC Physics, Multidisciplinary
SC Physics
GA JF0UF
UT WOS:000491101700012
OA DOAJ Gold
DA 2021-04-21
ER
PT J
AU Law, E
AF Law, Everest
TI Identifying clusters on a discrete periodic lattice via machine learning
SO COMPUTER PHYSICS COMMUNICATIONS
LA English
DT Article
DE Hierarchical clustering; Lattice simulations; Breadth-first search;
Periodic boundary conditions
ID PARALLEL ALGORITHMS; SIMULATIONS
AB Given the ubiquity of lattice models in physics, it is imperative for researchers to possess robust methods for quantifying clusters on the lattice - whether they be Ising spins or clumps of molecules. Inspired by biophysical studies, we present Python code for handling clusters on a 2D periodic lattice. Properties of individual clusters, such as their area, can be obtained with a few function calls. Our code invokes an unsupervised machine learning method called hierarchical clustering, which is simultaneously effective for the present problem and simple enough for non-experts to grasp qualitatively. Moreover, our code transparently merges clusters neighboring each other across periodic boundaries using breadth-first search (BFS), an algorithm well-documented in computer science pedagogy. The fact that our code is written in Python - instead of proprietary languages - further enhances its value for reproducible science. (C) 2019 Elsevier B.V. All rights reserved.
C1 [Law, Everest] Univ Southern Calif, Dept Phys & Astron, Los Angeles, CA 90089 USA.
[Law, Everest] Univ Southern Calif, Mol Computat Biol Program, Los Angeles, CA 90089 USA.
[Law, Everest] Univ Southern Calif, Dept Biol Sci, Los Angeles, CA 90089 USA.
RP Law, E (corresponding author), Univ Southern Calif, Dept Phys & Astron, Los Angeles, CA 90089 USA.; Law, E (corresponding author), Univ Southern Calif, Mol Computat Biol Program, Los Angeles, CA 90089 USA.; Law, E (corresponding author), Univ Southern Calif, Dept Biol Sci, Los Angeles, CA 90089 USA.
EM everestl@usc.edu
OI Law, Everest/0000-0003-1283-999X
FU National Science Foundation (NSF)National Science Foundation (NSF)
[DMR-1554716]; USC Center for High Performance Computing
FX This work was supported by National Science Foundation (NSF) award
number DMR-1554716 and the USC Center for High Performance Computing.
CR [Anonymous], 2004, PLOS BIOL, V2, pe168, DOI [10.1371/journal.pbio.0020168, DOI 10.1371/JOURNAL.PBIO.0020168]
Dahlhaus E, 2000, J ALGORITHMS, V36, P205, DOI 10.1006/jagm.2000.1090
Dash M, 2004, LECT NOTES COMPUT SC, V3149, P363
Domany E, 1999, COMPUT PHYS COMMUN, V121, P5, DOI 10.1016/S0010-4655(99)00267-2
Erban R., 2007, ARXIV07041908PHYSICS
Fange D, 2012, BIOINFORMATICS, V28, P3155, DOI 10.1093/bioinformatics/bts584
Gillespie DT, 2014, J CHEM PHYS, V140, DOI 10.1063/1.4863990
Hastie T., 2016, ELEMENTS STAT LEARNI, V2nd
James G., 2017, INTRO STAT LEARNING
Li YW, 2017, PHYS REV E, V95, DOI 10.1103/PhysRevE.95.052406
Mullner, 2013, J STAT SOFTW, DOI [10.18637/jss.v053.i09, DOI 10.18637/JSS.V053.I09]
MURTAGH FD, 1988, COMPUT PHYS COMMUN, V52, P15, DOI 10.1016/0010-4655(88)90166-X
OLSON CF, 1995, PARALLEL COMPUT, V21, P1313, DOI 10.1016/0167-8191(95)00017-I
Roberts E, 2013, J COMPUT CHEM, V34, P245, DOI 10.1002/jcc.23130
Shomar A, 2017, PLOS COMPUT BIOL, V13, DOI 10.1371/journal.pcbi.1005668
Smith S., 2018, B MATH BIOL, P1, DOI 10.1007/s11538-018-0443-1
Szklarczyk OM, 2013, PLOS COMPUT BIOL, V9, DOI 10.1371/journal.pcbi.1003310
Tang AH, 2016, NATURE, V536, P210, DOI 10.1038/nature19058
Verma Mudita, 2017, IOP Conference Series: Materials Science and Engineering, V263, DOI 10.1088/1757-899X/263/4/042094
NR 19
TC 0
Z9 0
U1 0
U2 3
PU ELSEVIER SCIENCE BV
PI AMSTERDAM
PA PO BOX 211, 1000 AE AMSTERDAM, NETHERLANDS
SN 0010-4655
EI 1879-2944
J9 COMPUT PHYS COMMUN
JI Comput. Phys. Commun.
PD OCT
PY 2019
VL 243
BP 106
EP 109
DI 10.1016/j.cpc.2019.05.004
PG 4
WC Computer Science, Interdisciplinary Applications; Physics, Mathematical
SC Computer Science; Physics
GA IH2HX
UT WOS:000474316900010
DA 2021-04-21
ER
PT J
AU Piccione, N
Militello, B
Napoli, A
Bellomo, B
AF Piccione, Nicolo
Militello, Benedetto
Napoli, Anna
Bellomo, Bruno
TI Simple scheme for extracting work with a single bath
SO PHYSICAL REVIEW E
LA English
DT Article
ID ENTROPY PRODUCTION; PYTHON FRAMEWORK; QUANTUM; DYNAMICS; PHYSICS; QUTIP
AB We propose a simple protocol exploiting the thermalization of a storage bipartite system S to extract work from a resource system R. The protocol is based on a recent work definition involving only a single bath. A general description of the protocol is provided without specifying the characteristics of S. We quantify both the extracted work and the ideal efficiency of the process, also giving maximum bounds for them. Then, we apply the protocol to two cases: two interacting qubits and the Rabi model. In both cases, for very strong couplings, an extraction of work comparable with the bare energies of the subsystems of S is obtained and its peak is reached for finite values of the bath temperature, T. We finally show, in the Rabi model at T = 0, how to transfer the work stored in S to an external device, permitting thus a cyclic implementation of the whole work-extraction protocol. Our proposal makes use of simple operations not needing fine control.
C1 [Piccione, Nicolo; Bellomo, Bruno] Univ Bourgogne Franche Comte, Observ Sci Univers THETA, Inst UTINAM, CNRS UMR 6213, 41 Bis Ave Observ, F-25010 Besancon, France.
[Militello, Benedetto; Napoli, Anna] Univ Palermo, Dipartimento Fis & Chim Emilio Segre, Via Archirafi 36, I-90123 Palermo, Italy.
[Militello, Benedetto; Napoli, Anna] Ist Nazl Fis Nucl, Sez Catania, Via Santa Sofia 64, I-95123 Catania, Italy.
RP Piccione, N (corresponding author), Univ Bourgogne Franche Comte, Observ Sci Univers THETA, Inst UTINAM, CNRS UMR 6213, 41 Bis Ave Observ, F-25010 Besancon, France.
EM nicolo.piccione@univ-fcomte.fr
RI Bellomo, Bruno/J-4520-2012
OI Bellomo, Bruno/0000-0002-3336-2441; Piccione, Nicolo/0000-0001-6391-2187
FU Erasmus+ program of the European Union; Institut UTINAM
FX N.P. acknowledges the financial support of the Erasmus+ program of the
European Union and of the Institut UTINAM for the development of this
program. N.P. and B.B. acknowledge useful suggestions by Felipe
Fernandes Fanchini and thank the IT team of the Institut UTINAM for its
technical support.
CR Beaudoin F, 2011, PHYS REV A, V84, DOI 10.1103/PhysRevA.84.043832
Beretta GP, 2014, PHYS REV E, V90, DOI 10.1103/PhysRevE.90.042113
Beretta GP, 2009, REP MATH PHYS, V64, P139, DOI 10.1016/S0034-4877(09)90024-6
Braak D, 2011, PHYS REV LETT, V107, DOI 10.1103/PhysRevLett.107.100401
Brandao F, 2015, P NATL ACAD SCI USA, V112, P3275, DOI 10.1073/pnas.1411728112
Breuer HP., 2002, THEORY OPEN QUANTUM
Casanova J, 2010, PHYS REV LETT, V105, DOI 10.1103/PhysRevLett.105.263603
Chen QH, 2012, PHYS REV A, V86, DOI 10.1103/PhysRevA.86.023822
Dawson CM, 2004, PHYS REV A, V69, DOI 10.1103/PhysRevA.69.052316
Decordi GL, 2017, OPT COMMUN, V387, P366, DOI 10.1016/j.optcom.2016.10.017
Esposito M, 2010, NEW J PHYS, V12, DOI 10.1088/1367-2630/12/1/013013
Gallego R, 2016, NEW J PHYS, V18, DOI 10.1088/1367-2630/18/10/103017
Gemmer J, 2001, PHYS REV LETT, V86, P1927, DOI 10.1103/PhysRevLett.86.1927
Haroche S., 2006, EXPLORING QUANTUM AT
Hewgill A, 2018, PHYS REV A, V98, DOI 10.1103/PhysRevA.98.042102
Horodecki M, 2013, NAT COMMUN, V4, DOI 10.1038/ncomms3059
Humphrey TE, 2002, PHYS REV LETT, V89, DOI 10.1103/PhysRevLett.89.116801
Irish EK, 2007, PHYS REV LETT, V99, DOI 10.1103/PhysRevLett.99.173601
James M., 2016, NEW J PHYS, V18, P11002
Johansson JR, 2013, COMPUT PHYS COMMUN, V184, P1234, DOI 10.1016/j.cpc.2012.11.019
Johansson JR, 2012, COMPUT PHYS COMMUN, V183, P1760, DOI 10.1016/j.cpc.2012.02.021
Kadowaki T, 1998, PHYS REV E, V58, P5355, DOI 10.1103/PhysRevE.58.5355
Kim SW, 2011, PHYS REV LETT, V106, DOI 10.1103/PhysRevLett.106.070401
Langford NK, 2017, NAT COMMUN, V8, DOI 10.1038/s41467-017-01061-x
Leggio B, 2015, PHYS REV A, V91, DOI 10.1103/PhysRevA.91.012117
Lostaglio M., 2016, RESOURCE THEORY QUAN
Lostaglio M, 2018, QUANTUM-AUSTRIA, V2, DOI 10.22331/q-2018-02-08-52
Lostaglio M, 2015, NAT COMMUN, V6, DOI 10.1038/ncomms7383
Maissen C, 2014, PHYS REV B, V90, DOI 10.1103/PhysRevB.90.205309
Martyushev LM, 2006, PHYS REP, V426, P1, DOI 10.1016/j.physrep.2005.12.001
Maruyama K, 2009, REV MOD PHYS, V81, P1, DOI 10.1103/RevModPhys.81.1
Militello B, 2018, PHYS REV E, V97, DOI 10.1103/PhysRevE.97.052113
Perry C, 2018, PHYS REV X, V8, DOI 10.1103/PhysRevX.8.041049
Popescu S, 2006, NAT PHYS, V2, P754, DOI 10.1038/nphys444
Quan HT, 2006, PHYS REV LETT, V97, DOI 10.1103/PhysRevLett.97.180402
Quan HT, 2007, PHYS REV E, V76, DOI 10.1103/PhysRevE.76.031105
Skrzypczyk P, 2014, NAT COMMUN, V5, DOI 10.1038/ncomms5185
Vinjanampathy S, 2016, CONTEMP PHYS, V57, P545, DOI 10.1080/00107514.2016.1201896
Wang YM, 2015, PHYS LETT A, V379, P779, DOI 10.1016/j.physleta.2014.12.052
wiklinski P., 2015, PHYS REV LETT, V115
Xie QT, 2017, J PHYS A-MATH THEOR, V50, DOI 10.1088/1751-8121/aa5a65
Yoshihara F, 2017, NAT PHYS, V13, P44, DOI [10.1038/NPHYS3906, 10.1038/nphys3906]
NR 42
TC 2
Z9 2
U1 1
U2 3
PU AMER PHYSICAL SOC
PI COLLEGE PK
PA ONE PHYSICS ELLIPSE, COLLEGE PK, MD 20740-3844 USA
SN 2470-0045
EI 2470-0053
J9 PHYS REV E
JI Phys. Rev. E
PD SEP 27
PY 2019
VL 100
IS 3
AR 032143
DI 10.1103/PhysRevE.100.032143
PG 12
WC Physics, Fluids & Plasmas; Physics, Mathematical
SC Physics
GA JB0SA
UT WOS:000488266000003
PM 31639978
DA 2021-04-21
ER
PT J
AU Syben, C
Michen, M
Stimpel, B
Seitz, S
Ploner, S
Maier, AK
AF Syben, Christopher
Michen, Markus
Stimpel, Bernhard
Seitz, Stephan
Ploner, Stefan
Maier, Andreas K.
TI Technical Note: PYRO-NN: Python reconstruction operators in neural
networks
SO MEDICAL PHYSICS
LA English
DT Article; Early Access
DE inverse problems; known operator learning; machine learning; open
source; reconstruction
ID IMAGE-RECONSTRUCTION; DEEP; FRAMEWORK; DOMAIN
AB Purpose Recently, several attempts were conducted to transfer deep learning to medical image reconstruction. An increasingly number of publications follow the concept of embedding the computed tomography (CT) reconstruction as a known operator into a neural network. However, most of the approaches presented lack an efficient CT reconstruction framework fully integrated into deep learning environments. As a result, many approaches use workarounds for mathematically unambiguously solvable problems. Methods PYRO-NN is a generalized framework to embed known operators into the prevalent deep learning framework Tensorflow. The current status includes state-of-the-art parallel-, fan-, and cone-beam projectors, and back-projectors accelerated with CUDA provided as Tensorflow layers. On top, the framework provides a high-level Python API to conduct FBP and iterative reconstruction experiments with data from real CT systems. Results The framework provides all necessary algorithms and tools to design end-to-end neural network pipelines with integrated CT reconstruction algorithms. The high-level Python API allows a simple use of the layers as known from Tensorflow. All algorithms and tools are referenced to a scientific publication and are compared to existing non-deep learning reconstruction frameworks. To demonstrate the capabilities of the layers, the framework comes with baseline experiments, which are described in the supplementary material. The framework is available as open-source software under the Apache 2.0 licence at . Conclusions PYRO-NN comes with the prevalent deep learning framework Tensorflow and allows to setup end-to-end trainable neural networks in the medical image reconstruction context. We believe that the framework will be a step toward reproducible research and give the medical physics community a toolkit to elevate medical image reconstruction with new deep learning techniques.
C1 [Syben, Christopher; Michen, Markus; Stimpel, Bernhard; Seitz, Stephan; Ploner, Stefan; Maier, Andreas K.] Friedich Alexander Univ Erlangen Nurnberg, Pattern Recognit Lab, D-91058 Erlangen, Germany.
RP Syben, C (corresponding author), Friedich Alexander Univ Erlangen Nurnberg, Pattern Recognit Lab, D-91058 Erlangen, Germany.
EM christopher.syben@fau.de
FU European Research Council (ERC) under European UnionEuropean Research
Council (ERC) [810316]; Emerging Fields Initiative (EFI) of the
Friedrich-Alexander University Erlangen-Nurnberg (FAU); European
Research CouncilEuropean Research Council (ERC)European Commission
[810316] Funding Source: Medline; Friedrich-Alexander University
Erlangen-Nurnberg Funding Source: Medline
FX The research leading to these results has received funding from the
European Research Council (ERC) under the European Union's Horizon 2020
research and innovation program (ERC Grant No. 810316). Additional
financial support for this project was granted by the Emerging Fields
Initiative (EFI) of the Friedrich-Alexander University Erlangen-Nurnberg
(FAU).
CR Abadi M, 2016, PROCEEDINGS OF OSDI'16: 12TH USENIX SYMPOSIUM ON OPERATING SYSTEMS DESIGN AND IMPLEMENTATION, P265
Adler J, 2018, IEEE T MED IMAGING, V37, P1322, DOI 10.1109/TMI.2018.2799231
Antun V, 2019, ARXIV190205300
Chen H, 2018, IEEE T MED IMAGING, V37, P1333, DOI 10.1109/TMI.2018.2805692
FELDKAMP LA, 1984, J OPT SOC AM A, V1, P612, DOI 10.1364/JOSAA.1.000612
Galigekere RR, 2003, IEEE T MED IMAGING, V22, P1202, DOI 10.1109/TMI.2003.817787
Greenspan H, 2016, IEEE T MED IMAGING, V35, P1153, DOI 10.1109/TMI.2016.2553401
Gursoy D, 2014, J SYNCHROTRON RADIAT, V21, P1188, DOI 10.1107/S1600577514013939
Hammernik K., 2017, BILDVERARBEITUNG MED, V2017, P92, DOI DOI 10.1007/978-3-662-54345-0_25
Jin KH, 2017, IEEE T IMAGE PROCESS, V26, P4509, DOI 10.1109/TIP.2017.2713099
Kak A.C., 1988, PRINCIPLES COMPUTERI
Kofler A, 2018, LECT NOTES COMPUT SC, V11074, P91, DOI 10.1007/978-3-030-00129-2_11
Kohr H, OPERATOR DISCRETIZAT
Krizhevsky A, 2017, COMMUN ACM, V60, P84, DOI 10.1145/3065386
LeCun Y, 2015, NATURE, V521, P436, DOI 10.1038/nature14539
Maier A, 2018, ARXIV181005401
Maier A, 2018, INT C PATT RECOG, P183, DOI 10.1109/ICPR.2018.8545553
Maier A, 2013, MED PHYS, V40, DOI 10.1118/1.4824926
Ronneberger O, 2015, LECT NOTES COMPUT SC, V9351, P234, DOI 10.1007/978-3-319-24574-4_28
Scherl H, 2007, IEEE NUCL SCI CONF R, P4464, DOI 10.1109/NSSMIC.2007.4437102
SHEPP LA, 1974, IEEE T NUCL SCI, VNS21, P21, DOI 10.1109/TNS.1974.6499235
Stimpel B, 2017, ARXIV171007498
Syben C, 2017, PYCONRAD SOFTWARE
Syben C, 2018, PATTERN RECOGNITION
Syben C., 2018, P 5 INT C IM FORM XR, P386
Syben C., 2019, CODE OCEAN, DOI [10.24433/CO.1164752.v1, DOI 10.24433/CO.1164752.V1]
van Aarle W, 2016, OPT EXPRESS, V24, P25129, DOI 10.1364/OE.24.025129
van den Oord A., 2016, ABS160903499 CORR
Wang G, 2018, IEEE T MED IMAGING, V37, P1289, DOI 10.1109/TMI.2018.2833635
Wurfl T, 2018, IEEE T MED IMAGING, V37, P1454, DOI 10.1109/TMI.2018.2833499
Wurfl Tobias, 2016, Medical Image Computing and Computer-Assisted Intervention - MICCAI 2016. 19th International Conference. Proceedings: LNCS 9902, P432, DOI 10.1007/978-3-319-46726-9_50
Ye JC, 2018, SIAM J IMAGING SCI, V11, P991, DOI 10.1137/17M1141771
Zeng GSL, 2000, IEEE T MED IMAGING, V19, P548, DOI 10.1109/42.870265
Zhu B, 2018, NATURE, V555, P487, DOI 10.1038/nature25988
NR 34
TC 4
Z9 4
U1 0
U2 4
PU WILEY
PI HOBOKEN
PA 111 RIVER ST, HOBOKEN 07030-5774, NJ USA
SN 0094-2405
EI 2473-4209
J9 MED PHYS
JI Med. Phys.
DI 10.1002/mp.13753
PG 6
WC Radiology, Nuclear Medicine & Medical Imaging
SC Radiology, Nuclear Medicine & Medical Imaging
GA IU6RV
UT WOS:000483714300001
PM 31389023
OA Green Published, Other Gold
DA 2021-04-21
ER
PT J
AU Pesce-Rollins, M
Di Lalla, N
Omodei, N
Baldini, L
AF Pesce-Rollins, Melissa
Di Lalla, Niccolo
Omodei, Nicola
Baldini, Luca
TI An observation-simulation and analysis framework for the Imaging X-ray
Polarimetry Explorer (IXPE)
SO NUCLEAR INSTRUMENTS & METHODS IN PHYSICS RESEARCH SECTION A-ACCELERATORS
SPECTROMETERS DETECTORS AND ASSOCIATED EQUIPMENT
LA English
DT Review
DE Detector techniques for cosmology; Astroparticle physics
AB We present a new simulation framework, based on the Python programming language and specifically developed for the Imaging X-ray Polarimetry Explorer (IXPE) mission. Starting from an arbitrary source model (including morphological, temporal, spectral and polarimetric information), this framework uses the instrument response functions to produce fast and realistic observation-simulations. The generated event lists can be directly fed into the standard X-ray visualization and analysis tools, including XSPEC-which make this framework a useful tool not only for simulating observations of astronomical sources, but also to develop and test end-to-end analysis chains. We will give an overview of the basic architecture of the software and we will present a few physically interesting case studies in the context of the IXPE mission.
C1 [Pesce-Rollins, Melissa; Di Lalla, Niccolo; Baldini, Luca] Ist Nazl Fis Nucl, Pisa, Italy.
[Di Lalla, Niccolo; Baldini, Luca] Univ Pisa, Pisa, Italy.
[Omodei, Nicola] Stanford Univ, Stanford, CA 94305 USA.
RP Pesce-Rollins, M (corresponding author), Ist Nazl Fis Nucl, Pisa, Italy.
EM melissa.pesce.rollins@pi.infn.it
RI Rollins, Melissa Pesce/AAB-4010-2021
OI Baldini, Luca/0000-0002-9785-7726
CR Helder EA, 2008, ASTROPHYS J, V686, P1094, DOI 10.1086/591242
NR 1
TC 3
Z9 3
U1 1
U2 2
PU ELSEVIER
PI AMSTERDAM
PA RADARWEG 29, 1043 NX AMSTERDAM, NETHERLANDS
SN 0168-9002
EI 1872-9576
J9 NUCL INSTRUM METH A
JI Nucl. Instrum. Methods Phys. Res. Sect. A-Accel. Spectrom. Dect. Assoc.
Equip.
PD AUG 21
PY 2019
VL 936
BP 224
EP 226
DI 10.1016/j.nima.2018.10.041
PG 3
WC Instruments & Instrumentation; Nuclear Science & Technology; Physics,
Nuclear; Physics, Particles & Fields
SC Instruments & Instrumentation; Nuclear Science & Technology; Physics
GA ID6YL
UT WOS:000471828100080
DA 2021-04-21
ER
PT J
AU Martone, R
Guidorzi, C
Mundell, CG
Kobayashi, S
Cucchiara, A
Gomboc, A
Jordana, N
Laskar, T
Marongiu, M
Morris, DC
Smith, RJ
Steele, IA
AF Martone, R.
Guidorzi, C.
Mundell, C. G.
Kobayashi, S.
Cucchiara, A.
Gomboc, A.
Jordana, N.
Laskar, T.
Marongiu, M.
Morris, D. C.
Smith, R. J.
Steele, I. A.
TI A robotic pipeline for fast GRB followup with the Las Cumbres
observatory network
SO EXPERIMENTAL ASTRONOMY
LA English
DT Article
DE GRB; LCO; Telegram
ID GAMMA-RAY BURST; OPTICAL-EMISSION; TELESCOPE; POLARIZATION; RADIATION;
FLASHES; LIGHT
AB In the era of multi-messenger astronomy the exploration of the early emission from transients is key for understanding the encoded physics. At the same time, current generation networks of fully-robotic telescopes provide new opportunities in terms of fast followup and sky coverage. This work describes our pipeline designed for robotic optical followup of gamma-ray bursts with the Las Cumbres Observatory network. We designed a Python code to promptly submit observation requests to the Las Cumbres Observatory network within 3 minutes of the receipt of the socket notice. Via Telegram the pipeline keeps the users informed, allowing them to take control upon request. Our group was able to track the early phases of the evolution of the optical output from gamma-ray bursts with a fully-robotic procedure and here we report the case of GRB180720B as an example. The developed pipeline represents a key ingredient for any reliable and rapid (minutes timescale) robotic telescope system. While successfully utilized and adapted for LCO, it can also be adapted to any other robotic facilities.
C1 [Martone, R.; Guidorzi, C.; Marongiu, M.] Univ Ferrara, Dept Phys & Earth Sci, Via Saragat 1, I-44122 Ferrara, Italy.
[Martone, R.; Marongiu, M.] ICRANet, Piazzale Repubbl 10, I-65122 Pescara, Italy.
[Mundell, C. G.; Jordana, N.; Laskar, T.] Univ Bath, Dept Phys, Bath BA2 7AY, Avon, England.
[Kobayashi, S.; Smith, R. J.; Steele, I. A.] Liverpool John Moores Univ, Astrophys Res Inst, Liverpool L3 5RF, Merseyside, England.
[Cucchiara, A.; Morris, D. C.] Univ Virgin Isl, 2 John Brewers Bay, St Thomas, VI 00802 USA.
[Gomboc, A.] Univ Nova Gorica, Ctr Astrophys & Cosmol, Vipavska 11c, Ajdovscina 5270, Slovenia.
RP Martone, R (corresponding author), Univ Ferrara, Dept Phys & Earth Sci, Via Saragat 1, I-44122 Ferrara, Italy.; Martone, R (corresponding author), ICRANet, Piazzale Repubbl 10, I-65122 Pescara, Italy.
EM mrtrnt@unife.it
OI Martone, Renato/0000-0002-0335-319X; Guidorzi,
Cristiano/0000-0001-6869-0835; Marongiu, Marco/0000-0002-5817-4009;
Kobayashi, Shiho/0000-0001-7946-4200; Smith, Rory/0000-0001-8516-3324;
Jordana-Mitjans, Nuria/0000-0002-5467-8277; Laskar,
Tanmoy/0000-0003-1792-2338
FU Universita di Ferrara
FX We thank Leo Singer for his help and support in the developing of the
code. Support for this work was provided by Universita di Ferrara
through grant FIR 2018 "A Broad-band study of Cosmic Gamma-Ray Burst
Prompt and Afterglow Emission" (PI Guidorzi).
CR Abbott BP, 2017, ASTROPHYS J LETT, V848, DOI 10.3847/2041-8213/aa91c9
Akerlof C, 1999, NATURE, V398, P400, DOI 10.1038/18837
Akerlof CW, 2003, PUBL ASTRON SOC PAC, V115, P132, DOI 10.1086/345490
BARTHELMY SD, 1995, ASTROPHYS SPACE SCI, V231, P235, DOI 10.1007/BF00658623
Boer M, 1999, ASTRON ASTROPHYS SUP, V138, P579, DOI 10.1051/aas:1999356
Brown TM, 2013, PUBL ASTRON SOC PAC, V125, P1031, DOI 10.1086/673168
Castro-Tirado AJ, 1999, ASTRON ASTROPHYS SUP, V138, P583, DOI 10.1051/aas:1999362
EICHLER D, 1989, NATURE, V340, P126, DOI 10.1038/340126a0
Gehrels N, 2004, ASTROPHYS J, V611, P1005, DOI 10.1086/422091
Gomboc A, 2008, ASTROPHYS J, V687, P443, DOI 10.1086/592062
Gomboc A, 2009, AIP CONF PROC, V1133, P145, DOI 10.1063/1.3155867
Guidorzi C, 2006, PUBL ASTRON SOC PAC, V118, P288, DOI 10.1086/499289
Japelj J, 2014, ASTROPHYS J, V785, DOI 10.1088/0004-637X/785/2/84
KLEBESADEL RW, 1973, ASTROPHYS J, V182, pL85, DOI 10.1086/181225
Kopac D, 2015, ASTROPHYS J, V813, DOI 10.1088/0004-637X/813/1/1
Laskar T, 2018, ASTROPHYS J, V862, DOI 10.3847/1538-4357/aacbcc
Laskar T, 2016, ASTROPHYS J, V833, DOI 10.3847/1538-4357/833/1/88
Lavigne JM, 1998, NUCL PHYS B, P69, DOI 10.1016/S0920-5632(97)00502-1
Lewis F, 2010, ADV ASTRON, V2010, DOI 10.1155/2010/873059
Lipunov V, 2010, ADV ASTRON, V2010, DOI 10.1155/2010/349171
LIPUNOV VM, 1995, ASTROPHYS J, V454, P593, DOI 10.1086/176512
Martone R., 2018, GRB COORDINATES NETW
Meegan C, 2009, ASTROPHYS J, V702, P791, DOI 10.1088/0004-637X/702/1/791
Melandri A, 2008, ASTROPHYS J, V686, P1209, DOI 10.1086/591243
Monfardini A, 2006, ASTROPHYS J, V648, P1125, DOI 10.1086/506170
Mundell CG, 2007, ASTROPHYS J, V660, P489, DOI 10.1086/512605
Mundell CG, 2013, NATURE, V504, P119, DOI 10.1038/nature12814
Mundell CG, 2010, ADV ASTRON, V2010, DOI 10.1155/2010/718468
Mundell CG, 2007, SCIENCE, V315, P1822, DOI 10.1126/science.1138484
NARAYAN R, 1992, ASTROPHYS J, V395, pL83, DOI 10.1086/186493
Paczynski B., 1991, Acta Astronomica, V41, P257
Schlegel DJ, 1998, ASTROPHYS J, V500, P525, DOI 10.1086/305772
Siegel M., 2018, GRB COORDINATES NETW
Steele IA, 2009, NATURE, V462, P767, DOI 10.1038/nature08590
Steele IA, 2004, P SOC PHOTO-OPT INS, V5489, P679, DOI 10.1117/12.551456
Troja E, 2017, NATURE, V547, P425, DOI 10.1038/nature23289
Tsapras Y, 2009, ASTRON NACHR, V330, P4, DOI 10.1002/asna.200811130
vanParadijs J, 1997, NATURE, V386, P686, DOI 10.1038/386686a0
Virgili FJ, 2013, ASTROPHYS J, V778, DOI 10.1088/0004-637X/778/1/54
Woosley SE, 2012, CAMBRIDGE ASTROPHYS, V51, P191
NR 40
TC 0
Z9 0
U1 0
U2 0
PU SPRINGER
PI DORDRECHT
PA VAN GODEWIJCKSTRAAT 30, 3311 GZ DORDRECHT, NETHERLANDS
SN 0922-6435
EI 1572-9508
J9 EXP ASTRON
JI Exp. Astron.
PD AUG
PY 2019
VL 48
IS 1
BP 25
EP 48
DI 10.1007/s10686-019-09634-y
PG 24
WC Astronomy & Astrophysics
SC Astronomy & Astrophysics
GA IR9JA
UT WOS:000481758700002
OA Green Accepted
DA 2021-04-21
ER
PT J
AU Blyth, D
Alcaraz, J
Binet, S
Chekanov, SV
AF Blyth, D.
Alcaraz, J.
Binet, S.
Chekanov, S. V.
TI ProIO: An event-based I/O stream format for protobuf messages
SO COMPUTER PHYSICS COMMUNICATIONS
LA English
DT Article
DE Protobuf; I/O; Event; Stream
AB ProIO is a new event-oriented streaming data format which utilizes Google's Protocol Buffers (protobuf) to be flexible and highly language-neutral. The ProIO concept is described here along with its software implementations. The performance of the ProIO concept for a dataset with Monte-Carlo event records used in high-energy physics was benchmarked and compared/contrasted with ROOT I/O. Various combinations of general-purpose compression and variable-length integer encoding available in protobuf were used to investigate the relationship between I/O performance and size-on-disk in a few key scenarios.
Program summary
Program Title: ProlO
Program Files doi: http://dx.doi.org/10.17632/mfxsg2d5x5.1
Licensing provisions: BSD 3-clause
Programming language: Python, Go, C++, Java
Nature of problem: In high-energy and nuclear physics (HEP and NP), Google's Protocol Buffers (protobufs) can be a useful tool for the persistence of data. However, protobufs are not well-suited for describing large, rich datasets. Additionally, features such as direct event access, lazy event decoding, general-purpose compression, and self-description are features that are important to HEP and NP, but that are missing from protobuf.
Solution method: The solution adopted here is to describe and implement a streaming format for wrapping protobufs in an event structure. This solution requires small (typically less than 1000 lines of code) implementations of the format in the desired programming languages. With this approach, most of the I/O heavy lifting is done by the protobufs, and ProlO adds the necessary physics-oriented features. Published by Elsevier B.V.
C1 [Blyth, D.; Chekanov, S. V.] Argonne Natl Lab, HEP Div, 9700 S Cass Ave, Argonne, IL 60439 USA.
[Binet, S.] Univ Clermont Auvergne, CNRS, IN2P3, LPC, F-63000 Clermont Ferrand, France.
[Alcaraz, J.] Northern Illinois Univ, De Kalb, IL 60115 USA.
[Blyth, D.] Radiat Detect & Imaging RDI Technol LLC, 21215 N 36th PI, Phoenix, AZ 85050 USA.
RP Blyth, D (corresponding author), Argonne Natl Lab, HEP Div, 9700 S Cass Ave, Argonne, IL 60439 USA.
EM dblyth@radiationimaging.com
OI Blyth, David/0000-0001-8452-516X; Chekanov, Sergei/0000-0001-7314-7247
FU U.S. Department of Energy Office of Science laboratoryUnited States
Department of Energy (DOE) [DE-AC02-06CH11357]; U.S. Department of
Energy, Office of High Energy PhysicsUnited States Department of Energy
(DOE) [DE-AC02-06CH11357]; ANL LDRD project [2017-058]; EIC eRD20
project
FX The submitted manuscript has been created by UChicago Argonne, LLC,
Operator of Argonne National Laboratory ("Argonne"). Argonne, a U.S.
Department of Energy Office of Science laboratory, is operated under
Contract No. DE-AC02-06CH11357. The U.S. Government retains for itself,
and others acting on its behalf, a paid-up nonexclusive, irrevocable
worldwide license in said article to reproduce, prepare derivative
works, distribute copies to the public, and perform publicly and display
publicly, by or on behalf of the Government. The Department of Energy
will provide public access to these results of federally sponsored
research in accordance with the DOE Public Access Plan.
http://energy.gov/downloads/doe-public-access-plan.Part of Argonne
National Laboratory's work was funded by the U.S. Department of Energy,
Office of High Energy Physics under contract DE-AC02-06CH11357. The work
of D. Blyth was supported by an ANL LDRD project 2017-058. The work of
J. Alcaraz was supported by the EIC eRD20 project.
CR Antcheva I, 2009, COMPUT PHYS COMMUN, V180, P2499, DOI 10.1016/j.cpc.2009.08.005
Binet S., 2018, ARXIV180806529
Blomer J, 2018, J PHYS CONF SER, V1085, DOI 10.1088/1742-6596/1085/3/032020
Chekanov SV, 2015, ADV HIGH ENERGY PHYS, V2015, DOI 10.1155/2015/136093
Chekanov SV, 2014, COMPUT PHYS COMMUN, V185, P2629, DOI 10.1016/j.cpc.2014.06.016
Gaede F., 2003, ARXIV PREPRINT
Sjostrand T, 2008, COMPUT PHYS COMMUN, V178, P852, DOI 10.1016/j.cpc.2008.01.036
NR 7
TC 2
Z9 2
U1 0
U2 0
PU ELSEVIER SCIENCE BV
PI AMSTERDAM
PA PO BOX 211, 1000 AE AMSTERDAM, NETHERLANDS
SN 0010-4655
EI 1879-2944
J9 COMPUT PHYS COMMUN
JI Comput. Phys. Commun.
PD AUG
PY 2019
VL 241
BP 98
EP 112
DI 10.1016/j.cpc.2019.03.018
PG 15
WC Computer Science, Interdisciplinary Applications; Physics, Mathematical
SC Computer Science; Physics
GA IF8YW
UT WOS:000473380200012
DA 2021-04-21
ER
PT J
AU Staub, F
AF Staub, Florian
TI xSLHA: An Les Houches Accord reader for Python and Mathematica
SO COMPUTER PHYSICS COMMUNICATIONS
LA English
DT Article
DE BSM; SLHA; Parser; Spectrum files
ID SUSY; SPHENO; MODELS; FLAVOR; TOOL
AB The format defined by the SUSY Les Houches Accord (SLHA) is widely used in high energy physics to store and exchange information. It is no longer applied only to a few supersymmetric models, but the general structure is adapted to all kinds of models. Therefore, it is helpful to have parsers at hand which can import files in the SLHA format into high-level languages as Python and Mathematica in order to further process the data. The focus of the xSLHA package, which exists now for Python and Mathematica, was on a fast read-in of large data samples. Moreover, also some blocks used by different tools, as HiggsBounds for instance, deviate from the standard conventions. These are also supported by xSLHA.
Program summary
Program Title: xSLHA
Program Files doi: http://dx.doLorg/10.17632/cj958d76pf.1
Licensing provisions: MIT
Programming language: Python, Mathematica
Nature of problem: Many numerical computer tools in phenomenological high-energy physics store the results in the so called SUSY Les Houches Accord (SLHA) format. In order to process the data with high-level languages as Mathematica or Python, these files must be translated into these languages. This can be very time consuming for large data samples.
Solution method: xSLHA is a pretty fast parser to import SLHA files into Python or Mathematica. It is also the first fully general SLHA reader written for Mathematica at all. In order to speed up the import of a large data sample, it provides the possibility to pre-process the SLHA files using very efficient shell tools as grep or cat. This improves the speed easily by an order of magnitude and more. (C) 2019 Elsevier B.V. All rights reserved.
C1 [Staub, Florian] Karlsruhe Inst Technol, ITP, Engesserstr 7, D-76128 Karlsruhe, Germany.
[Staub, Florian] Karlsruhe Inst Technol, Inst Nucl Phys IKP, Hermann von Helmholtz Pl 1, D-76344 Eggenstein Leopoldshafen, Germany.
RP Staub, F (corresponding author), Karlsruhe Inst Technol, ITP, Engesserstr 7, D-76128 Karlsruhe, Germany.
EM florian.staub@kit.edu
OI Staub, Florian/0000-0001-5911-5804
FU ERC Recognition Award of the Helmholtz Association [ERC-RA-0008]
FX I thank Martin Gabelmann Toby Opferkuch for testing xSLHA under Linux
and MacOS. This work is supported by the ERC Recognition Award
ERC-RA-0008 of the Helmholtz Association.
CR Allanach BC, 2009, COMPUT PHYS COMMUN, V180, P8, DOI 10.1016/j.cpc.2008.08.004
Basso L, 2013, COMPUT PHYS COMMUN, V184, P698, DOI 10.1016/j.cpc.2012.11.004
Bechtle P, 2011, COMPUT PHYS COMMUN, V182, P2605, DOI 10.1016/j.cpc.2011.07.015
Bechtle P, 2010, COMPUT PHYS COMMUN, V181, P138, DOI 10.1016/j.cpc.2009.09.003
Bechtle P, 2014, EUR PHYS J C, V74, DOI 10.1140/epjc/s10052-013-2693-2
Buckley A, 2015, EUR PHYS J C, V75, DOI 10.1140/epjc/s10052-015-3638-8
Hahn T, 2009, COMPUT PHYS COMMUN, V180, P1681, DOI 10.1016/j.cpc.2009.03.012
HAHN T, 2004, HEPPH0408283
Mahmoudi F, 2012, COMPUT PHYS COMMUN, V183, P285, DOI 10.1016/j.cpc.2011.10.006
Marquard P, 2014, COMPUT PHYS COMMUN, V185, P1153, DOI 10.1016/j.cpc.2013.12.005
Porod W, 2003, COMPUT PHYS COMMUN, V153, P275, DOI 10.1016/S0010-4655(03)00222-4
Porod W, 2012, COMPUT PHYS COMMUN, V183, P2458, DOI 10.1016/j.cpc.2012.05.021
Sjostrand T, 2004, J HIGH ENERGY PHYS
Staub F, 2014, COMPUT PHYS COMMUN, V185, P1773, DOI 10.1016/j.cpc.2014.02.018
Staub F, 2013, COMPUT PHYS COMMUN, V184, P1792, DOI 10.1016/j.cpc.2013.02.019
Staub F, 2012, COMPUT PHYS COMMUN, V183, P2165, DOI 10.1016/j.cpc.2012.04.013
Staub F, 2011, COMPUT PHYS COMMUN, V182, P808, DOI 10.1016/j.cpc.2010.11.030
Staub F, 2010, COMPUT PHYS COMMUN, V181, P1077, DOI 10.1016/j.cpc.2010.01.011
NR 18
TC 2
Z9 2
U1 0
U2 2
PU ELSEVIER SCIENCE BV
PI AMSTERDAM
PA PO BOX 211, 1000 AE AMSTERDAM, NETHERLANDS
SN 0010-4655
EI 1879-2944
J9 COMPUT PHYS COMMUN
JI Comput. Phys. Commun.
PD AUG
PY 2019
VL 241
BP 132
EP 138
DI 10.1016/j.cpc.2019.03.013
PG 7
WC Computer Science, Interdisciplinary Applications; Physics, Mathematical
SC Computer Science; Physics
GA IF8YW
UT WOS:000473380200015
DA 2021-04-21
ER
PT J
AU Pintaldi, S
Li, JM
Sethuyenkatraman, S
White, S
Rosengarten, G
AF Pintaldi, Sergio
Li, Jiaming
Sethuyenkatraman, Subbu
White, Stephen
Rosengarten, Gary
TI Model predictive control of a high efficiency solar thermal cooling
system with thermal storage
SO ENERGY AND BUILDINGS
LA English
DT Article
DE Non linear model predictive control; Genetic algorithm; Solar cooling;
Thermal energy storage; Triple-effect absorption chiller
ID PERFORMANCE; ALGORITHMS; COLLECTORS; BUILDINGS; PLANT; PART
AB This work presents the benefits of using a model predictive control (MPC) approach for controlling a high efficiency absorption chiller-based solar cooling system with thermal energy storage, incorporating perfect solar resource and load forecasting information. A dynamic physics-based model of the solar air-conditioning system has been built for studying the system behavior. A genetic algorithm based predictive controller is utilized to minimize backup energy consumption while satisfying the cooling demand. The simulations have been carried out using the open-source programming language Python. Detailed investigation of the role of the predictive controller and its decision strategy have been carried out using ten and fifty days simulations. Effect of storage tank heat losses has been investigated.
For the simulated example case pertaining to a building, results show the model predictive controller usage delivers about 10% reduction in auxiliary energy use in the system. This is achieved through reduction in tank heat losses, better utilization of heat stored in the tank. It is seen that the MPC based controller enables new system operational capabilities by running the solar collector pump in variable flow mode and allowing the simultaneous heat delivery from storage and backup devices. Opportunities to improve the MPC benefits have been identified. The benefits of the MPC are seen to be sensitive to the system parameters and specific constraints. In summary, this paper provides valuable insights into solar cooling system design and control. Crown Copyright (C) 2019 Published by Elsevier B.V. All rights reserved.
C1 [Pintaldi, Sergio; Rosengarten, Gary] RMIT Univ, Sch Aerosp Mech & Mfg Engn, Melbourne, Vic, Australia.
[Pintaldi, Sergio; Sethuyenkatraman, Subbu; White, Stephen] CSIRO, Energy, Newcastle, NSW, Australia.
[Li, Jiaming] CSIRO, Data61, Sydney, NSW, Australia.
RP Pintaldi, S (corresponding author), RMIT Univ, Sch Aerosp Mech & Mfg Engn, Melbourne, Vic, Australia.; Pintaldi, S (corresponding author), CSIRO, Energy, Newcastle, NSW, Australia.
EM sergio.pintaldi@switchdin.com
OI Sethuvenkatraman, Subbu/0000-0001-7197-2307
FU Australian Renewable Energy Agency (ARENA), Micro Urban Solar Integrated
Concentrators project [1-USO MUSIC]
FX The authors would like to thank Thermax Limited for providing the data
of the triple effect absorption chiller, the Australian Renewable Energy
Agency (ARENA) for providing funds to carry out this research work as a
part of Micro Urban Solar Integrated Concentrators (grant no. "1-USO
MUSIC") project and the developers of the Python packages Pandas and
Matplotlib [51,521 for making their tools freely available with full
documentation, and finally Dr Cristian Perfumo for his support related
to the model predictive controller development.
CR ACADS-BSG PtyLtd Elms consulting engineers, 2002, ABCB EN MOD OFF BUIL
Balaras CA, 2007, RENEW SUST ENERG REV, V11, P299, DOI 10.1016/j.rser.2005.02.003
Berkenkamp F, 2014, ENERG BUILDINGS, V84, P233, DOI 10.1016/j.enbuild.2014.07.052
Blasco X., 1998, APPL CONTROL NONLINE, P428, DOI [10.1007/3-540-64582-9_773, DOI 10.1007/3-540-64582-9_773]
Bujedo LA, 2011, SOL ENERGY, V85, P1302, DOI 10.1016/j.solener.2011.03.009
Cabrera FJ, 2013, RENEW SUST ENERG REV, V20, P103, DOI 10.1016/j.rser.2012.11.081
Camacho EF, 2012, ADV IND CONTROL, P1, DOI 10.1007/978-0-85729-916-1
Coley D.A., 1999, INTRO GENETIC ALGORI
Drosou VN, 2014, RENEW SUST ENERG REV, V29, P463, DOI 10.1016/j.rser.2013.08.019
Eicker U, 2015, RENEW ENERG, V80, P827, DOI 10.1016/j.renene.2015.02.019
Ernst D, 2009, IEEE T SYST MAN CY B, V39, P517, DOI 10.1109/TSMCB.2008.2007630
Fiorentini M, 2017, APPL ENERG, V187, P465, DOI 10.1016/j.apenergy.2016.11.041
Fortin FA, 2012, J MACH LEARN RES, V13, P2171
Garcia-Gabin W, 2009, CONTROL ENG PRACT, V17, P652, DOI 10.1016/j.conengprac.2008.10.015
Gholamibozanjani G, 2018, APPL ENERG, V231, P959, DOI 10.1016/j.apenergy.2018.09.181
Goldberg D. E, 1989, GENETIC ALGORITHM SE
Goyal A, 2019, INT J REFRIG, V97, P1, DOI 10.1016/j.ijrefrig.2018.08.026
Halvgaard R, 2012, ENRGY PROCED, V30, P270, DOI 10.1016/j.egypro.2012.11.032
Han YM, 2009, RENEW SUST ENERG REV, V13, P1014, DOI 10.1016/j.rser.2008.03.001
Henning HM, 2007, APPL THERM ENG, V27, P1734, DOI 10.1016/j.applthermaleng.2006.07.021
Hunter JD, 2007, COMPUT SCI ENG, V9, P90, DOI 10.1109/MCSE.2007.55
International Energy Agency (IEA), 2015, WORLD EN OUTL SPEC R
Killian M, 2016, BUILD ENVIRON, V105, P403, DOI 10.1016/j.buildenv.2016.05.034
Kuhn A., 2005, PROC 1ST INT CONF SO, V10, P5
Li SW, 2015, SOL ENERGY, V113, P139, DOI 10.1016/j.solener.2014.11.024
Marc O, 2010, ENERG BUILDINGS, V42, P774, DOI 10.1016/j.enbuild.2009.12.006
Menchinelli P, 2008, EUR J CONTROL, V14, P501, DOI 10.3166/EJC.14.501-515
Millman J., 2010, P56, DOI DOI 10.1016/S0168-0102(02)00204-3
Mugnier D., 2017, SOLAR COOLING DESIGN
Oliphant TE, 2007, COMPUT SCI ENG, V9, P10, DOI 10.1109/MCSE.2007.58
Onnen C, 1997, CONTROL ENG PRACT, V5, P1363, DOI 10.1016/S0967-0661(97)00133-0
Pichler MF, 2014, SOL ENERGY, V101, P203, DOI 10.1016/j.solener.2013.12.015
Pintaldi S, 2017, APPL ENERG, V188, P160, DOI 10.1016/j.apenergy.2016.11.123
Pintaldi S, 2015, RENEW SUST ENERG REV, V41, P975, DOI 10.1016/j.rser.2014.08.062
Powell KM, 2013, P AMER CONTR CONF, P2946
Rennard J. P, 2000, INTRO GENETIC ALGORI
Rodriguez M, 2008, EUR J CONTROL, V14, P484, DOI 10.3166/EJC.14.484-500
Rossetti A, 2018, INT J REFRIG, V93, P213, DOI 10.1016/j.ijrefrig.2018.06.004
Serale G, 2018, ENERG CONVERS MANAGE, V173, P438, DOI 10.1016/j.enconman.2018.07.099
Wang SW, 2008, HVAC&R RES, V14, P3, DOI 10.1080/10789669.2008.10390991
Shirazi A, 2016, ENERG CONVERS MANAGE, V114, P258, DOI 10.1016/j.enconman.2016.01.070
Siroky J, 2011, APPL ENERG, V88, P3079, DOI 10.1016/j.apenergy.2011.03.009
Sonntag C, 2008, EUR J CONTROL, V14, P451, DOI 10.3166/EJC.14.451-463
Sousa JM, 1997, CONTROL ENG PRACT, V5, P1395, DOI 10.1016/S0967-0661(97)00136-6
Vukovic PD, 2001, FUZZY SET SYST, V122, P107, DOI 10.1016/S0165-0114(00)00048-8
Weeratunge H, 2018, ENERGY, V152, P974, DOI 10.1016/j.energy.2018.03.079
[ Xue Fuzhen], 2004, [, Journal of University of Science and Technology of China], V34, P593
Yabase H., 2012, INT REFR AIR COND C, P1272
Yang Jian-jun, 2003, Control and Decision, V18, P141
Zambrano D, 2002, PROCEEDINGS OF THE 2002 IEEE INTERNATIONAL CONFERENCE ON CONTROL APPLICATIONS, VOLS 1 & 2, P1230, DOI 10.1109/CCA.2002.1038781
Zambrano D, 2008, EUR J CONTROL, V14, P464, DOI 10.3166/EJC.14.464-483
NR 51
TC 3
Z9 3
U1 0
U2 18
PU ELSEVIER SCIENCE SA
PI LAUSANNE
PA PO BOX 564, 1001 LAUSANNE, SWITZERLAND
SN 0378-7788
EI 1872-6178
J9 ENERG BUILDINGS
JI Energy Build.
PD AUG 1
PY 2019
VL 196
BP 214
EP 226
DI 10.1016/j.enbuild.2019.05.008
PG 13
WC Construction & Building Technology; Energy & Fuels; Engineering, Civil
SC Construction & Building Technology; Energy & Fuels; Engineering
GA IE2VO
UT WOS:000472242900019
DA 2021-04-21
ER
PT J
AU Martin, RD
Cai, Q
Garrow, T
Kapahi, C
AF Martin, R. D.
Cai, Q.
Garrow, T.
Kapahi, C.
TI QExpy: A python-3 module to support undergraduate physics laboratories
SO SOFTWAREX
LA English
DT Article
DE Physics; Uncertainties; Error; Plotting; Fitting; Data-analysis
AB QExpy is an open source python-3 module that was developed in order to simplify the analysis of data in undergraduate physics laboratories. Through the use of this module, students can focus their time on understanding the science and the data from their experiments, rather than on processing their data. In particular, the module allows users to easily propagate uncertainties from measured quantities using a variety of techniques (derivatives, Monte Carlo), as well as to plot and fit functions to data. The interface is designed to be pedagogical so that students with no prior programming experience can be eased into using python in their introductory physics laboratories. (C) 2019 The Authors. Published by Elsevier B.V.
C1 [Martin, R. D.; Cai, Q.; Garrow, T.; Kapahi, C.] Queens Univ, Dept Phys Engn Phys & Astron, Kingston, ON, Canada.
[Kapahi, C.] Univ Waterloo, Inst Quantum Comp, Waterloo, ON, Canada.
[Kapahi, C.] Univ Waterloo, Dept Phys, Waterloo, ON, Canada.
RP Martin, RD (corresponding author), Queens Univ, Dept Phys Engn Phys & Astron, Kingston, ON, Canada.
EM ryan.martin@queensu.ca
OI Kapahi, Connor/0000-0001-6051-6859
FU Department of Physics, Engineering Physics & Astronomy at Queen's
University
FX We would like to thank the Department of Physics, Engineering Physics &
Astronomy at Queen's University for their support of students to develop
this software. We would also like to thank Prof. A.B. McLean for the all
of the valuable support and input in developing QExpy, and for the
original idea of developing a ``professor approved'' python module for
error propagation. We also would like to thank the many students from
the department for their valuable feedback.
CR Bokeh Development Team, 2018, BOK PYTH LIB INT VIS
Hunter JD, 2007, COMPUT SCI ENG, V9, P90, DOI 10.1109/MCSE.2007.55
Jones E., 2001, SCIPY OPEN SOURCE SC
Lebigot E.O, 2017, UNCERTAINTIES PYTHON
MARQUARDT DW, 1963, J SOC IND APPL MATH, V11, P431, DOI 10.1137/0111030
Martin RD, 2016, QEXPY GITHUB REPOSIT
PICUP, 2019, PICUP PARTNERSHIP IN
van der Walt S, 2011, COMPUT SCI ENG, V13, P22, DOI 10.1109/MCSE.2011.37
NR 8
TC 0
Z9 0
U1 0
U2 1
PU ELSEVIER
PI AMSTERDAM
PA RADARWEG 29, 1043 NX AMSTERDAM, NETHERLANDS
SN 2352-7110
J9 SOFTWAREX
JI SoftwareX
PD JUL-DEC
PY 2019
VL 10
AR 100273
DI 10.1016/j.softx.2019.100273
PG 6
WC Computer Science, Software Engineering
SC Computer Science
GA JX9RT
UT WOS:000504065000049
OA DOAJ Gold
DA 2021-04-21
ER
PT J
AU Olarinoye, IO
Odiaga, RI
Paul, S
AF Olarinoye, I. O.
Odiaga, R., I
Paul, S.
TI EXABCal: A program for calculating photon exposure and energy absorption
buildup factors
SO HELIYON
LA English
DT Article
DE Atomic physics; Nuclear physics; Nuclear engineering; Physics methods;
Photons; Buildup factors; Shielding; Radiation dose; Equivalent atomic
number
ID MFP
AB This research presents a new Windows compatible program (EXABCal) for photon exposure and energy absorption buildup factors for standard energy grid from 0.015-15 MeV for elements, mixtures and compound. This program was written using Python programming language and the calculation of buildup factors was based on the well-known Geometric Progression (GP) fitting procedure. The equivalent atomic numbers and GP fitting parameters of mixtures and compounds can also be evaluated using this program. The program has been used to evaluate the photon exposure and energy absorption buildup factors for standard energy grid from 0.015-15 MeV for water, air and concrete, compared with values from the American Nuclear Society (ANS) standard reference data (ANSI-6.4.3) and found to be of high accurate with minimal errors. The program is fast and easy to use and will be of valuable interest to medical Physicist, radiation Physicists, Radiation shielding design engineers, students, teachers and researchers and other experts working in areas where nuclear radiation is applied.
C1 [Olarinoye, I. O.; Odiaga, R., I; Paul, S.] Fed Univ Technol, Dept Phys, Minna, Nigeria.
RP Olarinoye, IO (corresponding author), Fed Univ Technol, Dept Phys, Minna, Nigeria.
EM leke.olarinoye@futminna.edu.ng
RI Olarinoye, Oyeleke/AAD-7898-2021
OI Olarinoye, Oyeleke/0000-0002-3433-5250
CR [Anonymous], 1991, ANSIANS643
Gerward L, 2004, RADIAT PHYS CHEM, V71, P653, DOI 10.1016/j.radphyschem.2004.04.040
HARIMA Y, 1993, RADIAT PHYS CHEM, V41, P631, DOI 10.1016/0969-806X(93)90317-N
HARIMA Y, 1983, NUCL SCI ENG, V83, P299, DOI 10.13182/NSE83-A18222
HARIMA Y, 1986, NUCL SCI ENG, V94, P24, DOI 10.13182/NSE86-A17113
Hirayama H, 1995, J NUCL SCI TECHNOL, V32, P1201, DOI 10.3327/jnst.32.1201
Hubbell J. H., 1987, PHOTON CROSS SECTION, V87, P3597
Issa S. A. M., 2018, MAT CHEM PHYS
James E.M., 2006, PHYS RAD PROTECTION, P822
Kucuk N, 2010, EXPERT SYST APPL, V37, P3762, DOI 10.1016/j.eswa.2009.11.047
Kurudirek M, 2013, ANN NUCL ENERGY, V53, P485, DOI 10.1016/j.anucene.2012.08.002
Mann KS, 2013, ANN NUCL ENERGY, V51, P81, DOI 10.1016/j.anucene.2012.08.024
Obaid SS, 2018, RADIAT PHYS CHEM, V148, P86, DOI 10.1016/j.radphyschem.2018.02.026
Olarinoye I.O., 2017, J NUCL RES DEV, V13, P57
Sayyed MI, 2017, RESULTS PHYS, V7, P2528, DOI 10.1016/j.rinp.2017.07.028
Sayyed MI, 2017, RADIAT PHYS CHEM, V139, P33, DOI 10.1016/j.radphyschem.2017.05.013
Shimizu A, 2002, J NUCL SCI TECHNOL, V39, P477, DOI 10.3327/jnst.39.477
Singh Vishwanath P., 2012, International Journal of Nuclear Energy Science and Technology, V7, P75, DOI 10.1504/IJNEST.2012.046987
NR 18
TC 17
Z9 17
U1 0
U2 0
PU ELSEVIER SCI LTD
PI OXFORD
PA THE BOULEVARD, LANGFORD LANE, KIDLINGTON, OXFORD OX5 1GB, OXON, ENGLAND
SN 2405-8440
J9 HELIYON
JI Heliyon
PD JUL
PY 2019
VL 5
IS 7
AR e02017
DI 10.1016/j.heliyon.2019.e02017
PG 7
WC Multidisciplinary Sciences
SC Science & Technology - Other Topics
GA IN4RL
UT WOS:000478663100099
PM 31360782
OA DOAJ Gold, Green Published
DA 2021-04-21
ER
PT J
AU Hayen, L
Severijns, N
AF Hayen, L.
Severijns, N.
TI Beta Spectrum Generator: High precision allowed beta spectrum shapes
SO COMPUTER PHYSICS COMMUNICATIONS
LA English
DT Article
DE Beta decay; Standard model; Spectrum shape
ID NUCLEAR; DECAY; TRANSITIONS; PARTICLE; ELEMENTS; TABLE; TESTS
AB Several searches for Beyond Standard Model physics rely on an accurate and highly precise theoretical description of the allowed beta spectrum. Following recent theoretical advances, a C++ implementation of an analytical description of the allowed beta spectrum shape was constructed. It implements all known corrections required to give a theoretical description accurate to a few parts in 10(4). The remaining nuclear structure-sensitive input can optionally be calculated in an extreme single-particle approximation with a variety of nuclear potentials, or obtained through an interface with more state-of-the-art computations. Due to its relevance in modern neutrino physics, the corresponding (anti)neutrino spectra are readily available with appropriate radiative corrections. In the interest of user-friendliness, a graphical interface was developed in Python with a coupling to a variety of nuclear databases. We present several test cases and illustrate potential usage of the code. Our work can be used as the foundation for current and future high-precision experiments related to the beta decay process.
Source code: https://github.com/leenderthayen/BSG
Documentation: http://bsg.readthedocs.io
Program summary
Program Title: BSG
Program Files doi: http://dx.doi.org/10.17632/gx6yrpn22x.1
Licensing provisions: MIT
Programming language: C++ and Python
Nature of problem: The theoretical allowed beta spectrum contains a large variety of corrections from different areas of physics, each of which is important in certain energy ranges. A high precision description is required for new physics searches throughout the entire nuclear chart.
Solution method: We implement the analytical corrections described in recent theoretical work. Nuclear matrix elements in allowed Gamow-Teller,6 decay are calculated in a spherical harmonic oscillator basis. Wave functions can be calculated in an extreme single-particle approximation using different nuclear potentials, or provided by the user as the output from more sophisticated routines. Corresponding neutrino spectra are calculated with appropriate radiative corrections. A graphical user interface written in Python additionally provides connections to a variety of nuclear databases.
Additional comments: CPC Library subprograms used: ABOV_v1_0 (C) 2019 Elsevier B.V. All rights reserved.
C1 [Hayen, L.; Severijns, N.] Katholieke Univ Leuven, Inst Kern & Stralingsfys, Celestijnenlaan 200D, B-3001 Leuven, Belgium.
RP Hayen, L (corresponding author), Katholieke Univ Leuven, Inst Kern & Stralingsfys, Celestijnenlaan 200D, B-3001 Leuven, Belgium.
EM leendert.hayen@kuleuven.be
OI Hayen, Leendert/0000-0002-9471-0964
FU Belgian Federal Science Policy OfficeBelgian Federal Science Policy
OfficeEuropean Commission [IUAP EP/12-c]; Fund for Scientific Research
Flanders (FWO)FWO
FX The authors would like to thank L De Keukeleere and S. Vanlangendonck
for their valuable feedback. This work has been partly funded by the
Belgian Federal Science Policy Office, under Contract No. IUAP EP/12-c
and the Fund for Scientific Research Flanders (FWO).
CR Angeli I, 2013, ATOM DATA NUCL DATA, V99, P69, DOI 10.1016/j.adt.2011.12.006
Bao M, 2016, PHYS REV C, V94, DOI 10.1103/PhysRevC.94.064315
BEHRENS H, 1970, NUCL PHYS A, VA150, P481, DOI 10.1016/0375-9474(70)90413-6
Behrens H., 1982, ELECT RADIAL WAVE FU
BERTHIER J, 1966, NUCL PHYS, V78, P448, DOI 10.1016/0029-5582(66)90619-5
Bhattacharya T, 2012, PHYS REV D, V85, DOI 10.1103/PhysRevD.85.054512
Broussard L. J., 2019, Hyperfine Interactions, V240, DOI 10.1007/s10751-018-1538-7
Brown BA, 2014, NUCL DATA SHEETS, V120, P115, DOI 10.1016/j.nds.2014.07.022
Brown BA, 2006, PHYS REV C, V74, DOI 10.1103/PhysRevC.74.034315
DAVIDSON JP, 1968, COLLECTIVE MODELS NU
deShalit A., 1974, THEORETICAL NUCL PHY, VV1
DUDEK J, 1980, NUCL PHYS A, V341, P253, DOI 10.1016/0375-9474(80)90312-7
DUDEK J, 1982, PHYS REV C, V26, P1712, DOI 10.1103/PhysRevC.26.1712
ELTON LRB, 1958, NUCL PHYS, V5, P173, DOI 10.1016/0029-5582(58)90016-6
Fang DL, 2013, PHYS REV C, V88, DOI 10.1103/PhysRevC.88.034304
Fenker B, 2018, PHYS REV LETT, V120, DOI 10.1103/PhysRevLett.120.062502
GALLAGHER CJ, 1958, PHYS REV, V111, P1282, DOI 10.1103/PhysRev.111.1282
Hardy JC, 2015, PHYS REV C, V91, DOI 10.1103/PhysRevC.91.025501
Hayen L, 2019, PHYS REV C, V99, DOI 10.1103/PhysRevC.99.031301
Hayen L, 2018, REV MOD PHYS, V90, DOI 10.1103/RevModPhys.90.015008
Hayes AC, 2016, ANNU REV NUCL PART S, V66, P219, DOI 10.1146/annurev-nucl-102115-044826
HIRD B, 1973, COMPUT PHYS COMMUN, V6, P30, DOI 10.1016/0010-4655(73)90020-9
HJORTHJENSEN M, 1995, PHYS REP, V261, P126
Holstein BR, 2014, J PHYS G NUCL PARTIC, V41, DOI 10.1088/0954-3899/41/11/114001
HOLSTEIN BR, 1974, REV MOD PHYS, V46, P789, DOI 10.1103/RevModPhys.46.789
JACKSON JD, 1957, PHYS REV, V106, P517, DOI 10.1103/PhysRev.106.517
KLAPDOR HV, 1985, FORTSCHR PHYS, V33, P1, DOI 10.1002/prop.2190330102
LEE TD, 1956, PHYS REV, V104, P254, DOI 10.1103/PhysRev.104.254
Moller P, 2016, ATOM DATA NUCL DATA, V109, P1, DOI 10.1016/j.adt.2015.10.002
Mougeot X, 2014, PHYS REV A, V90, DOI 10.1103/PhysRevA.90.012501
Mueller TA, 2011, PHYS REV C, V83, DOI 10.1103/PhysRevC.83.054615
Naviliat-Cuncic O, 2013, ANN PHYS-BERLIN, V525, P600, DOI 10.1002/andp.201300072
Nilsson S. G., DAN MAT FYS MEDD, V29
Perkowski M, 2018, ACTA PHYS POL B, V49, P261, DOI 10.5506/APhysPolB.49.261
ROSE ME, 1954, PHYS REV, V93, P1326, DOI 10.1103/PhysRev.93.1326
Severijns N, 2014, J PHYS G NUCL PARTIC, V41, DOI 10.1088/0954-3899/41/11/114006
Severijns N, 2008, PHYS REV C, V78, DOI 10.1103/PhysRevC.78.055501
Sirlin A, 2011, PHYS REV D, V84, DOI 10.1103/PhysRevD.84.014021
Soti G, 2014, PHYS REV C, V90, DOI 10.1103/PhysRevC.90.035502
Sternberg MG, 2015, PHYS REV LETT, V115, DOI 10.1103/PhysRevLett.115.182501
Stone NJ, 2005, ATOM DATA NUCL DATA, V90, P75, DOI 10.1016/j.adt.2005.04.001
Suhonen JT, 2017, FRONT PHYS-LAUSANNE, V5, DOI 10.3389/fphy.2017.00055
Suzuki T, 2012, PHYS REV C, V85, DOI 10.1103/PhysRevC.85.015802
Towner IS, 2015, PHYS REV C, V91, DOI 10.1103/PhysRevC.91.015501
Vos KK, 2015, REV MOD PHYS, V87, P1483, DOI 10.1103/RevModPhys.87.1483
WILKINSON DH, 1993, NUCL INSTRUM METH A, V335, P182, DOI 10.1016/0168-9002(93)90271-I
NR 46
TC 3
Z9 3
U1 0
U2 4
PU ELSEVIER SCIENCE BV
PI AMSTERDAM
PA PO BOX 211, 1000 AE AMSTERDAM, NETHERLANDS
SN 0010-4655
EI 1879-2944
J9 COMPUT PHYS COMMUN
JI Comput. Phys. Commun.
PD JUL
PY 2019
VL 240
BP 152
EP 164
DI 10.1016/j.cpc.2019.02.012
PG 13
WC Computer Science, Interdisciplinary Applications; Physics, Mathematical
SC Computer Science; Physics
GA IH2GJ
UT WOS:000474312900014
DA 2021-04-21
ER
PT J
AU Aebischer, J
Kumar, J
Stangl, P
Straub, DM
AF Aebischer, Jason
Kumar, Jacky
Stangl, Peter
Straub, David M.
TI A global likelihood for precision constraints and flavour anomalies
SO EUROPEAN PHYSICAL JOURNAL C
LA English
DT Article
ID ELECTROWEAK MEASUREMENTS; PHYSICS; SEARCH; COLLISIONS; VIOLATION; DECAYS
AB We present a global likelihood function in the space of dimension-six Wilson coefficients in the Standard Model Effective Field Theory. The likelihood includes contributions from flavour-changing neutral current B decays, lepton flavour universality tests in charged- and neutral-current B and K decays, meson-antimeson mixing observables in the K, B, and D systems, direct CP violation in K, charged lepton flavour violating B, tau, and muon decays, electroweak precision tests on the Z and W poles, the anomalous magnetic moments of the electron, muon, and tau, and several other precision observables, 265 in total. The Wilson coefficients can be specified at any scale, with the one-loop running above and below the electroweak scale automatically taken care of. The implementation of the likelihood function is based on the open source tools flavio and wilson as well as the open Wilson coefficient exchange format (WCxf) and can be installed as a Python package. It can serve as a basis either for model-independent fits or for testing dynamical models, in particular models built to address the anomalies in B physics. We discuss a number of example applications, reproducing results from the EFT and model building literature.
C1 [Aebischer, Jason; Straub, David M.] Tech Univ Munich, Excellence Cluster Universe, Boltzmannstr 2, D-85748 Garching, Germany.
[Kumar, Jacky] Univ Montreal, Phys Particules, CP 6128,Succ Ctr Ville, Montreal, PQ H3C 3J7, Canada.
[Stangl, Peter] Univ Savoie Mt Blanc, Lab Annecy Le Vieux Phys Theor, UMR5108, 9 Chemin Bellevue,BP 110, F-74941 Annecy Le Vieux, France.
[Stangl, Peter] CNRS, 9 Chemin Bellevue,BP 110, F-74941 Annecy Le Vieux, France.
RP Aebischer, J (corresponding author), Tech Univ Munich, Excellence Cluster Universe, Boltzmannstr 2, D-85748 Garching, Germany.
EM jason.aebischer@tum.de; jacky.kumar@umontreal.ca;
peter.stangl@lapth.cnrs.fr; david.straub@tum.de
OI Stangl, Peter/0000-0002-8485-4091; Kumar, Jacky/0000-0001-9053-0731;
Straub, David/0000-0001-5762-7339
FU DFG cluster of excellence "Origin and Structure of the Universe"German
Research Foundation (DFG); NSERC of CanadaNatural Sciences and
Engineering Research Council of Canada (NSERC)
FX We thank Wolfgang Altmannshofer, Christoph Bobeth, IlariaBrivio, Andreas
Crivellin, Martin Jung, AneeshManohar, and Jordy de Vries for
discussions. We thank Alejandro Celis, Meril Reboud, and Olcyr Sumensari
for pointing out typos. We thank Martin Gonzalez-Alonso, Admir Greljo,
andMarco Nardecchia for useful comments. The work of D. S. and J. A. is
supported by the DFG cluster of excellence "Origin and Structure of the
Universe". The work of J. K. is financially supported by NSERC of
Canada.
CR Aaboud M, 2018, EUR PHYS J C, V78, DOI 10.1140/epjc/s10052-017-5475-4
Aaboud M., ARXIV180504000 ATLAS
Aaboud M., 2018, JHEP
Aad G, 2016, J HIGH ENERGY PHYS, DOI 10.1007/JHEP08(2016)045
Aad G, 2014, PHYS REV D, V90, DOI 10.1103/PhysRevD.90.072010
Aaij R, 2018, PHYS REV LETT, V120, DOI 10.1103/PhysRevLett.120.171802
Aaij R, 2018, J HIGH ENERGY PHYS, DOI 10.1007/JHEP03(2018)078
Aaij R, 2017, PHYS REV LETT, V118, DOI 10.1103/PhysRevLett.118.251802
Aaij R, 2017, J HIGH ENERGY PHYS, DOI 10.1007/JHEP04(2017)142
Aaij R, 2017, PHYS REV LETT, V118, DOI 10.1103/PhysRevLett.118.191801
Aaij R, 2016, J HIGH ENERGY PHYS, DOI 10.1007/JHEP11(2016)047
Aaij R, 2016, J HIGH ENERGY PHYS, DOI 10.1007/JHEP02(2016)104
Aaij R, 2015, PHYS REV LETT, V115, DOI 10.1103/PhysRevLett.115.159901
Aaij R, 2015, J HIGH ENERGY PHYS, DOI 10.1007/JHEP09(2015)179
Aaij R, 2015, PHYS REV LETT, V115, DOI 10.1103/PhysRevLett.115.111803
Aaij R, 2015, J HIGH ENERGY PHYS, DOI 10.1007/JHEP06(2015)115
Aaij R, 2015, J HIGH ENERGY PHYS, DOI 10.1007/JHEP04(2015)064
Aaij R, 2014, PHYS REV LETT, V113, DOI 10.1103/PhysRevLett.113.151601
Aaij R, 2014, J HIGH ENERGY PHYS, DOI 10.1007/JHEP06(2014)133
Aaij R, 2013, NUCL PHYS B, V867, P1, DOI 10.1016/j.nuclphysb.2012.09.013
Aaij R., 2017, HEP, V08
Aaij R., 2018, JHEP
Aaltonen T, 2013, PHYS REV D, V88, DOI 10.1103/PhysRevD.88.052018
Abdesselam A., ARXIV170201521 BELL
Abdesselam A., ARXIV180903290 BELL
Abreu P, 1997, Z PHYS C PART FIELDS, V73, P243, DOI 10.1007/s002880050315
Adamczyk Karol, 2019, 10 INT WORKSH CKM UN
Aebischer J., 2019, ARXIV190310434
Aebischer J., ARXIV180405033
Aebischer J., ARXIV180702520
Aebischer J., ARXIV180800466
Aebischer J., ARXIV180701709
Aebischer J, 2016, J HIGH ENERGY PHYS, DOI 10.1007/JHEP05(2016)037
Aebischer J, 2018, COMPUT PHYS COMMUN, V232, P71, DOI 10.1016/j.cpc.2018.05.022
Aebischer J, 2017, J HIGH ENERGY PHYS, DOI 10.1007/JHEP09(2017)158
Aguilar-Arevalo A, 2015, PHYS REV LETT, V115, DOI 10.1103/PhysRevLett.115.071801
Aguilar-Saavedra JA, 2011, NUCL PHYS B, V843, P638, DOI 10.1016/j.nuclphysb.2010.10.015
Ahn JK, 2019, PHYS REV LETT, V122, DOI 10.1103/PhysRevLett.122.021802
Akeroyd AG, 2017, PHYS REV D, V96, DOI 10.1103/PhysRevD.96.075011
AKERS R, 1995, Z PHYS C PART FIELDS, V67, P555
Alioli S, 2017, J HIGH ENERGY PHYS, DOI 10.1007/JHEP05(2017)086
Alonso R, 2017, PHYS REV LETT, V118, DOI 10.1103/PhysRevLett.118.081802
Alonso R, 2014, J HIGH ENERGY PHYS, DOI 10.1007/JHEP04(2014)159
Altmannshofer W, 2017, PHYS REV D, V96, DOI 10.1103/PhysRevD.96.055008
Altmannshofer W, 2017, EUR PHYS J C, V77, DOI 10.1140/epjc/s10052-017-4952-0
Altmannshofer W, 2015, EUR PHYS J C, V75, DOI 10.1140/epjc/s10052-015-3602-7
Altmannshofer W, 2014, PHYS REV LETT, V113, DOI 10.1103/PhysRevLett.113.091801
Amhis Y, 2017, EUR PHYS J C, V77, DOI 10.1140/epjc/s10052-017-5058-4
Amhis Y., ARXIV14127515 HFAG C
Artamonov AV, 2009, PHYS REV D, V79, DOI 10.1103/PhysRevD.79.092004
Aubert B, 2007, PHYS REV LETT, V98, DOI 10.1103/PhysRevLett.98.051801
Aubert B, 2006, PHYS REV LETT, V96, DOI 10.1103/PhysRevLett.96.251802
Aubert B, 2006, PHYS REV D, V73, DOI 10.1103/PhysRevD.73.092001
Aubert B, 2008, PHYS REV D, V77, DOI 10.1103/PhysRevD.77.091104
Bai Z, 2015, PHYS REV LETT, V115, DOI 10.1103/PhysRevLett.115.212001
Barbieri R, 2016, EUR PHYS J C, V76, DOI 10.1140/epjc/s10052-016-3905-3
Barducci D, ARXIV180207237
Becirevic D, 2003, J HIGH ENERGY PHYS, DOI 10.1088/1126-6708/2003/05/007
Becirevic D, 2016, EUR PHYS J C, V76, DOI 10.1140/epjc/s10052-016-3985-0
Beirevi D., 2018, PHYS REV D, V98
Bharucha A, 2016, J HIGH ENERGY PHYS, DOI 10.1007/JHEP08(2016)098
Bhattacharya B, 2015, PHYS LETT B, V742, P370, DOI 10.1016/j.physletb.2015.02.011
Bhattacharya S., ARXIV180508222
Bjorn M, 2016, PHYS LETT B, V762, P426, DOI 10.1016/j.physletb.2016.10.003
Black D, 2002, PHYS REV D, V66, DOI 10.1103/PhysRevD.66.053002
Blum T, 2015, PHYS REV D, V91, DOI 10.1103/PhysRevD.91.074502
Bobeth C, 2015, J HIGH ENERGY PHYS, DOI 10.1007/JHEP09(2015)018
Bobeth C, 2015, EUR PHYS J C, V75, DOI 10.1140/epjc/s10052-015-3535-1
Bobeth C, 2013, ACTA PHYS POL B, V44, P127, DOI 10.5506/APhysPolB.44.127
Boer P, 2015, J HIGH ENERGY PHYS, DOI 10.1007/JHEP01(2015)155
Bordone M, 2016, EUR PHYS J C, V76, DOI 10.1140/epjc/s10052-016-4274-7
Brignole A, 2004, NUCL PHYS B, V701, P3, DOI 10.1016/j.nuclphysb.2004.08.037
Brivio I., ARXIV170608945
Brivio I, 2017, J HIGH ENERGY PHYS, DOI 10.1007/JHEP07(2017)148
Brod J, 2015, PHYS REV D, V92, DOI 10.1103/PhysRevD.92.033002
Brod J, 2015, J HIGH ENERGY PHYS, DOI 10.1007/JHEP02(2015)141
BUCHMULLER W, 1986, NUCL PHYS B, V268, P621, DOI 10.1016/0550-3213(86)90262-2
Buckley A, 2015, PHYS REV D, V92, DOI 10.1103/PhysRevD.92.091501
Buras AJ, 2001, NUCL PHYS B, V605, P600, DOI 10.1016/S0550-3213(01)00207-3
Buras AJ, 2015, J HIGH ENERGY PHYS, DOI 10.1007/JHEP11(2015)202
Buras AJ, 2015, J HIGH ENERGY PHYS, DOI 10.1007/JHEP02(2015)184
Buras AJ, 2009, PHYS REV D, V79, DOI 10.1103/PhysRevD.79.053010
Buttazzo D, 2017, J HIGH ENERGY PHYS, DOI 10.1007/JHEP11(2017)044
Butter A, 2016, J HIGH ENERGY PHYS, DOI 10.1007/JHEP07(2016)152
Camargo-Molina JE, 2018, PHYS LETT B, V784, P284, DOI 10.1016/j.physletb.2018.07.051
Capdevila B, 2018, J HIGH ENERGY PHYS, DOI 10.1007/JHEP01(2018)093
[CDF Collaboration C. Collaboration], PREC MEAS EXCL B SMU
Celis Alejandro, 2013, Journal of High Energy Physics, DOI 10.1007/JHEP01(2013)054
Celis A, 2017, PHYS REV D, V96, DOI 10.1103/PhysRevD.96.035026
Celis A, 2017, EUR PHYS J C, V77, DOI 10.1140/epjc/s10052-017-4967-6
Celis A, 2017, PHYS LETT B, V771, P168, DOI 10.1016/j.physletb.2017.05.037
Celis A, 2014, PHYS REV D, V89, DOI 10.1103/PhysRevD.89.095014
Charles J, 2017, EUR PHYS J C, V77, DOI 10.1140/epjc/s10052-017-4767-z
Chatrchyan S, 2013, PHYS REV LETT, V111, DOI 10.1103/PhysRevLett.111.101804
Chobanova V, 2018, J HIGH ENERGY PHYS, DOI 10.1007/JHEP05(2018)024
Cirigliano V, 2016, PHYS REV D, V94, DOI 10.1103/PhysRevD.94.034031
Cirigliano V., ARXIV180901161
Cirigliano V, 2013, J HIGH ENERGY PHYS, DOI 10.1007/JHEP02(2013)046
Ciuchini M, 2017, EUR PHYS J C, V77, DOI 10.1140/epjc/s10052-017-5270-2
[CMSCollaboration C. Collaboration], MEAS P1 P5 ANG PAR D
Crivellin A., ARXIV180702068
David Andre, 2016, Reviews in Physics, V1, P13, DOI 10.1016/j.revip.2016.01.001
de Blas J, 2015, J HIGH ENERGY PHYS, DOI 10.1007/JHEP09(2015)189
de Boer S, 2017, J HIGH ENERGY PHYS, DOI 10.1007/JHEP08(2017)091
de Boer S, 2016, PHYS REV D, V93, DOI 10.1103/PhysRevD.93.074001
De Bruyn K, 2012, PHYS REV LETT, V109, DOI 10.1103/PhysRevLett.109.041801
De Bruyn K, 2012, PHYS REV D, V86, DOI 10.1103/PhysRevD.86.014027
de Florian D., HDB LHC HIGGS CROSS
Dedes A, 2018, J HIGH ENERGY PHYS, DOI 10.1007/JHEP08(2018)103
Dedes A, 2017, J HIGH ENERGY PHYS, DOI 10.1007/JHEP06(2017)143
Dekens W, 2019, J HIGH ENERGY PHYS, DOI 10.1007/JHEP01(2019)069
del Amo Sanchez P, 2010, Physical Review D, V82, DOI 10.1103/PhysRevD.82.112002
Descotes-Genon S., ARXIV181208163
Descotes-Genon S, 2016, J HIGH ENERGY PHYS, P1, DOI 10.1007/JHEP06(2016)092
Detmold W, 2016, PHYS REV D, V93, DOI 10.1103/PhysRevD.93.074501
Dutta D, 2015, PHYS REV D, V91, DOI 10.1103/PhysRevD.91.011101
Efrati A, 2015, J HIGH ENERGY PHYS, DOI 10.1007/JHEP07(2015)018
Ellis J, 2018, J HIGH ENERGY PHYS, DOI 10.1007/JHEP06(2018)146
Fajfer S, 2015, EUR PHYS J C, V75, DOI 10.1140/epjc/s10052-015-3801-2
Falkowski A, 2017, J HIGH ENERGY PHYS, DOI 10.1007/JHEP08(2017)123
Falkowski A, 2016, J HIGH ENERGY PHYS, DOI 10.1007/JHEP02(2016)086
Falkowski A, 2016, PHYS REV LETT, V116, DOI 10.1103/PhysRevLett.116.011801
Faroughy DA, 2017, PHYS LETT B, V764, P126, DOI 10.1016/j.physletb.2016.11.011
Feruglio F., ARXIV180610155
Feruglio F, 2017, J HIGH ENERGY PHYS, DOI 10.1007/JHEP09(2017)061
Freytsis M, 2015, PHYS REV D, V92, DOI 10.1103/PhysRevD.92.054018
GEIREGAT D, 1990, PHYS LETT B, V245, P271, DOI 10.1016/0370-2693(90)90146-W
Gonzalez-Alonso M, 2019, PROG PART NUCL PHYS, V104, P165, DOI 10.1016/j.ppnp.2018.08.002
Gonzalez-Alonso M, 2016, J HIGH ENERGY PHYS, DOI 10.1007/JHEP12(2016)052
Grygier J, 2018, PHYS REV D, V97, DOI 10.1103/PhysRevD.97.099902
Grygier J, 2017, PHYS REV D, V96, DOI 10.1103/PhysRevD.96.091101
Grzadkowski B, 2010, J HIGH ENERGY PHYS, DOI [10.1007/JHEP10(2010)85, 10.1007/JHEP10(2010)085]
Haller J, 2018, EUR PHYS J C, V78, DOI 10.1140/epjc/s10052-018-6131-3
Hamer P, 2016, PHYS REV D, V93, DOI 10.1103/PhysRevD.93.032007
Hartman C, 2015, J HIGH ENERGY PHYS, DOI 10.1007/JHEP07(2015)151
Hartmann C, 2015, PHYS REV LETT, V115, DOI 10.1103/PhysRevLett.115.191801
Hayasaka K, 2010, PHYS LETT B, V687, P139, DOI 10.1016/j.physletb.2010.03.037
Hirose S, 2017, PHYS REV LETT, V118, DOI 10.1103/PhysRevLett.118.211801
Hu Q.-Y., ARXIV181004939
Huber T, 2015, J HIGH ENERGY PHYS, DOI 10.1007/JHEP06(2015)176
Hurth T, 2017, PHYS REV D, V96, DOI 10.1103/PhysRevD.96.095034
Huschle M, 2015, PHYS REV D, V92, DOI 10.1103/PhysRevD.92.072014
Jenkins EE, 2018, J HIGH ENERGY PHYS, DOI 10.1007/JHEP03(2018)016
Jenkins EE, 2018, J HIGH ENERGY PHYS, DOI 10.1007/JHEP01(2018)084
Jenkins EE, 2014, J HIGH ENERGY PHYS, DOI 10.1007/JHEP01(2014)035
Jenkins EE, 2013, J HIGH ENERGY PHYS, DOI 10.1007/JHEP10(2013)087
Jung M., ARXIV180101112
Kallen G, 1964, ELEMENTARY PARTICLE
Kamenik JF, 2018, PHYS REV D, V97, DOI 10.1103/PhysRevD.97.035002
Khachatryan V, 2016, PHYS LETT B, V753, P424, DOI 10.1016/j.physletb.2015.12.020
Kumar J., ARXIV180607403
Kuno Y, 2001, REV MOD PHYS, V73, P151, DOI 10.1103/RevModPhys.73.151
Lees JP, 2017, PHYS REV LETT, V118, DOI 10.1103/PhysRevLett.118.031802
Lees JP, 2014, PHYS REV LETT, V112, DOI 10.1103/PhysRevLett.112.211802
Lees JP, 2013, PHYS REV D, V88, DOI 10.1103/PhysRevD.88.072012
Lees JP, 2013, PHYS REV D, V87, DOI 10.1103/PhysRevD.87.112005
Lees JP, 2012, PHYS REV D, V86, DOI 10.1103/PhysRevD.86.012004
Li XQ, 2016, J HIGH ENERGY PHYS, DOI 10.1007/JHEP08(2016)054
Lutz O, 2013, PHYS REV D, V87, DOI 10.1103/PhysRevD.87.111103
MISHRA SR, 1991, PHYS REV LETT, V66, P3117, DOI 10.1103/PhysRevLett.66.3117
Misiak M, 2015, PHYS REV LETT, V114, DOI 10.1103/PhysRevLett.114.221801
Misiak M, 2017, EUR PHYS J C, V77, DOI 10.1140/epjc/s10052-017-4776-y
Olive KA, 2014, CHINESE PHYS C, V38, DOI 10.1088/1674-1137/38/9/090001
Patrignani C, 2016, CHINESE PHYS C, V40, DOI 10.1088/1674-1137/40/10/100001
Paul A, 2017, J HIGH ENERGY PHYS, DOI 10.1007/JHEP04(2017)027
Petrin A. B., 2016, Applied Physics, P11
Pich A, 2014, PROG PART NUCL PHYS, V75, P41, DOI 10.1016/j.ppnp.2013.11.002
Romao JC, 2012, INT J MOD PHYS A, V27, DOI 10.1142/S0217751X12300256
Sato Y., 2016, PHYS REV D, V94
Schael S, 2013, PHYS REP, V532, P119, DOI 10.1016/j.physrep.2013.07.004
Schael S, 2006, PHYS REP, V427, P257, DOI 10.1016/j.physrep.2005.12.006
Straub D.M., FLAVIO FLAVOUR PHENO
Tanabashi M, 2018, PHYS REV D, V98, DOI 10.1103/PhysRevD.98.030001
Wells JD, 2016, J HIGH ENERGY PHYS, DOI 10.1007/JHEP01(2016)123
NR 174
TC 26
Z9 26
U1 0
U2 0
PU SPRINGER
PI NEW YORK
PA 233 SPRING ST, NEW YORK, NY 10013 USA
SN 1434-6044
EI 1434-6052
J9 EUR PHYS J C
JI Eur. Phys. J. C
PD JUN 15
PY 2019
VL 79
IS 6
AR 509
DI 10.1140/epjc/s10052-019-6977-z
PG 31
WC Physics, Particles & Fields
SC Physics
GA ID4ED
UT WOS:000471628700006
OA DOAJ Gold
DA 2021-04-21
ER
PT J
AU Heagy, LJ
Kang, S
Cockett, R
Oldenburg, DW
AF Heagy, Lindsey J.
Kang, Seogi
Cockett, Rowan
Oldenburg, Douglas W.
TI Open-source software for simulations and inversions of airborne
electromagnetic data
SO EXPLORATION GEOPHYSICS
LA English
DT Article
DE Airborne electromagnetics; electromagnetic geophysics; inversion;
programming; 3D modelling
AB Inversion of airborne electromagnetic data is often an iterative process, not only requiring that the researcher be able to explore the impact of changing components, such as the choice of regularisation functional or model parameterisation, but also often requiring that forward simulations be run and fields and fluxes visualised in order to build an understanding of the physical processes governing what we observe in the data. In the hope of facilitating this exploration and promoting the reproducibility of geophysical simulations and inversions, we have developed the open-source software package SimPEG. The software has been designed to be modular and extensible, with the goal of allowing researchers to interrogate all of the components and to facilitate the exploration of new inversion strategies. We present an overview of the software in its application to airborne electromagnetics and demonstrate its use for visualising fields and fluxes in a forward simulation, as well as its flexibility in formulating and solving the inverse problem. We invert a line of airborne time-domain electromagnetic data over a conductive vertical plate using a 1D voxel inversion, a 2D voxel inversion and a parametric inversion, where all of the forward modelling is done on a 3D grid. The results in this paper can be reproduced using the provided Jupyter notebooks. The Python software can also be modified to allow users to experiment with parameters and explore the physics of the electromagnetics and intricacies of inversion.
C1 [Heagy, Lindsey J.; Kang, Seogi; Cockett, Rowan; Oldenburg, Douglas W.] Univ British Columbia, Earth Ocean & Atmospher Sci, Vancouver, BC, Canada.
RP Heagy, LJ (corresponding author), Univ British Columbia, Earth Ocean & Atmospher Sci, Vancouver, BC, Canada.
EM lheagy@eos.ubc.ca
OI Heagy, Lindsey/0000-0002-1551-5926; KANG, SEOGI/0000-0002-9963-936X;
Oldenburg, Douglas/0000-0002-4327-2124
CR Cockett R, 2015, COMPUT GEOSCI-UK, V85, P142, DOI 10.1016/j.cageo.2015.09.015
Heagy LJ, 2017, COMPUT GEOSCI-UK, V107, P1, DOI 10.1016/j.cageo.2017.06.018
Kang S., 2015, SEG TECHN PROGR, P5000, DOI [10.1190/segam201 5- 5930379.1., DOI 10.1190/SEGAM2015-5930379.1]
Kang S, 2016, GEOPHYS J INT, V207, P174, DOI 10.1093/gji/ggw256
McMillan MS, 2015, GEOPHYSICS, V80, pK25, DOI 10.1190/GEO2015-0141.1
Oldenburg D. W., 2017, GEOPHYS ELECTROMAGNE
Pereira Fabio, 2015, 2015 IEEE Power & Energy Society General Meeting, P1, DOI 10.1109/PESGM.2015.7286381
Tikhonov A. N., 1977, MATH COMPUT, V32, P491, DOI DOI 10.2307/2006360
Viezzoli A, 2008, GEOPHYSICS, V73, pF105, DOI 10.1190/1.2895521
Viezzoli A, 2009, EXPLOR GEOPHYS, V40, P173, DOI 10.1071/EG08027
Yang DK, 2014, GEOPHYS J INT, V196, P1492, DOI 10.1093/gji/ggt465
NR 11
TC 2
Z9 2
U1 2
U2 9
PU TAYLOR & FRANCIS LTD
PI ABINGDON
PA 2-4 PARK SQUARE, MILTON PARK, ABINGDON OR14 4RN, OXON, ENGLAND
SN 0812-3985
EI 1834-7533
J9 EXPLOR GEOPHYS
JI Explor. Geophys.
PD JAN 2
PY 2020
VL 51
IS 1
BP 38
EP 44
DI 10.1080/08123985.2019.1583538
PG 7
WC Geochemistry & Geophysics
SC Geochemistry & Geophysics
GA OU8PT
UT WOS:000481374100001
DA 2021-04-21
ER
PT J
AU Hickstein, DD
Gibson, ST
Yurchak, R
Das, DD
Ryazanov, M
AF Hickstein, Daniel D.
Gibson, Stephen T.
Yurchak, Roman
Das, Dhrubajyoti D.
Ryazanov, Mikhail
TI A direct comparison of high-speed methods for the numerical Abel
transform
SO REVIEW OF SCIENTIFIC INSTRUMENTS
LA English
DT Article
ID SOOT VOLUME FRACTION; INVERSION; RECONSTRUCTION; IMAGES; FLAME
AB The Abel transform is a mathematical operation that transforms a cylindrically symmetric three-dimensional (3D) object into its two-dimensional (2D) projection. The inverse Abel transform reconstructs the 3D object from the 2D projection. Abel transforms have wide application across numerous fields of science, especially chemical physics, astronomy, and the study of laser-plasma plumes. Consequently, many numerical methods for the Abel transform have been developed, which makes it challenging to select the ideal method for a specific application. In this work, eight published transform methods have been incorporated into a single, open-source Python software package (PyAbel) to provide a direct comparison of the capabilities, advantages, and relative computational efficiency of each transform method. Most of the tested methods provide similar, high-quality results. However, the computational efficiency varies across several orders of magnitude. By optimizing the algorithms, we find that some transform methods are sufficiently fast to transform 1-megapixel images at more than 100 frames per second on a desktop personal computer. In addition, we demonstrate the transform of gigapixel images.
C1 [Hickstein, Daniel D.] Kapteyn Murnane Labs Inc, Boulder, CO 80301 USA.
[Gibson, Stephen T.] Australian Natl Univ, Res Sch Phys & Engn, Canberra, ACT 2601, Australia.
[Yurchak, Roman] Symerio, F-91120 Palaiseau, France.
[Das, Dhrubajyoti D.] Yale Univ, Dept Chem & Environm Engn, New Haven, CT 06511 USA.
[Ryazanov, Mikhail] Natl Inst Stand & Technol, JILA, Boulder, CO 80309 USA.
[Ryazanov, Mikhail] Univ Colorado, Boulder, CO 80309 USA.
RP Hickstein, DD (corresponding author), Kapteyn Murnane Labs Inc, Boulder, CO 80301 USA.
EM danhickstein@gmail.com
RI ; Hickstein, Daniel/I-8532-2012
OI Das, Dhrubajyoti/0000-0001-9731-2489; Yurchak,
Roman/0000-0002-2565-4444; Hickstein, Daniel/0000-0003-1277-847X;
Gibson, Stephen/0000-0002-3767-6114
FU Australian Research Council Discovery ProjectAustralian Research Council
[DP160102585]
FX S.T.G.'s research was supported by the Australian Research Council
Discovery Project, Grant No. DP160102585.
CR Behnel S, 2011, COMPUT SCI ENG, V13, P31, DOI 10.1109/MCSE.2010.118
Bordas C, 1996, REV SCI INSTRUM, V67, P2257, DOI 10.1063/1.1147044
Brady DJ, 2012, NATURE, V486, P386, DOI 10.1038/nature11150
CHANDLER DW, 1987, J CHEM PHYS, V87, P1445, DOI 10.1063/1.453276
Cignoli F, 2001, APPL OPTICS, V40, P5370, DOI 10.1364/AO.40.005370
COPPERSMITH D, 1990, J SYMB COMPUT, V9, P251, DOI 10.1016/S0747-7171(08)80013-2
CRAIG IJD, 1979, ASTRON ASTROPHYS, V79, P121
Das DD, 2017, P COMBUST INST, V36, P871, DOI 10.1016/j.proci.2016.06.047
DASCH CJ, 1992, APPL OPTICS, V31, P1146, DOI 10.1364/AO.31.001146
Daun KJ, 2006, APPL OPTICS, V45, P4638, DOI 10.1364/AO.45.004638
De Iuliis S, 1998, COMBUST FLAME, V115, P253, DOI 10.1016/S0010-2180(97)00357-X
De Micheli E, 2017, APPL MATH COMPUT, V301, P12, DOI 10.1016/j.amc.2016.12.009
Dick B, 2014, PHYS CHEM CHEM PHYS, V16, P570, DOI 10.1039/c3cp53673d
Dribinski V, 2002, REV SCI INSTRUM, V73, P2634, DOI 10.1063/1.1482156
Duzor M. V., 2010, J CHEM PHYS, V133
Garcia GA, 2004, REV SCI INSTRUM, V75, P4989, DOI 10.1063/1.1807578
Gascooke J. R., 2000, THESIS
Gascooke JR, 2017, J CHEM PHYS, V147, DOI 10.1063/1.4981024
Gerber T, 2013, REV SCI INSTRUM, V84, DOI 10.1063/1.4793404
Gibson S., 2019, PYABEL PYABEL V0 8 2, DOI 10.5281/zenodo.3243413
Gladstone GR, 2016, SCIENCE, V351, DOI 10.1126/science.aad8866
GLASSER J, 1978, APPL OPTICS, V17, P3750, DOI 10.1364/AO.17.003750
HANSEN EW, 1985, J OPT SOC AM A, V2, P510, DOI 10.1364/JOSAA.2.000510
HANSEN EW, 1985, IEEE T ACOUST SPEECH, V33, P666, DOI 10.1109/TASSP.1985.1164579
Harrison GR, 2018, J CHEM PHYS, V148, DOI 10.1063/1.5025057
Liu C, 2014, IEEE T INSTRUM MEAS, V63, P3067, DOI 10.1109/TIM.2014.2315737
Lumpe JD, 2007, J GEOPHYS RES-ATMOS, V112, DOI 10.1029/2006JD008076
Rallis CE, 2014, REV SCI INSTRUM, V85, DOI 10.1063/1.4899267
Renth F, 2006, REV SCI INSTRUM, V77, DOI 10.1063/1.2176056
Ryazanov M., 2012, THESIS
Snelling DR, 1999, APPL OPTICS, V38, P2478, DOI 10.1364/AO.38.002478
van der Walt S, 2011, COMPUT SCI ENG, V13, P22, DOI 10.1109/MCSE.2011.37
Whitaker B. J., 2003, IMAGING MOL DYNAMICS
Yurchak R., 2015, THESIS
NR 34
TC 14
Z9 14
U1 1
U2 10
PU AMER INST PHYSICS
PI MELVILLE
PA 1305 WALT WHITMAN RD, STE 300, MELVILLE, NY 11747-4501 USA
SN 0034-6748
EI 1089-7623
J9 REV SCI INSTRUM
JI Rev. Sci. Instrum.
PD JUN
PY 2019
VL 90
IS 6
AR 065115
DI 10.1063/1.5092635
PG 9
WC Instruments & Instrumentation; Physics, Applied
SC Instruments & Instrumentation; Physics
GA IH6JI
UT WOS:000474601100043
PM 31255037
OA Green Published
DA 2021-04-21
ER
PT J
AU Glaser, C
Nelles, A
Plaisier, I
Welling, C
Barwick, SW
Garcia-Fernandez, D
Gaswint, G
Lahmann, R
Persichilli, C
AF Glaser, Christian
Nelles, Anna
Plaisier, Ilse
Welling, Christoph
Barwick, Steven W.
Garcia-Fernandez, Daniel
Gaswint, Geoffrey
Lahmann, Robert
Persichilli, Christopher
TI NuRadioReco: a reconstruction framework for radio neutrino detectors
SO EUROPEAN PHYSICAL JOURNAL C
LA English
DT Article
ID AIR-SHOWERS; COSMIC-RAYS; EMISSION; ASKARYAN
AB While the radio detection of cosmic rays has advanced to a standard method in astroparticle physics, the radio detection of neutrinos is just about to start its full bloom. The successes of pilot-arrays have to be accompanied by the development of modern and flexible software tools to ensure rapid progress in reconstruction algorithms and data processing. We present NuRadioReco as such a modern Python-based data analysis tool. It includes a suitable data-structure, a database-implementation of a time-dependent detector, modern browser-based data visualization tools, and fully separated analysis modules. We describe the framework and examples, as well as new reconstruction algorithms to obtain the full three-dimensional electric field from distributed antennas which is needed for high-precision energy reconstruction of particle showers.
C1 [Glaser, Christian; Barwick, Steven W.; Gaswint, Geoffrey; Lahmann, Robert; Persichilli, Christopher] Univ Calif Irvine, Dept Phys & Astron, Irvine, CA 92697 USA.
[Nelles, Anna; Plaisier, Ilse; Welling, Christoph; Garcia-Fernandez, Daniel] DESY, Platanenallee 6, D-15738 Zeuthen, Germany.
[Nelles, Anna; Plaisier, Ilse; Welling, Christoph; Garcia-Fernandez, Daniel; Lahmann, Robert] Friedrich Alexander Univ Erlangen Nurnberg, Erlangen Ctr Astroparticle Phys, D-91058 Erlangen, Germany.
RP Glaser, C (corresponding author), Univ Calif Irvine, Dept Phys & Astron, Irvine, CA 92697 USA.
EM christian.glaser@uci.edu; anna.nelles@desy.de
RI Lahmann, Robert/L-7461-2015; Nelles, Anna/AAU-1193-2020
OI Nelles, Anna/0000-0002-1720-6350; Glaser, Christian/0000-0001-5998-2553
FU German research foundation (DFG)German Research Foundation (DFG) [GL
914/1-1, NE 2031/2-1]; U.S. National Science Foundation-Physics
DivisionNational Science Foundation (NSF) [NSF-1607719]
FX We acknowledge funding from the German research foundation (DFG) under
Grants GL 914/1-1 and NE 2031/2-1, and the U.S. National Science
Foundation-Physics Division (Grant NSF-1607719).
CR Aab A, 2018, J COSMOL ASTROPART P, DOI 10.1088/1475-7516/2018/10/026
Aab A, 2016, PHYS REV LETT, V116, DOI 10.1103/PhysRevLett.116.241101
Aab A, 2016, PHYS REV D, V93, DOI 10.1103/PhysRevD.93.122005
Aab A, 2014, PHYS REV D, V89, DOI 10.1103/PhysRevD.89.052002
Aartsen MG, 2017, PHYS REV LETT, V19, DOI 10.1103/PhysRevLett.119.259902
Aartsen MG, 2016, PHYS REV LETT, V117, DOI 10.1103/PhysRevLett.117.241101
Abreu P, 2012, J INSTRUM, V7, DOI 10.1088/1748-0221/7/10/P10011
Abreu P, 2011, NUCL INSTRUM METH A, V635, P92, DOI 10.1016/j.nima.2011.01.049
Allison P, 2016, PHYS REV D, V93, DOI 10.1103/PhysRevD.93.082003
Allison P, 2015, ASTROPART PHYS, V70, P62, DOI 10.1016/j.astropartphys.2015.04.006
Alvarez-Muniz J, 2014, ASTROPART PHYS, V59, P29, DOI 10.1016/j.astropartphys.2014.04.004
[Anonymous], 2019, DASH
[Anonymous], 2019, BOOTSTRAP
[Anonymous], 2019, NURADIORECO
[Anonymous], 2019, TINYDB
[Anonymous], 2019, NURADIOMC
Argiro S, 2007, NUCL INSTRUM METH A, V580, P1485, DOI 10.1016/j.nima.2007.07.010
ASKARYAN GA, 1965, SOV PHYS JETP-USSR, V21, P658
Barwick S, 2005, J GLACIOL, V51, P231, DOI 10.3189/172756505781829467
Barwick SW, 2017, ASTROPART PHYS, V90, P50, DOI 10.1016/j.astropartphys.2017.02.003
Barwick SW, 2015, ASTROPART PHYS, V70, P12, DOI 10.1016/j.astropartphys.2015.04.002
BERESINSKY VS, 1969, PHYS LETT B, VB 28, P423, DOI 10.1016/0370-2693(69)90341-4
Bezyazeekov PA, 2015, NUCL INSTRUM METH A, V802, P89, DOI 10.1016/j.nima.2015.08.061
Bretz HP, 2012, J INSTRUM, V7, DOI 10.1088/1748-0221/7/08/T08005
Buitink S, 2016, NATURE, V531, P70, DOI 10.1038/nature16976
Buitink S, 2014, PHYS REV D, V90, DOI 10.1103/PhysRevD.90.082003
Burke G., 1983, TECH REP
Corstanje A, 2017, ASTROPART PHYS, V89, P23, DOI 10.1016/j.astropartphys.2017.01.009
Dookayka K., 2011, THESIS
Farr E. G., 2011, SENSOR SIMULATION NO
Glaser C., 2017, THESIS, DOI [10.18154/RWTH-2017-02960, DOI 10.18154/RWTH-2017-02960]
Glaser C., 2018, EPJC CATANIA S UNPUB
Glaser C., 2019, P COSPAR 2018 UNPUB
Glaser C, 2019, ASTROPART PHYS, V104, P64, DOI 10.1016/j.astropartphys.2018.08.004
Glaser C, 2016, J COSMOL ASTROPART P, DOI 10.1088/1475-7516/2016/09/024
Gottowik M, 2018, ASTROPART PHYS, V103, P87, DOI 10.1016/j.astropartphys.2018.07.004
GREISEN K, 1966, PHYS REV LETT, V16, P748, DOI 10.1103/PhysRevLett.16.748
Huege T, 2013, AIP CONF PROC, V1535, P128, DOI 10.1063/1.4807534
Huege T, 2016, PHYS REP, V620, P1, DOI 10.1016/j.physrep.2016.02.001
Kelley J. L., 2018, EPJC CATANIA S UNPUB
Kolundzija B, 2011, 2011 5th European Conference on Antennas and Propagation (EuCAP), P2844
Kravchenko I., ARXIV07054491
Kunz K. S., 1993, FINITE DIFFERENCE TI
Nelles A, 2015, J COSMOL ASTROPART P, DOI 10.1088/1475-7516/2015/05/018
Nelles A., 2019, 18 INT WORKSH NEUTR
Nelles A, 2015, ASTROPART PHYS, V60, P13, DOI 10.1016/j.astropartphys.2014.05.001
Oliphant T.E., 2006, A GUIDE TO NUMPY, VVolume 1
Rossant C., 2016, SHOULD YOU USE HDF5
Rossant C., 2016, MOVING AWAY HDF5
Schellart P, 2014, J COSMOL ASTROPART P, DOI 10.1088/1475-7516/2014/10/014
Schellart P, 2013, ASTRON ASTROPHYS, V560, DOI 10.1051/0004-6361/201322683
Scholten O, 2016, PHYS REV D, V94, DOI 10.1103/PhysRevD.94.103010
Schroder FG, 2017, PROG PART NUCL PHYS, V93, P1, DOI 10.1016/j.ppnp.2016.12.002
van Haarlem MP, 2013, ASTRON ASTROPHYS, V556, DOI 10.1051/0004-6361/201220873
ZATSEPIN GT, 1966, JETP LETT-USSR, V4, P78
NR 55
TC 7
Z9 7
U1 0
U2 2
PU SPRINGER
PI NEW YORK
PA 233 SPRING ST, NEW YORK, NY 10013 USA
SN 1434-6044
EI 1434-6052
J9 EUR PHYS J C
JI Eur. Phys. J. C
PD JUN 1
PY 2019
VL 79
IS 6
AR 464
DI 10.1140/epjc/s10052-019-6971-5
PG 21
WC Physics, Particles & Fields
SC Physics
GA IA8BL
UT WOS:000469782400002
OA DOAJ Gold, Green Published
DA 2021-04-21
ER
PT J
AU Kurchin, R
Romano, G
Buonassisi, T
AF Kurchin, Rachel
Romano, Giuseppe
Buonassisi, Tonio
TI Bayesim: A tool for adaptive grid model fitting with Bayesian inference
SO COMPUTER PHYSICS COMMUNICATIONS
LA English
DT Article
DE Bayesian inference; Photovoltaics
AB Bayesian inference is a widely used and powerful analytical technique in fields such as astronomy and particle physics but has historically been underutilized in some other disciplines including semiconductor devices. In this work, we introduce bayesim, a Python package that utilizes adaptive grid sampling to efficiently generate a probability distribution over multiple input parameters to a forward model using a collection of experimental measurements. We discuss the implementation choices made in the code, showcase two examples in photovoltaics, and discuss general prerequisites for the approach to apply to other systems. (C) 2019 Elsevier B.V. All rights reserved.
C1 [Kurchin, Rachel; Romano, Giuseppe; Buonassisi, Tonio] MIT, 77 Massachusetts Ave, Cambridge, MA 02139 USA.
RP Kurchin, R; Buonassisi, T (corresponding author), MIT, 77 Massachusetts Ave, Cambridge, MA 02139 USA.
EM rkurchin@alum.mit.edu; buonassisi@mit.edu
OI /0000-0002-2147-4809; Romano, Giuseppe/0000-0003-0026-8237
FU Blue Waters Graduate Fellowship, USA; TOTAL SA research, USA grant
through MITei; MIT Energy Initiative; U.S. Department of Energy, Office
of ScienceUnited States Department of Energy (DOE)
FX R.C.K. acknowledges the funding of a Blue Waters Graduate Fellowship,
USA. The experimental data acquisition shown was supported by a TOTAL SA
research, USA grant funded through MITei, the MIT Energy Initiative and
the computational work was supported as part of the Center for
Next-Generation Materials by Design, USA, an Energy Frontier Research
Center, USA funded by the U.S. Department of Energy, Office of Science.
The authors also acknowledge I.M. Peters for helpful conversations.
CR Brandt RE, 2017, JOULE, V1, P843, DOI 10.1016/j.joule.2017.10.001
Burgelman M, 2000, THIN SOLID FILMS, V361, P527, DOI 10.1016/S0040-6090(99)00825-1
de Blas J, 2016, NUCL PART PHYS P, V273, P834, DOI 10.1016/j.nuclphysbps.2015.09.128
Feroz F, 2009, MON NOT R ASTRON SOC, V398, P1601, DOI 10.1111/j.1365-2966.2009.14548.x
Hunter JD, 2007, COMPUT SCI ENG, V9, P90, DOI 10.1109/MCSE.2007.55
Jones E., 2001, SCIPY OPEN SOURCE SC
Kurchin R. C., 2018, P 2018 7 WORLD C PHO
Kurchin R. C., 2018, BAYESIM
Kurchin R. C., BAYESIM
Larsson G., DEEPDISH
Millman J., 2010, P56, DOI DOI 10.1016/S0168-0102(02)00204-3
Oliphant T.E., 2006, A GUIDE TO NUMPY
Scheres SHW, 2012, J STRUCT BIOL, V180, P519, DOI 10.1016/j.jsb.2012.09.006
Trotta R, 2008, CONTEMP PHYS, V49, P71, DOI 10.1080/00107510802066753
Trotta R, 2008, J HIGH ENERGY PHYS, DOI 10.1088/1126-6708/2008/12/024
Ueno Tsuyoshi, 2016, Materials Discovery, V4, P18, DOI 10.1016/j.md.2016.04.001
Wilkinson DJ, 2007, BRIEF BIOINFORM, V8, P109, DOI 10.1093/bib/bbm007
Xue DZ, 2016, P NATL ACAD SCI USA, V113, P13301, DOI 10.1073/pnas.1607412113
2001, SCIENCE, V294, P2310, DOI DOI 10.1126/SCIENCE.1065889
NR 19
TC 2
Z9 2
U1 0
U2 3
PU ELSEVIER
PI AMSTERDAM
PA RADARWEG 29, 1043 NX AMSTERDAM, NETHERLANDS
SN 0010-4655
EI 1879-2944
J9 COMPUT PHYS COMMUN
JI Comput. Phys. Commun.
PD JUN
PY 2019
VL 239
BP 161
EP 165
DI 10.1016/j.cpc.2019.01.022
PG 5
WC Computer Science, Interdisciplinary Applications; Physics, Mathematical
SC Computer Science; Physics
GA HV8QN
UT WOS:000466248000015
OA Bronze
DA 2021-04-21
ER
PT J
AU Krivenko, I
Harland, M
AF Krivenko, Igor
Harland, Malte
TI TRIQS/SOM: Implementation of the stochastic optimization method for
analytic continuation
SO COMPUTER PHYSICS COMMUNICATIONS
LA English
DT Article
DE Quantum Monte Carlo; Analytic continuation; Stochastic optimization;
Python
ID QUANTUM MONTE-CARLO; HUBBARD-MODEL
AB We present the TRIQS/SOM analytic continuation package, an efficient implementation of the Stochastic Optimization Method proposed by Mishchenko et al. (2000). TRIQS/SOM strives to provide a high quality open source (distributed under the GNU General Public License version 3) alternative to the more widely adopted Maximum Entropy continuation programs. It supports a variety of analytic continuation problems encountered in the field of computational condensed matter physics. Those problems can be formulated in terms of response functions of imaginary time, Matsubara frequencies or in the Legendre polynomial basis representation. The application is based on the TRIQS C++/Python framework, which allows for easy interoperability with TRIQS-based quantum impurity solvers, electronic band structure codes and visualization tools. Similar to other TRIQS packages, it comes with a convenient Python interface. (C) 2019 Elsevier B.V. All rights reserved.
C1 [Krivenko, Igor; Harland, Malte] Univ Hamburg, Inst Theoret Phys 1, Jungiusstr 9, D-20355 Hamburg, Germany.
RP Krivenko, I (corresponding author), Univ Michigan, Dept Phys, Ann Arbor, MI 48109 USA.
EM ikrivenk@physnet.uni-hamburg.de; mharland@physnet.uni-hamburg.de
FU Deutschen Forschungsgemeinschaft (DFG)German Research Foundation (DFG)
[SFB668]; DFG, GermanyGerman Research Foundation (DFG) [SFB925]
FX Authors are grateful to Alexander Lichtenstein and Olga Goulko for
fruitful discussions, to Roberto Mozara for his valuable comments on the
usability of the code, and to Andrey Mishchenko for giving access to the
original FORTRAN implementation of the SOM algorithm. We would also like
to thank Vladislav Pokorny and Hanna Terletska, who stimulated writing
of this paper. I.K. acknowledges support from Deutschen
Forschungsgemeinschaft (DFG) via project SFB668 (subproject A3,
"Electronic structure and magnetism of correlated systems"). M.H.
acknowledges financial support by the DFG, Germany in the framework of
the SFB925. The computations were performed with resources provided by
the North -German Supercomputing Alliance (HLRN).
CR Abramowitz M., 1972, HDB MATH FUNCTIONS, P443
Beach K.S.D., 2004, ARXIVCONDMAT0403055
Beach KSD, 2000, PHYS REV B, V61, P5147, DOI 10.1103/PhysRevB.61.5147
Bergeron D., TRIQS INTERFACE OMEG
Bergeron D, 2016, PHYS REV E, V94, DOI 10.1103/PhysRevE.94.023303
BLANKENBECLER R, 1981, PHYS REV D, V24, P2278, DOI 10.1103/PhysRevD.24.2278
Boehnke L, 2011, PHYS REV B, V84, DOI 10.1103/PhysRevB.84.075145
BRYAN RK, 1990, EUR BIOPHYS J, V18, P165, DOI 10.1007/BF02427376
CARLITZ L, 1957, DUKE MATH J, V24, P151, DOI 10.1215/S0012-7094-57-02421-3
Chen HT, 2017, J CHEM PHYS, V146, DOI 10.1063/1.4974329
Chen HT, 2017, J CHEM PHYS, V146, DOI 10.1063/1.4974328
Fuchs S, 2010, J PHYS CONF SER, V200, DOI 10.1088/1742-6596/200/1/012041
Georges A, 1996, REV MOD PHYS, V68, P13, DOI 10.1103/RevModPhys.68.13
GEORGES A, 1992, PHYS REV B, V45, P6479, DOI 10.1103/PhysRevB.45.6479
Goulko O, 2017, PHYS REV B, V95, DOI 10.1103/PhysRevB.95.014102
Groetsch C W, 2007, Journal of Physics: Conference Series, V73, P012001, DOI 10.1088/1742-6596/73/1/012001
Gull E, 2008, EPL-EUROPHYS LETT, V82, DOI 10.1209/0295-5075/82/57003
HASTINGS WK, 1970, BIOMETRIKA, V57, P97, DOI 10.2307/2334940
Jarrell M, 1996, PHYS REP, V269, P133, DOI 10.1016/0370-1573(95)00074-7
Kotliar G, 2006, REV MOD PHYS, V78, P865, DOI 10.1103/RevModPhys.78.865
Kraberger G. J., MAXENT
Kraberger GJ, 2017, PHYS REV B, V96, DOI 10.1103/PhysRevB.96.155128
Levy R, 2017, COMPUT PHYS COMMUN, V215, P149, DOI 10.1016/j.cpc.2017.01.018
METROPOLIS N, 1953, J CHEM PHYS, V21, P1087, DOI 10.1063/1.1699114
METZNER W, 1989, PHYS REV LETT, V62, P324, DOI 10.1103/PhysRevLett.62.324
Mishchenko A. S., 2012, REIHE MODELING SIMUL, V2
Mishchenko AS, 2000, PHYS REV B, V62, P6317, DOI 10.1103/PhysRevB.62.6317
Moeller G, 1999, PHYS REV B, V59, P6846, DOI 10.1103/PhysRevB.59.6846
Otsuki J, 2017, PHYS REV E, V95, DOI 10.1103/PhysRevE.95.061302
Otsuki J, 2007, J PHYS SOC JPN, V76, DOI 10.1143/JPSJ.76.114707
Parcollet O, 2015, COMPUT PHYS COMMUN, V196, P398, DOI 10.1016/j.cpc.2015.04.023
Potthoff M, 1997, PHYS REV B, V55, P16132, DOI 10.1103/PhysRevB.55.16132
Prokof'ev NV, 2013, JETP LETT+, V97, P649, DOI 10.1134/S002136401311009X
Prokof'ev N, 2007, PHYS REV LETT, V99, DOI 10.1103/PhysRevLett.99.250201
Rubtsov AN, 2005, PHYS REV B, V72, DOI 10.1103/PhysRevB.72.035122
Sandvik AW, 1998, PHYS REV B, V57, P10287, DOI 10.1103/PhysRevB.57.10287
Seth P, 2016, COMPUT PHYS COMMUN, V200, P274, DOI 10.1016/j.cpc.2015.10.023
Vafayi K, 2007, PHYS REV B, V76, DOI 10.1103/PhysRevB.76.035115
VIDBERG HJ, 1977, J LOW TEMP PHYS, V29, P179, DOI 10.1007/BF00655090
Werner P, 2010, PHYS REV B, V81, DOI 10.1103/PhysRevB.81.035108
Werner P, 2009, PHYS REV B, V79, DOI 10.1103/PhysRevB.79.035320
Zhu XL, 2002, J COMPUT APPL MATH, V148, P341, DOI 10.1016/S0377-0427(02)00554-X
2008, PHYS REV B, V78
2008, PHYS REV B, V77
2011, REV MOD PHYS, V83, P349, DOI DOI 10.1103/REVMODPHYS.83.349
2006, PHYS REV B, V74
2016, PHYS REV B, V94
2008, PHYS REV LETT, V100
NR 48
TC 4
Z9 4
U1 0
U2 5
PU ELSEVIER SCIENCE BV
PI AMSTERDAM
PA PO BOX 211, 1000 AE AMSTERDAM, NETHERLANDS
SN 0010-4655
EI 1879-2944
J9 COMPUT PHYS COMMUN
JI Comput. Phys. Commun.
PD JUN
PY 2019
VL 239
BP 166
EP 183
DI 10.1016/j.cpc.2019.01.021
PG 18
WC Computer Science, Interdisciplinary Applications; Physics, Mathematical
SC Computer Science; Physics
GA HV8QN
UT WOS:000466248000016
DA 2021-04-21
ER
PT J
AU Rykhlevskii, A
Bae, JW
Huff, KD
AF Rykhlevskii, Andrei
Bae, Jin Whan
Huff, Kathryn D.
TI Modeling and simulation of online reprocessing in the thorium-fueled
molten salt breeder reactor
SO ANNALS OF NUCLEAR ENERGY
LA English
DT Article
DE Molten salt reactor; Molten salt breeder reactor; Python; Depletion;
Online reprocessing; Nuclear fuel cycle; Salt treatment
ID ANALYSIS CAPABILITIES; CORE PHYSICS; CYCLE; NEUTRONICS
AB In the search for new ways to generate carbon-free, reliable base-load power, interest in advanced nuclear energy technologies, particularly Molten Salt Reactors (MSRs), has resurged with multiple new companies pursuing MSR commercialization. To further develop these MSR concepts, researchers need simulation tools for analyzing liquid-fueled MSR depletion and fuel processing. However, most contemporary nuclear reactor physics software is unable to perform high-fidelity full-core depletion calculations for a reactor design with online reprocessing. This paper introduces a Python package, SaltProc, which couples with the Monte Carlo code, SERPENT2 to simulate MSR online reprocessing by modeling the changing isotopic composition of MSR fuel salt. This work demonstrates SaltProc capabilities for a full-core, high-fidelity model of the commercial Molten Salt Breeder Reactor (MSBR) concept and verifies these results to results in the literature from independent, lower-fidelity analyses. (C) 2019 Elsevier Ltd. All rights reserved.
C1 [Rykhlevskii, Andrei; Bae, Jin Whan; Huff, Kathryn D.] Univ Illinois, Dept Nucl Plasma & Radiol Engn, Urbana, IL 61801 USA.
RP Huff, KD (corresponding author), Univ Illinois, Dept Nucl Plasma & Radiol Engn, Urbana, IL 61801 USA.
EM kdhuff@illinois.edu
OI Huff, Katy/0000-0002-7075-6802; Rykhlevskii, Andrei/0000-0001-5786-3649
FU National Science FoundationNational Science Foundation (NSF)
[OCI-0725070, ACI-1238993]; state of Illinois; DOE ARPA-E MEITNER
programUnited States Department of Energy (DOE) [1798-1576]; DOE Nuclear
Energy University Program [16-10512]; Nuclear Regulatory Commission
Faculty Development Program; National Center for Supercomputing
Applications; NNSA Office of Defense Nuclear Nonproliferation R&D
through the Consortium for Verfication Technologies; NNSA Office of
Defense Nuclear Nonproliferation R&D through the Consortium for
Nonproliferation Enabling Capabilities; International Institute for
Carbon Neutral Energy Research (WPI-I2CNER) - Japanese Ministry of
Education, Culture, Sports, Science and Technology
FX This research is part of the Blue Waters sustained-petascale computing
project, which is supported by the National Science Foundation (awards
OCI-0725070 and ACI-1238993) and the state of Illinois. Blue Waters is a
joint effort of the University of Illinois at Urbana-Champaign and its
National Center for Supercomputing Applications; The authors contributed
to this work as described below. Andrei Rykhlevskii conceived and
designed the simulations, wrote the paper, prepared figures and/or
tables, performed the computation work, contributed to the software
product, and reviewed drafts of the paper. Jin Whan Bae conceived and
designed the simulations, wrote the paper, contributed to the software
product, and reviewed drafts of the paper. Andrei Rykhlevskii is
supported by DOE ARPA-E MEITNER program award 1798-1576. Jin Whan Bae is
supported by funding received from the DOE Nuclear Energy University
Program (Project 16-10512) 'Demand-Driven Cycamore Archetypes'.; Kathryn
D. Huff directed and supervised the work, conceived and designed the
simulations, contributed to the software product, and reviewed drafts of
the paper. Prof. Huff is supported by the Nuclear Regulatory Commission
Faculty Development Program, the National Center for Supercomputing
Applications, the NNSA Office of Defense Nuclear Nonproliferation R&D
through the Consortium for Verfication Technologies and the Consortium
for Nonproliferation Enabling Capabilities, the International Institute
for Carbon Neutral Energy Research (WPI-I2CNER), sponsored by the
Japanese Ministry of Education, Culture, Sports, Science and Technology,
and DOE ARPA-E MEITNER program award 1798-1576.
CR Ahmad A, 2015, ANN NUCL ENERGY, V75, P261, DOI 10.1016/j.anucene.2014.08.014
Alfonsi A., 2013, P M C2013 INT TOP M
Ashraf O, 2018, J PHYS CONF SER, V1133, DOI 10.1088/1742-6596/1133/1/012026
Aufiero M, 2013, J NUCL MATER, V441, P473, DOI 10.1016/j.jnucmat.2013.06.026
Bauman H. F., 1971, ROD NUCL FUEL CYCLE, DOI [10.2172/4741221, DOI 10.2172/4741221]
Betzler B. R., 2017, P M C 2017 INT C MAT
Betzler B. R., 2016, P INT C PHYS
Betzler BR, 2018, ANN NUCL ENERGY, V119, P396, DOI 10.1016/j.anucene.2018.04.043
Betzler BR, 2017, ANN NUCL ENERGY, V101, P489, DOI 10.1016/j.anucene.2016.11.040
Bowman SM, 2011, NUCL TECHNOL, V174, P126, DOI 10.13182/NT10-163
Croff A.G., 1980, ORNLTM7175
Derstine K., 1984, DIF3D CODE SOLVE ONE
DoE U S, 2002, TECHN ROADM GEN 4 NU, P48
Doligez X, 2014, ANN NUCL ENERGY, V64, P430, DOI 10.1016/j.anucene.2013.09.009
Fiorina C, 2013, PROG NUCL ENERG, V68, P153, DOI 10.1016/j.pnucene.2013.06.006
Forget B., 2018, INTEGRAL FULL CORE M
Gauld IC, 2011, NUCL TECHNOL, V174, P169, DOI 10.13182/NT11-3
Goluoglu S, 2011, NUCL TECHNOL, V174, P214, DOI 10.13182/NT10-124
Goorley J. T., 2013, LACP1300634 LOS AL L
HAUBENREICH PN, 1970, NUCL APPL TECHNOL, V8, P118, DOI 10.13182/NT8-2-118
Heuer D, 2014, ANN NUCL ENERGY, V64, P421, DOI 10.1016/j.anucene.2013.08.002
Heuer D., 2010, REV GEN NUCL, P95, DOI [10.1051/rgn/20106095, DOI 10.1051/rgn/20106095]
Jeong Y., 2014, PHYSOR 2014
Jeong Y, 2016, J NUCL SCI TECHNOL, V53, P529, DOI 10.1080/00223131.2015.1062812
Kee C. W., 1976, ORNLTM4210
LeBlanc D, 2010, NUCL ENG DES, V240, P1644, DOI 10.1016/j.nucengdes.2009.12.033
Leppanen J, 2015, ANN NUCL ENERGY, V82, P142, DOI 10.1016/j.anucene.2014.08.024
MCNP, 2004, GEN MONT CARL N PART
Nuttin A, 2005, PROG NUCL ENERG, V46, P77, DOI 10.1016/j.pnucene.2004.11.001
O. D. Bank, 2014, 24 JEFF OECDNEA
Park J, 2015, INT J ENERG RES, V39, P1673, DOI 10.1002/er.3371
Powers J. J., 2014, PHYSOR 2014
Powers J. J., 2013, NEW APPROACH MODELIN, P60526
Robertson R.C, 1971, ORNL4541
Ruggieri J., 2006, TECH REP, P60526
Rykhlevskii A., 2017, T AM NUCL SOC
Scopatz A., 2012, T AM NUCL SOC, V107
Serp J, 2014, PROG NUCL ENERG, V77, P308, DOI 10.1016/j.pnucene.2014.02.014
Sheu RJ, 2013, ANN NUCL ENERGY, V53, P1, DOI 10.1016/j.anucene.2012.10.017
T. H. Group, 1997, HIER DAT FORM VER 5
Xu Z., 2008, TECH REP
Zhou SC, 2018, ANN NUCL ENERGY, V114, P369, DOI 10.1016/j.anucene.2017.10.040
NR 42
TC 7
Z9 8
U1 1
U2 14
PU PERGAMON-ELSEVIER SCIENCE LTD
PI OXFORD
PA THE BOULEVARD, LANGFORD LANE, KIDLINGTON, OXFORD OX5 1GB, ENGLAND
SN 0306-4549
J9 ANN NUCL ENERGY
JI Ann. Nucl. Energy
PD JUN
PY 2019
VL 128
BP 366
EP 379
DI 10.1016/j.anucene.2019.01.030
PG 14
WC Nuclear Science & Technology
SC Nuclear Science & Technology
GA HU1TM
UT WOS:000465054700038
DA 2021-04-21
ER
PT J
AU Mehta, P
Bukov, M
Wang, CH
Day, AGR
Richardson, C
Fisher, CK
Schwab, DJ
AF Mehta, Pankaj
Bukov, Marin
Wang, Ching-Hao
Day, Alexandre G. R.
Richardson, Clint
Fisher, Charles K.
Schwab, David J.
TI A high-bias, low-variance introduction to Machine Learning for
physicists
SO PHYSICS REPORTS-REVIEW SECTION OF PHYSICS LETTERS
LA English
DT Review
ID BAYESIAN FEATURE-SELECTION; NEURAL-NETWORKS; INFORMATION-THEORY; DEEP;
DIMENSIONALITY; ALGORITHM; MODELS; REGULARIZATION; TUTORIAL; CAPACITY
AB Machine Learning (ML) is one of the most exciting and dynamic areas of modern research and application. The purpose of this review is to provide an introduction to the core concepts and tools of machine learning in a manner easily understood and intuitive to physicists. The review begins by covering fundamental concepts in ML and modern statistics such as the bias-variance tradeoff, overfitting, regularization, generalization, and gradient descent before moving on to more advanced topics in both supervised and unsupervised learning. Topics covered in the review include ensemble models, deep learning and neural networks, clustering and data visualization, energy-based models (including MaxEnt models and Restricted Boltzmann Machines), and variational methods. Throughout, we emphasize the many natural connections between ML and statistical physics. A notable aspect of the review is the use of Python Jupyter notebooks to introduce modern ML/statistical packages to readers using physics-inspired datasets (the Ising Model and Monte-Carlo simulations of supersymmetric decays of proton proton collisions). We conclude with an extended outlook discussing possible uses of machine learning for furthering our understanding of the physical world as well as open problems in ML where physicists may be able to contribute. (C) 2019 The Author(s). Published by Elsevier B.V.
C1 [Mehta, Pankaj; Wang, Ching-Hao; Day, Alexandre G. R.; Richardson, Clint] Boston Univ, Dept Phys, 590 Commonwealth Ave, Boston, MA 02215 USA.
[Bukov, Marin] Univ Calif Berkeley, Dept Phys, Berkeley, CA 94720 USA.
[Fisher, Charles K.] Unlearn AI, San Francisco, CA 94108 USA.
[Schwab, David J.] CUNY, Grad Ctr, Initiat Theoret Sci, 365 Fifth Ave, New York, NY 10016 USA.
RP Bukov, M (corresponding author), Univ Calif Berkeley, Dept Phys, Berkeley, CA 94720 USA.
EM pankajm@bu.edu; mgbukov@berkeley.edu
OI Bukov, Marin/0000-0002-3688-9599
FU Simon's Foundation; NIH MIRA programUnited States Department of Health &
Human ServicesNational Institutes of Health (NIH) - USA [1R35GM119461];
Emergent Phenomena in Quantum Systems initiative of the Gordon and Betty
Moore Foundation; ERC synergy grant UQUAM; U.S. Department of Energy,
Office of Science, Office of Advanced Scientific Computing Research,
Quantum Algorithm Teams ProgramUnited States Department of Energy (DOE);
NIHUnited States Department of Health & Human ServicesNational
Institutes of Health (NIH) - USA [GM098875-02]; NSFNational Science
Foundation (NSF) [PHYD1066293]; National Science FoundationNational
Science Foundation (NSF) [NSF PHY-1748958]; NATIONAL INSTITUTE OF
GENERAL MEDICAL SCIENCESUnited States Department of Health & Human
ServicesNational Institutes of Health (NIH) - USANIH National Institute
of General Medical Sciences (NIGMS) [R35GM119461, R35GM119461,
R35GM119461, R35GM119461] Funding Source: NIH RePORTER
FX PM and DJS would like to thank Anirvan Sengupta, Justin Kinney, and Ilya
Nemenman for useful conversations during the ACP working group. The
authors are also grateful to all readers who provided valuable feedback
on this manuscript while it was under peer review. We encourage readers
to help keep the Notebooks which accompany the review up-to-date, by
contributing to them on Github at
https://github.com/drckf/mlreview_notebooks.PM, CHW, and AD were
supported by Simon's Foundation in the form of a Simons Investigator in
the MMLS and NIH MIRA program grant: 1R35GM119461. MB acknowledges
support from the Emergent Phenomena in Quantum Systems initiative of the
Gordon and Betty Moore Foundation, the ERC synergy grant UQUAM, and the
U.S. Department of Energy, Office of Science, Office of Advanced
Scientific Computing Research, Quantum Algorithm Teams Program. DJS was
supported as a Simons Investigator in the MMLS and by NIH K25 grant
GM098875-02. PM and DJS would like to thank the NSF Grant: PHYD1066293
for supporting the Aspen Center for Physics (ACP) for facilitating
discussions leading to this work. This research was supported in part by
the National Science Foundation under Grant No. NSF PHY-1748958. The
authors are pleased to acknowledge that the computational work reported
on in this paper was performed on the Shared Computing Cluster which is
administered by Boston University's Research Computing Services.
CR Abu-Mostafa Y. S., 2012, LEARNING FROM DATA, V4
Ackley D. H., 1987, READINGS COMPUTER VI, P522, DOI DOI 10.1016/B978-0-08-051581-6.50053-2
Adam A, 2006, ARTIFICIAL KNOWING G
Advani M, 2016, PHYS REV X, V6, DOI 10.1103/PhysRevX.6.031034
Advani M, 2013, J STAT MECH-THEORY E, DOI 10.1088/1742-5468/2013/03/P03014
Aitchison L, 2016, PLOS COMPUT BIOL, V12, DOI 10.1371/journal.pcbi.1005110
Albarran-Arriagada F., 2018, ARXIV 1803 05340
Alemi A., 2017, ARXIV170507441
Alemi A. A., 2016, ARXIV161200410
Amit D. J., 1992, MODELING BRAIN FUNCT
AMIT DJ, 1985, PHYS REV A, V32, P1007, DOI 10.1103/PhysRevA.32.1007
Andrieu C, 2003, MACH LEARN, V50, P5, DOI 10.1023/A:1020281327116
Arai Shunta, 2017, ARXIV171200371
ARIMOTO S, 1972, IEEE T INFORM THEORY, V18, P14, DOI 10.1109/TIT.1972.1054753
Arsenault LF, 2014, PHYS REV B, V90, DOI 10.1103/PhysRevB.90.155136
Arunachalam S., 2017, ARXIV170106806
August M, 2018, ARXIV180204063
Aurisano A, 2016, J INSTRUM, V11, DOI 10.1088/1748-0221/11/09/P09001
Baireuther P, 2017, ARXIV170507855
Baity-Jesi M., 2018, ARXIV180306969
Baldassi C, 2018, J PHYS CONF SER, V955, DOI 10.1088/1742-6596/955/1/012001
Baldassi Carlo, 2017, ARXIV171009825
Baldi P, 2014, NAT COMMUN, V5, DOI 10.1038/ncomms5308
Barber D., 2012, BAYESIAN REASONING M
BARNES J, 1986, NATURE, V324, P446, DOI 10.1038/324446a0
Barra A, 2012, NEURAL NETWORKS, V34, P1, DOI 10.1016/j.neunet.2012.06.003
Barra Adriano, 2017, ARXIV170205882
Barra Adriano, 2016, ARXIV161203132
BATTITI R, 1992, NEURAL COMPUT, V4, P141, DOI 10.1162/neco.1992.4.2.141
Benedetti Marcello, 2016, ARXIV160902542
Benedetti Marcello, 2017, ARXIV170809784
Bengio Yoshua, 2012, Neural Networks: Tricks of the Trade. Second Edition: LNCS 7700, P437, DOI 10.1007/978-3-642-35289-8_26
BENNETT RS, 1969, IEEE T INFORM THEORY, V15, P517, DOI 10.1109/TIT.1969.1054365
Beny C., 2018, ARXIV180205756
Berger JO., 1992, BAYESIAN STAT 4, P35
Bickel PJ, 2006, TEST-SPAIN, V15, P271, DOI 10.1007/BF02607055
BICKEL PJ, 1981, ANN STAT, V9, P1196, DOI 10.1214/aos/1176345637
Bishop C., 1995, NEURAL NETWORKS PATT
BISHOP CM, 1995, NEURAL COMPUT, V7, P108, DOI 10.1162/neco.1995.7.1.108
Bishop CM., 2006, PATTERN RECOGN
BLAHUT RE, 1972, IEEE T INFORM THEORY, V18, P460, DOI 10.1109/TIT.1972.1054855
Bottou Leon, 2012, Neural Networks: Tricks of the Trade. Second Edition: LNCS 7700, P421, DOI 10.1007/978-3-642-35289-8_25
Bowman S. R., 2015, ARXIV151106349
Boyd S., 2004, CONVEX OPTIMIZATION
Bradde S, 2017, J STAT PHYS, V167, P462, DOI 10.1007/s10955-017-1770-6
Breiman L, 2001, MACH LEARN, V45, P5, DOI 10.1023/A:1010933404324
Breiman L, 1996, MACH LEARN, V24, P123, DOI 10.1007/bf00058655
Breuckmann Nikolas P., 2017, ARXIV171009489
Broecker P., 2017, ARXIV170700663
Bromley Thomas R., 2018, ARXIV180307039
Bukov M, 2018, PHYS REV X, V8, DOI 10.1103/PhysRevX.8.031086
Burges CJC, 1998, DATA MIN KNOWL DISC, V2, P121, DOI 10.1023/A:1009715923555
Caio M, 2019, ARXIV190103346
Caldeira J., 2018, ARXIV181001483
Canabarro Askery, 2018, ARXIV180807069
Cardenas -Lopez F. A., 2017, ARXIV170907848
Carleo G, 2017, SCIENCE, V355, P602, DOI 10.1126/science.aag2302
Carleo Giuseppe, 2018, COMMUNICATION
Carleo Giuseppe, 2018, ARXIV180209558
Carrasquilla J, 2017, NAT PHYS, V13, P431, DOI [10.1038/NPHYS4035, 10.1038/nphys4035]
Carrasquilla Juan, 2018, ARXIV181010584
Ch'ng Kelvin, 2017, ARXIV170803350
Chalk M., 2016, ADV NEURAL INFORM PR, P1957
Chamberland C, 2018, ARXIV180206441
Nguyen HC, 2017, ADV PHYS, V66, P197, DOI 10.1080/00018732.2017.1341604
Chechik G, 2005, J MACH LEARN RES, V6, P165
Chen CL, 2014, IEEE T NEUR NET LEAR, V25, P920, DOI 10.1109/TNNLS.2013.2283574
Chen J, 2018, PHYS REV B, V97, DOI 10.1103/PhysRevB.97.085104
Chen Jun-Jie, 2019, ARXIV190108748
Chen TQ, 2016, KDD'16: PROCEEDINGS OF THE 22ND ACM SIGKDD INTERNATIONAL CONFERENCE ON KNOWLEDGE DISCOVERY AND DATA MINING, P785, DOI 10.1145/2939672.2939785
Cheng Song, 2017, ARXIV171204144
Ciliberto Carlo, 2017, ARXIV170708561
Cohen N., 2016, C LEARN THEOR, P698
Colabrese S, 2017, PHYS REV LETT, V118, DOI 10.1103/PhysRevLett.118.158004
Cossu Guido, 2018, ARXIV181011503
Cox T. F., 2000, MULTIDIMENSIONAL SCA
Cristoforetti M., 2017, ARXIV170509524
Dahl G, 2010, ADV NEURAL INFORM PR, P469
Daskin Ammar, 2018, QUANTA, P7, DOI DOI 10.12743/QUANTA.V7I1.65
Davaasuren A., 2018, ARXIV180104377
Day AGR, 2019, PHYS REV LETT, V122, DOI 10.1103/PhysRevLett.122.020601
Day Alexandre G. R., 2018, VALIDATED AGGL UNPUB
Decelle Aurelien, 2018, ARXIV180301960
Decelle Aurelien, 2017, ARXIV170802917
DEMPSTER AP, 1977, J ROY STAT SOC B MET, V39, P1, DOI 10.1111/j.2517-6161.1977.tb01600.x
Deng DL, 2017, PHYS REV X, V7, DOI 10.1103/PhysRevX.7.021021
Dietterich TG, 2000, LECT NOTES COMPUT SC, V1857, P1, DOI 10.1007/3-540-45014-9_1
Do CB, 2008, NAT BIOTECHNOL, V26, P897, DOI 10.1038/nbt1406
Domingos P, 2012, COMMUN ACM, V55, P78, DOI 10.1145/2347736.2347755
Donoho DL, 2006, IEEE T INFORM THEORY, V52, P1289, DOI 10.1109/TIT.2006.871582
Dreyfus HubertL., 1965, ALCHEMY ARTIFICIAL I
Du Simon S, 2017, ARTICLE, V68, P1067
Duchi J, 2011, J MACH LEARN RES, V12, P2121
Dunjko V., 2017, ARXIV170902779
Dunjko V, 2017, ARXIV171011160
Efron B, 2004, ANN STAT, V32, P407, DOI 10.1214/009053604000000067
EFRON B, 1979, ANN STAT, V7, P1, DOI 10.1214/aos/1176344552
Eisen MB, 1998, P NATL ACAD SCI USA, V95, P14863, DOI 10.1073/pnas.95.25.14863
Elith J, 2011, DIVERS DISTRIB, V17, P43, DOI 10.1111/j.1472-4642.2010.00725.x
Ernst Oliver K., 2018, ARXIV180301063
Ester M., 1996, KDD-96 Proceedings. Second International Conference on Knowledge Discovery and Data Mining, P226
FADDEEV LD, 1967, PHYS LETT B, VB 25, P29, DOI 10.1016/0370-2693(67)90067-6
Fbsel Thomas, 2018, ARXIV180205267
Finol David, 2018, ARXIV180105733
Fisher CK, 2015, NEURAL COMPUT, V27, P2411, DOI 10.1162/NECO_a_00780
Fisher CK, 2015, BIOINFORMATICS, V31, P1754, DOI 10.1093/bioinformatics/btv037
Foreman S., 2017, ARXIV171002079
Freitas Nahuel, 2018, ARXIV180302118
Freund Y., 1999, Journal of Japanese Society for Artificial Intelligence, V14, P771
Freund Y, 1997, J COMPUT SYST SCI, V55, P119, DOI 10.1006/jcss.1997.1504
Friedman J. H., 2003, J MACHINE LEARNING R, P94305
Friedman Jerome., 2001, ELEMENTS STAT LEARNI, V1
Friedman JH, 2001, ANN STAT, V29, P1189, DOI 10.1214/aos/1013203451
Friedman JH, 2002, COMPUT STAT DATA AN, V38, P367, DOI 10.1016/S0167-9473(01)00065-2
Fu MC, 2006, HBK OPERAT RES MANAG, V13, P575, DOI 10.1016/S0927-0507(06)13019-4
Funai S. S., 2018, ARXIV181008179
Gao Jun, 2017, ARXIV171200456
Gao Xun, 2017, ARXIV170105039
Gelman A., 2014, BAYESIAN DATA ANAL, V2
Gersho A., 2012, VECTOR QUANTIZATION, V159
Geurts P, 2006, MACH LEARN, V63, P3, DOI 10.1007/s10994-006-6226-1
Glorot X, 2010, P249, DOI DOI 10.1177/1753193409103364.
Goldt Sebastian, 2017, ARXIV170609713
Goodfellow I, 2016, ADAPT COMPUT MACH LE, P1
Goodfellow Ian., 2016, ARXIV170100160
Goodfellow IJ, 2014, ADV NEUR IN, V27, P2672
Greplova E., 2017, ARXIV171105238
Grisafi Andrea, 2017, ARXIV170906757
Han Zhao-Yu, 2017, ARXIV170901662
He KM, 2016, PROC CVPR IEEE, P770, DOI 10.1109/CVPR.2016.90
He KM, 2015, IEEE I CONF COMP VIS, P1026, DOI 10.1109/ICCV.2015.123
Heimel Theo, 2018, ARXIV180808979
Higgins I., 2016, BETA VAE LEARNING BA
Hinton GE, 2006, SCIENCE, V313, P504, DOI 10.1126/science.1127647
Hinton G. E, 2012, NEURAL NETWORKS TRIC, P599, DOI DOI 10.1007/978-3-642-35289-8_32
Hinton GE, 2002, NEURAL COMPUT, V14, P1771, DOI 10.1162/089976602760128018
Hinton GE, 2006, NEURAL COMPUT, V18, P1527, DOI 10.1162/neco.2006.18.7.1527
Ho TK, 1998, IEEE T PATTERN ANAL, V20, P832, DOI 10.1109/34.709601
HOPFIELD JJ, 1982, P NATL ACAD SCI-BIOL, V79, P2554, DOI 10.1073/pnas.79.8.2554
Huang HP, 2017, J STAT MECH-THEORY E, DOI 10.1088/1742-5468/aa6ddc
Huang Haiping, 2017, ARXIV171001467
Huang Hengfeng, 2018, ARXIV180103334
HUBBARD J, 1959, PHYS REV LETT, V3, P77, DOI 10.1103/PhysRevLett.3.77
Huggins W, 2019, QUANTUM SCI TECHNOL, V4, DOI 10.1088/2058-9565/aaea94
Innocenti L., 2018, ARXIV180307119
Iso S., 2018, ARXIV180107172
James M, 2006, IMPROV LEARN TLRP, P14
Jarzynski C, 1997, PHYS REV LETT, V78, P2690, DOI 10.1103/PhysRevLett.78.2690
Jaynes Edwin T, 2003, PROBABILITY THEORY L
JAYNES ET, 1957, PHYS REV, V106, P620, DOI 10.1103/PhysRev.106.620
JAYNES ET, 1968, IEEE T SYST SCI CYB, VSSC4, P227, DOI 10.1109/TSSC.1968.300117
JAYNES ET, 1957, PHYS REV, V108, P171, DOI 10.1103/PhysRev.108.171
Jaynes ET., 1996, PROBABILITY THEORY L
JEFFREYS H, 1946, PROC R SOC LON SER-A, V186, P453, DOI 10.1098/rspa.1946.0056
Jin C., 2017, ARXIV171110456
Jordan MI, 1999, MACH LEARN, V37, P183, DOI 10.1023/A:1007665907178
Jordan Michael, 2018, ARTIFICIAL INTELLIGE
Kalantre Sandesh S., 2017, ARXIV171204914
Katz Y., 2017, MANUFACTURING ARTIFI
Kerenidis lordanis, 2017, ARXIV170404992
Keskar N., 2016, ARXIV160904836
Kingma D.P., 2014, ARXIV PREPRINT ARXIV
Kingma D. P., 2017, THESIS
Kingma Diederik P, 2013, ARXIV13126114
Kleijnen JPC, 1996, EUR J OPER RES, V88, P413, DOI 10.1016/0377-2217(95)00107-7
Kleinberg Jon, 2016, ARXIV160905807
Koch-Janusz Maciej, 2017, NEUROCOMPUTING
Kong QK, 2019, SEISMOL RES LETT, V90, P3, DOI 10.1785/0220180259
Krastanov Stefan, 2017, ARXIV170509334
Kriegel HP, 2009, ACM T KNOWL DISCOV D, V3, DOI 10.1145/1497577.1497578
Krizhevsky Alex, 2012, ADV NEURAL INFORM PR, P1097, DOI DOI 10.1145/3065386
Krzakala F, 2012, PHYS REV X, V2, DOI 10.1103/PhysRevX.2.021005
Krzakala F, 2014, IEEE INT SYMP INFO, P1499, DOI 10.1109/ISIT.2014.6875083
Krzakala F, 2012, J STAT MECH-THEORY E, DOI 10.1088/1742-5468/2012/08/P08009
Lake B. M., 2017, BEHAV BRAIN SCI, V40
lakovlev I. A., 2018, ARXIV180306682
Lamata L, 2017, SCI REP, V7
Larsen B., 1999, P16, DOI DOI 10.1145/312129.312186
Le QV, 2013, INT CONF ACOUST SPEE, P8595, DOI 10.1109/ICASSP.2013.6639343
LeCun Y, 1998, LECT NOTES COMPUT SC, V1524, P9, DOI 10.1007/3-540-49430-8_2
Lecun Y, 1998, P IEEE, V86, P2278, DOI 10.1109/5.726791
LeCun Y, 1995, HDB BRAIN THEORY NEU, V3361
Lee Jason D, 2017, ARXIV171007406
Lehmann E.L., 2006, TESTING STAT HYPOTHE
Lehmann E.L., 2006, THEORY POINT ESTIMAT
Levine Y., 2017, ARXIV170401552
Li Bo, 2017, ARXIV170801422
Li Chian -De, 2017, ARXIV170302369
Li RY, 2018, NPJ QUANTUM INFORM, V4, DOI 10.1038/s41534-018-0060-8
Li S. -H., 2018, ARXIV180202840
Lim TS, 2000, MACH LEARN, V40, P203, DOI 10.1023/A:1007608224229
Lin HW, 2017, J STAT PHYS, V168, P1223, DOI 10.1007/s10955-017-1836-5
Linderman GC, 2017, ARXIV171209005
Liu Z., 2017, ARXIV171004987
Loh WY, 2011, WIRES DATA MIN KNOWL, V1, P14, DOI 10.1002/widm.8
Louppe G., 2014, ARXIV14077502
Lu SR, 2018, PHYS REV A, V98, DOI 10.1103/PhysRevA.98.012315
MacKay DJ., 2003, INFORM THEORY INFERE
Marin A, 2018, PHYS REV E, V97, DOI 10.1103/PhysRevE.97.021102
Marsland III R, 2019, ARXIV190109673
Maskara N, 2018, ARXIV180208680
Masson JB, 2009, J PHYS A-MATH THEOR, V42, DOI 10.1088/1751-8113/42/43/434009
Mattingly HH, 2018, P NATL ACAD SCI USA, V115, P1760, DOI 10.1073/pnas.1715306115
McDermott Drew, 1985, AI MAG, V6, P122
McInnes L., 2018, UMAP UNIFORM MANIFOL, P1
Mehta P., 2014, ARXIV14103831
Mehta P, 2011, J STAT PHYS, V142, P1187, DOI 10.1007/s10955-010-0102-x
Mehta Pankaj, 2015, BIG DATAS RADICAL PO
Melko R.G, 2017, ARXIV170804622
Melnikov Alexey A., 2017, ARXIV170600868
Metz Cade, 2017, MOVE CODERS PHYS WIL
Mezard M., 2009, INFORM PHYS COMPUTAT
Mhaskar H., 2016, ARXIV160300988
Milliner Daniel, 2011, ARXIV11092378
Mitarai K., 2018, ARXIV180300745
Mnih V, 2015, NATURE, V518, P529, DOI 10.1038/nature14236
Muehlhauser L., 2016, WHAT SHOULD WE LEARN
Murphy K.P, 2012, MACHINE LEARNING PRO
Nagai Yuki, 2017, ARXIV170506724
Neal R. M., 2011, HDB MARKOV CHAIN MON, V2
Neal RM, 1998, NATO ADV SCI I D-BEH, V89, P355
Neal RM, 2001, STAT COMPUT, V11, P125, DOI 10.1023/A:1008923215028
Nesterov Y., 1983, SOV MATH DOKL, V27, P372
Neukart Florian, 2017, ARXIV170809354
Nielsen M.A., 2015, NEURAL NETWORKS DEEP
Niu M. Y., 2018, ARXIV180301857
Nomura Yusuke, 2017, ARXIV170906475
O'Neil C., 2017, WEAPONS MATH DESTRUC
Ohtsuki T, 2017, J PHYS SOC JPN, V86, DOI 10.7566/JPSJ.86.044708
Papanikolaou S., 2017, ARXIV170908225
Pedregosa F, 2011, J MACH LEARN RES, V12, P2825
Perdomo-Ortiz Alejandro, 2017, ARXIV170809757
Polyak B.T., 1964, USSR COMPUT MATH MAT, V4, P1, DOI [10.1016/0041-5553(64)90137-5, DOI 10.1016/0041-5553(64)90137-5]
Qian N, 1999, NEURAL NETWORKS, V12, P145, DOI 10.1016/S0893-6080(98)00116-6
RABINER LR, 1989, P IEEE, V77, P257, DOI 10.1109/5.18626
Radford Alec., 2015, ARXIV2015151106434
Raiko T., 2016, ADV NEURAL INFORM PR, P3738
Ramezanali Mohammad, 2015, ARXIV150908995
Ramezanpour A, 2017, PHYS REV A, V96, DOI 10.1103/PhysRevA.96.052307
Rao Wen-Jia, 2017, ARXIV170902597
Ravanbakhsh S, 2017, THIRTY-FIRST AAAI CONFERENCE ON ARTIFICIAL INTELLIGENCE, P1488
Rebentrost P, 2016, ARXIV161201789
Rebentrost Patrick, 2017, ARXIV171003599
Reddy G, 2016, P NATL ACAD SCI USA, V113, pE4877, DOI 10.1073/pnas.1606075113
Reddy G, 2016, J STAT PHYS, V163, P1454, DOI 10.1007/s10955-016-1521-0
Rem B S, 2018, ARXIV180905519
Rezende Danilo Jimenez, 2014, ICML
Rocchetto A, 2017, ARXIV171200127
Rocchetto A, 2018, NPJ QUANTUM INFORM, V4, DOI 10.1038/s41534-018-0077-z
Rockafellar R. T, 2015, CONVEX ANAL
Rodriguez A, 2014, SCIENCE, V344, P1492, DOI 10.1126/science.1242072
Rokach L, 2005, DATA MINING AND KNOWLEDGE DISCOVERY HANDBOOK, P321, DOI 10.1007/0-387-25465-X_15
Roweis ST, 2000, SCIENCE, V290, P2323, DOI 10.1126/science.290.5500.2323
Rudelius T., 2018, ARXIV181005159
Ruder S., 2016, ARXIV160904747
RUMELHART DE, 1985, COGNITIVE SCI, V9, P75, DOI 10.1207/s15516709cog0901_5
RUMELHART DE, 1986, NATURE, V323, P533, DOI 10.1038/323533a0
Ruscher Celine, 2018, ARXIV180906487
Saito Hiroki, 2017, ARXIV170905468
Salazar Domingos S. P., 2017, ARXIV170408724
Sander J, 1998, DATA MIN KNOWL DISC, V2, P169, DOI 10.1023/A:1009745219419
Saxe A. M., 2013, ARXIV13126120
Saxe AM, 2017, ARXIV171003667
Schapire R. E., 2012, BOOSTING FDN ALGORIT
Schindler F, 2017, PHYS REV B, V95, DOI 10.1103/PhysRevB.95.245134
Schmidhuber J, 2015, NEURAL NETWORKS, V61, P85, DOI 10.1016/j.neunet.2014.09.003
Schneidman E, 2006, NATURE, V440, P1007, DOI 10.1038/nature04701
Schoenholz Samuel S., 2017, ARXIV170908015
Schuld M., 2017, ARXIV170310793
Schuld M., 2018, ARXIV180307128
Schuld M, 2015, CONTEMP PHYS, V56, P172, DOI 10.1080/00107514.2014.964942
Schuld Maria, 2017, ARXIV170402146
Schwab DJ, 2014, PHYS REV LETT, V113, DOI 10.1103/PhysRevLett.113.068102
Shanahan Phiala E., 2018, ARXIV180105784
SHANNON CE, 1949, BELL SYST TECH J, V28, P656, DOI 10.1002/j.1538-7305.1949.tb00928.x
Shen Huitao, 2018, ARXIV180101127
Shinjo Kazuya, 2019, ARXIV190107900
Shlens J, 2014, TUTORIAL PRINCIPAL C
Sidky Hythem, 2017, ARXIV171202840
Simonite T., 2018, WIRED
SINGH K, 1981, ANN STAT, V9, P1187, DOI 10.1214/aos/1176345636
Slonim N, 2005, ARXIVCS0502017
Springenberg Jost Tobias, 2014, ARXIV14126806
Sriarunothai T., 2017, ARXIV170901366
Srivastava N, 2014, J MACH LEARN RES, V15, P1929
Stoudenmire E., 2016, ADV NEURAL INFORM PR, P4799
Stoudenmire EM, 2012, ANNU REV CONDEN MA P, V3, P111, DOI 10.1146/annurev-conmatphys-020911-125018
Stoudenmire E. M., 2018, ARXIV180100315
Stratonovich R. L., 1957, SOV PHYS DOKL, V2, P416
Strouse DJ, 2017, NEURAL COMPUT, V29, P1611, DOI 10.1162/NECO_a_00961
Suchsland Philippe, 2018, ARXIV180209876
Sutskever I, 2013, INT C MACH LEARN, P1139
Sutton R. S., 1998, REINFORCEMENT LEARNI, V1
Swaddle Michael, 2017, ARXIV170310743
Sweke R., 2018, ARXIV181007207
Szekely G. J., 2003, TECHNICAL REPORT, V3, P1
Tanaka A., 2017, ARXIV171203893
Tanaka A, 2017, J PHYS SOC JPN, V86, DOI 10.7566/JPSJ.86.063001
Tenenbaum JB, 2000, SCIENCE, V290, P2319, DOI 10.1126/science.290.5500.2319
Tibshirani RJ, 2013, ELECTRON J STAT, V7, P1456, DOI 10.1214/13-EJS815
Tieleman T., 2012, MACH LEARN, V4, P26
Tieleman T., 2009, P 26 ANN INT C MACH, P1033, DOI DOI 10.1145/1553374.1553506
Tishby N, 2017, ARXIV170300810
Tishby N., 2000, ARXIVPHYS0004057
Tomczak P, 2018, PHYS REV B, V98, DOI 10.1103/PhysRevB.98.144415
Torgerson W. S., 1958, THEORY METHODS SCALI
Torlai G, 2018, NAT PHYS, V14, P447, DOI 10.1038/s41567-018-0048-5
Torlai G, 2017, PHYS REV LETT, V119, DOI 10.1103/PhysRevLett.119.030501
Tramel Eric W., 2017, ARXIV170203260
Tubiana J, 2017, PHYS REV LETT, V118, DOI 10.1103/PhysRevLett.118.138301
van der Maaten L, 2014, J MACH LEARN RES, V15, P3221
van der Maaten L, 2008, J MACH LEARN RES, V9, P2579
van Nieuwenburg Evert, 2017, ARXIV171200450
van Nieuwenburg EPL, 2017, NAT PHYS, V13, P435, DOI [10.1038/nphys4037, 10.1038/NPHYS4037]
Venderley Jordan, 2017, ARXIV171100020
Vergassola M, 2007, NATURE, V445, P406, DOI 10.1038/nature05464
Vidal G, 2007, PHYS REV LETT, V99, DOI 10.1103/PhysRevLett.99.220405
Wainwright MJ, 2008, FOUND TRENDS MACH LE, V1, P1, DOI 10.1561/2200000001
Wang Ce, 2018, ARXIV180301205
Wang Ce, 2017, ARXIV170607977
Wang Lei, 2017, ARXIV170208586
Wang Y. - N., 2018, ARXIV180407296
Wasserman L., 2013, ALL STAT CONCISE COU
Wattenberg Martin, 2016, DISTILL, DOI [10.23915/DISTILL.00002, DOI 10.23915/DISTILL.00002, 10.23915/distill.00002]
WEI Q, 2017, PHYS REV E, V95
Weigt M, 2009, P NATL ACAD SCI USA, V106, P67, DOI 10.1073/pnas.0805923106
Weinstein S., 2017, ARXIV170703114
Wetzel Sebastian Johann, 2017, ARXIV170505582
Wetzel SJ, 2017, ARXIV170302435
WHITE SR, 1992, PHYS REV LETT, V69, P2863, DOI 10.1103/PhysRevLett.69.2863
White T., 2016, ARXIV160904468
Wilson A.C., 2017, ARXIV170508292
Wilson K. G., 1974, Physics Reports. Physics Letters Section C, V12c, P75, DOI 10.1016/0370-1573(74)90023-4
Witte R.S., 2013, STATISTICS
Wu Yadong, 2018, ARXIV180203930
Xie Junyuan, 2016, INT C MACH LEARN, P478
Yang T., 2006, IEEE WORKSH COMP INT
Yang Xu-Chen, 2017, ARXIV170800238
Yedidia J. S., 2003, UNDERSTANDING BELIEF
Yedidia JS, 2001, NEU INF PRO, P21
Yoshioka Nobuyuki, 2017, ARXIV170905790
You Yi-Zhuang, 2017, ARXIV170901223
Yu C.H., 2017, ARXIV170709524
Zdeborova L, 2016, ADV PHYS, V65, P453, DOI 10.1080/00018732.2016.1211393
Zeiler M. D., 2012, ARXIV12125701
Zhang Chengxian, 2018, ARXIV181007914
Zhang Chiyuan, 2016, ARXIV161103530
Zhang Pengfei, 2017, ARXIV170809401
Zhang Xiao-Ming, 2018, ARXIV180209248
Zhang Yi, 2017, ARXIV170501947
Zimek Arthur, 2012, Statistical Analysis and Data Mining, V5, P363, DOI 10.1002/sam.11161
Zou H, 2005, J R STAT SOC B, V67, P301, DOI 10.1111/j.1467-9868.2005.00503.x
NR 352
TC 117
Z9 119
U1 15
U2 59
PU ELSEVIER
PI AMSTERDAM
PA RADARWEG 29, 1043 NX AMSTERDAM, NETHERLANDS
SN 0370-1573
EI 1873-6270
J9 PHYS REP
JI Phys. Rep.-Rev. Sec. Phys. Lett.
PD MAY 30
PY 2019
VL 810
BP 1
EP 124
DI 10.1016/j.physrep.2019.03.001
PG 124
WC Physics, Multidisciplinary
SC Physics
GA ID5TR
UT WOS:000471739400001
PM 31404441
OA Other Gold, Green Accepted
DA 2021-04-21
ER
PT J
AU Zhang, F
AF Zhang, F.
CA CMS Collaboration
TI Development of the CMS Phase-1 Pixel Online Monitoring System and the
Evolution of Pixel Leakage Current
SO JOURNAL OF INSTRUMENTATION
LA English
DT Article; Proceedings Paper
CT 9th International Workshop on Semiconductor Pixel Detectors for
Particles and Imaging (PIXEL)
CY DEC 10-14, 2018
CL Activ Ctr Acad Sinica, Taipei, TAIWAN
HO Activ Ctr Acad Sinica
DE Performance of High Energy Physics Detectors; Detector cooling and
thermo-stabilization; Simulation methods and programs; Software
architectures (event data models frameworks and databases)
AB A new pixel online monitoring system has been developed to give a fast and intuitive view of the detector performance both offline and online. The source script was written modularly in Python programming language in association with the SQLite and Java languages. It establishes a connection with the CMS detector monitoring database, and extracts and stores detector information into a local database. Among all of the monitored detector parameters, the pixel leakage current is one of the most interesting, as it reflects the accumulated radiation damage of the silicon sensors. The leakage currents obtained from different module positions in the pixel detector are highly correlated with the distance from the beam pipe. Based on the new monitoring system, we have analyzed the pixel detector leakage current evolution since the recent Phase-1 upgrade of the pixel detector and its dependence on the environmental temperature influenced by the cooling loop arrangement inside the pixel detector. The results provide a crucial reference on the detector performance for the re-design of the detector in the Phase-2 upgrade.
C1 [Zhang, F.; CMS Collaboration] Univ Calif Davis, One Shields Ave, Davis, CA 95616 USA.
RP Zhang, F (corresponding author), Univ Calif Davis, One Shields Ave, Davis, CA 95616 USA.
EM fengwangdong.zhang@cern.ch
CR CMS Collaboration, CERNLHCC2012016 CMS
Moll M., 1999, THESIS
Renner J., 2017, THESIS
NR 3
TC 0
Z9 0
U1 0
U2 2
PU IOP PUBLISHING LTD
PI BRISTOL
PA TEMPLE CIRCUS, TEMPLE WAY, BRISTOL BS1 6BE, ENGLAND
SN 1748-0221
J9 J INSTRUM
JI J. Instrum.
PD MAY
PY 2019
VL 14
AR C05008
DI 10.1088/1748-0221/14/05/C05008
PG 9
WC Instruments & Instrumentation
SC Instruments & Instrumentation
GA HZ3CQ
UT WOS:000468725500003
DA 2021-04-21
ER
PT J
AU Guzman, HV
Tretyakov, N
Kobayashi, H
Fogarty, AC
Kreis, K
Krajniak, J
Junghans, C
Kremer, K
Stuehn, T
AF Guzman, Horacio V.
Tretyakov, Nikita
Kobayashi, Hideki
Fogarty, Aoife C.
Kreis, Karsten
Krajniak, Jakub
Junghans, Christoph
Kremer, Kurt
Stuehn, Torsten
TI ESPResSo++2.0: Advanced methods for multiscale molecular simulation
SO COMPUTER PHYSICS COMMUNICATIONS
LA English
DT Article
DE Molecular dynamics; Multiscale modeling; Coarse graining; Soft matter;
Lattice Boltzmann; Molecular simulations; High performance computer;
Computational physics
ID SOFT MATTER; LATTICE BOLTZMANN; POLYMER MELTS; DYNAMICS; MODELS;
PACKAGE; QUANTUM; EQUILIBRATION; ALGORITHMS; EFFICIENT
AB Molecular simulation is a scientific tool used in many fields including material science and biology. This requires constant development and enhancement of algorithms within molecular simulation software packages. Here, we present computational tools for multiscale modeling developed and implemented within the ESPResSo++ package. These include the latest applications of the adaptive resolution scheme, the hydrodynamic interactions through a lattice Boltzmann solvent coupled to particle-based molecular dynamics, the implementation of the hierarchical strategy for equilibrating long-chained polymer melts and a heterogeneous spatial domain decomposition.
The software design of ESPResSo++ has kept its highly modular C++ kernel with a Python user interface. Moreover, it has been enhanced by automatic scripts that parse configurations from other established packages, providing scientists with the ability to rapidly set up their simulations. (C) 2019 Elsevier B.V. All rights reserved.
C1 [Guzman, Horacio V.; Tretyakov, Nikita; Kobayashi, Hideki; Fogarty, Aoife C.; Kreis, Karsten; Kremer, Kurt; Stuehn, Torsten] Max Planck Inst Polymer Res, Ackermannweg 10, D-55128 Mainz, Germany.
[Krajniak, Jakub] Katholieke Univ Leuven, Dept Comp Sci, Celestijnenlaan 200A, B-3001 Leuven, Belgium.
[Junghans, Christoph] Los Alamos Natl Lab, Comp Computat & Stat Sci Div, Los Alamos, NM 87545 USA.
RP Stuehn, T (corresponding author), Max Planck Inst Polymer Res, Ackermannweg 10, D-55128 Mainz, Germany.
EM stuehn@mpip-mainz.mpg.de
RI Junghans, Christoph/G-4238-2010; Kremer, Kurt/G-5652-2011; Kobayashi,
Hideki/P-2191-2017
OI Junghans, Christoph/0000-0003-0925-1458; Kremer,
Kurt/0000-0003-1842-9369; Krajniak, Jakub/0000-0001-9372-6975;
Kobayashi, Hideki/0000-0002-4024-1752; Vargas Guzman, Horacio
Andres/0000-0003-2564-3005
FU Deutsche ForschungsgemeinschaftGerman Research Foundation (DFG)
[SFB-TRR146]; European Union's Horizon 2020 research and innovation
program [676531]; European Research Council under the European Union's
Seventh Framework Programme (FP7/2007-2 013)/ERC [340906-MOLPROCOMP];
U.S. Department of EnergyUnited States Department of Energy (DOE)
[DE-AC52-06NA25396]; Los Alamos National LaboratoryUnited States
Department of Energy (DOE)Los Alamos National Laboratory
[LA-UR-18-24195]
FX T. Stuehn, N. Tretyakov and H.V. Guzman acknowledge financial support
under the project SFB-TRR146 of the Deutsche Forschungsgemeinschaft. H.
Kobayashi acknowledges the European Union's Horizon 2020 research and
innovation program under the grant agreement No. 676531(project E-CAM).
A.C. Fogarty acknowledges research funding through the European Research
Council under the European Union's Seventh Framework Programme
(FP7/2007-2 013)/ERC grant agreement no. 340906-MOLPROCOMP. J. Krajniak
acknowledges "Strategic Initiative Materials" in Flanders (SIM) under
the InterPoCo program and VSC (Flemish Supercomputer Center; Hercules
Foundation and the Flemish Government - department EWI). This work has
been partially authored by an employee of Los Alamos National Security,
LLC, operator of the Los Alamos National Laboratory under Contract No.
DE-AC52-06NA25396 with the U.S. Department of Energy. The United States
Government retains and the publisher, by accepting this work for
publication, acknowledges that the United States Government retains a
nonexclusive, paid-up, irrevocable, world-wide license to publish or
reproduce this work, or allow others to do so for United States
Government purposes. C.J. thanks Los Alamos National Laboratory for a
Director's Postdoctoral fellowship supporting the early stage of this
work. Assigned: LA-UR-18-24195.
CR Ahlrichs P, 1999, J CHEM PHYS, V111, P8225, DOI 10.1063/1.480156
Aidun CK, 2010, ANNU REV FLUID MECH, V42, P439, DOI 10.1146/annurev-fluid-121108-145519
[Anonymous], 2011, DOXYGEN DOCUMENTATIO
Arnold A, 2005, ADV POLYM SCI, V185, P59, DOI 10.1007/b136793
Attig N., 2004, NIC LECT NOTES, V23
Auhl R, 2003, J CHEM PHYS, V119, P12718, DOI 10.1063/1.1628670
BENZI R, 1992, PHYS REP, V222, P145, DOI 10.1016/0370-1573(92)90090-M
Booch G, 1991, BENJAMIN CUMMINGS SE
D'Adamo G, 2013, J CHEM PHYS, V138, DOI 10.1063/1.4810881
d'Humieres D, 2002, PHILOS T ROY SOC A, V360, P437, DOI 10.1098/rsta.2001.0955
de Buyl P, 2015, J CHEM PHYS, V142, DOI 10.1063/1.4916313
de Buyl P, 2014, COMPUT PHYS COMMUN, V185, P1546, DOI 10.1016/j.cpc.2014.01.018
DEGENNES PG, 1971, J CHEM PHYS, V55, P572, DOI 10.1063/1.1675789
Delgado-Buscalioni R, 2009, J CHEM PHYS, V131, DOI 10.1063/1.3272265
Delgado-Buscalioni R, 2008, J CHEM PHYS, V128, DOI 10.1063/1.2890729
Delle Site L, 2007, PHYS REV E, V76, DOI 10.1103/PhysRevE.76.047701
Delle Site L, 2017, PHYS REP, V693, P1, DOI 10.1016/j.physrep.2017.05.007
DOI M, 1978, J CHEM SOC FARAD T 2, V74, P1789, DOI 10.1039/f29787401789
Doi M., 1988, THEORY POLYM DYNAMIC, V73
Dunweg B, 2009, ADV POLYM SCI, V221, P89, DOI 10.1007/12_2008_4
Dunweg B, 2007, PHYS REV E, V76, DOI 10.1103/PhysRevE.76.036704
E WN, 2003, PHYS REV B, V67, DOI 10.1103/PhysRevB.67.092101
Espanol P, 2015, J CHEM PHYS, V142, DOI 10.1063/1.4907006
Feynman R. P., 1965, QUANTUM MECH PATH IN
Fiorentini R, 2017, J CHEM PHYS, V146, DOI 10.1063/1.4989486
Fogarty AC, 2016, PROTEINS, V84, P1902, DOI 10.1002/prot.25173
Fogarty AC, 2015, J CHEM PHYS, V142, DOI 10.1063/1.4921347
Fritsch S, 2012, PHYS REV LETT, V108, DOI 10.1103/PhysRevLett.108.170602
Fritsch S, 2012, J CHEM THEORY COMPUT, V8, P398, DOI 10.1021/ct200706f
Guzman HV, 2017, PHYS REV E, V96, DOI 10.1103/PhysRevE.96.053311
Guzman HV, 2017, BEILSTEIN J NANOTECH, V8, P968, DOI 10.3762/bjnano.8.98
Halverson JD, 2013, COMPUT PHYS COMMUN, V184, P1129, DOI 10.1016/j.cpc.2012.12.004
Hess B, 2008, J CHEM THEORY COMPUT, V4, P435, DOI 10.1021/ct700301q
Holm C, 2005, ADV POLYM SCI, V173, P1
Holm C., 2005, ADV POLYM SCI, V221
Jones E., 2001, SCIPY OPEN SOURCE SC
Karlsson B., 2005, C PLUS PLUS STANDARD
Kirkwood JG, 1935, J CHEM PHYS, V3, P300, DOI 10.1063/1.1749657
Krajniak J, 2018, J COMPUT CHEM, V39, P1764, DOI 10.1002/jcc.25348
Krajniak J, 2018, J COMPUT CHEM, V39, P648, DOI 10.1002/jcc.25129
Krajniak J, 2016, J CHEM THEORY COMPUT, V12, P5549, DOI 10.1021/acs.jctc.6b00595
Kreis K, 2015, EUR PHYS J-SPEC TOP, V224, P2289, DOI 10.1140/epjst/e2015-02412-1
Kreis K, 2017, J CHEM PHYS, V147, DOI 10.1063/1.5000701
Kreis K, 2016, J CHEM THEORY COMPUT, V12, P4067, DOI 10.1021/acs.jctc.6b00440
Kreis K, 2016, J CHEM PHYS, V145, DOI 10.1063/1.4959169
Kreis K, 2016, J CHEM THEORY COMPUT, V12, P3030, DOI 10.1021/acs.jctc.6b00242
Kreis K, 2014, EPL-EUROPHYS LETT, V108, DOI 10.1209/0295-5075/108/30007
Kremer K, 2002, MOL SIMULAT, V28, P729, DOI 10.1080/0892702021000002458
KREMER K, 1990, J CHEM PHYS, V92, P5057, DOI 10.1063/1.458541
Lambeth BP, 2010, J CHEM PHYS, V133, DOI 10.1063/1.3522773
Lattner C., 2008, BSD C, P1
Lenz O., 2018, ESPRESSOPP ESPRESSO
Li XZ, 2010, PHYS REV LETT, V104, DOI 10.1103/PhysRevLett.104.066102
Limbach HJ, 2006, COMPUT PHYS COMMUN, V174, P704, DOI 10.1016/j.cpc.2005.10.005
Liu AJ, 2015, SOFT MATTER, V11, P2326, DOI 10.1039/c4sm02344g
Louis AA, 2002, J PHYS-CONDENS MAT, V14, P9187, DOI 10.1088/0953-8984/14/40/311
Lowe S., 2015, USING FORK AND BRANC
Mashayak SY, 2015, PLOS ONE, V10, DOI 10.1371/journal.pone.0131754
Meier K, 2013, ANGEW CHEM INT EDIT, V52, P2820, DOI 10.1002/anie.201205408
Millman J., 2010, P56, DOI DOI 10.1016/S0168-0102(02)00204-3
Moreira L A, 2015, THEORY SIMUL, V24, P419
Mukherji D, 2013, MACROMOLECULES, V46, P9158, DOI 10.1021/ma401877c
Mukherji D, 2012, J CHEM THEORY COMPUT, V8, P375, DOI 10.1021/ct200709h
Nagata Y, 2012, PHYS REV LETT, V109, DOI 10.1103/PhysRevLett.109.226101
Netz PA, 2016, J CHEM PHYS, V145, DOI 10.1063/1.4972014
Oliphant T.E., 2006, A GUIDE TO NUMPY
Pamuk B, 2012, PHYS REV LETT, V108, DOI 10.1103/PhysRevLett.108.193003
Pedregosa F, 2011, J MACH LEARN RES, V12, P2825
Perez A, 2010, J AM CHEM SOC, V132, P11510, DOI 10.1021/ja102004b
Perez F, 2007, COMPUT SCI ENG, V9, P21, DOI 10.1109/MCSE.2007.53
Peter C, 2010, FARADAY DISCUSS, V144, P9, DOI 10.1039/b919800h
Peters JH, 2016, PHYS REV E, V94, DOI 10.1103/PhysRevE.94.023309
Phillips JC, 2005, J COMPUT CHEM, V26, P1781, DOI 10.1002/jcc.20289
PLIMPTON S, 1995, J COMPUT PHYS, V117, P1, DOI 10.1006/jcph.1995.1039
Poblete S, 2010, J CHEM PHYS, V132, DOI 10.1063/1.3357982
Poma AB, 2011, PHYS CHEM CHEM PHYS, V13, P10510, DOI 10.1039/c0cp02865g
Poma AB, 2010, PHYS REV LETT, V104, DOI 10.1103/PhysRevLett.104.250201
Potestio R, 2012, J CHEM PHYS, V136, DOI 10.1063/1.3678587
Potestio R, 2014, ENTROPY-SWITZ, V16, P4199, DOI 10.3390/e16084199
Potestio R, 2013, PHYS REV LETT, V111, DOI 10.1103/PhysRevLett.111.060601
Potestio R, 2013, PHYS REV LETT, V110, DOI 10.1103/PhysRevLett.110.108301
Praprotnik M, 2005, J CHEM PHYS, V123, DOI 10.1063/1.2132286
Praprotnik M, 2011, PHYS REV LETT, V107, DOI 10.1103/PhysRevLett.107.099801
Praprotnik M, 2008, ANNU REV PHYS CHEM, V59, P545, DOI 10.1146/annurev.physchem.59.032607.093707
Praprotnik M, 2008, COMPUT PHYS COMMUN, V179, P51, DOI 10.1016/j.cpc.2008.01.018
Praprotnik M, 2007, J PHYS-CONDENS MAT, V19, DOI 10.1088/0953-8984/19/29/292201
Praprotnik M, 2007, PHYS REV E, V75, DOI 10.1103/PhysRevE.75.017701
Praprotnik M, 2007, J PHYS A-MATH THEOR, V40, pF281, DOI 10.1088/1751-8113/40/15/F03
Praprotnik M, 2006, PHYS REV E, V73, DOI 10.1103/PhysRevE.73.066701
Praprotnik M, 2011, J STAT PHYS, V145, P946, DOI 10.1007/s10955-011-0312-x
Praprotnik M, 2009, J PHYS-CONDENS MAT, V21, DOI 10.1088/0953-8984/21/49/499801
QIAN YH, 1992, EUROPHYS LETT, V17, P479, DOI 10.1209/0295-5075/17/6/001
RAPAPORT DC, 1991, COMPUT PHYS COMMUN, V62, P198, DOI 10.1016/0010-4655(91)90095-3
Roehm D, 2015, COMPUT PHYS COMMUN, V192, P138, DOI 10.1016/j.cpc.2015.03.006
Sablic J, 2016, SOFT MATTER, V12, P2416, DOI 10.1039/c5sm02604k
Scherer MK, 2015, J CHEM THEORY COMPUT, V11, P5525, DOI 10.1021/acs.jctc.5b00743
Shaw DE, 2005, J COMPUT CHEM, V26, P1318, DOI 10.1002/jcc.20267
Shaw DE., 2009, P C HIGH PERF COMP N, P1, DOI DOI 10.1145/1654059.1654099
Stillinger FH, 2002, J CHEM PHYS, V117, P288, DOI 10.1063/1.1480863
Succi S., 2001, LATTICE BOLTZMANN EQ
The HDF Group, 1997, HIER DAT FORM VERS 5
Tom P. W., SEMANTIC VERSIONING
Tretyakov N., 2017, COMPUT PHYS COMMUN, V216
TUCKERMAN M, 1992, J CHEM PHYS, V97, P1990, DOI 10.1063/1.463137
Tuckerman M. E., 2010, STAT MECH THEORY MOL
Vettorel T, 2010, SOFT MATTER, V6, P2282, DOI 10.1039/b921159d
Voth G. A., 2008, COARSE GRAINING COND
Wang H, 2013, PHYS REV X, V3, DOI 10.1103/PhysRevX.3.011018
Wang H, 2009, EUR PHYS J E, V28, P221, DOI 10.1140/epje/i2008-10413-5
Wang L, 2014, P NATL ACAD SCI USA, V111, P18454, DOI 10.1073/pnas.1417923111
Zavadlav J, 2015, J CHEM THEORY COMPUT, V11, P5035, DOI 10.1021/acs.jctc.5b00596
Zavadlav J, 2014, J CHEM PHYS, V140, DOI 10.1063/1.4863329
Zhang GJ, 2015, J CHEM PHYS, V142, DOI 10.1063/1.4922538
Zhang GJ, 2014, ACS MACRO LETT, V3, P198, DOI 10.1021/mz5000015
2005, ADV POLYM SCI, V173, P1
NR 115
TC 5
Z9 5
U1 4
U2 27
PU ELSEVIER SCIENCE BV
PI AMSTERDAM
PA PO BOX 211, 1000 AE AMSTERDAM, NETHERLANDS
SN 0010-4655
EI 1879-2944
J9 COMPUT PHYS COMMUN
JI Comput. Phys. Commun.
PD MAY
PY 2019
VL 238
BP 66
EP 76
DI 10.1016/j.cpc.2018.12.017
PG 11
WC Computer Science, Interdisciplinary Applications; Physics, Mathematical
SC Computer Science; Physics
GA HR0FD
UT WOS:000462802800005
DA 2021-04-21
ER
PT J
AU Alden, B
Hallman, EJ
Rapetti, D
Burns, JO
Datta, A
AF Alden, B.
Hallman, E. J.
Rapetti, D.
Burns, J. O.
Datta, A.
TI The galaxy cluster Typeline' for X-ray temperature maps: ClusterPyXT
SO ASTRONOMY AND COMPUTING
LA English
DT Article
DE Galaxy clusters; ClusterPyXT; Chandra; X-ray; Python
ID LARGE-SCALE STRUCTURE; SHOCK-WAVES; RADIO HALOS; EMISSION
AB ClusterPyXT is a new software pipeline to generate spectral temperature, X-ray surface brightness, pressure, and density maps from X-ray observations of galaxy clusters. These data products help to elucidate the physics of processes occurring within clusters of galaxies, including turbulence, shock fronts, nonthermal phenomena, and the overall dynamics of cluster mergers. ClusterPyXT automates the creation of these data products with minimal user interaction, and allows for rapid analyses of archival data with user defined parameters and the ability to straightforwardly incorporate additional observations. In this paper, we describe in detail the use of this code and release it as an open source Python project on GitHub. (C) 2019 The Authors. Published by Elsevier B.V.
C1 [Alden, B.; Hallman, E. J.; Rapetti, D.; Burns, J. O.; Datta, A.] Univ Colorado, Ctr Astrophys & Space Astron, Dept Astrophys & Planetary Sci, 389 UCB, Boulder, CO 80309 USA.
[Rapetti, D.] NASA, Ames Res Ctr, Moffett Field, CA 94035 USA.
[Datta, A.] Indian Inst Technol, Indore, Madhya Pradesh, India.
RP Alden, B (corresponding author), Univ Colorado, Ctr Astrophys & Space Astron, Dept Astrophys & Planetary Sci, 389 UCB, Boulder, CO 80309 USA.
EM brian.alden@colorado.edu
OI BURNS, JACK/0000-0002-4468-2117; Datta, Abhirup/0000-0002-5333-1095;
Alden, Brian/0000-0003-0025-6762
FU NASA ADAP grant [NNX15AE17G]; US Department of Veterans Affairs
Vocational Rehabilitation and Employment program; NASA Postdoctoral
Program Senior Fellowship at NASA's Ames Research Center
FX This research was supported by NASA ADAP grant NNX15AE17G. BA is
supported by the US Department of Veterans Affairs Vocational
Rehabilitation and Employment program. DR is supported by a NASA
Postdoctoral Program Senior Fellowship at NASA's Ames Research Center,
administered by the Universities Space Research Association under
contract with NASA.
CR BALUCINSKACHURCH M, 1992, ASTROPHYS J, V400, P699, DOI 10.1086/172032
Bruggen M, 2012, SPACE SCI REV, V166, P187, DOI 10.1007/s11214-011-9785-9
Burns JO, 1998, SCIENCE, V280, P400, DOI 10.1126/science.280.5362.400
Cassano R, 2010, ASTROPHYS J LETT, V721, pL82, DOI 10.1088/2041-8205/721/2/L82
Condon JJ, 1998, ASTRON J, V115, P1693, DOI 10.1086/300337
Datta A, 2014, ASTROPHYS J, V793, DOI 10.1088/0004-637X/793/2/80
Diehl S, 2006, MON NOT R ASTRON SOC, V368, P497, DOI 10.1111/j.1365-2966.2006.10125.x
Dolag K, 2000, ASTRON ASTROPHYS, V362, P151
Ensslin TA, 1998, ASTRON ASTROPHYS, V332, P395
Feretti L, 2012, ASTRON ASTROPHYS REV, V20, DOI 10.1007/s00159-012-0054-z
Freeman PE, 2001, PROC SPIE, V4477, P76, DOI 10.1117/12.447161
Fruscione A, 2006, PROC SPIE, V6270, DOI 10.1117/12.671760
Giacintucci S, 2014, ASTROPHYS J, V781, DOI 10.1088/0004-637X/781/1/9
Hallman EJ, 2018, ASTROPHYS J, V859, DOI 10.3847/1538-4357/aabf3a
Kravtsov AV, 2012, ANNU REV ASTRON ASTR, V50, P353, DOI 10.1146/annurev-astro-081811-125502
Loewenstein M., 2004, ORIGIN EVOLUTION ELE, P422
Randall S, 2008, ASTROPHYS J, V688, P208, DOI 10.1086/592324
Randall SW, 2010, ASTROPHYS J, V722, P825, DOI 10.1088/0004-637X/722/1/825
ROETTIGER K, 1993, ASTROPHYS J, V407, pL53, DOI 10.1086/186804
Ryu D, 2003, ASTROPHYS J, V593, P599, DOI 10.1086/376723
Sanders JS, 2006, MON NOT R ASTRON SOC, V371, P829, DOI 10.1111/j.1365-2966.2006.10716.x
SARAZIN CL, 1986, REV MOD PHYS, V58, P1, DOI 10.1103/RevModPhys.58.1
Schenck DE, 2014, ASTRON J, V148, DOI 10.1088/0004-6256/148/1/23
Skillman SW, 2013, ASTROPHYS J, V765, DOI 10.1088/0004-637X/765/1/21
Skillman SW, 2011, ASTROPHYS J, V735, DOI 10.1088/0004-637X/735/2/96
Smith RK, 2001, ASTROPHYS J, V556, pL91, DOI 10.1086/322992
van Weeren RJ, 2016, ASTROPHYS J, V818, DOI 10.3847/0004-637X/818/2/204
NR 27
TC 2
Z9 2
U1 0
U2 0
PU ELSEVIER SCIENCE BV
PI AMSTERDAM
PA PO BOX 211, 1000 AE AMSTERDAM, NETHERLANDS
SN 2213-1337
EI 2213-1345
J9 ASTRON COMPUT
JI Astron. Comput.
PD APR
PY 2019
VL 27
BP 147
EP 155
DI 10.1016/j.ascom.2019.04.001
PG 9
WC Astronomy & Astrophysics; Computer Science, Interdisciplinary
Applications
SC Astronomy & Astrophysics; Computer Science
GA IB0VQ
UT WOS:000469980300010
OA Other Gold
DA 2021-04-21
ER
PT J
AU Asfandiyarov, R
Bayes, R
Blackmore, V
Bogomilov, M
Coiling, D
Dobbs, AJ
Drielsma, F
Drews, M
Ellis, M
Fedorov, M
Franchini, P
Gardener, R
Greis, JR
Hanlet, PM
Heidt, C
Hunt, C
Kafka, G
Karadzhov, Y
Kurup, A
Kyberd, P
Littlefield, M
Liu, A
Long, K
Maletic, D
Martyniak, J
Middleton, S
Mohayai, T
Nebrensky, JJ
Nugent, JC
Overton, E
Pec, V
Pidcott, CE
Rajaram, D
Rayner, M
Reid, ID
Rogers, CT
Santos, E
Savic, M
Taylor, I
Torun, Y
Tunnell, CD
Uchida, MA
Verguilov, V
Walaron, K
Winter, M
Wilbur, S
AF Asfandiyarov, R.
Bayes, R.
Blackmore, V
Bogomilov, M.
Coiling, D.
Dobbs, A. J.
Drielsma, F.
Drews, M.
Ellis, M.
Fedorov, M.
Franchini, P.
Gardener, R.
Greis, J. R.
Hanlet, P. M.
Heidt, C.
Hunt, C.
Kafka, G.
Karadzhov, Y.
Kurup, A.
Kyberd, P.
Littlefield, M.
Liu, A.
Long, K.
Maletic, D.
Martyniak, J.
Middleton, S.
Mohayai, T.
Nebrensky, J. J.
Nugent, J. C.
Overton, E.
Pec, V
Pidcott, C. E.
Rajaram, D.
Rayner, M.
Reid, I. D.
Rogers, C. T.
Santos, E.
Savic, M.
Taylor, I
Torun, Y.
Tunnell, C. D.
Uchida, M. A.
Verguilov, V.
Walaron, K.
Winter, M.
Wilbur, S.
TI MAUS: the MICE analysis user software
SO JOURNAL OF INSTRUMENTATION
LA English
DT Article
DE Data reduction methods; Simulation methods and programs; Software
architectures (event data models, frameworks and databases); Accelerator
modelling and simulations (multiparticle dynamics; single-particle
dynamics)
ID SIMULATION; GEOMETRY; PHYSICS
AB The Muon Ionization Cooling Experiment (MICE) collaboration has developed the MICE Analysis User Software (MAUS) to simulate and analyze experimental data. It serves as the primary codebase for the experiment, providing for offline batch simulation and reconstruction as well as online data quality checks. The software provides both traditional particle-physics functionalities such as track reconstruction and particle identification, and accelerator physics functions, such as calculating transfer matrices and emittances. The code design is object orientated, but has a top-level structure based on the Map-Reduce model. This allows for parallelization to support live data reconstruction during data-taking operations. MAUS allows users to develop in either Python or C++ and provides APIs for both. Various software engineering practices from industry are also used to ensure correct and maintainable code, including style, unit and integration tests, continuous integration and load testing, code reviews, and distributed version control. The software framework and the simulation and reconstruction capabilities are described.
C1 [Asfandiyarov, R.; Drielsma, F.; Karadzhov, Y.; Verguilov, V.] Univ Geneva, Sect Phys, DPNC, Geneva, Switzerland.
[Bayes, R.; Nugent, J. C.; Walaron, K.] Univ Glasgow, Sch Phys & Astron, Kelvin Bldg, Glasgow, Lanark, Scotland.
[Blackmore, V; Coiling, D.; Dobbs, A. J.; Ellis, M.; Hunt, C.; Kurup, A.; Long, K.; Martyniak, J.; Middleton, S.; Santos, E.; Uchida, M. A.] Imperial Coll London, Dept Phys, Blackett Lab, London, England.
[Bogomilov, M.] St Kliment Ohridski Univ Sofia, Dept Atom Phys, Sofia, Bulgaria.
[Fedorov, M.] Radboud Univ Nijmegen, Nijmegen, Netherlands.
[Franchini, P.; Greis, J. R.; Pidcott, C. E.; Taylor, I] Univ Warwick, Dept Phys, Coventry, W Midlands, England.
[Gardener, R.; Kyberd, P.; Littlefield, M.; Nebrensky, J. J.; Reid, I. D.] Brunel Univ, Uxbridge, Middx, England.
[Drews, M.; Hanlet, P. M.; Kafka, G.; Mohayai, T.; Rajaram, D.; Torun, Y.; Winter, M.] IIT, Dept Phys, Chicago, IL 60616 USA.
[Heidt, C.] Univ Calif Riverside, Riverside, CA 92521 USA.
[Liu, A.; Mohayai, T.] Fermilab Natl Accelerator Lab, Batavia, IL USA.
[Maletic, D.; Savic, M.] Univ Belgrade, Inst Phys, Belgrade, Serbia.
[Overton, E.; Pec, V; Wilbur, S.] Univ Sheffield, Dept Phys & Astron, Sheffield, S Yorkshire, England.
[Rayner, M.; Tunnell, C. D.] Univ Oxford, Dept Phys, Denys Wilkinson Bldg, Oxford, England.
[Long, K.; Rogers, C. T.] STFC Rutherford Appleton Lab, Didcot, Oxon, England.
RP Rajaram, D (corresponding author), IIT, Dept Phys, Chicago, IL 60616 USA.
EM durga@fnal.gov
OI Nugent, John/0000-0002-2210-8480; Nebrensky, Henry/0000-0002-8412-4259;
Tunnell, Christopher/0000-0001-8158-7795; Long,
Kenneth/0000-0002-3181-0351; Franchini, Paolo/0000-0002-1419-1368;
Pidcott, Celeste/0000-0001-5598-5685
FU Department of Energy (U.S.A.)United States Department of Energy (DOE);
National Science Foundation (U.S.A.)National Science Foundation (NSF);
Instituto Nazionale di Fisica Nucleare (Italy); Science and Technology
Facilities Council (U.K.)UK Research & Innovation (UKRI)Science &
Technology Facilities Council (STFC); European Community under the
European Commission Framework Programme 7 (AIDA project) [262025]; Japan
Society for the Promotion of ScienceMinistry of Education, Culture,
Sports, Science and Technology, Japan (MEXT)Japan Society for the
Promotion of Science; Swiss National Science FoundationSwiss National
Science Foundation (SNSF)European Commission; European Community under
the European Commission Framework Programme 7 (TIARA project) [261905];
European Community under the European Commission Framework Programme 7
(EuCARD)
FX The work described here was made possible by grants from Department of
Energy and National Science Foundation (U.S.A.), the Instituto Nazionale
di Fisica Nucleare (Italy), the Science and Technology Facilities
Council (U.K.), the European Community under the European Commission
Framework Programme 7 (AIDA project, grant agreement no. 262025, TIARA
project, grant agreement no. 261905, and EuCARD), the Japan Society for
the Promotion of Science and the Swiss National Science Foundation, in
the framework of the SCOPES programme. We gratefully acknowledge all
sources of support. We are grateful to the support given to us by the
staff of the STFC Rutherford Appleton and Daresbury Laboratories. We
acknowledge the use of Grid computing resources deployed and operated by
GridPP [41] in the U.K.
CR Adams D, 2019, EUR PHYS J C, V79, DOI 10.1140/epjc/s10052-019-6674-y
Adams D, 2013, EUR PHYS J C, V73, DOI 10.1140/epjc/s10052-013-2582-8
Adinolfi M, 2002, NUCL INSTRUM METH A, V494, P326, DOI 10.1016/S0168-9002(02)01488-2
Agostinelli S, 2003, NUCL INSTRUM METH A, V506, P250, DOI 10.1016/S0168-9002(03)01368-8
Ambrosino F, 2009, NUCL INSTRUM METH A, V598, P239, DOI 10.1016/j.nima.2008.08.097
Apostolakis J, 2009, RADIAT PHYS CHEM, V78, P859, DOI 10.1016/j.radphyschem.2009.04.026
Asfandiyarov R, 2016, J INSTRUM, V11, DOI 10.1088/1748-0221/11/10/T10007
Bertoni R, 2010, NUCL INSTRUM METH A, V615, P14, DOI 10.1016/j.nima.2009.12.065
Brun R, 1997, NUCL INSTRUM METH A, V389, P81, DOI 10.1016/S0168-9002(97)00048-X
Chytracek R, 2006, IEEE T NUCL SCI, V53, P2892, DOI 10.1109/TNS.2006.881062
Dean J, 2004, P OSDI 04, V04, P137
Dobbs A, 2016, J INSTRUM, V11, DOI 10.1088/1748-0221/11/12/T12001
Fernow R. C., 1999, Proceedings of the 1999 Particle Accelerator Conference (Cat. No.99CH36366), P3020, DOI 10.1109/PAC.1999.792132
Geer S, 2009, ANNU REV NUCL PART S, V59, P347, DOI 10.1146/annurev.nucl.010909.083736
IDS- NF collaboration, 2011, INT DES STUD NEUTR F
MICE collaboration, 2016, J I, V11
MICE collaboration, 2012, JINST, V7
MICE collaboration, 2015, J I, V10
MICE collaboration, 2018, P INT PART ACC C VAN, P5035
Middleton S. C., 2018, THESIS
Penn G, 2000, PHYS REV LETT, V85, P764, DOI 10.1103/PhysRevLett.85.764
Roberts Thomas J, 2007, 2007 IEEE Particle Accelerator Conference, P3468, DOI 10.1109/PAC.2007.4440461
Rogers CT, 2013, PHYS REV SPEC TOP-AC, V16, DOI 10.1103/PhysRevSTAB.16.040104
Ryne R., 2015, MICENOTE461
Ryne R. D., 2006, P 2006 INT COMP ACC, P157
2011, NUCL INSTRUM METH A, V659, P136, DOI DOI 10.1016/J.NIMA.2011.04.041
2009, IEEE T NUCL SCI, V56, P1475, DOI DOI 10.1109/TNS.2009.2021266
NR 27
TC 1
Z9 1
U1 0
U2 1
PU IOP PUBLISHING LTD
PI BRISTOL
PA TEMPLE CIRCUS, TEMPLE WAY, BRISTOL BS1 6BE, ENGLAND
SN 1748-0221
J9 J INSTRUM
JI J. Instrum.
PD APR
PY 2019
VL 14
AR T04005
DI 10.1088/1748-0221/14/04/T04005
PG 23
WC Instruments & Instrumentation
SC Instruments & Instrumentation
GA HW9KK
UT WOS:000467009500001
DA 2021-04-21
ER
PT J
AU Louboutin, M
Lange, M
Luporini, F
Kukreja, N
Witte, PA
Herrmann, FJ
Velesko, P
Gorman, GJ
AF Louboutin, Mathias
Lange, Michael
Luporini, Fabio
Kukreja, Navjot
Witte, Philipp A.
Herrmann, Felix J.
Velesko, Paulius
Gorman, Gerard J.
TI Devito (v3.1.0): an embedded domain-specific language for finite
differences and geophysical exploration
SO GEOSCIENTIFIC MODEL DEVELOPMENT
LA English
DT Article
ID WAVE-FORM INVERSION; ADJOINT; IMPLEMENTATION
AB We introduce Devito, a new domain-specific language for implementing high-performance finite-difference partial differential equation solvers. The motivating application is exploration seismology for which methods such as full-waveform inversion and reverse-time migration are used to invert terabytes of seismic data to create images of the Earth's subsurface. Even using modern supercomputers, it can take weeks to process a single seismic survey and create a useful subsurface image The computational cost is dominated by the numerical solution of wave equations and their corresponding adjoints. Therefore, a great deal of effort is invested in aggressively optimizing the performance of these wave-equation propagators for different computer architectures. Additionally, the actual set of partial differential equations being solved and their numerical discretization is under constant innovation as increasingly realistic representations of the physics are developed, further ratcheting up the cost of practical solvers. By embedding a domain-specific language within Python and making heavy use of SymPy, a symbolic mathematics library, we make it possible to develop finite-difference simulators quickly using a syntax that strongly resembles the mathematics. The Devito compiler reads this code and applies a wide range of analysis to generate highly optimized and parallel code. This approach can reduce the development time of a verified and optimized solver from months to days.
C1 [Louboutin, Mathias; Witte, Philipp A.; Herrmann, Felix J.] Georgia Inst Technol, Sch Computat Sci & Engn, Atlanta, GA 30332 USA.
[Lange, Michael] ECMWF, Reading, Berks, England.
[Luporini, Fabio; Kukreja, Navjot; Velesko, Paulius; Gorman, Gerard J.] Imperial Coll London, Earth Sci & Engn, London, England.
RP Louboutin, M (corresponding author), Georgia Inst Technol, Sch Computat Sci & Engn, Atlanta, GA 30332 USA.
EM mlouboutin3@gatech.edu
RI Witte, Philipp/AAA-1287-2021
OI Herrmann, Felix/0000-0003-1180-2167; Louboutin,
Mathias/0000-0002-1255-2107; Kukreja, Navjot/0000-0003-0016-3785
FU Imperial College London Intel Parallel Computing Centre; SINBAD II
project; EPSRCUK Research & Innovation (UKRI)Engineering & Physical
Sciences Research Council (EPSRC) [EP/M011054/1, EP/L000407/1]; SINBAD
Consortium
FX The development of Devito was primarily supported through the Imperial
College London Intel Parallel Computing Centre. We would also like to
acknowledge support from the SINBAD II project and support from the
member organizations of the SINBAD Consortium as well as EPSRC grants
EP/M011054/1 and EP/L000407/1.
CR Alnaes MS, 2014, ACM T MATH SOFTWARE, V40, DOI 10.1145/2566630
Andreolli C., 2015, HIGH PERFORMANCE PAR, P377
Arbona A, 2018, COMPUT PHYS COMMUN, V229, P170, DOI 10.1016/j.cpc.2018.03.015
Asanovic K., 2006, LANDSCAPE PARALLEL C
Backus J, ACM SIGPLAN NOTICES, V13, P165
Barba L. A, 2018, J OPEN SOURCE ED, V9, P21, DOI [10.21105/jose.00021, DOI 10.21105/JOSE.00021]
Bondhugula U, 2008, PLDI'08: PROCEEDINGS OF THE 2008 SIGPLAN CONFERENCE ON PROGRAMMING LANGUAGE DESIGN & IMPLEMENTATION, P101, DOI 10.1145/1375581.1375595
CARDENAS AF, 1970, COMMUN ACM, V13, P184, DOI 10.1145/362052.362059
Cauchy A. Me, 1847, COMP REND SCI PARIS, P536
Christen M., 2011, Proceedings of the 25th IEEE International Parallel & Distributed Processing Symposium (IPDPS 2011), P676, DOI 10.1109/IPDPS.2011.70
CLAYTON R, 1977, B SEISMOL SOC AM, V67, P1529
Colella P., 2004, DEFINING SOFTWARE RE
Cook Jr G. O, 1988, TECH REP
DONGARRA JJ, 1988, LECT NOTES COMPUT SC, V297, P456
Farrell PE, 2013, SIAM J SCI COMPUT, V35, pC369, DOI 10.1137/120873558
Fomel S., 2013, J OPEN RES SOFTWARE, V1, pE8, DOI [10.5334/jors.ag, DOI 10.5334/JORS.AG, DOI 10.5334/J0RS.AG]
Griewank A, 2000, ACM T MATH SOFTWARE, V26, P19, DOI 10.1145/347837.347846
Haber E, 2012, SIAM J OPTIMIZ, V22, P739, DOI 10.1137/11081126X
Hawick K. A, 2013, 11 INT C SOFTW ENG R
Henretty T., 2013, P 27 INT ACM C INT C, P13
Hopper G M, 1952, P 1952 ACM NAT M PIT, P243
Igel H., 2016, COMPUTATIONAL SEISMO
Intel Corporation, 2016, INT VTUNE PERF AN
Iverson KE, 1962, PROGRAMMING LANGUAGE
Jacobs C. T, 2016, ABS160901277 CORR
Jones J. L., 1954, THESIS
Kim S, 2007, APPL NUMER MATH, V57, P402, DOI 10.1016/j.apnum.2006.05.003
Koster M, 2014, P 1 INT WORKSH HIGH, P1
Kukreja N., 2018, ARXIV180202474
Kumar R, 2015, GEOPHYSICS, V80, pWD73, DOI 10.1190/GEO2015-0108.1
Lange M, 2017, PYTH SCI C P, P89
Lions J. L, OPTIMAL CONTROL SYST
Liu Y, 2011, GEOPHYSICS, V76, pV69, DOI [10.1190/geo2010-0231.1, 10.1190/GEO2010-0231.1]
Logg A, 2012, AUTOMATED SOLUTION D, V84, DOI [10.1007/978-3-642-23099-8, DOI 10.1007/978-3-642-23099-8]
Louboutin M, 2017, LEADING EDGE, V36, P1033, DOI [10.1190/tle36121033.1, DOI 10.1190/TLE36121033.1]
Louboutin M, 2017, COMPUT GEOSCI-UK, V105, P148, DOI 10.1016/j.cageo.2017.04.014
Luporini F., 2018, ABS180703032 CORR
Luporini F, 2014, ACM T ARCHIT CODE OP, V11, DOI 10.1145/2687415
McCalpin J. D., 1991, TECH REP
MCMECHAN GA, 1983, GEOPHYS PROSPECT, V31, P413, DOI 10.1111/j.1365-2478.1983.tb01060.x
Membarth R, 2012, 2012 SC COMPANION: HIGH PERFORMANCE COMPUTING, NETWORKING, STORAGE AND ANALYSIS (SCC), P1133, DOI 10.1109/SC.Companion.2012.136
Meurer A, 2017, PEERJ COMPUT SCI, DOI 10.7717/peerj-cs.103
MITTET R, 1994, GEOPHYSICS, V59, P1894, DOI 10.1190/1.1443576
Naghizadeh M, 2009, GEOPHYSICS, V74, pV9, DOI 10.1190/1.3008547
Osuna C, 2014, PLATF ADV SCI COMP P
Patterson D. A., 2007, COMPUTER ORG DESIGN
Peters Bas, 2017, Leading Edge, V36, P94, DOI 10.1190/tle36010094.1
Plessix RE, 2006, GEOPHYS J INT, V167, P495, DOI 10.1111/j.1365-246X.2006.02978.x
Raknes EB, 2016, GEOPHYSICS, V81, pR45, DOI 10.1190/GEO2015-0185.1
Rathgeber F, 2015, ABS150101809 CORR
Schmidt M., 2009, ARTIFICIAL INTELLIGE, V5, P456
Sun D., 2010, 1006 RIC U DEP COMP
Symes W. W, 2015, ACOUSTIC STAGGERED G, P141
Symes W. W, 2015, IWAVE STRUCTURE BASI, P85
Symes WW, 2011, GEOPHYS PROSPECT, V59, P814, DOI 10.1111/j.1365-2478.2011.00977.x
Tang Y, 2011, SPAA 11: PROCEEDINGS OF THE TWENTY-THIRD ANNUAL SYMPOSIUM ON PARALLELISM IN ALGORITHMS AND ARCHITECTURES, P117, DOI 10.1145/1989493.1989508
TARANTOLA A, 1984, GEOPHYSICS, V49, P1259, DOI 10.1190/1.1441754
Umetani Y., 1985, P IFIP TC2 WG22, V5, P147
Unat Didem, 2011, P INT C SUP, P214, DOI DOI 10.1145/1995896.1995932
Van Engelen Robert, 1996, P 10 INT C SUP NEW Y, P86
van Leeuwen T, 2016, INVERSE PROBL, V32, DOI 10.1088/0266-5611/32/1/015007
Versteeg R., 1994, LEADING EDGE, V13, P927, DOI [10.1190/1.1437051, DOI 10.1190/1.1437051]
Virieux J, 2009, GEOPHYSICS, V74, pWCC1, DOI 10.1190/1.3238367
VIRIEUX J, 1986, GEOPHYSICS, V51, P889, DOI 10.1190/1.1442147
Wang S, 2017, COMPUTING RES REPOSI
Warner M., 2014, ADAPTIVE WAVEFORM IN, P1089, DOI 10.1190/segam2014-0371.1
Wason H, 2017, GEOPHYSICS, V82, pP15, DOI 10.1190/GEO2016-0252.1
Watanabe K., 2015, GREENS FUNCTIONS LAP, P33, DOI DOI 10.1007/978-3-319-17455-6_2
Weiss RM, 2013, GEOPHYSICS, V78, pF7, DOI 10.1190/GEO2012-0063.1
Williams S, 2009, COMMUN ACM, V52, P65, DOI 10.1145/1498765.1498785
Witte Philipp, 2018, Leading Edge, V37, P142, DOI 10.1190/tle37020142.1
Witte PA, 2019, GEOPHYSICS, V84, pF57, DOI 10.1190/GEO2018-0174.1
Yount C, 2015, IEEE I C EMBED SOFTW, P865, DOI 10.1109/HPCC-CSS-ICESS.2015.27
Zhang YY, 2012, INT SYM COMPUT INTEL, P155, DOI 10.1109/ISCID.2012.191
NR 74
TC 9
Z9 9
U1 0
U2 1
PU COPERNICUS GESELLSCHAFT MBH
PI GOTTINGEN
PA BAHNHOFSALLEE 1E, GOTTINGEN, 37081, GERMANY
SN 1991-959X
EI 1991-9603
J9 GEOSCI MODEL DEV
JI Geosci. Model Dev.
PD MAR 27
PY 2019
VL 12
IS 3
BP 1165
EP 1187
DI 10.5194/gmd-12-1165-2019
PG 23
WC Geosciences, Multidisciplinary
SC Geology
GA HQ7JR
UT WOS:000462596800001
OA DOAJ Gold
DA 2021-04-21
ER
PT J
AU Vicentini, F
Minganti, F
Biella, A
Orso, G
Ciuti, C
AF Vicentini, Filippo
Minganti, Fabrizio
Biella, Alberto
Orso, Giuliano
Ciuti, Cristiano
TI Optimal stochastic unraveling of disordered open quantum systems:
Application to driven-dissipative photonic lattices
SO PHYSICAL REVIEW A
LA English
DT Article
ID PYTHON FRAMEWORK; DYNAMICS; SIMULATION; INSULATOR; STATES; QUTIP; JULIA
AB We propose an efficient numerical method to compute configuration averages of observables in disordered open quantum systems whose dynamics can be unraveled via stochastic trajectories. We prove that the optimal sampling of trajectories and disorder configurations is simply achieved by considering one random disorder configuration for each individual trajectory. As a first application, we exploit the present method to study the role of disorder on the physics of the driven-dissipative Bose-Hubbard model in two different regimes: (i) for strong interactions, we explore the dissipative physics of fermionized bosons in disordered one-dimensional chains; (ii) for weak interactions, we investigate the role of on-site inhomogeneities on a first-order dissipative phase transition in a two-dimensional square lattice.
C1 [Vicentini, Filippo; Minganti, Fabrizio; Biella, Alberto; Orso, Giuliano; Ciuti, Cristiano] Univ Paris Diderot, Lab Mat & Phenomenes Quant, CNRS, UMR7162, F-75013 Paris, France.
RP Vicentini, F (corresponding author), Univ Paris Diderot, Lab Mat & Phenomenes Quant, CNRS, UMR7162, F-75013 Paris, France.
RI Ciuti, Cristiano/K-7248-2012; Biella, Alberto/Q-6998-2016; Minganti,
Fabrizio/AAX-4108-2020
OI Ciuti, Cristiano/0000-0002-1134-7013; Minganti,
Fabrizio/0000-0003-4850-1130; Orso, Giuliano/0000-0002-0816-9084;
Vicentini, Filippo/0000-0003-3333-3648
FU ERC (via Consolidator CORPHO) [616233]
FX We thank N. Bartolo, N. Carlon-Zambon, and I. Carusotto for stimulating
discussions. We acknowledge support from ERC (via Consolidator CORPHO
Grant No. 616233). This work was granted access to the HPC resources of
TGCC under the allocations 2018-AP010510493 and 2018-A0050510601
attributed by GENCI ("Grand Equipement National de Calcul Intensif").
CR Albert VV, 2014, PHYS REV A, V89, DOI 10.1103/PhysRevA.89.022118
Aspelmeyer M, 2014, REV MOD PHYS, V86, P1391, DOI 10.1103/RevModPhys.86.1391
Bardyn CE, 2013, NEW J PHYS, V15, DOI 10.1088/1367-2630/15/8/085001
Basko DM, 2006, ANN PHYS-NEW YORK, V321, P1126, DOI 10.1016/j.aop.2005.11.014
Bezanson J, 2017, SIAM REV, V59, P65, DOI 10.1137/141000671
Biella A, 2013, EPL-EUROPHYS LETT, V103, DOI 10.1209/0295-5075/103/57009
Biella A, 2018, PHYS REV B, V97, DOI 10.1103/PhysRevB.97.035103
Biella A, 2017, PHYS REV A, V96, DOI 10.1103/PhysRevA.96.023839
Biella A, 2015, PHYS REV A, V91, DOI 10.1103/PhysRevA.91.053815
Breuer H.-P., 2007, THEORY OPEN QUANTUM
Carmichael H. J, 1998, STAT METHODS QUANTUM
Carmichael H.J, 2008, STAT METHODS QUANTUM
CARMICHAEL HJ, 1993, PHYS REV LETT, V70, P2273, DOI 10.1103/PhysRevLett.70.2273
Carusotto I, 2005, PHYS REV B, V72, DOI 10.1103/PhysRevB.72.125335
Carusotto I, 2009, PHYS REV LETT, V103, DOI 10.1103/PhysRevLett.103.033601
Carusotto I, 2013, REV MOD PHYS, V85, DOI 10.1103/RevModPhys.85.299
Casteels W, 2018, PHYS REV A, V97, DOI 10.1103/PhysRevA.97.062107
Celardo GL, 2013, FORTSCHR PHYS, V61, P250, DOI 10.1002/prop.201200082
Celardo G. L., ARXIV170204506
Cui J, 2015, PHYS REV LETT, V114, DOI 10.1103/PhysRevLett.114.220601
Daley AJ, 2014, ADV PHYS, V63, P77, DOI 10.1080/00018732.2014.933502
DALIBARD J, 1992, PHYS REV LETT, V68, P580, DOI 10.1103/PhysRevLett.68.580
Diehl S, 2008, NAT PHYS, V4, P878, DOI 10.1038/nphys1073
Finazzi S, 2015, PHYS REV LETT, V115, DOI 10.1103/PhysRevLett.115.080604
Fitzpatrick M, 2017, PHYS REV X, V7, DOI 10.1103/PhysRevX.7.011016
Foss-Feig M, 2017, PHYS REV A, V95, DOI 10.1103/PhysRevA.95.043826
Gambetta J, 2008, PHYS REV A, V77, DOI 10.1103/PhysRevA.77.012112
Gardiner C.W., 2004, SPRINGER SERIES SYNE
Georgescu IM, 2014, REV MOD PHYS, V86, P153, DOI 10.1103/RevModPhys.86.153
GIRARDEAU M, 1960, J MATH PHYS, V1, P516, DOI 10.1063/1.1703687
Grujic T, 2012, NEW J PHYS, V14, DOI 10.1088/1367-2630/14/10/103025
Hartmann MJ, 2016, J OPTICS-UK, V18, DOI 10.1088/2040-8978/18/10/104005
Houck AA, 2012, NAT PHYS, V8, P292, DOI [10.1038/NPHYS2251, 10.1038/nphys2251]
Hrahsheh F, 2012, PHYS REV B, V86, DOI 10.1103/PhysRevB.86.214204
Ilievski E, 2015, PHYS REV LETT, V115, DOI 10.1103/PhysRevLett.115.157201
IMRY Y, 1979, PHYS REV B, V19, P3580, DOI 10.1103/PhysRevB.19.3580
IMRY Y, 1975, PHYS REV LETT, V35, P1399, DOI 10.1103/PhysRevLett.35.1399
Jacqmin T, 2014, PHYS REV LETT, V112, DOI 10.1103/PhysRevLett.112.116402
Jager SB, 2017, PHYS REV A, V95, DOI 10.1103/PhysRevA.95.063852
Jaschke D, 2019, QUANTUM SCI TECHNOL, V4, DOI 10.1088/2058-9565/aae724
Jin JS, 2018, PHYS REV B, V98, DOI 10.1103/PhysRevB.98.241108
Jin JS, 2016, PHYS REV X, V6, DOI 10.1103/PhysRevX.6.031011
Johansson JR, 2013, COMPUT PHYS COMMUN, V184, P1234, DOI 10.1016/j.cpc.2012.11.019
Johansson JR, 2012, COMPUT PHYS COMMUN, V183, P1760, DOI 10.1016/j.cpc.2012.02.021
Kapit E, 2017, QUANTUM SCI TECHNOL, V2, DOI 10.1088/2058-9565/aa7e5d
Kenney J. F., 1947, MATH STAT, V1
Kramer S, 2018, COMPUT PHYS COMMUN, V227, P109, DOI 10.1016/j.cpc.2018.02.004
Kshetrimayum A, 2017, NAT COMMUN, V8, DOI 10.1038/s41467-017-01511-6
Kulaitis G, 2013, PHYS REV A, V87, DOI 10.1103/PhysRevA.87.013840
Labouvie R, 2016, PHYS REV LETT, V116, DOI 10.1103/PhysRevLett.116.235302
Langen T, 2015, SCIENCE, V348, P207, DOI 10.1126/science.1257026
Le Boite A, 2013, PHYS REV LETT, V110, DOI 10.1103/PhysRevLett.110.233601
Lebreuilly J, 2017, PHYS REV A, V96, DOI 10.1103/PhysRevA.96.033828
Lee TE, 2013, PHYS REV LETT, V110, DOI 10.1103/PhysRevLett.110.257204
Ma RC, 2019, NATURE, V566, P51, DOI 10.1038/s41586-019-0897-9
Maghrebi MF, 2017, PHYS REV B, V96, DOI 10.1103/PhysRevB.96.174304
Maghrebi MF, 2016, PHYS REV B, V93, DOI 10.1103/PhysRevB.93.014307
Marino J, 2016, PHYS REV LETT, V116, DOI 10.1103/PhysRevLett.116.070407
Mascarenhas E., ARXIV171200987
Mascarenhas E, 2015, PHYS REV A, V92, DOI 10.1103/PhysRevA.92.022116
Maximo CE, 2015, PHYS REV A, V92, DOI 10.1103/PhysRevA.92.062702
Minganti F, 2018, PHYS REV A, V98, DOI 10.1103/PhysRevA.98.042118
MOLMER K, 1993, J OPT SOC AM B, V10, P524, DOI 10.1364/JOSAB.10.000524
Muller M, 2012, ADV ATOM MOL OPT PHY, V61, P1, DOI 10.1016/B978-0-12-396482-3.00001-6
Nagy A, 2018, PHYS REV A, V97, DOI 10.1103/PhysRevA.97.052129
Nandkishore R, 2015, ANNU REV CONDEN MA P, V6, P15, DOI 10.1146/annurev-conmatphys-031214-014726
Nigro D., ARXIV180306279
Noh C, 2017, REP PROG PHYS, V80, DOI 10.1088/0034-4885/80/1/016401
Oganesyan V, 2007, PHYS REV B, V75, DOI 10.1103/PhysRevB.75.155111
Orus R, 2014, ANN PHYS-NEW YORK, V349, P117, DOI 10.1016/j.aop.2014.06.013
Plenio MB, 1998, REV MOD PHYS, V70, P101, DOI 10.1103/RevModPhys.70.101
Proctor TC, 2014, PHYS REV LETT, V112, DOI 10.1103/PhysRevLett.112.097201
Rackauckas C., 2017, THE J, V5, P15, DOI [10.5334/jors.151, DOI 10.5334/JORS.151]
Rackauckas C., ARXIV180404344
Rackauckas C, 2017, DISCRETE CONT DYN-B, V22, P2731, DOI 10.3934/dcdsb.2017133
Rodriguez SRK, 2017, PHYS REV LETT, V118, DOI 10.1103/PhysRevLett.118.247402
Roncaglia M, 2010, PHYS REV LETT, V104, DOI 10.1103/PhysRevLett.104.096803
Rota R, 2018, NEW J PHYS, V20, DOI 10.1088/1367-2630/aab703
Rota R, 2017, PHYS REV B, V95, DOI 10.1103/PhysRevB.95.134431
Rota R., PHYS REV LETT
Sachdev S, 2001, QUANTUM PHASE TRANSI
Saffman M, 2010, REV MOD PHYS, V82, P2313, DOI 10.1103/RevModPhys.82.2313
Santos LF, 2016, PHYS REV LETT, V116, DOI 10.1103/PhysRevLett.116.250402
Scarlatella O, 2019, PHYS REV B, V99, DOI 10.1103/PhysRevB.99.064511
Schiro M, 2016, PHYS REV LETT, V116, DOI 10.1103/PhysRevLett.116.143603
Schutz S, 2016, PHYS REV LETT, V117, DOI 10.1103/PhysRevLett.117.083001
See T. F., PHYS REV A
Shammah N, 2018, PHYS REV A, V98, DOI 10.1103/PhysRevA.98.063815
Sieberer LM, 2016, REP PROG PHYS, V79, DOI 10.1088/0034-4885/79/9/096001
Vakulchyk I, 2018, PHYS REV B, V98, DOI 10.1103/PhysRevB.98.020202
Verstraelen W, 2018, APPL SCI-BASEL, V8, DOI 10.3390/app8091427
Vicentini F, 2018, PHYS REV A, V97, DOI 10.1103/PhysRevA.97.013853
Viteau M, 2012, PHYS REV LETT, V109, DOI 10.1103/PhysRevLett.109.053002
VOGEL K, 1989, PHYS REV A, V39, P4675, DOI 10.1103/PhysRevA.39.4675
VOGEL K, 1988, PHYS REV A, V38, P2409, DOI 10.1103/PhysRevA.38.2409
Werner AH, 2016, PHYS REV LETT, V116, DOI 10.1103/PhysRevLett.116.237201
Wilson RM, 2016, PHYS REV A, V94, DOI 10.1103/PhysRevA.94.033801
Wolf A, 2011, EPL-EUROPHYS LETT, V95, DOI 10.1209/0295-5075/95/60008
Xu XS, 2018, PHYS REV B, V97, DOI 10.1103/PhysRevB.97.140201
NR 99
TC 4
Z9 4
U1 0
U2 3
PU AMER PHYSICAL SOC
PI COLLEGE PK
PA ONE PHYSICS ELLIPSE, COLLEGE PK, MD 20740-3844 USA
SN 1050-2947
EI 1094-1622
J9 PHYS REV A
JI Phys. Rev. A
PD MAR 14
PY 2019
VL 99
IS 3
AR 032115
DI 10.1103/PhysRevA.99.032115
PG 11
WC Optics; Physics, Atomic, Molecular & Chemical
SC Optics; Physics
GA HP7UU
UT WOS:000461896700007
OA Green Accepted
DA 2021-04-21
ER
PT J
AU Linga, G
Bolet, A
Mathiesen, J
AF Linga, Gaute
Bolet, Asger
Mathiesen, Joachim
TI Bernaise: A Flexible Framework for Simulating Two-Phase
Electrohydrodynamic Flows in Complex Domains
SO FRONTIERS IN PHYSICS
LA English
DT Article
DE electrokinectic; electrohydrodynamics (EHD); porous flow; phase field
method; capillarity; numerical simulation; finite element method (FEM)
ID DIFFUSE INTERFACE MODELS; BENCHMARK COMPUTATIONS; FLUID-DYNAMICS; STABLE
SCHEMES; SALINITY; DROP
AB Bernaise (Binary Electrohydrodynamic Solver) is a flexible high-level finite element solver of two-phase electrohydrodynamic flow in complex geometries. Two-phase flow with electrolytes is relevant across a broad range of systems and scales, from "lab-on-a-chip" devices for medical diagnostics to enhanced oil recovery at the reservoir scale. For the strongly coupled multi-physics problem, we employ a recently developed thermodynamically consistent model which combines a generalized Nernst-Planck equation for ion transport, the Poisson equation for electrostatics, the Cahn-Hilliard equation for the phase field (describing the interface separating the phases), and the Navier-Stokes equations for fluid flow. We present an efficient linear, decoupled numerical scheme which sequentially solves the three sets of equations. The scheme is validated by comparison to cases where analytical solutions are available, benchmark cases, and by the method of manufactured solution. The solver operates on unstructured meshes and is therefore well suited to handle arbitrarily shaped domains and problem set-ups where, e.g., very different resolutions are required in different parts of the domain. Bernaise is implemented in Python via the FEniCS framework, which effectively utilizes MPI and domain decomposition. Further, new solvers and problem set-ups can be specified and added with ease to the Bernaise framework by experienced Python users.
C1 [Linga, Gaute; Bolet, Asger; Mathiesen, Joachim] Univ Copenhagen, Niels Bohr Inst, Copenhagen, Denmark.
RP Linga, G (corresponding author), Univ Copenhagen, Niels Bohr Inst, Copenhagen, Denmark.
EM linga@nbi.dk
RI ; Linga, Gaute/J-5699-2015; Bolet, Asger Johannes Skjode/E-6342-2015
OI Mathiesen, Joachim/0000-0002-5621-5487; Linga,
Gaute/0000-0002-0987-8704; Bolet, Asger Johannes
Skjode/0000-0002-3394-7159
FU European Union's Horizon 2020 research and innovation program through a
Marie Curie initial training networks [642976]; Villum Fonden through
the grant Earth Patterns
FX This project has received funding from the European Union's Horizon 2020
research and innovation program through a Marie Curie initial training
networks under grant agreement no. 642976 (NanoHeal), and from the
Villum Fonden through the grant Earth Patterns.
CR Abels H, 2012, MATH MOD METH APPL S, V22, DOI 10.1142/S0218202511500138
Aland S, 2012, INT J NUMER METH FL, V69, P747, DOI 10.1002/fld.2611
Anderson DM, 1998, ANNU REV FLUID MECH, V30, P139, DOI 10.1146/annurev.fluid.30.1.139
Balay S., 2017, PETSC WEB PAGE
BENI G, 1981, J APPL PHYS, V52, P6011, DOI 10.1063/1.329822
BENI G, 1981, APPL PHYS LETT, V38, P207, DOI 10.1063/1.92322
BENI G, 1982, APPL PHYS LETT, V40, P912, DOI 10.1063/1.92952
Berry JD, 2013, J COMPUT PHYS, V251, P209, DOI 10.1016/j.jcp.2013.05.026
Bolet A, 2018, PHYS REV E, V97, DOI 10.1103/PhysRevE.97.043114
Bonn D, 2009, REV MOD PHYS, V81, P739, DOI 10.1103/RevModPhys.81.739
Brenner S., 2007, MATH THEORY FINITE E, V15
Campillo-Funollet E, 2012, SIAM J APPL MATH, V72, P1899, DOI 10.1137/120861333
Carlson A, 2012, PHYS REV E, V85, DOI 10.1103/PhysRevE.85.045302
Chorin A.J., 1967, Journal of Computational Physics, V2, P12, DOI 10.1016/0021-9991(67)90037-X
CHORIN AJ, 1968, MATH COMPUT, V22, P745, DOI 10.2307/2004575
DEGENNES PG, 1985, REV MOD PHYS, V57, P827, DOI 10.1103/RevModPhys.57.827
Eck C, 2009, INTERFACE FREE BOUND, V11, P259
Ervik A, 2016, J COMPUT PHYS, V327, P576, DOI 10.1016/j.jcp.2016.09.039
Fiorentino EA, 2017, GEOPHYS J INT, V208, P1139, DOI 10.1093/gji/ggw417
Fiorentino EA, 2016, GEOPHYS J INT, V205, P648, DOI 10.1093/gji/ggw041
Fylladitakis ED, 2014, IEEE T PLASMA SCI, V42, P358, DOI 10.1109/TPS.2013.2297173
Grun G, 2014, J COMPUT PHYS, V257, P708, DOI 10.1016/j.jcp.2013.10.028
Grun G, 2016, COMMUN COMPUT PHYS, V19, P1473, DOI 10.4208/cicp.scpde14.39s
Guermond JL, 2006, COMPUT METHOD APPL M, V195, P6011, DOI 10.1016/j.cma.2005.10.010
Guillen-Gonzalez F, 2014, J COMPUT MATH, V32, P643, DOI 10.4208/jcm.1405-m4410
Hassenkam T, 2011, COLLOID SURFACE A, V390, P179, DOI 10.1016/j.colsurfa.2011.09.025
Hayes RA, 2003, NATURE, V425, P383, DOI 10.1038/nature01988
Hilner E, 2015, SCI REP-UK, V5, DOI 10.1038/srep09933
HOHENBERG PC, 1977, REV MOD PHYS, V49, P435, DOI 10.1103/RevModPhys.49.435
Huang JJ, 2015, INT J NUMER METH FL, V77, P123, DOI 10.1002/fld.3975
Hysing S, 2009, INT J NUMER METH FL, V60, P1259, DOI 10.1002/fld.1934
Langtangen H.P., 2017, SOLVING PDES PYTHON, VI, DOI [10.1007/978-3-319-52462-7., DOI 10.1007/978-3-319-52462-7, 10.1007/978-3-319-52462-7]
Langtangen HP, 2002, ADV WATER RESOUR, V25, P1125, DOI 10.1016/S0309-1708(02)00052-0
Lee J, 2000, J MICROELECTROMECH S, V9, P171, DOI 10.1109/84.846697
Lin Y, 2012, INT J MULTIPHAS FLOW, V45, P1, DOI 10.1016/j.ijmultiphaseflow.2012.04.002
Linga G, 2018, BERNAISE GIT REPOSIT
Linga G, 2018, PHYS REV E, V98, DOI 10.1103/PhysRevE.98.013101
Lippmann G., 1875, RELATIONS ENTRE PHEN
Logg A, 2010, ACM T MATH SOFTWARE, V37, DOI 10.1145/1731022.1731030
Logg Anders, 2012, AUTOMATED SOLUTION D, V84
Lopez-Herrera JM, 2011, J COMPUT PHYS, V230, P1939, DOI 10.1016/j.jcp.2010.11.042
Lowengrub J, 1998, P ROY SOC A-MATH PHY, V454, P2617, DOI 10.1098/rspa.1998.0273
Lu HW, 2007, J FLUID MECH, V590, P411, DOI 10.1017/S0022112007008154
MELCHER JR, 1969, ANNU REV FLUID MECH, V1, P111, DOI 10.1146/annurev.fl.01.010169.000551
Metzger Stefan, 2015, Proceedings in Applied Mathematics and Mechanics, V15, P715, DOI 10.1002/pamm.201510346
Metzger S, 2019, NUMER ALGORITHMS, V80, P1361, DOI 10.1007/s11075-018-0530-2
Mitscha-Baude G, 2017, J COMPUT PHYS, V338, P452, DOI 10.1016/j.jcp.2017.02.072
Monroe CW, 2006, PHYS REV LETT, V97, DOI 10.1103/PhysRevLett.97.136102
Monroe CW, 2006, J PHYS-CONDENS MAT, V18, P2837, DOI 10.1088/0953-8984/18/10/009
Mortensen M, 2015, COMPUT PHYS COMMUN, V188, P177, DOI 10.1016/j.cpc.2014.10.026
Mugele F, 2007, J PHYS-CONDENS MAT, V19, DOI 10.1088/0953-8984/19/37/375112
Mugele F, 2005, J PHYS-CONDENS MAT, V17, pR705, DOI 10.1088/0953-8984/17/28/R01
Mugele F, 2010, ADV COLLOID INTERFAC, V161, P115, DOI 10.1016/j.cis.2009.11.002
Mugele F, 2009, NATURE, V461, P356, DOI 10.1038/461356a
Nelson WC, 2012, J ADHES SCI TECHNOL, V26, P1747, DOI 10.1163/156856111X599562
Nielsen CP, 2014, PHYS REV E, V90, DOI 10.1103/PhysRevE.90.043020
Nochetto RH, 2014, MATH MOD METH APPL S, V24, DOI 10.1142/S0218202513500474
Nurnberg R, 2017, EUR J APPL MATH, V28, P470, DOI 10.1017/S0956792516000395
Pedersen NR, 2016, ENERG FUEL, V30, P3768, DOI 10.1021/acs.energyfuels.5b02562
Pillai R, 2015, PHYS REV E, V92, DOI 10.1103/PhysRevE.92.013007
Plumper O, 2017, NAT GEOSCI, V10, P685, DOI [10.1038/ngeo3009, 10.1038/NGEO3009]
Pollack MG, 2002, LAB CHIP, V2, P96, DOI 10.1039/b110474h
PRIDE SR, 1991, GEOPHYSICS, V56, P914, DOI 10.1190/1.1443125
Prosperetti A., 2009, COMPUTATIONAL METHOD
Qian TZ, 2006, J FLUID MECH, V564, P333, DOI 10.1017/S0022112006001935
Qian TZ, 2003, PHYS REV E, V68, DOI 10.1103/PhysRevE.68.016306
RezaeiDoust A, 2009, ENERG FUEL, V23, P4479, DOI 10.1021/ef900185q
Ristenpart WD, 2009, NATURE, V461, P377, DOI 10.1038/nature08294
Saville DA, 1997, ANNU REV FLUID MECH, V29, P27, DOI 10.1146/annurev.fluid.29.1.27
Schnitzer O, 2015, J FLUID MECH, V773, P1, DOI 10.1017/jfm.2015.242
Schoch RB, 2008, REV MOD PHYS, V80, P839, DOI 10.1103/RevModPhys.80.839
Shen J, 2015, SIAM J NUMER ANAL, V53, P279, DOI 10.1137/140971154
Siria A, 2017, NAT REV CHEM, V1, DOI 10.1038/s41570-017-0091
Snoeijer JH, 2013, ANNU REV FLUID MECH, V45, P269, DOI 10.1146/annurev-fluid-011212-140734
Squires TM, 2005, REV MOD PHYS, V77, P977, DOI 10.1103/RevModPhys.77.977
Srinivasan V, 2004, LAB CHIP, V4, P310, DOI 10.1039/b403341h
Sui Y, 2014, ANNU REV FLUID MECH, V46, P97, DOI 10.1146/annurev-fluid-010313-141338
TAYLOR G, 1966, PROC R SOC LON SER-A, V291, P159, DOI 10.1098/rspa.1966.0086
Teigen KE, 2011, J COMPUT PHYS, V230, P375, DOI 10.1016/j.jcp.2010.09.020
Teigen KE, 2010, PHYS FLUIDS, V22, DOI 10.1063/1.3504271
Tomar G, 2007, J COMPUT PHYS, V227, P1267, DOI 10.1016/j.jcp.2007.09.003
Walker SW, 2006, J MICROELECTROMECH S, V15, P986, DOI 10.1109/JMEMS.2006.878876
Walker SW, 2009, PHYS FLUIDS, V21, DOI 10.1063/1.3254022
Yang C, 1996, COLLOID SURFACE A, V113, P51, DOI 10.1016/0927-7757(96)03544-3
Yang QZ, 2014, INT J HEAT MASS TRAN, V78, P820, DOI 10.1016/j.ijheatmasstransfer.2014.07.039
Yang QZ, 2013, INT J MULTIPHAS FLOW, V57, P1, DOI 10.1016/j.ijmultiphaseflow.2013.06.006
Zeng J, 2011, INT J MOL SCI, V12, P1633, DOI 10.3390/ijms12031633
Zholkovskij EK, 2002, J FLUID MECH, V472, P1, DOI 10.1017/S0022112002001441
NR 88
TC 2
Z9 2
U1 1
U2 5
PU FRONTIERS MEDIA SA
PI LAUSANNE
PA AVENUE DU TRIBUNAL FEDERAL 34, LAUSANNE, CH-1015, SWITZERLAND
SN 2296-424X
J9 FRONT PHYS-LAUSANNE
JI Front. Physics
PD MAR 4
PY 2019
VL 7
AR 21
DI 10.3389/fphy.2019.00021
PG 25
WC Physics, Multidisciplinary
SC Physics
GA HN7EU
UT WOS:000460352500001
OA DOAJ Gold
DA 2021-04-21
ER
PT J
AU D'Urso, F
Santoro, C
Santoro, FF
AF D'Urso, Fabio
Santoro, Corrado
Santoro, Federico Fausto
TI An integrated framework for the realistic simulation of multi-UAV
applications
SO COMPUTERS & ELECTRICAL ENGINEERING
LA English
DT Article
DE UAV; Flock; Swarm; Simulation framework; Autonomous UAV simulation;
Multi-UAV; Network simulation; MANET; FANET; Internet-of-Things
AB This paper describes the software architecture of an integrated simulator for the realistic simulation of multi Unmanned aerial vehicle applications. The integrated simulator exploits some already existing tools to simulate a specific part of the overall Unmanned aerial vehicle hardware and software structure: a 3D visualization engine, a physics simulator, a flight control stack and a network simulator to handle communications among Unmanned aerial vehicles. These features are provided by the tools Gazebo, ArduCopter and ns-3 that, however, are not designed to work together in an integrated manner. The solution proposed in this paper is based on a software middleware that coordinates all of these tools, which may optionally be run on multiple interconnected computers, and lets them have a common notion of simulated time during the simulation; moreover, the middleware coordinates the activities of the High-level Logic, which is the software part that implements the strategy and control of the multi Unmanned aerial vehicle application. A Python API is provided to allow developers to write their Unmanned aerial vehicle application (cooperative missions, flocking, etc.) in such a way as to be first simulated and then run onto the real platform with no or few modifications. (C) 2019 Elsevier Ltd. All rights reserved.
C1 [D'Urso, Fabio; Santoro, Corrado; Santoro, Federico Fausto] Univ Catania, Dept Math & Informat, Viale Andrea Doria 6, I-95125 Catania, Italy.
RP Santoro, FF (corresponding author), Univ Catania, Dept Math & Informat, Viale Andrea Doria 6, I-95125 Catania, Italy.
EM durso@dmi.unict.it; santoro@dmi.unict.it; federico.sanroto@unict.it
FU Italian MIURMinistry of Education, Universities and Research (MIUR)
[CLARA SCN_00451]
FX This work is supported by project CLARA SCN_00451 funded by the Italian
MIUR.
CR Bouragadi N, 2009, P 4 NAT C CONTR ARCH
Cai GW, 2014, UNMANNED SYST, V2, P175, DOI 10.1142/S2301385014300017
Cavalcante TRF, 2017, 2017 VII BRAZILIAN SYMPOSIUM ON COMPUTING SYSTEMS ENGINEERING (SBESC), P9, DOI 10.1109/SBESC.2017.8
Chmaj G, 2015, ADV INTELL SYST, V366, P449, DOI 10.1007/978-3-319-08422-0_66
Ciarletta L, 2014, INT CONF UNMAN AIRCR, P95, DOI 10.1109/ICUAS.2014.6842244
De Benedetti M, 2017, J NETW COMPUT APPL, V96, P14, DOI 10.1016/j.jnca.2017.08.004
De Benedetti M, 2017, 18 WORKSH DAGL OGG A
De Benedetti M, 2015, 2015 10TH INTERNATIONAL CONFERENCE ON P2P, PARALLEL, GRID, CLOUD AND INTERNET COMPUTING (3PGCIC), P248, DOI 10.1109/3PGCIC.2015.78
Furrer F, 2016, STUD COMPUT INTELL, V625, P595, DOI 10.1007/978-3-319-26054-9_23
Gomes C., 2017, CORR
Hauert S., 2011, 2011 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS 2011), P5015, DOI 10.1109/IROS.2011.6048729
Hayat S, 2016, IEEE COMMUN SURV TUT, V18, P2624, DOI 10.1109/COMST.2016.2560343
Koenig N., 2004, 2004 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS) (IEEE Cat. No.04CH37566), P2149
Kudelski M, 2013, ROBOT AUTON SYST, V61, P483, DOI 10.1016/j.robot.2013.01.003
Kudelski M, 2012, IEEE INT C INT ROBOT, P5018, DOI 10.1109/IROS.2012.6385998
Luke S, 2005, SIMUL-T SOC MOD SIM, V81, P517, DOI 10.1177/0037549705058073
Madey AG, 2013, P AG DIR SIM S ADSS
Marconato EA, 2017, AVENS ANOVEL FLYING
Meier L, 2015, ROB AUT ICRA 2015 IE
Nethi S, 2007, WORLD WIR MOB MULT N, P1
Pace P, 2016, MOBILE NETW APPL, V21, P708, DOI 10.1007/s11036-016-0726-4
Reynolds C., 1987, SIGGRAPH COMPUT GRAP, V21, P25, DOI DOI 10.1145/37402.37406
Riley G.F., 2010, NS 3 NETWORK SIMULAT, P15, DOI DOI 10.1007/978-3-642-12331-3_2
Schmittle M, 2018, P 9 ACM IEEE INT C C, P130
Sklar E, 2007, ARTIF LIFE, V13, P303, DOI 10.1162/artl.2007.13.3.303
TATARA E, 2006, P AG 2006 C SOC AG R
Varga A., 2008, P 1 INT C SIM TOOLS, V60, P1, DOI [DOI 10.4108/ICST.SIMUT00LS20083027, DOI 10.1109/ITSC.2002.1041322]
Vasarhelyi G, 2014, IEEE INT C INT ROBOT, P3866, DOI 10.1109/IROS.2014.6943105
Viragh C, 2014, BIOINSPIR BIOMIM, V9, DOI 10.1088/1748-3182/9/2/025012
Wei Y, 2013, PROCEDIA COMPUT SCI, V18, P1949, DOI 10.1016/j.procs.2013.05.364
NR 30
TC 5
Z9 5
U1 2
U2 11
PU PERGAMON-ELSEVIER SCIENCE LTD
PI OXFORD
PA THE BOULEVARD, LANGFORD LANE, KIDLINGTON, OXFORD OX5 1GB, ENGLAND
SN 0045-7906
EI 1879-0755
J9 COMPUT ELECTR ENG
JI Comput. Electr. Eng.
PD MAR
PY 2019
VL 74
BP 196
EP 209
DI 10.1016/j.compeleceng.2019.01.016
PG 14
WC Computer Science, Hardware & Architecture; Computer Science,
Interdisciplinary Applications; Engineering, Electrical & Electronic
SC Computer Science; Engineering
GA IH7GO
UT WOS:000474672200015
DA 2021-04-21
ER
PT J
AU Liu, ZQ
Zhao, ZL
Yang, YW
Gao, YC
Meng, HY
Gao, QY
AF Liu, Zhao-Qing
Zhao, Ze-Long
Yang, Yong-Wei
Gao, Yu-Cui
Meng, Hai-Yan
Gao, Qing-Yu
TI Development and validation of depletion code system IMPC-Burnup for ADS
SO NUCLEAR SCIENCE AND TECHNIQUES
LA English
DT Article
DE ADS-coupled proton-neutron transport; Burnup calculation; IMPC-Burnup;
FLUKA; OpenMC; ORIGEN2
AB Depletion calculation is important for studying the transmutation efficiency of minor actinides and long-life fission products in accelerator-driven subcritical reactor system (ADS). Herein the Python language is used to develop a burnup code system called IMPC-Burnup by coupling FLUKA, OpenMC, and ORIGEN2. The program is preliminarily verified by OECD-NEA pin cell and IAEA-ADS benchmarking by comparison with experimental values and calculated results from other studies. Moreover, the physics design scheme of the CIADS subcritical core is utilized to test the feasibility of IMPC-Burnup program in the burnup calculation of ADS system. Reference results are given by the COUPLE3.0 program. The results of IMPC-Burnup show good agreement with those of COUPLE3.0. In addition, since the upper limit of the neutron transport energy for OpenMC is 20 MeV, neutrons with energies greater than 20 MeV in the CIADS subcritical core cannot be transported; thus, an equivalent flux method has been proposed to consider neutrons above 20 MeV in the OpenMC transport calculation. The results are compared to those that do not include neutrons greater than 20 MeV. The conclusion is that the accuracy of the actinide nuclide mass in the burnup calculation is improved when the equivalent flux method is used. Therefore, the IMPC-Burnup code is suitable for burnup analysis of the ADS system.
C1 [Liu, Zhao-Qing; Zhao, Ze-Long; Yang, Yong-Wei; Gao, Yu-Cui; Meng, Hai-Yan; Gao, Qing-Yu] Chinese Acad Sci, Inst Modern Phys, Lanzhou 730000, Gansu, Peoples R China.
[Liu, Zhao-Qing; Zhao, Ze-Long; Yang, Yong-Wei; Gao, Qing-Yu] Univ Chinese Acad Sci, Sch Nucl Sci & Technol, Beijing 100049, Peoples R China.
RP Zhao, ZL; Yang, YW (corresponding author), Chinese Acad Sci, Inst Modern Phys, Lanzhou 730000, Gansu, Peoples R China.; Zhao, ZL; Yang, YW (corresponding author), Univ Chinese Acad Sci, Sch Nucl Sci & Technol, Beijing 100049, Peoples R China.
EM zhaozelong@impcas.ac.cn; yangyongwei@impcas.ac.cn
FU "Strategic Priority Research Program" of Chinese Academy of Sciences
[XDA03030102]
FX This work was supported by the "Strategic Priority Research Program" of
Chinese Academy of Sciences (No. XDA03030102).
CR Croff A.G., 1980, ORNLTM7175
DEHART MD, 1996, ORNL6901
Fasso A., 2005, LANCET, DOI [10.2172/877507, DOI 10.2172/877507]
Gul A, 2017, ANN NUCL ENERGY, V99, P321, DOI 10.1016/j.anucene.2016.09.016
Hong S, 2016, CHINESE PHYS C, V40, DOI 10.1088/1674-1137/40/11/114102
Jiang XF, 2004, ANN NUCL ENERGY, V31, P213, DOI 10.1016/S0306-4549(03)00205-6
Leppanen J, 2014, SNA + MC 2013 - JOINT INTERNATIONAL CONFERENCE ON SUPERCOMPUTING IN NUCLEAR APPLICATIONS + MONTE CARLO, DOI 10.1051/snamc/201406021
Li Gang, 2013, High Power Laser and Particle Beams, V25, P158, DOI 10.3788/HPLPB20132501.0178
Li H. Q., 2004, C CHIN REACT PHYS QI
Li JY, 2017, NUCL ENG DES, V324, P360, DOI 10.1016/j.nucengdes.2017.09.012
Li XZ, 2015, ATOM ENERGY SCI TECH, V49, P371, DOI DOI 10.7538/YZK.2015.49.S0.0371
Martin WR, 2012, NUCL ENG TECHNOL, V44, P151, DOI 10.5516/NET.01.2012.502
Moore R.L., 1995, INEL950523
Pelowitz D., 2011, MCNPX USERS MANUAL 2
Romano PK, 2015, ANN NUCL ENERGY, V82, P90, DOI 10.1016/j.anucene.2014.07.048
Romano PK, 2013, ANN NUCL ENERGY, V51, P274, DOI 10.1016/j.anucene.2012.06.040
Slessarev I., 1997, IAEA ADS BENCHMARK R
Stanculescu A, 2013, ANN NUCL ENERGY, V62, P607, DOI 10.1016/j.anucene.2013.02.006
Stankovskiy A., 2012, PHYSOR 2012 C ADV RE
Talamo A, 2006, ANN NUCL ENERGY, V33, P1176, DOI 10.1016/j.anucene.2006.08.006
Wang K, 2015, ANN NUCL ENERGY, V82, P121, DOI 10.1016/j.anucene.2014.08.048
[ Yu Ganglin], 2003, [, Atomic Energy Science and Technology], V37, P250
NR 22
TC 5
Z9 5
U1 1
U2 9
PU SPRINGER SINGAPORE PTE LTD
PI SINGAPORE
PA #04-01 CENCON I, 1 TANNERY RD, SINGAPORE 347719, SINGAPORE
SN 1001-8042
EI 2210-3147
J9 NUCL SCI TECH
JI Nucl. Sci. Tech.
PD MAR
PY 2019
VL 30
IS 3
AR 44
DI 10.1007/s41365-019-0560-z
PG 10
WC Nuclear Science & Technology; Physics, Nuclear
SC Nuclear Science & Technology; Physics
GA HQ3CF
UT WOS:000462284000009
DA 2021-04-21
ER
PT J
AU Landour, M
El Bardouni, T
Chakir, E
Benaalilou, K
Mohammed, M
Bougueniz, H
El Yaakoubi, H
AF M Landour
El Bardouni, T.
Chakir, E.
Benaalilou, K.
Mohammed, M.
Bougueniz, H.
El Yaakoubi, H.
TI NTP-ERSN: A new package for solving the multigroup neutron transport
equation in a slab geometry
SO APPLIED RADIATION AND ISOTOPES
LA English
DT Article
DE Package; NTP-ERSN; Neutron transport equation; FORTRAN90; GUI; Python;
CP; SN; MOC; Eigenvalue calculation
AB The package, called NTP-ERSN (N eutron T ransport P ackage from the R adiations and N uclear S ystems G roup), is an open-source code written in FORTRAN90 for a pedagogical purpose to solve the steady-state multigroup neutron transport equation. This package is based on three classical methods, namely the collision probability (CP) method, the discrete ordinates (S-N) method and the method of characteristics (MOC). These methods are employed to obtain scalar and angular flux distributions in homogeneous and heterogeneous slab geometry with isotropic and anisotropic scattering. The source code algorithms are very simple to be comprehensive by engineering students. In addition, NTP-ERSN is a simple framework to add and test new algorithms. On the other hand, a graphical user interface written in Python programing language has been developed to simplify the use of NTP-ERSN. Numerical results are given to illustrate the NTP-ERSN code's accuracy. Finally, the present software can be useful as an academic tool for teaching reactor physics. It is freely available for download on GitHub (https://github.com/mohamedlandour/NTP-ERSN).
C1 [M Landour; El Bardouni, T.; Benaalilou, K.; Mohammed, M.; Bougueniz, H.; El Yaakoubi, H.] Univ Abdelmalek Essaadi, Fac Sci Tetuan, Radiat & Nucl Syst Lab, Tetouan, Morocco.
[Chakir, E.] Ibn Tofail Univ, Fac Sci, SIMO LAB, Kenitra, Morocco.
RP Landour, M (corresponding author), Univ Abdelmalek Essaadi, Fac Sci Tetuan, Radiat & Nucl Syst Lab, Tetouan, Morocco.
EM mlandour@uae.ac.ma
OI LAHDOUR, Mohamed/0000-0003-3347-132X; Chakir, El
Mahjoub/0000-0002-1653-4635
CR Abbate Maximo J., 1983, METHODS STEADY STATE
Batistela CHF, 1999, ANN NUCL ENERGY, V26, P761, DOI 10.1016/S0306-4549(98)00096-6
Boyd W, 2014, ANN NUCL ENERGY, V68, P43, DOI 10.1016/j.anucene.2013.12.012
Bray T., 2017, 8259 RFC
Brinkley F. W., 1995, DIFFUSION ACCELERATE
Caldeira AD, 2001, ANN NUCL ENERGY, V28, P1563, DOI 10.1016/S0306-4549(00)00119-5
Clark Melville, 1964, ELSEVIER SCI
Dahl J. A., 2000, PHYS 2000 INT TOP M
Hebert A., 2009, APPL REACTOR PHYS
Hebert A, 2006, NUCL SCI ENG, V154, P134, DOI 10.13182/NSE06-A2623
Hebert A, 2016, NUCL SCI ENG, V184, P591, DOI 10.13182/NSE16-82
Hebert A, 2011, NUCL SCI ENG, V169, P81, DOI 10.13182/NSE10-39
Kornreich DE, 2004, ANN NUCL ENERGY, V31, P1477, DOI 10.1016/j.anucene.2004.03.012
Lathrop K. D., 1969, Journal of Computational Physics, V4, P475, DOI 10.1016/0021-9991(69)90015-1
Lewis E.E., 1984, COMPUTATIONAL METHOD
Mazumdar T, 2015, ANN NUCL ENERGY, V77, P522, DOI 10.1016/j.anucene.2014.12.029
Peterson P, 2009, INT J COMPUT SCI ENG, V4, P296, DOI 10.1504/IJCSE.2009.029165
Roseman Mark, 2012, MODERN TKINTER BUSY
Samuel Glasstone, 1970, NUCL REACTOR THEORY
Sentis Remi, 1997, MATH APPL
Sood A, 2003, PROG NUCL ENERG, V42, P55, DOI 10.1016/S0149-1970(02)00098-7
Suslov Igor R, 1997, P INT C MATH METH SU, V5
Tosi S., 2009, MATPLOTLIB PYTHON DE
NR 23
TC 3
Z9 3
U1 0
U2 2
PU PERGAMON-ELSEVIER SCIENCE LTD
PI OXFORD
PA THE BOULEVARD, LANGFORD LANE, KIDLINGTON, OXFORD OX5 1GB, ENGLAND
SN 0969-8043
J9 APPL RADIAT ISOTOPES
JI Appl. Radiat. Isot.
PD MAR
PY 2019
VL 145
BP 73
EP 84
DI 10.1016/j.apradiso.2018.12.004
PG 12
WC Chemistry, Inorganic & Nuclear; Nuclear Science & Technology; Radiology,
Nuclear Medicine & Medical Imaging
SC Chemistry; Nuclear Science & Technology; Radiology, Nuclear Medicine &
Medical Imaging
GA HP1ED
UT WOS:000461407300012
PM 30583139
DA 2021-04-21
ER
PT J
AU Weik, F
Weeber, R
Szuttor, K
Breitsprecher, K
de Graaf, J
Kuron, M
Landsgesell, J
Menke, H
Sean, D
Holm, C
AF Weik, Florian
Weeber, Rudolf
Szuttor, Kai
Breitsprecher, Konrad
de Graaf, Joost
Kuron, Michael
Landsgesell, Jonas
Menke, Henri
Sean, David
Holm, Christian
TI ESPResSo 4.0-an extensible software package for simulating soft matter
systems
SO EUROPEAN PHYSICAL JOURNAL-SPECIAL TOPICS
LA English
DT Article; Proceedings Paper
CT Concerence of the Collaborative-Research-Center (SFB) 716 on Particle
Methods in Natural Science and Engineering
CY SEP 24-26, 2018
CL Heidelberg, GERMANY
ID CALCULATING ELECTROSTATIC INTERACTIONS; LATTICE BOLTZMANN SIMULATIONS;
MOLECULAR-DYNAMICS; MONTE-CARLO; PHASE-TRANSITION; WEAK
POLYELECTROLYTES; COLLOIDAL SYSTEMS; POLARIZABLE MODEL; CHARGE
REGULATION; SUMMATION METHOD
AB ESPResSo is an extensible simulation package for research on soft matter. This versatile molecular dynamics program was originally developed for coarse-grained simulations of charged systems [H.J. Limbach et al., Comput. Phys. Commun. 174, 704 (2006)]. The scope of the software has since broadened considerably: ESPResSo can now be used to simulate systems with length scales spanning from the molecular to the colloidal. Examples include, self-propelled particles in active matter, membranes in biological systems, and the aggregation of soot particles in process engineering. ESPResSo also includes solvers for hydrodynamic and electrokinetic problems, both on the continuum and on the explicit particle level. Since our last description of version 3.1 [A. Arnold et al., Meshfree methods for partial di_erential equations VI, Lect. Notes Comput. Sci. Eng. 89, 1 (2013)], the software has undergone considerable restructuring. The biggest change is the replacement of the Tcl scripting interface with a much more powerful Python interface. In addition, many new simulation methods have been implemented. In this article, we highlight the changes and improvements made to the interface and code, as well as the new simulation techniques that enable a user of ESPResSo 4.0 to simulate physics that is at the forefront of soft matter research.
C1 [Weik, Florian; Weeber, Rudolf; Szuttor, Kai; Breitsprecher, Konrad; Kuron, Michael; Landsgesell, Jonas; Menke, Henri; Sean, David; Holm, Christian] Univ Stuttgart, Inst Comp Phys, Allmandring 3, D-70569 Stuttgart, Germany.
[de Graaf, Joost] Univ Utrecht, Inst Theoret Phys, Ctr Extreme Matter & Emergent Phenomena, Princetonpl 5, NL-3584 CC Utrecht, Netherlands.
[Menke, Henri] Univ Otago, Dept Phys, POB 56, Dunedin 9054, New Zealand.
RP Weik, F (corresponding author), Univ Stuttgart, Inst Comp Phys, Allmandring 3, D-70569 Stuttgart, Germany.
EM fweik@icp.uni-stuttgart.de; holm@icp.uni-stuttgart.de
RI Holm, Christian/C-2134-2009
OI Holm, Christian/0000-0003-2739-310X; Menke, Henri/0000-0001-5245-5400;
Kuron, Michael/0000-0002-3231-3330
FU German Science Foundation (DFG) through the collaborative research
centerGerman Research Foundation (DFG) [SFB 716]; SimTech cluster of
excellence [EXC 310]; DFGGerman Research Foundation (DFG)European
Commission [SPP 1726]; NWO Rubicon Grant [680501210]; Marie
Sklodowska-Curie Intra European Fellowship within Horizon 2020 [654916];
Ministry of Science, Research and Arts; Universities of the State of
Baden-Wurttemberg, Germany; [HO 1108/25-1]; [HO 1108/26-1]; [AR
593/7-1]; [HO 1108/28-1]
FX We would like to acknowledge more than 100 researchers who contributed
over the last fifteen years to the ESPResSo software, and whose names
can be found on our website http://espressomd.org or in the AUTHORS file
distributed with ESPResSo. We are also grateful to our colleagues of the
SFB 716 for numerous suggestions for extending the capabilities of
ESPResSo and in helping us to improve our software. As part of a
collaboration [108], Milena Smiljanic contributed code to the cluster
analysis framework, particularly to the analysis routines on the single
cluster level. CH, KS, and FW gratefully acknowledge funding by the
German Science Foundation (DFG) through the collaborative research
center SFB 716 within TP C5, the SimTech cluster of excellence (EXC
310), and grants HO 1108/25-1, HO 1108/26-1, AR 593/7-1, HO 1108/28-1.
MK, JdG, and CH thank the DFG for funding through the SPP 1726
Microswimmers-From Single Particle Motion to Collective Behavior. JdG
further acknowledges funding from an NWO Rubicon Grant (#680501210) and
a Marie Sklodowska-Curie Intra European Fellowship (G.A. No. 654916)
within Horizon 2020. The simulations were partially performed on the
bwUniCluster funded by the Ministry of Science, Research and Arts and
the Universities of the State of Baden-Wurttemberg, Germany, within the
framework program bwHPC.
CR Ahlrichs P, 1999, J CHEM PHYS, V111, P8225, DOI 10.1063/1.480156
ALDER BJ, 1962, PHYS REV, V127, P359, DOI 10.1103/PhysRev.127.359
ALDER BJ, 1957, J CHEM PHYS, V27, P1208, DOI 10.1063/1.1743957
Arnold A, 2006, LECT NOTES PHYS, V703, P193, DOI 10.1007/3-540-35273-2_6
Arnold A, 2005, J CHEM PHYS, V123, DOI 10.1063/1.2052647
Arnold A, 2005, ADV POLYM SCI, V185, P59, DOI 10.1007/b136793
Arnold A, 2002, CHEM PHYS LETT, V354, P324, DOI 10.1016/S0009-2614(02)00131-8
Arnold A, 2002, J CHEM PHYS, V117, P2496, DOI 10.1063/1.1491955
Arnold A, 2002, COMPUT PHYS COMMUN, V148, P327, DOI 10.1016/S0010-4655(02)00586-6
Arnold A., 2013, MESHFREE METHODS PAR, P1, DOI DOI 10.1007/978-3-642-32979-1_1
Arnold A, 2013, PHYS REV E, V88, DOI 10.1103/PhysRevE.88.063308
Arnold A, 2013, ENTROPY-SWITZ, V15, P4569, DOI 10.3390/e15114569
Attili A, 2014, COMBUST FLAME, V161, P1849, DOI 10.1016/j.combustflame.2014.01.008
Ayachit U, 2015, PARAVIEW GUIDE PARAL
Bacher C, 2017, PHYS REV FLUIDS, V2, DOI 10.1103/PhysRevFluids.2.013102
Ballerini M, 2008, P NATL ACAD SCI USA, V105, P1232, DOI 10.1073/pnas.0711437105
Barrat J.-L., 2003, BASIC CONCEPTS FO SI
Bates MA, 1998, J CHEM PHYS, V109, P6193, DOI 10.1063/1.477248
BERENDSEN HJC, 1995, COMPUT PHYS COMMUN, V91, P43, DOI 10.1016/0010-4655(95)00042-E
Berg J. M., 2015, BIOCHEMISTRY
Bordin JR, 2016, EUR PHYS J-SPEC TOP, V225, P1693, DOI 10.1140/epjst/e2016-60150-1
Breitsprecher K, 2018, ACS NANO, V12, P9733, DOI 10.1021/acsnano.8b04785
Brodka A, 2004, CHEM PHYS LETT, V400, P62, DOI 10.1016/j.cplett.2004.10.086
Butter K, 2003, NAT MATER, V2, P88, DOI 10.1038/nmat811
Camp PJ, 2000, PHYS REV LETT, V84, P115, DOI 10.1103/PhysRevLett.84.115
Campbell A.I., 2018, ARXIV180204600
Case DA, 2005, J COMPUT CHEM, V26, P1668, DOI 10.1002/jcc.20290
Castelnovo M, 2000, EUR PHYS J E, V1, P115, DOI 10.1007/PL00014591
Cates ME, 2012, REP PROG PHYS, V75, DOI 10.1088/0034-4885/75/4/042601
Cerda JJ, 2008, J PHYS-CONDENS MAT, V20, DOI 10.1088/0953-8984/20/20/204125
Cerda JJ, 2008, J CHEM PHYS, V129, DOI 10.1063/1.3000389
Chandrasekhar S., 1992, LIQUID CRYSTALS
Chatterji A, 2005, J CHEM PHYS, V122, DOI 10.1063/1.1890905
Chollet F., 2017, DEEP LEARNING PYTHON
Cimrak I, 2014, COMPUT PHYS COMMUN, V185, P900, DOI 10.1016/j.cpc.2013.12.013
Cimrak I, 2012, COMPUT MATH APPL, V64, P278, DOI 10.1016/j.camwa.2012.01.062
Cimrak I., 2013, 3 INT C PART BAS MET, V2013, P133
Colby RH, 2003, POLYM PHYS
Collette A, 2013, PYTHON AND HDF5
de Buyl P, 2014, COMPUT PHYS COMMUN, V185, P1546, DOI 10.1016/j.cpc.2014.01.018
de Graaf J., 2016, J CHEM PHYS, V1440, P134106, DOI [10.1063/1.4944962, DOI 10.1063/1.4944962]
de Graaf J., 2017, COMMUNICATION
de Graaf J, 2017, PHYS REV E, V95, DOI 10.1103/PhysRevE.95.023302
de Graaf J, 2016, SOFT MATTER, V12, P4704, DOI 10.1039/c6sm00939e
de Graaf J, 2015, J CHEM PHYS, V143, DOI 10.1063/1.4928503
de Paula, 2010, PHYS CHEM
DEGENNES PG, 1992, REV MOD PHYS, V64, P645, DOI 10.1103/RevModPhys.64.645
Deserno M, 1998, J CHEM PHYS, V109, P7694, DOI 10.1063/1.477415
Deserno M, 1998, J CHEM PHYS, V109, P7678, DOI 10.1063/1.477414
Dobrynin AV, 1996, MACROMOLECULES, V29, P2974, DOI 10.1021/ma9507958
Doi M., 1988, THEORY POLYM DYNAMIC, V73
Doi M., 2013, SOFT MATTER PHYS
Dommert F, 2013, PHYS CHEM CHEM PHYS, V15, P2037, DOI 10.1039/c2cp43698a
Dommert F, 2012, CHEMPHYSCHEM, V13, P1625, DOI 10.1002/cphc.201100997
Donaldson JG, 2015, NANOSCALE, V7, P3217, DOI 10.1039/c4nr07101h
Drescher K, 2011, P NATL ACAD SCI USA, V108, P10940, DOI 10.1073/pnas.1019079108
Drescher K, 2010, PHYS REV LETT, V105, DOI 10.1103/PhysRevLett.105.168101
Dunweg B, 2009, ADV POLYM SCI, V221, P89, DOI 10.1007/12_2008_4
Ebeling W, 1999, BIOSYSTEMS, V49, P17, DOI 10.1016/S0303-2647(98)00027-6
Fahrenberger F, 2014, PHYS REV E, V90, DOI 10.1103/PhysRevE.90.063304
Fischer LP, 2015, J CHEM PHYS, V143, DOI 10.1063/1.4928502
Fletcher M., 2005, PYOPENGL PYTHON OPEN
Fodor E, 2018, PHYSICA A, V504, P106, DOI 10.1016/j.physa.2017.12.137
Frenkel D, 2002, SCIENCE, V296, P65, DOI 10.1126/science.1070865
Frenkel D., 1996, UNDERSTANDING MOL SI
Frey E, 2005, ANN PHYS-BERLIN, V14, P20, DOI 10.1002/andp.200410132
Geyer VF, 2013, P NATL ACAD SCI USA, V110, P18058, DOI 10.1073/pnas.1300895110
Guckenberger A, 2016, COMPUT PHYS COMMUN, V207, P1, DOI 10.1016/j.cpc.2016.04.018
Guzman H. V., 2018, ARXIV180610841
Hamley I. V., 2003, INTRO SOFT MATTER
Harris E.H., 2013, CHLAMYDOMONAS SOURCE
Helbing D, 2000, NATURE, V407, P487, DOI 10.1038/35035023
Hockney R.W., 1988, COMPUTER SIMULATION
Howse JR, 2007, PHYS REV LETT, V99, DOI 10.1103/PhysRevLett.99.048102
Humphrey W, 1996, J MOL GRAPH MODEL, V14, P33, DOI 10.1016/0263-7855(96)00018-5
Hunter JD, 2007, COMPUT SCI ENG, V9, P90, DOI 10.1109/MCSE.2007.55
Ilse SE, 2016, J CHEM PHYS, V145, DOI 10.1063/1.4963804
Inci G, 2017, FLOW TURBUL COMBUST, V98, P1065, DOI 10.1007/s10494-016-9797-3
Inci G, 2014, AEROSOL SCI TECH, V48, P842, DOI 10.1080/02786826.2014.932942
JOHNSON JK, 1994, MOL PHYS, V81, P717, DOI 10.1080/00268979400100481
Jones E., 2001, SCIPY OPEN SOURCE SC
Kantsler V, 2013, P NATL ACAD SCI USA, V110, P1187, DOI 10.1073/pnas.1210548110
Katz Y, 2011, P NATL ACAD SCI USA, V108, P18720, DOI 10.1073/pnas.1107583108
Klapp SHL, 2004, J MOL LIQ, V109, P55, DOI 10.1016/j.molliq.2003.08.003
Klinkigt M, 2013, SOFT MATTER, V9, P3535, DOI 10.1039/c2sm27290c
Kluyver T, 2016, POSITIONING AND POWER IN ACADEMIC PUBLISHING: PLAYERS, AGENTS AND AGENDAS, P87, DOI 10.3233/978-1-61499-649-1-87
Kohagen M, 2016, J PHYS CHEM B, V120, P1454, DOI 10.1021/acs.jpcb.5b05221
Kratzer K, 2015, SOFT MATTER, V11, P2174, DOI 10.1039/c4sm02365j
Kratzer K, 2014, COMPUT PHYS COMMUN, V185, P1875, DOI 10.1016/j.cpc.2014.03.013
Kratzer K, 2013, J CHEM PHYS, V138, DOI 10.1063/1.4801866
Kummel F, 2013, PHYS REV LETT, V110, DOI 10.1103/PhysRevLett.110.198302
Lamoureux G, 2006, CHEM PHYS LETT, V418, P245, DOI 10.1016/j.cplett.2005.10.135
Lamoureux G, 2003, J CHEM PHYS, V119, P3025, DOI 10.1063/1.1589749
Landsgesell J, 2017, EUR PHYS J-SPEC TOP, V226, P725, DOI 10.1140/epjst/e2016-60324-3
Landsgesell J, 2017, J CHEM THEORY COMPUT, V13, P852, DOI 10.1021/acs.jctc.6b00791
Lechner W, 2008, J CHEM PHYS, V129, DOI 10.1063/1.2977970
Leontyev I, 2011, PHYS CHEM CHEM PHYS, V13, P2613, DOI 10.1039/c0cp01971b
Leunissen ME, 2005, NATURE, V437, P235, DOI 10.1038/nature03946
Levental I, 2007, SOFT MATTER, V3, P299, DOI 10.1039/b610522j
Limbach HJ, 2006, COMPUT PHYS COMMUN, V174, P704, DOI 10.1016/j.cpc.2005.10.005
Limbach HJ, 2002, EUROPHYS LETT, V60, P566, DOI 10.1209/epl/i2002-00256-8
Lobaskin V, 2004, NEW J PHYS, V6, DOI 10.1088/1367-2630/6/1/054
Lowen H, 2001, J PHYS-CONDENS MAT, V13, pR415, DOI 10.1088/0953-8984/13/24/201
Lund M, 2005, BIOCHEMISTRY-US, V44, P5722, DOI 10.1021/bi047630o
Lund M, 2013, Q REV BIOPHYS, V46, P265, DOI 10.1017/S003358351300005X
Ma R, 2014, PHYS REV LETT, V113, DOI 10.1103/PhysRevLett.113.048101
Maggi C, 2016, SMALL, V12, P446, DOI 10.1002/smll.201502391
Mann BA, 2004, EUROPHYS LETT, V67, P786, DOI 10.1209/epl/i2004-10121-x
Martys NS, 1999, PHYS REV E, V59, P3733, DOI 10.1103/PhysRevE.59.3733
Meurer A, 2017, PEERJ COMPUT SCI, DOI 10.7717/peerj-cs.103
Micka U, 1999, LANGMUIR, V15, P4033, DOI 10.1021/la981191a
Millman J., 2010, P56, DOI DOI 10.1016/S0168-0102(02)00204-3
MITCHELL PJ, 1993, J PHYS-CONDENS MAT, V5, P1031, DOI 10.1088/0953-8984/5/8/006
Morozov K, 2009, PHYS REV E, V79, DOI 10.1103/PhysRevE.79.040801
Nash RW, 2008, PHYS REV E, V77, DOI 10.1103/PhysRevE.77.026709
Nash R. W., 2010, THESIS
Nelson MT, 1996, INT J SUPERCOMPUT AP, V10, P251, DOI 10.1177/109434209601000401
Nelson P.C., 2004, BIOL PHYS ENERGY INF
Nestler F, 2016, APPL NUMER MATH, V105, P25, DOI 10.1016/j.apnum.2016.01.003
Palacci J, 2013, SCIENCE, V339, P936, DOI 10.1126/science.1230020
Paszke A, 2017, NEURAL INFORM PROCES
Paxton WF, 2004, J AM CHEM SOC, V126, P13424, DOI 10.1021/ja047697z
PEARLMAN DA, 1995, COMPUT PHYS COMMUN, V91, P1, DOI 10.1016/0010-4655(95)00041-D
Pedregosa F, 2011, J MACH LEARN RES, V12, P2825
Phillips J. C., 2014, P 1 1 WORKSH HIGH PE, P6
Phillips JC, 2005, J COMPUT CHEM, V26, P1781, DOI 10.1002/jcc.20289
PLIMPTON S, 1995, J COMPUT PHYS, V117, P1, DOI 10.1006/jcph.1995.1039
Polarz S, 2002, CHEM COMMUN, P2593, DOI 10.1039/b205708p
Polin M, 2009, SCIENCE, V325, P487, DOI 10.1126/science.1172667
Pronk S, 2013, BIOINFORMATICS, V29, P845, DOI 10.1093/bioinformatics/btt055
PUSEY PN, 1994, J PHYS-CONDENS MAT, V6, pA29, DOI 10.1088/0953-8984/6/23A/004
Radu M, 2014, EPL-EUROPHYS LETT, V105, DOI 10.1209/0295-5075/105/26001
Raikher YL, 2003, J MAGN MAGN MATER, V258, P477, DOI 10.1016/S0304-8853(02)01102-2
Ramachandran P, 2011, COMPUT SCI ENG, V13, P40, DOI 10.1109/MCSE.2011.35
REED CE, 1992, J CHEM PHYS, V96, P1609, DOI 10.1063/1.462145
Reith D, 2003, J COMPUT CHEM, V24, P1624, DOI 10.1002/jcc.10307
Rempfer G, 2016, J CHEM PHYS, V145, DOI 10.1063/1.4958950
Reufer M, 2014, BIOPHYS J, V106, P37, DOI 10.1016/j.bpj.2013.10.038
Riedel IH, 2005, SCIENCE, V309, P300, DOI 10.1126/science.1110329
Roehm D, 2012, EUR PHYS J-SPEC TOP, V210, P89, DOI 10.1140/epjst/e2012-01639-6
Roehm D, 2014, SOFT MATTER, V10, P5503, DOI 10.1039/c4sm00686k
Rohm D., 2011, THESIS
Samin S, 2013, PHYS REV E, V87, DOI 10.1103/PhysRevE.87.052128
Schmidt J, 2010, J PHYS CHEM B, V114, P6150, DOI 10.1021/jp910771q
Schneider S, 2002, EUR PHYS J E, V8, P457, DOI 10.1140/epje/i2002-10043-y
Schober C., 2016, P 7 INT C COUPL PROB
Schwarz-Linek J, 2016, COLLOID SURFACE B, V137, P2, DOI 10.1016/j.colsurfb.2015.07.048
Seifert U, 2012, REP PROG PHYS, V75, DOI 10.1088/0034-4885/75/12/126001
Shi CY, 2012, BIOPHYS J, V102, P1590, DOI 10.1016/j.bpj.2012.02.021
Shreiner D., 1999, OPENGL REFERENCE MAN
Silverberg JL, 2013, PHYS REV LETT, V110, DOI 10.1103/PhysRevLett.110.228701
Smiljanic M., EUR PHYS J SPE UNPUB
SMITH ER, 1994, J STAT PHYS, V77, P449, DOI 10.1007/BF02186852
Sokolov A, 2007, PHYS REV LETT, V98, DOI 10.1103/PhysRevLett.98.158102
Stein W., 2005, SIGSAM Bulletin, V39, P61
Stenhammar J, 2013, PHYS REV LETT, V111, DOI 10.1103/PhysRevLett.111.145702
STEVENS MJ, 1993, PHYS REV LETT, V71, P2228, DOI 10.1103/PhysRevLett.71.2228
The HDF Group, HIER DAT FORM VERS 5
Theurkauff I, 2012, PHYS REV LETT, V108, DOI 10.1103/PhysRevLett.108.268303
THOLE BT, 1981, CHEM PHYS, V59, P341, DOI 10.1016/0301-0104(81)85176-2
Turner CH, 2008, MOL SIMULAT, V34, P119, DOI 10.1080/08927020801986564
Tyagi S, 2007, J CHEM PHYS, V127, DOI 10.1063/1.2790428
Tyagi S, 2010, J CHEM PHYS, V132, DOI 10.1063/1.3376011
Tyagi S, 2008, J CHEM PHYS, V129, DOI 10.1063/1.3021064
Ubbink J, 2008, SOFT MATTER, V4, P1569, DOI 10.1039/b802183j
Van der Spoel D, 2005, J COMPUT CHEM, V26, P1701, DOI 10.1002/jcc.20291
van Roij R, 1999, PHYS REV E, V59, P2010, DOI 10.1103/PhysRevE.59.2010
Verwey E., 1948, THEORY STABILITY LYO
VICSEK T, 1995, PHYS REV LETT, V75, P1226, DOI 10.1103/PhysRevLett.75.1226
Wang FG, 2001, PHYS REV E, V64, DOI 10.1103/PhysRevE.64.056101
Wang ZW, 2002, PHYS REV E, V66, DOI 10.1103/PhysRevE.66.021405
Weeber R., 2018, ARXIV180810341
Weeber R, 2018, J PHYS-CONDENS MAT, V30, DOI 10.1088/1361-648X/aaa344
Weeber R, 2013, J CHEM PHYS, V139, DOI 10.1063/1.4832239
Weeber R, 2012, SOFT MATTER, V8, P9923, DOI 10.1039/c2sm26097b
WEEKS JD, 1971, J CHEM PHYS, V54, P5237, DOI 10.1063/1.1674820
Wensink HH, 2014, PHYS REV E, V89, DOI 10.1103/PhysRevE.89.010302
Wong JE, 2006, PROG COLL POL SCI S, V133, P45, DOI 10.1007/2882_056
Woolley DM, 2003, REPRODUCTION, V126, P259, DOI 10.1530/rep.0.1260259
Yan QL, 2003, PHYS REV LETT, V91, DOI 10.1103/PhysRevLett.91.018301
Yu HB, 2003, J CHEM PHYS, V118, P221, DOI 10.1063/1.1523915
Zhang J., 2013, TRAFFIC GRANULAR FLO, P241, DOI DOI 10.1007/978-3-642-39669-4_23
Zheng X, 2013, PHYS REV E, V88, DOI 10.1103/PhysRevE.88.032304
NR 183
TC 41
Z9 41
U1 3
U2 11
PU SPRINGER HEIDELBERG
PI HEIDELBERG
PA TIERGARTENSTRASSE 17, D-69121 HEIDELBERG, GERMANY
SN 1951-6355
EI 1951-6401
J9 EUR PHYS J-SPEC TOP
JI Eur. Phys. J.-Spec. Top.
PD MAR
PY 2019
VL 227
IS 14
BP 1789
EP 1816
DI 10.1140/epjst/e2019-800186-9
PG 28
WC Physics, Multidisciplinary
SC Physics
GA HO3LZ
UT WOS:000460825700022
DA 2021-04-21
ER
PT J
AU Reuter, K
Kofinger, J
AF Reuter, Klaus
Koefinger, Juergen
TI CADISHI: Fast parallel calculation of particle-pair distance histograms
on CPUs and GPUs
SO COMPUTER PHYSICS COMMUNICATIONS
LA English
DT Article
DE Radial distribution function; Pair-distance distribution function;
Two-point correlation function; Distance histogram; GPU; CUDA
ID MOLECULAR-DYNAMICS
AB We report on the design, implementation, optimization, and performance of the CADISHI software package, which calculates histograms of pair-distances of ensembles of particles on CPUs and GPUs. These histograms represent 2-point spatial correlation functions and are routinely calculated from simulations of soft and condensed matter, where they are referred to as radial distribution functions, and in the analysis of the spatial distributions of galaxies and galaxy clusters. Although conceptually simple, the calculation of radial distribution functions via distance binning requires the evaluation of O(N-2)particle pair distances where N is the number of particles under consideration. CADISHI provides fast parallel implementations of the distance histogram algorithm for the CPU and the GPU, written in templated C++ and CUDA. Orthorhombic and general triclinic periodic boxes are supported, in addition to the non periodic case. The CPU kernels feature cache blocking, vectorization and thread-parallelization to obtain high performance. The GPU kernels are tuned to exploit the memory and processor features of current GPUs, demonstrating histogramming rates of up to a factor 40 higher than on a high-end multi-core CPU. To enable high-throughput analyses of molecular dynamics trajectories, the compute kernels are driven by the Python-based CADISHI engine. It implements a producer-consumer data processing pattern and thereby enables the complete utilization of all the CPU and GPU resources available on a specific computer, independent of special libraries such as MPI, covering commodity systems up to high-end high-performance computing nodes. Data input and output are performed efficiently via HDF5. In addition, our CPU and GPU kernels can be compiled into a standard C library and used with any application, independent from the CADISHI engine or Python. The CADISHI software is freely available under the MIT license.
Program summary
Program Title: CADISHI
Program Files doi: http://dx.doi.org/10.17632/82b8sdft79.1
Licensing provisions: MIT
Programming language: C++, CUDA, Python
Nature of problem: Radial distribution functions are of fundamental importance in soft and condensed matter physics and astrophysics. However, the calculation of distance histograms scales quadratically with the particle number. To be able to analyze large data sets, fast and efficient implementations of distance histogramming are crucial.
Solution method: CADISHI provides parallel, highly optimized implementations of distance histogramming. On the CPU, high performance is achieved via an advanced cache blocking scheme in combination with vectorization and threading. On the GPU, the problem is decomposed via a tiling scheme to exploit the GPU's massively parallel architecture and hierarchy of global, constant, and shared memory efficiently, resulting in significant speedups compared to the CPU. Moreover, CADISHI exploits all the resources (GPUs, CPUs) available on a compute node in parallel.
Additional comments including restrictions and unusual features: Additionally to the non-periodic case CADISHI implements the minimum image convention for orthorhombic and general triclinic periodic boxes. We provide Python interfaces and the option to compile the kernels into a plain C library. (C) 2018 The Authors. Published by Elsevier B.V.
C1 [Reuter, Klaus] Max Planck Comp & Data Facil, Giessenbachstr 2, D-85748 Garching, Germany.
[Koefinger, Juergen] Max Planck Inst Biophys, Max von Laue Str 3, D-60438 Frankfurt, Germany.
RP Reuter, K (corresponding author), Max Planck Comp & Data Facil, Giessenbachstr 2, D-85748 Garching, Germany.
EM klaus.reuter@mpcdf.mpg.de; juergen.koefinger@biophys.mpg.de
OI Kofinger, Jurgen/0000-0001-8367-1077
FU Max Planck Society, GermanyMax Planck Society
FX We thank Dr. Joseph Curtis, Prof. Gerhard Hummer, Max Linke, Dr. Markus
Rampp, and Dr. Hailiang Zhang for fruitful discussions. We thank Prof.
Kei-ichi Okazaki for providing an initial NAMD setup for
F1-ATPase. We acknowledge financial support by the Max Planck
Society, Germany.
CR Anandakrishnan A, 2013, PHYS REV D, V87, DOI 10.1103/PhysRevD.87.055005
[Anonymous], 2011, CURR BIOL, V21, pR68, DOI [10.1016/J.CUB.2010.11.062, DOI 10.1016/J.CUB.2010.11.062]
Augonnet C, 2011, CONCURR COMP-PRACT E, V23, P187, DOI 10.1002/cpe.1631
Caldeira A.B., 2016, IBM POWER SYSTEM S82
DEBYE P, 1947, J PHYS COLLOID CHEM, V51, P18, DOI 10.1021/j150451a002
Frenkel D., 2002, UNDERSTANDING MOL SI, P63, DOI [10.1016/B978-012267351-1/50006-7., DOI 10.1016/B978-012267351-1/50006-7, 10.1016/B978-012267351-1/50006-7]
Gowers R., 2016, P 15 PYTH SCI C, P98
Hansen J.P., 2013, THEORY SIMPLE LIQUID, V4th
Humphrey W, 1996, J MOL GRAPH MODEL, V14, P33, DOI 10.1016/0263-7855(96)00018-5
Intel Corporation, 2017, INT XEON PLAT 8164 P
Kerscher M, 2000, ASTROPHYS J, V535, pL13, DOI 10.1086/312702
Kofinger J., 2018, CAPRIQORN SOFTWARE P
Kutzner C, 2015, J COMPUT CHEM, V36, P1990, DOI 10.1002/jcc.24030
Levine BG, 2011, J CHEM THEORY COMPUT, V7, P4135, DOI 10.1021/ct2005193
Levine BG, 2011, J COMPUT PHYS, V230, P3556, DOI 10.1016/j.jcp.2011.01.048
Likos CN, 2001, PHYS REP, V348, P267, DOI 10.1016/S0370-1573(00)00141-1
McQuarrie D.A., 1975, STAT MECH, P641
Michaud-Agrawal N, 2011, J COMPUT CHEM, V32, P2319, DOI 10.1002/jcc.21787
NVIDIA Corporation,, 2018, CUDA C PROGRAMMING G
NVIDIA Corporation, 2017, NVIDIA TESL V100 GPU
NVIDIA Corporation, 2017, NVIDIA DGX 1 TESL V1
NVIDIA Corporation, 2016, NVIDIA GEFORCE GTX 1
NVIDIA Corporation, 2016, NVIDIA TESL P100
Ornstein LS, 1914, P K AKAD WET-AMSTERD, V17, P793
Phillips JC, 2005, J COMPUT CHEM, V26, P1781, DOI 10.1002/jcc.20289
Python Software Foundation, 2018, PYTH STAND LIB
Reuter K., 2018, CADISHI SOFTWARE PAC
Springel V, 2005, NATURE, V435, P629, DOI 10.1038/nature03597
The HDF Group, 1997, HIER DAT FORM VERS 5
Tuckerman M., 2011, STAT MECH THEORY MOL
Volkov Vasily, 2010, BETTER PERFORMANCE L
Wassenaar T. A., 2006, THESIS
NR 32
TC 1
Z9 1
U1 0
U2 5
PU ELSEVIER SCIENCE BV
PI AMSTERDAM
PA PO BOX 211, 1000 AE AMSTERDAM, NETHERLANDS
SN 0010-4655
EI 1879-2944
J9 COMPUT PHYS COMMUN
JI Comput. Phys. Commun.
PD MAR
PY 2019
VL 236
BP 274
EP 284
DI 10.1016/j.cpc.2018.10.018
PG 11
WC Computer Science, Interdisciplinary Applications; Physics, Mathematical
SC Computer Science; Physics
GA HK8GJ
UT WOS:000458227100025
OA Other Gold
DA 2021-04-21
ER
PT J
AU Blanchard, JB
Damblin, G
Martinez, JM
Arnaud, G
Gaudier, F
AF Blanchard, Jean-Baptiste
Damblin, Guillaume
Martinez, Jean-Marc
Arnaud, Gilles
Gaudier, Fabrice
TI The Uranie platform: an open-source software for optimisation,
meta-modelling and uncertainty analysis
SO EPJ NUCLEAR SCIENCES & TECHNOLOGIES
LA English
DT Article
ID GLOBAL SENSITIVITY-ANALYSIS; SMALL FAILURE PROBABILITIES;
IMPLEMENTATION; CALIBRATION; PREDICTION; INDEXES; DESIGNS; MODELS
AB The high-performance computing resources and the constant improvement of both numerical simulation accuracy and the experimental measurements with which they are confronted bring a new compulsory step to strengthen the credence given to the simulation results: uncertainty quantification. This can have different meanings, according to the requested goals (rank uncertainty sources, reduce them, estimate precisely a critical threshold or an optimal working point), and it could request mathematical methods with greater or lesser complexity. This paper introduces the Uranie platform, an open-source framework developed at the Alternative Energies and Atomic Energy Commission (CEA), in the nuclear energy division, in order to deal with uncertainty propagation, surrogate models, optimisation issues, code calibration, etc. This platform benefits from both its dependencies and from personal developments, to offer an efficient data handling model, a C++ and Python interface, advanced graphi graphical tools, several parallelisation solutions, etc. These methods can then be applied to many kinds of code (considered as black boxes by Uranie) so to many fields of physics as well. In this paper, the example of thermal exchange between a plate-sheet and a fluid is introduced to show how Uranie can be used to perform a large range of analysis.
C1 [Blanchard, Jean-Baptiste; Damblin, Guillaume; Martinez, Jean-Marc; Arnaud, Gilles; Gaudier, Fabrice] Univ Paris Saclay, CEA, Den Serv Thermohydraul & Mecan Fluides STMF, F-91191 Gif Sur Yvette, France.
RP Blanchard, JB (corresponding author), Univ Paris Saclay, CEA, Den Serv Thermohydraul & Mecan Fluides STMF, F-91191 Gif Sur Yvette, France.
EM jean.blanchard@cea.fr
CR Adams B. M., SAND20102183 SAND NA
ANDERSON TW, 1952, ANN MATH STAT, V23, P193, DOI 10.1214/aoms/1177729437
ANDERSON TW, 1962, ANN MATH STAT, V33, P1148, DOI 10.1214/aoms/1177704477
Au SK, 2001, PROBABILIST ENG MECH, V16, P263, DOI 10.1016/S0266-8920(01)00019-4
Bachoc F., 2013, THESIS, P7
Bachoc F, 2014, NUCL SCI ENG, V176, P81, DOI 10.13182/NSE12-55
Baudin M., 2010, 42 JOURN STAT MARS F
Baudin M., HDB UNCERTAINTY QUAN, P2001, DOI [10.1007/978-3-319-12385-1_64, DOI 10.1007/978-3-319-12385-1_64]
Bayarri MJ, 2007, TECHNOMETRICS, V49, P138, DOI 10.1198/004017007000000092
Bettonvil B, 1997, EUR J OPER RES, V96, P180, DOI 10.1016/S0377-2217(96)00156-7
Blanchard J.-B., DENDANSDM2SSTMFLGLSR
Brun R, 1997, NUCL INSTRUM METH A, V389, P81, DOI 10.1016/S0168-9002(97)00048-X
Bucher C. G., 1991, COMPUTATIONAL STOCHA, P301
CAMERON RH, 1947, ANN MATH, V48, P385, DOI 10.2307/1969178
CASELLA G, 1992, AM STAT, V46, P167, DOI 10.2307/2685208
CHIB S, 1995, AM STAT, V49, P327, DOI 10.2307/2684568
Da Veiga S, 2015, J STAT COMPUT SIM, V85, P1283, DOI 10.1080/00949655.2014.945932
Damblin G, 2013, J SIMUL, V7, P276, DOI 10.1057/jos.2013.16
de Crecy A, 2001, DETERMINATION UNCERT
De Rocquigny E., 2008, UNCERTAINTY IND PRAC
Drepper U, 2007, WHAT EVERY PROGRAMME
Fang K.-T., 2005, COMPUTER SCI DATA AN
Feathers M., 2002, CPPUNIT COOKBOOK
Frigo M, 2005, P IEEE, V93, P216, DOI 10.1109/JPROC.2004.840301
Gabriel E, 2004, LECT NOTES COMPUT SC, V3241, P97
GHANEM R., 2017, SPRINGER HDB UNCERTA
Ghanem R., 1991, STOCHASTIC FINITE EL
HALTON JH, 1964, COMMUN ACM, V7, P701, DOI 10.1145/355588.365104
HASOFER AM, 1974, J ENG MECH DIV-ASCE, V100, P111
Hinton GE, 2007, PROG BRAIN RES, V165, P535, DOI 10.1016/S0079-6123(06)65034-6
Homma T, 1996, RELIAB ENG SYST SAFE, V52, P1, DOI 10.1016/0951-8320(96)00002-6
Huang XX, 2016, STRUCT SAF, V59, P86, DOI 10.1016/j.strusafe.2015.12.003
Iman R.L.S., 1985, FORTRAN 77 PROGRAM U
IMAN RL, 1982, COMMUN STAT B-SIMUL, V11, P311, DOI 10.1080/03610918208812265
Iooss B., 2015, UNCERTAINTY MANAGEME, P101, DOI DOI 10.1007/978-1-4899-7547-8_5
Johnson S. G., 2008, NLOPT NONLINEAR OPTI
Jones DR, 1998, J GLOBAL OPTIM, V13, P455, DOI 10.1023/A:1008306431147
Kennedy MC, 2001, J R STAT SOC B, V63, P425, DOI 10.1111/1467-9868.00294
Kluyver T, 2016, POSITIONING AND POWER IN ACADEMIC PUBLISHING: PLAYERS, AGENTS AND AGENDAS, P87, DOI 10.3233/978-1-61499-649-1-87
Kolmogoroff A, 1933, GIORNALE DELLISTITUT, V4, P83, DOI DOI 10.1016/J.SAB.2008.10.045
Lemaitre P, 2015, J STAT COMPUT SIM, V85, P1200, DOI 10.1080/00949655.2013.873039
Mara TA, 2008, J STAT COMPUT SIM, V78, P167, DOI 10.1080/10629360600964454
Marelli Stefano, 2014, Vulnerability, Uncertainty, and Risk. Quantification, Mitigation, and Management. Second International Conference on Vulnerability and Risk Analysis and Management (ICVRAM) and the Sixth International Symposium on Uncertainty Modeling and Analysis (ISUMA). Proceedings, P2554
Martin Abadi, 2015, TENSORFLOW LARGE SCA
Martin K, 2007, IEEE SOFTWARE, V24, P46, DOI 10.1109/MS.2007.5
Martinez J, 2011, TECHNICAL REPORT
Matheron G., CAHIERS CTR MORPHOLO
Mcculloch W S, 1943, B MATH BIOPHYS, V5, P115, DOI DOI 10.1007/BF02478259
McKay MD, 1999, COMPUT PHYS COMMUN, V117, P44, DOI 10.1016/S0010-4655(98)00155-6
Mckay MD, 2000, TECHNOMETRICS, V42, P55, DOI 10.2307/1271432
MCRAE GJ, 1982, COMPUT CHEM ENG, V6, P15, DOI 10.1016/0098-1354(82)80003-3
Meza JC, 2007, ACM T MATH SOFTWARE, V33, DOI 10.1145/1236463.1236467
Monod H, 2006, WORKING DYNAMIC CROP
MORRIS MD, 1995, J STAT PLAN INFER, V43, P381, DOI 10.1016/0378-3758(94)00035-T
Nanty S, 2017, COMPUTATION STAT, V32, P559, DOI 10.1007/s00180-016-0676-0
Neal R. M., HDB MARKOV CHAIN MON, V2
Nvidia C., 2011, NVIDIA CORPORATION, V120
Owen AB, 2014, SIAM-ASA J UNCERTAIN, V2, P245, DOI 10.1137/130936233
Petras K, 2001, NUMER ALGORITHMS, V26, P93, DOI 10.1023/A:1016676624575
Pronzato L, 2012, STAT COMPUT, V22, P681, DOI 10.1007/s11222-011-9242-3
Rasmussen C. E., 2006, GAUSSIAN PROCESS MAC
Robinson T., 1991, Computer Speech and Language, V5, P259, DOI 10.1016/0885-2308(91)90010-N
Rosenblatt F., 1962, PRINCIPLES NEURODYNA
Saltelli A, 1998, COMPUT STAT DATA AN, V26, P445, DOI 10.1016/S0167-9473(97)00043-1
Saltelli A, 2002, COMPUT PHYS COMMUN, V145, P280, DOI 10.1016/S0010-4655(02)00280-1
Saltelli A., 2008, GLOBAL SENSITIVITY A
Saltelli A., 2004, SENSITIVITY ANAL PRA
Saltelli A., 2008, SENSITIVITY ANAL
Sobol I.M., 1967, COMP MATH MATH PHYS+, V7, P86, DOI [10.1016/0041-5553(67)90144-9, DOI 10.1016/0041-5553(67)90144-9]
Tarantola S, 2006, RELIAB ENG SYST SAFE, V91, P717, DOI 10.1016/j.ress.2005.06.003
Tissot JY, 2012, RELIAB ENG SYST SAFE, V107, P205, DOI 10.1016/j.ress.2012.06.010
van Heesch D., 2008, DOXYGEN SOURCE CODE
Wiener N, 1938, AM J MATH, V60, P897, DOI 10.2307/2371268
Zhang QF, 2007, IEEE T EVOLUT COMPUT, V11, P712, DOI 10.1109/TEVC.2007.892759
Zhang XY, 2015, IEEE T EVOLUT COMPUT, V19, P761, DOI 10.1109/TEVC.2014.2378512
Zhao YG, 1999, STRUCT SAF, V21, P95, DOI 10.1016/S0167-4730(99)00008-9
Zitzler E, 2004, LECT NOTES COMPUT SC, V3242, P832
NR 77
TC 2
Z9 2
U1 0
U2 1
PU EDP SCIENCES S A
PI LES ULIS CEDEX A
PA 17, AVE DU HOGGAR, PA COURTABOEUF, BP 112, F-91944 LES ULIS CEDEX A,
FRANCE
SN 2491-9292
J9 EPJ NUCL SCI TECHNOL
JI EPJ Nucl. Sci. Technol.
PD FEB 28
PY 2019
VL 5
AR 4
DI 10.1051/epjn/2018050
PG 32
WC Nuclear Science & Technology
SC Nuclear Science & Technology
GA HQ2CQ
UT WOS:000462207500001
OA DOAJ Gold, Green Published
DA 2021-04-21
ER
PT J
AU Edwards, TDP
Kavanagh, BJ
Weniger, C
Baum, S
Drukier, AK
Freese, K
Gorski, M
Stengel, P
AF Edwards, Thomas D. P.
Kavanagh, Bradley J.
Weniger, Christoph
Baum, Sebastian
Drukier, Andrzej K.
Freese, Katherine
Gorski, Maciej
Stengel, Patrick
TI Digging for dark matter: Spectral analysis and discovery potential of
paleo-detectors
SO PHYSICAL REVIEW D
LA English
DT Article
ID MAGNETIC MONOPOLES; TRACKS; SCATTERING; PHYSICS; SEARCH; PYTHON; MICA
AB Paleo-detectors are a recently proposed method for the direct detection of dark matter (DM). In such detectors, one would search for the persistent damage features left by DM-nucleus interactions in ancient minerals. Initial sensitivity projections have shown that paleo-detectors could probe much of the remaining weakly interacting massive particle (WIMP) parameter space. In this paper, we improve upon the cut-and-count approach previously used to estimate the sensitivity by performing a full spectral analysis of the background-and DM-induced signal spectra. We consider two scenarios for the systematic errors on the background spectra: (i) systematic errors on the normalization only, and (ii) systematic errors on the shape of the backgrounds. We find that the projected sensitivity is rather robust to imperfect knowledge of the backgrounds. Finally, we study how well the parameters of the true WIMP model could be reconstructed in the hypothetical case of a WIMP discovery.
C1 [Edwards, Thomas D. P.; Kavanagh, Bradley J.; Weniger, Christoph] Univ Amsterdam, Inst Theoret Phys Amsterdam, Gravitat Astroparticle Phys Amsterdam GRAPPA, Sci Pk 904, NL-1098 XH Amsterdam, Netherlands.
[Edwards, Thomas D. P.; Kavanagh, Bradley J.; Weniger, Christoph] Univ Amsterdam, Delta Inst Theoret Phys, Sci Pk 904, NL-1098 XH Amsterdam, Netherlands.
[Baum, Sebastian; Drukier, Andrzej K.; Freese, Katherine; Stengel, Patrick] Stockholm Univ, Dept Phys, Oskar Klein Ctr Cosmoparticle Phys, S-10691 Stockholm, Sweden.
[Baum, Sebastian; Freese, Katherine] NORDITA, KTH Royal Inst Technol, Roslagstullsbacken 23, S-10691 Stockholm, Sweden.
[Baum, Sebastian; Freese, Katherine] Stockholm Univ, Roslagstullsbacken 23, S-10691 Stockholm, Sweden.
[Freese, Katherine] Univ Michigan, Leinweber Ctr Theoret Phys, Ann Arbor, MI 48109 USA.
[Gorski, Maciej] Natl Ctr Nucl Res, PL-05400 Otwock, Swierk, Poland.
RP Edwards, TDP; Kavanagh, BJ; Weniger, C (corresponding author), Univ Amsterdam, Inst Theoret Phys Amsterdam, Gravitat Astroparticle Phys Amsterdam GRAPPA, Sci Pk 904, NL-1098 XH Amsterdam, Netherlands.; Edwards, TDP; Kavanagh, BJ; Weniger, C (corresponding author), Univ Amsterdam, Delta Inst Theoret Phys, Sci Pk 904, NL-1098 XH Amsterdam, Netherlands.; Baum, S; Drukier, AK; Freese, K; Stengel, P (corresponding author), Stockholm Univ, Dept Phys, Oskar Klein Ctr Cosmoparticle Phys, S-10691 Stockholm, Sweden.; Baum, S; Freese, K (corresponding author), NORDITA, KTH Royal Inst Technol, Roslagstullsbacken 23, S-10691 Stockholm, Sweden.; Baum, S; Freese, K (corresponding author), Stockholm Univ, Roslagstullsbacken 23, S-10691 Stockholm, Sweden.; Freese, K (corresponding author), Univ Michigan, Leinweber Ctr Theoret Phys, Ann Arbor, MI 48109 USA.; Gorski, M (corresponding author), Natl Ctr Nucl Res, PL-05400 Otwock, Swierk, Poland.
EM t.d.p.edwards@uva.nl; b.j.kavanagh@uva.nl; c.weniger@uva.nl;
sbaum@fysik.su.se; adrukier@gmail.com; ktfreese@umich.edu;
maciej.gorski@ncbj.gov.pl; patrick.stengel@fysik.su.se
RI Kavanagh, Bradley/AAB-1239-2021; Stengel, Patrick/ABB-9455-2020
OI Kavanagh, Bradley/0000-0002-3634-4679; Baum,
Sebastian/0000-0001-6792-9381; Gorski, Maciej/0000-0003-2146-187X
FU NWO through the VIDI research program "Probing the Genesis of Dark
Matter" [680-47-532]; Vetenskapsradet (Swedish Research Council)Swedish
Research Council [638-2013-8993]; Oskar Klein Centre for Cosmoparticle
Physics; DoEUnited States Department of Energy (DOE) [DE-SC007859]; LCTP
at the University of MichiganUniversity of Michigan System
FX We thank Niki Klop and Adri Duivenvoorden for bringing this wonderful
collaboration together. S. B. would like to thank GRAPPA and the
University of Amsterdam, where part of this work was completed, for
hospitality. This research is partially funded by NWO through the VIDI
research program "Probing the Genesis of Dark Matter" (680-47-532; T.
E., B. K., C. W.). S. B., A. K. D., K. F., and P. S. acknowledge support
by the Vetenskapsradet (Swedish Research Council) through Contract No.
638-2013-8993 and the Oskar Klein Centre for Cosmoparticle Physics. S.
B., K. F., and P. S. also acknowledge support from DoE Grant No.
DE-SC007859 and the LCTP at the University of Michigan. We also greatly
thank the makers of WINE, for making it possible for us to run SRIM.
Finally, we acknowledge the use of a number of packages for scientific
computing in PYTHON [115-121].
CR AALBERS J, 2016, J COSMOL ASTROPART P
Aalseth CE, 2018, EUR PHYS J PLUS, V133, DOI 10.1140/epjp/i2018-11973-4
Agnes P, 2018, PHYS REV D, V98, DOI 10.1103/PhysRevD.98.102006
Agnese R, 2018, PHYS REV D, V97, DOI 10.1103/PhysRevD.97.022002
Agnese R, 2017, PHYS REV D, V95, DOI 10.1103/PhysRevD.95.082002
Agnese R., ARXIV180809098 SUPER
Agostini M., ARXIV170900756 BOR C
Akerib DS, 2017, PHYS REV LETT, V118, DOI 10.1103/PhysRevLett.118.021303
Akerib D. S., ARXIV180206039 LUXZE
Algeri S., ARXIV180709273
Amaudruz PA, 2018, PHYS REV LETT, V121, DOI 10.1103/PhysRevLett.121.071801
Angloher G, 2017, EUR PHYS J C, V77, DOI 10.1140/epjc/s10052-017-5223-9
Angloher G, 2014, PHYS DARK UNIVERSE, V3, P41, DOI 10.1016/j.dark.2014.03.004
Aprile E, 2018, PHYS REV LETT, V121, DOI 10.1103/PhysRevLett.121.111302
Aprile E, 2016, J COSMOL ASTROPART P, DOI 10.1088/1475-7516/2016/04/027
Baltz E. A., 1998, PHYS REV D, V59
Baum S., ARXIV180605991
Baum S, 2019, PHYS LETT B, V789, P262, DOI 10.1016/j.physletb.2018.12.036
Beacom JF, 2010, ANNU REV NUCL PART S, V60, P439, DOI 10.1146/annurev.nucl.010909.083331
Bernabei R, 2008, EUR PHYS J C, V56, P333, DOI 10.1140/epjc/s10052-008-0662-y
Bernabei R, 2013, EUR PHYS J C, V73, DOI 10.1140/epjc/s10052-013-2648-7
Bernabei R, 2018, UNIVERSE-BASEL, V4, DOI 10.3390/universe4110116
Billard J, 2014, PHYS REV D, V89, DOI 10.1103/PhysRevD.89.023524
Billard J, 2012, PHYS REV D, V85, DOI 10.1103/PhysRevD.85.035006
Bottino A, 2008, PHYS REV D, V78, DOI 10.1103/PhysRevD.78.083520
Bovy J, 2012, ASTROPHYS J, V759, DOI 10.1088/0004-637X/759/2/131
Bozorgnia N, 2018, J COSMOL ASTROPART P, DOI 10.1088/1475-7516/2018/12/013
Catena R, 2016, J COSMOL ASTROPART P, DOI 10.1088/1475-7516/2016/05/039
Cerdefio D.G., 2010, PARTICLE DARK MATTER, P347
Chang S, 2009, PHYS REV D, V79, DOI 10.1103/PhysRevD.79.115011
COLLAR JI, 1995, NUCL INSTRUM METH B, V95, P349, DOI 10.1016/0168-583X(94)00543-5
Cowan G, 2011, EUR PHYS J C, V71, DOI 10.1140/epjc/s10052-011-1554-0
Cui XY, 2017, PHYS REV LETT, V119, DOI 10.1103/PhysRevLett.119.181302
de Jonge N, 2010, ULTRAMICROSCOPY, V110, P1114, DOI 10.1016/j.ultramic.2010.04.001
DRUKIER A, 1984, PHYS REV D, V30, P2295, DOI 10.1103/PhysRevD.30.2295
Drukier A. K., ARXIV181106844
DRUKIER AK, 1986, PHYS REV D, V33, P3495, DOI 10.1103/PhysRevD.33.3495
Duda G, 2007, J COSMOL ASTROPART P, DOI 10.1088/1475-7516/2007/04/012
Echlin MP, 2015, MATER CHARACT, V100, P1, DOI 10.1016/j.matchar.2014.10.023
Edwards T.D.P., ARXIV171205401
Edwards TDP, 2018, PHYS REV LETT, V121, DOI 10.1103/PhysRevLett.121.181101
Edwards TDP, 2018, J COSMOL ASTROPART P, DOI 10.1088/1475-7516/2018/02/021
ENGEL J, 1995, PHYS REV C, V52, P2216, DOI 10.1103/PhysRevC.52.2216
Evans N. Wyn, ARXIV181011468
Fairbairn M, 2009, J COSMOL ASTROPART P, DOI 10.1088/1475-7516/2009/01/037
Feldstein B, 2014, J COSMOL ASTROPART P, DOI 10.1088/1475-7516/2014/08/065
FLEISCHER RL, 1964, PHYS REV A-GEN PHYS, V133, P1443, DOI 10.1103/PhysRev.133.A1443
FLEISCHER RL, 1965, ANN REV NUCL SCI, V15, P1, DOI 10.1146/annurev.ns.15.120165.000245
FLEISCHER RL, 1965, SCIENCE, V149, P383, DOI 10.1126/science.149.3682.383
Fox PJ, 2011, PHYS REV D, V83, DOI 10.1103/PhysRevD.83.103514
Freese K, 2013, REV MOD PHYS, V85, P1561, DOI 10.1103/RevModPhys.85.1561
Gallagher K, 1998, ANNU REV EARTH PL SC, V26, P519, DOI 10.1146/annurev.earth.26.1.519
Gelmini GB, 2017, J COSMOL ASTROPART P, DOI 10.1088/1475-7516/2017/12/039
Gondolo P, 2005, PHYS REV D, V71, DOI 10.1103/PhysRevD.71.123520
GOODMAN MW, 1985, PHYS REV D, V31, P3059, DOI 10.1103/PhysRevD.31.3059
Gradstein JG., 2012, GEOLOGIC TIME SCALE
Green A. M., 2008, P SCI IDM IDM2008, V2008, P108
Green AM, 2008, J COSMOL ASTROPART P, DOI 10.1088/1475-7516/2008/07/005
Green AM, 2007, J COSMOL ASTROPART P, DOI 10.1088/1475-7516/2007/08/022
Green AM, 2017, J PHYS G NUCL PARTIC, V44, DOI 10.1088/1361-6471/aa7819
Green AM, 2012, MOD PHYS LETT A, V27, DOI 10.1142/S0217732312300042
Gutlein A, 2010, ASTROPART PHYS, V34, P90, DOI 10.1016/j.astropartphys.2010.06.002
Guo S.-L., 2012, HDB RADIOACTIVITY AN, P233, DOI [10.1016/B978-0-12-384873-4.00004-9, DOI 10.1016/B978-0-12-384873-4.00004-9]
Hehn L, 2016, EUR PHYS J C, V76, DOI 10.1140/epjc/s10052-016-4388-y
HELM RH, 1956, PHYS REV, V104, P1466, DOI 10.1103/PhysRev.104.1466
Herrero-Garcia J, 2018, PHYS REV D, V98, DOI 10.1103/PhysRevD.98.123007
Hill R, 2012, ADV IMAG ELECT PHYS, V170, P65, DOI 10.1016/B978-0-12-394396-5.00002-6
Holler M, 2014, SCI REP-UK, V4, DOI 10.1038/srep03857
Hunter JD, 2007, COMPUT SCI ENG, V9, P90, DOI 10.1109/MCSE.2007.55
Ibarra A, 2018, J COSMOL ASTROPART P, DOI 10.1088/1475-7516/2018/12/018
Joens MS, 2013, SCI REP-UK, V3, DOI 10.1038/srep03514
KANG S, 2018, J COSMOL ASTROPART P, DOI DOI 10.1088/1475-7516/2018/07/016
Kavanagh BJ, 2013, PHYS REV LETT, V111, DOI 10.1103/PhysRevLett.111.031302
KIRZ J, 1985, REV SCI INSTRUM, V56, P1, DOI 10.1063/1.1138464
Koposov SE, 2010, ASTROPHYS J, V712, P260, DOI 10.1088/0004-637X/712/1/260
Lewin JD, 1996, ASTROPART PHYS, V6, P87, DOI 10.1016/S0927-6505(96)00047-3
Lider VV, 2017, PHYS-USP+, V60, P187, DOI [10.3367/UFNe.2016.06.037830, 10.3367/UFNr.2016.06.037830]
Lombardo JJ, 2012, J SYNCHROTRON RADIAT, V19, P789, DOI 10.1107/S0909049512027252
Madland D. G., 1999, LA13639MS LOS AL NAT, DOI [10.2172/15215, DOI 10.2172/15215]
McCabe C, 2011, PHYS REV D, V84, DOI 10.1103/PhysRevD.84.043525
Millman KJ, 2011, COMPUT SCI ENG, V13, P9, DOI 10.1109/MCSE.2011.36
Monroe J, 2007, PHYS REV D, V76, DOI 10.1103/PhysRevD.76.033007
O'Hare CAJ, 2017, PHYS REV D, V96, DOI 10.1103/PhysRevD.96.083011
O'Hare CAJ, 2016, PHYS REV D, V94, DOI 10.1103/PhysRevD.94.063527
Oliphant T.E., 2015, GUIDE NUMPY
Oliphant TE, 2007, COMPUT SCI ENG, V9, P10, DOI 10.1109/MCSE.2007.58
Pedregosa F, 2011, J MACH LEARN RES, V12, P2825
Perez F, 2007, COMPUT SCI ENG, V9, P21, DOI 10.1109/MCSE.2007.53
Peter AHG, 2014, PHYS DARK UNIVERSE, V5-6, P45, DOI 10.1016/j.dark.2014.10.006
Peter AHG, 2011, PHYS REV D, V83, DOI 10.1103/PhysRevD.83.125029
Petricca F., ARXIV171107692 CRESS
Petriello FJ, 2008, J HIGH ENERGY PHYS, DOI 10.1088/1126-6708/2008/09/047
Pfeifenberger MJ, 2017, MATER DESIGN, V121, P109, DOI 10.1016/j.matdes.2017.02.012
Piffl T, 2014, ASTRON ASTROPHYS, V562, DOI 10.1051/0004-6361/201322531
PRICE PB, 1984, PHYS REV LETT, V52, P1265, DOI 10.1103/PhysRevLett.52.1265
PRICE PB, 1986, PHYS REV LETT, V56, P1226, DOI 10.1103/PhysRevLett.56.1226
Randolph SJ, 2018, J VAC SCI TECHNOL B, V36, DOI 10.1116/1.5047806
Read JI, 2014, J PHYS G NUCL PARTIC, V41, DOI 10.1088/0954-3899/41/6/063101
Rodriguez MD, 2014, NUCL INSTRUM METH B, V326, P150, DOI 10.1016/j.nimb.2013.10.076
Savage C, 2009, J COSMOL ASTROPART P, DOI 10.1088/1475-7516/2009/09/036
Savage C, 2009, J COSMOL ASTROPART P, DOI 10.1088/1475-7516/2009/04/010
Savage C, 2011, PHYS REV D, V83, DOI 10.1103/PhysRevD.83.055002
Schaff F, 2015, NATURE, V527, P353, DOI 10.1038/nature16060
SNOWDENIFFT DP, 1995, NUCL INSTRUM METH B, V101, P247, DOI 10.1016/0168-583X(95)00483-1
SnowdenIfft DP, 1997, PHYS REV LETT, V78, P1628, DOI 10.1103/PhysRevLett.78.1628
SNOWDENIFFT DP, 1995, PHYS REV LETT, V74, P4133, DOI 10.1103/PhysRevLett.74.4133
SPERGEL DN, 1988, PHYS REV D, V37, P1353, DOI 10.1103/PhysRevD.37.1353
Stevens SM, 2008, CHEM COMMUN, P3894, DOI 10.1039/b804440f
Strege C, 2012, PHYS REV D, V86, DOI 10.1103/PhysRevD.86.023507
Strigari LE, 2016, PHYS REV D, V93, DOI 10.1103/PhysRevD.93.103534
Strigari LE, 2009, J COSMOL ASTROPART P, DOI 10.1088/1475-7516/2009/11/019
Strigari LE, 2009, NEW J PHYS, V11, DOI 10.1088/1367-2630/11/10/105011
Undagoitia TM, 2016, J PHYS G NUCL PARTIC, V43, DOI 10.1088/0954-3899/43/1/013001
Urban KW, 2009, NAT MATER, V8, P260, DOI 10.1038/nmat2407
van den Haute P., 1998, ADV FISSION TRACK GE
van Gastel R, 2012, MICROELECTRON RELIAB, V52, P2104, DOI 10.1016/j.microrel.2012.06.130
Wilks SS, 1938, ANN MATH STAT, V9, P60, DOI 10.1214/aoms/1177732360
WILSON WD, 1977, PHYS REV B, V15, P2458, DOI 10.1103/PhysRevB.15.2458
Ziegler J.F., 1985, STOPPING RANGE IONS
Ziegler JF, 2010, NUCL INSTRUM METH B, V268, P1818, DOI 10.1016/j.nimb.2010.02.091
NR 120
TC 7
Z9 7
U1 0
U2 1
PU AMER PHYSICAL SOC
PI COLLEGE PK
PA ONE PHYSICS ELLIPSE, COLLEGE PK, MD 20740-3844 USA
SN 2470-0010
EI 2470-0029
J9 PHYS REV D
JI Phys. Rev. D
PD FEB 27
PY 2019
VL 99
IS 4
AR 043541
DI 10.1103/PhysRevD.99.043541
PG 17
WC Astronomy & Astrophysics; Physics, Particles & Fields
SC Astronomy & Astrophysics; Physics
GA HN0XZ
UT WOS:000459913000001
OA Green Published, Other Gold
DA 2021-04-21
ER
PT J
AU Angelopoulos, V
Cruce, P
Drozdov, A
Grimes, EW
Hatzigeorgiu, N
King, DA
Larson, D
Lewis, JW
McTiernan, JM
Roberts, DA
Russell, CL
Hori, T
Kasahara, Y
Kumamoto, A
Matsuoka, A
Miyashita, Y
Miyoshi, Y
Shinohara, I
Teramoto, M
Faden, JB
Halford, AJ
McCarthy, M
Millan, RM
Sample, JG
Smith, DM
Woodger, LA
Masson, A
Narock, AA
Asamura, K
Chang, TF
Chiang, CY
Kazama, Y
Keika, K
Matsuda, S
Segawa, T
Seki, K
Shoji, M
Tam, SWY
Umemura, N
Wang, BJ
Wang, SY
Redmon, R
Rodriguez, JV
Singer, HJ
Vandegriff, J
Abe, S
Nose, M
Shinbori, A
Tanaka, YM
UeNo, S
Andersson, L
Dunn, P
Fowler, C
Halekas, JS
Hara, T
Harada, Y
Lee, CO
Lillis, R
Mitchell, DL
Argall, MR
Bromund, K
Burch, JL
Cohen, IJ
Galloy, M
Giles, B
Jaynes, AN
Le Contel, O
Oka, M
Phan, TD
Walsh, BM
Westlake, J
Wilder, FD
Bale, SD
Livi, R
Pulupa, M
Whittlesey, P
DeWolfe, A
Harter, B
Lucas, E
Auster, U
Bonnell, JW
Cully, CM
Donovan, E
Ergun, RE
Frey, HU
Jackel, B
Keiling, A
Korth, H
McFadden, JP
Nishimura, Y
Plaschke, F
Robert, P
Turner, DL
Weygand, JM
Candey, RM
Johnson, RC
Kovalick, T
Liu, MH
McGuire, RE
Breneman, A
Kersten, K
Schroeder, P
AF Angelopoulos, V.
Cruce, P.
Drozdov, A.
Grimes, E. W.
Hatzigeorgiu, N.
King, D. A.
Larson, D.
Lewis, J. W.
McTiernan, J. M.
Roberts, D. A.
Russell, C. L.
Hori, T.
Kasahara, Y.
Kumamoto, A.
Matsuoka, A.
Miyashita, Y.
Miyoshi, Y.
Shinohara, I.
Teramoto, M.
Faden, J. B.
Halford, A. J.
McCarthy, M.
Millan, R. M.
Sample, J. G.
Smith, D. M.
Woodger, L. A.
Masson, A.
Narock, A. A.
Asamura, K.
Chang, T. F.
Chiang, C. -Y.
Kazama, Y.
Keika, K.
Matsuda, S.
Segawa, T.
Seki, K.
Shoji, M.
Tam, S. W. Y.
Umemura, N.
Wang, B. -J.
Wang, S. -Y.
Redmon, R.
Rodriguez, J. V.
Singer, H. J.
Vandegriff, J.
Abe, S.
Nose, M.
Shinbori, A.
Tanaka, Y. -M.
UeNo, S.
Andersson, L.
Dunn, P.
Fowler, C.
Halekas, J. S.
Hara, T.
Harada, Y.
Lee, C. O.
Lillis, R.
Mitchell, D. L.
Argall, M. R.
Bromund, K.
Burch, J. L.
Cohen, I. J.
Galloy, M.
Giles, B.
Jaynes, A. N.
Le Contel, O.
Oka, M.
Phan, T. D.
Walsh, B. M.
Westlake, J.
Wilder, F. D.
Bale, S. D.
Livi, R.
Pulupa, M.
Whittlesey, P.
DeWolfe, A.
Harter, B.
Lucas, E.
Auster, U.
Bonnell, J. W.
Cully, C. M.
Donovan, E.
Ergun, R. E.
Frey, H. U.
Jackel, B.
Keiling, A.
Korth, H.
McFadden, J. P.
Nishimura, Y.
Plaschke, F.
Robert, P.
Turner, D. L.
Weygand, J. M.
Candey, R. M.
Johnson, R. C.
Kovalick, T.
Liu, M. H.
McGuire, R. E.
Breneman, A.
Kersten, K.
Schroeder, P.
TI The Space Physics Environment Data Analysis System (SPEDAS)
SO SPACE SCIENCE REVIEWS
LA English
DT Review
DE Space plasmas; Magnetospheric physics; Planetary magnetospheres;
Solarwind; Ionospheric physics; Geospace science
ID MISSION; SCIENCE; SUPERDARN
AB With the advent of the Heliophysics/Geospace System Observatory (H/GSO), acomplement of multi-spacecraft missions and ground-based observatories to study the space environment, data retrieval, analysis, and visualization of space physics data can be daunting. The Space Physics Environment Data Analysis System (SPEDAS), agrass-roots software development platform (www.spedas.org), is now officially supported by NASA Heliophysics as part of its data environment infrastructure. It serves more than a dozen space missions and ground observatories and can integrate the full complement of past and upcoming space physics missions with minimal resources, following clear, simple, and well-proven guidelines. Free, modular and configurable to the needs of individual missions, it works in both command-line (ideal for experienced users) and Graphical User Interface (GUI) mode (reducing the learning curve for first-time users). Both options have crib-sheets, user-command sequences in ASCII format that can facilitate record-and-repeat actions, especially for complex operations and plotting. Crib-sheets enhance scientific interactions, as users can move rapidly and accurately from exchanges of technical information on data processing to efficient discussions regarding data interpretation and science. SPEDAS can readily query and ingest all International Solar Terrestrial Physics (ISTP)-compatible products from the Space Physics Data Facility (SPDF), enabling access to a vast collection of historic and current mission data. The planned incorporation of Heliophysics Application Programmer's Interface (HAPI) standards will facilitate data ingestion from distributed datasets that adhere to these standards. Although SPEDAS is currently Interactive Data Language (IDL)-based (and interfaces to Java-based tools such as Autoplot), efforts are under-way to expand it further to work with python (first as an interface tool and potentially even receiving an under-the-hood replacement). We review the SPEDAS development history, goals, and current implementation. We explain its modes of use with examples geared for users and outline its technical implementation and requirements with software developers in mind. We also describe SPEDAS personnel and software management, interfaces with other organizations, resources and support structure available to the community, and future development plans.
C1 [Angelopoulos, V.; Cruce, P.; Drozdov, A.; Grimes, E. W.; Russell, C. L.; Weygand, J. M.] Univ Calif Los Angeles, Dept Earth Planetary & Space Sci, Los Angeles, CA 90095 USA.
[Angelopoulos, V.; Cruce, P.; Drozdov, A.; Grimes, E. W.; Russell, C. L.; Weygand, J. M.] Univ Calif Los Angeles, Inst Geophys & Planetary Phys, Los Angeles, CA 90024 USA.
[Hatzigeorgiu, N.; King, D. A.; Larson, D.; Lewis, J. W.; McTiernan, J. M.; Dunn, P.; Hara, T.; Lee, C. O.; Lillis, R.; Mitchell, D. L.; Oka, M.; Phan, T. D.; Bale, S. D.; Livi, R.; Pulupa, M.; Whittlesey, P.; Bonnell, J. W.; Frey, H. U.; Keiling, A.; McFadden, J. P.; Schroeder, P.] Univ Calif Berkeley, Space Sci Lab, Berkeley, CA 94720 USA.
[Roberts, D. A.; Bromund, K.; Giles, B.; Candey, R. M.; McGuire, R. E.] NASA, Goddard Space Flight Ctr, Greenbelt, MD USA.
[Hori, T.; Miyoshi, Y.; Teramoto, M.; Chang, T. F.; Matsuda, S.; Segawa, T.; Shoji, M.; Umemura, N.; Nose, M.; Shinbori, A.] Nagoya Univ, Inst Space Earth Environm Res, Nagoya, Aichi, Japan.
[Kasahara, Y.] Kanazawa Univ, Kanazawa, Ishikawa, Japan.
[Kumamoto, A.] Tohoku Univ, Aoba Ku, 6-3 Aoba, Sendai, Miyagi 9808578, Japan.
[Matsuoka, A.; Shinohara, I.; Asamura, K.] Japan Aerosp Explorat Agcy, Inst Space & Astronaut Sci, Sagamihara, Kanagawa, Japan.
[Miyashita, Y.] Korea Astron & Space Sci Inst, Daejeon, South Korea.
[Faden, J. B.] Cottage Syst, Iowa City, IA USA.
[Halford, A. J.] Aerosp Corp, Space Sci Dept, Chantilly, VA USA.
[McCarthy, M.] Univ Washington, Dept Earth & Space Sci, Seattle, WA 98195 USA.
[Millan, R. M.; Woodger, L. A.] Dartmouth Coll, Dept Phys & Astron, Hanover, NH 03755 USA.
[Sample, J. G.] Montana State Univ, Dept Phys, Bozeman, MT USA.
[Smith, D. M.] Univ Calif Santa Cruz, Santa Cruz Inst Particle Phys, Santa Cruz, CA 95064 USA.
[Smith, D. M.] Univ Calif Santa Cruz, Dept Phys, Santa Cruz, CA 95064 USA.
[Masson, A.] SCI OPD, ESAC, European Space Agcy, Madrid, Spain.
[Narock, A. A.; Johnson, R. C.; Kovalick, T.; Liu, M. H.] NASA, ADNET Syst Inc, Goddard Space Flight Ctr, Greenbelt, MD USA.
[Chiang, C. -Y.; Tam, S. W. Y.] Natl Cheng Kung Univ, Inst Space & Plasma Sci, Tainan, Taiwan.
[Kazama, Y.; Wang, B. -J.; Wang, S. -Y.] Acad Sinica, Inst Astron & Astrophys, Taipei, Taiwan.
[Keika, K.; Seki, K.] Univ Tokyo, Grad Sch Sci, Dept Earth & Planetary Sci, Tokyo, Japan.
[Wang, B. -J.] Natl Cent Univ, Grad Inst Space Sci, Taoyuan, Taiwan.
[Redmon, R.; Rodriguez, J. V.] Natl Ocean & Atmospher Adm, Natl Ctr Environm Informat, Boulder, CO USA.
[Rodriguez, J. V.] Univ Colorado, CIRES, Boulder, CO 80309 USA.
[Singer, H. J.] Natl Ocean & Atmospher Adm, Space Weather Predict Ctr, Boulder, CO USA.
[Vandegriff, J.; Cohen, I. J.; Westlake, J.; Korth, H.] Johns Hopkins Univ, Appl Phys Lab, Laurel, MD USA.
[Abe, S.] Kyushu Univ, Int Ctr Space Weather Sci & Educ, Fukuoka, Fukuoka, Japan.
[Nose, M.] Kyoto Univ, Kyoto Data Anal Ctr Geomagnetism & Space Magnetis, World Data Ctr Geomagnetism, Kyoto, Japan.
[Tanaka, Y. -M.] Natl Inst Polar Res, Tokyo, Japan.
[UeNo, S.] Kyoto Univ, Hida Observ, Kyoto, Japan.
[Andersson, L.; Fowler, C.; Wilder, F. D.; DeWolfe, A.; Harter, B.; Lucas, E.; Ergun, R. E.] Univ Colorado, Lab Atmospher & Space Phys, Boulder, CO USA.
[Halekas, J. S.; Jaynes, A. N.] Univ Iowa, Dept Phys & Astron, Iowa City, IA 52242 USA.
[Harada, Y.] Kyoto Univ, Dept Geophys, Kyoto, Japan.
[Argall, M. R.] Univ New Hampshire, Dept Phys, Durham, NH 03824 USA.
[Argall, M. R.] Univ New Hampshire, Ctr Space Sci, Durham, NH 03824 USA.
[Burch, J. L.] Southwest Res Inst, San Antonio, TX USA.
[Galloy, M.] Natl Ctr Atmospher Res, POB 3000, Boulder, CO 80307 USA.
[Le Contel, O.; Robert, P.] Univ Paris Sud, Sorbonne Univ, Observ Paris, Lab Phys Plasmas,CNRS,Ecole Polytech, Paris, France.
[Walsh, B. M.] Boston Univ, Ctr Space Phys, Dept Mech Engn, Boston, MA 02215 USA.
[Auster, U.] Tech Univ Carolo Wilhelmina Braunschweig, Inst Geophys & Extraterr Phys, Braunschweig, Germany.
[Cully, C. M.; Donovan, E.; Jackel, B.] Univ Calgary, Calgary, ON, Canada.
[Nishimura, Y.] Boston Univ, Ctr Space Phys, Boston, MA 02215 USA.
[Nishimura, Y.] Boston Univ, Dept Elect & Comp Engn, Boston, MA 02215 USA.
[Plaschke, F.] Karl Franzens Univ Graz, Inst Phys, Austrian Acad Sci, Space Res Inst, Graz, Austria.
[Turner, D. L.] Aerosp Corp, El Segundo, CA 90245 USA.
[Breneman, A.; Kersten, K.] Univ Minnesota, Minneapolis, MN USA.
RP Angelopoulos, V (corresponding author), Univ Calif Los Angeles, Dept Earth Planetary & Space Sci, Los Angeles, CA 90095 USA.
EM vassilis@ucla.edu
RI JAYNES, ALLISON N/F-7876-2018; Cohen, Ian/P-1710-2019; Miyoshi,
Yoshizumi/B-5834-2015; Drozdov, Alexander/Y-2161-2019; Nose,
Masahito/S-3086-2019; Candey, Robert M/D-4639-2012; Tam, Sunny W.
Y./AAP-2715-2020; Turner, Drew L/G-3224-2012; Wong, Dixon/E-3370-2019;
Shoji, Masafumi/S-8304-2019; Giles, Barbara L/J-7393-2017; ,
Shiang-Yu Wang/AAZ-2695-2020; Halford, Alexa Jean/AAH-2098-2019;
Westlake, Joseph/AAZ-9404-2020; Walsh, Brian/C-4899-2016
OI JAYNES, ALLISON N/0000-0002-1470-4266; Cohen, Ian/0000-0002-9163-6009;
Miyoshi, Yoshizumi/0000-0001-7998-1240; Drozdov,
Alexander/0000-0002-5334-2026; Nose, Masahito/0000-0002-2789-3588;
Turner, Drew L/0000-0002-2425-7818; Wong, Dixon/0000-0002-0654-3431;
Giles, Barbara L/0000-0001-8054-825X; , Shiang-Yu
Wang/0000-0001-6491-1901; Westlake, Joseph/0000-0003-0472-8640; Harada,
Yuki/0000-0002-4001-6352; Halford, Alexa/0000-0002-5383-4602; Narock,
Ayris/0000-0001-6746-7455; Sample, John/0000-0002-9516-9292; Harter,
Bryan/0000-0002-3908-9001; Plaschke, Ferdinand/0000-0002-5104-6282;
Masson, Arnaud/0000-0002-5602-1957; Cully,
Christopher/0000-0001-7242-8872; Hori, Tomoaki/0000-0001-8451-6941;
Donovan, Eric/0000-0002-8557-4155; Walsh, Brian/0000-0001-7426-5413;
Ware, Alexandria/0000-0003-4647-9718; Teramoto,
Mariko/0000-0001-5983-1149; Vandegriff, Jon/0000-0002-0781-1565; Seki,
Kanako/0000-0001-5557-9062
FU NASANational Aeronautics & Space Administration (NASA) [NNG17PZ01C,
NNG04EB99C, NAS5-02099, NNX16AP95G, NNN06AA01C, NNX08AM58G, NNX17AL22G];
German Ministry for Economy and Technology; German Center for Aviation
and Space (DLR)Helmholtz AssociationGerman Aerospace Centre (DLR) [50 OC
0302]; JSPS KAKENHIMinistry of Education, Culture, Sports, Science and
Technology, Japan (MEXT)Japan Society for the Promotion of
ScienceGrants-in-Aid for Scientific Research (KAKENHI) [JP15H05816];
GEMSIS project of ISEE at Nagoya University; Japan's JSPS KAKENHI
[15H05815, 15H05747, 16H06286, 16H04056]; Taiwan grant [MOST
105-3111-Y-001-042, MOST 106-2111-M-001-011, MOST 107-2111-M006-003];
NSFNational Science Foundation (NSF) [AGS-1737823, AGS-1004736,
AGS-1004814]
FX The core SPEDAS team acknowledges support from NASA contract NNG17PZ01C
(for SPEDAS community support) to UCLA, contract NNG04EB99C (as
subcontract from SwRI for MMS SPEDAS plug-in development) to UCLA, and
contract NAS5-02099 (THEMIS support of TDAS maintenance and SPEDAS
infrastructure) to UCB, UCLA and BU. UCB also acknowledges support from
NASA grant NNX16AP95G (for WIND 3DP work), NASA contract NNN06AA01C (for
PSP/FIELDS work).; The THEMIS team also acknowledges contributions from
F. S. Mozer for use of the EFI data; S. Mende for use of the ASI data,
the Canadian Space Agency for logistical support in fielding and data
retrieval from the GBO stations; K. H. Glassmeier and W. Baumjohann for
the use of the FGM data provided under the lead of the Technical
University of Braunschweig and with financial support through the German
Ministry for Economy and Technology and the German Center for Aviation
and Space (DLR) under contract 50 OC 0302.; The IUGONET team
acknowledges support from JSPS KAKENHI grant JP15H05816.; ERG work was
done by the ERG-Science Center operated by ISAS/JAXA and at ISEE/Nagoya
University. ERG work was also partially supported by the GEMSIS project
of ISEE (formerly STEL) at Nagoya University. Y. Miyoshi acknowledges
support from Japan's JSPS KAKENHI grants 15H05815, 15H05747, and
16H06286. Y. Kasahara acknowledges support from Japan's JSPS KAKENHI
grant 16H04056. S.-Y. Wang acknowledges support from Taiwan grants MOST
105-3111-Y-001-042, and MOST 106-2111-M-001-011, and C.-Y. Chiang and
S.W.Y. Tam acknowledge support from Taiwan grant MOST
107-2111-M006-003.; BARREL authors acknowledge NASA grant NNX08AM58G and
the BARREL team for development of the BDAS software. BARREL data and
software can be obtained through the SPEDAS software package
distribution as well as from the CDAWeb website.; Y. Nishimura
acknowledges grants NASA NNX17AL22G and NSF AGS-1737823.; GIMNAST work
for SPEDAS was supported by NSF's AGS-1004736 and AGS-1004814.
CR Allen AJ, 2010, ASTROPHYSICS SPACE, P225, DOI 10.1007/978-90-481-3499-1_14
Angelopoulos V, 2008, SPACE SCI REV, V141, P453, DOI 10.1007/s11214-008-9378-4
Angelopoulos V., 1998, PREFACE SCI CLOSURE
Angelopoulos V., 2009, THE THEMIS MISSION
[Anonymous], 2013, SOL SPAC PHYS SCI TE
Baker M, 2016, NATURE, V533, P452, DOI 10.1038/533452a
Burch JL, 2016, SPACE SCI REV, V199, P5, DOI 10.1007/s11214-015-0164-9
Delory G.T., 1998, SCI CLOSURE ENABLING, P22
Donovan E., 2017, AM GEOPH UN M
Escoubet CP, 2001, ANN GEOPHYS-GERMANY, V19, P1197, DOI 10.5194/angeo-19-1197-2001
Faden JB, 2010, EARTH SCI INFORM, V3, P41, DOI 10.1007/s12145-010-0049-0
Fanelli D, 2018, P NATL ACAD SCI USA, V115, P2628, DOI 10.1073/pnas.1708272114
Fox NJ, 2016, SPACE SCI REV, V204, P7, DOI 10.1007/s11214-015-0211-6
Fox N.J., 2013, PREFACE VANALLEN PRO, P1
GREENWALD RA, 1995, SPACE SCI REV, V71, P761, DOI 10.1007/BF00751350
Hayashi H, 2013, DATA SCI J, V12, DOI [10.2481/dsj.WDS-030, DOI 10.2481/DSJ.WDS-030]
Hori T, 2015, J SPACE SCI INFO JPN, V4, P75
Hori Tomoaki, 2013, Advances in Polar Science, V24, P69, DOI 10.3724/SP.J.1085.2013.00069
Immel TJ, 2018, SPACE SCI REV, V214, DOI 10.1007/s11214-017-0449-2
Jakosky BM, 2015, SPACE SCI REV, V195, P3, DOI 10.1007/s11214-015-0139-x
Kato M, 2010, SPACE SCI REV, V154, P3, DOI 10.1007/s11214-010-9678-3
Keika K, 2017, EARTH PLANETS SPACE, V69, DOI 10.1186/s40623-017-0761-9
LIN RP, 1995, SPACE SCI REV, V71, P125, DOI 10.1007/BF00751328
Mauk BH, 2016, SPACE SCI REV, V199, P471, DOI 10.1007/s11214-014-0055-5
McFadden J.P., 2009, THE THEMIS MISSION
McFadden JP, 2001, SPACE SCI REV, V98, P169, DOI 10.1023/A:1013179624253
Mende SB, 2008, SPACE SCI REV, V141, P357, DOI 10.1007/s11214-008-9380-x
Millan RM, 2011, J ATMOS SOL-TERR PHY, V73, P1425, DOI 10.1016/j.jastp.2011.01.006
Miyoshi Y., 2016, VARIABILITY SUN ITS, P2
Miyoshi Y, 2018, EARTH PLANETS SPACE, V70, DOI 10.1186/s40623-018-0862-0
Miyoshi Y, 2018, EARTH PLANETS SPACE, V70, DOI 10.1186/s40623-018-0867-8
Morley S.K., 2010, P 9 PYTH SCI C SCIPY
OGAWARA Y, 1991, SOL PHYS, V136, P1, DOI 10.1007/BF00151692
Pfaff R, 2001, SPACE SCI REV, V98, P1, DOI 10.1023/A:1013187826070
Reme H, 1997, SPACE SCI REV, V79, P303, DOI 10.1023/A:1004929816409
RUSSELL CT, 1995, SPACE SCI REV, V71, P1, DOI 10.1007/BF00751322
Siscoe G., 1998, SCI CLOSURE ENABLING, P1
Tsyganenko NA, 2013, ANN GEOPHYS-GERMANY, V31, P1745, DOI 10.5194/angeo-31-1745-2013
Umemura N, 2017, J SPACE SCI INF JPN, V6, P25, DOI [10.20637/JAXA-RR-16-007/0003, DOI 10.20637/JAXA-RR-16-007/0003]
Williams D.J., 1980, SOLAR INTERPLANETARY
Wiltberger M., 2015, THE GEM MESSENGER, V25, P30
Wolkovitch D.Y., 1997, EOS T AGU, V78, P5301
Woodger LA, 2015, J GEOPHYS RES-SPACE, V120, P4922, DOI 10.1002/2014JA020874
Yumoto K., 2012, 2012 AM GEOPH UN FAL
NR 44
TC 107
Z9 107
U1 3
U2 32
PU SPRINGER
PI DORDRECHT
PA VAN GODEWIJCKSTRAAT 30, 3311 GZ DORDRECHT, NETHERLANDS
SN 0038-6308
EI 1572-9672
J9 SPACE SCI REV
JI Space Sci. Rev.
PD FEB
PY 2019
VL 215
IS 1
AR 9
DI 10.1007/s11214-018-0576-4
PG 46
WC Astronomy & Astrophysics
SC Astronomy & Astrophysics
GA HI5CS
UT WOS:000456470700001
PM 30880847
OA Green Published, Other Gold
DA 2021-04-21
ER
PT J
AU Gripaios, B
Sutherland, D
AF Gripaios, Ben
Sutherland, Dave
TI DEFT: a program for operators in EFT
SO JOURNAL OF HIGH ENERGY PHYSICS
LA English
DT Article
DE Effective Field Theories; Beyond Standard Model
AB We describe a Python-based computer program, DEFT, for manipulating operators in effective field theories (EFTs). In its current incarnation, DEFT can be applied to 4-dimensional, Poincare invariant theories with gauge group SU(3) x SU(2) x U(1), such as the Standard Model (SM), but a variety of extensions (e.g. to lower dimensions or to an arbitrary product of unitary gauge groups) are possible. Amongst other features, the program is able to: (i) check whether an input list of Lagrangian operators (of a given dimension in the EFT expansion) is a basis for the space of operators contributing to S-matrix elements, once redundancies (such as Fierz-Pauli identities, integration by parts, and equations of motion) are taken into account; (ii) generate such a basis (where possible) from an input algorithm; (iii) carry out a change of basis. We describe applications to the SM (where we carry out a number of non-trivial cross-checks) and extensions thereof, and outline how the program may be of use in precision tests of the SM and in the ongoing search for new physics at the LHC and elsewhere. The code and instructions can be downloaded from http://web.physics.ucsb.edu/dwsuth/DEFT/.
C1 [Gripaios, Ben] Univ Cambridge, Cavendish Lab, JJ Thomson Ave, Cambridge CB3 0HE, England.
[Sutherland, Dave] Univ Calif Santa Barbara, UCSB Broida Hall, Santa Barbara, CA 93106 USA.
RP Gripaios, B (corresponding author), Univ Cambridge, Cavendish Lab, JJ Thomson Ave, Cambridge CB3 0HE, England.
EM gripaios@hep.phy.cam.ac.uk; dwsuth@ucsb.edu
FU STFCUK Research & Innovation (UKRI)Science & Technology Facilities
Council (STFC) [ST/L000385/1, ST/P000681/1]; King's College, Cambridge;
Science and Technology Facilities CouncilUK Research & Innovation
(UKRI)Science & Technology Facilities Council (STFC); Emmanuel College,
Cambridge; Department of EnergyUnited States Department of Energy (DOE)
[DE-SC0014129]; Center for Scientific Computing from the CNSI, MRL: an
NSF MRSEC [DMR-1121053]; NSFNational Science Foundation (NSF)
[CNS-0960316]
FX We thank T. You and other members of the Cambridge SUSY Working group
for discussions. BG was supported by STFC grants ST/L000385/1 and
ST/P000681/1 and King's College, Cambridge. DS acknowledges support from
the Science and Technology Facilities Council; Emmanuel College,
Cambridge; the Department of Energy (DE-SC0014129), and the Center for
Scientific Computing from the CNSI, MRL: an NSF MRSEC (DMR-1121053) and
NSF CNS-0960316.
CR ARZT C, 1995, PHYS LETT B, V342, P189, DOI 10.1016/0370-2693(94)01419-D
BUCHMULLER W, 1986, NUCL PHYS B, V268, P621, DOI 10.1016/0550-3213(86)90262-2
Cheung C, 2015, PHYS REV LETT, V115, DOI 10.1103/PhysRevLett.115.071601
Criado JC, 2018, COMPUT PHYS COMMUN, V227, P42, DOI 10.1016/j.cpc.2018.02.016
Dreiner HK, 2010, PHYS REP, V494, P1, DOI 10.1016/j.physrep.2010.05.002
Einhorn MB, 2013, NUCL PHYS B, V876, P556, DOI 10.1016/j.nuclphysb.2013.08.023
Falkowski A., 2015, LHCHXSWGINT2015001 C
Falkowski A, 2015, EUR PHYS J C, V75, DOI 10.1140/epjc/s10052-015-3806-x
Gripaios B, 2016, J HIGH ENERGY PHYS, DOI 10.1007/JHEP08(2016)103
Grzadkowski B, 2010, J HIGH ENERGY PHYS, DOI [10.1007/JHEP10(2010)85, 10.1007/JHEP10(2010)085]
Henning B, 2017, J HIGH ENERGY PHYS, DOI 10.1007/JHEP10(2017)199
Henning B, 2017, J HIGH ENERGY PHYS, DOI 10.1007/JHEP08(2017)016
Henning B, 2016, COMMUN MATH PHYS, V347, P363, DOI 10.1007/s00220-015-2518-2
Lehman L, 2016, J HIGH ENERGY PHYS, DOI 10.1007/JHEP02(2016)081
Lehman L, 2015, PHYS REV D, V91, DOI 10.1103/PhysRevD.91.105014
Meurer A, 2017, PEERJ COMPUT SCI, DOI 10.7717/peerj-cs.103
NR 16
TC 19
Z9 19
U1 1
U2 2
PU SPRINGER
PI NEW YORK
PA 233 SPRING ST, NEW YORK, NY 10013 USA
SN 1029-8479
J9 J HIGH ENERGY PHYS
JI J. High Energy Phys.
PD JAN 15
PY 2019
IS 1
AR 128
DI 10.1007/JHEP01(2019)128
PG 21
WC Physics, Particles & Fields
SC Physics
GA HJ9EM
UT WOS:000457501600001
OA DOAJ Gold, Green Published
DA 2021-04-21
ER
PT J
AU Imtiaz, B
Wan, YP
Cai, YF
AF Imtiaz, Batool
Wan, Youping
Cai, Yi-Fu
TI Two-field cosmological phase transitions and gravitational waves in the
singlet Majoron model
SO EUROPEAN PHYSICAL JOURNAL C
LA English
DT Article
ID FALSE VACUUM; RADIATION; SCALE; CONSTRAINTS; FATE
AB In the singlet Majoron model, we study cosmological phase transitions (PTs) and their resulting gravitational waves (GWs), in the two-field phase space, without freezing any of the field directions. We first calculate the effective potential, at one loop and at finite temperature, of the Standard Model Higgs doublet together with one extra Higgs singlet. We make use of the public available Python package CosmoTransitions' to simulate the two-dimensional (2D) cosmological PTs and evaluate the gravitational waves generated by first-order PTs. With the full 2D simulation, we are able not only to confirm the PTs' properties previously discussed in the literature, but also we find new patterns, such as strong first-order PTs tunneling from a vacuum located on one axis to another vacuum located on the second axis. The two-field phase space analysis presents a richer panel of cosmological PT patterns compared to analysis with a single-field approximation. The PTGW amplitudes turn out to be out of the reach for the space-borne gravitational wave interferometers such as LISA, DECIGO, BBO, TAIJI and TianQin when constraints from colliders physics are taken into account.
C1 [Imtiaz, Batool; Wan, Youping; Cai, Yi-Fu] Univ Sci & Technol China, Sch Phys Sci, Dept Astron, Hefei 230026, Anhui, Peoples R China.
[Imtiaz, Batool; Wan, Youping; Cai, Yi-Fu] Univ Sci & Technol China, CAS Key Lab Res Galaxies & Cosmol, Hefei 230026, Anhui, Peoples R China.
[Imtiaz, Batool; Wan, Youping; Cai, Yi-Fu] Univ Sci & Technol China, Sch Astron & Space Sci, Hefei 230026, Anhui, Peoples R China.
RP Cai, YF (corresponding author), Univ Sci & Technol China, Sch Phys Sci, Dept Astron, Hefei 230026, Anhui, Peoples R China.; Cai, YF (corresponding author), Univ Sci & Technol China, CAS Key Lab Res Galaxies & Cosmol, Hefei 230026, Anhui, Peoples R China.; Cai, YF (corresponding author), Univ Sci & Technol China, Sch Astron & Space Sci, Hefei 230026, Anhui, Peoples R China.
EM yifucai@ustc.edu.cn
RI Cai, Yi-Fu/M-8162-2013
OI Cai, Yi-Fu/0000-0003-0706-8465
FU NSFCNational Natural Science Foundation of China (NSFC) [11722327,
11653002, 11421303, J1310021]; CAST Young Elite Scientists Sponsorship
Program [2016QNRC001]; National Youth Thousand Talents Program of China;
Fundamental Research Funds for the Central UniversitiesFundamental
Research Funds for the Central Universities; Postdoctoral Science
Foundation of ChinaChina Postdoctoral Science Foundation [2017M621999]
FX We are grateful to Andrea Addazi, Jim Cline, Ryusuke Jinno, Antonino
Marciano and Pierre Zhang for valuable comments. We also thank two
reviewers for insightful suggestions on the manuscript. YPW would like
to thank Carroll L. Wainwright for useful discussions of the package of
CosmoTransitions. This work is supported in part by the NSFC (nos.
11722327, 11653002, 11421303, J1310021), by CAST Young Elite Scientists
Sponsorship Program (2016QNRC001), by the National Youth Thousand
Talents Program of China, by the Fundamental Research Funds for the
Central Universities, and by the Postdoctoral Science Foundation of
China (2017M621999). All numerical simulations are operated on the
computer clusters Linda & Judy in the particle cosmology group at USTC.
CR Aad G, 2016, J HIGH ENERGY PHYS, DOI 10.1007/JHEP01(2016)172
Abbott B. P., 2016, PHYS REV LETT, V116
Addazi A, 2018, PHYS LETT B, V782, P732, DOI 10.1016/j.physletb.2018.06.015
Addazi A, 2018, CHINESE PHYS C, V42, DOI 10.1088/1674-1137/42/2/023105
AKHMEDOV EK, 1993, PHYS LETT B, V299, P90, DOI 10.1016/0370-2693(93)90887-N
ANDERSON GW, 1992, PHYS REV D, V45, P2685, DOI 10.1103/PhysRevD.45.2685
Apreda R, 2002, NUCL PHYS B, V631, P342, DOI 10.1016/S0550-3213(02)00264-X
ARNOLD P, 1994, PHYS REV D, V50, P6662, DOI 10.1103/PhysRevD.50.6662.2
ARNOLD P, 1993, PHYS REV D, V47, P3546, DOI 10.1103/PhysRevD.47.3546
Audley H, ARXIV170200786ASTROP
Basler P, 2018, J HIGH ENERGY PHYS, DOI 10.1007/JHEP03(2018)061
Basler P, 2017, J HIGH ENERGY PHYS, DOI 10.1007/JHEP02(2017)121
BEREZHIANI ZG, 1992, PHYS LETT B, V291, P99, DOI 10.1016/0370-2693(92)90126-O
Berezhiani Z, 2016, EUR PHYS J C, V76, DOI 10.1140/epjc/s10052-016-4564-0
Bodeker D, 2009, J COSMOL ASTROPART P, DOI 10.1088/1475-7516/2009/05/009
Cai R. G., 2017, J COSMOL ASTROPART P, V1708
Cai YF, 2007, PHYS LETT B, V657, P1, DOI 10.1016/j.physletb.2007.09.068
Cai YF, 2018, PHYS REV D, V97, DOI 10.1103/PhysRevD.97.103513
Cai YF, 2016, SCI CHINA PHYS MECH, V59, DOI 10.1007/s11433-016-0178-x
Cai YF, 2015, NUCL PHYS B, V900, P517, DOI 10.1016/j.nuclphysb.2015.09.025
CALLAN CG, 1977, PHYS REV D, V16, P1762, DOI 10.1103/PhysRevD.16.1762
Caprini C, 2016, J COSMOL ASTROPART P, DOI 10.1088/1475-7516/2016/04/001
Caprini C, 2008, PHYS REV D, V77, DOI 10.1103/PhysRevD.77.124015
Chao W, 2017, J COSMOL ASTROPART P, DOI 10.1088/1475-7516/2017/09/009
Chen YD, 2018, J HIGH ENERGY PHYS, DOI 10.1007/JHEP05(2018)178
CHIKASHIGE Y, 1981, PHYS LETT B, V98, P265, DOI 10.1016/0370-2693(81)90011-3
Clarke JD, 2014, J HIGH ENERGY PHYS, DOI 10.1007/JHEP02(2014)123
Cline JM, 1993, ASTROPART PHYS, V1, P387, DOI 10.1016/0927-6505(93)90005-X
Cline JM, 2009, J HIGH ENERGY PHYS, DOI 10.1088/1126-6708/2009/07/040
Cline JM, 1997, PHYS REV D, V55, P3873, DOI 10.1103/PhysRevD.55.3873
COLEMAN S, 1977, PHYS REV D, V16, P1248, DOI 10.1103/PhysRevD.16.1248
COLEMAN S, 1977, PHYS REV D, V15, P2929, DOI 10.1103/PhysRevD.15.2929
Corbin V, 2006, CLASSICAL QUANT GRAV, V23, P2435, DOI 10.1088/0264-9381/23/7/014
Cutting D, 2018, PHYS REV D, V97, DOI 10.1103/PhysRevD.97.123513
DELAUNAY C, 2008, J HIGH ENERGY PHYS
Dev PSB, 2016, PHYS REV D, V93, DOI 10.1103/PhysRevD.93.104001
Dorsch GC, 2017, J HIGH ENERGY PHYS, DOI 10.1007/JHEP12(2017)086
Dorsch GC, 2013, J HIGH ENERGY PHYS, DOI 10.1007/JHEP10(2013)029
Ellis J., ARXIV180908242HEPPH
ENQVIST K, 1993, NUCL PHYS B, V403, P749, DOI 10.1016/0550-3213(93)90369-Z
Espinosa JR, 2012, NUCL PHYS B, V854, P592, DOI 10.1016/j.nuclphysb.2011.09.010
Espinosa JR, 2008, PHYS REV D, V78, DOI 10.1103/PhysRevD.78.123528
Espinosa JR, 2010, J COSMOL ASTROPART P, DOI 10.1088/1475-7516/2010/06/028
Espinosa JR, 2007, PHYS REV D, V76, DOI 10.1103/PhysRevD.76.076004
ESPINOSA JR, 1993, PHYS LETT B, V305, P98, DOI 10.1016/0370-2693(93)91111-Y
Ezquiaga J. M., ARXIV180709241ASTROP
Falkowski A, 2015, J HIGH ENERGY PHYS, DOI 10.1007/JHEP05(2015)057
FODOR Z, 1995, NUCL PHYS B, V439, P147, DOI 10.1016/0550-3213(95)00038-T
FUKUGITA M, 1990, PHYS REV D, V42, P1285, DOI 10.1103/PhysRevD.42.1285
GELMINI GB, 1981, PHYS LETT B, V99, P411, DOI 10.1016/0370-2693(81)90559-1
Gong XF, 2015, J PHYS CONF SER, V610, DOI 10.1088/1742-6596/610/1/012011
Grojean C, 2005, PHYS REV D, V71, DOI 10.1103/PhysRevD.71.036001
Hashino K., ARXIV180904994HEPPH
Hindmarsh M, 2017, PHYS REV D, V96, DOI 10.1103/PhysRevD.96.103520
Hindmarsh M, 2015, PHYS REV D, V92, DOI 10.1103/PhysRevD.92.123009
Hindmarsh M, 2014, PHYS REV LETT, V112, DOI 10.1103/PhysRevLett.112.041301
HOGAN CJ, 1986, MON NOT R ASTRON SOC, V218, P629, DOI 10.1093/mnras/218.4.629
Hu XC, 2018, CLASSICAL QUANT GRAV, V35, DOI 10.1088/1361-6382/aab52f
Huang FP, 2019, PHYS LETT B, V788, P288, DOI 10.1016/j.physletb.2018.11.024
Huang FP, 2016, PHYS REV D, V94, DOI 10.1103/PhysRevD.94.041702
Huang FP, 2016, PHYS REV D, V93, DOI 10.1103/PhysRevD.93.103515
Huang FP, 2015, PHYS REV D, V92, DOI 10.1103/PhysRevD.92.075014
Huang P., 2016, PHYS REV D, V94, DOI 10.1103/PhysRevD.94.075008
Huber SJ, 2016, J COSMOL ASTROPART P, DOI 10.1088/1475-7516/2016/03/036
Huber SJ, 2008, J COSMOL ASTROPART P, DOI 10.1088/1475-7516/2008/09/022
Huber SJ, 2008, J COSMOL ASTROPART P, DOI 10.1088/1475-7516/2008/05/017
Jinno R., ARXIV170703111HEPPH
Jinno R, 2017, PHYS REV D, V95, DOI 10.1103/PhysRevD.95.024009
Kajantie K, 1996, NUCL PHYS B, V466, P189, DOI 10.1016/0550-3213(96)00052-1
KAJANTIE K, 1993, NUCL PHYS B, V407, P356, DOI 10.1016/0550-3213(93)90062-T
KAMIONKOWSKI M, 1994, PHYS REV D, V49, P2837, DOI 10.1103/PhysRevD.49.2837
Kawamura S, 2006, CLASSICAL QUANT GRAV, V23, pS125, DOI 10.1088/0264-9381/23/8/S17
Kawamura S, 2011, CLASSICAL QUANT GRAV, V28, DOI 10.1088/0264-9381/28/9/094011
Khachatryan V, 2017, J HIGH ENERGY PHYS, DOI 10.1007/JHEP02(2017)135
KONDO Y, 1991, PHYS LETT B, V263, P93, DOI 10.1016/0370-2693(91)91712-5
KOSOWSKY A, 1992, PHYS REV D, V45, P4514, DOI 10.1103/PhysRevD.45.4514
Kudoh H, 2006, PHYS REV D, V73, DOI 10.1103/PhysRevD.73.064006
KUZMIN VA, 1985, PHYS LETT B, V155, P36, DOI 10.1016/0370-2693(85)91028-7
LI C, 2018, JCAP, V1810, P1
Li H., 2018, NATL SCI REV
Luo J, 2016, CLASSICAL QUANT GRAV, V33, DOI 10.1088/0264-9381/33/3/035010
Nicolis A, 2004, CLASSICAL QUANT GRAV, V21, pL27, DOI 10.1088/0264-9381/21/4/L05
PARWANI RR, 1993, PHYS REV D, V48, P5965, DOI 10.1103/PhysRevD.48.5965.2
PARWANI RR, 1992, PHYS REV D, V45, P4695, DOI 10.1103/PhysRevD.45.4695
Patrignani C, 2016, CHINESE PHYS C, V40, DOI 10.1088/1674-1137/40/10/100001
Profumo S, 2007, J HIGH ENERGY PHYS, DOI 10.1088/1126-6708/2007/08/010
Queiroz FS, 2014, PHYS LETT B, V735, P69, DOI 10.1016/j.physletb.2014.06.016
Robens T, 2015, EUR PHYS J C, V75, DOI 10.1140/epjc/s10052-015-3323-y
Schwaller P, 2015, PHYS REV LETT, V115, DOI 10.1103/PhysRevLett.115.181101
SEI N, 1993, PHYS LETT B, V299, P286, DOI 10.1016/0370-2693(93)90261-F
Seto N, 2001, PHYS REV LETT, V87, DOI 10.1103/PhysRevLett.87.221103
Wainwright CL, 2012, COMPUT PHYS COMMUN, V183, P2006, DOI 10.1016/j.cpc.2012.04.004
WITTEN E, 1984, PHYS REV D, V30, P272, DOI 10.1103/PhysRevD.30.272
ZHANG XM, 1993, PHYS REV D, V47, P3065, DOI 10.1103/PhysRevD.47.3065
NR 94
TC 2
Z9 2
U1 0
U2 9
PU SPRINGER
PI NEW YORK
PA 233 SPRING ST, NEW YORK, NY 10013 USA
SN 1434-6044
EI 1434-6052
J9 EUR PHYS J C
JI Eur. Phys. J. C
PD JAN 14
PY 2019
VL 79
IS 1
AR 25
DI 10.1140/epjc/s10052-019-6532-y
PG 15
WC Physics, Particles & Fields
SC Physics
GA HH4DL
UT WOS:000455670800002
OA DOAJ Gold
DA 2021-04-21
ER
PT S
AU Bevan, A
Charman, T
Hays, J
AF Bevan, Adrian
Charman, Thomas
Hays, Jonathan
BE Forti, A
Betev, L
Litmaath, M
Smirnova, O
Hristov, P
TI A Python package for particle physics analyses
SO 23RD INTERNATIONAL CONFERENCE ON COMPUTING IN HIGH ENERGY AND NUCLEAR
PHYSICS (CHEP 2018)
SE EPJ Web of Conferences
LA English
DT Proceedings Paper
CT 23rd International Conference on Computing in High Energy and Nuclear
Physics (CHEP)
CY JUL 09-13, 2018
CL Sofia, BULGARIA
SP Inst Adv Phys Studies, Sofia Univ, Plovdiv Univ, New Bulgarian Univ , IICT-BAS, Burgas Free Univ, BAS, INRNE, BAS, IICT, RHEA Grp, T Syst, Intel, Chaos Grp
ID NEURAL-NETWORKS; MODEL
AB HIPSTER (Heavily Ionising Particle Standard Toolkit for Event Recognition) is an open source Python package designed to facilitate the use of TensorFlow in a high energy physics analysis context. The core functionality of the software is presented, with images from the MoEDAL experiment Nuclear Track Detectors (NTDs) serving as an example dataset. Convolutional neural networks are selected as the classification algorithm for this dataset and the process of training a variety of models with different hyper-parameters is detailed. Next the results are shown for the MoEDAL problem demonstrating the rich information output by HIPSTER that enables the user to probe the performance of their model in detail.
C1 [Bevan, Adrian; Charman, Thomas; Hays, Jonathan] Queen Mary Univ London, Sch Phys & Astron, GO Jones Bldg,327 Mile End Rd, London E1 4NS, England.
RP Charman, T (corresponding author), Queen Mary Univ London, Sch Phys & Astron, GO Jones Bldg,327 Mile End Rd, London E1 4NS, England.
EM t.p.charman@qmul.ac.uk
OI Charman, Thomas/0000-0001-6288-5236
FU Alfred P Sloan FoundationAlfred P. Sloan Foundation; Global Impact Award
from Google; Science and Technology Facilities Council (UK)UK Research &
Innovation (UKRI)Science & Technology Facilities Council (STFC)
FX This publication uses data generated via the Zooniverse.org platform,
development of which is funded by generous support, including a Global
Impact Award from Google, and by a grant from the Alfred P Sloan
Foundation. This authors would specifically like to thank the following
University ofAlabama undergraduate students Zach Buckley, Sarah Deutsch,
Thomas (Hank) Richards, Cameron Roberts, Andie Wall and Alex Watts, for
their work in labelling data. The authors would like to thank the Nvidia
corporation for providing hardware that was used to carry out this
research. The authors would like to thank the Science and Technology
Facilities Council (UK) for providing funding for this research.
CR Abadi M., 2015, ARXIV160304467
Adam-Bourdarios C., 2015, J MACHINE LEARNING R, V42, P19, DOI DOI 10.1088/1742-6596/664/7/072015
Albertsson K., 2018, CISC VIS NETW IND GL, DOI [10.1088/1742-6596/1085/2/022008, DOI 10.1088/1742-6596/1085/2/022008]
Brun R, 1997, NUCL INSTRUM METH A, V389, P81, DOI 10.1016/S0168-9002(97)00048-X
Chollet F., 2015, KERAS
ELMAN JL, 1990, COGNITIVE SCI, V14, P179, DOI 10.1207/s15516709cog1402_1
Evans L, 2008, J INSTRUM, V3, DOI 10.1088/1748-0221/3/08/S08001
Fisher RA, 1936, ANN EUGENIC, V7, P179, DOI 10.1111/j.1469-1809.1936.tb02137.x
FUKUSHIMA K, 1980, BIOL CYBERN, V36, P193, DOI 10.1007/BF00344251
Hochreiter S, 1997, NEURAL COMPUT, V9, P1735, DOI 10.1162/neco.1997.9.8.1735
Hoecker A., 2007, POS ACAT, V040
HOPFIELD JJ, 1982, P NATL ACAD SCI-BIOL, V79, P2554, DOI 10.1073/pnas.79.8.2554
Jordan M.I., 1997, ADV PSYCHOL, V121, P471, DOI [10.1016/s0166-4115(97)80111-2, DOI 10.1016/S0166-4115(97)80111-2]
LeCun Y., 1999, OBJECT RECOGNITION G, P319, DOI DOI 10.1007/3-540-46805-6_19
Pinfold J, 2017, EPJ WEB CONF, V145, DOI 10.1051/epjconf/201714512002
Reeve A., NPTDMS
ROSENBLATT F, 1958, PSYCHOL REV, V65, P386, DOI 10.1037/h0042519
Srivastava N, 2014, J MACH LEARN RES, V15, P1929
Sutskever I., 2014, ADV NEURAL INFORM PR, P3104, DOI DOI 10.1007/S10107-014-0839-0
Travis E O, 2006, A GUIDE TO NUMPY
NR 20
TC 0
Z9 0
U1 0
U2 0
PU E D P SCIENCES
PI CEDEX A
PA 17 AVE DU HOGGAR PARC D ACTIVITES COUTABOEUF BP 112, F-91944 CEDEX A,
FRANCE
SN 2100-014X
J9 EPJ WEB CONF
PY 2019
VL 214
AR 06027
DI 10.1051/epjconf/201921406027
PG 8
WC Computer Science, Artificial Intelligence; Computer Science, Information
Systems; Computer Science, Software Engineering; Computer Science,
Theory & Methods; Physics, Nuclear; Physics, Particles & Fields
SC Computer Science; Physics
GA BP9PI
UT WOS:000570241300291
OA DOAJ Gold
DA 2021-04-21
ER
PT S
AU Pivarski, J
Nandi, J
Lange, D
Elmer, P
AF Pivarski, Jim
Nandi, Jaydeep
Lange, David
Elmer, Peter
BE Forti, A
Betev, L
Litmaath, M
Smirnova, O
Hristov, P
TI Columnar data processing for HEP analysis
SO 23RD INTERNATIONAL CONFERENCE ON COMPUTING IN HIGH ENERGY AND NUCLEAR
PHYSICS (CHEP 2018)
SE EPJ Web of Conferences
LA English
DT Proceedings Paper
CT 23rd International Conference on Computing in High Energy and Nuclear
Physics (CHEP)
CY JUL 09-13, 2018
CL Sofia, BULGARIA
SP Inst Adv Phys Studies, Sofia Univ, Plovdiv Univ, New Bulgarian Univ , IICT-BAS, Burgas Free Univ, BAS, INRNE, BAS, IICT, RHEA Grp, T Syst, Intel, Chaos Grp
AB In the last stages of data analysis, physicists are often forced to choose between simplicity and execution speed. In High Energy Physics (HEP), high-level languages like Python are known for ease of use but also very slow execution. However, Python is used in speed-critical data analysis in other fields of science and industry. In those fields, most operations are performed on Numpy arrays in an array programming style; this style can be adopted for HEP by introducing variable-sized, nested data structures. We describe how array programming may be extended for HEP use-cases and an implementation known as awkward-array. We also present integration with ROOT, Apache Arrow, and Parquet, as well as preliminary performance results.
C1 [Pivarski, Jim; Lange, David; Elmer, Peter] Princeton Univ, Princeton, NJ 08544 USA.
[Nandi, Jaydeep] Natl Inst Technol, Silchar, India.
RP Pivarski, J (corresponding author), Princeton Univ, Princeton, NJ 08544 USA.
FU National Science FoundationNational Science Foundation (NSF)
[ACI1450377, PHY-1624356]
FX This work was supported by the National Science Foundation under grants
ACI1450377 and PHY-1624356.
CR Arrow Development Team, AP ARR
Brun Rene, 1996, ROOT OBJECT I O SYST
Dask Development Team, 2016, DASK LIB DYN TASK SC
Griffith Blake, 2017, NEP 13 MECH OVERRIDI
Iverson KE, 1962, PROGRAMMING LANGUAGE
Le Dem Julien, 2015, APACHE PARQUET
Millman J., 2010, P56, DOI DOI 10.1016/S0168-0102(02)00204-3
Nandi Jaydeep, 2018, VECTORIZED PROOF CON
Okuta R., 2017, P WORKSH MACH LEARN
Pivarski Jim, 2018, AWKWARD ARRAY SOFTWA
Pivarski Jim, 2018, UPROOT SOFTWARE RELE
Saad Youcef, 1990, TECHNICAL REPORT
Travis E O, 2006, A GUIDE TO NUMPY
NR 13
TC 0
Z9 0
U1 0
U2 0
PU E D P SCIENCES
PI CEDEX A
PA 17 AVE DU HOGGAR PARC D ACTIVITES COUTABOEUF BP 112, F-91944 CEDEX A,
FRANCE
SN 2100-014X
J9 EPJ WEB CONF
PY 2019
VL 214
AR 06026
DI 10.1051/epjconf/201921406026
PG 8
WC Computer Science, Artificial Intelligence; Computer Science, Information
Systems; Computer Science, Software Engineering; Computer Science,
Theory & Methods; Physics, Nuclear; Physics, Particles & Fields
SC Computer Science; Physics
GA BP9PI
UT WOS:000570241300290
OA DOAJ Gold, Green Published
DA 2021-04-21
ER
PT S
AU Rodrigues, E
AF Rodrigues, Eduardo
BE Forti, A
Betev, L
Litmaath, M
Smirnova, O
Hristov, P
TI The Scikit-HEP Project
SO 23RD INTERNATIONAL CONFERENCE ON COMPUTING IN HIGH ENERGY AND NUCLEAR
PHYSICS (CHEP 2018)
SE EPJ Web of Conferences
LA English
DT Proceedings Paper
CT 23rd International Conference on Computing in High Energy and Nuclear
Physics (CHEP)
CY JUL 09-13, 2018
CL Sofia, BULGARIA
SP Inst Adv Phys Studies, Sofia Univ, Plovdiv Univ, New Bulgarian Univ , IICT-BAS, Burgas Free Univ, BAS, INRNE, BAS, IICT, RHEA Grp, T Syst, Intel, Chaos Grp
AB The Scikit-HEP project is a community-driven and community oriented effort with the aim of providing Particle Physics at large with a Python scientific toolset containing core and common tools. The project builds on five pillars that embrace the major topics involved in a physicist's analysis work: datasets, data aggregations, modelling, simulation and visualisation. The vision is to build a user and developer community engaging collaboration across experiments, to emulate scikit-learn's unified interface with Astropy's embrace of third-party packages, and to improve discoverability of relevant tools.
C1 [Rodrigues, Eduardo] Univ Cincinnati, Cincinnati, OH 45221 USA.
RP Rodrigues, E (corresponding author), Univ Cincinnati, Cincinnati, OH 45221 USA.
EM eduardo.rodrigues@uc.edu
CR Cacciari M, 2012, EUR PHYS J C, V72, DOI 10.1140/epjc/s10052-012-1896-2
Jones E., SCIPY OPEN SOURCE SC
Robitaille TP, 2013, ASTRON ASTROPHYS, V558, DOI 10.1051/0004-6361/201322068
VanderPlas Jake, 2017, PYCON 2017
NR 4
TC 3
Z9 3
U1 0
U2 0
PU E D P SCIENCES
PI CEDEX A
PA 17 AVE DU HOGGAR PARC D ACTIVITES COUTABOEUF BP 112, F-91944 CEDEX A,
FRANCE
SN 2100-014X
J9 EPJ WEB CONF
PY 2019
VL 214
AR 06005
DI 10.1051/epjconf/201921406005
PG 6
WC Computer Science, Artificial Intelligence; Computer Science, Information
Systems; Computer Science, Software Engineering; Computer Science,
Theory & Methods; Physics, Nuclear; Physics, Particles & Fields
SC Computer Science; Physics
GA BP9PI
UT WOS:000570241300269
OA DOAJ Gold
DA 2021-04-21
ER
PT S
AU Reininghaus, M
Ulrich, R
AF Reininghaus, Maximilian
Ulrich, Ralf
CA CORSIKA 8 Developers
BE LhenryYvon, I
Biteau, J
Deligny, O
Ghia, P
TI CORSIKA 8-Towards a modern framework for the simulation of extensive air
showers
SO ULTRA HIGH ENERGY COSMIC RAYS 2018 (UHECR 2018)
SE EPJ Web of Conferences
LA English
DT Proceedings Paper
CT Conference on Ultra High Energy Cosmic Rays (UHECR)
CY OCT 08-12, 2018
CL Paris, FRANCE
AB Current and future challenges in astroparticle physics require novel simulation tools to achieve higher precision and more flexibility. For three decades the FORTRAN version of CORSIKA served the community in an excellent way. However, the effort to maintain and further develop this complex package is getting increasingly difficult. To overcome existing limitations, and designed as a very open platform for all particle cascade simulations in astroparticle physics, we are developing CORSIKA 8 based on modern C++ and Python concepts. Here, we give a brief status report of the project.
C1 [Reininghaus, Maximilian; Ulrich, Ralf] Karlsruher Inst Technol KIT, Inst Kernphys, Karlsruhe, Germany.
[Reininghaus, Maximilian] Karlsruher Inst Technol KIT, Inst Expt Teilchenphys, Karlsruhe, Germany.
RP Reininghaus, M (corresponding author), Karlsruher Inst Technol KIT, Inst Kernphys, Karlsruhe, Germany.; Reininghaus, M (corresponding author), Karlsruher Inst Technol KIT, Inst Expt Teilchenphys, Karlsruhe, Germany.
OI Ulrich, Ralf/0000-0002-2535-402X
FU DFGGerman Research Foundation (DFG)European Commission
FX M.R. acknowledges support by the DFG-funded Doctoral School "Karlsruhe
School of Elementary and Astroparticle Physics: Science and Technology".
CR Agostinelli S, 2003, NUCL INSTRUM METH A, V506, P250, DOI 10.1016/S0168-9002(03)01368-8
Allison J, 2006, IEEE T NUCL SCI, V53, P270, DOI 10.1109/TNS.2006.869826
Allison J, 2016, NUCL INSTRUM METH A, V835, P186, DOI 10.1016/j.nima.2016.06.125
Baack D., 2016, TECH REP, DOI [10.17877/DE290R-19158, DOI 10.17877/DE290R-19158]
Capdevielle J.N., 1992, KFK4998
Engel Ralph, 2019, Computing and Software for Big Science, V3, DOI 10.1007/s41781-018-0013-0
GILS HJ, 1989, COMPUT PHYS COMMUN, V56, P105, DOI 10.1016/0010-4655(89)90011-8
Heck D., 2009, FZKA7495
Heck D., 1998, FZKA6019
Klages HO, 1997, NUCL PHYS B, P92, DOI 10.1016/S0920-5632(96)00852-3
Nievergelt Y, 1996, SIAM REV, V38, P136, DOI 10.1137/1038007
Ulrich R., 2006, COAST
Zwart SP, 2018, SCIENCE, V361, P979, DOI 10.1126/science.aau3206
NR 13
TC 0
Z9 0
U1 0
U2 0
PU E D P SCIENCES
PI CEDEX A
PA 17 AVE DU HOGGAR PARC D ACTIVITES COUTABOEUF BP 112, F-91944 CEDEX A,
FRANCE
SN 2100-014X
J9 EPJ WEB CONF
PY 2019
VL 210
AR 02011
DI 10.1051/epjconf/201921002011
PG 4
WC Astronomy & Astrophysics; Physics, Applied; Physics, Particles & Fields
SC Astronomy & Astrophysics; Physics
GA BP9QE
UT WOS:000570466200021
OA DOAJ Gold, Green Published
DA 2021-04-21
ER
PT S
AU Cristiano, KL
Triana, DA
Ortiz, R
Pico, M
Estupinan, AF
AF Cristiano, K. L.
Triana, D. A.
Ortiz, R.
Pico, M.
Estupinan, A. F.
GP IOP
TI Analytical and experimental determination of gravity and moment of
inertia using a physical pendulum
SO 5TH INTERNATIONAL MEETING FOR RESEARCHERS IN MATERIALS AND PLASMA
TECHNOLOGY (5TH IMRMPT)
SE Journal of Physics Conference Series
LA English
DT Proceedings Paper
CT 5th International Meeting for Researchers in Materials and Plasma
Technology (IMRMPT)
CY MAY 28-31, 2019
CL San Jose de Cucuta, COLOMBIA
SP Fdn Researchers Sci & Technol Mat, Univ Francisco Paula Santander
AB Searching to encourage and increase the desire of students to seek a vocation in the study of engineering and science, we wanted to implement and validate experimentally and numerically, the study of the movement of a mechanical oscillator using, in this case, a physical pendulum, formed by a bar and a disk. In this article has done the study the physical pendulum, combining a methodology that involves an experimental arrangement and the implementation of simulations developed in Python, with the aim objective of offering to students a visual and interactive experience, so that they can understand in a simpler way topics covered in the theoretical physics course, in such a way that is different from the typical physical-mathematical formalism. This study was carried out with low cost materials and easy access, in addition to the great social impact that I had against the acceptance and assessment by the students with whom this work was applied. This work was developed in three phases: first, to measure the period of oscillation of a physical pendulum experimentally. Second, the approach of the analytical model to compare with the experimental results. Third, the development of a dynamic simulator according to the predictions of the theoretical model. The students found a didactic and different way of studying the physical pendulum. Finally, it was possible to demonstrate a self-consistency between the experimental and numerical results of the system studied in this work.
C1 [Cristiano, K. L.; Triana, D. A.] Univ Ind Santander, Escuela Fis, Bucaramanga, Colombia.
[Ortiz, R.; Pico, M.] Univ Autonoma Bucaramanga, Bucaramanga, Colombia.
[Estupinan, A. F.] Univ Invest & Desarrollo, Bucaramanga, Colombia.
RP Triana, DA (corresponding author), Univ Ind Santander, Escuela Fis, Bucaramanga, Colombia.
EM dantrica@saber.uis.edu.co
RI Lopez, Alex Francisco Estupinan/ABH-9945-2020
OI Lopez, Alex Francisco Estupinan/0000-0003-3067-6821; Triana Camacho,
Daniel Andres/0000-0001-6852-6277
FU Universidad Industrial de Santander
FX We want give special thanks at the Universidad Industrial de Santander
for the funds to show this work. Also, we give thanks the Physics
Department and his Director, Jorge Martinez Tellez. We would like to
thank the Universidad Autonoma de Bucaramanga, for lend us the
installations and materials for carry to this experiment with which the
analytical model presented in this paper could be validated.
CR Angel Duarte Jesus Sanchez J A R O, 2019, ICONIC RES ENG J, V3, P6
Backer A, 2007, COMPUT SCI ENG, V9, P30, DOI 10.1109/MCSE.2007.48
Baker GL., 2005, PENDULUM CASE STUDY
Cristiano KL, 2019, J PHYS CONF SER, V1247, DOI 10.1088/1742-6596/1247/1/012044
Cristiano KL, 2019, J PHYS CONF SER, V1161, DOI 10.1088/1742-6596/1161/1/012020
Giancoli D.C., 2005, PHYS PRINCIPLES APPL
Janert P.K., 2010, GNUPLOT ACTION UNDER
Landau RH, 2015, COMPUTATIONAL PHYS P
Monteiro M, 2014, PHYS TEACH, V52, P180, DOI 10.1119/1.4865529
NELSON RA, 1986, AM J PHYS, V54, P112, DOI 10.1119/1.14703
Rodriguez R, 2019, TEACHER ED CURRICULU, V4, P33
Sears FW, 1987, U PHYS
Serway R A, 2005, FISICA CIENCIAS INGE, V1
Tipler P A, 2003, FISICA CIENCIA TEC A, V1A
VanderPlas J, 2016, PYTHON DATA SCI HDB
WATTERS A, 1996, INTERNET PROGRAMMING
Zill D G, 2013, MATEMATICAS AVANZADA, V1
NR 17
TC 0
Z9 0
U1 0
U2 0
PU IOP PUBLISHING LTD
PI BRISTOL
PA DIRAC HOUSE, TEMPLE BACK, BRISTOL BS1 6BE, ENGLAND
SN 1742-6588
EI 1742-6596
J9 J PHYS CONF SER
PY 2019
VL 1386
AR 012139
DI 10.1088/1742-6596/1386/1/012139
PG 7
WC Materials Science, Multidisciplinary; Physics, Fluids & Plasmas
SC Materials Science; Physics
GA BP7HU
UT WOS:000562059600138
OA Bronze
DA 2021-04-21
ER
PT S
AU Jatmiko, ATP
Yusuf, M
Putra, M
AF Jatmiko, Agus T. P.
Yusuf, M.
Putra, M.
BE Natalia, D
Basar, K
Neswan, O
Arifyanto, MI
Mujahidin, D
TI Light curve analyses of eclipsing binary system ASAS 172533-1221.4
SO 6TH INTERNATIONAL CONFERENCE ON MATHEMATICS AND NATURAL SCIENCES
SE Journal of Physics Conference Series
LA English
DT Proceedings Paper
CT 6th International Conference on Mathematics and Natural Sciences (CMNS)
- On the Road Towards Sustainable Development
CY NOV 02-03, 2016
CL Bandung, INDONESIA
SP ITB, Fac Math & Nat Sci
AB Using the data taken from our 0.36 m f/7.2 robotic telescope, we performed a very first light curve (LC) analyses of eclipsing binary ASAS 172533-1221.4, one of target stars which is part of program stars in our variable star survey project. The LC of this star was constructed by using LEMON, a semi-automatic photometric pipeline written in Python. We refined a Time of Minima (ToM, T-0) and variability period of this system, P and updated its ephemerides as HJD(min I) = 2457200.255578 + 0.678861 *phi. The LC modeling of the system was conducted with the PHOEBE (PHysics Of Eclipsing BinariEs) software built on top of the widely used WD program. The assorted LC modeling solutions are shown as follows. mass ratio q = 0.811 +/- 0.009, inclination i = 70.62 +/- 0.01 degrees, temperature of primary and secondary component T-1 = 5559.23 +/- 83.51 K and T-2 = 3871.64 +/- 43.66 K, respectively, and modified Kopal potentials which are a function of primary's and secondary's radii Omega(1) = 3.436 +/- 0.018 and Omega 2 = Omega(cr) = 2.980, respectively. It is concluded that ASAS 1725533-1221.4 is found to be near-contact system with almost similar size between primary and secondary components, with its secondary component is already filling its Roche lobe.
C1 [Jatmiko, Agus T. P.; Yusuf, M.; Putra, M.] Inst Teknol Bandung, Bosscha Observ, Jl Peneropongan Bintang, Bandung, West Java, Indonesia.
[Putra, M.] Inst Teknol Bandung, Astron Study Program, Jl Ganesha 10, Bandung, West Java, Indonesia.
RP Jatmiko, ATP (corresponding author), Inst Teknol Bandung, Bosscha Observ, Jl Peneropongan Bintang, Bandung, West Java, Indonesia.
EM agustrionopj@alumni.itb.ac.id
CR EGGLETON PP, 1983, ASTROPHYS J, V268, P368, DOI 10.1086/160960
Hidayat T, 2014, EXP ASTRON, V37, P85, DOI 10.1007/s10686-013-9369-7
Pojmanski G, 1997, ACTA ASTRONOM, V47, P467
Prsa A, 2005, ASTROPHYS J, V628, P426, DOI 10.1086/430591
STELLINGWERF RF, 1978, ASTROPHYS J, V224, P953, DOI 10.1086/156444
Terron V, 2011, HIGHLIGHTS SPANISH A, P755
VANHAMME W, 1993, ASTRON J, V106, P2096, DOI 10.1086/116788
WILSON RE, 1971, ASTROPHYS J, V166, P605, DOI 10.1086/150986
Yusuf M., 2016, 6 ICMNS UNPUB
NR 9
TC 0
Z9 0
U1 0
U2 0
PU IOP PUBLISHING LTD
PI BRISTOL
PA DIRAC HOUSE, TEMPLE BACK, BRISTOL BS1 6BE, ENGLAND
SN 1742-6588
EI 1742-6596
J9 J PHYS CONF SER
PY 2019
VL 1127
AR 012048
DI 10.1088/1742-6596/1127/1/012048
PG 5
WC Mathematics, Applied
SC Mathematics
GA BP6OF
UT WOS:000560237900046
OA Bronze
DA 2021-04-21
ER
PT B
AU Behzadan, V
Minton, J
Munir, A
AF Behzadan, Vahid
Minton, James
Munir, Arslan
GP Assoc Comp Machinery
TI TrolleyMod v1.0: An Open-Source Simulation and Data Collection Platform
for Ethical Decision-Making in Autonomous Vehicles
SO AIES '19: PROCEEDINGS OF THE 2019 AAAI/ACM CONFERENCE ON AI, ETHICS, AND
SOCIETY
LA English
DT Proceedings Paper
CT 2nd AAAI/ACM Conference on AI, Ethics, and Society (AIES)
CY JAN 27-28, 2019
CL Honolulu, HI
SP AAAI, Assoc Comp Machinery, ACM SIGAI, Berkeley Existential Risk Initiat, DeepMind Eth & Soc, Google, Natl Sci Fdn, IBM Res, Facebook, Amazon, PricewaterhouseCoopers, Future Life Inst, Partnership AI
DE Ethical Decision-Making; Autonomous Vehicles; Social Choice; Artificial
Intelligence; Simulation
AB This paper presents TrolleyMod v1.0, an open-source platform based on the CARLA simulator for the collection of ethical decision making data for autonomous vehicles. This platform is designed to facilitate experiments aiming to observe and record human decisions and actions in high-fidelity simulations of ethical dilemmas that occur in the context of driving. Targeting experiments in the class of trolley problems, TrolleyMod provides a seamless approach to creating new experimental settings and environments with the realistic physics-engine and the high-quality graphical capabilities of CARLA and the Unreal Engine. Also, TrolleyMod provides a straightforward interface between the CARLA environment and Python to enable the implementation of custom controllers, such as deep reinforcement learning agents. The results of such experiments can be used for sociological analyses, as well as the training and tuning of value-aligned autonomous vehicles based on social values that are inferred from observations.
C1 [Behzadan, Vahid; Minton, James; Munir, Arslan] Kansas State Univ, Manhattan, KS 66506 USA.
RP Behzadan, V (corresponding author), Kansas State Univ, Manhattan, KS 66506 USA.
EM behzadan@ksu.edu; jzm@ksu.edu; amunir@ksu.edu
RI Behzadan, Vahid/AAZ-5344-2020
OI Behzadan, Vahid/0000-0002-6229-9365
CR Abel David, 2016, AAAI WORKSH AI ETH S, V92
Arnold Thomas, 2017, 3 INT WORKSH AI ETH
Awad Edmond, 2018, NATURE, P1
Dosovitskiy A., 2017, ARXIV171103938
Frison AK, 2016, AUTOMOTIVEUI 2016: 8TH INTERNATIONAL CONFERENCE ON AUTOMOTIVE USER INTERFACES AND INTERACTIVE VEHICULAR APPLICATIONS, P117, DOI 10.1145/3004323.3004336
GENANDER JACOB, CONTROL SELF DRIVING
Goodall NJ, 2014, LECT N MOBIL, P93, DOI 10.1007/978-3-319-05990-7_9
Greene J, 2016, THIRTIETH AAAI CONFERENCE ON ARTIFICIAL INTELLIGENCE, P4147
Kasenberg Daniel, 2018, P 1 AAAI ACM C ART I
Kim Richard, 2017, COMPUTATIONAL MODEL
Kim Richard, 2018, ARXIV180104346
Leonard Thomas C, 2008, NUDGE IMPROVING DECI
Liang Xiaodan, 2018, ARXIV180703776, P1
Noothigattu Ritesh, 2017, ARXIV170906692
Taylor J., 2016, ALIGNMENT ADV MACHIN
THOMSON JJ, 1985, YALE LAW J, V94, P1395, DOI 10.2307/796133
Wallach W., 2008, MORAL MACHINES TEACH
Wang Yijia, 2017, ARXIV171105905
NR 18
TC 0
Z9 0
U1 1
U2 1
PU ASSOC COMPUTING MACHINERY
PI NEW YORK
PA 1515 BROADWAY, NEW YORK, NY 10036-9998 USA
BN 978-1-4503-6324-2
PY 2019
BP 391
EP 395
DI 10.1145/3306618.3314239
PG 5
WC Computer Science, Artificial Intelligence
SC Computer Science
GA BP5KP
UT WOS:000556121100054
DA 2021-04-21
ER
PT B
AU Rupe, A
Kumar, N
Epifanov, V
Kashinath, K
Pavlyk, O
Schlimbach, F
Patwary, M
Maidanov, S
Lee, V
Prabhat
Crutchfield, JP
AF Rupe, Adam
Kumar, Nalini
Epifanov, Vladislav
Kashinath, Karthik
Pavlyk, Oleksandr
Schlimbach, Frank
Patwary, Mostofa
Maidanov, Sergey
Lee, Victor
Prabhat
Crutchfield, James P.
GP IEEE
TI DisCo: Physics-Based Unsupervised Discovery of Coherent Structures in
Spatiotemporal Systems
SO PROCEEDINGS OF 2019 5TH IEEE/ACM WORKSHOP ON MACHINE LEARNING IN HIGH
PERFORMANCE COMPUTING ENVIRONMENTS (MLHPC 2019)
LA English
DT Proceedings Paper
CT 5th IEEE/ACM Workshop on Machine Learning in High Performance Computing
Environments (MLHPC)
CY NOV 17-22, 2019
CL Denver, CO
SP IEEE, ACM, IEEE Comp Soc, TCHPC, SIGHPC
ID BIG DATA; INTENSITY
AB Extracting actionable insight from complex unlabeled scientific data is an open challenge and key to unlocking data-driven discovery in science. Complementary and alternative to supervised machine learning approaches, unsupervised physics-based methods based on behavior-driven theories hold great promise. Due to computational limitations, practical application on real-world domain science problems has lagged far behind theoretical development. We present our first step towards bridging this divide - DisCo - a high-performance distributed workflow for the behavior-driven local causal state theory. DisCo provides a scalable unsupervised physics-based representation learning method that decomposes spatiotemporal systems into their structurally relevant components, which are captured by the latent local causal state variables. Complex spatiotemporal systems are generally highly structured and organize around a lower-dimensional skeleton of coherent structures, and in several firsts we demonstrate the efficacy of DisCo in capturing such structures from observational and simulated scientific data. To the best of our knowledge, DisCo is also the first application software developed entirely in Python to scale to over 1000 machine nodes, providing good performance along with ensuring domain scientists' productivity. We developed scalable, performant methods optimized for Intel many-core processors that will be upstreamed to open-source Python library packages. Our capstone experiment, using newly developed DisCo workflow and libraries, performs unsupervised spacetime segmentation analysis of CAM5.1 climate simulation data, processing an unprecedented 89.5 TB in 6.6 minutes end-to-end using 1024 Intel Haswell nodes on the Cori supercomputer obtaining 91% weak-scaling and 64% strong-scaling efficiency.
C1 [Rupe, Adam; Crutchfield, James P.] Univ Calif Davis, Complex Sci Ctr, One Shields Ave, Davis, CA 95616 USA.
[Rupe, Adam; Crutchfield, James P.] Univ Calif Davis, Dept Phys, One Shields Ave, Davis, CA 95616 USA.
[Kumar, Nalini; Epifanov, Vladislav; Pavlyk, Oleksandr; Schlimbach, Frank; Maidanov, Sergey; Lee, Victor] Intel Corp, 3600 Juliette Ln, Santa Clara, CA 95035 USA.
[Kashinath, Karthik; Prabhat] Lawrence Berkeley Natl Lab, 1 Cyclotron Rd,M-S 59R4010A, Berkeley, CA 94720 USA.
[Patwary, Mostofa] Baidu Res, 1195 Bordeaux Dr, Sunnyvale, CA 94089 USA.
RP Rupe, A (corresponding author), Univ Calif Davis, Complex Sci Ctr, One Shields Ave, Davis, CA 95616 USA.; Rupe, A (corresponding author), Univ Calif Davis, Dept Phys, One Shields Ave, Davis, CA 95616 USA.
FU Intel(R); Intel(R) Big Data Center; U.S. Army Research LaboratoryUnited
States Department of DefenseUS Army Research Laboratory (ARL); U.S. Army
Research Office [W911NF-13-1-0390, W911NF-18-1-0028]; Office of Science
of the U.S. Department of EnergyUnited States Department of Energy (DOE)
[DE-AC02-05CH11231]
FX AR and JPC would like to acknowledge Intel (R) for supporting the IPCC
at UC Davis. KK and P were supported by the Intel (R) Big Data Center.
This research is based upon work supported by, or in part by, the U.S.
Army Research Laboratory and the U.S. Army Research Office under
contracts W911NF-13-1-0390 and W911NF-18-1-0028, and used resources of
the National Energy Research Scientific Computing Center, a DOE Office
of Science User Facility supported by the Office of Science of the U.S.
Department of Energy under Contract No. DE-AC02-05CH11231.
CR ANDERSON PW, 1972, SCIENCE, V177, P393, DOI 10.1126/science.177.4047.393
Ankerst M, 1999, SIGMOD RECORD, VOL 28, NO 2 - JUNE 1999, P49
Arthur D, 2007, PROCEEDINGS OF THE EIGHTEENTH ANNUAL ACM-SIAM SYMPOSIUM ON DISCRETE ALGORITHMS, P1027
Bell G, 2009, SCIENCE, V323, P1297, DOI 10.1126/science.1170411
Bhimji W, 2018, J PHYS CONF SER, V1085, DOI 10.1088/1742-6596/1085/4/042034
Butler KT, 2018, NATURE, V559, P547, DOI 10.1038/s41586-018-0337-2
Carleo G, 2017, SCIENCE, V355, P602, DOI 10.1126/science.aag2302
Chahin M., 2017, BIG DATA ADV PHYS
Chivers T., 2018, BIG DATA IS CHANGING
Chiyu M. J., 2019, INT C LEARN REPR
Crutchfield JP, 2014, WILEY INTERDISCIP RE, V6, P75, DOI 10.1002/wics.1290
Crutchfield JP, 2012, NAT PHYS, V8, P17
Emanuel K, 2003, ANNU REV EARTH PL SC, V31, P75, DOI 10.1146/annurev.earth.31.100901.141259
EMANUEL KA, 1987, NATURE, V326, P483, DOI 10.1038/326483a0
Epps B, 2017, 55 AIAA AER SCI M, P0989
Ester M., 1996, KDD, V96, P226
Faghmous JH, 2014, BIG DATA-US, V2, P155, DOI 10.1089/big.2014.0026
Farazmand M, 2016, J FLUID MECH, V795, P278, DOI 10.1017/jfm.2016.203
Gillet LC, 2016, ANNU REV ANAL CHEM, V9, P449, DOI 10.1146/annurev-anchem-071015-041535
Goerg G., 2012, ARXIV12062398
Gotz M., 2015, P WORKSH MACH LEARN, P2
GRASSBERGER P, 1986, INT J THEOR PHYS, V25, P907, DOI 10.1007/BF00668821
Guo Y., 2002, FAST PARALLEL CLUSTE
Hadjighasem A, 2017, CHAOS, V27, DOI 10.1063/1.4982720
Hadjighasem A, 2016, SIAM REV, V58, P69, DOI 10.1137/140983665
Haller G, 2016, J FLUID MECH, V795, P136, DOI 10.1017/jfm.2016.151
Holmes P, 2012, CAMB MG MEC, P1, DOI 10.1017/CBO9780511919701
Hu X, 2017, ARXIV171101034
Intel, FAST SCAL EAS MACH L
Jaenicke H, 2007, IEEE T VIS COMPUT GR, V13, P1384, DOI 10.1109/TVCG.2007.70615
Jolliffe Ian, 2011, PRINCIPAL COMPONENT
Jones N, 2017, NATURE, V548, P379, DOI 10.1038/548379a
Kurth T, 2018, P INT C HIGH PERF CO, P51
Larranaga P, 2006, BRIEF BIOINFORM, V7, P86, DOI 10.1093/bib/bbk007
Lee S, 2016, 2016 IEEE INTERNATIONAL CONFERENCE ON BIG DATA (BIG DATA), P2251, DOI 10.1109/BigData.2016.7840856
Liandeng Li, 2018, SC18: International Conference for High Performance Computing, Networking, Storage and Analysis. Proceedings, P160, DOI 10.1109/SC.2018.00016
Maidanov S., PERFORMING NUMERICAL
MARIMONT RB, 1979, J I MATH APPL, V24, P59
Mathuriya Amrita, 2018, SC18: International Conference for High Performance Computing, Networking, Storage and Analysis. Proceedings, P819, DOI 10.1109/SC.2018.00068
McInnes L., 2017, J OPEN SOURCE SOFTWA, V2, P205, DOI [DOI 10.21105/joss.00205, 10.21105/joss.00205]
Min S, 2017, BRIEF BIOINFORM, V18, P851, DOI 10.1093/bib/bbw068
Mjolsness E, 2001, SCIENCE, V293, P2051, DOI 10.1126/science.293.5537.2051
Montanez G. D., 2015, ARXIV150602686
Mudigonda Mayur, 2017, DEEP LEARN PHYS SCI
NASA, JUP CLOUD SEQ CASS
Overpeck JT, 2011, SCIENCE, V331, P700, DOI 10.1126/science.1197869
Patwary M. A., 2013, P INT C HIGH PERF CO, P49
Patwary M.M.A., 2015, INT C HIGH PERF COMP, P1
Patwary M. M. A., 2015, PDSDBSCAN SOURCE COD
Prabhat, 2012, PROCEDIA COMPUT SCI, V9, P866, DOI 10.1016/j.procs.2012.04.093
Reichstein M, 2019, NATURE, V566, P195, DOI 10.1038/s41586-019-0912-1
Ronaghi Z., 2017, P 7 WORKSH PYTH HIGH, P4
Runge J, 2015, NAT COMMUN, V6, DOI 10.1038/ncomms9502
Rupe A., 2018, ARXIV181211597
Rupe A, 2018, CHAOS, V28, DOI 10.1063/1.5021130
Salakhutdinov R., 2019, P 36 INT C MACH LEAR, V97, P1321
Sejnowski TJ, 2014, NAT NEUROSCI, V17, P1440, DOI 10.1038/nn.3839
Shadden SC, 2012, TRANSPORT AND MIXING IN LAMINAR FLOWS: FROM MICROFLUIDICS TO OCEANIC CURRENTS, P59
Shalizi C., 2003, DISCRETE MATH THEORE
Shalizi CR, 2006, PHYS REV E, V73, DOI 10.1103/PhysRevE.73.036104
Shalizi CR, 2001, J STAT PHYS, V104, P817, DOI 10.1023/A:1010388907793
Shields CA, 2018, GEOSCI MODEL DEV, V11, P2455, DOI 10.5194/gmd-11-2455-2018
Tantet A, 2015, CHAOS, V25, DOI 10.1063/1.4908174
Tu Jonathan H., 2014, J COMPUT DYNAM, V1, P391, DOI DOI 10.3934/JCD.2014.1.391
Venderley J, 2018, PHYS REV LETT, V120, DOI 10.1103/PhysRevLett.120.257204
Vesselinov V. V., 2019, J COMPUTATIONAL PHYS
Webster PJ, 2005, SCIENCE, V309, P1844, DOI 10.1126/science.1116448
Wehner MF, 2014, J ADV MODEL EARTH SY, V6, P980, DOI 10.1002/2013MS000276
Wehner MF, 2010, ADV METEOROL, V2010, DOI 10.1155/2010/915303
Williams MO, 2015, J NONLINEAR SCI, V25, P1307, DOI 10.1007/s00332-015-9258-5
WOLFRAM S, 1984, COMMUN MATH PHYS, V96, P15, DOI 10.1007/BF01217347
Ye H, 2015, P NATL ACAD SCI USA, V112, pE1569, DOI 10.1073/pnas.1417063112
Zenil H, 2019, NAT MACH INTELL, V1, P58, DOI 10.1038/s42256-018-0005-0
Zimek Arthur, 2012, Statistical Analysis and Data Mining, V5, P363, DOI 10.1002/sam.11161
NR 74
TC 2
Z9 2
U1 0
U2 0
PU IEEE COMPUTER SOC
PI LOS ALAMITOS
PA 10662 LOS VAQUEROS CIRCLE, PO BOX 3014, LOS ALAMITOS, CA 90720-1264 USA
BN 978-1-7281-5985-0
PY 2019
BP 75
EP 87
DI 10.1109/MLHPC49564.2019.00013
PG 13
WC Computer Science, Artificial Intelligence; Computer Science, Theory &
Methods
SC Computer Science
GA BO9CE
UT WOS:000530690500008
DA 2021-04-21
ER
PT S
AU Chrin, J
Aiba, M
Snuverink, J
AF Chrin, J.
Aiba, M.
Snuverink, J.
GP IOP
TI A Channel Access Software Platform for Beam Dynamics Applications in
Scripting Languages
SO 10TH INTERNATIONAL PARTICLE ACCELERATOR CONFERENCE
SE Journal of Physics Conference Series
LA English
DT Proceedings Paper
CT 10th International Particle Accelerator Conference (IPAC)
CY MAY 19-24, 2019
CL Melbourne, AUSTRALIA
AB To facilitate the seamless integration of EPICS (Experimental Physics and Industrial Control System) into high-level applications in particle accelerators, a dedicated modern C++ Channel Access interface library, CAFE, provides a comprehensive and user-friendly interface to the underlying control system. Functionality is provided for synchronous and asynchronous interaction of single and composite groups of channels, coupled with an abstract layer tailored towards beam dynamics applications and complex modelling of virtual accelerators. Equivalent consumable solutions in scripting and domain-specific languages can then be accelerated by providing bindings to the relevant methods of the interface platform. This is exemplified by CAFE's extensive MATLAB (R) interface, incarnated through a single MATLAB executable (MEX) file, and a high performance Python interface written in the Cython programming language. A number of gratifying particularities specific to these language extension modules are revealed.
C1 [Chrin, J.; Aiba, M.; Snuverink, J.] Paul Scherrer Inst, CH-5232 Villigen, Switzerland.
RP Chrin, J (corresponding author), Paul Scherrer Inst, CH-5232 Villigen, Switzerland.
EM jan.chrin@psi.ch
CR Aiba M, 2013, PHYS REV SPEC TOP-AC, V16, DOI 10.1103/PhysRevSTAB.16.012802
Boge M, 2000, P 7 EUR PART ACC C V, P1354
Chen J, 1995, P 1995 INT C ACC LAR
Chrin J, 2015, P 15 INT C ACC LARG, P1013
Chrin J, 2013, P 14 INT C ACC LARG, P453
Chrin J, 2016, P 11 INT WORKSH PERS, P21, DOI [10.18429/JACoW-PCAPAC2016-WEUIPLCO04, DOI 10.18429/JACOW-PCAPAC2016-WEUIPLCO04]
Fuchsberger K, 2011, P IPAC2011 SAN SEB S, P2289
Hill JO, 2009, EPICS R3 14 CHANNEL
Portmann G, 2005, P PAC05 KNOXV MAY 16, P4009
Schietinger T, 2016, PHYS REV ACCEL BEAMS, V19, DOI 10.1103/PhysRevAccelBeams.19.100702
Streun A, 2018, J SYNCHROTRON RADIAT, V25, P631, DOI 10.1107/S1600577518002722
Terebilo T, 2001, P 8 INT C ACC LARG E, P543
Yamamoto N, 1998, PROCEEDINGS OF THE 1997 PARTICLE ACCELERATOR CONFERENCE, VOLS 1-3, P2455, DOI 10.1109/PAC.1997.751238
Zimoch D, 2009, CHANNEL ACCESS CLIEN
NR 14
TC 0
Z9 0
U1 0
U2 0
PU IOP PUBLISHING LTD
PI BRISTOL
PA DIRAC HOUSE, TEMPLE BACK, BRISTOL BS1 6BE, ENGLAND
SN 1742-6588
EI 1742-6596
J9 J PHYS CONF SER
PY 2019
VL 1350
AR 012155
DI 10.1088/1742-6596/1350/1/012155
PG 6
WC Physics, Particles & Fields
SC Physics
GA BO7YR
UT WOS:000526100000154
OA Green Accepted, Bronze
DA 2021-04-21
ER
PT J
AU Gautier, G
Polito, G
Bardenet, R
Valko, M
AF Gautier, Guillaume
Polito, Guillermo
Bardenet, Remi
Valko, Michal
TI DPPy: DPP Sampling with Python
SO JOURNAL OF MACHINE LEARNING RESEARCH
LA English
DT Article
DE determinantal point processes; sampling; MCMC; random matrices; Python
AB Determinantal point processes (DPPs) are specific probability distributions over clouds of points that are used as models and computational tools across physics, probability, statistics, and more recently machine learning. Sampling from DPPs is a challenge and therefore we present DPPy, a Python toolbox that gathers known exact and approximate sampling algorithms for both finite and continuous DPPs. The project is hosted on GitHubc) and equipped with an extensive documentation.
C1 [Gautier, Guillaume; Polito, Guillermo; Bardenet, Remi; Valko, Michal] Univ Lille, CNRS, UMR 9189, Cent Lille,CRIStAL, F-59651 Villeneuve Dascq, France.
[Gautier, Guillaume; Polito, Guillermo; Valko, Michal] Inria Lille Nord Europe, 40 Ave Halley, F-59650 Villeneuve Dascq, France.
[Valko, Michal] DeepMind Paris, 14 Rue Londres, F-75009 Paris, France.
RP Gautier, G (corresponding author), Univ Lille, CNRS, UMR 9189, Cent Lille,CRIStAL, F-59651 Villeneuve Dascq, France.; Gautier, G (corresponding author), Inria Lille Nord Europe, 40 Ave Halley, F-59650 Villeneuve Dascq, France.
EM G.GAUTIER@INRIA.FR; GUILLERMO.POLITO@UNIV-LILLE.FR;
REMI.BARDENET@GMAIL.COM; VALKOM@DEEPMIND.COM
FU European CHIST-ERA project DELTA; French Ministry of Higher Education
and Research; Nord-Pas-de-Calais Regional CouncilRegion Hauts-de-France;
Inria; Otto-von-Guericke-Universitat Magdeburg associated-team
north-European project Allocate; French National Research Agency project
BoBFrench National Research Agency (ANR) [ANR-16-CE23-0003]
FX We acknowledge funding by European CHIST-ERA project DELTA, the French
Ministry of Higher Education and Research, the Nord-Pas-de-Calais
Regional Council, Inria and Otto-von-Guericke-Universitat Magdeburg
associated-team north-European project Allocate, and French National
Research Agency project BoB (n.ANR-16-CE23-0003).
CR Affandi R. H., 2013, INT C ART INT STAT A
Anari N., 2016, C LEARN THEOR COLT
Baddeley A., 2005, J STAT SOFTWARE
Bardenet R., 2019, ANN APPL PROBABILITY
Borodin A, 2010, B AM MATH SOC
Burt D, 2019, INT C MACH LEARN ICM
Derezinski M., 2019, ADV NEURAL INFORM PR
Dumitriu I, 2002, J MATH PHYS
Dupuy C, 2018, INT C ART INT STAT A
Gartrell M., 2019, ARXIV190512962
Gartrell M, 2016, AAAI C ART INT
Gautier G., 2019, ADV NEURAL INFORM PR
Gautier G, 2017, INT C MACH LEARN ICM
Gillenwater J., 2014, THESIS
Hough J. B., 2006, PROBABILITY SURVEYS
Kammoun M. S., 2018, ELECT J PROBABILITY
Kathuria Tarun, 2016, ADV NEURAL INFORM PR
Killip R, 2004, INT MATH RES NOTICES
Konig W, 2004, PROBABILITY SURVEYS
Kulesza A, 2012, FDN TRENDS MACHINE L
Launay Claire, 2018, ARXIV180208429
Lavancier F, 2012, J ROYAL STAT SOC B
Li C., 2016, ADV NEURAL INFORM PR
Macchi O., 1975, ADV APPL PROBABILITY
Mazoyer A, 2019, ARXIV190102099
Moller J., 2004, STAT INFERENCE SIMUL
Pathria R., 2011, STAT MECH
Poulson J., 2019, ARXIV190500165
Propp J. G, 1998, J ALGORITHMS
Rasmussen CE, 2005, ADAPT COMPUT MACH LE, P1
Soshnikov A, 2000, RUSSIAN MATH SURVEYS
Tremblay N., 2018, ARXIV180208471
NR 32
TC 4
Z9 4
U1 0
U2 1
PU MICROTOME PUBL
PI BROOKLINE
PA 31 GIBBS ST, BROOKLINE, MA 02446 USA
SN 1532-4435
J9 J MACH LEARN RES
JI J. Mach. Learn. Res.
PY 2019
VL 20
AR 180
PG 7
WC Automation & Control Systems; Computer Science, Artificial Intelligence
SC Automation & Control Systems; Computer Science
GA KB3ME
UT WOS:000506403100020
DA 2021-04-21
ER
PT S
AU Lazar, Z
Bidaux, Y
Roos, M
Close, GF
AF Lazar, Zsombor
Bidaux, Yves
Roos, Markus
Close, Gael F.
GP IEEE
TI Model-Based Engineering of Magnetic Sensors
SO 2019 16TH INTERNATIONAL CONFERENCE ON SYNTHESIS, MODELING, ANALYSIS AND
SIMULATION METHODS AND APPLICATIONS TO CIRCUIT DESIGN (SMACD 2019)
SE International Conference on Synthesis Modeling Analysis and Simulation
Methods and Applications to Circuit Design
LA English
DT Proceedings Paper
CT 16th International Conference on Synthesis, Modeling, Analysis and
Simulation Methods and Applications to Circuit Design (SMACD)
CY JUL 15-18, 2019
CL Lausanne, SWITZERLAND
SP Dialog Semicond, Ams AG, Cadence Acad Network, Coilcraft, Melexis Inspired Engn, Springer, IEEE, IEEE Circuits & Syst Soc, IEEE Council Elect Design Automat, EPFL, Tecnico Lisboa, Inst Telecomunicacoes, Univ Lisbon, Inst Super Tecnico
DE multiphysics system modeling; magnetic position sensors; automotive
electronics
AB A modern car contains about 30 magnetic sensors on average. Their performances depend on the readout circuit imperfections, the magnetic environment, and mechanical assembly tolerances. For angle sensors, this yields a complex physics with magnet tilt and off-axis rotation. Traditionally these effects are studied separately in finite-element-method (FEM) simulators, separated from traditional system and circuit design simulation tools. This creates a simulation gap. A new modeling method for physical modeling of magnetic position sensors is presented. The method is based on spherical harmonic decomposition, and is implemented in Matlab. In this representation, any rigid-body 3-D motion of the magnet and sensor is modeled by matrix operations. Critical physical effects for the sensor accuracy can then be explored directly in system simulation tools such as Matlab, Simulink, Python or even in an integrated circuit simulator. The method maintains FEM accuracy. It represents a paradigm shift in magnetic position sensor design, and brings FEM accuracy to a much wider range of users.
C1 [Lazar, Zsombor; Bidaux, Yves; Close, Gael F.] Melexis, CH-2022 Bevaix, Switzerland.
[Roos, Markus] NM Numer Modelling GmbH, CH-6300 Zug, Switzerland.
RP Close, GF (corresponding author), Melexis, CH-2022 Bevaix, Switzerland.
EM lzs@melexis.com; ybi@melexis.com; markus.roos@nmtec.ch; gcl@melexis.com
CR Bronzan J. B., 1982, Electromagnetism. Paths to research, P171
Fummi F, 2005, DES AUT TEST EUROPE, P798, DOI 10.1109/DATE.2005.327
Hackner M., 2009, SENS TEST C 2009 P A, VII, P23
Halder Abhishek, 2017, 2017 IEEE Power & Energy Society General Meeting, DOI 10.1109/PESGM.2017.8274310
Heinssen S., 2017, 14 INT C SYNTH MOD A, P1
Leroy S, 2017, ESSCIRC 2017 - 43RD IEEE EUROPEAN SOLID STATE CIRCUITS CONFERENCE, P360, DOI 10.1109/ESSCIRC.2017.8094600
Schwarz P, 2001, PROC SPIE, V4407, P10, DOI 10.1117/12.425295
Yole, 2017, MAGN SENS MARK TECHN
NR 8
TC 1
Z9 1
U1 0
U2 0
PU IEEE
PI NEW YORK
PA 345 E 47TH ST, NEW YORK, NY 10017 USA
SN 2575-4874
EI 2575-4890
BN 978-1-7281-1201-5
J9 INT C SYNTH MODEL AN
PY 2019
BP 105
EP 108
PG 4
WC Engineering, Electrical & Electronic
SC Engineering
GA BO1YP
UT WOS:000503265100027
DA 2021-04-21
ER
PT S
AU Cristiano, KL
Estupinan, A
Triana, DA
AF Cristiano, K. L.
Estupinan, A.
Triana, D. A.
BE PerezTaborda, JA
Bernal, AGA
TI Python script used as a simulator for the teaching of the electric field
in electromagnetism course
SO 6TH NATIONAL CONFERENCE ON ENGINEERING PHYSICS AND THE 1ST INTERNATIONAL
CONFERENCE ON APPLIED PHYSICS ENGINEERING & INNOVATION
SE Journal of Physics Conference Series
LA English
DT Proceedings Paper
CT 6th National Conference on Engineering Physics (VICNIF) / 1st
International Conference on Applied Physics Engineering and Innovation
(APEI)
CY OCT 22-26, 2018
CL Univ Ind Santander, Bucaramanga, COLOMBIA
SP Colombian Soc Engn Phys, Univ EAFIT, Univ Natl Colombia, Univ Andes, Fac Ingn, Dept Ingn Electrica & Electronica, Vortex Co, Andina Tecnologias, Bruker, Horiba, Rigaku, Tescan, JEOL, S & S Ingn, A P P Machines, Newport, Inst Nacl Metrologia Colombia, Opt Soc, SPIE, Aust Nt, Spectra Phys
HO Univ Ind Santander
AB We present in this article a Python script, based on a methodology to obtain the electric field produced by n electric charges. This tool was implemented in courses of electromagnetism and its laboratory in three institutions of higher education. The aim of this work was to incorporate information and communication technologies (ICTs) at the physics subjects, in accordance with the programs promoted by the Colombian Ministry of Education. We wanted to connect the students with sensory experiences of the physical phenomena that allow them to improve their experience of learning of subjects traditionally studied through the board. Finally, in this work, an interactive computational code was obtained, in which the electric field of the discrete and continuous charge distributions can be calculated, for the classical problems that are shown in an electrical physics course.
C1 [Cristiano, K. L.; Triana, D. A.] UIS, Escuela Fis, CIMBIOS, Bucaramanga, Colombia.
[Estupinan, A.] Univ Santander UDES, Fac Ciencias Exactas Nat & Agr, Dept Matemat Fis, Bucaramanga, Colombia.
[Estupinan, A.; Triana, D. A.] Univ Auntonoma Bucaramanga UNAB, Dept Matemat, Bucaramanga, Colombia.
RP Triana, DA (corresponding author), UIS, Escuela Fis, CIMBIOS, Bucaramanga, Colombia.
EM dantrica@saber.uis.edu.co
RI Lopez, Alex Francisco Estupinan/ABH-9945-2020
OI Lopez, Alex Francisco Estupinan/0000-0003-3067-6821; Triana Camacho,
Daniel Andres/0000-0001-6852-6277
CR Bassi S, 2007, PLOS COMPUT BIOL, V3, P2052, DOI 10.1371/journal.pcbi.0030199
Burbano de Ercilla S, 1993, FISICA GEN
Caraballo Clavijo S V, 2012, THESIS
De Ercilla S B, 2003, FISICA GEN
Furio-Mas C, 1998, ENSEN CIENC, V16, P131
Giancoli D C, 2002, FISICA U, P530
Langtangen H. P., 2009, PRIMER SCI PROGRAMMI, V2
Miranda DA, 2017, J PHYS CONF SER, V850, DOI 10.1088/1742-6596/850/1/012015
Pollack G.L, 2005, ELECTROMAGNETISM
Serway R A, 2005, FISICA CIENCIAS INGE, V6
Wieman C E, 2008, OERSTED MEDAL LECT 2
Young HD, 2006, SEARS ZEMANSKYS U PH, V1
NR 12
TC 1
Z9 1
U1 1
U2 1
PU IOP PUBLISHING LTD
PI BRISTOL
PA DIRAC HOUSE, TEMPLE BACK, BRISTOL BS1 6BE, ENGLAND
SN 1742-6588
EI 1742-6596
J9 J PHYS CONF SER
PY 2019
VL 1247
AR 012044
DI 10.1088/1742-6596/1247/1/012044
PG 6
WC Engineering, Multidisciplinary; Physics, Multidisciplinary
SC Engineering; Physics
GA BN3OD
UT WOS:000480435000044
OA Bronze
DA 2021-04-21
ER
PT J
AU Steppa, C
Holch, TL
AF Steppa, Constantin
Holch, Tim L.
TI HexagDLy-Processing hexagonally sampled data with CNNs in PyTorch
SO SOFTWAREX
LA English
DT Article
DE Convolutional neural networks; Hexagonal grid; PyTorch; Astroparticle
physics
AB HexagDLy is a Python-library extending the PyTorch deep learning framework with convolution and pooling operations on hexagonal grids. It aims to ease the access to convolutional neural networks for applications that rely on hexagonally sampled data as, for example, commonly found in ground-based astroparticle physics experiments. (C) 2019 The Authors. Published by Elsevier B.V.
C1 [Steppa, Constantin] Univ Potsdam, Dept Phys & Astron, Expt Astroparticle Phys, Karl Liebknecht Str 24-25, D-14476 Potsdam, Germany.
[Holch, Tim L.] Humboldt Univ, Dept Phys, Expt Particle Phys, Newtonstr 15, D-12489 Berlin, Germany.
RP Steppa, C (corresponding author), Univ Potsdam, Dept Phys & Astron, Expt Astroparticle Phys, Karl Liebknecht Str 24-25, D-14476 Potsdam, Germany.
EM steppa@uni-potsdam.de; holchtim@physik.hu-berlin.de
OI Holch, Tim Lukas/0000-0001-5161-1168
CR Aartsen MG, 2015, EUR PHYS J C, V75, DOI 10.1140/epjc/s10052-015-3713-1
Birch CPD, 2007, ECOL MODEL, V206, P347, DOI 10.1016/j.ecolmodel.2007.03.041
Cherenkov T., 2017, ARXIV170907997
Cohen Taco S., 2018, INT C LEARN REPR
Erdmann M, 2018, ASTROPART PHYS, V97, P46, DOI 10.1016/j.astropartphys.2017.10.006
Feng Q, 2016, P INT ASTRONOMICAL U, V12
Holch TL, POS ICRC2017 FLUOR D, P795, DOI [10.22323/1.301.0795,arXiv:1711.06298, DOI 10.22323/1.301.0795,ARXIV:1711.06298]
Hoogeboom E., 2018, ARXIV180302108
Huennefeld M, 2017, POS ICRC2017, P1057, DOI [10.22323/1.301.1057, DOI 10.22323/1.301.1057]
LeCun Y, 2015, NATURE, V521, P436, DOI 10.1038/nature14539
Mangano S, 2018, LECT NOTES ARTIF INT, V11081, P243, DOI 10.1007/978-3-319-99978-4_19
Masci J, 2015, SHAPENET CONVOLUTION
Paszke A., 2017, NIPS W
Perraudin N., 2018, ARXIV181012186
Richard C., 1990, P SOC PHOTO-OPT INS, V1194
Sahr K., 2011, ARCH, V22, P363
Satoh M, 2014, PROG EARTH PLANET SC, V1, DOI 10.1186/s40645-014-0018-1
Shilon I, 2019, ASTROPART PHYS, V105, P44, DOI 10.1016/j.astropartphys.2018.10.003
STAUNTON RC, 1989, IMAGE VISION COMPUT, V7, P162, DOI 10.1016/0262-8856(89)90040-1
1979, P IEEE, V67, P930, DOI DOI 10.1109/PROC.1979.11356
2009, ASTROPART PHYS, V31, P383, DOI DOI 10.1016/J.ASTROPARTPHYS.2009.04.001
NR 21
TC 3
Z9 3
U1 0
U2 0
PU ELSEVIER
PI AMSTERDAM
PA RADARWEG 29, 1043 NX AMSTERDAM, NETHERLANDS
SN 2352-7110
J9 SOFTWAREX
JI SoftwareX
PD JAN-JUN
PY 2019
VL 9
BP 193
EP 198
DI 10.1016/j.softx.2019.02.010
PG 6
WC Computer Science, Software Engineering
SC Computer Science
GA HW6RS
UT WOS:000466818600030
OA DOAJ Gold
DA 2021-04-21
ER
PT J
AU Tian, L
Li, L
Ding, J
Mousseau, N
AF Tian, Liang
Li, Lin
Ding, Jun
Mousseau, Normand
TI ART_data_analyzer: Automating parallelized computations to study the
evolution of materials
SO SOFTWAREX
LA English
DT Article
DE Activation and relaxation techniques; Kinetics; Automation and
parallelization; Machine learning
ID RELAXATION; ORDER
AB The kinetics and dynamic evolution of material structures need a comprehensive understanding of the potential energy landscape at current sample state. The Activation-Relaxation Technique (ART) is an efficient way to probe the potential energy landscape by sampling a large amount of events (a single event involves initial, saddle and final state) from which a statistical distribution of activation energy barrier can be extracted. However, there has been a lack of a user-friendly toolkit to automate the parallelization of running of ART simulations and post-processing of data from ART simulations to extract useful physics information and insights. The ART_data_analyzer Python package has been developed to serve this purpose and fill in this gap for the broad community of scientific researchers interested in the kinetics and dynamic transitions of material structures. As a demo, we utilized this software package to demonstrate the user-friendly workflow of studying ZrCuAl metallic glass sample prepared by molecular dynamics. (C) 2019 The Authors. Published by Elsevier B.V.
C1 [Tian, Liang; Li, Lin] Univ Alabama, Dept Met & Mat Engn, Tuscaloosa, AL 35404 USA.
[Tian, Liang] Univ Michigan, Dept Mat Sci & Engn, Ann Arbor, MI 48109 USA.
[Ding, Jun] Lawrence Berkeley Natl Lab, Mat Sci Div, Berkeley, CA 94720 USA.
[Mousseau, Normand] Univ Montreal, Dept Phys, CP 6128,Succ Ctr Ville, Montreal, PQ, Canada.
RP Tian, L (corresponding author), 1024 North Engn Res Ctr, Tuscaloosa, AL 35404 USA.
EM liangtianisu@gmail.com
RI Tian, Liang/R-8452-2019
OI Tian, Liang/0000-0003-4876-968X
FU U.S. Department of Energy, Office of Science, Basic Energy Sciences
(BES), USAUnited States Department of Energy (DOE) [DE-SC0016164]
FX LT and LL acknowledge the financial support by the U.S. Department of
Energy, Office of Science, Basic Energy Sciences (BES), USA, by Award
no. DE-SC0016164. LT and LL appreciate the valuable discussions with Dr
Yue Fan at University of Michigan, Ann Arbor.
CR Andreas H., 2008, J PHYS CONDENS MATT, V20
Athenes M, 2012, J CHEM PHYS, V137, DOI 10.1063/1.4766458
Barkema GT, 1996, PHYS REV LETT, V77, P4358, DOI 10.1103/PhysRevLett.77.4358
Cheng YQ, 2008, ACTA MATER, V56, P5263, DOI 10.1016/j.actamat.2008.07.011
Fan Y, 2017, NAT COMMUN, V8, DOI 10.1038/ncomms15417
Fan Y, 2014, NAT COMMUN, V5, DOI 10.1038/ncomms6083
Khoddam S, 2018, ADV ENG MATER, V20, DOI 10.1002/adem.201800048
Li F, 2018, J APPL PHYS, V123, DOI 10.1063/1.5008841
Li J., 2005, LEAST SQUARE ATOMIC
Ma E, 2015, NAT MATER, V14, P547, DOI 10.1038/nmat4300
Machado-Charry E, 2011, J CHEM PHYS, V135, DOI 10.1063/1.3609924
Malek R, 2000, PHYS REV E, V62, P7723, DOI 10.1103/PhysRevE.62.7723
Ong SP, 2013, COMP MATER SCI, V68, P314, DOI 10.1016/j.commatsci.2012.10.028
Pedregosa F, 2011, SCIKIT LEARN MACHINE
PLIMPTON S, 1995, J COMPUT PHYS, V117, P1, DOI 10.1006/jcph.1995.1039
Rycroft CH, 2009, CHAOS, V19, DOI 10.1063/1.3215722
Stukowski A, 2010, MODEL SIMUL MATER SC, V18, DOI 10.1088/0965-0393/18/1/015012
Tian L, 2018, INT J CURR ENG TECHN, V8, P236, DOI DOI 10.14741/IJCET/V.8.2.7
Wang BB, 2018, NPJ COMPUT MATER, V4, DOI 10.1038/s41524-018-0097-4
NR 19
TC 4
Z9 4
U1 1
U2 5
PU ELSEVIER
PI AMSTERDAM
PA RADARWEG 29, 1043 NX AMSTERDAM, NETHERLANDS
SN 2352-7110
J9 SOFTWAREX
JI SoftwareX
PD JAN-JUN
PY 2019
VL 9
BP 238
EP 243
DI 10.1016/j.softx.2019.03.002
PG 6
WC Computer Science, Software Engineering
SC Computer Science
GA HW6RS
UT WOS:000466818600037
OA DOAJ Gold
DA 2021-04-21
ER
PT S
AU Pinzon, EF
Lizcano, AR
Martinez, JH
Patino, GA
Miranda, DA
AF Pinzon, E. F.
Lizcano, A. R.
Martinez, J. H.
Patino, G. A.
Miranda, D. A.
GP IOP
TI Student perception of the implementation of a teaching strategy based on
Just in Time mediated learning and the use of information and
communications technologies in the physics I laboratory course
SO FIRST INTERNATIONAL CONFERENCE ON VIRTUAL EDUCATION: CHALLENGES AND
OPPORTUNITIES
SE Journal of Physics Conference Series
LA English
DT Proceedings Paper
CT 1st International Conference on Virtual Education - Challenges and
Opportunities (ViEduc)
CY OCT 08-12, 2018
CL Bucaramanga, COLOMBIA
SP Inst Reg Project & Distance Educ, iPred, Acad Vice Chancellorship, Sch Educ, Univ Ind Santander, CEDEDUIS, Ctr Dev Teaching
AB A Teaching strategy based on Just in Time Teaching, mediated learning techniques and the use of information and communication technologies was implemented in physics I laboratory courses to improve research skills of second-level engineering students. In this work, a study into student's perception of application of this strategy was carried out through statistical analysis of the responses given by students in perception surveys applied at the end of each semester. The study consisted of two parts: a quantitative analysis of closed test answers (questions with predefined answers) and a qualitative analysis of open answers (answers written by students). Programming codes in Python language were developed to perform the descriptive analysis of student responses from the first academic semester of 2016 (2016/I) to the first academic semester of 2018 (2018/I). Student percentage who answered "yes, they are enough" to the question if tools available in the Moodle virtual learning platform are useful for verification of the physical concepts, presented a tendency to rise (65% to 80%) along academic semesters. Also, qualitative analysis of the responses showed the need to train students in use of new technological tools (data collection software and devices) implemented in the laboratory projects in the last semesters. The above was concluded from the increased percentage of students who expressed difficulties in handling laboratory tools and devices (3.0% in 2016/I to 29% in 2018/I).
C1 [Pinzon, E. F.; Lizcano, A. R.; Martinez, J. H.; Patino, G. A.; Miranda, D. A.] Univ Ind Santander, CIMBIOS, Bucaramanga, Colombia.
RP Miranda, DA (corresponding author), Univ Ind Santander, CIMBIOS, Bucaramanga, Colombia.
EM dalemir@uis.edu.co
RI Mercado, David Alejandro Miranda/P-5560-2016
OI Mercado, David Alejandro Miranda/0000-0003-3130-3314; Pinzon Nieto,
Edgar Fabian/0000-0001-7782-1462; Lizcano, Adriana/0000-0001-6135-1662
CR Deslauriers L, 2011, SCIENCE, V332, P862, DOI 10.1126/science.1201783
Driscoll M, 2002, SCI RES, V332, P862
Hakkarainen K, 2009, INT J COMP-SUPP COLL, V4, P213, DOI 10.1007/s11412-009-9064-x
McParland M, 2004, MED EDUC, V38, P859, DOI 10.1111/j.1365-2929.2004.01818.x
Miranda DA, 2017, J PHYS CONF SER, V850, DOI 10.1088/1742-6596/850/1/012015
Prince M, 2001, J ENG EDUC, V93, P223
Simkins S, 2004, SOC SCI COMPUT REV, V22, P444, DOI 10.1177/0894439304268643
Torres V, 2005, TEACH PROF DEV, V6, P177
Trujillo F., 2011, ENFOQUE COMPETENCIAS
Universidad Industrial de Santander, AUT I
Valek J, 2012, PROCD SOC BEHV, V69, P1866, DOI 10.1016/j.sbspro.2012.12.139
Woods DR, 2014, IND ENG CHEM RES, V53, P5337, DOI 10.1021/ie401202k
NR 12
TC 0
Z9 0
U1 0
U2 1
PU IOP PUBLISHING LTD
PI BRISTOL
PA DIRAC HOUSE, TEMPLE BACK, BRISTOL BS1 6BE, ENGLAND
SN 1742-6588
EI 1742-6596
J9 J PHYS CONF SER
PY 2019
VL 1161
AR 012013
DI 10.1088/1742-6596/1161/1/012013
PG 7
WC Computer Science, Interdisciplinary Applications; Education &
Educational Research
SC Computer Science; Education & Educational Research
GA BM5YN
UT WOS:000465687000013
OA Bronze
DA 2021-04-21
ER
PT S
AU Kimm, H
Chaffers, A
AF Kimm, Haklin
Chaffers, Alexander
GP IEEE
TI Interdisciplinary Internship Projects Utilizing Legacy Robotic Equipment
under Budget Constraints at a Small-sized Institution
SO 2019 9TH IEEE INTEGRATED STEM EDUCATION CONFERENCE (ISEC)
SE Integrated STEM Education Conference
LA English
DT Proceedings Paper
CT 9th IEEE Integrated STEM Education Conference (ISEC)
CY MAR 16, 2019
CL Princeton, NJ
SP IEEE
DE Image Processing; Python; Raspberry Pi; Robotic Vision; STEM
AB In this paper we would like to share interdisciplinary project experiences with other small universities who intend or hold an interest in participating interdisciplinary STEM projects with their students. The interdisciplinary science and engineering projects attract students from various disciplines and backgrounds such as computer science, electrical engineering, physics, mathematics, and others. When initiating a robotic vision project at East Stroudsburg University, it began with open discussions upon what resources were available to us, what topics we were interested in, and and what we might be albe to accomplish. The students participating in the project were exposed to diverse challenging problems which were hardly seen in their class course works, and to encounter technical difficulties related to the project. The students were encouraged to workfirst upon their interests and specialties such as hardware, software, and programming languages. The students routinely presented their understanding of possible problems and solutions to other team members because the projects require sharing the knowledge and helping each other to achieve a common goal of the project success. All the team members including faculty adviser were generally in discussions upon finding and solving the problems, with self-motivated learning and helping each other, and in playing their roles for the project development, management, and technological entrepreneurship of rebuilding the old robot arms within university budget constraints. The projects using robot arms seemed to have outperformed in the students' learning outcomes, motivated the students to enhance their career interests into research fields, and prepared them to an advanced science and technology level in solving practical problems.
C1 [Kimm, Haklin; Chaffers, Alexander] East Stroudsburg Univ, Comp Sci Dept, East Stroudsburg, PA 18301 USA.
RP Kimm, H (corresponding author), East Stroudsburg Univ, Comp Sci Dept, East Stroudsburg, PA 18301 USA.
EM hkimm@esu.edu; achaffers@live.esu.edu
CR Fagin B., SIGCSE 03 P 34 SIGCS, P307
Koski M., 2008, KOLI 08 P 8 INT C CO
McCartney R, 1996, COMPUT SCI ED, V7, P135
NR 3
TC 0
Z9 0
U1 0
U2 0
PU IEEE
PI NEW YORK
PA 345 E 47TH ST, NEW YORK, NY 10017 USA
SN 2330-331X
BN 978-1-7281-1502-3
J9 INTEGR STEM EDU CONF
PY 2019
BP 177
EP 182
PG 6
WC Education, Scientific Disciplines; Engineering, Multidisciplinary
SC Education & Educational Research; Engineering
GA BQ6AD
UT WOS:000609974500040
DA 2021-04-21
ER
PT J
AU Aebischer, J
Kumar, J
Straub, DM
AF Aebischer, Jason
Kumar, Jacky
Straub, David M.
TI : a Python package for the running and matching of Wilson coefficients
above and below the electroweak scale
SO EUROPEAN PHYSICAL JOURNAL C
LA English
DT Article
ID MODEL
AB wilson is a Python library for matching and running Wilson coefficients of higher-dimensional operators beyond the Standard Model. Provided with the numerical values of the Wilson coefficients at a high new physics scale, it automatically performs the renormalization group evolution within the Standard Model effective field theory (SMEFT), matching onto the weak effective theory (WET) at the electroweak scale, and QCD/QED renormalization group evolution below the electroweak scale down to hadronic scales relevant for low-energy precision tests. The matching and running encompasses the complete set of dimension-six operators in both SMEFT and WET. The program builds on the Wilson coefficient exchange format (WCxf) and can thus be easily combined with a number of existing public codes.
C1 [Aebischer, Jason; Straub, David M.] TUM, Excellence Cluster Universe, Boltzmannstr 2, D-85748 Garching, Germany.
[Kumar, Jacky] Indian Inst Sci Educ & Res, Dept Phys, Mohali 140036, Punjab, India.
RP Aebischer, J (corresponding author), TUM, Excellence Cluster Universe, Boltzmannstr 2, D-85748 Garching, Germany.
EM jason.aebischer@tum.de; jkumar@iisermohali.ac.in; david.straub@tum.de
OI Kumar, Jacky/0000-0001-9053-0731; Straub, David/0000-0001-5762-7339
FU DFG cluster of excellence "Origin and Structure of the Universe"German
Research Foundation (DFG)
FX The work of D.S. and J.A. is supported by the DFG cluster of excellence
"Origin and Structure of the Universe". We thank Xuanyou Pan and
Matthias Schoffel for important technical support in the development
phase. J.K. thanks Michael Paraskevas for discussions.
CR Aaij R, 2017, J HIGH ENERGY PHYS, DOI 10.1007/JHEP08(2017)055
Aaij R, 2015, PHYS REV LETT, V115, DOI 10.1103/PhysRevLett.115.159901
Aaij R, 2015, PHYS REV LETT, V115, DOI 10.1103/PhysRevLett.115.111803
Aaij R, 2014, PHYS REV LETT, V113, DOI 10.1103/PhysRevLett.113.151601
Abdesselam A., 2016, ARXIV160806391
Aebischer J., 2018, ARXIV181007698
Aebischer J., 2018, ARXIV180702520
Aebischer J., 2018, ARXIV180800466
Aebischer J., 2018, ARXIV180701709
Aebischer J, 2016, J HIGH ENERGY PHYS, DOI 10.1007/JHEP05(2016)037
Aebischer J, 2018, COMPUT PHYS COMMUN, V232, P71, DOI 10.1016/j.cpc.2018.05.022
Aebischer J, 2017, J HIGH ENERGY PHYS, DOI 10.1007/JHEP09(2017)158
Alok AK, 2017, PHYS REV D, V96, DOI 10.1103/PhysRevD.96.095009
Alonso R, 2014, J HIGH ENERGY PHYS, DOI 10.1007/JHEP04(2014)159
Altmannshofer W, 2017, PHYS REV D, V96, DOI 10.1103/PhysRevD.96.055008
APPELQUIST T, 1975, PHYS REV D, V11, P2856, DOI 10.1103/PhysRevD.11.2856
BARDEEN WA, 1978, PHYS REV D, V18, P3998, DOI 10.1103/PhysRevD.18.3998
Bhattacharya B, 2017, J HIGH ENERGY PHYS, DOI 10.1007/JHEP01(2017)015
Buchalla G, 1996, REV MOD PHYS, V68, P1125, DOI 10.1103/RevModPhys.68.1125
BUCHMULLER W, 1986, NUCL PHYS B, V268, P621, DOI 10.1016/0550-3213(86)90262-2
Buras A. J., 2011, ARXIV11025650
Calibbi L, 2015, PHYS REV LETT, V115, DOI 10.1103/PhysRevLett.115.181801
CALLAN CG, 1969, PHYS REV, V177, P2247, DOI 10.1103/PhysRev.177.2247
Capdevila B, 2018, J HIGH ENERGY PHYS, DOI 10.1007/JHEP01(2018)093
Celis A, 2017, EUR PHYS J C, V77, DOI 10.1140/epjc/s10052-017-4967-6
Ciuchini M, 2017, EUR PHYS J C, V77, DOI 10.1140/epjc/s10052-017-5270-2
COLEMAN S, 1969, PHYS REV, V177, P2239, DOI 10.1103/PhysRev.177.2239
Cornella C, 2018, J HIGH ENERGY PHYS, DOI 10.1007/JHEP11(2018)012
Dedes A, 2017, J HIGH ENERGY PHYS, DOI 10.1007/JHEP06(2017)143
Ellis J., 2018, YOU, V2018, P146, DOI [10. 1007/JHEP06(2018)146, DOI 10.1007/JHEP06(2018)146]
Feruglio F, 2017, J HIGH ENERGY PHYS, DOI 10.1007/JHEP09(2017)061
Feruglio F, 2017, PHYS REV LETT, V118, DOI 10.1103/PhysRevLett.118.011801
Geng LS, 2017, PHYS REV D, V96, DOI 10.1103/PhysRevD.96.093006
GLASHOW SL, 1961, NUCL PHYS, V22, P579, DOI 10.1016/0029-5582(61)90469-2
Gonzalez-Alonso M, 2016, J HIGH ENERGY PHYS, DOI 10.1007/JHEP12(2016)052
Grzadkowski B, 2010, J HIGH ENERGY PHYS, DOI [10.1007/JHEP10(2010)85, 10.1007/JHEP10(2010)085]
Herren F, 2018, COMPUT PHYS COMMUN, V224, P333, DOI 10.1016/j.cpc.2017.11.014
Huschle M, 2015, PHYS REV D, V92, DOI 10.1103/PhysRevD.92.072014
Jenkins EE, 2018, J HIGH ENERGY PHYS, DOI 10.1007/JHEP03(2018)016
Jenkins EE, 2018, J HIGH ENERGY PHYS, DOI 10.1007/JHEP01(2018)084
Jenkins EE, 2014, J HIGH ENERGY PHYS, DOI 10.1007/JHEP01(2014)035
Jenkins EE, 2013, J HIGH ENERGY PHYS, DOI 10.1007/JHEP10(2013)087
Jung M., 2018, ARXIV180101112
Kniehl BA, 2016, COMPUT PHYS COMMUN, V206, P84, DOI 10.1016/j.cpc.2016.04.017
Kumar J, 2018, ARXIV180607403
Lees JP, 2013, PHYS REV D, V88, DOI 10.1103/PhysRevD.88.072012
Salam A, 1968, C P C, V680519, P367, DOI DOI 10.1142/9789812795915_0034
Straub D. M., 2018, ARXIV181008132
WEINBERG S, 1980, PHYS LETT B, V91, P51, DOI 10.1016/0370-2693(80)90660-7
WEINBERG S, 1967, PHYS REV LETT, V19, P1264, DOI 10.1103/PhysRevLett.19.1264
WILSON KG, 1983, REV MOD PHYS, V55, P583, DOI 10.1103/RevModPhys.55.583
NR 51
TC 32
Z9 33
U1 0
U2 0
PU SPRINGER
PI NEW YORK
PA 233 SPRING ST, NEW YORK, NY 10013 USA
SN 1434-6044
EI 1434-6052
J9 EUR PHYS J C
JI Eur. Phys. J. C
PD DEC 19
PY 2018
VL 78
IS 12
AR 1026
DI 10.1140/epjc/s10052-018-6492-7
PG 8
WC Physics, Particles & Fields
SC Physics
GA HF1OO
UT WOS:000453936200002
OA DOAJ Gold, Green Published
DA 2021-04-21
ER
PT J
AU Yan, HP
Bromley, BP
Dugal, C
Colton, AV
AF Yan, Huiping
Bromley, Blair Patrick
Dugal, Cliff
Colton, Ashlea, V
TI MODELING STUDIES AND CODE-TO-CODE COMPARISONS FOR PRESSURE TUBE HEAVY
WATER REACTOR CORES
SO CNL NUCLEAR REVIEW
LA English
DT Article
DE PT-HWR; MCNP; RFSP; modeling; benchmark; thorium
ID THORIUM; FUELS
AB Preliminary, conceptual studies have been performed previously using deterministic lattice physics (WIMS-AECL) and core physics codes (RFSP) to estimate performance and safety characteristics of various thorium-based fuels and uranium-based fuels augmented by small amounts of thorium for use in pressure tube heavy-water reactors (PT-HWRs). To confirm the validity of the results, the WIMS-AECL/RFSP results are compared against predictions made with the stochastic neutron transport code MCNP. This paper describes the development of a method for setting up an MCNP core model of at PT-HWR for comparison with WIMS-AECL/ RFSP results, using a core with 37-element natural uranium fuel bundles as a test case for sensitivity studies. These studies included evaluating the sensitivity of the bias of the effective neutron multiplication factor (k(eff) ) , a source convergence study, uncertainties correction with multiple independent simulations, the impact of irradiation map binning methods, and the impact of reflector models. A Python-based software scripting tool was developed to automate the creation, execution, and post-processing of reactor physics data from the MCNP models. The software tool and algorithm for creating an MCNP core model using data from the WIMS-AECL and RFSP models are described in this paper. Based on the preliminary evaluations of the simulation parameters with the base model, reactor physics analyses were performed for PT-HWR cores with thorium-based fuels in a 35-element bundle type. Code-to-code results demonstrate good agreement between MCNP and RFSP, giving confidence in the method developed and its applicability to other fuels and core types.
C1 [Yan, Huiping; Bromley, Blair Patrick; Dugal, Cliff; Colton, Ashlea, V] Canadian Nucl Labs, Chalk River, ON K0J 1J0, Canada.
RP Bromley, BP (corresponding author), Canadian Nucl Labs, Chalk River, ON K0J 1J0, Canada.
EM blair.bromley@cnl.ca
FU Canadian Nuclear Laboratory's Federal Science and Technology program
FX The authors acknowledge the following staff at CNL for their assistance:
Fred Adams, Jeremy Pencer, Tina Wilson, Darren Radford, and Brock
Sanderson. Funding for this work was provided by the Canadian Nuclear
Laboratory's Federal Science and Technology program, which is
administered by Atomic Energy of Canada Limited (AECL) on behalf of the
Government of Canada.
CR AINSCOUGH JB, 1983, NUCL TECHNOL, V61, P521, DOI 10.13182/NT83-A33177
ALTIPARMAKOV D.V., 2008, P PHYSOR 2008 INT SW
ALTIPARMAKOV D.V., 2010, P PHYSOR 2010 PITTSB
[Anonymous], 2002, IAEA TECHNICAL REPOR, V407
[Anonymous], 2014, UR 2014 RES PROD DEM
Bromley BP, 2014, NUCL TECHNOL, V186, P17, DOI 10.13182/NT13-86
Brown F. B., 2009, P ANS NCSD 2009 RICH
Brown F. B., 2006, P PHYSOR 2006 VANC B
Chow J. C., 2015, 7 INT C MOD SIM NUCL
Collette A., 2013, OREILLY MEDIA
Colton A. V, 2016, ANS T, V115, P1101
Colton AV, 2018, NUCL TECHNOL, V203, P146, DOI 10.1080/00295450.2018.1444898
Colton AV, 2017, NUCL SCI ENG, V186, P48, DOI 10.1080/00295639.2016.1273021
Colton AV, 2016, NUCL TECHNOL, V196, P1, DOI 10.13182/NT16-70
Griffiths J., 1983, AECL7615
Hendricks J.S., 2008, LAUR082216 LOS AL NA
Liang T., 2008, P 29 ANN C CAN NUCL
Mao J., 2009, P ADV NUCL FUEL MANA
McDonald M, 2018, CNL NUCL REV, V7, P147, DOI 10.12943/CNR.2017.00009
Milgram M.S., 1984, AECL8326
MILGRAM M. S., 1982, AECL7516
Oliphant T.E., 2006, A GUIDE TO NUMPY, VVolume 1
Ovanes M., 2012, P PHYSOR 2012 KNOXV
Romano PK, 2015, ANN NUCL ENERGY, V82, P90, DOI 10.1016/j.anucene.2014.07.048
Rouben B, 2003, CANDU FUEL MANAGEMEN
ROUBEN B., 2002, P 13 PAC BAS NUCL C
SciPy Team, 2017, SCI COMP TOOLS PYTH
Shen W., 2016, DEV PRODUCTION MCNP, P531
The Matplotlib Development Team, 2017, MATPL INTR
Trellue H. R., 2012, LAUR1225804 LOS AL N
Ueki T, 2003, NUCL SCI ENG, V145, P279, DOI 10.13182/NSE03-04
X-5 Monte Carlo Team, 2005, LAUR031987 LOS AL NA, V1-3
Xia L., 2012, ELECT THESIS DISSERT, V898
Yan HP, 2018, ANN NUCL ENERGY, V120, P642, DOI 10.1016/j.anucene.2018.06.036
NR 34
TC 0
Z9 0
U1 0
U2 1
PU CANADIAN NUCLEAR LABORATORIES
PI CHALK RIVER
PA CHALK RIVER LABORATORIES, CHALK RIVER, ON K0J 1J0, CANADA
SN 2369-6923
EI 2369-6931
J9 CNL NUCL REV
JI CNL Nucl. Rev.
PD DEC
PY 2018
VL 7
IS 2
BP 177
EP 200
DI 10.12943/CNR.2018.00003
PG 24
WC Nuclear Science & Technology
SC Nuclear Science & Technology
GA HB1SG
UT WOS:000450805300005
DA 2021-04-21
ER
PT J
AU Consiglio, R
de Salas, PF
Mangano, G
Miele, G
Pastor, S
Pisanti, O
AF Consiglio, R.
de Salas, P. F.
Mangano, G.
Miele, G.
Pastor, S.
Pisanti, O.
TI PArthENoPE reloaded
SO COMPUTER PHYSICS COMMUNICATIONS
LA English
DT Article
DE Primordial nucleosynthesis; Cosmology; Neutrino physics
ID NUCLEOSYNTHESIS; ELEMENTS; COMPILATION; RATES
AB We describe the main features of a new and updated version of the program PArthENoPE, which computes the abundances of light elements produced during Big Bang Nucleosynthesis. As the previous first release in 2008, the new one, PArthENoPE2.0, is publicly available and distributed from the code site, http://parthenope.na.infn.it . Apart from minor changes, which will be also detailed, the main improvements are as follows. The powerful, but not freely accessible, NAG routines have been substituted by ODEPACK libraries, without any significant loss in precision. Moreover, we have developed a Graphical User Interface (GUI) which allows a friendly use of the code and a simpler implementation of running for grids of input parameters.
New Version program summary
Program Title: PArthENoPE2.0
Program Files doi : http://dx.doi.org/10.17632/wvgr7d8yt9.1
Licensing provisions: GPLv3
Programming language: Fortran 77 and Python
Supplementary material: User Manual available on the web page http://parthenope.na.infn.it
Journal reference of previous version: Comput. Phys. Commun. 178 (2008) 956 971
Does the new version supersede the previous version?: Yes
Reasons for the new version: Make the code more versatile and user friendly
Summary of revisions: (1) Publicly available libraries (2) GUI for configuration
Nature of problem: Computation of yields of light elements synthesized in the primordial universe
Solution method: Livermore Solver for Ordinary Differential Equations (LSODE) for stiff and nonstiff systems (C) 2018 Elsevier B.V. All rights reserved.
C1 [Consiglio, R.; Miele, G.; Pisanti, O.] Univ Napoli Federico II, Dipartimento Fis E Pancini, Via Cintia, I-80126 Naples, Italy.
[de Salas, P. F.; Pastor, S.] Univ Valencia, CSIC, Inst Fis Corpuscular, C Catedrat Jose Beltran 2, Paterna 46980, Valencia, Spain.
[Mangano, G.; Miele, G.; Pisanti, O.] INFN, Sez Napoli, Via Cintia, I-80126 Naples, Italy.
RP Pisanti, O (corresponding author), Univ Napoli Federico II, Dipartimento Fis E Pancini, Via Cintia, I-80126 Naples, Italy.
EM pisanti@na.infn.it
RI Pastor, Sergio/J-6902-2014; de Salas, Pablo Fernandez/U-5544-2017;
Miele, Gennaro/AAG-6782-2020
OI Pastor, Sergio/0000-0003-0933-7710; de Salas, Pablo
Fernandez/0000-0003-3890-6441; Miele, Gennaro/0000-0002-2028-0578
FU INFN, under grant Iniziativa Specifica TASP; Fondi della regione
Campania, "L.R. num. 5/2002 - annualita 2007"; MINECO [FPA2017-85216-P,
SEV-2014-0398]; Generalitat ValencianaGeneralitat ValencianaEuropean
Commission [PROMETEOII/2014/084]; MECD [FPU13/03729]
FX We are grateful to many users of PArthENoPE, who during these years,
gave important feedbacks and suggestions to improve the code. This work
was supported by INFN, under grant Iniziativa Specifica TASP, and by
Fondi della regione Campania, "L.R. num. 5/2002 - annualita 2007".
P.F.d.S. and S.P. were supported by the Spanish grants FPA2017-85216-P
and SEV-2014-0398 (MINECO), PROMETEOII/2014/084 (Generalitat Valenciana)
and FPU13/03729 (MECD).
CR Abazajian K. N., ARXIV12045379HEPPH
Ade PAR, 2016, ASTRON ASTROPHYS, V594, DOI 10.1051/0004-6361/201525830
Adelberger EG, 2011, REV MOD PHYS, V83, P195, DOI 10.1103/RevModPhys.83.195
Angulo C, 1999, NUCL PHYS A, V656, P3, DOI 10.1016/S0375-9474(99)00030-5
Arbey A, 2012, COMPUT PHYS COMMUN, V183, P1822, DOI 10.1016/j.cpc.2012.03.018
Birrell J, 2015, NUCL PHYS B, V890, P481, DOI 10.1016/j.nuclphysb.2014.11.020
Capozzi F, 2017, PHYS REV D, V95, DOI 10.1103/PhysRevD.95.096014
Castorina E, 2012, PHYS REV D, V86, DOI 10.1103/PhysRevD.86.023517
Cyburt RH, 2004, PHYS REV D, V70, DOI 10.1103/PhysRevD.70.023505
de Salas PF, 2018, PHYS LETT B, V782, P633, DOI 10.1016/j.physletb.2018.06.019
de Salas PF, 2016, J COSMOL ASTROPART P, DOI 10.1088/1475-7516/2016/07/051
Dolgov AD, 1997, NUCL PHYS B, V503, P426, DOI 10.1016/S0550-3213(97)00479-3
Esposito S, 1999, NUCL PHYS B, V540, P3, DOI 10.1016/S0550-3213(98)00757-3
Esteban I, 2017, J HIGH ENERGY PHYS, DOI 10.1007/JHEP01(2017)087
Grohs E, 2017, NUCL PHYS B, V923, P222, DOI 10.1016/j.nuclphysb.2017.07.019
HANNESTAD S, 1995, PHYS REV D, V52, P1764, DOI 10.1103/PhysRevD.52.1764
Hinshaw G, 2013, ASTROPHYS J SUPPL S, V208, DOI 10.1088/0067-0049/208/2/19
Iocco F, 2007, PHYS REV D, V75, DOI 10.1103/PhysRevD.75.087304
Iocco F, 2009, PHYS REP, V472, P1, DOI 10.1016/j.physrep.2009.02.002
KAWANO L, FERMILABPUB88034A
Lopez RE, 1999, PHYS REV D, V59, DOI 10.1103/PhysRevD.59.103502
Mangano G, 2005, NUCL PHYS B, V729, P221, DOI 10.1016/j.nuclphysb.2005.09.041
Mangano G, 2012, PHYS LETT B, V708, P1, DOI 10.1016/j.physletb.2012.01.015
Pisanti O, 2008, COMPUT PHYS COMMUN, V178, P956, DOI 10.1016/j.cpc.2008.02.015
Pitrou C., ARXIV180108023ASTROP, DOI [10.1016/j.physrep.2018.04.005, DOI 10.1016/J.PHYSREP.2018.04.005]
PRESS WH, NUMERICAL RECIPES FO, V77
Sbordone L, 2010, ASTRON ASTROPHYS, V522, DOI 10.1051/0004-6361/200913282
Serpico PD, 2004, J COSMOL ASTROPART P, DOI 10.1088/1475-7516/2004/12/010
SMITH MS, 1993, ASTROPHYS J SUPPL S, V85, P219, DOI 10.1086/191763
Tanabashi M, 2018, PHYS REV D, V98, DOI 10.1103/PhysRevD.98.030001
WAGONER RV, 1969, ASTROPHYS J SUPPL S, V18, P247, DOI 10.1086/190191
WAGONER RV, 1973, ASTROPHYS J, V179, P343, DOI 10.1086/151873
WAGONER RV, 1967, ASTROPHYS J, V148, P3, DOI 10.1086/149126
Xu Y, 2013, NUCL PHYS A, V918, P61, DOI 10.1016/j.nuclphysa.2013.09.007
NR 34
TC 22
Z9 22
U1 1
U2 7
PU ELSEVIER SCIENCE BV
PI AMSTERDAM
PA PO BOX 211, 1000 AE AMSTERDAM, NETHERLANDS
SN 0010-4655
EI 1879-2944
J9 COMPUT PHYS COMMUN
JI Comput. Phys. Commun.
PD DEC
PY 2018
VL 233
BP 237
EP 242
DI 10.1016/j.cpc.2018.06.022
PG 6
WC Computer Science, Interdisciplinary Applications; Physics, Mathematical
SC Computer Science; Physics
GA GT6YP
UT WOS:000444667100020
DA 2021-04-21
ER
PT J
AU Carnall, AC
McLure, RJ
Dunlop, JS
Dave, R
AF Carnall, A. C.
McLure, R. J.
Dunlop, J. S.
Dave, R.
TI Inferring the star formation histories of massive quiescent galaxies
with BAGPIPES: evidence for multiple quenching mechanisms
SO MONTHLY NOTICES OF THE ROYAL ASTRONOMICAL SOCIETY
LA English
DT Article
DE methods: statistical; galaxies: evolution; galaxies: star formation
ID STELLAR POPULATION SYNTHESIS; FORMATION RATES; FORMING GALAXIES; MASSES;
DUST; EVOLUTION; EMISSION; SEQUENCE; COLORS; ULTRAVIOLET
AB We present Bayesian Analysis of Galaxies for Physical Inference and Parameter EStimation, or BAGPIPES, a new PYTHON tool that can be used to rapidly generate complex model galaxy spectra and to fit these to arbitrary combinations of spectroscopic and photometric data using the MULTINEST nested sampling algorithm. We extensively test our
ability to recover realistic star formation histories (SFHs) by fitting mock observations of quiescent galaxies from the MUFASA simulation. We then perform a detailed analysis of the SFHs of a sample of 9289 quiescent galaxies from UltraVISTA with stellar masses, M-* > 10(10) M-circle dot and redshifts 0.25 < z < 3.75. The majority of our sample exhibit SFHs that rise gradually then quench relatively rapidly over 1 -2 Gyr. This behaviour is consistent with recent cosmological hydrodynamic simulations, where AGN-driven feedback in the low-accretion (jet) mode is the dominant quenching mechanism. At z > 1, we also find a class of objects with SFHs that rise and fall very rapidly, with quenching time-scales of <1 Gyr, consistent with quasar-mode AGN feedback. Finally, at z < 1 we find a population with SFHs which quench more slowly than they rise, over >3 Gyr, which we speculate to be the result of diminishing overall cosmic gas supply. We confirm the mass-accelerated evolution (downsizing) trend, and a trend towards more rapid quenching at higher stellar masses. However, our results suggest that the latter is a natural consequence of mass-accelerated evolution, rather than a change in quenching physics with stellar mass. We find 61 +/- 8 per cent of z > 1.5 massive-quenched galaxies undergo significant further evolution by z = 0.5. BAGPIPES is available at bagpipes.readthedocs.io.
C1 [Carnall, A. C.; McLure, R. J.; Dunlop, J. S.; Dave, R.] Univ Edinburgh, Royal Observ, Inst Astron, SUPA, Edinburgh EH9 3HJ, Midlothian, Scotland.
RP Carnall, AC (corresponding author), Univ Edinburgh, Royal Observ, Inst Astron, SUPA, Edinburgh EH9 3HJ, Midlothian, Scotland.
EM adamc@roe.ac.uk
OI Dave, Romeel/0000-0003-2842-9434
FU UK Science and Technology Facilities CouncilUK Research & Innovation
(UKRI)Science & Technology Facilities Council (STFC); ESO Telescopes at
La Silla Paranal Observatory under ESO programme [179. A2005]
FX The authors all acknowledge the support of the UK Science and Technology
Facilities Council. This work is based on data products from
observations made with ESO Telescopes at La Silla Paranal Observatory
under ESO programme ID 179. A2005, and on data products produced by
TERAPIX and the Cambridge Astronomy Survey Unit on behalf of the
UltraVISTA consortium. This work is based in part on observations made
with the Spitzer Space Telescope, which is operated by the Jet
Propulsion Laboratory, California Institute of Technology under a NASA
contract. This research made use of ASTROPY, a community-developed core
PYTHON package for Astronomy (Astropy Collaboration et al. 2013).
CR Abramson LE, 2016, ASTROPHYS J, V832, DOI 10.3847/0004-637X/832/1/7
ANDERS E, 1989, GEOCHIM COSMOCHIM AC, V53, P197, DOI 10.1016/0016-7037(89)90286-X
Ashby MLN, 2013, ASTROPHYS J, V769, DOI 10.1088/0004-637X/769/1/80
Baldry IK, 2006, MON NOT R ASTRON SOC, V373, P469, DOI 10.1111/j.1365-2966.2006.11081.x
BALDWIN JA, 1981, PUBL ASTRON SOC PAC, V93, P5, DOI 10.1086/130766
Barro G, 2013, ASTROPHYS J, V765, DOI 10.1088/0004-637X/765/2/104
Behroozi PS, 2013, ASTROPHYS J, V770, DOI 10.1088/0004-637X/770/1/57
Bell EF, 2004, ASTROPHYS J, V608, P752, DOI 10.1086/420778
BELL EF, 2012, ASTROPHYS J, V753
Brammer GB, 2009, ASTROPHYS J LETT, V706, pL173, DOI 10.1088/0004-637X/706/1/L173
Brinchmann J, 2013, MON NOT R ASTRON SOC, V432, P2112, DOI 10.1093/mnras/stt551
Bruzual G, 2003, MON NOT R ASTRON SOC, V344, P1000, DOI 10.1046/j.1365-8711.2003.06897.x
Buchner J, 2014, ASTRON ASTROPHYS, V564, DOI 10.1051/0004-6361/201322971
Byler N, 2017, ASTROPHYS J, V840, DOI 10.3847/1538-4357/aa6c66
Calzetti D, 2000, ASTROPHYS J, V533, P682, DOI 10.1086/308692
CARDELLI JA, 1989, ASTROPHYS J, V345, P245, DOI 10.1086/167900
Carson DP, 2010, MON NOT R ASTRON SOC, V408, P213, DOI 10.1111/j.1365-2966.2010.17151.x
Casey CM, 2012, MON NOT R ASTRON SOC, V425, P3094, DOI 10.1111/j.1365-2966.2012.21455.x
Charlot S, 2000, ASTROPHYS J, V539, P718, DOI 10.1086/309250
Charlot S, 2001, MON NOT R ASTRON SOC, V323, P887, DOI 10.1046/j.1365-8711.2001.04260.x
Chevallard J, 2016, MON NOT R ASTRON SOC, V462, P1415, DOI 10.1093/mnras/stw1756
Choi J, 2014, ASTROPHYS J, V792, DOI 10.1088/0004-637X/792/2/95
Chuter RW, 2011, MON NOT R ASTRON SOC, V413, P1678, DOI 10.1111/j.1365-2966.2011.18241.x
Citro A, 2016, ASTRON ASTROPHYS, V592, DOI 10.1051/0004-6361/201527772
Croton DJ, 2006, MON NOT R ASTRON SOC, V365, P11, DOI 10.1111/j.1365-2966.2005.09675.x
Cullen F, 2018, MON NOT R ASTRON SOC, V476, P3218, DOI 10.1093/mnras/sty469
Cullen F, 2017, MON NOT R ASTRON SOC, V470, P3006, DOI 10.1093/mnras/stx1451
Dave R, 2017, MNRAS, V471, P1671
Dave R, 2016, MON NOT R ASTRON SOC, V462, P3265, DOI 10.1093/mnras/stw1862
Diemer B, 2017, ASTROPHYS J, V839, DOI 10.3847/1538-4357/aa68e5
Dopita MA, 2000, ASTROPHYS J, V542, P224, DOI 10.1086/309538
Eldridge JJ, 2009, MON NOT R ASTRON SOC, V400, P1019, DOI 10.1111/j.1365-2966.2009.15514.x
Faber SM, 2007, ASTROPHYS J, V665, P265, DOI 10.1086/519294
FALCONBARROSO J, 2011, A A, V532
Fang JJ, 2018, ASTROPHYS J, V858, DOI 10.3847/1538-4357/aabcba
Ferland GJ, 2017, REV MEX ASTRON ASTR, V53, P385
Fernandes RC, 2005, MON NOT R ASTRON SOC, V358, P363, DOI 10.1111/j.1365-2966.2005.08752.x
Feroz F, 2008, MON NOT R ASTRON SOC, V384, P449, DOI 10.1111/j.1365-2966.2007.12353.x
Feroz F, 2009, MON NOT R ASTRON SOC, V398, P1601, DOI 10.1111/j.1365-2966.2009.14548.x
Feroz F., 2013, ARXIV13062144
Foreman-Mackey D., 2016, J OPEN SOURCE SOFTW, V24
Foreman-Mackey D, 2013, PUBL ASTRON SOC PAC, V125, P306, DOI 10.1086/670067
Forster Schreiber N.M, 2014, APJ, V787, P38
Fumagalli M, 2016, ASTROPHYS J, V822, DOI 10.3847/0004-637X/822/1/1
Furusawa H, 2016, ASTROPHYS J, V822, DOI 10.3847/0004-637X/822/1/46
Gabor JM, 2015, MON NOT R ASTRON SOC, V447, P374, DOI 10.1093/mnras/stu2399
Gabor JM, 2011, MON NOT R ASTRON SOC, V417, P2676, DOI 10.1111/j.1365-2966.2011.19430.x
Gabor JM, 2010, MON NOT R ASTRON SOC, V407, P749, DOI 10.1111/j.1365-2966.2010.16961.x
Gallazzi A, 2014, ASTROPHYS J, V788, DOI 10.1088/0004-637X/788/1/72
Gladders MD, 2013, ASTROPHYS J, V770, DOI 10.1088/0004-637X/770/1/64
Goodman J, 2010, COMM APP MATH COM SC, V5, P65, DOI 10.2140/camcos.2010.5.65
Hartley WG, 2010, MON NOT R ASTRON SOC, V407, P1212, DOI 10.1111/j.1365-2966.2010.16972.x
Heavens A, 2004, NATURE, V428, P625, DOI 10.1038/nature02474
Heavens AF, 2000, MON NOT R ASTRON SOC, V317, P965, DOI 10.1046/j.1365-8711.2000.03692.x
HILDEBRAND RH, 1983, Q J ROY ASTRON SOC, V24, P267
Hogg D.W., 2010, ARXIV10084686
Hudelot P., 2012, VIZIER ONLINE DATA C
Inoue AK, 2014, MON NOT R ASTRON SOC, V442, P1805, DOI 10.1093/mnras/stu936
Jorgensen I, 2013, ASTRON J, V145, DOI 10.1088/0004-6256/145/3/77
Kennicutt RC, 2012, ANNU REV ASTRON ASTR, V50, P531, DOI 10.1146/annurev-astro-081811-125610
Kewley LJ, 2013, ASTROPHYS J, V774, DOI 10.1088/0004-637X/774/2/100
Kriek M, 2013, ASTROPHYS J LETT, V775, DOI 10.1088/2041-8205/775/1/L16
Kroupa P, 2002, MON NOT R ASTRON SOC, V336, P1188, DOI 10.1046/j.1365-8711.2002.05848.x
Law-Smith J, 2017, ASTROPHYS J, V836, DOI 10.3847/1538-4357/836/1/87
Leja J, 2017, ASTROPHYS J, V837, DOI 10.3847/1538-4357/aa5ffe
Lonoce I, 2014, MON NOT R ASTRON SOC, V444, P2048, DOI 10.1093/mnras/stu1593
Lotz JM, 2011, ASTROPHYS J, V742, DOI 10.1088/0004-637X/742/2/103
MADAU P, 1995, ASTROPHYS J, V441, P18, DOI 10.1086/175332
Maiolino R, 2012, MON NOT R ASTRON SOC, V425, pL66, DOI 10.1111/j.1745-3933.2012.01303.x
Maraston C, 2010, MON NOT R ASTRON SOC, V407, P830, DOI 10.1111/j.1365-2966.2010.16973.x
McCracken HJ, 2012, ASTRON ASTROPHYS, V544, DOI 10.1051/0004-6361/201219507
McLure RJ, 2018, MON NOT R ASTRON SOC, V479, P25, DOI 10.1093/mnras/sty1213
McLure RJ, 2018, MON NOT R ASTRON SOC, V476, P3991, DOI 10.1093/mnras/sty522
McLure RJ, 2013, MON NOT R ASTRON SOC, V428, P1088, DOI 10.1093/mnras/sts092
McLure RJ, 2011, MON NOT R ASTRON SOC, V418, P2074, DOI 10.1111/j.1365-2966.2011.19626.x
Mobasher B, 2015, ASTROPHYS J, V808, DOI 10.1088/0004-637X/808/1/101
Moresco M, 2010, ASTRON ASTROPHYS, V524, DOI 10.1051/0004-6361/201014044
Mortlock A, 2017, MON NOT R ASTRON SOC, V465, P672, DOI 10.1093/mnras/stw2728
Moustakas J, 2006, ASTROPHYS J, V642, P775, DOI 10.1086/500964
Nelson D, 2018, MON NOT R ASTRON SOC, V475, P624, DOI 10.1093/mnras/stx3040
Nogueira-Cavalcante JP, 2018, MON NOT R ASTRON SOC, V473, P1346, DOI 10.1093/mnras/stx2399
Onodera M, 2015, ASTROPHYS J, V808, DOI 10.1088/0004-637X/808/2/161
Onodera M, 2012, ASTROPHYS J, V755, DOI 10.1088/0004-637X/755/1/26
Pacifici C, 2016, ASTROPHYS J, V832, DOI 10.3847/0004-637X/832/1/79
Pacifici C, 2012, MON NOT R ASTRON SOC, V421, P2002, DOI 10.1111/j.1365-2966.2012.20431.x
Panter B, 2007, MON NOT R ASTRON SOC, V378, P1550, DOI 10.1111/j.1365-2966.2007.11909.x
Papovich C, 2012, ASTROPHYS J, V750, DOI 10.1088/0004-637X/750/2/93
Peng Y, 2015, NATURE, V521, P192, DOI 10.1038/nature14439
Peng YJ, 2012, ASTROPHYS J, V757, DOI 10.1088/0004-637X/757/1/4
Pentericci L, 2018, ARXIV180307373
Pforr J, 2012, MON NOT R ASTRON SOC, V422, P3285, DOI 10.1111/j.1365-2966.2012.20848.x
Reddy NA, 2012, ASTROPHYS J, V754, DOI 10.1088/0004-637X/754/1/25
Robitaille TP, 2013, ASTRON ASTROPHYS, V558, DOI 10.1051/0004-6361/201322068
Schawinski K, 2014, MON NOT R ASTRON SOC, V440, P889, DOI 10.1093/mnras/stu327
Siudek M, 2017, ASTRON ASTROPHYS, V597, DOI 10.1051/0004-6361/201628951
Skilling J, 2006, BAYESIAN ANAL, V1, P833, DOI 10.1214/06-BA127
Smethurst RJ, 2018, MON NOT R ASTRON SOC, V473, P2679, DOI 10.1093/mnras/stx2547
Steinhardt CL, 2014, ASTROPHYS J LETT, V791, DOI 10.1088/2041-8205/791/2/L25
Straatman CMS, 2016, ASTROPHYS J, V830, DOI 10.3847/0004-637X/830/1/51
Straatman CMS, 2014, ASTROPHYS J LETT, V783, DOI 10.1088/2041-8205/783/1/L14
Strateva I, 2001, ASTRON J, V122, P1861, DOI 10.1086/323301
Strazzullo V, 2013, ASTROPHYS J, V772, DOI 10.1088/0004-637X/772/2/118
Thomas D, 2010, MON NOT R ASTRON SOC, V404, P1775, DOI 10.1111/j.1365-2966.2010.16427.x
THOMAS R, 2017, ASTRON ASTROPHYS, V602
Tomczak AR, 2014, ASTROPHYS J, V783, DOI 10.1088/0004-637X/783/2/85
van der Wel A, 2014, ASTROPHYS J, V788, DOI 10.1088/0004-637X/788/1/28
Vogelsberger M, 2014, MON NOT R ASTRON SOC, V444, P1518, DOI 10.1093/mnras/stu1536
Whitaker KE, 2013, ASTROPHYS J LETT, V770, DOI 10.1088/2041-8205/770/2/L39
Whitaker KE, 2011, ASTROPHYS J, V735, DOI 10.1088/0004-637X/735/2/86
Wilkinson DM, 2017, MON NOT R ASTRON SOC, V472, P4297, DOI 10.1093/mnras/stx2215
Williams RJ, 2009, ASTROPHYS J, V691, P1879, DOI 10.1088/0004-637X/691/2/1879
WORTHEY G, 1994, ASTROPHYS J SUPPL S, V94, P687, DOI 10.1086/192087
Wuyts S, 2011, ASTROPHYS J, V738, DOI 10.1088/0004-637X/738/1/106
Younger JD, 2009, MON NOT R ASTRON SOC, V394, P1685, DOI 10.1111/j.1365-2966.2009.14455.x
NR 114
TC 44
Z9 44
U1 1
U2 3
PU OXFORD UNIV PRESS
PI OXFORD
PA GREAT CLARENDON ST, OXFORD OX2 6DP, ENGLAND
SN 0035-8711
EI 1365-2966
J9 MON NOT R ASTRON SOC
JI Mon. Not. Roy. Astron. Soc.
PD NOV
PY 2018
VL 480
IS 4
BP 4379
EP 4401
DI 10.1093/mnras/sty2169
PG 23
WC Astronomy & Astrophysics
SC Astronomy & Astrophysics
GA GZ6XX
UT WOS:000449617100009
OA Green Published
DA 2021-04-21
ER
PT J
AU Krischer, L
Aiman, YA
Bartholomaus, T
Donner, S
van Driel, M
Duru, K
Garina, K
Gessele, K
Gunawan, T
Hable, S
Hadziioannou, C
Koymans, M
Leeman, J
Lindner, F
Ling, A
Megies, T
Nunn, C
Rijal, A
Salvermoser, J
Soza, ST
Tape, C
Taufiqurrahman, T
Vargas, D
Wassermann, J
Wolfl, F
Williams, M
Wollherr, S
Igel, H
AF Krischer, Lion
Aiman, Yongki Andita
Bartholomaus, Timothy
Donner, Stefanie
van Driel, Martin
Duru, Kenneth
Garina, Kristina
Gessele, Kilian
Gunawan, Tomy
Hable, Sarah
Hadziioannou, Celine
Koymans, Mathijs
Leeman, John
Lindner, Fabian
Ling, Angel
Megies, Tobias
Nunn, Ceri
Rijal, Ashim
Salvermoser, Johannes
Soza, Sujania Talavera
Tape, Carl
Taufiqurrahman, Taufiq
Vargas, David
Wassermann, Joachim
Woelfl, Florian
Williams, Mitch
Wollherr, Stephanie
Igel, Heiner
TI seismo-live: An Educational Online Library of Jupyter Notebooks for
Seismology
SO SEISMOLOGICAL RESEARCH LETTERS
LA English
DT Article
ID PYTHON PACKAGE; DATABASE; OBSPY
AB Efficient computer programming is becoming a central requirement in quantitative Earth science education. This applies not only to the early career stage but-due to the rapid evolution of programming paradigms-also throughout professional life. At universities, workshops, or any software training events, efficient practical programming exercises are hampered by the heterogeneity of hardware and software setups of participants. Jupyter notebooks offer an attractive solution by providing a platform-independent concept and allowing the combination of text-editing, program execution, and plotting. Here, we document a growing library with dozens of Jupyter notebooks for training in seismology. The library is made "live" through a server that allows accessing and running the notebooks in the browser on any system (PC, laptop, tablet, smartphone), provided there is internet access. The library seismo-live contains notebooks on many aspects of seismology, including data processing, computational seismology, and earthquake physics, as well as reproducible papers and graphics. It is a community effort and is intended to benefit from continuous interaction with seismologists around the world.
C1 [Krischer, Lion; Aiman, Yongki Andita; Donner, Stefanie; Duru, Kenneth; Garina, Kristina; Gunawan, Tomy; Hable, Sarah; Hadziioannou, Celine; Lindner, Fabian; Ling, Angel; Megies, Tobias; Nunn, Ceri; Rijal, Ashim; Salvermoser, Johannes; Soza, Sujania Talavera; Taufiqurrahman, Taufiq; Vargas, David; Wassermann, Joachim; Woelfl, Florian; Williams, Mitch; Wollherr, Stephanie; Igel, Heiner] Ludwig Maximilians Univ Munchen, Dept Earth & Environm Sci, Theresienstr 41, D-80333 Munich, Bavaria, Germany.
[Bartholomaus, Timothy] Univ Idaho, Moscow, ID 83843 USA.
[Krischer, Lion; van Driel, Martin; Lindner, Fabian] Eidgenoss Tech Hsch Zurich, Zurich, Switzerland.
[Koymans, Mathijs] Royal Netherlands Meteorol Inst KNMI, De Bilt, Netherlands.
[Leeman, John] Leeman Geophys LLC, Mead, CO USA.
[Tape, Carl] Univ Alaska, Fairbanks, AK 99701 USA.
[Donner, Stefanie] Bundesanstalten Geowissensch & Rohstoffe, Hannover, Germany.
[Hadziioannou, Celine] Univ Hamburg, Ctr Earth Syst Res & Sustainabil CEN, Hamburg, Germany.
[Soza, Sujania Talavera] Univ Utrecht, Utrecht, Netherlands.
RP Krischer, L (corresponding author), Ludwig Maximilians Univ Munchen, Dept Earth & Environm Sci, Theresienstr 41, D-80333 Munich, Bavaria, Germany.; Krischer, L (corresponding author), Eidgenoss Tech Hsch Zurich, Zurich, Switzerland.
EM lion.krischer@erdw.ethz.ch; yongki.aiman@gmail.com;
tbartholomaus@uidaho.edu; stefanie.donner@bgr.de; vandriel@erdw.ethz.ch;
kenneth.c.duru@gmail.com; kristinagarina@gmail.com; kilian@gessele.de;
gunawantomy.pgr3@gmail.com; shable@geophysik.uni-muenchen.de;
celine.hadziioannou@uni-hamburg.de; koymans@knmi.nl; jleeman@ucar.edu;
lindner@vaw.baug.ethz.ch; okling92@gmail.com;
megies@geophysik.uni-muenchen.de; ceri.nunn@gmail.com;
rijalashim@gmail.com; salv_johannes@gmx.de; sujaniaasereth@gmail.com;
ctape@alaska.edu; taufiqurrahman@hotmail.com; davofis123@gmail.com;
joachim.wassermann@geophysik.uni-muenchen.de; flo.woelfl@web.de;
mcbw00@gmail.com; wollherr@geophysik.uni-muenchen.de; heiner.igel@lmu.de
RI Igel, Heiner/AAF-2865-2019; Igel, Heiner/E-9580-2010; Igel,
Heiner/AAK-2038-2021
OI Igel, Heiner/0000-0002-7242-6399; Donner, Stefanie/0000-0001-7351-8079;
Duru, Kenneth/0000-0002-5260-7942; Hadziioannou,
Celine/0000-0002-5312-2226; Talavera-Soza, Sujania/0000-0003-0947-917X
FU European Science FoundationEuropean Science Foundation (ESF)European
Commission; EU FP7 VERCE; EU H2020 EPOS project; European Research
Council (ERC-ADV Grant ROMY)
FX The authors gratefully acknowledge the European Science Foundation for
funding the TIme DEpendent Seismology (TIDES) project (coordinated by
Andrea Morelli, University of Bologna). The idea for the seismo-live
platform originated at the TIDES Meeting in Bertinoro 2015. The authors
also gratefully acknowledge the support of the Leibniz Supercomputing
Centre Munich (Anton Frank, Dieter Kranzlmuller) for their continuous
support and the provision of computational resources. Additionally the
authors are grateful for fruitful discussions and support in
seismo-live's early phase within the EU FP7 VERCE and the EU H2020 EPOS
project. H.I. acknowledges support from the European Research Council
(ERC-ADV Grant ROMY). The authors acknowledge the contribution of Lane
Johnson providing his original code solving Lamb's problem.
CR Achterberg H, 2017, SOFTWARE CARPENTRY P
Aiken JM, 2018, SEISMOL RES LETT, V89, P1165, DOI 10.1785/0220170246
Aki K., 2002, QUANTITATIVE SEISMOL
[Anonymous], 2016, NATURE, V533, P437, DOI 10.1038/533437a
Beyreuther M, 2010, SEISMOL RES LETT, V81, P530, DOI 10.1785/gssrl.81.3.530
Chamberlain CJ, 2018, SEISMOL RES LETT, V89, P173, DOI 10.1785/0220170151
Chen C, 2016, SEISMOL RES LETT, V87, P1384, DOI 10.1785/0220160019
Durand S, 2018, SEISMOL RES LETT, V89, P658, DOI 10.1785/0220170142
Hunter JD, 2007, COMPUT SCI ENG, V9, P90, DOI 10.1109/MCSE.2007.55
Igel H., 2016, COMPUTATIONAL SEISMO
Igel H., 2015, ENCY COMPLEXITY SYST
JOHNSON LR, 1974, GEOPHYS J ROY ASTR S, V37, P99, DOI 10.1111/j.1365-246X.1974.tb02446.x
Kluyver T, 2016, POSITIONING AND POWER IN ACADEMIC PUBLISHING: PLAYERS, AGENTS AND AGENDAS, P87, DOI 10.3233/978-1-61499-649-1-87
Krischer Lion, 2015, Computational Science and Discovery, V8, DOI 10.1088/1749-4699/8/1/014003
Krischer L, 2017, SEISMOL RES LETT, V88, P1127, DOI 10.1785/0220160210
Krischer L, 2016, GEOPHYS J INT, V207, P1003, DOI 10.1093/gji/ggw319
Krischer L, 2015, SEISMOL RES LETT, V86, P1198, DOI 10.1785/0220140248
Lecocq T, 2014, SEISMOL RES LETT, V85, P715, DOI 10.1785/0220130073
MacCarthy JK, 2014, SEISMOL RES LETT, V85, P905, DOI 10.1785/0220140013
Megies T, 2011, ANN GEOPHYS-ITALY, V54, P47, DOI 10.4401/ag-4838
Nissen-Meyer T, 2014, SOLID EARTH, V5, P425, DOI 10.5194/se-5-425-2014
Perkel JM, 2015, NATURE, V518, P125, DOI 10.1038/518125a
Salvermoser J, 2017, SEISMOL RES LETT, V88, P935, DOI 10.1785/0220160184
Schmelzbach C, 2018, GEOPHYSICS, V83, pWC53, DOI 10.1190/GEO2017-0492.1
Shen H, 2014, NATURE, V515, P151, DOI 10.1038/515151a
van Driel M, 2015, SOLID EARTH, V6, P701, DOI 10.5194/se-6-701-2015
van Rossum Jr G, 2011, PYTHON LANGUAGE REFE
NR 27
TC 5
Z9 5
U1 4
U2 13
PU SEISMOLOGICAL SOC AMER
PI ALBANY
PA 400 EVELYN AVE, SUITE 201, ALBANY, CA 94706-1375 USA
SN 0895-0695
EI 1938-2057
J9 SEISMOL RES LETT
JI Seismol. Res. Lett.
PD NOV-DEC
PY 2018
VL 89
IS 6
BP 2413
EP 2419
DI 10.1785/0220180167
PG 7
WC Geochemistry & Geophysics
SC Geochemistry & Geophysics
GA GY7HX
UT WOS:000448780200046
DA 2021-04-21
ER
PT J
AU Aebischer, J
Brivio, I
Celis, A
Evans, JA
Jiang, Y
Kumar, J
Pan, XY
Porod, W
Rosiek, J
Shih, D
Staub, F
Straub, DM
van Dyk, D
Vicente, A
AF Aebischer, Jason
Brivio, Ilaria
Celis, Alejandro
Evans, Jared A.
Jiang, Yun
Kumar, Jacky
Pan, Xuanyou
Porod, Werner
Rosiek, Janusz
Shih, David
Staub, Florian
Straub, David M.
van Dyk, Danny
Vicente, Avelino
TI WCxf : An exchange format for Wilson coefficients beyond the Standard
Model
SO COMPUTER PHYSICS COMMUNICATIONS
LA English
DT Article
DE High energy physics and computing; Models beyond the standard model
ID EFFECTIVE-FIELD THEORY; LOOP CALCULATIONS; FLAVOR; FEYNARTS; PROGRAM;
SPHENO; MSSM
AB We define a data exchange format for numerical values of Wilson coefficients of local operators parameterising low-energy effects of physics beyond the Standard Model. The format facilitates interfacing model-specific Wilson coefficient calculators, renormalisation group (RG) runners, and observable calculators. It is designed to be unambiguous (defining a non-redundant set of operators with fixed normalisation in each basis), extensible (allowing the addition of new EFTs or bases by the user), and robust (being based on industry standard file formats with parsers implemented in many programming languages). We have implemented the format for the Standard Model EFT (SMEFT) and for the weak effective theory (WET) below the electroweak scale and have added interfaces to a number of public codes dealing with SMEFT or WET. We also provide command-line utilities and a Python module for convenient manipulation of WCxf files, including translation between different bases and matching from SMEFT to WET. (C) 2018 Elsevier B.V. All rights reserved.
C1 [Aebischer, Jason; Pan, Xuanyou; Straub, David M.] TUM, Excellence Cluster Universe, Boltzmannstr 2, D-85748 Garching, Germany.
[Brivio, Ilaria; Jiang, Yun] Univ Copenhagen, Niels Bohr Inst, Niels Bohr Int Acad & Discovery Ctr, Blegdamsvej 17, DK-2100 Copenhagen O, Denmark.
[Celis, Alejandro] Ludwig Maximilians Univ Munchen, Fak Phys, Arnold Sommerfeld Ctr Theoret Phys, D-80333 Munich, Germany.
[Evans, Jared A.] Univ Cincinnati, Dept Phys, Cincinnati, OH 45221 USA.
[Porod, Werner] Univ Wurzburg, Inst Theoret Phys & Astrophys, D-97074 Wurzburg, Germany.
[Rosiek, Janusz] Univ Warsaw, Fac Phys, Pasteura 5, PL-02093 Warsaw, Poland.
[Shih, David] Rutgers State Univ, Dept Phys, Piscataway, NJ 08854 USA.
[Staub, Florian] Karlsruhe Inst Technol, ITP, Engesserstr 7, D-76128 Karlsruhe, Germany.
[Staub, Florian] Karlsruhe Inst Technol, IKP, Hermann von Helmholtz Pl 1, D-76344 Eggenstein Leopoldshafen, Germany.
[van Dyk, Danny] Tech Univ Munich, Phys Dept, James Franck Str 1, D-85748 Garching, Germany.
[Vicente, Avelino] Univ Valencia, CSIC, Inst Fis Corpuscular, Valencia 46071, Spain.
[Kumar, Jacky] Harish Chandra Res Inst, Chhatnag Rd, Allahabad 211019, Uttar Pradesh, India.
RP Straub, DM (corresponding author), TUM, Excellence Cluster Universe, Boltzmannstr 2, D-85748 Garching, Germany.
EM david.straub@tum.de
RI Brivio, Ilaria/ABD-6976-2020; Vicente, Avelino/A-6905-2017; Jiang,
Yun/E-4869-2015
OI Brivio, Ilaria/0000-0002-0396-5866; Vicente,
Avelino/0000-0002-1137-4695; Staub, Florian/0000-0001-5911-5804; Straub,
David/0000-0001-5762-7339; Jiang, Yun/0000-0002-4898-0787; Kumar,
Jacky/0000-0001-9053-0731; Porod, Werner/0000-0002-0248-6556; Rosiek,
Janusz/0000-0002-1653-1274
FU DFG cluster of excellence "Origin and Structure of the Universe"German
Research Foundation (DFG); DFGGerman Research Foundation (DFG)European
Commission [BU 1391/2-1, PO 1337-7/1]; Villum Foundation, NBIA;
Discovery Centre at Copenhagen University; Danish National Research
Foundation (DNRF91); "Juan de la Cierva" program - Spanish MINECO
[27-13-463B-731]; MINECO [FPA2014-58183-P, FPA2017-85216-P,
SEV-2014-0398]; Generalitat ValencianaGeneralitat ValencianaEuropean
Commission [PROMETEOII/ 2014/084]; Deutsche Forschungsgemeinschaft (DFG)
within the Emmy Noether programmeGerman Research Foundation (DFG) [DY
130/1-1]; DFG Collaborative Research Center 110 "Symmetries and the
Emergence of Structure in QCD"; National Science Center, PolandNational
Science Centre, Poland [DEC-2015/19/13/ST2/02848,
DEC-2015/18/M/ST2/00054]; ERC Recognition Award of the Helmholtz
Association [ERC-RA-0008]
FX The work of D.S., J.A., and X.P. was supported by the DFG cluster of
excellence "Origin and Structure of the Universe". The work of A.C. was
supported by the DFG grant BU 1391/2-1. I.B. and Y.J. were supported by
the Villum Foundation, NBIA, the Discovery Centre at Copenhagen
University and the Danish National Research Foundation (DNRF91). A.V.
acknowledges financial support from the "Juan de la Cierva" program
(27-13-463B-731) funded by the Spanish MINECO as well as from the grants
FPA2014-58183-P, FPA2017-85216-P and SEV-2014-0398 (MINECO), and
PROMETEOII/ 2014/084 (Generalitat Valenciana). D.v.D. is supported by
the Deutsche Forschungsgemeinschaft (DFG) within the Emmy Noether
programme under grant DY 130/1-1 and through the DFG Collaborative
Research Center 110 "Symmetries and the Emergence of Structure in QCD".
The work of J.R. was supported in part by the National Science Center,
Poland, under research grants DEC-2015/19/13/ST2/02848 and
DEC-2015/18/M/ST2/00054. W.P. was supported by the DFG, project no. PO
1337-7/1. F.S. was supported by the ERC Recognition Award ERC-RA-0008 of
the Helmholtz Association. I.B. and Y.J. also thank Michael Trott for
valuable discussions and suggestions in designing the SMEFTsim
interface. A.C. and A.V. thank Javier Fuentes-Martin and Javier Virto
for their collaboration in the development of DsixTools. D.S. thanks
Alex Arbey, Christoph Bobeth, Nazila Mahmoudi, Ayan Paul, and Mauro
Valli for useful discussions.
CR ABBOTT LF, 1980, PHYS REV D, V22, P2208, DOI 10.1103/PhysRevD.22.2208
Aebischer J., 2018, ARXIV180405033HEPPH
Aebischer J, 2016, J HIGH ENERGY PHYS, DOI 10.1007/JHEP05(2016)037
Aebischer J, 2017, J HIGH ENERGY PHYS, DOI 10.1007/JHEP09(2017)158
Allanach BC, 2009, COMPUT PHYS COMMUN, V180, P8, DOI 10.1016/j.cpc.2008.08.004
Alloul A, 2014, COMPUT PHYS COMMUN, V185, P2250, DOI 10.1016/j.cpc.2014.04.012
Alonso R, 2014, PHYS LETT B, V734, P302, DOI 10.1016/j.physletb.2014.05.065
Alonso R, 2014, J HIGH ENERGY PHYS, DOI 10.1007/JHEP04(2014)159
Alwall J, 2014, J HIGH ENERGY PHYS, DOI 10.1007/JHEP07(2014)079
Bishara F., 2017, ARXIV170802678HEPPH
Bjornson Z., MYAML
Brivio I., 2017, ARXIV170608945HEPPH
Brivio I., SMEFTSIM
Brivio I., 2017, ARXIV170906492HEPPH
BUCHMULLER W, 1986, NUCL PHYS B, V268, P621, DOI 10.1016/0550-3213(86)90262-2
Celis A., DSIXTOOLS
Celis A, 2017, EUR PHYS J C, V77, DOI 10.1140/epjc/s10052-017-4967-6
Christensen ND, 2009, COMPUT PHYS COMMUN, V180, P1614, DOI 10.1016/j.cpc.2009.02.018
Criado JC, 2018, COMPUT PHYS COMMUN, V227, P42, DOI 10.1016/j.cpc.2018.02.016
D'Ambrosio G, 2002, NUCL PHYS B, V645, P155, DOI 10.1016/S0550-3213(02)00836-2
de Blas J., 2017, ARXIV171110391HEPPH
de Florian D., 2016, HDB LHC HIGGS CROSS, DOI [10.23731/CYRM-2017-002, DOI 10.23731/CYRM-2017-002]
Dedes A, 2017, J HIGH ENERGY PHYS, DOI 10.1007/JHEP06(2017)143
Dedes A., SMEFT FEYNMAN RULES
Degrande C, 2012, COMPUT PHYS COMMUN, V183, P1201, DOI 10.1016/j.cpc.2012.01.022
Evans J. A., FORMFLAVO
Evans J. A., 2016, ARXIV160600003HEPPH
Falkowski A, 2015, EUR PHYS J C, V75, DOI 10.1140/epjc/s10052-015-3806-x
Grzadkowski B, 2010, J HIGH ENERGY PHYS, DOI [10.1007/JHEP10(2010)85, 10.1007/JHEP10(2010)085]
Hahn T, 1999, COMPUT PHYS COMMUN, V118, P153, DOI 10.1016/S0010-4655(98)00173-8
Hahn T, 2004, NUCL PHYS B-PROC SUP, V135, P333, DOI 10.1016/j.nuclphysbps.2004.09.018
Hahn T, 2001, COMPUT PHYS COMMUN, V140, P418, DOI 10.1016/S0010-4655(01)00290-9
Hahn T, 2000, NUCL PHYS B-PROC SUP, V89, P231, DOI 10.1016/S0920-5632(00)00848-3
Hahn T., 2005, 2005 INT LIN COLL WO
Henning B., 2017, J HIGH ENERGY PHYS, V8, DOI [10.1007/JHEP08(2017)016, DOI 10.1007/JHEP08(2017)016]
Jenkins E. E., 2017, ARXIV171105270HEPPH
Jenkins E. E., 2017, ARXIV170904486HEPPH
Jenkins EE, 2014, J HIGH ENERGY PHYS, DOI 10.1007/JHEP01(2014)035
Jenkins EE, 2013, J HIGH ENERGY PHYS, DOI 10.1007/JHEP10(2013)087
Lehman L, 2014, PHYS REV D, V90, DOI 10.1103/PhysRevD.90.125023
Mahmoudi F, 2008, COMPUT PHYS COMMUN, V178, P745, DOI 10.1016/j.cpc.2007.12.006
Mahmoudi F, 2012, COMPUT PHYS COMMUN, V183, P285, DOI 10.1016/j.cpc.2011.10.006
Nejad BC, 2014, J PHYS CONF SER, V523, DOI 10.1088/1742-6596/523/1/012050
Patrignani C, 2016, CHINESE PHYS C, V40, DOI 10.1088/1674-1137/40/10/100001
Porod W, 2003, COMPUT PHYS COMMUN, V153, P275, DOI 10.1016/S0010-4655(03)00222-4
Porod W, 2012, COMPUT PHYS COMMUN, V183, P2458, DOI 10.1016/j.cpc.2012.05.021
Porod W., FLAVORKIT
Porod W, 2014, EUR PHYS J C, V74, P1, DOI 10.1140/epjc/s10052-014-2992-2
Skands P. Z., 2004, HIGH ENERG PHYS, V7, DOI 10.1088/1126-6708/2004/07/036
Staub F., 2008, ARXIV08060538HEPPH
Staub F, 2014, COMPUT PHYS COMMUN, V185, P1773, DOI 10.1016/j.cpc.2014.02.018
Staub F, 2013, COMPUT PHYS COMMUN, V184, P1792, DOI 10.1016/j.cpc.2013.02.019
Staub F, 2011, COMPUT PHYS COMMUN, V182, P808, DOI 10.1016/j.cpc.2010.11.030
Staub F, 2010, COMPUT PHYS COMMUN, V181, P1077, DOI 10.1016/j.cpc.2010.01.011
Straub D.M., FLAVIO FLAVOUR PHENO, DOI [10.5281/zenodo.594587, DOI 10.5281/ZENODO.594587]
van Dyk D., EOS HEP PROGRAMM FLA
WEINBERG S, 1979, PHYS REV LETT, V43, P1566, DOI 10.1103/PhysRevLett.43.1566
NR 57
TC 36
Z9 36
U1 0
U2 6
PU ELSEVIER SCIENCE BV
PI AMSTERDAM
PA PO BOX 211, 1000 AE AMSTERDAM, NETHERLANDS
SN 0010-4655
EI 1879-2944
J9 COMPUT PHYS COMMUN
JI Comput. Phys. Commun.
PD NOV
PY 2018
VL 232
BP 71
EP 83
DI 10.1016/j.cpc.2018.05.022
PG 13
WC Computer Science, Interdisciplinary Applications; Physics, Mathematical
SC Computer Science; Physics
GA GR0FQ
UT WOS:000442190200006
DA 2021-04-21
ER
PT J
AU Lian, C
Hu, SQ
Guan, MX
Meng, S
AF Lian, Chao
Hu, Shi-Qi
Guan, Meng-Xue
Meng, Sheng
TI Momentum-resolved TDDFT algorithm in atomic basis for real time tracking
of electronic excitation
SO JOURNAL OF CHEMICAL PHYSICS
LA English
DT Article
ID DENSITY-FUNCTIONAL THEORY; ORDER HARMONIC-GENERATION; CHARGE-TRANSFER
DYNAMICS; SOLAR-CELLS; 1ST-PRINCIPLES ELECTRON; NONADIABATIC DYNAMICS;
MOLECULAR-DYNAMICS; PYTHON FRAMEWORK; LASER FIELDS; APPROXIMATION
AB Ultrafast electronic dynamics in solids lies at the core of modern condensed matter and materials physics. To build up a practical ab initio method for studying solids under photoexcitation, we develop a momentum-resolved real-time time dependent density functional theory (rt-TDDFT) algorithm using numerical atomic basis, together with the implementation of both the length and vector gauge of the electromagnetic field. When applied to simulate elementary excitations in two-dimensional materials such as graphene, different excitation modes, only distinguishable in momentum space, are observed. The momentum-resolved rt-TDDFT is important and computationally efficient for the study of ultrafast dynamics in extended systems. Published by AIP Publishing.
C1 [Lian, Chao; Hu, Shi-Qi; Guan, Meng-Xue; Meng, Sheng] Chinese Acad Sci, Beijing Natl Lab Condensed Matter Phys, Beijing 100190, Peoples R China.
[Lian, Chao; Hu, Shi-Qi; Guan, Meng-Xue; Meng, Sheng] Chinese Acad Sci, Inst Phys, Beijing 100190, Peoples R China.
[Meng, Sheng] Collaborat Innovat Ctr Quantum Matter, Beijing 100190, Peoples R China.
RP Meng, S (corresponding author), Chinese Acad Sci, Beijing Natl Lab Condensed Matter Phys, Beijing 100190, Peoples R China.; Meng, S (corresponding author), Chinese Acad Sci, Inst Phys, Beijing 100190, Peoples R China.; Meng, S (corresponding author), Collaborat Innovat Ctr Quantum Matter, Beijing 100190, Peoples R China.
EM smeng@iphy.ac.cn
RI Lian, Chao/N-5909-2016; Meng, Sheng/A-7171-2010
OI Lian, Chao/0000-0002-2583-9334; Meng, Sheng/0000-0002-1553-1432
FU MOST [2016YFA0300902, 2015CB921001]; NSFCNational Natural Science
Foundation of China (NSFC) [11774396, 11474328, 91850120]; CASChinese
Academy of Sciences [XDB07030100]
FX We acknowledge partial financial support from MOST (Grant Nos.
2016YFA0300902 and 2015CB921001), NSFC (Grant Nos. 11774396, 11474328,
and 91850120), and CAS (Grant No. XDB07030100).
CR Agostini F, 2015, J CHEM PHYS, V142, DOI 10.1063/1.4908133
Andrade X, 2015, PHYS CHEM CHEM PHYS, V17, P31371, DOI 10.1039/c5cp00351b
Barbry M, 2015, NANO LETT, V15, P3410, DOI 10.1021/acs.nanolett.5b00759
BECKE AD, 1988, PHYS REV A, V38, P3098, DOI 10.1103/PhysRevA.38.3098
Bertsch GF, 2000, PHYS REV B, V62, P7998, DOI 10.1103/PhysRevB.62.7998
Bruner A, 2016, J CHEM THEORY COMPUT, V12, P3741, DOI 10.1021/acs.jctc.6b00511
Castro A, 2006, PHYS STATUS SOLIDI B, V243, P2465, DOI 10.1002/pssb.200642067
Chapman CT, 2013, J PHYS CHEM A, V117, P2687, DOI 10.1021/jp312525j
Chapman CT, 2011, J PHYS CHEM LETT, V2, P1189, DOI 10.1021/jz200339y
Ding FZ, 2015, J CHEM PHYS, V143, DOI 10.1063/1.4930985
Ding FZ, 2015, J CHEM PHYS, V142, DOI 10.1063/1.4906083
Ding FZ, 2014, J CHEM PHYS, V140, DOI 10.1063/1.4884388
Dion M, 2004, PHYS REV LETT, V92, DOI 10.1103/PhysRevLett.92.246401
Donati G, 2018, J PHYS CHEM C, V122, P10621, DOI 10.1021/acs.jpcc.8b02425
Donati G, 2017, J PHYS CHEM LETT, V8, P5283, DOI 10.1021/acs.jpclett.7b02320
Donati G, 2017, J PHYS CHEM C, V121, P15368, DOI 10.1021/acs.jpcc.7b04451
Donati G, 2016, J PHYS CHEM A, V120, P7255, DOI 10.1021/acs.jpca.6b06419
Elliott P, 2016, NEW J PHYS, V18, DOI 10.1088/1367-2630/18/1/013014
Elliott P, 2012, PHYS REV LETT, V109, DOI 10.1103/PhysRevLett.109.266404
Fernando RG, 2015, J CHEM THEORY COMPUT, V11, P646, DOI 10.1021/ct500943m
Fischer SA, 2015, J CHEM THEORY COMPUT, V11, P4294, DOI 10.1021/acs.jctc.5b00473
Ganeev RA, 2015, J APPL PHYS, V117, DOI 10.1063/1.4905902
Gao SW, 2015, J CHEM PHYS, V142, DOI 10.1063/1.4922490
Gao Y, 2011, SOLID STATE COMMUN, V151, P1009, DOI 10.1016/j.ssc.2011.05.001
Goings JJ, 2018, WIRES COMPUT MOL SCI, V8, DOI 10.1002/wcms.1341
Goings JJ, 2016, J CHEM PHYS, V145, DOI 10.1063/1.4962422
Hack MD, 2000, J PHYS CHEM A, V104, P7917, DOI 10.1021/jp001629r
Haruyama J, 2012, PHYS REV A, V85, DOI 10.1103/PhysRevA.85.062511
Haruyama J, 2012, PHYS REV A, V85, DOI 10.1103/PhysRevA.85.012516
Heslar J, 2007, INT J QUANTUM CHEM, V107, P3159, DOI 10.1002/qua.21491
Hu CP, 2013, PHYS REV B, V87, DOI 10.1103/PhysRevB.87.035421
Isborn CM, 2007, J CHEM PHYS, V126, DOI 10.1063/1.2713391
Jiao Y, 2013, CHEM PHYS LETT, V586, P97, DOI 10.1016/j.cplett.2013.09.008
Jiao Y, 2011, PHYS CHEM CHEM PHYS, V13, P13196, DOI 10.1039/c1cp20540d
Johansson JR, 2013, COMPUT PHYS COMMUN, V184, P1234, DOI 10.1016/j.cpc.2012.11.019
Johansson JR, 2012, COMPUT PHYS COMMUN, V183, P1760, DOI 10.1016/j.cpc.2012.02.021
Kasper JM, 2018, J CHEM THEORY COMPUT, V14, P1998, DOI 10.1021/acs.jctc.7b01279
Krasheninnikov AV, 2007, PHYS REV LETT, V99, DOI 10.1103/PhysRevLett.99.016104
Krausz F, 2009, REV MOD PHYS, V81, P163, DOI 10.1103/RevModPhys.81.163
Krieger K, 2015, J CHEM THEORY COMPUT, V11, P4870, DOI 10.1021/acs.jctc.5b00621
Kruchinin SY, 2018, REV MOD PHYS, V90, DOI 10.1103/RevModPhys.90.021002
LEE CT, 1988, PHYS REV B, V37, P785, DOI 10.1103/PhysRevB.37.785
Lestrange PJ, 2015, J CHEM PHYS, V143, DOI 10.1063/1.4937410
Li XS, 2005, PHYS CHEM CHEM PHYS, V7, P233, DOI 10.1039/b415849k
Li XS, 2005, J CHEM PHYS, V123, DOI 10.1063/1.2008258
LIAN C, 2016, PHYS REV B, V94, DOI DOI 10.1103/PHYSREVB.94.184310
Liang W, 2012, J PHYS CHEM A, V116, P1884, DOI 10.1021/jp2123899
Lion C, 2018, ADV THEOR SIMUL, V1, DOI 10.1002/adts.201800055
Lopata K, 2012, J CHEM THEORY COMPUT, V8, P3284, DOI 10.1021/ct3005613
Lopata K, 2013, J CHEM THEORY COMPUT, V9, P4939, DOI 10.1021/ct400569s
Lopata K, 2011, J CHEM THEORY COMPUT, V7, P1344, DOI 10.1021/ct200137z
Ma J, 2015, NAT COMMUN, V6, DOI 10.1038/ncomms10107
Ma W, 2014, J PHYS CHEM C, V118, P16447, DOI 10.1021/jp410982e
Ma W, 2013, PHYS CHEM CHEM PHYS, V15, P17187, DOI 10.1039/c3cp52458b
Maitra NT, 2005, INT J QUANTUM CHEM, V102, P573, DOI 10.1002/qua.20465
Maitra NT, 2002, PHYS REV LETT, V89, DOI 10.1103/PhysRevLett.89.023002
Manjavacas A, 2014, ACS NANO, V8, P7630, DOI 10.1021/nn502445f
Marques M. A. L., 2012, LECT NOTES PHYS, V837
Marques MAL, 2003, COMPUT PHYS COMMUN, V151, P60, DOI 10.1016/S0010-4655(02)00686-0
Meng S, 2008, J CHEM PHYS, V129, DOI 10.1063/1.2960628
Meng S, 2008, NANO LETT, V8, P3266, DOI 10.1021/nl801644d
Meng S, 2010, NANO LETT, V10, P1238, DOI 10.1021/nl100442e
Miyamoto Y, 2007, PHYS STATUS SOLIDI A, V204, P1925, DOI 10.1002/pssa.200675331
Miyamoto Y, 2006, PHYS REV LETT, V97, DOI 10.1103/PhysRevLett.97.126104
Miyamoto Y, 2013, APPL PHYS LETT, V103, DOI 10.1063/1.4820781
Miyamoto Y, 2010, PHYS REV LETT, V104, DOI 10.1103/PhysRevLett.104.208302
Miyamoto Y, 2007, APPL PHYS LETT, V91, DOI 10.1063/1.2785952
MONKHORST HJ, 1976, PHYS REV B, V13, P5188, DOI 10.1103/PhysRevB.13.5188
Nguyen PD, 2012, J PHYS CHEM LETT, V3, P2898, DOI 10.1021/jz301042f
Nguyen TS, 2015, J CHEM THEORY COMPUT, V11, P2918, DOI 10.1021/acs.jctc.5b00262
Nobusada K, 2004, PHYS REV A, V70, DOI 10.1103/PhysRevA.70.043411
Ordejon P, 1996, PHYS REV B, V53, P10441, DOI 10.1103/PhysRevB.53.R10441
Otobe T, 2016, PHYS REV B, V93, DOI 10.1103/PhysRevB.93.045124
Otobe T, 2008, PHYS REV B, V77, DOI 10.1103/PhysRevB.77.165104
Otobe T, 2009, J PHYS-CONDENS MAT, V21, DOI 10.1088/0953-8984/21/6/064224
Ozaki T, 2003, PHYS REV B, V67, DOI 10.1103/PhysRevB.67.155108
Parandekar PV, 2006, J CHEM THEORY COMPUT, V2, P229, DOI 10.1021/ct050213k
Pellegrini C, 2016, REV MOD PHYS, V88, DOI 10.1103/RevModPhys.88.015006
Perdew JP, 1996, PHYS REV LETT, V77, P3865, DOI 10.1103/PhysRevLett.77.3865
PERDEW JP, 1981, PHYS REV B, V23, P5048, DOI 10.1103/PhysRevB.23.5048
Petrone A, 2014, PHYS CHEM CHEM PHYS, V16, P24457, DOI 10.1039/c4cp04000g
Provorse MR, 2015, J CHEM THEORY COMPUT, V11, P4791, DOI 10.1021/acs.jctc.5b00559
Qian XF, 2006, PHYS REV B, V73, DOI 10.1103/PhysRevB.73.035408
Raghunathan S, 2012, J CHEM THEORY COMPUT, V8, P806, DOI 10.1021/ct200905z
Ren J, 2010, MOL PHYS, V108, P1829, DOI 10.1080/00268976.2010.491489
Repisky M, 2015, J CHEM THEORY COMPUT, V11, P980, DOI 10.1021/ct501078d
Rohringer N, 2006, PHYS REV A, V74, DOI 10.1103/PhysRevA.74.042512
Roman-Perez G, 2009, PHYS REV LETT, V103, DOI 10.1103/PhysRevLett.103.096102
RUNGE E, 1984, PHYS REV LETT, V52, P997, DOI 10.1103/PhysRevLett.52.997
Sato SA, 2015, PHYS REV B, V92, DOI 10.1103/PhysRevB.92.205413
Sato SA, 2015, J CHEM PHYS, V143, DOI 10.1063/1.4937379
Schlegel HB, 2001, J CHEM PHYS, V114, P9758, DOI 10.1063/1.1372182
Schleife A, 2012, J CHEM PHYS, V137, DOI 10.1063/1.4758792
Shinohara Y, 2010, PHYS REV B, V82, DOI 10.1103/PhysRevB.82.155110
Shinohara Y, 2010, J PHYS-CONDENS MAT, V22, DOI 10.1088/0953-8984/22/38/384212
Silaeva EP, 2015, PHYS REV B, V92, DOI 10.1103/PhysRevB.92.155401
Soler JM, 2002, J PHYS-CONDENS MAT, V14, P2745, DOI 10.1088/0953-8984/14/11/302
Song P, 2012, PHYS REV B, V86, DOI 10.1103/PhysRevB.86.121410
Song P, 2011, J CHEM PHYS, V134, DOI 10.1063/1.3554420
Tong XM, 2001, PHYS REV A, V64, DOI 10.1103/PhysRevA.64.013417
Tong XM, 1998, PHYS REV A, V57, P452, DOI 10.1103/PhysRevA.57.452
Tong XM, 1997, CHEM PHYS, V217, P119, DOI 10.1016/S0301-0104(97)00063-3
Townsend E, 2012, NANO LETT, V12, P429, DOI 10.1021/nl2037613
TROULLIER N, 1991, PHYS REV B, V43, P8861, DOI 10.1103/PhysRevB.43.8861
Tsolakidis A, 2002, PHYS REV B, V66, DOI 10.1103/PhysRevB.66.235416
TULLY JC, 1990, J CHEM PHYS, V93, P1061, DOI 10.1063/1.459170
Tussupbayev S, 2015, J CHEM THEORY COMPUT, V11, P1102, DOI 10.1021/ct500763y
Ullrich CA, 2006, J CHEM PHYS, V125, DOI 10.1063/1.2406069
Wachter G, 2014, PHYS REV LETT, V113, DOI 10.1103/PhysRevLett.113.087401
Williams-Young D, 2016, J CHEM THEORY COMPUT, V12, P5333, DOI 10.1021/acs.jctc.6b00693
Yabana K, 2006, PHYS STATUS SOLIDI B, V243, P1121, DOI 10.1002/pssb.200642005
Yabana K, 2012, PHYS REV B, V85, DOI 10.1103/PhysRevB.85.045134
Yabana K, 1996, PHYS REV B, V54, P4484, DOI 10.1103/PhysRevB.54.4484
Yamamoto T, 2006, PHYS REV B, V74, DOI 10.1103/PhysRevB.74.121409
Yan J, 2011, PHYS REV B, V84, DOI 10.1103/PhysRevB.84.235430
Yan J, 2007, PHYS REV LETT, V98, DOI 10.1103/PhysRevLett.98.216602
Yan L, 2016, ACS NANO, V10, P5452, DOI 10.1021/acsnano.6b01840
Yost DC, 2017, PHYS REV B, V96, DOI 10.1103/PhysRevB.96.115134
Zhang H, 2012, PHYS REV B, V85, DOI 10.1103/PhysRevB.85.201409
Zhang H, 2012, PHYS REV B, V85, DOI 10.1103/PhysRevB.85.033402
Zhang H, 2009, APPL PHYS LETT, V95, DOI 10.1063/1.3196317
Zhang JF, 2017, ADV SCI, V4, DOI 10.1002/advs.201600343
Zheng J, 2016, J PHYS CHEM C, V120, P1375, DOI 10.1021/acs.jpcc.5b09921
NR 123
TC 6
Z9 6
U1 5
U2 17
PU AMER INST PHYSICS
PI MELVILLE
PA 1305 WALT WHITMAN RD, STE 300, MELVILLE, NY 11747-4501 USA
SN 0021-9606
EI 1089-7690
J9 J CHEM PHYS
JI J. Chem. Phys.
PD OCT 21
PY 2018
VL 149
IS 15
AR 154104
DI 10.1063/1.5036543
PG 12
WC Chemistry, Physical; Physics, Atomic, Molecular & Chemical
SC Chemistry; Physics
GA GY0NB
UT WOS:000448216600006
PM 30342439
DA 2021-04-21
ER
PT J
AU Jonsson, M
AF Jonsson, Marius
TI Standard error estimation by an automated blocking method
SO PHYSICAL REVIEW E
LA English
DT Article
ID BOOTSTRAP
AB The sample mean (X) over bar is probably the most popular estimator of the expected value in all sciences and var((X) over bar) measures the error (standard- and mean-square-errors). Here, an alternative approach to estimation of var(X) for time series data is presented. The method has an accuracy similar to dependent bootstrapping, but scales in O(n) time, and applies to stationary time series, including stationary Markov chains. The computational complexity is bounded by 12n floating point operations, but this can be reduced to n + O(1) in large computations. Convergence in relative error squared is faster than n(-1/2) and the method is insensitive to the probability distribution of the observations. It is proven that a small part of the correlation structure is relevant to the convergence rate of the method. From this, proof of the Blocking method [Flyvbjerg and Petersen, J. Chem. Phys. 91, 461 (1989)] follows as a corollary. The result is also used to propose a hypothesis test surveying the relevant part of the correlation structure. It yields a fully automatic method which is sufficiently robust to operate without supervision. An algorithm and sample code showing the implementation is available for PYTHON, C++, and R [www.github.com/computative/block]. Method validation using autoregressive AR(1) and AR(2) processes and physics applications is included. Method self-evaluation is provided by bias and mean-square-error statistics. The method is easily adapted to multithread applications and data larger than computing cluster memory, such as ultralong time series or data streams. This way, the paper provides a stringent and modern treatment of the Blocking method using rigorous linear algebra, multivariate probability theory, real analysis, and Fisherian statistical inference.
C1 [Jonsson, Marius] Univ Oslo, Dept Phys, N-0316 Oslo, Norway.
RP Jonsson, M (corresponding author), Univ Oslo, Dept Phys, N-0316 Oslo, Norway.
CR Agresti A, 2015, FDN LINEAR GEN LINEA
[Anonymous], 2011, PHYS REV A, V83
Borowski Ephraim J., 1989, DICT MATH
BRADLEY RC, 1987, ROCKY MT J MATH, V17, P95, DOI 10.1216/RMJ-1987-17-1-95
Brockwell P. J., 2016, INTRO TIME SERIES FO
DeGroot M.H., 2014, PROBABILITY STAT
Devore JL, 2012, MODERN MATH STAT APP
EFRON B, 1986, AM STAT, V40, P1, DOI 10.2307/2683111
FLYVBJERG H, 1989, J CHEM PHYS, V91, P461, DOI 10.1063/1.457480
Gardiner C.W., 1985, HDB STOCHASTIC METHO, V2nd
Gelman A., 2014, BAYESIAN DATA ANAL
Hazewinkel M., 1989, ENCY MATH
Hazewinkel M., 1993, ENCY MATH
IBRAGIMOV IA, 1975, TEOR VER PRIM, V20, P134
Jones G.L., 2004, PROBAB SURVEY, V1, P299, DOI [DOI 10.1214/154957804100000051, 10.1214/154957804100000051]
Lay D., 2012, LINEAR ALGEBRA ITS A
Lee RM, 2011, PHYS REV E, V83, DOI 10.1103/PhysRevE.83.066706
Mathai A., 1992, QUADRATIC FORMS RAND
McDonald J. N., 2013, COURSE REAL ANAL
METROPOLIS N, 1953, J CHEM PHYS, V21, P1087, DOI 10.1063/1.1699114
PARR WC, 1985, STAT PROBABIL LETT, V3, P97, DOI 10.1016/0167-7152(85)90033-1
Plischke M., 2006, EQUILIBRIUM STAT PHY
Politis D. N., 2006, ECON REV, V23, P53
POLITIS DN, 1994, J AM STAT ASSOC, V89, P1303, DOI 10.2307/2290993
Riley K. F., 2011, ESSENTIAL MATH METHO
Schafer J, 2005, STAT APPL GENET MO B, V4, DOI 10.2202/1544-6115.1175
Shumway R. H., 2017, TIME SERIES ANAL ITS
Stein C., 1956, P 3 BERK S MATH STAT, V1, P197
Wolff U, 2004, COMPUT PHYS COMMUN, V156, P143, DOI 10.1016/S0010-4655(03)00467-3
NR 29
TC 4
Z9 4
U1 0
U2 0
PU AMER PHYSICAL SOC
PI COLLEGE PK
PA ONE PHYSICS ELLIPSE, COLLEGE PK, MD 20740-3844 USA
SN 2470-0045
EI 2470-0053
J9 PHYS REV E
JI Phys. Rev. E
PD OCT 15
PY 2018
VL 98
IS 4
AR 043304
DI 10.1103/PhysRevE.98.043304
PG 17
WC Physics, Fluids & Plasmas; Physics, Mathematical
SC Physics
GA GW9KM
UT WOS:000447305400011
OA Green Published
DA 2021-04-21
ER
PT J
AU Li, L
Lange, CF
Xu, Z
Jiang, PY
Ma, YS
AF Li, Lei
Lange, Carlos F.
Xu, Zhen
Jiang, Pingyu
Ma, Yongsheng
TI Feature-based intelligent system for steam simulation using
computational fluid dynamics
SO ADVANCED ENGINEERING INFORMATICS
LA English
DT Article
DE Artificial intelligence; Feature-based modeling; Computational fluid
dynamics; Robust simulation; Information consistency
ID DESIGN; MODEL
AB In the development of products involving fluids, computational fluid dynamics (CFD) has been increasingly applied to investigate the flow associated with various product operating conditions or product designs. The batch simulation is usually conducted when CFD is heavily used, which is not able to respond to the changes in flow regime when the fluid domain changes. In order to overcome this defect, a rule-based intelligent CFD simulation system for steam simulation is proposed to analyze the specific product design and generate the corresponding robust simulation model with accurate results. The rules used in the system are based on physical knowledge and CFD best practices which make this system easy to be applied in other application scenarios by changing the relevant knowledge base. Fluid physics features and dynamic physics features are used to model the intelligent functions of the system. Incorporating CAE boundary features, the CFD analysis view is fulfilled, which maintains the information consistency in a multi-view feature modeling environment. The prototype software tool is developed by Python 3 with separated logics and settings. The effectiveness of the proposed system is proven by the case study of a disk-type gate valve and a pipe reducer in a piping system.
C1 [Li, Lei; Lange, Carlos F.; Ma, Yongsheng] Univ Alberta, Dept Mech Engn, Edmonton, AB, Canada.
[Xu, Zhen] Univ Alberta, Dept Elect & Comp Engn, Edmonton, AB, Canada.
[Jiang, Pingyu] Xi An Jiao Tong Univ, Sch Mech Engn, Xian, Shaanxi, Peoples R China.
RP Ma, YS (corresponding author), Univ Alberta, Dept Mech Engn, Edmonton, AB, Canada.
EM yongsheng.ma@ualberta.ca
RI Ma, Yongsheng/ABE-8840-2020
OI Ma, Yongsheng/0000-0002-6155-0167; Li, Lei/0000-0002-5259-407X
FU Natural Sciences and Engineering Research Council of Canada
(NSERC)Natural Sciences and Engineering Research Council of Canada
(NSERC); China Scholarship Council (CSC)China Scholarship Council;
University of AlbertaUniversity of Alberta; Alberta Innovates Technology
Futures (AITF); RGL Reservoir Management
FX The authors would like to thank Natural Sciences and Engineering
Research Council of Canada (NSERC), RGL Reservoir Management, China
Scholarship Council (CSC), University of Alberta and Alberta Innovates
Technology Futures (AITF) for the financial support.
CR Ahmed AQ, 2017, APPL THERM ENG, V110, P821, DOI 10.1016/j.applthermaleng.2016.08.217
Bathe KJ, 2009, COMPUT STRUCT, V87, P604, DOI 10.1016/j.compstruc.2009.01.017
Blevins RD, 1992, APPL FLUID DYNAMICS
Bonte MHA, 2008, STRUCT MULTIDISCIP O, V35, P571, DOI 10.1007/s00158-007-0206-3
Bronsvoort WF, 2004, COMPUT AIDED DESIGN, V36, P929, DOI 10.1016/j.cad.2003.09.008
C. ANSYS, 2013, ANSYS CFX SOLV MOD G
Cao Y, 2016, COMPUT FLUIDS, V137, P36, DOI 10.1016/j.compfluid.2016.07.013
Casey M., 2000, BEST PRACTICE GUIDEL
Cengel Y., 2014, HEAT MASS TRANSFER F
Chan CK, 2003, COMPUT AIDED DESIGN, V35, P1315, DOI 10.1016/S0010-4485(03)00062-9
Cheng ZR, 2017, ADV ENG INFORM, V33, P1, DOI 10.1016/j.aei.2017.04.003
Deng YM, 2002, ENG COMPUT-GERMANY, V18, P80, DOI 10.1007/s003660200007
Ferziger JH., 2002, COMPUTATIONAL METHOD, V3rd, DOI [10.1007/978-3-642-56026-2, DOI 10.1007/978-3-642-56026-2]
Gao SM, 2010, COMPUT AIDED DESIGN, V42, P1178, DOI 10.1016/j.cad.2010.05.010
Garcia M, 2015, INT J INTERACT DES M, V9, P235, DOI 10.1007/s12008-014-0236-1
GOMES AJP, 1991, COMPUT GRAPH, V15, P217, DOI 10.1016/0097-8493(91)90075-S
Gross C, 2011, BASIN FUTURES: WATER REFORM IN THE MURRAY-DARLING BASIN, P149
Hamri O, 2010, ADV ENG SOFTW, V41, P1211, DOI 10.1016/j.advengsoft.2010.07.003
Salvador FJ, 2014, INT J MULTIPHAS FLOW, V65, P108, DOI 10.1016/j.ijmultiphaseflow.2014.06.003
Khabbazi MR, 2018, ASSEMBLY AUTOM, V38, P97, DOI 10.1108/AA-07-2016-084
Lee SH, 2005, COMPUT AIDED DESIGN, V37, P941, DOI 10.1016/j.cad.2004.09.021
Li L, 2016, COMPUT AIDED DESIGN, V13, P208, DOI DOI 10.1080/
Li L., 2018, COMPUT AIDED DES APP, V15, P643, DOI [10.1080/16864360.2018, DOI 10.1080/16864360.2018]
Li LL, 2017, ANN OTO RHINOL LARYN, V126, P236, DOI 10.1177/0003489416672476
Liu JK, 2017, STRUCT MULTIDISCIP O, V55, P1281, DOI 10.1007/s00158-016-1565-4
Liu JK, 2017, COMPUT METHOD APPL M, V324, P595, DOI 10.1016/j.cma.2017.06.021
Liu JK, 2015, STRUCT MULTIDISCIP O, V52, P563, DOI 10.1007/s00158-015-1263-7
Liu JL, 2016, STOCH ENV RES RISK A, V30, P713, DOI 10.1007/s00477-015-1141-2
Ma YS, 2008, J INTELL MANUF, V19, P625, DOI 10.1007/s10845-008-0128-y
Montgomery DC., 2012, DESIGN ANAL EXPT, V8
Mun DW, 2003, COMPUT AIDED DESIGN, V35, P1171, DOI 10.1016/S0010-4485(03)00022-8
Murena F, 2009, ATMOS ENVIRON, V43, P2303, DOI 10.1016/j.atmosenv.2009.01.038
Oberkampf WL, 2002, PROG AEROSP SCI, V38, P209, DOI 10.1016/S0376-0421(02)00005-2
Phillips TS, 2017, J COMPUT PHYS, V330, P46, DOI 10.1016/j.jcp.2016.11.002
Rezaeiha A, 2017, RENEW ENERG, V107, P373, DOI 10.1016/j.renene.2017.02.006
Sanfilippo EM, 2016, COMPUT AIDED DESIGN, V80, P9, DOI 10.1016/j.cad.2016.07.001
Shah J. J., 1991, Research in Engineering Design, V2, P93, DOI 10.1007/BF01579254
Shah J.J., 1995, PARAMETRIC FEATURE B
Shah MS, 2012, CHEM ENG SCI, V71, P300, DOI 10.1016/j.ces.2011.11.022
Shephard MS, 2004, FINITE ELEM ANAL DES, V40, P1575, DOI 10.1016/j.finel.2003.11.004
Skousen PL., 2011, VALVE HDB, V3rd ed.
Su J, 2017, DISCRETE CONT DYN-B, V22, P3421, DOI 10.3934/dcdsb.2017173
Tang S.-H., 2013, SEMANTIC MODELING IN, DOI [10.1007/978-1-4471-5073-2_4, DOI 10.1007/978-1-4471-5073-2_4]
Tonomura O, 2004, CHEM ENG J, V101, P397, DOI 10.1016/j.cej.2003.10.022
Venkatakrishnan V., 1998, BARRIERS CHALLENGES, DOI [10.1007/978-94-011-5169-6, DOI 10.1007/978-94-011-5169-6]
Versteeg H K, 2007, INTRO COMPUTATIONAL
White FM., 2011, FLUID MECH-SOV RES
Xia ZH, 2015, ADV ENG SOFTW, V87, P68, DOI 10.1016/j.advengsoft.2015.05.005
Yan X, 2000, COMPUT AIDED DESIGN, V32, P605, DOI 10.1016/S0010-4485(00)00045-2
Yin CG, 2012, ADV ENG INFORM, V26, P539, DOI 10.1016/j.aei.2012.02.010
NR 50
TC 4
Z9 4
U1 0
U2 19
PU ELSEVIER SCI LTD
PI OXFORD
PA THE BOULEVARD, LANGFORD LANE, KIDLINGTON, OXFORD OX5 1GB, OXON, ENGLAND
SN 1474-0346
EI 1873-5320
J9 ADV ENG INFORM
JI Adv. Eng. Inform.
PD OCT
PY 2018
VL 38
BP 357
EP 369
DI 10.1016/j.aei.2018.08.011
PG 13
WC Computer Science, Artificial Intelligence; Engineering,
Multidisciplinary
SC Computer Science; Engineering
GA HF6WJ
UT WOS:000454378700027
DA 2021-04-21
ER
PT J
AU Xu, SK
Morel, P
Gurcan, OD
AF Xu, Shaokang
Morel, P.
Gurcan, O. D.
TI Logarithmically discretized model of bounce averaged gyrokinetics and
its implications on tokamak turbulence
SO PHYSICS OF PLASMAS
LA English
DT Article
ID TRAPPED-ELECTRON; NUMERICAL-SIMULATION; DRIVEN TURBULENCE; ENERGY
CASCADE; ZONAL FLOWS; ION MODES; PLASMAS; INSTABILITIES; PYTHON
AB A logarithmically discretized model, which consists of writing the system in log polar coordinates in wave-number domain and reducing the nonlinear interactions to a sum over neighboring scales that satisfy the triad conditions, is proposed for bounce averaged gyrokinetics, where the energy dependence is kept over a semi-regular grid that allows quadrature calculations in order to guarantee quasi-neutrality. The resulting model is a cheaper implementation of nonlinear multi-scale physics involving trapped electron modes, trapped ion modes, and zonal flows, which can handle anisotropy. The resulting wave-number spectrum is anisotropic at large scales, where the energy injection is clearly anisotropic, but is isotropised rapidly, leading generally towards an isotropic k(-4) spectrum for spectral potential energy density for fully kinetic system and a k(-5) spectrum for the system with one adiabatic species. Zonal flow damping, which is necessary for reaching a steady state in this model, plays an important role along with electron adiabaticity. Interesting dynamics akin to predator-prey evolution is observed among zonal flows and similarly large scale but radially elongated structures. Published by AIP Publishing.
C1 [Xu, Shaokang; Morel, P.; Gurcan, O. D.] UPMC Univ Paris 06, Univ Paris Sud, Univ Paris Saclay,PSL Res Univ, LPP,CNRS,Ecole Polytech,Observ Paris,Sorbonne Uni, F-91128 Palaiseau, France.
RP Xu, SK (corresponding author), UPMC Univ Paris 06, Univ Paris Sud, Univ Paris Saclay,PSL Res Univ, LPP,CNRS,Ecole Polytech,Observ Paris,Sorbonne Uni, F-91128 Palaiseau, France.
EM shaokang.xu@lpp.polytechnique.fr
RI Gurcan, Ozgur D/A-1362-2013
OI Gurcan, Ozgur D/0000-0002-2278-1544
FU Chinese Scholarship Council (CSC)China Scholarship Council
FX The authors would like to thank Professor E. Gravier, Dr. M. Lesur,
Professor P.H. Diamond, and Professor T.S. Hahm for stimulating
discussions and the participants and organizers of the 2017 Festival de
Theorie in Aix en Provence. Mr. Shaokang Xu would like to thank the
Chinese Scholarship Council (CSC) for the financial support that they
provided for his Ph.D. studies, which this paper is a part of.
CR Biferale L, 2003, ANNU REV FLUID MECH, V35, P441, DOI 10.1146/annurev.fluid.35.101101.161122
BIGLARI H, 1989, PHYS FLUIDS B-PLASMA, V1, P109, DOI 10.1063/1.859206
Brizard AJ, 2007, REV MOD PHYS, V79, P421, DOI 10.1103/RevModPhys.79.421
Citrin J, 2012, PHYS PLASMAS, V19, DOI 10.1063/1.4719697
COHEN BI, 1976, NUCL FUSION, V16, P971, DOI 10.1088/0029-5515/16/6/009
Dagum L, 1998, IEEE COMPUT SCI ENG, V5, P46, DOI 10.1109/99.660313
Dannert T, 2005, PHYS PLASMAS, V12, DOI 10.1063/1.1947447
Depret G, 2000, PLASMA PHYS CONTR F, V42, P949, DOI 10.1088/0741-3335/42/9/302
Diamond PH, 2005, PLASMA PHYS CONTR F, V47, pR35, DOI 10.1088/0741-3335/47/5/R01
Dif-Pradalier G, 2015, PHYS REV LETT, V114, DOI 10.1103/PhysRevLett.114.085004
Drouot T, 2015, PHYS PLASMAS, V22, DOI 10.1063/1.4927920
Drouot T, 2014, EUR PHYS J D, V68, DOI 10.1140/epjd/e2014-50151-2
Ernst DR, 2004, PHYS PLASMAS, V11, P2637, DOI 10.1063/1.1705653
Fong BH, 1999, PHYS PLASMAS, V6, P188, DOI 10.1063/1.873272
Garbet X, 2010, NUCL FUSION, V50, DOI 10.1088/0029-5515/50/4/043002
Gorler T, 2008, PHYS PLASMAS, V15, DOI 10.1063/1.3006086
Grandgirard V, 2008, COMMUN NONLINEAR SCI, V13, P81, DOI 10.1016/j.cnsns.2007.05.016
Gravier E, 2017, NUCL FUSION, V57, DOI 10.1088/1741-4326/aa8c4c
Gravier E, 2016, PHYS PLASMAS, V23, DOI 10.1063/1.4962845
Gurcan OD, 2015, J PHYS A-MATH THEOR, V48, DOI 10.1088/1751-8113/48/29/293001
Gurcan OD, 2016, PHYS REV E, V94, DOI 10.1103/PhysRevE.94.033106
Gurcan OD, 2009, PHYS REV LETT, V102, DOI 10.1103/PhysRevLett.102.255002
Gurcan O. D., SPIRAL CHAINS UNPUB
HAMMETT GW, 1993, PLASMA PHYS CONTR F, V35, P973, DOI 10.1088/0741-3335/35/8/006
Idomura Y, 2009, NUCL FUSION, V49, DOI 10.1088/0029-5515/49/6/065029
KADOMTSEV BB, 1971, NUCL FUSION, V11, P67, DOI 10.1088/0029-5515/11/1/010
Kim EJ, 2003, PHYS REV LETT, V91, DOI 10.1103/PhysRevLett.91.075003
KINGSBURY OT, 1994, PHYS PLASMAS, V1, P2319, DOI 10.1063/1.870629
KRAICHNAN RH, 1971, J FLUID MECH, V47, P525, DOI 10.1017/S0022112071001216
Krommes JA, 2012, ANNU REV FLUID MECH, V44, P175, DOI 10.1146/annurev-fluid-120710-101223
Kwon JM, 2012, NUCL FUSION, V52, DOI 10.1088/0029-5515/52/1/013004
L'vov VS, 1998, PHYS REV E, V58, P1811, DOI 10.1103/PhysRevE.58.1811
Lin Z, 1998, SCIENCE, V281, P1835, DOI 10.1126/science.281.5384.1835
Miki K, 2012, PHYS PLASMAS, V19, DOI 10.1063/1.4753931
Morel P, 2014, PLASMA PHYS CONTR F, V56, DOI 10.1088/0741-3335/56/1/015002
OHKITANI K, 1989, PROG THEOR PHYS, V81, P329, DOI 10.1143/PTP.81.329
Oliphant TE, 2007, COMPUT SCI ENG, V9, P10, DOI 10.1109/MCSE.2007.58
PARKER SE, 1993, PHYS REV LETT, V71, P2042, DOI 10.1103/PhysRevLett.71.2042
Peeters AG, 2005, PHYS PLASMAS, V12, DOI 10.1063/1.1848111
Peterson P, 2009, INT J COMPUT SCI ENG, V4, P296, DOI 10.1504/IJCSE.2009.029165
REWOLDT G, 1990, PHYS FLUIDS B-PLASMA, V2, P318, DOI 10.1063/1.859320
TANG WM, 1977, PHYS FLUIDS, V20, P430, DOI 10.1063/1.861879
Xu SK, 2018, PHYS PLASMAS, V25, DOI 10.1063/1.5020145
XU XQ, 1991, PHYS FLUIDS B-PLASMA, V3, P627, DOI 10.1063/1.859862
NR 44
TC 1
Z9 1
U1 0
U2 5
PU AMER INST PHYSICS
PI MELVILLE
PA 1305 WALT WHITMAN RD, STE 300, MELVILLE, NY 11747-4501 USA
SN 1070-664X
EI 1089-7674
J9 PHYS PLASMAS
JI Phys. Plasmas
PD OCT
PY 2018
VL 25
IS 10
AR 102306
DI 10.1063/1.5049681
PG 14
WC Physics, Fluids & Plasmas
SC Physics
GA GY9OF
UT WOS:000448976400030
DA 2021-04-21
ER
PT J
AU Szott, M
Wang, Z
Ruzic, DN
AF Szott, M.
Wang, Z.
Ruzic, D. N.
TI Reconstruction and analysis of exploding wire particle trajectories via
automatic calibration of stereo images
SO REVIEW OF SCIENTIFIC INSTRUMENTS
LA English
DT Article; Proceedings Paper
CT 22nd Biannual Topical Conference on High-Temperature Plasma Diagnostics
(HTPD)
CY APR 16-19, 2018
CL Gen Atom, San Diego, CA
SP Appl Nanotools, DtAcq Solut, Palomar Sci Instruments, Sydor Technologies, TAE Technologies, Telops
HO Gen Atom
AB Quantitative understanding of the physics of dust or granular matter transport significantly impacts several aspects of burning plasma science and technology. This work takes machine vision techniques popular in robotics and self-driving cars and applies them to identification and analysis of microparticles generated from exploding wires. Using only the image frames and knowledge of the intrinsic properties of the cameras, a Python code was written to identify the particles, automatically calibrate the relative image positions, and extract trajectory data. After identifying approximately 50 particles based on the timing of secondary particle explosions, the eight point and random sample consensus algorithms were used to determine the geometric correlation between the cameras. Over 100 particle matches were found between the two camera views. These correlated trajectories were used in subsequent 3D track reconstruction and analysis of the physics behind the particle motion. The 3D reconstruction resulted in accurate positioning of the particles with respect to the experimental setup. The particle motion was consistent with the effects of a 1 g gravitational field modified by drag forces. The methods and analyses presented here can be used in many facets of high temperature plasma diagnostics. Published by AIP Publishing.
C1 [Szott, M.; Ruzic, D. N.] Univ Illinois, Dept Nucl Plasma & Radiol Engn, Urbana, IL 61801 USA.
[Wang, Z.] Los Alamos Natl Lab, Los Alamos, NM 87545 USA.
RP Szott, M (corresponding author), Univ Illinois, Dept Nucl Plasma & Radiol Engn, Urbana, IL 61801 USA.
EM szott1@illinois.edu
OI Wang, Zhehui/0000-0001-7826-4063
FU Department of Energy Office of Science Graduate Student Research
ProgramUnited States Department of Energy (DOE)
FX This work was supported by the Department of Energy Office of Science
Graduate Student Research Program.
CR Allan D, 2016, TRACKPY V0 3 2
Baylor L. R., 2010, IAEACN180
Boeglin WU, 2008, REV SCI INSTRUM, V79, DOI 10.1063/1.2965001
Crocker JC, 1996, J COLLOID INTERF SCI, V179, P298, DOI 10.1006/jcis.1996.0217
Dorf LA, 2006, REV SCI INSTRUM, V77, DOI 10.1063/1.2336790
FISCHLER MA, 1981, COMMUN ACM, V24, P381, DOI 10.1145/358669.358692
Hartley RI, 1997, IEEE T PATTERN ANAL, V19, P580, DOI 10.1109/34.601246
Mansfield DK, 2013, NUCL FUSION, V53, DOI 10.1088/0029-5515/53/11/113023
Prince S. J., 2012, COMPUTER VISION MODE
Schindelin J, 2012, NAT METHODS, V9, P676, DOI [10.1038/NMETH.2019, 10.1038/nmeth.2019]
Ticos CM, 2006, PHYS PLASMAS, V13, DOI 10.1063/1.2356316
Wang ZH, 2007, PHYS PLASMAS, V14, DOI 10.1063/1.2778416
Wang ZB, 2016, 2016 INTERNATIONAL CONFERENCE ON ARCHITECTURE AND CIVIL ENGINEERING (ICACE 2016), P87
NR 13
TC 2
Z9 2
U1 0
U2 3
PU AMER INST PHYSICS
PI MELVILLE
PA 1305 WALT WHITMAN RD, STE 300, MELVILLE, NY 11747-4501 USA
SN 0034-6748
EI 1089-7623
J9 REV SCI INSTRUM
JI Rev. Sci. Instrum.
PD OCT
PY 2018
VL 89
IS 10
AR 10K118
DI 10.1063/1.5039373
PG 5
WC Instruments & Instrumentation; Physics, Applied
SC Instruments & Instrumentation; Physics
GA GZ1QT
UT WOS:000449144500257
PM 30399670
DA 2021-04-21
ER
PT J
AU Neher, SH
Klein, H
Kuhs, WF
AF Neher, Sigmund H.
Klein, Helmut
Kuhs, Werner F.
TI A fast X-ray-diffraction-based method for the determination of crystal
size distributions (FXD-CSD)
SO JOURNAL OF APPLIED CRYSTALLOGRAPHY
LA English
DT Article
DE crystal size distribution; X-ray diffraction; FXD-CSD; spotty
diffraction patterns; two-dimensional detectors
ID DARWIN TRANSFER EQUATIONS; LINE-BROADENING ANALYSIS; GRAIN-SIZE; GROWTH;
EXTINCTION; STRENGTH; VALIDITY; LIMIT; TEXTURE; STRESS
AB A procedure for a fast X-ray-diffraction-based crystal size distribution analysis, named FXD-CSD, is presented. The method enables the user, with minimal sample preparation, to determine the crystal size distribution (CSD) of crystalline powders or polycrystalline materials, derived via an intensity scaling procedure from the diffraction intensities of single Bragg spots measured in spotty diffraction patterns with a two-dimensional detector. The method can be implemented on any single-crystal laboratory diffractometer and any synchrotron-based instrument with a fast-readout two-dimensional detector and a precise sample scanning axis. The intensity scaling is achieved via the measurement of a reference sample with known CSD under identical conditions; the only other prerequisite is that the structure (factors) of both sample and reference material must be known. The data analysis is done with a software package written in Python. A detailed account is given of each step of the procedure, including the measurement strategy and the demands on the spottiness of the diffraction rings, the data reduction and the intensity corrections needed, and the data evaluation and the requirements for the reference material. Using commercial laboratory X-ray equipment, several corundum crystal size fractions with precisely known CSD were measured and analysed to verify the accuracy and precision of the FXD-CSD method; a comparison of known and deduced CSDs shows good agreement both in mean size and in the shape of the size distribution. For the used material and diffractometer setup, the crystal size application range is one to several tens of micrometres; this range is highly material and X-ray source dependent and can easily be extended on synchrotron sources to cover the range from below 0.5 mu m to over 100 mu m. FXD-CSD has the potential to become a generally applicable method for CSD determination in the field of materials science and pharmaceutics, including development and quality management, as well as in various areas of fundamental research in physics, chemistry, chemical engineering, crystallography, the geological sciences and bio-crystallization. It can be used also under in situ conditions for studying crystal coarsening phenomena, and delivers precise and accurate CSDs, permitting experimental tests of various theories developed to predict their evolution.
C1 [Neher, Sigmund H.; Klein, Helmut; Kuhs, Werner F.] Univ Gottingen, GZG Crystallog, Goldschmidtstr 1, D-37077 Gottingen, Germany.
RP Neher, SH; Kuhs, WF (corresponding author), Univ Gottingen, GZG Crystallog, Goldschmidtstr 1, D-37077 Gottingen, Germany.
EM sneher@gwdg.de; wkuhs1@gwdg.de
FU Deutsche ForschungsgemeinschaftGerman Research Foundation (DFG)
[KU920/20-1]; BMBFFederal Ministry of Education & Research (BMBF)
[03G0856B]
FX The following funding is acknowledged: Deutsche Forschungsgemeinschaft
(grant No. KU920/20-1). A small final part of this work was funded by
the BMBF in the framework of the SUGAR-III programme (grant 03G0856B).
CR ABBRUZZESE G, 1986, ACTA METALL MATER, V34, P905, DOI 10.1016/0001-6160(86)90064-7
ABBRUZZESE G, 1992, MATER SCI FORUM, V94, P77
Abu Bakar MR, 2009, CRYST GROWTH DES, V9, P1378, DOI 10.1021/cg800595v
Als-Nielsen J., 2011, ELEMENTS MODERN XRAY
Andersen M. S., 2013, CVXOPT PYTHON SOFTWA
Balzar D, 2004, J APPL CRYSTALLOGR, V37, P911, DOI 10.1107/S0021889804022551
BECKER PJ, 1974, ACTA CRYSTALLOGR A, VA 30, P129, DOI 10.1107/S0567739474000337
BECKER PJ, 1974, ACTA CRYSTALLOGR A, VA 30, P148, DOI 10.1107/S0567739474000349
BECKER PJ, 1975, ACTA CRYSTALLOGR A, V31, P417, DOI 10.1107/S0567739475000976
Berbenni S, 2007, INT J PLASTICITY, V23, P114, DOI 10.1016/j.ijplas.2006.03.004
BUNGE HJ, 1990, TEXTURE MICROSTRUCT, V13, P59, DOI 10.1155/TSM.13.59
CARNIGLIA SC, 1972, J AM CERAM SOC, V55, P243, DOI 10.1111/j.1151-2916.1972.tb11272.x
CHANTIKUL P, 1990, J AM CERAM SOC, V73, P2419, DOI 10.1111/j.1151-2916.1990.tb07607.x
Chaouachi M, 2017, CRYST GROWTH DES, V17, P2458, DOI 10.1021/acs.cgd.6b01875
Chavanne J, 1998, J SYNCHROTRON RADIAT, V5, P196, DOI 10.1107/S0909049597012855
Chen WM, 2011, CRYSTENGCOMM, V13, P3959, DOI 10.1039/c1ce05272a
Eberl DD, 1998, AM J SCI, V298, P499, DOI 10.2475/ajs.298.6.499
FELTHAM P, 1969, SCRIPTA METALL MATER, V3, P853, DOI 10.1016/0036-9748(69)90194-X
FREEDMAN D, 1981, Z WAHRSCHEINLICHKEIT, V57, P453, DOI 10.1007/BF01025868
Fujiwara M, 2005, J PROCESS CONTR, V15, P493, DOI 10.1016/j.jprocont.2004.08.003
German RM, 2010, CRIT REV SOLID STATE, V35, P263, DOI 10.1080/10408436.2010.525197
He B.B., 2009, 2 DIMENSIONAL XRAY D
He B. B., 2011, US Patent, Patent No. 7885383
HILLERT M, 1965, ACTA METALL MATER, V13, P227, DOI 10.1016/0001-6160(65)90200-2
Hiraoka N, 2005, J SYNCHROTRON RADIAT, V12, P670, DOI 10.1107/S0909049505022569
HIRSCH PB, 1952, ACTA CRYSTALLOGR, V5, P162, DOI 10.1107/S0365110X52000496
HIRSCH PB, 1954, BRIT J APPL PHYS, V5, P257, DOI 10.1088/0508-3443/5/7/306
Ida T., 2011, US Patent, Patent No. [2011/ 0064199, 20110064199]
Ingham B, 2014, J APPL CRYSTALLOGR, V47, P166, DOI 10.1107/S1600576713029713
KABSCH W, 1988, J APPL CRYSTALLOGR, V21, P916, DOI 10.1107/S0021889888007903
Kazaryan A., 2001, PHYS REV B, V63, P1
Klapp SA, 2007, GEOPHYS RES LETT, V34, DOI 10.1029/2006GL029134
Knudsen EB, 2013, J APPL CRYSTALLOGR, V46, P537, DOI 10.1107/S0021889813000150
Konert M, 1997, SEDIMENTOLOGY, V44, P523, DOI 10.1046/j.1365-3091.1997.d01-38.x
Krill CE, 1998, PHILOS MAG A, V77, P621, DOI 10.1080/01418619808224072
Kruzic JJ, 2008, J AM CERAM SOC, V91, P1986, DOI 10.1111/j.1551-2916.2008.02380.x
KURZYDLOWSKI KJ, 1993, ACTA METALL MATER, V41, P3141, DOI 10.1016/0956-7151(93)90044-S
Langford JI, 2000, J APPL CRYSTALLOGR, V33, P964, DOI 10.1107/S002188980000460X
LASAGA AC, 1998, PR S GEOCH
Lipson H., 2006, INT TABLES CRYSTAL C, P596
LOUAT NP, 1994, PHILOS MAG A, V69, P841, DOI 10.1080/01418619408242523
MILCH JR, 1974, J APPL CRYSTALLOGR, V7, P502, DOI 10.1107/S0021889874010284
Muller G., 1967, METHODS SEDIMENTARY
Nagy ZK, 2012, ANNU REV CHEM BIOMOL, V3, P55, DOI 10.1146/annurev-chembioeng-062011-081043
Nakamura D, 2016, ELECTR COMMUN JPN, V99, P58, DOI 10.1002/ecj.11874
Neher SH, 2018, J AM CERAM SOC, V101, P1381, DOI 10.1111/jace.15309
Nutzmann K., 2013, THESIS
Pieniazek A, 2016, OPT MATER EXPRESS, V6, P3741, DOI 10.1364/OME.6.003741
Poulsen HF, 2004, MATER SCI FORUM, V467-470, P1363, DOI 10.4028/www.scientific.net/MSF.467-470.1363
Raeisinia B, 2008, MODEL SIMUL MATER SC, V16, DOI 10.1088/0965-0393/16/2/025001
Rodriguez-Navarro AB, 2006, J AM CERAM SOC, V89, P2232, DOI 10.1111/j.1551-2916.2006.00998.x
ROUSE KD, 1970, ACTA CRYSTALL A-CRYS, VA 26, P682, DOI 10.1107/S0567739470001687
Scardi P, 2004, J APPL CRYSTALLOGR, V37, P381, DOI 10.1107/S0021889804004583
Schdanow HS, 1935, Z KRISTALLOGR, V90, P82
Scherrer P., 1918, MATHPHYS KL, V2, P98, DOI DOI 10.1007/978-3-662-33915-2
Schindelin J, 2012, NAT METHODS, V9, P676, DOI [10.1038/NMETH.2019, 10.1038/nmeth.2019]
Schneider CA, 2012, NAT METHODS, V9, P671, DOI 10.1038/nmeth.2089
Sharma H, 2012, J APPL CRYSTALLOGR, V45, P693, DOI 10.1107/S0021889812025563
Shi D, 2005, NANOTECHNOLOGY, V16, pS562, DOI 10.1088/0957-4484/16/7/034
Shimogaki T, 2014, APPL PHYS A-MATER, V117, P269, DOI 10.1007/s00339-014-8529-6
Skripnyak NV, 2017, PROCEEDINGS OF THE 7TH INTERNATIONAL CONFERENCE ON MECHANICS AND MATERIALS IN DESIGN (M2D2017), P1749
Sorensen HO, 2012, Z KRIST-CRYST MATER, V227, P63, DOI 10.1524/zkri.2012.1438
Stephen RA, 1937, J I MET, V60, P285
SWANSON PL, 1987, J AM CERAM SOC, V70, P279, DOI 10.1111/j.1151-2916.1987.tb04982.x
TAKAYAMA Y, 1992, MATER SCI FORUM, V94, P325
Tasaki R, 2018, APPL PHYS A-MATER, V124, DOI 10.1007/s00339-018-1596-3
Tommaseo CE, 2009, PRAKT METALLOGR-PR M, V46, P77, DOI 10.3139/147.110026
Tschentscher T, 1998, J SYNCHROTRON RADIAT, V5, P286, DOI 10.1107/S0909049597014775
Ungar T, 2001, J APPL CRYSTALLOGR, V34, P298, DOI 10.1107/S0021889801003715
van der Walt S., 2014, scikit-image: image processing in Python. PeerJ, V2, pE453, DOI [10.7717/peerj.453, DOI 10.7717/PEERJ.453]
VINCENT L, 1991, IEEE T PATTERN ANAL, V13, P583, DOI 10.1109/34.87344
Yager KG, 2014, J APPL CRYSTALLOGR, V47, P1855, DOI 10.1107/S1600576714020822
Yang WY, 2008, J AM CERAM SOC, V91, P2732, DOI 10.1111/j.1551-2916.2008.02483.x
NR 73
TC 1
Z9 1
U1 1
U2 14
PU INT UNION CRYSTALLOGRAPHY
PI CHESTER
PA 2 ABBEY SQ, CHESTER, CH1 2HU, ENGLAND
SN 1600-5767
J9 J APPL CRYSTALLOGR
JI J. Appl. Crystallogr.
PD OCT
PY 2018
VL 51
BP 1352
EP 1371
DI 10.1107/S1600576718010567
PN 5
PG 20
WC Chemistry, Multidisciplinary; Crystallography
SC Chemistry; Crystallography
GA GU8TI
UT WOS:000445614800010
DA 2021-04-21
ER
PT J
AU Varjas, D
Rosdahl, TO
Akhmerov, AR
AF Varjas, Daniel
Rosdahl, Tomas O.
Akhmerov, Anton R.
TI Qsymm: algorithmic symmetry finding and symmetric Hamiltonian generation
SO NEW JOURNAL OF PHYSICS
LA English
DT Article
DE symmetry; graphene; Majorana wire; SPT
ID QUANTUM; STATE
AB Symmetry is a guiding principle in physics that allows us to generalize conclusions between many physical systems. In the ongoing search for new topological phases of matter, symmetry plays a crucial role by protecting topological phases. We address two converse questions relevant to the symmetry classification of systems: is it possible to generate all possible single-body Hamiltonians compatible with a given symmetry group? Is it possible to find all the symmetries of a given family of Hamiltonians? We present numerically stable, deterministic polynomial time algorithms to solve both of these problems. Our treatment extends to all continuous or discrete symmetries of non-interacting lattice or continuum Hamiltonians. We implement the algorithms in the Qsymm Python package, and demonstrate their usefulness through applications in active research areas of condensed matter physics, including Majorana wires and Kekule graphene.
C1 [Varjas, Daniel] Delft Univ Technol, QuTech, POB 4056, NL-2600 GA Delft, Netherlands.
[Varjas, Daniel; Rosdahl, Tomas O.; Akhmerov, Anton R.] Delft Univ Technol, Kavli Inst Nanosci, POB 4056, NL-2600 GA Delft, Netherlands.
RP Varjas, D (corresponding author), Delft Univ Technol, QuTech, POB 4056, NL-2600 GA Delft, Netherlands.; Varjas, D (corresponding author), Delft Univ Technol, Kavli Inst Nanosci, POB 4056, NL-2600 GA Delft, Netherlands.
EM dvarjes@gmail.com; torosdahl@gmail.com
RI Rosdahl, Tomas/T-1074-2018
OI Rosdahl, Tomas/0000-0001-5126-6413; Varjas, Daniel/0000-0002-3283-6182;
Akhmerov, Anton/0000-0001-8031-1340
FU ERCEuropean Research Council (ERC)European Commission [638760];
Netherlands Organisation for Scientific Research (NWO/OCW); US Office of
Naval ResearchOffice of Naval Research
FX We thank N V Gnezdilov, M Hastings, A Lau, B Nijholt, VP Ostroukh, R
Skolasinski and J B Weston for fruitful discussions. This work was
supported by ERC Starting Grant 638760, the Netherlands Organisation for
Scientific Research (NWO/OCW), and the US Office of Naval Research.
CR Altland A, 1997, PHYS REV B, V55, P1142, DOI 10.1103/PhysRevB.55.1142
Ando Y, 2015, ANNU REV CONDEN MA P, V6, P361, DOI 10.1146/annurev-conmatphys-031214-014501
BEAUZAMY B, 1990, J NUMBER THEORY, V36, P219, DOI 10.1016/0022-314X(90)90075-3
Bir G.L., 1974, SYMMETRY STRAIN INDU
Bradlyn B, 2018, PHYS REV B, V97, DOI 10.1103/PhysRevB.97.035138
Bradlyn B, 2017, NATURE, V547, P298, DOI 10.1038/nature23268
Cappelluti E, 2013, PHYS REV B, V88, DOI 10.1103/PhysRevB.88.075409
Fang SA, 2015, PHYS REV B, V92, DOI 10.1103/PhysRevB.92.205108
Fei ZY, 2017, NAT PHYS, V13, P677, DOI [10.1038/nphys4091, 10.1038/NPHYS4091]
Fu LA, 2011, PHYS REV LETT, V106, DOI 10.1103/PhysRevLett.106.106802
Fu L, 2009, PHYS REV LETT, V103, DOI 10.1103/PhysRevLett.103.266801
Fulga IC, 2014, PHYS REV B, V89, DOI 10.1103/PhysRevB.89.155424
Fulton W, 2004, REPRESENTATION THEOR, DOI [10.1007/978-1-4612-0979-9, DOI 10.1007/978-1-4612-0979-9]
Gamayun OV, 2018, NEW J PHYS, V20, DOI 10.1088/1367-2630/aaa7e5
Gottlieb LA, 2010, LECT NOTES COMPUT SC, V6302, P205, DOI 10.1007/978-3-642-15369-3_16
Groth CW, 2014, NEW J PHYS, V16, DOI 10.1088/1367-2630/16/6/063065
Hall B. C., 2015, LIE GROUPS LIE ALGEB, DOI [10.1007/978-3-319-13467-3, DOI 10.1007/978-3-319-13467-3]
Hasan MZ, 2010, REV MOD PHYS, V82, P3045, DOI 10.1103/RevModPhys.82.3045
Hou CY, 2007, PHYS REV LETT, V98, DOI 10.1103/PhysRevLett.98.186809
Hsieh TH, 2012, NAT COMMUN, V3, DOI 10.1038/ncomms1969
Jancu JM, 1998, PHYS REV B, V57, P6493, DOI 10.1103/PhysRevB.57.6493
Jia ZY, 2017, PHYS REV B, V96, DOI 10.1103/PhysRevB.96.041108
Jones E., 2001, SCIPY OPEN SOURCE SC
Kane CL, 2014, NAT PHYS, V10, P39, DOI [10.1038/NPHYS2835, 10.1038/nphys2835]
Kitaev A, 2009, AIP CONF PROC, V1134, P22, DOI 10.1063/1.3149495
Kitagawa T, 2010, PHYS REV B, V82, DOI 10.1103/PhysRevB.82.235114
Kluyver T, 2016, POSITIONING AND POWER IN ACADEMIC PUBLISHING: PLAYERS, AGENTS AND AGENDAS, P87, DOI 10.3233/978-1-61499-649-1-87
Konig M, 2007, SCIENCE, V318, P766, DOI 10.1126/science.1148047
Kormanyos A, 2015, 2D MATER, V2, DOI 10.1088/2053-1583/2/2/022001
Kruthoff J, 2017, PHYS REV X, V7, DOI 10.1103/PhysRevX.7.041069
Lau A, 2018, UNPUB
Lau A, 2018, ARXIV180409574
Lehoucq R, 1997, SOFTW ENV TOOLS, V6
Leijnse M, 2012, SEMICOND SCI TECH, V27, DOI 10.1088/0268-1242/27/12/124003
Li J, 2014, PHYS REV B, V90, DOI 10.1103/PhysRevB.90.235433
Liu GB, 2013, PHYS REV B, V88, DOI 10.1103/PhysRevB.88.085433
LUTTINGER JM, 1955, PHYS REV, V97, P869, DOI 10.1103/PhysRev.97.869
Meurer A, 2017, PEERJ COMPUT SCI, DOI 10.7717/peerj-cs.103
Muechler L, 2016, PHYS REV X, V6, DOI 10.1103/PhysRevX.6.041069
Murakami S, 2017, SCI ADV, V3, DOI 10.1126/sciadv.1602680
Nijholt B, 2016, PHYS REV B, V93, DOI 10.1103/PhysRevB.93.235434
Ningyuan J, 2015, PHYS REV X, V5, DOI 10.1103/PhysRevX.5.021031
Notomi M, 2000, PHYS REV B, V62, P10696, DOI 10.1103/PhysRevB.62.10696
Po HC, 2017, NAT COMMUN, V8, DOI 10.1038/s41467-017-00133-2
Resta R, 2000, J PHYS-CONDENS MAT, V12, pR107, DOI 10.1088/0953-8984/12/9/201
Sato M, 2017, REP PROG PHYS, V80, DOI 10.1088/1361-6633/aa6ac7
Schnyder AP, 2008, PHYS REV B, V78, DOI 10.1103/PhysRevB.78.195125
Slager RJ, 2013, NAT PHYS, V9, P98, DOI 10.1038/nphys2513
SLATER JC, 1954, PHYS REV, V94, P1498, DOI 10.1103/PhysRev.94.1498
Susstrunk R, 2015, SCIENCE, V349, P47, DOI 10.1126/science.aab0239
Tang SJ, 2017, NAT PHYS, V13, P683, DOI [10.1038/nphys4174, 10.1038/NPHYS4174]
Varjas D, 2018, QSYMM SYMMETRY FINDE, DOI [10.5281/zenodo.1287230, DOI 10.5281/ZENODO.1287230]
Varjas D, 2015, PHYS REV B, V92, DOI 10.1103/PhysRevB.92.195116
VOGL P, 1983, J PHYS CHEM SOLIDS, V44, P365, DOI 10.1016/0022-3697(83)90064-1
Wang QH, 2012, NAT NANOTECHNOL, V7, P699, DOI [10.1038/nnano.2012.193, 10.1038/NNANO.2012.193]
Wang XB, 2014, PHYS REV B, V90, DOI 10.1103/PhysRevB.90.054507
Watanabe H, 2018, SCI ADV, V4, DOI 10.1126/sciadv.aat8685
Wu SF, 2018, SCIENCE, V359, P76, DOI 10.1126/science.aan6003
Xiao D, 2012, PHYS REV LETT, V108, DOI 10.1103/PhysRevLett.108.196802
Ye JT, 2012, SCIENCE, V338, P1193, DOI 10.1126/science.1228006
Zheng FP, 2016, ADV MATER, V28, P4845, DOI 10.1002/adma.201600100
NR 61
TC 9
Z9 9
U1 0
U2 1
PU IOP PUBLISHING LTD
PI BRISTOL
PA TEMPLE CIRCUS, TEMPLE WAY, BRISTOL BS1 6BE, ENGLAND
SN 1367-2630
J9 NEW J PHYS
JI New J. Phys.
PD SEP 24
PY 2018
VL 20
AR 093026
DI 10.1088/1367-2630/aadf67
PG 18
WC Physics, Multidisciplinary
SC Physics
GA GU8RG
UT WOS:000445607600001
OA DOAJ Gold, Green Published
DA 2021-04-21
ER
PT J
AU Le, TP
Olaya-Castro, A
AF Le, Thao P.
Olaya-Castro, Alexandra
TI Objectivity (or lack thereof): Comparison between predictions of quantum
Darwinism and spectrum broadcast structure
SO PHYSICAL REVIEW A
LA English
DT Article
ID PYTHON FRAMEWORK; BROWNIAN-MOTION; DECOHERENCE; DYNAMICS; SYSTEMS;
STATES; ENVIRONMENT; PHYSICS; QUTIP
AB Quantum Darwinism and spectrum broadcast structure describe the emergence of objectivity in quantum systems. However, it is unclear whether these two frameworks lead to consistent predictions on the objectivity of the state of a quantum system in a given scenario. In this paper, we jointly investigate quantum Darwinism and spectrum broadcasting, as well as the subdivision of quantum Darwinism into accessible information and quantum discord, in a two-level system interacting with an N-level environment via a random matrix coupling. We propose a partial trace method to suitably and consistently partition the effective N-level environment and compare the predictions with those obtained using the partitioning method proposed by Perez [Phys. Rev. A 81, 052326 (2010)]. We find that quantum Darwinism can apparently emerge under the Perez trace even when spectrum broadcast structure does not emerge, and the majority of the quantum mutual information between system and environment fractions is in fact quantum in nature. This work therefore shows there can be discrepancies between quantum Darwinism and the nature of information and spectrum broadcast structure.
C1 [Le, Thao P.; Olaya-Castro, Alexandra] UCL, Dept Phys & Astron, Gower St, London WC1E 6BT, England.
RP Le, TP (corresponding author), UCL, Dept Phys & Astron, Gower St, London WC1E 6BT, England.
EM thao.le.16@ucl.ac.uk
OI Le, Thao/0000-0001-6309-1753
FU Engineering and Physical Sciences Research CouncilUK Research &
Innovation (UKRI)Engineering & Physical Sciences Research Council
(EPSRC) [EP/L015242/1]
FX We thank the anonymous referee for constructive feedback on previous
versions of this manuscript. This work was supported by the Engineering
and Physical Sciences Research Council (Grant No. EP/L015242/1).
CR Balaneskovic N, 2016, EUR PHYS J D, V70, DOI 10.1140/epjd/e2016-70174-9
Balaneskovic N, 2015, EUR PHYS J D, V69, DOI 10.1140/epjd/e2015-60319-9
Blume-Kohout R, 2005, FOUND PHYS, V35, P1857, DOI 10.1007/s10701-005-7352-5
Blume-Kohout R, 2006, PHYS REV A, V73, DOI 10.1103/PhysRevA.73.062310
Blume-Kohout R, 2008, PHYS REV LETT, V101, DOI 10.1103/PhysRevLett.101.240405
Brandao FGSL, 2015, NAT COMMUN, V6, DOI 10.1038/ncomms8908
BRODY TA, 1981, REV MOD PHYS, V53, P385, DOI 10.1103/RevModPhys.53.385
Bulgac A, 1998, PHYS REV E, V58, P196, DOI 10.1103/PhysRevE.58.196
Carrera M, 2014, PHYS REV A, V90, DOI 10.1103/PhysRevA.90.022107
Datta A., 2008, THESIS
Esposito M, 2003, PHYS REV E, V68, DOI 10.1103/PhysRevE.68.066113
Esposito M, 2010, NEW J PHYS, V12, DOI 10.1088/1367-2630/12/1/013013
Galve F, 2016, SCI REP-UK, V6, DOI 10.1038/srep19607
Giorgi GL, 2015, PHYS REV A, V92, DOI 10.1103/PhysRevA.92.022105
Gorin T, 2008, NEW J PHYS, V10, DOI 10.1088/1367-2630/10/11/115016
Henderson L, 2001, J PHYS A-MATH GEN, V34, P6899, DOI 10.1088/0305-4470/34/35/315
Horodecki R, 2015, PHYS REV A, V91, DOI 10.1103/PhysRevA.91.032122
Johansson JR, 2013, COMPUT PHYS COMMUN, V184, P1234, DOI 10.1016/j.cpc.2012.11.019
Johansson JR, 2012, COMPUT PHYS COMMUN, V183, P1760, DOI 10.1016/j.cpc.2012.02.021
Joos E., 2003, DECOHERENCE APPEARAN
Korbicz JK, 2014, PHYS REV LETT, V112, DOI 10.1103/PhysRevLett.112.120402
Lampo A, 2017, PHYS REV A, V96, DOI 10.1103/PhysRevA.96.012120
Lebowitz JL, 2015, J PHYS A-MATH THEOR, V48, DOI 10.1088/1751-8113/48/26/265201
Liu BH, 2011, NAT PHYS, V7, P931, DOI [10.1038/nphys2085, 10.1038/NPHYS2085]
Lutz E, 1999, PHYSICA A, V267, P354, DOI 10.1016/S0378-4371(99)00022-9
Mironowicz P, 2017, PHYS REV LETT, V118, DOI 10.1103/PhysRevLett.118.150501
Ollivier H, 2004, PHYS REV LETT, V93, DOI 10.1103/PhysRevLett.93.220401
Ollivier H, 2002, PHYS REV LETT, V88, DOI 10.1103/PhysRevLett.88.017901
Paz JP, 2009, PHYS REV A, V80, DOI 10.1103/PhysRevA.80.042111
Perez A, 2010, PHYS REV A, V81, DOI 10.1103/PhysRevA.81.052326
Pleasance G, 2017, PHYS REV A, V96, DOI 10.1103/PhysRevA.96.062105
Riedel CJ, 2010, PHYS REV LETT, V105, DOI 10.1103/PhysRevLett.105.020404
Salvail JZ, 2013, NAT PHOTONICS, V7, P316, DOI [10.1038/NPHOTON.2013.24, 10.1038/nphoton.2013.24]
Schlosshauer M, 2004, REV MOD PHYS, V76, P1267, DOI 10.1103/RevModPhys.76.1267
Schlosshauer M, 2007, FRONT COLLECT, P1
Tuziemski J, 2015, EPL-EUROPHYS LETT, V112, DOI 10.1209/0295-5075/112/40008
Tuziemski J, 2015, PHOTONICS, V2, P228, DOI 10.3390/photonics2010228
Weidenmuller HA, 2009, REV MOD PHYS, V81, P539, DOI 10.1103/RevModPhys.81.539
Zurek WH, 2003, REV MOD PHYS, V75, P715, DOI 10.1103/RevModPhys.75.715
ZUREK WH, 1993, PHYS REV LETT, V70, P1187, DOI 10.1103/PhysRevLett.70.1187
ZUREK WH, 1993, PROG THEOR PHYS, V89, P281, DOI 10.1143/PTP.89.281
Zurek WH, 2009, NAT PHYS, V5, P181, DOI 10.1038/NPHYS1202
Zwolak M, 2016, SCI REP-UK, V6, DOI 10.1038/srep25277
Zwolak M, 2014, PHYS REV LETT, V112, DOI 10.1103/PhysRevLett.112.140406
Zwolak M, 2013, SCI REP-UK, V3, DOI 10.1038/srep01729
Zwolak M, 2010, PHYS REV A, V81, DOI 10.1103/PhysRevA.81.062110
Zwolak M, 2009, PHYS REV LETT, V103, DOI 10.1103/PhysRevLett.103.110402
NR 47
TC 7
Z9 7
U1 0
U2 3
PU AMER PHYSICAL SOC
PI COLLEGE PK
PA ONE PHYSICS ELLIPSE, COLLEGE PK, MD 20740-3844 USA
SN 2469-9926
EI 2469-9934
J9 PHYS REV A
JI Phys. Rev. A
PD SEP 5
PY 2018
VL 98
IS 3
AR 032103
DI 10.1103/PhysRevA.98.032103
PG 12
WC Optics; Physics, Atomic, Molecular & Chemical
SC Optics; Physics
GA GS5BM
UT WOS:000443671600001
OA Green Published
DA 2021-04-21
ER
PT J
AU Jain, A
AF Jain, Abhinandan
TI An analytical workbench for system level multibody dynamics
SO MULTIBODY SYSTEM DYNAMICS
LA English
DT Article
DE Dynamics; Workbench
AB There has been considerable focus in the research community on developing accurate models, as well as on fast algorithms for solving the equations of motion of multibody systems required for simulating the dynamics of such systems. This paper focuses on the less explored complementary topic of evaluating system level dynamics properties of multibody systems. Examples of such dynamics properties are the system mass matrix, Jacobians and sensitivities of these quantities. These system level quantities manifest the dynamical properties of the system and are important for design, optimization and control. While such system level quantities are often used in theory to describe the underlying mathematical physics of the systems, due to their complexity there is a lack of a systematic methods for computing them when desired. In this paper we describe a computational workbench framework that provides a bridge between theory and the computation of such system level quantities. This workbench framework builds upon the Spatial Operator Algebra (SOA) that has been used for analysis and algorithm development for multibody dynamics. Mathematical operator expressions from the SOA can be transcribed literally to the workbench command line to allow the easy evaluation of complex dynamics quantities. We use a specific Python/C++ implementation of a workbench called PyCraft to illustrate the structure and use of such a workbench. Several examples illustrating operator-based analysis and corresponding PyCraft-based computation are included.
C1 [Jain, Abhinandan] CALTECH, Jet Prop Lab, 4800 Oak Grove Dr, Pasadena, CA 91109 USA.
RP Jain, A (corresponding author), CALTECH, Jet Prop Lab, 4800 Oak Grove Dr, Pasadena, CA 91109 USA.
EM Abhi.Jain@jpl.nasa.gov
CR Craig J. J, 1986, INTRO ROBOTICS
FIXMAN M, 1974, P NATL ACAD SCI USA, V71, P3050, DOI 10.1073/pnas.71.8.3050
Jain A, 1997, J COMPUT PHYS, V136, P289, DOI 10.1006/jcph.1997.5731
JAIN A, 1995, IEEE T ROBOTIC AUTOM, V11, P571, DOI 10.1109/70.406941
Jain A., 2016, 4 JOINT INT C MULT S
Jain A., 2013, 2013 IEEE INT C ROB
Jain A, 2012, NONLINEAR DYNAM, V67, P2153, DOI 10.1007/s11071-011-0136-x
Jain A, 2011, ROBOT AND MULTIBODY DYNAMICS: ANALYSIS AND ALGORITHMS, P3, DOI 10.1007/978-1-4419-7267-5
Khatib O., 1985, 3 INT S ROB RES PAR
KREUTZDELGADO K, 1992, INT J ROBOT RES, V11, P320, DOI 10.1177/027836499201100405
LUH JYS, 1980, J DYN SYST-T ASME, V102, P69, DOI 10.1115/1.3149599
RODRIGUEZ G, 1991, INT J ROBOT RES, V10, P371, DOI 10.1177/027836499101000406
WALKER MW, 1982, J DYN SYST-T ASME, V104, P205, DOI 10.1115/1.3139699
NR 13
TC 1
Z9 1
U1 0
U2 7
PU SPRINGER
PI DORDRECHT
PA VAN GODEWIJCKSTRAAT 30, 3311 GZ DORDRECHT, NETHERLANDS
SN 1384-5640
EI 1573-272X
J9 MULTIBODY SYST DYN
JI Multibody Syst. Dyn.
PD SEP
PY 2018
VL 44
IS 1
BP 57
EP 79
DI 10.1007/s11044-018-9623-x
PG 23
WC Mechanics
SC Mechanics
GA GP4CV
UT WOS:000440805700003
DA 2021-04-21
ER
PT J
AU Figueiras, E
Olivieri, D
Paredes, A
Michinel, H
AF Figueiras, Edgar
Olivieri, David
Paredes, Angel
Michinel, Humberto
TI An open source virtual laboratory for the Schrodinger equation
SO EUROPEAN JOURNAL OF PHYSICS
LA English
DT Article
DE virtual laboratory; Schrodinger equation; beam propagation method; open
source; Python
ID SOLITONS; PROPAGATION; SCIENCE; PHYSICS
AB A simple Python-based open source software library for the numerical simulation of the linear or nonlinear time-dependent Schrodinger equation in one and two dimensions is presented. The integration is performed using a first-order split-step pseudospectral method, relying on the fast Fourier transform. The software library could be useful for undergraduate courses in elementary quantum mechanics, wave optics and computational physics. It could also be of interest for graduate students working with nonlinear waves, in frameworks such as laser beam propagation in nonlinear optical materials, matter waves within ultracold gases, dark matter or superfluid dynamics, among others. The discussion is complemented by solved examples and suggestions for educational applications of the code.
C1 [Figueiras, Edgar; Olivieri, David] Univ Vigo, Dept Linguaxes & Sistemas Informat, As Lagoas S-N, E-32004 Orense, Spain.
[Paredes, Angel; Michinel, Humberto] Univ Vigo, Sch Aeronaut & Space Engn, Appl Phys Dept, As Lagoas S-N, E-32004 Orense, Spain.
RP Paredes, A (corresponding author), Univ Vigo, Sch Aeronaut & Space Engn, Appl Phys Dept, As Lagoas S-N, E-32004 Orense, Spain.
EM efgomez@esei.uvigo.es; olivieri@uvigo.es; angel.paredes@uvigo.es;
hmichinel@uvigo.es
RI Paredes, Angel/L-3126-2014; Michinel, Humberto/L-3214-2014
OI Paredes, Angel/0000-0003-3207-1586; Michinel,
Humberto/0000-0002-7854-7626
FU Ministerio de Economia y Competitividad (Spain) [FIS2014-58117-P,
FIS2017-83762-P]; Conselleria de Cultura, Educacion e Ordenacion
Universitaria (Xunta de Galicia)Xunta de Galicia [GPC2015/019]
FX We thank A F Biasi, J Blanco-Labrador and D Tommasini for testing the
code and commenting on the manuscript. This work is supported by grants
FIS2014-58117-P and FIS2017-83762-P from Ministerio de Economia y
Competitividad (Spain), and grant GPC2015/019 from Conselleria de
Cultura, Educacion e Ordenacion Universitaria (Xunta de Galicia).
CR Agrawal G. P., 2007, NONLINEAR FIBER OPTI
Biasi A, 2017, PHYS REV A, V96, DOI 10.1103/PhysRevA.96.053615
Briscese F, 2017, EUR PHYS J C, V77, DOI 10.1140/epjc/s10052-017-5209-7
Burger S, 1999, PHYS REV LETT, V83, P5198, DOI 10.1103/PhysRevLett.83.5198
Cambronero-Lopez F, 2017, EUR J PHYS, V38, DOI 10.1088/1361-6404/aa5a93
Carpentier AV, 2008, AM J PHYS, V76, P916, DOI 10.1119/1.2955792
Chhabra M, 2017, EUR J PHYS, V38, DOI 10.1088/0143-0807/38/1/015404
Couairon A, 2007, PHYS REP, V441, P47, DOI 10.1016/j.physrep.2006.12.005
DAVYDOV AS, 1979, PHYS SCRIPTA, V20, P387, DOI 10.1088/0031-8949/20/3-4/013
de Jong T, 2013, SCIENCE, V340, P305, DOI 10.1126/science.1230579
Fibich G., 2015, NONLINEAR SCHRODINGE
FLECK JA, 1976, APPL PHYS, V10, P129, DOI 10.1007/BF00896333
Galan D, 2017, EUR J PHYS, V38, DOI 10.1088/1361-6404/aa5dc1
Gould H, 2016, INTRO COMPUTER SIMUL
HASEGAWA A, 1973, APPL PHYS LETT, V23, P142, DOI 10.1063/1.1654836
Hatherly PA, 2009, EUR J PHYS, V30, P751, DOI 10.1088/0143-0807/30/4/008
Kim WS, 2000, PHYS LETT A, V266, P364, DOI 10.1016/S0375-9601(00)00080-3
Kiriushcheva N, 1998, AM J PHYS, V66, P867, DOI 10.1119/1.18985
Kivshar YS, 1998, PHYS REP, V298, P81, DOI 10.1016/S0370-1573(97)00073-2
Landau RH, 2015, COMPUTATIONAL PHYS P
Lehtovaara L, 2007, J COMPUT PHYS, V221, P148, DOI 10.1016/j.jcp.2006.06.006
Michinel H, 2002, PHYS REV E, V65, DOI 10.1103/PhysRevE.65.066604
Michinel H, 2012, PHYS REV A, V86, DOI 10.1103/PhysRevA.86.013620
Minner DD, 2010, J RES SCI TEACH, V47, P474, DOI 10.1002/tea.20347
Muslu GM, 2005, MATH COMPUT SIMULAT, V67, P581, DOI 10.1016/j.matcom.2004.08.002
Newman M., 2013, COMPUTATIONAL PHYS
Orquin I, 2007, COMPUT APPL ENG EDUC, V15, P124, DOI 10.1002/cae.20100
Paredes A, 2016, PHYS DARK UNIVERSE, V12, P50, DOI 10.1016/j.dark.2016.02.003
Paredes A, 2014, PHYS REV LETT, V112, DOI 10.1103/PhysRevLett.112.173901
Rodas-Verde MI, 2005, PHYS REV LETT, V95, DOI 10.1103/PhysRevLett.95.153903
Rogel-Salazar J, 2013, EUR J PHYS, V34, P247, DOI 10.1088/0143-0807/34/2/247
Schmidt J. D., 2010, NUMERICAL SIMULATION
Shao J., 2014, IEEE PHOTONICS J, V6, P1, DOI DOI 10.1109/JPH0T.2014.2340993)
Sulem PL, 1999, NONLINEAR SCHRODINGE
Teich MC., 2007, FUNDAMENTALS PHOTONI, V2nd edn
van Dijk W, 2014, AM J PHYS, V82, P955, DOI 10.1119/1.4885376
Wu W, 2013, PHYS REV A, V88
Zacharia ZC, 2008, J RES SCI TEACH, V45, P1021, DOI 10.1002/tea.20260
ZAKHAROV VE, 1972, SOV PHYS JETP-USSR, V34, P62
NR 39
TC 5
Z9 5
U1 0
U2 12
PU IOP PUBLISHING LTD
PI BRISTOL
PA TEMPLE CIRCUS, TEMPLE WAY, BRISTOL BS1 6BE, ENGLAND
SN 0143-0807
EI 1361-6404
J9 EUR J PHYS
JI Eur. J. Phys.
PD SEP
PY 2018
VL 39
IS 5
AR 055802
DI 10.1088/1361-6404/aac999
PG 13
WC Education, Scientific Disciplines; Physics, Multidisciplinary
SC Education & Educational Research; Physics
GA GL2FK
UT WOS:000436932800001
OA Green Published, Other Gold
DA 2021-04-21
ER
PT J
AU Zhao, CC
Song, JS
AF Zhao, Chenchao
Song, Jun S.
TI Quantum transport senses community structure in networks
SO PHYSICAL REVIEW E
LA English
DT Article
ID EFFICIENT ALGORITHM; DIFFUSION
AB Quantum time evolution exhibits rich physics, attributable to the interplay between the density and phase of a wave function. However, unlike classical heat diffusion, the wave nature of quantum mechanics has not yet been extensively explored in modern data analysis. We propose that the Laplace transform of quantum transport (QT) can be used to construct an ensemble of maps from a given complex network to a circle S-1, such that closely related nodes on the network are grouped into sharply concentrated clusters on S-1. The resulting QT clustering (QTC) algorithm is as powerful as the state-of-the-art spectral clustering in discerning complex geometric patterns and more robust when clusters show strong density variations or heterogeneity in size. The observed phenomenon of QTC can be interpreted as a collective behavior of the microscopic nodes that evolve as macroscopic cluster "orbitals" in an effective tight-binding model recapitulating the network. PYTHON source code implementing the algorithm and examples are available at Imps://githuh.com/issong-1ah/QTC.
C1 [Song, Jun S.] Univ Illinois, Dept Phys, Urbana, IL 61801 USA.
Univ Illinois, Carl R Woese Inst Genom Biol, Urbana, IL 61801 USA.
RP Song, JS (corresponding author), Univ Illinois, Dept Phys, Urbana, IL 61801 USA.
EM songj@illinois.edu
RI Song, Jun S/E-2526-2016
OI Song, Jun S/0000-0002-0422-2175
FU Sontag Foundation; Grainger Engineering Breakthroughs Initiative
FX We thank Alan Luu, Mohith Manjunath, and Yi Zhang for their help. This
work was supported by the Sontag Foundation and the Grainger Engineering
Breakthroughs Initiative.
CR ANDERSON PW, 1958, PHYS REV, V109, P1492, DOI 10.1103/PhysRev.109.1492
Arthur D, 2007, PROCEEDINGS OF THE EIGHTEENTH ANNUAL ACM-SIAM SYMPOSIUM ON DISCRETE ALGORITHMS, P1027
Brin S, 1998, COMPUT NETWORKS ISDN, V30, P107, DOI 10.1016/S0169-7552(98)00110-X
Cardillo A, 2013, PHYS REV A, V87, DOI 10.1103/PhysRevA.87.052312
Childs AM, 2002, QUANTUM INF PROCESS, V1, P35, DOI 10.1023/A:1019609420309
Coifman RR, 2006, APPL COMPUT HARMON A, V21, P5, DOI 10.1016/j.acha.2006.04.006
DEFAYS D, 1977, COMPUT J, V20, P364, DOI 10.1093/comjnl/20.4.364
Dunham I, 2012, NATURE, V489, P57, DOI 10.1038/nature11247
Ester M., 1996, KDD-96 Proceedings. Second International Conference on Knowledge Discovery and Data Mining, P226
Faccin M, 2014, PHYS REV X, V4, DOI 10.1103/PhysRevX.4.041012
Faccin M, 2013, PHYS REV X, V3, DOI 10.1103/PhysRevX.3.041007
Farhi E, 1998, PHYS REV A, V58, P915, DOI 10.1103/PhysRevA.58.915
FORGY EW, 1965, BIOMETRICS, V21, P768
Fudenberg G, 2011, NAT BIOTECHNOL, V29, P1109, DOI 10.1038/nbt.2049
Hastie T., 2013, ELEMENTS STAT LEARNI
Horn D, 2002, PHYS REV LETT, V88, DOI 10.1103/PhysRevLett.88.018702
Kaufman L, 2009, FINDING GROUPS DATA
Lafferty J, 2005, J MACH LEARN RES, V6, P129
Li HJ, 2012, PHYS REV E, V86, DOI 10.1103/PhysRevE.86.016109
LLOYD SP, 1982, IEEE T INFORM THEORY, V28, P129, DOI 10.1109/tit.1982.1056489
MARIMONT RB, 1979, J I MATH APPL, V24, P59
Novikov V. A., 1999, ITEP LECT PARTICLE P, V2, P201
Novikov V. A., 1999, ITEP LECT PARTICLE P, V1, P201
Reichardt J, 2004, PHYS REV LETT, V93, DOI 10.1103/PhysRevLett.93.218701
Sanchez-Burillo E, 2012, SCI REP-UK, V2, DOI 10.1038/srep00605
Shenvi N, 2003, PHYS REV A, V67, DOI 10.1103/PhysRevA.67.052307
SIBSON R, 1973, COMPUT J, V16, P30, DOI 10.1093/comjnl/16.1.30
von Luxburg U, 2007, STAT COMPUT, V17, P395, DOI 10.1007/s11222-007-9033-z
Yu GS, 2012, IEEE T IMAGE PROCESS, V21, P2481, DOI 10.1109/TIP.2011.2176743
Zhao C., 2018, FRONTIERS APPL MATH, V4, P129
Zhao CC, 2017, PHYS REV E, V95, DOI 10.1103/PhysRevE.95.042307
NR 31
TC 1
Z9 1
U1 0
U2 7
PU AMER PHYSICAL SOC
PI COLLEGE PK
PA ONE PHYSICS ELLIPSE, COLLEGE PK, MD 20740-3844 USA
SN 1539-3755
EI 1550-2376
J9 PHYS REV E
JI Phys. Rev. E
PD AUG 3
PY 2018
VL 98
IS 2
AR 022301
DI 10.1103/PhysRevE.98.022301
PG 13
WC Physics, Fluids & Plasmas; Physics, Mathematical
SC Physics
GA GP3DQ
UT WOS:000440721800004
PM 30253552
DA 2021-04-21
ER
PT J
AU Wang, F
Barklage, M
Lou, XT
van der Lee, S
Bina, CR
Jacobsen, SD
AF Wang, Fei
Barklage, Mitchell
Lou, Xiaoting
van der Lee, Suzan
Bina, Craig R.
Jacobsen, Steven D.
TI HyMaTZ: A Python Program for Modeling Seismic Velocities in Hydrous
Regions of the Mantle Transition Zone
SO GEOCHEMISTRY GEOPHYSICS GEOSYSTEMS
LA English
DT Article
ID SINGLE-CRYSTAL ELASTICITY; FE-BEARING WADSLEYITE; SHEAR-WAVE VELOCITIES;
SAN-CARLOS OLIVINE; EQUATION-OF-STATE; SOUND VELOCITIES; HIGH-PRESSURE;
TEMPERATURE-DEPENDENCE; STORAGE CAPACITY; EARTHS INTERIOR
AB Mapping the spatial distribution of water in the mantle transition zone (MTZ, 410- to 660-km depth) may be approached by combining thermodynamic and experimental mineral physics data with regional studies of seismic velocity and seismic discontinuity structure. HyMaTZ (Hydrous Mantle Transition Zone) is a Python program with graphical user interface, which calculates and displays seismic velocities for different scenarios of hydration in the MTZ for comparison to global or regional seismic-velocity models. The influence of water is applied through a regression to experimental data on how H2O influences the thermoelastic properties of (Mg, Fe)(2)SiO4 polymorphs: olivine, wadsleyite, and ringwoodite. Adiabatic temperature profiles are internally consistent with dry phase proportion models; however, modeling hydration in HyMaTZ affects only velocities and not phase proportions or discontinuity structure. For wadsleyite, adding 1.65 wt% H2O or increasing the iron content by 7 mol% leads to roughly equivalent reductions in VS as raising the temperature by 160 K with a pyrolite model in the upper part of the MTZ. The eastern U.S. low-velocity anomaly, which has been interpreted as the result of dehydration of the Farallon slab in the top of the lower mantle, is consistent with hydration of wadsleyite to about 20% of its water storage capacity in the upper MTZ. Velocity gradients with depth in absolute shear velocity models are steeper in all seismic models than all mineralogical models, suggesting that the seismic velocity gradients should be lowered or varied with depth and/ or an alternative compositional model is required.
Plain Language Summary Olivine and its high-pressure polymorphs, wadsleyite and ringwoodite, constitute at least half of the material in the Earth's upper mantle. In addition to magnesium, iron, silicon, and oxygen, these phases are known for their ability to incorporate H2O. Water is incorporated into their crystal structures as OH (hydroxyl) defects, charge-balanced primarily by cation vacancies. Hydration of olivine, wadsleyite, and ringwoodite through such defectsmodifies their density and elastic properties such as the bulk (K) and shear (G) moduli, which can lead to changes in seismic velocities as determined from analyses of seismograms using methods such as seismic tomography. This paper presents a tool for researchers to calculate and graph the influence of hydration in olivine, wadsleyite, and ringwoodite for comparison to any seismic-velocity model. While Hydrous Mantle Transition Zone accounts for elastic property changes due to hydration, the effect ofwater on thermodynamic phase proportions is not yet included. The goal of such studies is to assess the amount and regional distribution of water in the mantle transition zone.
C1 [Wang, Fei; Barklage, Mitchell; Lou, Xiaoting; van der Lee, Suzan; Bina, Craig R.; Jacobsen, Steven D.] Northwestern Univ, Dept Earth & Planetary Sci, Evanston, IL 60208 USA.
RP Wang, F (corresponding author), Northwestern Univ, Dept Earth & Planetary Sci, Evanston, IL 60208 USA.
EM feiwang2020@u.northwestern.edu
RI Jacobsen, Steven/F-3443-2013; van der Lee, Suzan/K-1144-2013
OI Jacobsen, Steven/0000-0002-9746-958X; van der Lee,
Suzan/0000-0003-1884-1185; Bina, Craig/0000-0001-5946-3737
FU National Science FoundationNational Science Foundation (NSF)
[EAR-1452344]
FX This research was supported by the National Science Foundation grant
EAR-1452344 to S. D. J. HyMaTZ can be downloaded from GitHub at github.
com/wangyefei/HyMaTZ. HyMaTZ can be redistributed and/or modified under
the terms of the GNU Library General Public License as published by the
Free Software Foundation. All of the data sets used in this paper are
provided in the software distribution.
CR Abramson EH, 1997, J GEOPHYS RES-SOL EA, V102, P12253, DOI 10.1029/97JB00682
Afonso JC, 2008, GEOCHEM GEOPHY GEOSY, V9, DOI 10.1029/2007GC001834
ANDERSON DL, 1986, NATURE, V320, P321, DOI 10.1038/320321a0
Barklage M, 2015, GEOCHEM GEOPHY GEOSY, V16, P681, DOI 10.1002/2014GC005627
Bedle H, 2009, J GEOPHYS RES-SOL EA, V114, DOI 10.1029/2008JB005949
BINA CR, 1992, ANNU REV EARTH PL SC, V20, P527, DOI 10.1146/annurev.ea.20.050192.002523
BOGGS PT, 1989, ACM T MATH SOFTWARE, V15, P348, DOI 10.1145/76909.76913
Bolfan-Casanova N, 2005, MINERAL MAG, V69, P229, DOI 10.1180/0026461056930248
Bolfan-Casanova N, 2012, AM MINERAL, V97, P1483, DOI 10.2138/am.2012.3869
Burdick S, 2017, SEISMOL RES LETT, V88, P319, DOI 10.1785/0220160186
Cammarano F, 2003, PHYS EARTH PLANET IN, V138, P197, DOI 10.1016/S0031-9201(03)00156-0
Chang YY, 2015, J GEOPHYS RES-SOL EA, V120, P8259, DOI 10.1002/2015JB012123
Chen JH, 2002, GEOPHYS RES LETT, V29, DOI 10.1029/2001GL014429
Connolly JAD, 2016, GEOPHYS RES LETT, V43, P5026, DOI 10.1002/2016GL068239
Connolly JAD, 2009, GEOCHEM GEOPHY GEOSY, V10, DOI 10.1029/2009GC002540
Connolly JAD, 2005, EARTH PLANET SC LETT, V236, P524, DOI 10.1016/j.epsl.2005.04.033
Cottaar S, 2014, GEOCHEM GEOPHY GEOSY, V15, P1164, DOI 10.1002/2013GC005122
Courtier AM, 2006, GEOPHYS MONOGR SER, V168, P181, DOI 10.1029/168GM14
Darling KL, 2004, PHYS EARTH PLANET IN, V143, P19, DOI 10.1016/j.pepi.2003.07.018
Demouchy S, 2005, AM MINERAL, V90, P1084, DOI 10.2138/am.2005.1751
DZIEWONSKI AM, 1975, PHYS EARTH PLANET IN, V10, P12, DOI 10.1016/0031-9201(75)90017-5
DZIEWONSKI AM, 1981, PHYS EARTH PLANET IN, V25, P297, DOI 10.1016/0031-9201(81)90046-7
Emry EL, 2015, GEOCHEM GEOPHY GEOSY, V16, P40, DOI 10.1002/2014GC005588
Fei HZ, 2017, SCI ADV, V3, DOI 10.1126/sciadv.1603024
French SW, 2014, GEOPHYS J INT, V199, P1303, DOI 10.1093/gji/ggu334
Frost DJ, 2007, EARTH PLANET SC LETT, V256, P182, DOI 10.1016/j.epsl.2007.01.023
Frost DJ, 2006, REV MINERAL GEOCHEM, V62, P243, DOI 10.2138/rmg.2006.62.11
Goes S, 2002, J GEOPHYS RES-SOL EA, V107, DOI 10.1029/2000JB000049
Goes S, 2000, J GEOPHYS RES-SOL EA, V105, P11153, DOI 10.1029/1999JB900300
GRAHAM EK, 1969, J GEOPHYS RES, V74, P5949, DOI 10.1029/JB074i025p05949
GRAND SP, 1994, J GEOPHYS RES-SOL EA, V99, P11591, DOI 10.1029/94JB00042
Grand SP, 2002, PHILOS T R SOC A, V360, P2475, DOI 10.1098/rsta.2002.1077
Green HW, 2010, NATURE, V467, P828, DOI 10.1038/nature09401
HASHIN Z, 1963, J MECH PHYS SOLIDS, V11, P127, DOI 10.1016/0022-5096(63)90060-7
Hauri EH, 2006, EARTH PLANET SC LETT, V248, P715, DOI 10.1016/j.epsl.2006.06.014
Higo Y, 2008, PHYS EARTH PLANET IN, V166, P167, DOI 10.1016/j.pepi.2008.01.003
Higo Y, 2006, PHYS EARTH PLANET IN, V159, P276, DOI 10.1016/j.pepi.2006.08.004
Hirschmann MM, 2006, ANNU REV EARTH PL SC, V34, P629, DOI 10.1146/annurev.earth.34.031405.125211
Hirschmann MM, 2005, EARTH PLANET SC LETT, V236, P167, DOI 10.1016/j.epsl.2005.04.022
Holland TJ, 2013, J PETROL, V54, P1901, DOI 10.1093/petrology/egt035
Inoue T, 2010, PHYS EARTH PLANET IN, V183, P245, DOI 10.1016/j.pepi.2010.08.003
IRIFUNE T, 1987, EARTH PLANET SC LETT, V86, P365, DOI 10.1016/0012-821X(87)90233-0
ISAAK DG, 1992, J GEOPHYS RES-SOL EA, V97, P1871, DOI 10.1029/91JB02675
Isaak DG, 2007, PHYS EARTH PLANET IN, V162, P22, DOI 10.1016/j.pepi.2007.02.010
Isaak DG, 2010, PHYS EARTH PLANET IN, V182, P107, DOI 10.1016/j.pepi.2010.06.014
Jackson JM, 2000, AM MINERAL, V85, P296, DOI 10.2138/am-2000-2-306
Jacobsen S. D, 2006, EARTHS DEEP WATER CY, DOI [10.1029/GM168, DOI 10.1029/GM168]
Jacobsen SD, 2006, GEOPHYS MONOGR SER, V168, P131
Jacobsen SD, 2006, REV MINERAL GEOCHEM, V62, P321, DOI 10.2138/rmg.2006.62.14
Jacobsen SD, 2009, GEOPHYS RES LETT, V36, DOI 10.1029/2009GL038660
Jacobsen SD, 2008, GEOPHYS RES LETT, V35, DOI 10.1029/2008GL034398
Karato S, 1998, EARTH PLANET SC LETT, V157, P193, DOI 10.1016/S0012-821X(98)00034-X
KARATO S, 1993, GEOPHYS RES LETT, V20, P1623, DOI 10.1029/93GL01767
KARATO S, 1990, REV GEOPHYS, V28, P399, DOI 10.1029/RG028i004p00399
Karato S, 2011, EARTH PLANET SC LETT, V301, P413, DOI 10.1016/j.epsl.2010.11.038
KENNETT BLN, 1991, GEOPHYS J INT, V105, P429, DOI 10.1111/j.1365-246X.1991.tb06724.x
KENNETT BLN, 1995, GEOPHYS J INT, V122, P108, DOI 10.1111/j.1365-246X.1995.tb03540.x
Kohlstedt DL, 1996, CONTRIB MINERAL PETR, V123, P345, DOI 10.1007/s004100050161
Komabayashi T, 2006, PHYS EARTH PLANET IN, V156, P89, DOI 10.1016/j.pepi.2006.02.002
KUMAZAWA M, 1969, J GEOPHYS RES, V74, P5961, DOI 10.1029/JB074i025p05961
Li B, 2007, P NATL ACAD SCI USA, V104, P9145, DOI 10.1073/pnas.0608609104
Li BS, 2000, AM MINERAL, V85, P292, DOI 10.2138/am-2000-2-305
Li BS, 2003, AM MINERAL, V88, P1312
Li BS, 2001, J GEOPHYS RES-SOL EA, V106, P30579, DOI 10.1029/2001JB000317
Liu Q, 2008, HIGH PRESSURE RES, V28, P405, DOI 10.1080/08957950802296287
Liu W, 2005, GEOPHYS RES LETT, V32, DOI 10.1029/2005GL023453
Liu W, 2009, PHYS EARTH PLANET IN, V174, P98, DOI 10.1016/j.pepi.2008.10.020
Liu Z, 2016, GEOPHYS RES LETT, V43, P2480, DOI 10.1002/2015GL067097
Mao Z, 2008, GEOPHYS RES LETT, V35, DOI 10.1029/2008GL035618
Mao Z, 2016, SCI CHINA EARTH SCI, V59, P873, DOI 10.1007/s11430-016-5277-9
Mao Z, 2012, EARTH PLANET SC LETT, V331, P112, DOI 10.1016/j.epsl.2012.03.001
Mao Z, 2011, AM MINERAL, V96, P1606, DOI 10.2138/am.2011.3807
Mayama N, 2005, PHYS EARTH PLANET IN, V148, P353, DOI 10.1016/j.pepi.2004.09.007
MINSTER JB, 1981, PHILOS T R SOC A, V299, P319, DOI 10.1098/rsta.1981.0025
Ohtani E, 2004, PHYS EARTH PLANET IN, V143, P255, DOI 10.1016/j.pepi.2003.09.015
Ohtani E, 2001, PHYS EARTH PLANET IN, V124, P105, DOI 10.1016/S0031-9201(01)00192-3
Pearson DG, 2014, NATURE, V507, P221, DOI 10.1038/nature13080
POLLACK HN, 1993, REV GEOPHYS, V31, P267, DOI 10.1029/93RG01249
RINGWOOD AE, 1967, EARTH PLANET SC LETT, V2, P130, DOI 10.1016/0012-821X(67)90114-8
Ritsema J, 2004, J GEOPHYS RES-SOL EA, V109, DOI 10.1029/2003JB002610
ROMANOWICZ BA, 1979, GEOPHYS J ROY ASTR S, V57, P479, DOI 10.1111/j.1365-246X.1979.tb04790.x
Schaeffer AJ, 2014, EARTH PLANET SC LETT, V402, P26, DOI 10.1016/j.epsl.2014.05.014
Schmandt B, 2014, GEOPHYS RES LETT, V41, P6342, DOI 10.1002/2014GL061231
Schmandt B, 2014, SCIENCE, V344, P1265, DOI [10.1126/science.1253358, 10.1126/science.1253258]
Sinogeikin SV, 2003, PHYS EARTH PLANET IN, V136, P41, DOI 10.1016/S0031-9201(03)00022-0
Sinogeikin SV, 1998, J GEOPHYS RES-SOL EA, V103, P20819, DOI 10.1029/98JB01819
Sinogeikin SV, 1997, GEOPHYS RES LETT, V24, P3265, DOI 10.1029/97GL03217
Smyth JR, 2002, GEOPHYS RES LETT, V29, DOI 10.1029/2001GL014418
SMYTH JR, 1994, AM MINERAL, V79, P1021
SMYTH JR, 1987, AM MINERAL, V72, P1051
Smyth JR, 2004, PHYS EARTH PLANET IN, V143, P271, DOI 10.1016/j.pepi.2003.08.011
Speziale S, 2004, J GEOPHYS RES-SOL EA, V109, DOI 10.1029/2004JB003162
Stixrude L, 2005, GEOPHYS J INT, V162, P610, DOI 10.1111/j.1365-246X.2005.02642.x
Stixrude L, 2012, ANNU REV EARTH PL SC, V40, P569, DOI 10.1146/annurev.earth.36.031207.124244
Stixrude L, 2011, GEOPHYS J INT, V184, P1180, DOI 10.1111/j.1365-246X.2010.04890.x
SUZUKI R, 1983, ACTA NEUROPATHOL, V60, P217, DOI 10.1007/BF00691869
Tenner TJ, 2012, CONTRIB MINERAL PETR, V163, P297, DOI 10.1007/s00410-011-0675-7
Thio V, 2016, PHYS EARTH PLANET IN, V250, P46, DOI 10.1016/j.pepi.2015.11.005
van der Lee S, 2008, EARTH PLANET SC LETT, V273, P15, DOI 10.1016/j.epsl.2008.04.041
van der Lee S, 2005, GEOPHYS MONOGR SER, V157, P67, DOI 10.1029/157GM05
van der Meijde M, 2003, SCIENCE, V300, P1556, DOI 10.1126/science.1083636
vanderHilst RD, 1997, NATURE, V386, P578, DOI 10.1038/386578a0
vanderLee S, 1997, J GEOPHYS RES-SOL EA, V102, P22815, DOI 10.1029/97JB01168
Wang JY, 2006, GEOPHYS RES LETT, V33, DOI 10.1029/2006GL026441
Wang JY, 2003, AM MINERAL, V88, P1608
Wang XB, 2015, GEOPHYS RES LETT, V42, P3289, DOI 10.1002/2015GL063436
WATT JP, 1976, REV GEOPHYS, V14, P541, DOI 10.1029/RG014i004p00541
WEBB SL, 1989, PHYS CHEM MINER, V16, P684
WEIDNER DJ, 1986, CHEM PHYSICS TERREST, P251, DOI DOI 10.1007/978-1-4612-4928-3_7
WOOD BJ, 1995, SCIENCE, V268, P74, DOI 10.1126/science.268.5207.74
Xu WB, 2008, EARTH PLANET SC LETT, V275, P70, DOI 10.1016/j.epsl.2008.08.012
Ye Y, 2012, AM MINERAL, V97, P573, DOI 10.2138/am.2012.4010
ZAUG JM, 1993, SCIENCE, V260, P1487, DOI 10.1126/science.260.5113.1487
Zha CS, 1997, EARTH PLANET SC LETT, V147, pE9, DOI 10.1016/S0012-821X(97)00010-1
Zha CS, 1996, J GEOPHYS RES-SOL EA, V101, P17535, DOI 10.1029/96JB01266
Zha CS, 1998, EARTH PLANET SC LETT, V159, P25, DOI 10.1016/S0012-821X(98)00063-6
NR 116
TC 5
Z9 5
U1 1
U2 12
PU AMER GEOPHYSICAL UNION
PI WASHINGTON
PA 2000 FLORIDA AVE NW, WASHINGTON, DC 20009 USA
SN 1525-2027
J9 GEOCHEM GEOPHY GEOSY
JI Geochem. Geophys. Geosyst.
PD AUG
PY 2018
VL 19
IS 8
BP 2308
EP 2324
DI 10.1029/2018GC007464
PG 17
WC Geochemistry & Geophysics
SC Geochemistry & Geophysics
GA HC9JQ
UT WOS:000452122500002
OA Bronze
DA 2021-04-21
ER
PT J
AU Toye, H
Kortas, S
Zhan, P
Hoteit, I
AF Toye, Habib
Kortas, Samuel
Zhan, Peng
Hoteit, Ibrahim
TI A fault-tolerant HPC scheduler extension for large and operational
ensemble data assimilation: Application to the Red Sea
SO JOURNAL OF COMPUTATIONAL SCIENCE
LA English
DT Article; Proceedings Paper
CT 2nd International Computational Science and Engineering Conference
CY OCT 23-24, 2017
CL Doha, QATAR
DE High performance computing; Ensemble data assimilation; Bayesian
filtering; Operational oceanography; Red Sea
ID ADAPTIVE COVARIANCE INFLATION; KALMAN FILTER; MODEL; OCEAN;
LOCALIZATION; FRAMEWORK
AB A fully parallel ensemble data assimilation and forecasting system has been developed for the Red Sea based on the MIT general circulation model (MITgcm) to simulate the Red Sea circulation and the Data Assimilation Research Testbed (DART) ensemble assimilation software. An important limitation of operational ensemble assimilation systems is the risk of ensemble members' collapse. This could happen in those situations when the filter update step imposes large corrections on one, or more, of the forecasted ensemble members that are not fully consistent with the model physics. Increasing the ensemble size is expected to improve the assimilation system performances, but obviously increases the risk of members' collapse. Hardware failure or slow numerical convergence encountered for some members should also occur more frequently. In this context, the manual steering of the whole process appears as a real challenge and makes the implementation of the ensemble assimilation procedure uneasy and extremely time consuming.
This paper presents our efforts to build an efficient and fault-tolerant MITgcm-DART ensemble assimilation system capable of operationally running thousands of members. Built on top of Decimate, a scheduler extension developed to ease the submission, monitoring and dynamic steering of workflow of dependent jobs in a fault-tolerant environment, we describe the assimilation system implementation and discuss in detail its coupling strategies. Within Decimate, only a few additional lines of Python is needed to define flexible convergence criteria and to implement any necessary actions to the forecast ensemble members, as for instance (i) restarting faulty job in case of job failure, (ii) changing the random seed in case of poor convergence or numerical instability, (iii) adjusting (reducing or increasing) the number of parallel forecasts on the fly, (iv) replacing members on the fly to enrich the ensemble with new members, etc.
We demonstrate the efficiency of the system with numerical experiments assimilating real satellites sea surface height and temperature observations in the Red Sea. (C) 2018 Elsevier B.V. All rights reserved.
C1 [Toye, Habib; Hoteit, Ibrahim] King Abdullah Univ Sci & Technol, Div Comp Elect & Math Sci & Engn, Jeddah 239556900, Saudi Arabia.
[Kortas, Samuel] King Abdullah Univ Sci & Technol, KAUST Supercomp Lab KSL, Jeddah 239556900, Saudi Arabia.
[Zhan, Peng; Hoteit, Ibrahim] King Abdullah Univ Sci & Technol, Div Phys Sci & Engn, Jeddah 239556900, Saudi Arabia.
RP Hoteit, I (corresponding author), King Abdullah Univ Sci & Technol, Div Comp Elect & Math Sci & Engn, Jeddah 239556900, Saudi Arabia.
EM ibrahim.hoteit@kaust.edu.sa
OI zhan, peng/0000-0002-3996-7011; TOYE, Habib/0000-0002-1186-837X; Hoteit,
Ibrahim/0000-0002-3751-4393
FU King Abdullah University of Science and Technology (KAUST)King Abdullah
University of Science & Technology; Saudi ARAMCO
FX The research reported in this manuscript was supported by King Abdullah
University of Science and Technology (KAUST) and Saudi ARAMCO, and made
use of the resources of the Supercomputing Core Laboratory of KAUST. The
data used in this study may be obtained from the authors upon request.
CR Anderson J, 2009, B AM METEOROL SOC, V90, P1283, DOI 10.1175/2009BAMS2618.1
Anderson JL, 2007, PHYSICA D, V230, P99, DOI 10.1016/j.physd.2006.02.011
Anderson JL, 2007, J ATMOS OCEAN TECH, V24, P1452, DOI 10.1175/JTECH2049.1
Anderson JL, 2007, TELLUS A, V59, P210, DOI 10.1111/j.1600-0870.2006.00216.x
Anderson JL, 2009, TELLUS A, V61, P72, DOI 10.1111/j.1600-0870.2008.00361.x
Anderson JL, 2003, MON WEATHER REV, V131, P634, DOI 10.1175/1520-0493(2003)131<0634:ALLSFF>2.0.CO;2
Anderson JL, 2001, MON WEATHER REV, V129, P2884, DOI 10.1175/1520-0493(2001)129<2884:AEAKFF>2.0.CO;2
Belyaev KP, 2001, APPL MATH MODEL, V25, P655, DOI 10.1016/S0307-904X(01)00003-8
Burgers G, 1998, MON WEATHER REV, V126, P1719, DOI 10.1175/1520-0493(1998)126<1719:ASITEK>2.0.CO;2
Edwards CA, 2015, ANNU REV MAR SCI, V7, P21, DOI 10.1146/annurev-marine-010814-015821
EVENSEN G, 1994, J GEOPHYS RES-OCEANS, V99, P10143, DOI 10.1029/94JC00572
Evensen G., 2001, OCEAN CLIMATE PREDIC, P37, DOI [10.1007/3-540-70734-4_6, DOI 10.1007/3-540-70734-4_6]
Fu WW, 2011, OCEAN MODEL, V40, P227, DOI 10.1016/j.ocemod.2011.09.004
Guo HD, 2015, ADV CLIM CHANG RES, V6, P108, DOI 10.1016/j.accre.2015.09.007
Hamill TM, 2011, MON WEATHER REV, V139, P117, DOI 10.1175/2010MWR3246.1
Hamill TM, 2001, MON WEATHER REV, V129, P2776, DOI 10.1175/1520-0493(2001)129<2776:DDFOBE>2.0.CO;2
Hoteit I, 2008, MON WEATHER REV, V136, P317, DOI 10.1175/2007MWR1927.1
Hoteit I, 2015, MON WEATHER REV, V143, P2918, DOI 10.1175/MWR-D-14-00088.1
Hoteit I, 2002, J MARINE SYST, V36, P101, DOI 10.1016/S0924-7963(02)00129-X
Hoteit I, 2012, MON WEATHER REV, V140, P528, DOI 10.1175/2011MWR3640.1
Houtekamer PL, 1998, MON WEATHER REV, V126, P796, DOI 10.1175/1520-0493(1998)126<0796:DAUAEK>2.0.CO;2
Houtekamer PL, 2001, MON WEATHER REV, V129, P123, DOI 10.1175/1520-0493(2001)129<0123:ASEKFF>2.0.CO;2
Kalman R.E., 1960, J BASIC ENG, V82, P35, DOI [10.1115/1.3662552, DOI 10.1115/1.3662552]
Kortas S., 2018, DECIMATE DOCUMENTATI
Kortas S., 2017, DECIMATE BETA DEV BR
Kortas S., 2017, DECIMATE STABLE BRAN
Liu B, 2016, J HYDROL, V535, P1, DOI 10.1016/j.jhydrol.2016.01.048
Liu B, 2016, MON WEATHER REV, V144, P781, DOI 10.1175/MWR-D-14-00292.1
Marshall J, 1997, J GEOPHYS RES-OCEANS, V102, P5753, DOI 10.1029/96JC02775
Marshall J., 1997, J GEOPHYS RES, V102, pC3, DOI [10.1029/96JC02775102, DOI 10.1029/96JC02775102]
Martin MJ, 2015, J OPER OCEANOGR, V8, pS28, DOI 10.1080/1755876X.2015.1022055
MEINHOLD RJ, 1983, AM STAT, V37, P123, DOI 10.2307/2685871
Oke PR, 2007, OCEAN DYNAM, V57, P32, DOI 10.1007/s10236-006-0088-8
Pham DT, 1998, J MARINE SYST, V16, P323
Sakov P, 2008, TELLUS A, V60, P361, DOI 10.1111/j.1600-0870.2007.00299.x
Sakov P, 2011, COMPUTAT GEOSCI, V15, P225, DOI 10.1007/s10596-010-9202-6
Toye H, 2017, OCEAN DYNAM, V67, P915, DOI 10.1007/s10236-017-1064-1
Verlaan M, 1997, STOCH HYDROL HYDRAUL, V11, P349, DOI 10.1007/BF02427924
Zhan P, 2016, J GEOPHYS RES-OCEANS, V121, P4732, DOI 10.1002/2015JC011589
Zhan P, 2014, J GEOPHYS RES-OCEANS, V119, P3909, DOI 10.1002/2013JC009563
NR 40
TC 2
Z9 2
U1 0
U2 2
PU ELSEVIER
PI AMSTERDAM
PA RADARWEG 29, 1043 NX AMSTERDAM, NETHERLANDS
SN 1877-7503
EI 1877-7511
J9 J COMPUT SCI-NETH
JI J. Comput. Sci.
PD JUL
PY 2018
VL 27
BP 46
EP 56
DI 10.1016/j.jocs.2018.04.018
PG 11
WC Computer Science, Interdisciplinary Applications; Computer Science,
Theory & Methods
SC Computer Science
GA GS4ZG
UT WOS:000443665800005
OA Green Published
DA 2021-04-21
ER
PT J
AU Bhat, PC
Prosper, HB
Sekmen, S
Stewart, C
AF Bhat, Pushpalatha C.
Prosper, Harrison B.
Sekmen, Sezen
Stewart, Chip
TI Optimizing event selection with the random grid search
SO COMPUTER PHYSICS COMMUNICATIONS
LA English
DT Article
DE Selection optimization; Grid search; LHC; Higgs; SUSY
ID PROGRAM
AB The random grid search (RGS) is a simple, but efficient, stochastic algorithm to find optimal cuts that was developed in the context of the search for the top quark at Fermilab in the mid-1990s. The algorithm, and associated code, have been enhanced recently with the introduction of two new cut types, one of which has been successfully used in searches for supersymmetry at the Large Hadron Collider. The RGS optimization algorithm is described along with the recent developments, which are illustrated with two examples from particle physics. One explores the optimization of the selection of vector boson fusion events in the four-lepton decay mode of the Higgs boson and the other optimizes SUSY searches using boosted objects and the razor variables.
Program summary
Program title: Random Grid Search
Program Files doi: http://dx.doi.org/10.17632/mpcmd7xb4.1
Licensing provisions: GNU General Public License 3 (GPL)
Programming language: c++, python
Nature of problem: We address the problem of scanning a large number of thresholds (cuts) on discriminating variables in order to find ones that maximize some measure of the degree of discrimination between classes of objects, for example, between signal and background events at the Large Hadron Collider (LHC).
Solution method: The cuts searched are determined by the distribution of the objects that are the focus of an analysis. For example, if one is searching for supersymmetric events at the LHC, the cuts are determined by the predicted distribution of the variables that discriminate between the supersymmetric signal and the standard model background. In effect, we search for cuts using importance sampling determined by the signal distribution, thereby mitigating the curse of dimensionality.
Additional comments including restrictions and unusual features: For cases with exceptionally large numbers of events, the program may take several hours to run. However, the system can be trivially parallelized, with no change to the program, by splitting large files into N smaller files and running the same set of cuts over the N files. The counts associated with each cut can then be summed over the N files. (C) 2018 Elsevier B.V. All rights reserved.
C1 [Bhat, Pushpalatha C.] Fermilab Natl Accelerator Lab, Batavia, IL 60510 USA.
[Prosper, Harrison B.] Florida State Univ, Dept Phys, Tallahassee, FL 32306 USA.
[Sekmen, Sezen] Kyungpook Natl Univ, Daegu, South Korea.
[Stewart, Chip] Broad Inst, Boston, MA USA.
RP Sekmen, S (corresponding author), Kyungpook Natl Univ, Daegu, South Korea.
EM pushpa@fnal.gov; harry@hep.fsu.edu; ssekmen@cern.ch;
stewart@broadinstitute.org
OI Sekmen, Sezen/0000-0003-1726-5681
FU U.S. Department of EnergyUnited States Department of Energy (DOE)
[DE-AC02-07CH11359, DE-SC0010102]; FermilabUnited States Department of
Energy (DOE)University of Chicago; National Research Foundation of Korea
(NRF)National Research Foundation of Korea; Ministry of Science ICT
[NRF-2008-00460]; U.S. Department of Energy through the Distinguished
Researcher Program from the Fermilab LHC Physics CenterUnited States
Department of Energy (DOE)
FX The work of PCB and HBP is supported in part by the U.S. Department of
Energy, under contract number DE-AC02-07CH11359 with Fermilab, and under
grant number DE-SC0010102, respectively. The work of SS is supported by
the financial support of the National Research Foundation of Korea
(NRF), funded by the Ministry of Science & ICT under contract
NRF-2008-00460 and by the U.S. Department of Energy through the
Distinguished Researcher Program from the Fermilab LHC Physics Center.
CR Aaboud M, 2016, J HIGH ENERGY PHYS, DOI 10.1007/JHEP11(2016)112
Aad G, 2016, J INSTRUM, V11, DOI 10.1088/1748-0221/11/04/P04008
Aad G, 2015, PHYS REV D, V91, DOI 10.1103/PhysRevD.91.012006
Aad G, 2012, PHYS LETT B, V716, P1, DOI 10.1016/j.physletb.2012.08.020
Aaij R, 2015, J INSTRUM, V10, DOI 10.1088/1748-0221/10/06/P06013
ABACHI S, 1995, PHYS REV LETT, V74, P2632, DOI 10.1103/PhysRevLett.74.2632
Abadi M, 2016, PROCEEDINGS OF OSDI'16: 12TH USENIX SYMPOSIUM ON OPERATING SYSTEMS DESIGN AND IMPLEMENTATION, P265
Abazov VM, 2009, PHYS REV LETT, V103, DOI 10.1103/PhysRevLett.103.092001
Abazov V. M., 2003, PHYS REV D, V67, DOI [10.11031PhysRevD.67.012004, DOI 10.11031/PHYSREVD.67.012004]
Abazov VM, 2001, PHYS REV D, V64, DOI [10.1103/PhysRevD.64.092004, 10.1103/PhysRevD.64.012004]
Abbott B, 1998, PHYS REV D, V58, DOI 10.1103/PhysRevD.58.052001
ABE F, 1995, PHYS REV LETT, V74, P2626, DOI 10.1103/PhysRevLett.74.2626
Acciarri R, 2017, J INSTRUM, V12, DOI 10.1088/1748-0221/12/03/P03011
Allanach BC, 2002, COMPUT PHYS COMMUN, V143, P305, DOI 10.1016/S0010-4655(01)00460-X
Amos NA, 1995, P INT C COMP HIGH EN, P215
Aurisano A, 2016, J INSTRUM, V11, DOI 10.1088/1748-0221/11/09/P09001
Barnreuther P, 2012, PHYS REV LETT, V109, DOI 10.1103/PhysRevLett.109.132001
Baldi P, 2015, PHYS REV LETT, V114, DOI 10.1103/PhysRevLett.114.111801
Baldi P, 2014, NAT COMMUN, V5, DOI 10.1038/ncomms5308
Baldi P, 2016, PHYS REV D, V93, DOI 10.1103/PhysRevD.93.094034
Ballintijn M, 2006, NUCL INSTRUM METH A, V559, P13, DOI 10.1016/j.nima.2005.11.100
Beenakker W, 1997, NUCL PHYS B, V492, P51, DOI 10.1016/S0550-3213(97)80027-2
Beenakker W., 1996, ARXIVHEPPH9611232
Bhat PC, 2011, ANNU REV NUCL PART S, V61, P281, DOI [10.1146/annurev.nucl.012809.10442, 10.1146/annurev.nucl.012809.104427]
Bishop CM., 2006, PATTERN RECOGN
Buckley A, 2015, EUR PHYS J C, V75, DOI 10.1140/epjc/s10052-015-3318-8
C Collaboration CMS Collaboration, 2016, SEARCH INV DEC HIGGS
Cacciari M, 2015, PHYS REV LETT, V115, DOI 10.1103/PhysRevLett.115.082002
Campbell JM, 2015, EUR PHYS J C, V75, DOI 10.1140/epjc/s10052-015-3461-2
Chatrchyan S, 2014, PHYS REV D, V89, DOI 10.1103/PhysRevD.89.092007
Chatrchyan S, 2013, J INSTRUM, V8, DOI 10.1088/1748-0221/8/11/P11002
Chatrchyan S, 2012, PHYS LETT B, V716, P30, DOI 10.1016/j.physletb.2012.08.021
Chatrchyan S, 2012, J HIGH ENERGY PHYS, DOI 10.1007/JHEP04(2012)033
Czakon M, 2014, COMPUT PHYS COMMUN, V185, P2930, DOI 10.1016/j.cpc.2014.06.021
Czakon M, 2013, PHYS REV LETT, V110, DOI 10.1103/PhysRevLett.110.252004
Czakon M, 2013, J HIGH ENERGY PHYS, DOI 10.1007/JHEP01(2013)080
Czakon M, 2012, J HIGH ENERGY PHYS, DOI 10.1007/JHEP12(2012)054
de Favereau J, 2014, J HIGH ENERGY PHYS, DOI 10.1007/JHEP02(2014)057
de Florian D., 2016, HDB LHC HIGGS CROSS, DOI [10.23731/CYRM-2017-002, DOI 10.23731/CYRM-2017-002]
Dulat S, 2016, PHYS REV D, V93, DOI 10.1103/PhysRevD.93.033006
Feindt M, 2006, NUCL INSTRUM METH A, V559, P190, DOI 10.1016/j.nima.2005.11.166
Guest D, 2016, PHYS REV D, V94, DOI 10.1103/PhysRevD.94.112002
Hoecker Andreas, 2007, ARXIVPHYSICS0703039
Khachatryan V, 2016, PHYS REV D, V93, DOI 10.1103/PhysRevD.93.092009
Khachatryan V, 2015, J INSTRUM, V10, DOI 10.1088/1748-0221/10/06/P06005
KULLBACK S, 1951, ANN MATH STAT, V22, P79, DOI 10.1214/aoms/1177729694
Martin Abadi, 2015, TENSORFLOW LARGE SCA
Muhlleitner MM, 2007, ACTA PHYS POL B, V38, P635
Nason P, 2014, EUR PHYS J C, V74, P1, DOI 10.1140/epjc/s10052-013-2702-5
Patrignani C, 2016, CHINESE PHYS C, V40, DOI 10.1088/1674-1137/40/10/100001
Pedregosa F, 2011, J MACH LEARN RES, V12, P2825
Prosper H. B., 2012, CMSIN2012012 CERN
Rogan C., 2010, ARXIV10062727
Searcy J., 2016, J ZHU PHYS REV D, V93, DOI 10.1103/PhysRevD.93.094033
Sirunyan A. M., 2017, ARXIV171207158
Sjostrand T, 2008, COMPUT PHYS COMMUN, V178, P852, DOI 10.1016/j.cpc.2008.01.036
Sjostrand T, 2006, J HIGH ENERGY PHYS, DOI 10.1088/1126-6708/2006/05/026
Strobbe N. C., 2011, THESIS
T. A. Collaboration ATLAS Collaboration, 2016, MEAS HIGGS BOS PROD
Thaler J, 2011, J HIGH ENERGY PHYS, DOI 10.1007/JHEP03(2011)015
Theano Development Team, 2016, ARXIV E PRINTS
NR 61
TC 7
Z9 8
U1 2
U2 7
PU ELSEVIER SCIENCE BV
PI AMSTERDAM
PA PO BOX 211, 1000 AE AMSTERDAM, NETHERLANDS
SN 0010-4655
EI 1879-2944
J9 COMPUT PHYS COMMUN
JI Comput. Phys. Commun.
PD JUL
PY 2018
VL 228
BP 245
EP 257
DI 10.1016/j.cpc.2018.02.018
PG 13
WC Computer Science, Interdisciplinary Applications; Physics, Mathematical
SC Computer Science; Physics
GA GH9PI
UT WOS:000434000900025
DA 2021-04-21
ER
PT J
AU Giorgino, T
AF Giorgino, Toni
TI How to differentiate collective variables in free energy codes:
Computer-algebra code generation and automatic differentiation
SO COMPUTER PHYSICS COMMUNICATIONS
LA English
DT Article
DE Molecular dynamics; Free energy; Biased sampling; Metadynamics;
Symbolic; C plus
ID MOLECULAR-DYNAMICS
AB The proper choice of collective variables (CVs) is central to biased-sampling free energy reconstruction methods in molecular dynamics simulations. The PLUMED 2 library, for instance, provides several sophisticated CV choices, implemented in a C++ framework; however, developing new CVs is still time consuming due to the need to provide code for the analytical derivatives of all functions with respect to atomic coordinates. We present two solutions to this problem, namely (a) symbolic differentiation and code generation, and (b) automatic code differentiation, in both cases leveraging open-source libraries (SymPy and Stan Math, respectively). The two approaches are demonstrated and discussed in detail implementing a realistic example CV, the local radius of curvature of a polymer. Users may use the code as a template to streamline the implementation of their own CVs using high-level constructs and automatic gradient computation.
Program summary
Program Title: Practical approaches to the differentiation of collective variables in free energy codes: computer-algebra code generation and automatic differentiation
Program Files doi: http:fidx.doi.org/10.17632/r4r67bvkdn.1
Licensing provisions: GNU Lesser General Public License Version 3 (LGPL-3)
Programming languages: C++, Python
Nature of problem: The C++ implementation of collective variables (CVs, functions of atomic coordinates to be used in biased sampling applications) in biasing libraries for atomistic simulations, such as PLUMED [1], requires computation of both the variable and its gradient with respect to the atomic coordinates; coding and testing the analytical derivatives complicate the implementation of new CVs.
Solution method: The paper shows two approaches to automate the computation of CV gradients, namely, symbolic differentiation with code generation and automatic code differentiation, demonstrating their implementation entirely with open-source software (respectively, SymPy and the Stan Math Library).
Additional comments: The paper's accompanying code serves as an example and template for the methods described in the paper; it is distributed as the two modules curvature_codegen and curvature_aut odiff integrated in PLUMED 2's source tree; the latest version is available at https://github. comitonigi/plumed2- automatic-gradients.
[1] Tribello GA, Bonomi M, Branduardi D, Camilloni C, Bussi G. PLUMED 2: New feathers for an old bird. Computer Physics Communications. 2014 Feb; 185(2):604-13. (C) 2018 Published by Elsevier B.V.
C1 [Giorgino, Toni] Natl Res Council CNR IN, Inst Neurosci, Corso Stati Uniti 4, I-35127 Padua, Italy.
[Giorgino, Toni] Univ Milan, Dipartimento Biosci, CNR, Ist Biofis,IBF, Via Celoria 26, I-20133 Milan, Italy.
RP Giorgino, T (corresponding author), Natl Res Council CNR IN, Inst Neurosci, Corso Stati Uniti 4, I-35127 Padua, Italy.; Giorgino, T (corresponding author), Univ Milan, Dipartimento Biosci, CNR, Ist Biofis,IBF, Via Celoria 26, I-20133 Milan, Italy.
EM toni.giorgino@cnr.it
RI Giorgino, Toni/H-1866-2011
OI Giorgino, Toni/0000-0001-6449-0596
FU CINECA awards under the ISCRA initiativeCINECA, Italy; Acellera Ltd.
FX I would like to thank Prof. G. Bussi and Prof. C. Camilloni for
discussions on the applications of automatic differentiation and
comments on the manuscript. I acknowledge CINECA awards under the ISCRA
initiative for the availability of high performance computing resources
and support. Research funding from Acellera Ltd. is gratefully
acknowledged.
CR Bonomi M, 2017, BIOINFORMATICS, V33, P3999, DOI 10.1093/bioinformatics/btx529
Branduardi D, 2007, J CHEM PHYS, V126, DOI 10.1063/1.2432340
Carpenter B., 2015, ARXIV150907164CS
Carpenter B, 2017, J STAT SOFTW, V76, P1, DOI 10.18637/jss.v076.i01
Fiorin G, 2013, MOL PHYS, V111, P3345, DOI 10.1080/00268976.2013.813594
Galvelis R, 2017, J CHEM THEORY COMPUT, V13, P2489, DOI 10.1021/acs.jctc.7b00188
Giorgino T, 2017, COMPUT PHYS COMMUN, V217, P204, DOI 10.1016/j.cpc.2017.04.009
Guennebaud G., 2010, EIGEN V3
Hamelberg D, 2004, J CHEM PHYS, V120, P11919, DOI 10.1063/1.1755656
Hindmarsh AC, 2005, ACM T MATH SOFTWARE, V31, P363, DOI 10.1145/1089014.1089020
Laio A, 2002, P NATL ACAD SCI USA, V99, P12562, DOI 10.1073/pnas.202427399
Laio A, 2008, REP PROG PHYS, V71, DOI 10.1088/0034-4885/71/12/126601
Meurer A, 2017, PEERJ COMPUT SCI, DOI 10.7717/peerj-cs.103
Mollica L, 2015, SCI REP-UK, V5, DOI 10.1038/srep11539
Mushtaq A, 2014, COMPUT PHYS COMMUN, V185, P1461, DOI 10.1016/j.cpc.2014.01.012
Perez F, 2007, COMPUT SCI ENG, V9, P21, DOI 10.1109/MCSE.2007.53
Salmaso V, 2017, STRUCTURE, V25, P655, DOI 10.1016/j.str.2017.02.009
Sun HY, 2017, J CHEM INF MODEL, V57, P1895, DOI 10.1021/acs.jcim.7b00075
TORRIE GM, 1977, J COMPUT PHYS, V23, P187, DOI 10.1016/0021-9991(77)90121-8
Tribello GA, 2017, J CHEM THEORY COMPUT, V13, P1317, DOI 10.1021/acs.jctc.6b01073
Tribello GA, 2014, COMPUT PHYS COMMUN, V185, P604, DOI 10.1016/j.cpc.2013.09.018
Weaver Vincent M., 2013, IEEE International Symposium on Performance Analysis of Systems and Software (ISPASS 2013), P215
NR 22
TC 2
Z9 2
U1 0
U2 9
PU ELSEVIER SCIENCE BV
PI AMSTERDAM
PA PO BOX 211, 1000 AE AMSTERDAM, NETHERLANDS
SN 0010-4655
EI 1879-2944
J9 COMPUT PHYS COMMUN
JI Comput. Phys. Commun.
PD JUL
PY 2018
VL 228
BP 258
EP 263
DI 10.1016/j.cpc.2018.02.017
PG 6
WC Computer Science, Interdisciplinary Applications; Physics, Mathematical
SC Computer Science; Physics
GA GH9PI
UT WOS:000434000900026
DA 2021-04-21
ER
PT J
AU Schmidt, B
Hartmann, C
AF Schmidt, Burkhard
Hartmann, Carsten
TI WavePacket: A Matlab package for numerical quantum dynamics. II: Open
quantum systems, optimal control, and model reduction
SO COMPUTER PHYSICS COMMUNICATIONS
LA English
DT Article
DE Schrodinger equation; Liouville-von Neumann equation; Optimal control;
Model order reduction
ID PYTHON FRAMEWORK; ALGORITHM; DISSOCIATION; PROPAGATION; REALIZATION;
TRANSITIONS; MOLECULES; LASERS; QUTIP
AB WavePacket is an open-source program package for numeric simulations in quantum dynamics. It can solve time-independent or time-dependent linear Schrodinger and Liouville-von Neumann-equations in one or more dimensions. Also coupled equations can be treated, which allows, e.g., to simulate molecular quantum dynamics beyond the Born-Oppenheimer approximation. Optionally accounting for the interaction with external electric fields within the semi-classical dipole approximation, WavePacket can be used to simulate experiments involving tailored light pulses in photo-induced physics or chemistry. Being highly versatile and offering visualization of quantum dynamics 'on the fly', WavePacket is well suited for teaching or research projects in atomic, molecular and optical physics as well as in physical or theoretical chemistry. Building on the previous Part I [Comp. Phys. Comm. 213, 223-234 (2017)] which dealt with closed quantum systems and discrete variable representations, the present Part II focuses on the dynamics of open quantum systems, with Lindblad operators modeling dissipation and dephasing. This part also describes the WavePacket function for optimal control of quantum dynamics, building on rapid monotonically convergent iteration methods. Furthermore, two different approaches to dimension reduction implemented in WavePacket are documented here. In the first one, a balancing transformation based on the concepts of controllability and observability Gramians is used to identify states that are neither well controllable nor well observable. Those states are either truncated or averaged out. In the other approach, the H2-error for a given reduced dimensionality is minimized by H2 optimal model reduction techniques, utilizing a bilinear iterative rational Krylov algorithm.
The present work describes the MATLAB version of WavePacket 5.3.0 which is hosted and further developed at the Sourceforge platform, where also extensive Wiki-documentation as well as numerous worked-out demonstration examples with animated graphics can be found.
Program summary
Program Title: WAVEPACKET
Program Files doi: http://dx.doi.org/10.17632/9g8b7jychy.1
Licensing provisions: GPLv3
Programming language: MATLAB
Journal reference of previous version: Comput. Phys. Comm. 213 (2017), 223.
Does the new version supersede the previous version?: The previous article focused on the treatment of closed quantum systems by discrete variable representations and implementation of various numerical algorithms for solving Schrobdinger's equations. Complementary to that, the present second part is concerned with open quantum systems and optimal control by external fields. In addition, two approaches to dimension reduction useful in modeling of quantum control are described.
Reasons for the new version: The reason for having a second article on the WavePacket software package lies in the fact that a complete description of the package would have exceeded the scope of a regular article. Several significant features of the WAVEPACKET package are introduced here which could not be mentioned in the first article, due to length constraints.
Summary of revisions: Here we describe the numerical treatment of open quantum systems dynamics modeled by Lindblad master equations. Moreover, we explain the WAVEPACKET functions for optimal control simulations, both for closed and open quantum systems. To address the problem of computational effort, two strategies for model reduction have been included.
Nature of problem: Dynamics of closed and open systems are described by Schrodinger or Liouvillevon Neumann equations, respectively, where the latter ones will be restricted to the Lindblad master equation. Emphasis is on the interaction of quantum system with external electric fields, treated within the semi-classical dipole approximation. Quantum optimal control simulations are used for the design of tailored fields achieving specified targets in quantum dynamics. With these features, WAVEPACKET can be instrumental for the simulation, understanding, and prediction of modern experiments in atomic, molecular and optical physics involving temporally shaped fields.
Solution method: Representing state vectors or reduced density matrices in a discrete basis, SchrOdinger or Liouville-von Neumann equations are cast into systems of ordinary differential equations. Those are treated by self-written or MATLAB'S built-in solvers, the latter ones offering adaptive time-stepping. The optimal control equations are solved by the rapid monotonically convergent iteration methods developed by Zhu, Rabitz, Ohtsuki and others. In order to reduce the dimensionality of large scale control problems, the balanced truncation method as well as H2-optimal model reduction approaches are available in WAVEPACKET.
Additional comments including restrictions and unusual features: The WAVEPACKET program package is rather easy and intuitive to use, providing visualization of quantum dynamics 'on the fly'. It is mainly intended for low-dimensional systems, typically not exceeding three to five degrees of freedom. Detailed user guides and reference manuals are available through numerous Wiki pages hosted at the SOURCEFORGE platform where also a large number of well documented demonstration examples can be found. (C) 2018 Elsevier B.V. All rights reserved.
C1 [Schmidt, Burkhard] Free Univ Berlin, Inst Math, Arnimallee 6, D-14195 Berlin, Germany.
[Hartmann, Carsten] Brandenburg Tech Univ Cottbus, Inst Math, Konrad Wachsmann Allee 1, D-03046 Cottbus, Germany.
RP Schmidt, B (corresponding author), Free Univ Berlin, Inst Math, Arnimallee 6, D-14195 Berlin, Germany.
EM burkhard.schmidt@fu-berlin.de; carsten.hartmann@b-tu.de
RI Schmidt, Burkhard/A-2358-2013
OI Schmidt, Burkhard/0000-0002-9658-499X
FU Einstein Center for Mathematics Berlin (ECMath) [SE 11, SE 20]
FX This work has been supported by the Einstein Center for Mathematics
Berlin (ECMath) through projects SE 11 and SE 20. Boris Schafer-Bung
(formerly at FU Berlin) and Tobias Breiten (U of Graz, Austria) are
acknowledged for setting up initial versions of the MATLAB codes for
dimension reduction. Finally, we are grateful to Ulf Lorenz (formerly at
U Potsdam) for his valuable help with all kind of questions around the
WAVEPACKET software package.
CR Andrianov I, 2006, J CHEM PHYS, V124, DOI 10.1063/1.2161191
Baer M, 2006, BORN OPPENHEIMER
Bai ZJ, 2006, LINEAR ALGEBRA APPL, V415, P406, DOI 10.1016/j.laa.2005.04.032
Beck MH, 2000, PHYS REP, V324, P1, DOI 10.1016/S0370-1573(99)00047-2
Benner P., 2017, ARXIV170609882
Benner P, 2012, SIAM J MATRIX ANAL A, V33, P859, DOI 10.1137/110836742
Breiten T, 2010, SYST CONTROL LETT, V59, P443, DOI 10.1016/j.sysconle.2010.06.003
Breuer HP., 2002, THEORY OPEN QUANTUM
BURKEY RS, 1984, J OPT SOC AM B, V1, P169, DOI 10.1364/JOSAB.1.000169
Damm T., 2008, LINEAR ALGEBRA APPL, V15, P53
de Vivie-Riedle R, 2007, CHEM REV, V107, P5082, DOI 10.1021/cr040094l
Domcke W., 2004, ADV SERIES PHYS CHEM, V15
Glaser SJ, 2015, EUR PHYS J D, V69, DOI 10.1140/epjd/e2015-60464-1
Grossmann F, 2008, SPRINGER SER ATOM OP, V48, P3
Hahn J, 2002, IND ENG CHEM RES, V41, P2204, DOI 10.1021/ie0106175
Hartmann C, 2013, SIAM J CONTROL OPTIM, V51, P2356, DOI 10.1137/100796844
Hartmann C, 2010, MULTISCALE MODEL SIM, V8, P1348, DOI 10.1137/080732717
Horenko I, 2004, J CHEM PHYS, V120, P8913, DOI 10.1063/1.1691015
Horenko I, 2002, J CHEM PHYS, V117, P11075, DOI 10.1063/1.1522712
Horenko I, 2002, J CHEM PHYS, V117, P4643, DOI 10.1063/1.1498467
Johansson JR, 2013, COMPUT PHYS COMMUN, V184, P1234, DOI 10.1016/j.cpc.2012.11.019
Johansson JR, 2012, COMPUT PHYS COMMUN, V183, P1760, DOI 10.1016/j.cpc.2012.02.021
JUDSON RS, 1992, PHYS REV LETT, V68, P1500, DOI 10.1103/PhysRevLett.68.1500
Kais S., 2014, ADV CHEM PHYS
Khan BA, 2014, J PHYS CHEM A, V118, P11451, DOI 10.1021/jp507459m
Khaneja N, 2005, J MAGN RESON, V172, P296, DOI 10.1016/j.jmr.2004.11.004
Korolkov MV, 1996, J CHEM PHYS, V105, P1862, DOI 10.1063/1.472058
Kossakowski A., 1972, Reports on Mathematical Physics, V3, P247, DOI 10.1016/0034-4877(72)90010-9
LAUB AJ, 1987, IEEE T AUTOMAT CONTR, V32, P115, DOI 10.1109/TAC.1987.1104549
Le Bris C., 2003, QUANTUM CONTROL MATH, P139
LEFORESTIER C, 1991, J COMPUT PHYS, V94, P59, DOI 10.1016/0021-9991(91)90137-A
Light JC, 2000, ADV CHEM PHYS, V114, P263, DOI 10.1002/9780470141731.ch4
LIGHT JC, 1985, J CHEM PHYS, V82, P1400, DOI 10.1063/1.448462
LINDBLAD G, 1976, COMMUN MATH PHYS, V48, P119, DOI 10.1007/BF01608499
Lockwood DM, 2001, CHEM PHYS, V268, P55, DOI 10.1016/S0301-0104(01)00306-8
Machnes S, 2011, PHYS REV A, V84, DOI 10.1103/PhysRevA.84.022305
Maday Y, 2003, J CHEM PHYS, V118, P8191, DOI 10.1063/1.1564043
May V., 2000, CHARGE ENERGY TRANSF
MECKE R, 1950, Z ELEKTROCHEM, V54, P38
Mendl CB, 2011, COMPUT PHYS COMMUN, V182, P1327, DOI 10.1016/j.cpc.2011.01.028
Ohtsuki Y, 2004, J CHEM PHYS, V120, P5509, DOI 10.1063/1.1650297
Ohtsuki Y, 1999, J CHEM PHYS, V110, P9825, DOI 10.1063/1.478036
Owschimikow N, 2011, PHYS CHEM CHEM PHYS, V13, P8671, DOI 10.1039/c0cp02260h
Rabitz H, 2000, SCIENCE, V288, P824, DOI 10.1126/science.288.5467.824
Rabitz H, 2003, SCIENCE, V299, P525, DOI 10.1126/science.1080683
Schafer-Bung B, 2011, J CHEM PHYS, V135, DOI 10.1063/1.3605243
Schleich W., 2001, QUANTUM OPTICS PHASE
Schmidt B, 2017, COMPUT PHYS COMMUN, V213, P223, DOI 10.1016/j.cpc.2016.12.007
Schmidt B, 2014, J CHEM PHYS, V140, DOI 10.1063/1.4864465
SEEL M, 1991, J CHEM PHYS, V95, P7806, DOI 10.1063/1.461816
SOMLOI J, 1993, CHEM PHYS, V172, P85, DOI 10.1016/0301-0104(93)80108-L
Steinfeld J.I., 1989, CHEM KINETICS DYNAMI
Subotnik JE, 2016, ANNU REV PHYS CHEM, V67, P387, DOI 10.1146/annurev-physchem-040215-112245
Sundermann K, 1999, J CHEM PHYS, V110, P1896, DOI 10.1063/1.477856
Sundstrom V, 2008, ANNU REV PHYS CHEM, V59, P53, DOI 10.1146/annurev.physchem.59.032607.093615
TANNOR D, 2004, INTRO QUANTUM MECH T
TOMBS MS, 1987, INT J CONTROL, V46, P1319, DOI 10.1080/00207178708933971
Tremblay JC, 2011, J CHEM PHYS, V134, DOI 10.1063/1.3532410
Wachspress EL, 1988, APPL MATH LETT, V1, P87
Weiss U., 1999, QUANTUM DISSIPATIVE
Werschnik J, 2007, J PHYS B-AT MOL OPT, V40, pR175, DOI 10.1088/0953-4075/40/18/R01
Zewail AH, 2000, J PHYS CHEM A, V104, P5660, DOI 10.1021/jp001460h
Zhang LQ, 2002, AUTOMATICA, V38, P205, DOI 10.1016/S0005-1098(01)00204-7
Zhang W., 2014, J COMPUT DYN, V1, P279, DOI DOI 10.3934/jcd.2014.1.279
Zhou K., 1998, ESSENTIALS ROBUST CO
Zhu J, 2013, J CHEM PHYS, V138, DOI 10.1063/1.4774058
Zhu WS, 1998, J CHEM PHYS, V108, P1953, DOI 10.1063/1.475576
Zhu WS, 1998, J CHEM PHYS, V109, P385, DOI 10.1063/1.476575
NR 68
TC 8
Z9 8
U1 5
U2 31
PU ELSEVIER SCIENCE BV
PI AMSTERDAM
PA PO BOX 211, 1000 AE AMSTERDAM, NETHERLANDS
SN 0010-4655
EI 1879-2944
J9 COMPUT PHYS COMMUN
JI Comput. Phys. Commun.
PD JUL
PY 2018
VL 228
BP 229
EP 244
DI 10.1016/j.cpc.2018.02.022
PG 16
WC Computer Science, Interdisciplinary Applications; Physics, Mathematical
SC Computer Science; Physics
GA GH9PI
UT WOS:000434000900024
DA 2021-04-21
ER
PT J
AU Bosnar, D
Matic, Z
Friscic, I
Zugec, P
Janci, H
AF Bosnar, D.
Matic, Z.
Friscic, I.
Zugec, P.
Janci, H.
TI A simple setup for cosmic muon lifetime measurements
SO EUROPEAN JOURNAL OF PHYSICS
LA English
DT Article
DE cosmic muons; scintillation detector; muon lifetime
ID MEAN LIFETIME
AB Elementary particle physics is a fascinating field of modern physics investigating the basic constituents of matter and their interactions. In the experiments large accelerators and very sophisticated detector systems are usually used. However, it is desirable to have simple experiments for undergraduate and even secondary school courses which can demonstrate complex investigations in this field. We have constructed a simple setup for the measurement of the lifetime of cosmic muons based on a single scintillation detector. Expensive and complicated professional particle physics equipment for the signal processing, which has mainly prevented the realization of this experiment by non-experts, is replaced by simple, inexpensive and commercially available electronic components. With our setup we register time stamps of the events detected in the scintillation detector and from this data we determine the muon lifetime. Also, a Python software package has been developed for data analysis and presentation of the results.
C1 [Bosnar, D.; Friscic, I.; Zugec, P.] Univ Zagreb, Fac Sci, Dept Phys, Zagreb, Croatia.
[Matic, Z.] Metall Nova Doo, Rijeka, Croatia.
[Janci, H.] Osnovna Skola Grgura Karlovcana, Djurdjevac, Croatia.
[Friscic, I.] MIT, LNS, 77 Massachusetts Ave, Cambridge, MA 02139 USA.
RP Bosnar, D (corresponding author), Univ Zagreb, Fac Sci, Dept Phys, Zagreb, Croatia.
EM bosnar@phy.hr
CR CERN, 2018, CERN ACC SCI
Coan T, 2006, AM J PHYS, V74, P161, DOI 10.1119/1.2135319
Evans R.D., 1955, ATOMIC NUCL
HALL RE, 1970, AM J PHYS, V38, P1196, DOI 10.1119/1.1976002
Hess VF, 1912, PHYS Z, V13, P1084
LEWIS RJ, 1982, AM J PHYS, V50, P894, DOI 10.1119/1.13013
Melissinos A C, 2003, EXPT MODERN PHYS
Muehry H, 2002, PHYS TEACH, V40, P294
Neddermeyer SH, 1937, PHYS REV, V51, P0884, DOI 10.1103/PhysRev.51.884
OWENS A, 1978, AM J PHYS, V46, P859, DOI 10.1119/1.11407
Particle Data Group, 2017, REV PART PHYS
Perkins D, 2009, PARTICLE ASTROPHYSIC
Riggi F, 2016, EUR J PHYS, V37, DOI 10.1088/0143-0807/37/4/045702
Thomson M., 2013, MODERN PARTICLE PHYS
WARD T, 1985, AM J PHYS, V53, P542, DOI 10.1119/1.14235
Webber DM, 2011, PHYS REV LETT, V106, DOI 10.1103/PhysRevLett.106.041803
NR 16
TC 1
Z9 1
U1 2
U2 14
PU IOP PUBLISHING LTD
PI BRISTOL
PA TEMPLE CIRCUS, TEMPLE WAY, BRISTOL BS1 6BE, ENGLAND
SN 0143-0807
EI 1361-6404
J9 EUR J PHYS
JI Eur. J. Phys.
PD JUL
PY 2018
VL 39
IS 4
AR 045801
DI 10.1088/1361-6404/aaadec
PG 9
WC Education, Scientific Disciplines; Physics, Multidisciplinary
SC Education & Educational Research; Physics
GA GD2OI
UT WOS:000430339400001
DA 2021-04-21
ER
PT J
AU Court, CJ
Cole, JM
AF Court, Callum J.
Cole, Jacqueline M.
TI Data Descriptor: Auto-generated materials database of Curie and Neel
temperatures via semi-supervised relationship extraction
SO SCIENTIFIC DATA
LA English
DT Article; Data Paper
ID INFORMATION; DISCOVERY; MAGNDATA
AB Large auto-generated databases of magnetic materials properties have the potential for great utility in materials science research. This article presents an auto-generated database of 39,822 records containing chemical compounds and their associated Curie and Neel magnetic phase transition temperatures. The database was produced using natural language processing and semi-supervised quaternary relationship extraction, applied to a corpus of 68,078 chemistry and physics articles. Evaluation of the database shows an estimated overall precision of 73%. Therein, records processed with the text-mining toolkit, ChemDataExtractor, were assisted by a modified Snowball algorithm, whose original binary relationship extraction capabilities were extended to quaternary relationship extraction. Consequently, its machine learning component can now train with <= 500 seeds, rather than the 4,000 originally used. Data processed with the modified Snowball algorithm affords 82% precision. Database records are available in MongoDB, CSV and JSON formats which can easily be read using Python, R, Java and MatLab. This makes the database easy to query for tackling big-data materials science initiatives and provides a basis for magnetic materials discovery.
[GRAPHICS]
.
C1 [Court, Callum J.; Cole, Jacqueline M.] Univ Cambridge, Dept Phys, Cavendish Lab, JJ Thomson Ave, Cambridge CB3 0HE, England.
[Cole, Jacqueline M.] STFC Rutherford Appleton Lab, ISIS Neutron & Muon Source, Harwell Sci & Innovat Campus, Didcot OX11 0QX, Oxon, England.
[Cole, Jacqueline M.] Argonne Natl Lab, 9700 South Cass Ave, Argonne, IL 60439 USA.
[Cole, Jacqueline M.] Univ Cambridge, Dept Chem Engn & Biotechnol, West Cambridge Site,Philippa Fawcett Dr, Cambridge CB3 0FS, England.
RP Cole, JM (corresponding author), Univ Cambridge, Dept Phys, Cavendish Lab, JJ Thomson Ave, Cambridge CB3 0HE, England.; Cole, JM (corresponding author), STFC Rutherford Appleton Lab, ISIS Neutron & Muon Source, Harwell Sci & Innovat Campus, Didcot OX11 0QX, Oxon, England.; Cole, JM (corresponding author), Argonne Natl Lab, 9700 South Cass Ave, Argonne, IL 60439 USA.; Cole, JM (corresponding author), Univ Cambridge, Dept Chem Engn & Biotechnol, West Cambridge Site,Philippa Fawcett Dr, Cambridge CB3 0FS, England.
EM jmc61@cam.ac.uk
RI Cole, Jacqueline/C-5991-2008
OI Court, Callum/0000-0002-3919-5605
FU EPSRC Computational Methods in Materials Science CentreUK Research &
Innovation (UKRI)Engineering & Physical Sciences Research Council
(EPSRC) [EP/L015552/1]; Royal Commission of the 1851 Great Exhibition;
DOE Office of Science, Office of Basic Energy SciencesUnited States
Department of Energy (DOE) [DE-AC02-06CH11357]
FX CJC would like to thank the EPSRC Computational Methods in Materials
Science Centre for Doctoral Training for PhD funding (reference,
EP/L015552/1). JMC is grateful to the Royal Commission of the 1851 Great
Exhibition for the 2014 Design Fellowship, and Argonne National
Laboratory where work done was supported by DOE Office of Science,
Office of Basic Energy Sciences, under Contract No. DE-AC02-06CH11357.
CR Agichtein E., 2000, ACM 2000. Digital Libraries. Proceedings of the Fifth ACM Conference on Digital Libraries, P85
[Anonymous], 2017, SPRINGER NATURE SPRI
Eltyeb S, 2014, J CHEMINFORMATICS, V6, DOI 10.1186/1758-2946-6-17
Fader A., 2011, P C EMP METH NAT LAN, P1535, DOI DOI 10.1234/12345678
Frakes W.B., 1992, INFORM RETRIEVAL DAT
Gallego SV, 2016, J APPL CRYSTALLOGR, V49, P1941, DOI 10.1107/S1600576716015491
Gallego SV, 2016, J APPL CRYSTALLOGR, V49, P1750, DOI 10.1107/S1600576716012863
Gong Y., 2001, P 6 INT C DOC AN REC, P903
Hawizy L, 2011, J CHEMINFORMATICS, V3, DOI 10.1186/1758-2946-3-17
Holden J, 2011, MAT GEN IN GLOB COMP
Jain A, 2013, APL MATER, V1, DOI 10.1063/1.4812323
Kim E, 2017, SCI DATA, V4, DOI 10.1038/sdata.2017.127
Krallinger M, 2017, CHEM REV, V117, P7673, DOI 10.1021/acs.chemrev.6b00851
Lawrence Berkeley National Laboratory, 2017, MAT PROJ
LUHN HP, 1957, IBM J RES DEV, V1, P309, DOI 10.1147/rd.14.0309
Olivares-Amaya R, 2011, ENERG ENVIRON SCI, V4, P4849, DOI 10.1039/c1ee02056k
Pyzer-Knapp EO, 2015, ADV FUNCT MATER, V25, P6495, DOI 10.1002/adfm.201501919
Swain MC, 2016, J CHEM INF MODEL, V56, P1894, DOI 10.1021/acs.jcim.6b00207
Yates A., 2007, P NAACL HLT, P25
NR 19
TC 23
Z9 23
U1 1
U2 12
PU NATURE PUBLISHING GROUP
PI LONDON
PA MACMILLAN BUILDING, 4 CRINAN ST, LONDON N1 9XW, ENGLAND
EI 2052-4463
J9 SCI DATA
JI Sci. Data
PD JUN 19
PY 2018
VL 5
AR 180111
DI 10.1038/sdata.2018.111
PG 12
WC Multidisciplinary Sciences
SC Science & Technology - Other Topics
GA GJ7CK
UT WOS:000435542300001
PM 29917013
OA DOAJ Gold, Green Published
DA 2021-04-21
ER
PT J
AU Ambrogi, F
Kraml, S
Kulkarni, S
Laa, U
Lessa, A
Magerl, V
Sonneveld, J
Traub, M
Waltenberger, W
AF Ambrogi, Federico
Kraml, Sabine
Kulkarni, Suchita
Laa, Ursula
Lessa, Andre
Magerl, Veronika
Sonneveld, Jory
Traub, Michael
Waltenberger, Wolfgang
TI SModelS v1.1 user manual: Improving simplified model constraints with
efficiency maps
SO COMPUTER PHYSICS COMMUNICATIONS
LA English
DT Article
DE LHC; Supersymmetry; Simplified models; Physics beyond the standard
model; Reinterpretation
AB SModelS is an automatized tool for the interpretation of simplified model results from the LHC. It allows to decompose models of new physics obeying a Z(2) symmetry into simplified model components, and to compare these against a large database of experimental results. The first release of SModelS, v1.0, used only cross section upper limit maps provided by the experimental collaborations. In this new release, v1.1, we extend the functionality of SModelS to efficiency maps. This increases the constraining power of the software, as efficiency maps allow to combine contributions to the same signal region from different simplified models. Other new features of version 1.1 include likelihood and X-2 calculations, extended information on the topology coverage, an extended database of experimental results as well as major speed upgrades for both the code and the database. We describe in detail the concepts and procedures used in SModelS v1.1, explaining in particular how upper limits and efficiency map results are dealt with in parallel. Detailed instructions for code usage are also provided.
Program summary
Program Title: SModelS
Program Files doi: http ://dx.doi.org/10.17632/w4nft4459w.1
Licensing provisions: GPLv3
Programming language: Python
Nature of problem: The results for searches for new physics beyond the Standard Model (BSM) at the Large Hadron Collider are often communicated by the experimental collaborations in terms of constraints on so-called simplified models spectra (SMS). Understanding how SMS constraints impact a realistic new physics model, where possibly a multitude of relevant production channels and decay modes are relevant, is a non-trivial task.
Solution method: We exploit the notion of simplified models to constrain full models by "decomposing" them into their SMS components. A database of SMS results obtained from the official results of the ATLAS and CMS collaborations, but in part also from 'recasting' the experimental analyses, can be matched against the decomposed model, resulting in a statement to what extent the model at hand is in agreement or contradiction with the experimental results. Further useful information on, e.g., the coverage of the models' signatures is also provided.
Additional comments including Restrictions and Unusual features: At present, the tool is limited to signatures with missing transverse energy, and only models with a Z(2)-like symmetry can be tested. Each SMS is defined purely by the vertex structure and the SM final state particles; BSM particles are described only by their masses, production cross sections and branching ratios. Possible differences in signal selection efficiencies arising, e.g., from different production mechanisms or from the spin of the BSM particles, are ignored in this approach. Since only part of the full model can be constrained by SMS results, SModelS will always remain more conservative (though orders of magnitude faster) than "full recasting" approaches. (C) 2018 Elsevier B.V. All rights reserved.
C1 [Ambrogi, Federico; Kulkarni, Suchita; Traub, Michael; Waltenberger, Wolfgang] Austrian Acad Sci, Inst Hochenergiephys, Nikolsdorfer Gasse 18, A-1050 Vienna, Austria.
[Kraml, Sabine; Laa, Ursula] Univ Grenoble Alpes, Lab Phys Subatom & Cosmol, CNRS, IN2P3, 53 Ave Martyrs, F-38026 Grenoble, France.
[Laa, Ursula] Univ Savoie Mt Blanc, CNRS, LAPTh, BP 110 Annecy le Vieux, F-74941 Annecy, France.
[Lessa, Andre] Univ Fed ABC, Ctr Ciencias Nat & Humanas, BR-09210580 Santo Andre, SP, Brazil.
[Magerl, Veronika] Albert Ludwigs Univ, Fak Math & Phys, D-79104 Freiburg, Germany.
[Sonneveld, Jory] Univ Hamburg, Inst Expt Phys, D-22761 Hamburg, Germany.
RP Waltenberger, W (corresponding author), Austrian Acad Sci, Inst Hochenergiephys, Nikolsdorfer Gasse 18, A-1050 Vienna, Austria.
EM wolfgang.waltenberger@gmail.com
RI Waltenberger, Wolfgang/H-9330-2018; Lessa, Andre/AAK-2194-2021
OI Waltenberger, Wolfgang/0000-0002-6215-7228; Lessa,
Andre/0000-0002-5251-7891; Kraml, Sabine/0000-0002-2613-7000; Laa,
Ursula/0000-0002-0249-6439
FU French ANRFrench National Research Agency (ANR) [DMAstro-LHC
ANR-12-BS05-0006]; Theory-LHC-France Initiative of the CNRS (INP/IN2P3);
Austrian FWFAustrian Science Fund (FWF) [P26896-N27]; "New Frontiers"
program of the Austrian Academy of Sciences; Investissements d'avenir,
Labex ENIGMASSFrench National Research Agency (ANR); Sao Paulo Research
Foundation (FAPESP)Fundacao de Amparo a Pesquisa do Estado de Sao Paulo
(FAPESP) [2015/20570-1, 2016/50338-6]; German Science Foundation
(DFG)German Research Foundation (DFG) [SFB676]; German Federal Ministry
of Education and Research (BMBF)Federal Ministry of Education & Research
(BMBF); Austrian Science Fund (FWF)Austrian Science Fund (FWF) [P 26896]
Funding Source: researchfish
FX This work was supported in part by the French ANR, project DMAstro-LHC
ANR-12-BS05-0006, and the Theory-LHC-France Initiative of the CNRS
(INP/IN2P3). F.A. is supported by the Austrian FWF, project P26896-N27,
Su.K. by the "New Frontiers" program of the Austrian Academy of
Sciences, U.L. by the "Investissements d'avenir, Labex ENIGMASS", and
A.L. by the Sao Paulo Research Foundation (FAPESP), projects
2015/20570-1 and 2016/50338-6. The work of J.S. is supported by the
collaborative research center SFB676 "Particles, Strings, and the Early
Universe" by the German Science Foundation (DFG) and by the German
Federal Ministry of Education and Research (BMBF).
CR Aad G, 2015, J HIGH ENERGY PHYS, DOI 10.1007/JHEP10(2015)134
Aad G, 2014, J HIGH ENERGY PHYS, P1, DOI 10.1007/JHEP05(2014)071
Aad G, 2013, J HIGH ENERGY PHYS, DOI 10.1007/JHEP10(2013)189
Aad G, 2013, J HIGH ENERGY PHYS, DOI 10.1007/JHEP10(2013)130
Alwall J, 2008, EUR PHYS J C, V53, P473, DOI 10.1140/epjc/s10052-007-0490-5
Alwall J, 2007, COMPUT PHYS COMMUN, V176, P300, DOI 10.1016/j.cpc.2006.11.010
Alwall J, 2014, J HIGH ENERGY PHYS, DOI 10.1007/JHEP07(2014)079
Barducci D., 2016, ARXIV160603834
Bein S., MADANALYSIS 5 IMPLEM, DOI 10.7484/INSPIREHEP.DATA.83GG.U5BW
Cacciari M, 2012, EUR PHYS J C, V72, DOI 10.1140/epjc/s10052-012-1896-2
CMS Collaboration, 2017, 2017001 CMS COLL
de Favereau J, 2014, J HIGH ENERGY PHYS, DOI 10.1007/JHEP02(2014)057
Dumont B., 2014, MADANALYSIS 5 IMPLEM, DOI 10.7484/INSPIREHEP.DATA.HLMR.T56W.2
Fuks B., ADANALYSIS5 IMPLEMEN, DOI 10.7484/INSPIREHEP.DATA.STLS.SAMT
Hoche S., 2005, HERA LHC WORKSH IM A, P288, DOI DOI 10.5170/CERN-2005-014.288
Mangano M., 2002, FNAL MATR EL MONT CA
Sjostrand T, 2004, J HIGH ENERGY PHYS
NR 17
TC 21
Z9 22
U1 0
U2 2
PU ELSEVIER
PI AMSTERDAM
PA RADARWEG 29, 1043 NX AMSTERDAM, NETHERLANDS
SN 0010-4655
EI 1879-2944
J9 COMPUT PHYS COMMUN
JI Comput. Phys. Commun.
PD JUN
PY 2018
VL 227
BP 72
EP 98
DI 10.1016/j.cpc.2018.02.007
PG 27
WC Computer Science, Interdisciplinary Applications; Physics, Mathematical
SC Computer Science; Physics
GA GC4LM
UT WOS:000429756000007
DA 2021-04-21
ER
PT J
AU Richardson, ML
Amini, B
AF Richardson, Michael L.
Amini, Behrang
TI Teaching Radiology Physics Interactively with Scientific Notebook
Software
SO ACADEMIC RADIOLOGY
LA English
DT Article
DE Physics; radiology education; simulation; scientific notebook software
ID ACHILLES-TENDON; RESIDENTS; INTENSITY; PYTHON
AB Rationale and Objectives: The goal of this study is to demonstrate how the teaching of radiology physics can be enhanced with the use of interactive scientific notebook software.
Methods: We used the scientific notebook software known as Project Jupyter, which is free, open-source, and available for the Macintosh, Windows, and Linux operating systems.
Results: We have created a scientific notebook that demonstrates multiple interactive teaching modules we have written for our residents using the Jupyter notebook system.
Conclusions: Scientific notebook software allows educators to create teaching modules in a form that combines text, graphics, images, data, interactive calculations, and image analysis within a single document These notebooks can be used to build interactive teaching modules, which can help explain complex topics in imaging physics to residents.
C1 [Richardson, Michael L.] Univ Washington, Dept Radiol, 4245 Roosevelt Way NE, Seattle, WA 98105 USA.
[Amini, Behrang] Univ Texas MD Anderson Canc Ctr, Dept Radiol, Houston, TX 77030 USA.
RP Richardson, ML (corresponding author), Univ Washington, Dept Radiol, 4245 Roosevelt Way NE, Seattle, WA 98105 USA.
EM mrich@u.washington.edu
OI Amini, Behrang/0000-0002-4962-3466
CR *AM BOARD RAD, 2017, COR EX
CHANG W, 2017, COOKBOOK FOR R
Chappell KE, 2004, AM J NEURORADIOL, V25, P431
ERICKSON SJ, 1991, RADIOLOGY, V181, P389, DOI 10.1148/radiology.181.2.1924777
Groch MW, 1998, RADIOGRAPHICS, V18, P1247, DOI 10.1148/radiographics.18.5.9747617
GRUBER J, 2017, MARKDOWN SYNTAX
Hayes CW, 1996, TOP MAGN RESON IMAG, V8, P51
Jacobs CT, 2016, J GEOSCI ED, V64, P183, DOI DOI 10.5408/15-101.1
Jones B, 2013, PYTHON COOKBOOK RECI
*MAPL, 2017, MAPL SOFTW MATH ONL
Marshall H, 2002, AM J ROENTGENOL, V179, P187, DOI 10.2214/ajr.179.1.1790187
*MATL, 2017, MATHWORKS MAK MATLAB
MILAS J, 2016, JUPYTER CLASSROOM
MITCHELL MR, 1984, INVEST RADIOL, V19, P350, DOI 10.1097/00004424-198409000-00004
Mittelbach Frank., 2004, THE LATEX COMPANION, V2nd
Oatridge A, 2003, CLIN RADIOL, V58, P384, DOI 10.1016/S0009-9260(02)00582-2
Osipov D, 2016, RISE DATA SCI NOTEBO
Perez F, 2007, COMPUT SCI ENG, V9, P21, DOI 10.1109/MCSE.2007.53
Perez F, 2011, COMPUT SCI ENG, V13, P13, DOI 10.1109/MCSE.2010.119
*PROJ JUP, 2017, PROJ JUP HOM
RICHARDSON ML, 1985, INVEST RADIOL, V20, P492, DOI 10.1097/00004424-198508000-00009
RICHARDSON ML, 2017, CURR PROBL DIAGN RAD, DOI DOI 10.1067/J.CPRADIOL.2017
RStudio Team, 2015, INT DEV ENV R
Shen H, 2014, NATURE, V515, P151, DOI 10.1038/515151a
Teetor P., 2011, R COOKBOOK
van der Walt S, 2014, PEERJ, V2, DOI 10.7717/peerj.453
van der Walt S, 2011, COMPUT SCI ENG, V13, P22, DOI 10.1109/MCSE.2011.37
VANNOORDEN R, 2014, NATURE, P16015, DOI DOI 10.1038/NATURE.2014.16015
*WOLFR LANG SYST D, 2017, US NOT BOOK INT
Zhang J, 2017, ACAD RADIOL, V24, P677, DOI 10.1016/j.acra.2017.01.015
2016, DIAGN RAD PHYS CURR
2017, STACK OV CONTR
2017, WRIT GITHUB BAS WRIT
NR 33
TC 4
Z9 4
U1 0
U2 3
PU ELSEVIER SCIENCE INC
PI NEW YORK
PA 360 PARK AVE SOUTH, NEW YORK, NY 10010-1710 USA
SN 1076-6332
EI 1878-4046
J9 ACAD RADIOL
JI Acad. Radiol.
PD JUN
PY 2018
VL 25
IS 6
BP 801
EP 810
DI 10.1016/j.acra.2017.11.024
PG 10
WC Radiology, Nuclear Medicine & Medical Imaging
SC Radiology, Nuclear Medicine & Medical Imaging
GA GG1BZ
UT WOS:000432415600020
PM 29751860
DA 2021-04-21
ER
PT J
AU Rosati, E
Madec, M
Kammerer, JB
Hebrard, L
Lallement, C
Haiech, J
AF Rosati, Elise
Madec, Morgan
Kammerer, Jean-Baptiste
Hebrard, Luc
Lallement, Christophe
Haiech, Jacques
TI Efficient Modeling and Simulation of Space-Dependent Biological Systems
SO JOURNAL OF COMPUTATIONAL BIOLOGY
LA English
DT Article
DE compact modeling; mesher; space-and-time modeling; SPICE; systems and
synthetic biology
ID OSCILLATIONS; CIRCUIT
AB We recently demonstrated the possibility to model and to simulate biological functions using hardware description languages (HDLs) and associated simulators traditionally used for microelectronics. Nevertheless, those languages are not suitable to model and simulate space-dependent systems described by partial differential equations. However, in more and more applications space- and time-dependent models are unavoidable. For this purpose, we investigated a new modeling approach to simulate molecular diffusion on a mesoscopic scale still based on HDL. Our work relies on previous investigations on an electrothermal simulation tool for integrated circuits, and analogies that can be drawn between electronics, thermodynamics, and biology. The tool is composed of four main parts: a simple but efficient mesher that divides space into parallelepipeds (or rectangles in 2D) of adaptable size, a set of interconnected biological models, a SPICE simulator that handles the model and Python scripts that interface the different tools. Simulation results obtained with our tool have been validated on simple cases for which an analytical solution exists and compared with experimental data gathered from literature. Compared with existing approaches, our simulator has three main advantages: a very simple algorithm providing a direct interface between the diffusion model and biological model of each cell, the use of a powerful and widely proven simulation core (SPICE) and the ability to interface biological models with other domains of physics, enabling the study of transdisciplinary systems.
C1 [Rosati, Elise; Madec, Morgan; Kammerer, Jean-Baptiste; Hebrard, Luc; Lallement, Christophe; Haiech, Jacques] Univ Strasbourg, Lab Sci Ingenieur Informat & Imagerie ICube, CNRS, UMR 7357, 300 Bd Sebastien Brandt, F-67412 Illkirch Graffenstaden, France.
[Haiech, Jacques] Univ Strasbourg, Lab Biotechnol & Signalisat Cellulaire BSC, CNRS, UMR 7242, 300 Bd Sebastien Brandt, F-67412 Illkirch Graffenstaden, France.
RP Rosati, E (corresponding author), Univ Strasbourg, Lab Sci Ingenieur Informat & Imagerie, CNRS, UMR 7357, 300 Bd Sebastien Brant,BP 10413, F-67412 Illkirch Graffenstaden, France.
EM elise.rosati@etu.unistra.fr
RI HEBRARD, Luc/AAG-3266-2019; Madec, Morgan/AAA-5910-2020; haiech, jacques
J/F-2890-2010
OI haiech, jacques/0000-0003-2908-8053; LALLEMENT,
christophe/0000-0002-0708-7212
CR Amar Patrick, 2004, Journal of Biological Physics and Chemistry, V4, P79
Arifin DY, 2006, ADV DRUG DELIVER REV, V58, P1274, DOI 10.1016/j.addr.2006.09.007
Balagadde FK, 2008, MOL SYST BIOL, V4, DOI 10.1038/msb.2008.24
Basu S, 2005, NATURE, V434, P1130, DOI 10.1038/nature03461
Blundell S., 2010, CONCEPTS THERMAL PHY
Butterworth Erik, 2013, F1000Res, V2, P288, DOI 10.12688/f1000research.2-288.v1
Calzone L, 2006, BIOINFORMATICS, V22, P1805, DOI 10.1093/bioinformatics/btl172
Digele G, 1997, IEEE T VLSI SYST, V5, P250, DOI 10.1109/92.609867
Dreij K, 2011, PLOS ONE, V6, DOI 10.1371/journal.pone.0023128
ERMENTROUT GB, 1993, J THEOR BIOL, V160, P97, DOI 10.1006/jtbi.1993.1007
Evans G., 2000, NUMERICAL METHODS PA
Fick A., 1855, MAGAZ J SCI, V10, P30, DOI [10.1080/14786445508641925, DOI 10.1080/14786445508641925]
Fujii T, 2013, ACS NANO, V7, P27, DOI 10.1021/nn3043572
Garci M, 2014, IEEE INT NEW CIRC, P125, DOI 10.1109/NEWCAS.2014.6933999
Gendrault Y, 2014, IEEE T BIO-MED ENG, V61, P1231, DOI 10.1109/TBME.2014.2298559
GOLDBETER A, 1990, P NATL ACAD SCI USA, V87, P1461, DOI 10.1073/pnas.87.4.1461
Haiech J, 2014, BBA-MOL CELL RES, V1843, P2348, DOI 10.1016/j.bbamcr.2014.03.013
Hecht F, 2012, J NUMER MATH, V20, P251, DOI 10.1515/jnum-2012-0013
Hoops S, 2006, BIOINFORMATICS, V22, P3067, DOI 10.1093/bioinformatics/btl485
Hucka M, 2003, BIOINFORMATICS, V19, P524, DOI 10.1093/bioinformatics/btg015
Keiter E., 2014, SAND20124805 SAND NA
Konkoli Z, 2011, THEOR BIOL MED MODEL, V8, DOI 10.1186/1742-4682-8-10
Krencker JC, 2014, MICROELECTRON J, V45, P491, DOI 10.1016/j.mejo.2014.02.001
Krencker J.-C., 2010, THERM INV ICS SYST T
Lannutti F., 2014, CUSPICE NGSPICE CUDA
Lopez CF, 2013, MOL SYST BIOL, V9, DOI 10.1038/msb.2013.1
Madec M, 2017, PLOS ONE, V12, DOI 10.1371/journal.pone.0182385
Madec M, 2012, IEEE ENG MED BIO, P5462, DOI 10.1109/EMBC.2012.6347230
Michaelis VL, 1913, BIOCHEMISTRY-US, V29, P332
Moraru II, 2008, IET SYST BIOL, V2, P352, DOI 10.1049/iet-syb:20080102
Nenzi P., 2011, NGSPICE USERS MANUAL
Rosati E, 2016, LECT NOTES COMPUT SC, V9597, P184, DOI 10.1007/978-3-319-31204-0_13
Schmidt H, 2006, BIOINFORMATICS, V22, P514, DOI 10.1093/bioinformatics/bti799
SOMOGYI R, 1991, J BIOL CHEM, V266, P11068
Takahashi K, 2005, FEBS LETT, V579, P1783, DOI 10.1016/j.febslet.2005.01.072
Tamsir A, 2011, NATURE, V469, P212, DOI 10.1038/nature09565
Vogelsong R.S., 1989, 1989 P IEEE CUST INT
Vollmer J., 2013, P COMSOL C ROTT NETH
Wunsche S, 1997, IEEE T VLSI SYST, V5, P277, DOI 10.1109/92.609870
Xie Z, 2011, SCIENCE, V333, P1307, DOI 10.1126/science.1205527
NR 40
TC 4
Z9 4
U1 0
U2 0
PU MARY ANN LIEBERT, INC
PI NEW ROCHELLE
PA 140 HUGUENOT STREET, 3RD FL, NEW ROCHELLE, NY 10801 USA
SN 1066-5277
EI 1557-8666
J9 J COMPUT BIOL
JI J. Comput. Biol.
PD AUG
PY 2018
VL 25
IS 8
BP 917
EP 933
DI 10.1089/cmb.2018.0012
PG 17
WC Biochemical Research Methods; Biotechnology & Applied Microbiology;
Computer Science, Interdisciplinary Applications; Mathematical &
Computational Biology; Statistics & Probability
SC Biochemistry & Molecular Biology; Biotechnology & Applied Microbiology;
Computer Science; Mathematical & Computational Biology; Mathematics
GA GP5XC
UT WOS:000432062600001
PM 29741924
DA 2021-04-21
ER
PT J
AU Vianello, G
AF Vianello, Giacomo
TI The Significance of an Excess in a Counting Experiment: Assessing the
Impact of Systematic Uncertainties and the Case with a Gaussian
Background
SO ASTROPHYSICAL JOURNAL SUPPLEMENT SERIES
LA English
DT Article
DE astroparticle physics; gamma-ray burst: general; methods: data analysis;
methods: statistical
ID GAMMA-RAY ASTRONOMY; LIKELIHOOD RATIO; SMALL NUMBERS; CONFIDENCE;
STATISTICS; EVENTS; LIMITS
AB Several experiments in high-energy physics and astrophysics can be treated as on/off measurements, where an observation potentially containing a new source or effect ("on" measurement) is contrasted with a background-only observation free of the effect ("off" measurement). In counting experiments, the significance of the new source or effect can be estimated with a widely used formula from Li & Ma, which assumes that both measurements are Poisson random variables. In this paper we study three other cases: (i) the ideal case where the background measurement has no uncertainty, which can be used to study the maximum sensitivity that an instrument can achieve, (ii) the case where the background estimate b in the off measurement has an additional systematic uncertainty, and (iii) the case where b is a Gaussian random variable instead of a Poisson random variable. The latter case applies when b comes from a model fitted on archival or ancillary data, or from the interpolation of a function fitted on data surrounding the candidate new source/effect. Practitioners typically use a formula that is only valid when b is large and when its uncertainty is very small, while we derive a general formula that can be applied in all regimes. We also develop simple methods that can be used to assess how much an estimate of significance is sensitive to systematic uncertainties on the efficiency or on the background. Examples of applications include the detection of short gamma-ray bursts and of new X-ray or.-ray sources. All the techniques presented in this paper are made available in a Python code that is ready to use.
C1 [Vianello, Giacomo] Stanford Univ, Hansen Expt Phys Lab, 450 Serra Mall, Stanford, CA 94305 USA.
RP Vianello, G (corresponding author), Stanford Univ, Hansen Expt Phys Lab, 450 Serra Mall, Stanford, CA 94305 USA.
EM giacomov@stanford.edu
OI Vianello, Giacomo/0000-0002-2553-0839
CR Abbott BP, 2017, ASTROPHYS J LETT, V848, DOI 10.3847/2041-8213/aa920c
Bhat PN, 2016, ASTROPHYS J SUPPL S, V223, DOI 10.3847/0067-0049/223/2/28
CASH W, 1979, ASTROPHYS J, V228, P939, DOI 10.1086/156922
Clopper CJ, 1934, BIOMETRIKA, V26, P404, DOI 10.1093/biomet/26.4.404
Cousins RD, 2008, NUCL INSTRUM METH A, V595, P480, DOI 10.1016/j.nima.2008.07.086
GEHRELS N, 1986, ASTROPHYS J, V303, P336, DOI 10.1086/164079
Gillessen S, 2005, ASTRON ASTROPHYS, V430, P355, DOI 10.1051/0004-6361:20035839
Goldstein A, 2017, ASTROPHYS J LETT, V848, DOI 10.3847/2041-8213/aa8f41
JAMES F, 1980, NUCL PHYS B, V172, P475, DOI 10.1016/0550-3213(80)90179-0
LI TP, 1983, ASTROPHYS J, V272, P317, DOI 10.1086/161295
Meegan C, 2009, ASTROPHYS J, V702, P791, DOI 10.1088/0004-637X/702/1/791
Protassov R, 2002, ASTROPHYS J, V571, P545, DOI 10.1086/339856
Reid N, 1995, STAT SCI, V10, P138, DOI 10.1214/ss/1177010027
Spengler G, 2015, ASTROPART PHYS, V67, P70, DOI 10.1016/j.astropartphys.2015.02.002
Szecsi D, 2013, ASTRON ASTROPHYS, V557, DOI 10.1051/0004-6361/201321068
Vasileiou V, 2013, ASTROPART PHYS, V48, P61, DOI 10.1016/j.astropartphys.2013.07.002
Vianello G., 2018, GV SIGNIFICANCE V1 0, DOI [10.5281/zenodo.1157308, DOI 10.5281/ZENODO.1157308]
Wilks SS, 1938, ANN MATH STAT, V9, P60, DOI 10.1214/aoms/1177732360
Zhang S. N., 1990, Experimental Astronomy, V1, P145, DOI 10.1007/BF00462037
NR 19
TC 15
Z9 15
U1 0
U2 0
PU IOP PUBLISHING LTD
PI BRISTOL
PA TEMPLE CIRCUS, TEMPLE WAY, BRISTOL BS1 6BE, ENGLAND
SN 0067-0049
EI 1538-4365
J9 ASTROPHYS J SUPPL S
JI Astrophys. J. Suppl. Ser.
PD MAY
PY 2018
VL 236
IS 1
AR 17
DI 10.3847/1538-4365/aab780
PG 11
WC Astronomy & Astrophysics
SC Astronomy & Astrophysics
GA GT5HM
UT WOS:000444537700012
DA 2021-04-21
ER
PT J
AU Hardwick, RJ
AF Hardwick, Robert J.
TI Multiple spectator condensates from inflation
SO JOURNAL OF COSMOLOGY AND ASTROPARTICLE PHYSICS
LA English
DT Article
DE inflation; physics of the early universe
ID QUANTUM; GENERATION; FIELD
AB We investigate the development of spectator (light test) field condensates due to their quantum fluctuations in a de Sitter inflationary background, making use of the stochastic formalism to describe the system. In this context, a condensate refers to the typical field value found after a coarse-graining using the Hubble scale H, which can be essential to seed the initial conditions required by various post-inflationary processes. We study models with multiple coupled spectators and for the first time we demonstrate that new forms of stationary solution exist (distinct from the standard exponential form) when the potential is asymmetric. Furthermore, we find a critical value for the inter-field coupling as a function of the number of fields above which the formation of stationary condensates collapses to H. Considering some simple two-field example potentials, we are also able to derive a lower limit on the coupling, below which the fluctuations are effectively decoupled, and the standard stationary variance formulae for each field separately can be trusted. These results are all numerically verified by a new publicly available python class (nfield) to solve the coupled Langevin equations over a large number of fields, realisations and timescales. Further applications of this new tool are also discussed.
C1 [Hardwick, Robert J.] Univ Portsmouth, Inst Cosmol & Gravitat, Dennis Sciama Bldg,Burnaby Rd, Portsmouth PO1 3FX, Hants, England.
RP Hardwick, RJ (corresponding author), Univ Portsmouth, Inst Cosmol & Gravitat, Dennis Sciama Bldg,Burnaby Rd, Portsmouth PO1 3FX, Hants, England.
EM robert.hardwick@port.ac.uk
RI Hardwick, Robert J./O-1098-2019
OI Hardwick, Robert J./0000-0001-8778-006X
FU U.K. Science and Technology Facilities CouncilUK Research & Innovation
(UKRI)Science & Technology Facilities Council (STFC) [ST/N5044245]; ICG;
SEPNet; University of Portsmouth
FX The author is supported by U.K. Science and Technology Facilities
Council grant ST/N5044245. Some numerical computations were done on the
Sciama High Performance Compute (HPC) cluster which is supported by the
ICG, SEPNet and the University of Portsmouth. The author would also like
to thank both Matthew Hull for fruitful discussions and Chris Pattison,
Vincent Vennin and David Wands for careful reading and helpful comments
on the manuscript.
CR Ashoorioon A, 2009, J COSMOL ASTROPART P, DOI 10.1088/1475-7516/2009/06/018
Assadullahi H, 2016, J COSMOL ASTROPART P, DOI 10.1088/1475-7516/2016/06/043
Bartolo N., 2002, PHYS REV, VD 65, P1213
Birrell N.D., 1984, CAMBRIDGE MONOGRAPHS
BUNCH TS, 1978, P ROY SOC LOND A MAT, V360, P117, DOI 10.1098/rspa.1978.0060
Burgess CP, 2010, J COSMOL ASTROPART P, DOI 10.1088/1475-7516/2010/10/017
Ema Y., ARXIV171110554
Enqvist K, 2002, NUCL PHYS B, V626, P395, DOI 10.1016/S0550-3213(02)00043-3
Enqvist K, 2018, J COSMOL ASTROPART P, DOI 10.1088/1475-7516/2018/02/006
Enqvist K, 2012, J COSMOL ASTROPART P, DOI 10.1088/1475-7516/2012/10/052
Fairbairn M, 2018, EUR PHYS J C, V78, DOI 10.1140/epjc/s10052-018-5830-0
Freese K., ARXIV171203791
Glavan D, 2018, EUR PHYS J C, V78, DOI 10.1140/epjc/s10052-018-5862-5
Grain J, 2017, J COSMOL ASTROPART P, DOI 10.1088/1475-7516/2017/05/045
GRISHCHUK L, 1992, PHYS REV D, V46, P1440, DOI 10.1103/PhysRevD.46.1440
GRISHCHUK LP, 1990, PHYS REV D, V42, P3413, DOI 10.1103/PhysRevD.42.3413
Hardwick R. J., UNPUB
Hardwick RJ, 2017, INT J MOD PHYS D, V26, DOI 10.1142/S0218271817430258
Hardwick RJ, 2017, J COSMOL ASTROPART P, DOI 10.1088/1475-7516/2017/10/018
HEIDMANN A, 1987, PHYS REV LETT, V59, P2555, DOI 10.1103/PhysRevLett.59.2555
Linde A, 1997, PHYS REV D, V56, pR535, DOI 10.1103/PhysRevD.56.R535
Lyth DH, 2002, PHYS LETT B, V524, P5, DOI 10.1016/S0370-2693(01)01366-1
MARKKANEN T, 2017, JHEP, V1
Moroi T, 2001, PHYS LETT B, V522, P215, DOI 10.1016/S0370-2693(01)01295-3
Risken H., 1989, SPINGER SERIES SYNER, V18
Roberts A. J., ARXIV12100933
STAROBINSKY AA, 1994, PHYS REV D, V50, P6357, DOI 10.1103/PhysRevD.50.6357
STAROBINSKY AA, 1982, PHYS LETT B, V117, P175, DOI 10.1016/0370-2693(82)90541-X
STAROBINSKY AA, 1985, PIS ZH EKSP TEOR FIZ, V42, P124
Tokuda J, 2018, J COSMOL ASTROPART P, DOI 10.1088/1475-7516/2018/02/014
Tsamis NC, 2005, NUCL PHYS B, V724, P295, DOI 10.1016/j.nuclphysb.2005.06.031
Woodard RP, 2008, PHYS REV LETT, V101, DOI 10.1103/PhysRevLett.101.081301
NR 32
TC 7
Z9 7
U1 0
U2 0
PU IOP PUBLISHING LTD
PI BRISTOL
PA TEMPLE CIRCUS, TEMPLE WAY, BRISTOL BS1 6BE, ENGLAND
SN 1475-7516
J9 J COSMOL ASTROPART P
JI J. Cosmol. Astropart. Phys.
PD MAY
PY 2018
IS 5
AR 054
DI 10.1088/1475-7516/2018/05/054
PG 19
WC Astronomy & Astrophysics; Physics, Particles & Fields
SC Astronomy & Astrophysics; Physics
GA GG7HO
UT WOS:000432869300001
DA 2021-04-21
ER
PT J
AU Schnepf, A
Leitner, D
Landl, M
Lobet, G
Mai, TH
Morandage, S
Sheng, C
Zorner, M
Vanderborght, J
Vereecken, H
AF Schnepf, Andrea
Leitner, Daniel
Landl, Magdalena
Lobet, Guillaume
Trung Hieu Mai
Morandage, Shehan
Sheng, Cheng
Zoerner, Mirjam
Vanderborght, Jan
Vereecken, Harry
TI CRootBox: a structural-functional modelling framework for root systems
SO ANNALS OF BOTANY
LA English
DT Article
DE C plus; Python; root architecture modelling; root-soil interaction; RSML
ID WATER-UPTAKE; HYDRAULIC ARCHITECTURE; MATHEMATICAL-MODEL;
NUTRIENT-UPTAKE; PLANT-ROOTS; SOIL-WATER; GROWTH; MAIZE; TRANSPORT;
NITROGEN
AB Background and Aims Root architecture development determines the sites in soil where roots provide input of carbon and take up water and solutes. However, root architecture is difficult to determine experimentally when grown in opaque soil. Thus, root architecture models have been widely used and been further developed into functional-structural models that simulate the fate of water and solutes in the soil-root system. The root architecture model CRootBox presented here is a flexible framework to model root architecture and its interactions with static and dynamic soil environments.
Methods CRootBox is a C++-based root architecture model with Python binding, so that CRootBox can be included via a shared library into any Python code. Output formats include VTP, DGF, RSML and a plain text file containing coordinates of root nodes. Furthermore, a database of published root architecture parameters was created. The capabilities of CRootBox for the unconfined growth of single root systems, as well as the different parameter sets, are highlighted in a freely available web application.
Key results The capabilities of CRootBox are demonstrated through five different cases: (1) free growth of individual root systems; (2) growth of root systems in containers as a way to mimic experimental setups; (3) fieldscale simulation; (4) root growth as affected by heterogeneous, static soil conditions; and (5) coupling CRootBox with code from the book Soil physics with Python to dynamically compute water flow in soil, root water uptake and water flow inside roots.
Conclusions CRootBox is a fast and flexible functional-structural root model that is based on state-of-the-art computational science methods. Its aim is to facilitate modelling of root responses to environmental conditions as well as the impact of roots on soil. In the future, this approach will be extended to the above-ground part of the plant.
C1 [Schnepf, Andrea; Landl, Magdalena; Lobet, Guillaume; Trung Hieu Mai; Morandage, Shehan; Sheng, Cheng; Zoerner, Mirjam; Vanderborght, Jan; Vereecken, Harry] Forschungszentrum Juelich GmbH, Agrosphere IBG 3, D-52428 Julich, Germany.
[Leitner, Daniel] Simulationswerkstatt, Ortmayrstr 20, A-4060 Leonding, Austria.
RP Schnepf, A (corresponding author), Forschungszentrum Juelich GmbH, Agrosphere IBG 3, D-52428 Julich, Germany.
EM a.schnepf@fz-juelich.de
RI Schnepf, Andrea/Q-4457-2019; Lobet, Guillaume/R-2063-2017; Vanderborght,
Jan/AAG-7753-2019; Landl, Magdalena/AAA-7084-2020; Schnepf,
Andrea/F-5203-2015
OI Schnepf, Andrea/0000-0003-2203-4466; Lobet,
Guillaume/0000-0002-5883-4572; Vanderborght, Jan/0000-0001-7381-3211;
Schnepf, Andrea/0000-0003-2203-4466; Mai, Trung
Hieu/0000-0002-3753-7245; Vereecken, Harry/0000-0002-8051-8517
FU German Federal Ministry of Education and Research (BMBF)Federal Ministry
of Education & Research (BMBF) [031B0026C]; German Research Foundation
DFGGerman Research Foundation (DFG) [PAK888, TR32-B4]; China Scholarship
Council (CSC)China Scholarship Council
FX We thank the editor and two anonymous reviewers for their constructive
and insightful comments. This work was funded by the German Federal
Ministry of Education and Research (BMBF) in the framework of the
funding initiative Soil as a Sustainable Resource for the Bioeconomy -
BonaRes, project BonaRes (Module A): Sustainable Subsoil Management -
Soil3; subproject 3 (grant 031B0026C) and by the German
Research Foundation DFG (grant numbers PAK888 and TR32-B4). C.S. has a
PhD scholarship of the China Scholarship Council (CSC).
CR BARRACLOUGH PB, 1989, J AGRON CROP SCI, V163, P352, DOI 10.1111/j.1439-037X.1989.tb00778.x
Bingham IJ, 2011, EUR J AGRON, V34, P181, DOI 10.1016/j.eja.2011.01.003
Bittelli M, 2015, SOIL PHYS PYTHON TRA
Bourion V, 2014, J EXP BOT, V65, P2365, DOI 10.1093/jxb/eru124
Chen YL, 2011, PLANT SOIL, V348, P345, DOI 10.1007/s11104-011-0939-z
Chimungu J.G., 2014, PLANT BIOTECHNOLOGY, P181
Chochois V, 2012, J EXP BOT, V63, P3467, DOI 10.1093/jxb/ers044
CLAUSNITZER V, 1994, PLANT SOIL, V164, P299, DOI 10.1007/BF00010082
DIGGLE AJ, 1988, PLANT SOIL, V105, P169, DOI 10.1007/BF02376780
Doussan C, 1998, ANN BOT-LONDON, V81, P225, DOI 10.1006/anbo.1997.0541
Doussan C, 2006, PLANT SOIL, V283, P99, DOI 10.1007/s11104-004-7904-z
Drouet JL, 2003, ECOL MODEL, V165, P147, DOI 10.1016/S0304-3800(03)00072-3
Dunbabin VM, 2013, PLANT SOIL, V372, P93, DOI 10.1007/s11104-013-1769-y
Dunbabin VM, 2002, PLANT SOIL, V239, P19, DOI 10.1023/A:1014939512104
Dupuy L, 2010, J EXP BOT, V61, P2131, DOI 10.1093/jxb/erp389
Feddes R. A., 1978, SIMULATION FIELD WAT
Fischer Gustav, 1982, WURZELATLAS MITTELEU
Flemisch B, 2011, ADV WATER RESOUR, V34, P1102, DOI 10.1016/j.advwatres.2011.03.007
Gregory P. J., 2006, PLANT ROOTS GROWTH A
Hochholdinger F, 2004, TRENDS PLANT SCI, V9, P42, DOI 10.1016/j.tplants.2003.11.003
Huber K, 2014, PLANT SOIL, V384, P93, DOI 10.1007/s11104-014-2188-4
Jacques D, 2006, J CONTAM HYDROL, V88, P197, DOI 10.1016/j.jconhyd.2006.06.008
Janott M, 2011, PLANT SOIL, V341, P233, DOI 10.1007/s11104-010-0639-0
Javaux M, 2008, VADOSE ZONE J, V7, P1079, DOI 10.2136/vzj2007.0115
Klepper B, 1991, ROOT SHOOT RELATIONS
Kutschera L., 1960, WURZELATLAS MITTELEU
Leitner D, 2014, FIELD CROP RES, V165, P125, DOI 10.1016/j.fcr.2014.05.009
Leitner D, 2014, PLANT PHYSIOL, V164, P24, DOI 10.1104/pp.113.227892
Leitner D, 2010, MATH COMP MODEL DYN, V16, P575, DOI 10.1080/13873954.2010.491360
Leitner D, 2010, PLANT SOIL, V332, P177, DOI 10.1007/s11104-010-0284-7
Lobet G, 2015, PLANT PHYSIOL, V167, P617, DOI 10.1104/pp.114.253625
Lobet G, 2014, PLANT PHYSIOL, V164, P1619, DOI 10.1104/pp.113.233486
Lynch JP, 1997, PLANT SOIL, V188, P139, DOI 10.1023/A:1004276724310
Osher Stanley, 2003, LEVEL SET METHODS DY, P17
Pages L, 2004, PLANT SOIL, V258, P103, DOI 10.1023/B:PLSO.0000016540.47134.03
PAGES L, 1989, PLANT SOIL, V119, P147, DOI 10.1007/BF02370279
Pages L, 1997, PLANT SOIL, V189, P81, DOI 10.1023/A:1004288430467
Pages L, 2014, ECOL MODEL, V290, P76, DOI 10.1016/j.ecolmodel.2013.11.014
Pages L, 2013, PLANT SOIL, V373, P723, DOI 10.1007/s11104-013-1820-z
Pages L, 2011, PLANT CELL ENVIRON, V34, P1749, DOI 10.1111/j.1365-3040.2011.02371.x
Perez F, 2011, COMPUT SCI ENG, V13, P13, DOI 10.1109/MCSE.2010.119
Pierret A, 2006, PLANT SOIL, V282, P117, DOI 10.1007/s11104-005-5373-7
Postma JA, 2017, NEW PHYTOL, V215, P1274, DOI 10.1111/nph.14641
Postma JA, 2011, PLANT PHYSIOL, V156, P1190, DOI 10.1104/pp.111.175489
Pradal C, 2008, FUNCT PLANT BIOL, V35, P751, DOI 10.1071/FP08084
RAATS PAC, 1974, SOIL SCI SOC AM J, V38, P717, DOI 10.2136/sssaj1974.03615995003800050012x
Roose T, 2004, J THEOR BIOL, V228, P173, DOI 10.1016/j.jtbi.2003.12.013
Roose T, 2001, J MATH BIOL, V42, P347, DOI 10.1007/s002850000075
Schnepf A, 2016, J R SOC INTERFACE, V13, DOI 10.1098/rsif.2016.0129
Schnepf A, 2012, VADOSE ZONE J, V11, DOI 10.2136/vzj2012.0001
Schroder N, 2014, PLANT SOIL, V377, P277, DOI 10.1007/s11104-013-1990-8
Schroder T, 2009, VADOSE ZONE J, V8, P783, DOI 10.2136/vzj2008.0116
Somma F, 1998, PLANT SOIL, V202, P281, DOI 10.1023/A:1004378602378
Spek LY, 1997, PLANT SOIL, V197, P9, DOI 10.1023/A:1004236626479
TSEGAYE T, 1995, PLANT SOIL, V172, P1, DOI 10.1007/BF00020855
Vansteenkiste J, 2014, PLANT SOIL, V375, P75, DOI 10.1007/s11104-013-1942-3
Wasson AP, 2017, FRONT PLANT SCI, V8, DOI 10.3389/fpls.2017.00282
Weaver J. E., 1927, ROOT DEV VEGETABLE C
Wu L, 2007, ECOL MODEL, V200, P343, DOI 10.1016/j.ecolmodel.2006.08.010
Zobel RW, 2010, PLANT BIOSYST, V144, P507, DOI 10.1080/11263501003764483
NR 60
TC 36
Z9 37
U1 3
U2 63
PU OXFORD UNIV PRESS
PI OXFORD
PA GREAT CLARENDON ST, OXFORD OX2 6DP, ENGLAND
SN 0305-7364
EI 1095-8290
J9 ANN BOT-LONDON
JI Ann. Bot.
PD APR
PY 2018
VL 121
IS 5
SI SI
BP 1033
EP 1053
DI 10.1093/aob/mcx221
PG 21
WC Plant Sciences
SC Plant Sciences
GA GD7FT
UT WOS:000430676600019
PM 29432520
OA Bronze, Green Published
DA 2021-04-21
ER
PT J
AU Jaschke, D
Wall, ML
Carr, LD
AF Jaschke, Daniel
Wall, Michael L.
Carr, Lincoln D.
TI Open source Matrix Product States: Opening ways to simulate entangled
many-body quantum systems in one dimension
SO COMPUTER PHYSICS COMMUNICATIONS
LA English
DT Article
DE Many-body quantum system; Entangled quantum dynamics; Matrix Product
State (MPS); Quantum simulator; Tensor network method; Density Matrix
Renormalization Group (DMRG)
ID MONTE-CARLO-SIMULATION; RENORMALIZATION-GROUP; ALGORITHM
AB Numerical simulations are a powerful tool to study quantum systems beyond exactly solvable systems lacking an analytic expression. For one-dimensional entangled quantum systems, tensor network methods, amongst them Matrix Product States (MPSs), have attracted interest from different fields of quantum physics ranging from solid state systems to quantum simulators and quantum computing. Our open source MPS code provides the community with a toolset to analyze the statics and dynamics of one-dimensional quantum systems. Here, we present our open source library, Open Source Matrix Product States (OSMPS), of MPS methods implemented in Python and Fortran2003. The library includes tools for ground state calculation and excited states via the variational ansatz. We also support ground states for infinite systems with translational invariance. Dynamics are simulated with different algorithms, including three algorithms with support for long-range interactions. Convenient features include built-in support for fermionic systems and number conservation with rotational U(1) and discrete Z(2) symmetries for finite systems, as well as data parallelism with MPI. We explain the principles and techniques used in this library along with examples of how to efficiently use the general interfaces to analyze the Ising and Bose-Hubbard models. This description includes the preparation of simulations as well as dispatching and post-processing of them.
Program summary
Program title: Open Source Matrix Product States (OSMPS), v2.0
Program Files doi: http://dx.doi.org/10.17632/vxm2mcmk4v.1
Licensing provisions: GNU GPL v3
Programming language: Python, Fortran2003, MPI for parallel computing
Compilers (Fortran): gfortran, ifort, g95
Dependencies: The minimal requirements in addition to the Fortran compiler are BIAS, LAPACK, ARPACK, python, numpy, scipy. Additional packages for plotting include matplotlib, dvipng, and LATEX packages. The Expokit package, available at the homepage http://www.maths.uq.edu.au/expokit/, is required to use the Local Runge-Kutta time evolution.
Supplementary material: We provide programs to reproduce selected figures in the Appendices.
Nature of problem: Solving the ground state and dynamics of a many-body entangled quantum system is a challenging problem; the Hilbert space grows exponentially with system size. Complete diagonalization of the Hilbert space to floating point precision is limited to less than forty qubits.
Solution method: Matrix Product States in one spatial dimension overcome the exponentially growing Hilbert space by truncating the least important parts of it. The error can be well controlled. Local neighboring sites are variationally optimized in order to minimize the energy of the complete system. We can target the ground state and low lying excited states. Moreover, we offer various methods to solve the time evolution following the many-body Schrodinger equation. These methods include e.g. the Suzuki-Trotter decompositions using local propagators or the Krylov method, both approximating the propagator on the complete Hilbert space. (C) 2017 Elsevier B.V. All rights reserved.
C1 [Jaschke, Daniel; Wall, Michael L.; Carr, Lincoln D.] Colorado Sch Mines, Dept Phys, Golden, CO 80401 USA.
[Wall, Michael L.] NIST, JILA, Boulder, CO 80309 USA.
[Wall, Michael L.] Univ Colorado, Boulder, CO 80309 USA.
[Wall, Michael L.] Johns Hopkins Univ, Appl Phys Lab, Laurel, MD 20723 USA.
RP Jaschke, D (corresponding author), Colorado Sch Mines, Dept Phys, Golden, CO 80401 USA.
EM djaschke@mines.edu
RI Carr, Lincoln/E-3819-2016
OI Carr, Lincoln/0000-0002-4848-7941; Wall, Michael/0000-0002-6223-0800;
Jaschke, Daniel/0000-0001-7658-3546
FU National Science FoundationNational Science Foundation (NSF)
[PHY-120881, PHY-1520915, OAC-1740130]; Air Force Office of Scientific
ResearchUnited States Department of DefenseAir Force Office of
Scientific Research (AFOSR) [FA9550-14-1-0287]; Division Of
PhysicsNational Science Foundation (NSF)NSF - Directorate for
Mathematical & Physical Sciences (MPS) [1520915] Funding Source:
National Science Foundation
FX We gratefully appreciate contributions from and discussions with A.
Dhar, B. Gardas, A. Glick, W. Han, D. M. Larue, and D. Vargas during the
development of OSMPS. We are equally thankful to the ALPS collaboration
[81,25] and to C. W. Clark, I. Danshita, R. Mishmash, B. I. Schneider,
and J. E. Williams who contributed heavily to the predecessor of OSMPS,
OpenTEBD [34]. The calculations were carried out using the high
performance computing resources provided by the Golden Energy Computing
Organization at the Colorado School of Mines. This work has been
supported by the National Science Foundation under the grants
PHY-120881, PHY-1520915, and OAC-1740130, and the Air Force Office of
Scientific Research under grant FA9550-14-1-0287.
CR Alvermann A, 2011, J COMPUT PHYS, V230, P5930, DOI 10.1016/j.jcp.2011.04.006
Anisimovas E, 2016, PHYS REV A, V94, DOI 10.1103/PhysRevA.94.063632
Bauer B, 2011, J STAT MECH-THEORY E, DOI 10.1088/1742-5468/2011/05/P05001
Bellotti FF, 2017, EUR PHYS J D, V71, DOI 10.1140/epjd/e2017-70650-8
Carr L., 2010, UNDERSTANDING QUANTU
Chan G. Kin-Lic, 160502611 ARXIV
Crosswhite GM, 2008, PHYS REV A, V78, DOI 10.1103/PhysRevA.78.012356
DALIBARD J, 1992, PHYS REV LETT, V68, P580, DOI 10.1103/PhysRevLett.68.580
Dargel P., MPS DMRG APPLET
De Chiara G, 2008, J COMPUT THEOR NANOS, V5, P1277, DOI 10.1166/jctn.2008.011
Dhar A, 2016, PHYS REV B, V94, DOI 10.1103/PhysRevB.94.075116
Dolfi M, 2014, COMPUT PHYS COMMUN, V185, P3430, DOI 10.1016/j.cpc.2014.08.019
DUM R, 1992, PHYS REV A, V45, P4879, DOI 10.1103/PhysRevA.45.4879
Dutta A, 2001, PHYS REV B, V64, DOI 10.1103/PhysRevB.64.184106
Eisert J, 2010, REV MOD PHYS, V82, P277, DOI 10.1103/RevModPhys.82.277
GALLOPOULOS E, 1992, SIAM J SCI STAT COMP, V13, P1236, DOI 10.1137/0913071
Garcia-Ripoll J., MATRIX PRODUCT STATE
Gardas B., 161205084 ARXIV
Garrison J. R., SIMPLE DMRG
Georges A, 1996, REV MOD PHYS, V68, P13, DOI 10.1103/RevModPhys.68.13
Gersgorin S., 1931, B ACAD SCI URSS, P749
Gobert D, 2005, PHYS REV E, V71, DOI 10.1103/PhysRevE.71.036102
Golub G., 1996, J HOPKINS STUDIES MA
Gong ZX, 2016, PHYS REV B, V93, DOI 10.1103/PhysRevB.93.205115
Gong ZX, 2016, PHYS REV B, V93, DOI 10.1103/PhysRevB.93.041102
Haegeman J, 2016, PHYS REV B, V94, DOI 10.1103/PhysRevB.94.165116
HUBBARD J, 1963, PROC R SOC LON SER-A, V276, P238, DOI 10.1098/rspa.1963.0204
Ising E, 1925, Z PHYS, V31, P253, DOI 10.1007/BF02980577
Jaschke D., OPEN SOURCE MATRIX P
Jaschke D., 161207437 ARXIV
Jordan P, 1928, Z PHYS, V47, P631, DOI 10.1007/BF01331938
Garcia-Ripoll JJ, 2006, NEW J PHYS, V8, DOI 10.1088/1367-2630/8/12/305
Koller AP, 2016, PHYS REV LETT, V117, DOI 10.1103/PhysRevLett.117.195302
Maghrebi M. F., 151001325 ARXIV
Manmana SR, 2005, AIP CONF PROC, V789, P269
McCulloch I. P., 08042509 ARXIV
McCulloch IP, 2007, J STAT MECH-THEORY E, DOI 10.1088/1742-5468/2007/10/P10014
Michel L, 10084667 ARXIV
Milsted A., EVOMPS
MIRSKY L, 1975, MONATSH MATH, V79, P303, DOI 10.1007/BF01647331
Moler C, 2003, SIAM REV, V45, P3, DOI 10.1137/S00361445024180
Nielsen M, 2007, QUANTUM COMPUTATION
Orus R, 2014, ANN PHYS-NEW YORK, V349, P117, DOI 10.1016/j.aop.2014.06.013
Polkovnikov A, 2010, ANN PHYS-NEW YORK, V325, P1790, DOI 10.1016/j.aop.2010.02.006
Prokof'ev NV, 1998, J EXP THEOR PHYS+, V87, P310, DOI 10.1134/1.558661
QI LQ, 1984, LINEAR ALGEBRA APPL, V56, P105, DOI 10.1016/0024-3795(84)90117-4
RUNGE E, 1984, PHYS REV LETT, V52, P997, DOI 10.1103/PhysRevLett.52.997
Russomanno A, 2016, EPL-EUROPHYS LETT, V115, DOI 10.1209/0295-5075/115/30006
SAAD Y, 1992, SIAM J NUMER ANAL, V29, P209, DOI 10.1137/0729014
Sachdev S., 2011, QUANTUM PHASE TRANSI
SANDVIK AW, 1991, PHYS REV B, V43, P5950, DOI 10.1103/PhysRevB.43.5950
Schachenmayer J, 2015, PHYS REV X, V5, DOI 10.1103/PhysRevX.5.011022
Schollwock U, 2005, REV MOD PHYS, V77, P259, DOI 10.1103/RevModPhys.77.259
Schollwock U, 2011, ANN PHYS-NEW YORK, V326, P96, DOI 10.1016/j.aop.2010.09.012
Shi YY, 2006, PHYS REV A, V74, DOI 10.1103/PhysRevA.74.022320
Sidje RB, 1998, ACM T MATH SOFTWARE, V24, P130, DOI 10.1145/285861.285868
Singh S, 2011, PHYS REV B, V83, DOI 10.1103/PhysRevB.83.115125
Singh S, 2010, PHYS REV A, V82, DOI 10.1103/PhysRevA.82.050301
Sornborger AT, 1999, PHYS REV A, V60, P1956, DOI 10.1103/PhysRevA.60.1956
Stoudenmire EM, 2010, NEW J PHYS, V12, DOI 10.1088/1367-2630/12/5/055026
Urbanek M, 2016, COMPUT PHYS COMMUN, V199, P170, DOI 10.1016/j.cpc.2015.10.016
Vargas D. L., 150807041 ARXIV
Verstraete F, 2008, ADV PHYS, V57, P143, DOI 10.1080/14789940801912366
Verstraete F, 2004, PHYS REV LETT, V93, DOI 10.1103/PhysRevLett.93.227205
Verstraete F, 2004, PHYS REV LETT, V93, DOI 10.1103/PhysRevLett.93.207204
Vidal G, 2003, PHYS REV LETT, V91, DOI 10.1103/PhysRevLett.91.147902
Vidal G, 2008, PHYS REV LETT, V101, DOI 10.1103/PhysRevLett.101.110501
Wall ML, 2013, NEW J PHYS, V15, DOI 10.1088/1367-2630/15/12/123005
Wall ML, 2013, PHYS REV A, V88, DOI 10.1103/PhysRevA.88.023605
Wall ML, 2012, NEW J PHYS, V14, DOI 10.1088/1367-2630/14/12/125015
Wang L, 2014, NEW J PHYS, V16, DOI 10.1088/1367-2630/16/10/103008
Weimer H, 2014, NEW J PHYS, V16, DOI 10.1088/1367-2630/16/9/093040
Werner AH, 2016, PHYS REV LETT, V116, DOI 10.1103/PhysRevLett.116.237201
WHITE SR, 1992, PHYS REV LETT, V69, P2863, DOI 10.1103/PhysRevLett.69.2863
WHITE SR, 1993, PHYS REV B, V48, P10345, DOI 10.1103/PhysRevB.48.10345
White SR, 2007, PHYS REV LETT, V99, DOI 10.1103/PhysRevLett.99.127004
Wouters S, 2014, COMPUT PHYS COMMUN, V185, P1501, DOI 10.1016/j.cpc.2014.01.019
Zaletel MP, 2015, PHYS REV B, V91, DOI 10.1103/PhysRevB.91.165112
Zwolak M, 2004, PHYS REV LETT, V93, DOI 10.1103/PhysRevLett.93.207205
NR 79
TC 23
Z9 23
U1 2
U2 13
PU ELSEVIER SCIENCE BV
PI AMSTERDAM
PA PO BOX 211, 1000 AE AMSTERDAM, NETHERLANDS
SN 0010-4655
EI 1879-2944
J9 COMPUT PHYS COMMUN
JI Comput. Phys. Commun.
PD APR
PY 2018
VL 225
BP 59
EP 91
DI 10.1016/j.cpc.2017.12.015
PG 33
WC Computer Science, Interdisciplinary Applications; Physics, Mathematical
SC Computer Science; Physics
GA GA0MC
UT WOS:000428006400006
DA 2021-04-21
ER
PT J
AU Zhu, GL
Lu, XY
Wan, LL
Yin, TS
Bin, Q
Wu, Y
AF Zhu, Gui-Lei
Lu, Xin-You
Wan, Liang-Liang
Yin, Tai-Shuang
Bin, Qian
Wu, Ying
TI Controllable nonlinearity in a dual-coupling optomechanical system under
a weak-coupling regime
SO PHYSICAL REVIEW A
LA English
DT Article
ID PYTHON FRAMEWORK; SINGLE PHOTONS; QUANTUM; CAVITY; LIGHT; DYNAMICS;
MOTION; STATE; QUTIP
AB Strong quantum nonlinearity gives rise to many interesting quantum effects and has wide applications in quantum physics. Herewe investigate the quantum nonlinear effect of an optomechanical system (OMS) consisting of both linear and quadratic coupling. Interestingly, a controllable optomechanical nonlinearity is obtained by applying a driving laser into the cavity. This controllable optomechanical nonlinearity can be enhanced into a strong coupling regime, even if the system is initially in the weak-coupling regime. Moreover, the system dissipation can be suppressed effectively, which allows the appearance of phonon sideband and photon blockade effects in the weak-coupling regime. This work may inspire the exploration of a dual-coupling optomechanical system as well as its applications in modern quantum science.
C1 [Zhu, Gui-Lei; Lu, Xin-You; Wan, Liang-Liang; Yin, Tai-Shuang; Bin, Qian; Wu, Ying] Huazhong Univ Sci & Technol, Sch Phys, Wuhan 430074, Hubei, Peoples R China.
RP Lu, XY; Wu, Y (corresponding author), Huazhong Univ Sci & Technol, Sch Phys, Wuhan 430074, Hubei, Peoples R China.
EM xinyoulu@hust.edu.cn; yingwu2@126.com
RI Wu, Ying/M-7844-2019
OI Bin, Qian/0000-0001-6823-2850; Zhu, Guilei/0000-0002-7731-2665
FU National Key Research and Development Program of China [2016YFA0301203];
National Science Foundation of ChinaNational Natural Science Foundation
of China (NSFC) [11374116, 11574104, 11375067]
FX This work is supported by the National Key Research and Development
Program of China Grant No. 2016YFA0301203, and the National Science
Foundation of China (Grants No. 11374116, No. 11574104, and No.
11375067).
CR Aspelmeyer M, 2014, REV MOD PHYS, V86, P1391, DOI 10.1103/RevModPhys.86.1391
Aspelmeyer M, 2012, PHYS TODAY, V65, P29, DOI 10.1063/PT.3.1640
Bhattacharya M, 2008, PHYS REV A, V77, DOI 10.1103/PhysRevA.77.033819
Bhattacharya M, 2007, PHYS REV LETT, V99, DOI 10.1103/PhysRevLett.99.073601
Bose S, 1997, PHYS REV A, V56, P4175, DOI 10.1103/PhysRevA.56.4175
Brooks DWC, 2012, NATURE, V488, P476, DOI 10.1038/nature11325
Buchmann LF, 2015, PHYS REV A, V92, DOI 10.1103/PhysRevA.92.013851
Chan J, 2012, APPL PHYS LETT, V101, DOI 10.1063/1.4747726
Chan J, 2011, NATURE, V478, P89, DOI 10.1038/nature10461
Chan J, 2009, OPT EXPRESS, V17, P3802, DOI 10.1364/OE.17.003802
Eichenfield M, 2009, NATURE, V462, P78, DOI 10.1038/nature08524
Fiore V, 2011, PHYS REV LETT, V107, DOI 10.1103/PhysRevLett.107.133601
Gan JH, 2016, OPT LETT, V41, P2676, DOI 10.1364/OL.41.002676
Groblacher S, 2009, NATURE, V460, P724, DOI 10.1038/nature08171
Heikkila TT, 2014, PHYS REV LETT, V112, DOI 10.1103/PhysRevLett.112.203603
Heinrich G, 2011, EPL-EUROPHYS LETT, V93, DOI 10.1209/0295-5075/93/18003
Hong FY, 2008, PHYS REV A, V78, DOI 10.1103/PhysRevA.78.013812
Johansson JR, 2014, PHYS REV A, V90, DOI 10.1103/PhysRevA.90.053833
Johansson JR, 2013, COMPUT PHYS COMMUN, V184, P1234, DOI 10.1016/j.cpc.2012.11.019
Johansson JR, 2012, COMPUT PHYS COMMUN, V183, P1760, DOI 10.1016/j.cpc.2012.02.021
Karuza M, 2013, PHYS REV A, V88, DOI 10.1103/PhysRevA.88.013804
Keller M, 2004, NATURE, V431, P1075, DOI 10.1038/nature02961
Kim EJ, 2015, PHYS REV A, V91, DOI 10.1103/PhysRevA.91.033835
Kippenberg TJ, 2008, SCIENCE, V321, P1172, DOI 10.1126/science.1156032
Kippenberg TJ, 2004, PHYS REV LETT, V93, DOI 10.1103/PhysRevLett.93.083904
Komar P, 2013, PHYS REV A, V87, DOI 10.1103/PhysRevA.87.013839
Krause AG, 2015, PHYS REV LETT, V115, DOI 10.1103/PhysRevLett.115.233601
Kronwald A, 2013, PHYS REV LETT, V111, DOI 10.1103/PhysRevLett.111.133601
Lemonde MA, 2016, NAT COMMUN, V7, DOI 10.1038/ncomms11338
Lemonde MA, 2015, PHYS REV A, V91, DOI 10.1103/PhysRevA.91.033836
Liao JQ, 2013, PHYS REV A, V88, DOI 10.1103/PhysRevA.88.023853
Lounis B, 2000, NATURE, V407, P491, DOI 10.1038/35035032
Lu XY, 2018, PHYS REV A, V97, DOI 10.1103/PhysRevA.97.033807
Lu XY, 2015, PHYS REV LETT, V114, DOI 10.1103/PhysRevLett.114.093602
Lu XY, 2013, SCI REP-UK, V3, DOI 10.1038/srep02943
Ludwig M, 2013, PHYS REV LETT, V111, DOI 10.1103/PhysRevLett.111.073603
Ludwig M, 2012, PHYS REV LETT, V109, DOI 10.1103/PhysRevLett.109.063601
Lu XY, 2015, PHYS REV A, V91, DOI 10.1103/PhysRevA.91.013834
Lund AP, 2004, PHYS REV A, V70, DOI 10.1103/PhysRevA.70.020101
Marquardt F., 2009, PHYSICS, V2, P40, DOI DOI 10.1103/PHYSICS.2.40
Marquardt F, 2007, PHYS REV LETT, V99, DOI 10.1103/PhysRevLett.99.093902
Massel F, 2012, NAT COMMUN, V3, DOI 10.1038/ncomms1993
Meystre P, 2013, ANN PHYS-BERLIN, V525, P215, DOI 10.1002/andp.201200226
Murch KW, 2008, NAT PHYS, V4, P561, DOI 10.1038/nphys965
Nunnenkamp A, 2011, PHYS REV LETT, V107, DOI 10.1103/PhysRevLett.107.063602
O'Connell AD, 2010, NATURE, V464, P697, DOI 10.1038/nature08967
PACE AF, 1993, PHYS REV A, V47, P3173, DOI 10.1103/PhysRevA.47.3173
Palomaki TA, 2013, SCIENCE, V342, P710, DOI 10.1126/science.1244563
Palomaki TA, 2013, NATURE, V495, P210, DOI 10.1038/nature11915
Pepper B, 2012, PHYS REV LETT, V109, DOI 10.1103/PhysRevLett.109.023601
Purdy TP, 2013, PHYS REV X, V3, DOI 10.1103/PhysRevX.3.031012
Rabl P, 2011, PHYS REV LETT, V107, DOI 10.1103/PhysRevLett.107.063601
Rimberg AJ, 2014, NEW J PHYS, V16, DOI 10.1088/1367-2630/16/5/055008
Safavi-Naeini AH, 2011, NATURE, V472, P69, DOI 10.1038/nature09933
Safavi-Naeini AH, 2013, NATURE, V500, P185, DOI 10.1038/nature12307
Safavi-Naeini AH, 2010, APPL PHYS LETT, V97, DOI 10.1063/1.3507288
Sankey JC, 2010, NAT PHYS, V6, P707, DOI 10.1038/NPHYS1707
Seok H, 2013, PHYS REV A, V88, DOI 10.1103/PhysRevA.88.063850
Si LG, 2017, PHYS REV A, V95, DOI 10.1103/PhysRevA.95.033803
Stannigel K, 2012, PHYS REV LETT, V109, DOI 10.1103/PhysRevLett.109.013603
Teufel JD, 2011, NATURE, V475, P359, DOI 10.1038/nature10261
Thompson JD, 2008, NATURE, V452, P72, DOI 10.1038/nature06715
Vahala KJ, 2003, NATURE, V424, P839, DOI 10.1038/nature01939
Vanner MR, 2011, PHYS REV X, V1, DOI 10.1103/PhysRevX.1.021011
Vitali D, 2007, PHYS REV LETT, V98, DOI 10.1103/PhysRevLett.98.030405
Wan LL, 2017, OPT EXPRESS, V25, P17364, DOI 10.1364/OE.25.017364
Weis S, 2010, SCIENCE, V330, P1520, DOI 10.1126/science.1195596
Wilson-Rae I, 2007, PHYS REV LETT, V99, DOI 10.1103/PhysRevLett.99.093901
Xiang ZL, 2013, REV MOD PHYS, V85, P623, DOI 10.1103/RevModPhys.85.623
Xiong H, 2015, SCI CHINA PHYS MECH, V58, DOI 10.1007/s11433-015-5648-9
Xuereb A, 2013, PHYS REV A, V87, DOI 10.1103/PhysRevA.87.023830
Yin TS, 2017, PHYS REV A, V95, DOI 10.1103/PhysRevA.95.053861
You JQ, 2011, NATURE, V474, P589, DOI 10.1038/nature10122
Zhou X, 2013, NAT PHYS, V9, P179, DOI 10.1038/nphys2527
NR 74
TC 20
Z9 22
U1 1
U2 42
PU AMER PHYSICAL SOC
PI COLLEGE PK
PA ONE PHYSICS ELLIPSE, COLLEGE PK, MD 20740-3844 USA
SN 2469-9926
EI 2469-9934
J9 PHYS REV A
JI Phys. Rev. A
PD MAR 16
PY 2018
VL 97
IS 3
AR 033830
DI 10.1103/PhysRevA.97.033830
PG 9
WC Optics; Physics, Atomic, Molecular & Chemical
SC Optics; Physics
GA FZ5KZ
UT WOS:000427632500019
DA 2021-04-21
ER
PT J
AU Saunders, WR
Grant, J
Muller, EH
AF Saunders, William Robert
Grant, James
Mueller, Erike Hermann
TI A domain specific language for performance portable molecular dynamics
algorithms
SO COMPUTER PHYSICS COMMUNICATIONS
LA English
DT Article
DE Molecular dynamics; Domain specific language; Performance portability;
Parallel computing; GPU
ID PARTICLE-MESH; PARALLEL; SIMULATIONS; ARCHITECTURES; COMPUTERS; GROMACS;
NAMD
AB Developers of Molecular Dynamics (MD) codes face significant challenges when adapting existing simulation packages to new hardware. In a continuously diversifying hardware landscape it becomes increasingly difficult for scientists to be experts both in their own domain (physics/chemistry/biology) and specialists in the low level parallelisation and optimisation of their codes. To address this challenge, we describe a "Separation of Concerns" approach for the development of parallel and optimised MD codes: the science specialist writes code at a high abstraction level in a domain specific language (DSL), which is then translated into efficient computer code by a scientific programmer. In a related context, an abstraction for the solution of partial differential equations with grid based methods has recently been implemented in the (Py)OP2 library. Inspired by this approach, we develop a Python code generation system for molecular dynamics simulations on different parallel architectures, including massively parallel distributed memory systems and GPUs. We demonstrate the efficiency of the auto-generated code by studying its performance and scalability on different hardware and compare it to other state-of-theart simulation packages. With growing data volumes the extraction of physically meaningful information from the simulation becomes increasingly challenging and requires equally efficient implementations. A particular advantage of our approach is the easy expression of such analysis algorithms. We consider two popular methods for deducing the crystalline structure of a material from the local environment of each atom, show how they can be expressed in our abstraction and implement them in the code generation framework. (C) 2017 Elsevier B.V. All rights reserved.
C1 [Saunders, William Robert; Mueller, Erike Hermann] Univ Bath, Dept Math Sci, Bath BA2 7AY, Avon, England.
[Grant, James] Univ Bath, Dept Chem, Bath BA2 7AY, Avon, England.
RP Muller, EH (corresponding author), Univ Bath, Dept Math Sci, Bath BA2 7AY, Avon, England.
EM w.r.saunders@bath.ac.uk; r.j.grant@bath.ac.uk; e.mueller@bath.ac.uk
OI Mueller, Eike/0000-0003-3006-3347; Grant, James/0000-0003-1362-2055
FU EPSRC studentshipUK Research & Innovation (UKRI)Engineering & Physical
Sciences Research Council (EPSRC); Engineering and Physical Sciences
Research CouncilUK Research & Innovation (UKRI)Engineering & Physical
Sciences Research Council (EPSRC) [1552071] Funding Source: researchfish
FX We would like to thank Alexander Stukowski (Darmstadt) for useful
correspondence to clarify the exact definitions of the quantities in the
common neighbour analysis algorithm. The PhD project of William Saunders
is funded by an EPSRC studentship. This research made use of the Balena
High Performance Computing (HPC) service at the University of Bath.
CR Abraham Mark James, 2015, SoftwareX, V1-2, P19, DOI 10.1016/j.softx.2015.06.001
Allen M. P., 1989, COMPUTER SIMULATION
Alnaes MS, 2014, ACM T MATH SOFTWARE, V40, DOI 10.1145/2566630
Anderson E., 1999, SOC IND APPL MATH
Anderson JA, 2008, J COMPUT PHYS, V227, P5342, DOI 10.1016/j.jcp.2008.01.047
BERENDSEN HJC, 1995, COMPUT PHYS COMMUN, V91, P43, DOI 10.1016/0010-4655(95)00042-E
Bertolli C., 2012, REVISED SELECTED P 1, P191
Bonomi M, 2009, COMPUT PHYS COMMUN, V180, P1961, DOI 10.1016/j.cpc.2009.05.011
Brown WM, 2012, COMPUT PHYS COMMUN, V183, P449, DOI 10.1016/j.cpc.2011.10.012
Brown WM, 2011, COMPUT PHYS COMMUN, V182, P898, DOI 10.1016/j.cpc.2010.12.021
Bush IJ, 2006, COMPUT PHYS COMMUN, V175, P323, DOI 10.1016/j.cpc.2006.05.001
Cickovski T, 2007, SIM S 2007 ANSS 07 4, P256
Eastman P, 2013, J CHEM THEORY COMPUT, V9, P461, DOI 10.1021/ct300857j
Ewald PP, 1921, ANN PHYS-BERLIN, V64, P253
Frenkel D., 2002, UNDERSTANDING MOL SI
Giles MB, 2013, J PARALLEL DISTR COM, V73, P1451, DOI 10.1016/j.jpdc.2012.07.008
Giles M. B., 2011, Performance Evaluation Review, V38, P9, DOI 10.1145/1964218.1964221
Glaser J, 2015, COMPUT PHYS COMMUN, V192, P97, DOI 10.1016/j.cpc.2015.02.028
GREENGARD L, 1987, J COMPUT PHYS, V73, P325, DOI 10.1016/0021-9991(87)90140-9
Gysi T., 2014, EGU GEN ASS C COP, V16, P8464
HONEYCUTT JD, 1987, J PHYS CHEM-US, V91, P4950, DOI 10.1021/j100303a014
Horowitz C., 2011, ARXIV11095095
Hu Y, 2016, 2016 IEEE 10TH INTERNATIONAL SYMPOSIUM ON EMBEDDED MULTICORE/MANY-CORE SYSTEMS-ON-CHIP (MCSOC), P361, DOI 10.1109/MCSoC.2016.37
Jia ZD, 2007, ESAIM-MATH MODEL NUM, V41, P333, DOI 10.1051/m2an:2007019
KARPLUS M, 1990, NATURE, V347, P631, DOI 10.1038/347631a0
Lawson C. L., 1979, ACM Transactions on Mathematical Software, V5, P308, DOI 10.1145/355841.355847
Leimkuhler B, 2011, J STAT PHYS, V143, P921, DOI 10.1007/s10955-011-0210-2
Mangiardi CM, 2017, COMPUT PHYS COMMUN, V219, P196, DOI 10.1016/j.cpc.2017.05.020
Maruyama N., 2011, P 2011 INT C HIGH PE, P1
Mickel W, 2013, J CHEM PHYS, V138, DOI 10.1063/1.4774084
Nelson MT, 1996, INT J SUPERCOMPUT AP, V10, P251, DOI 10.1177/109434209601000401
Newton I., 1846, MATH PRINCIPLES NATU, VII
Pall S, 2013, COMPUT PHYS COMMUN, V184, P2641, DOI 10.1016/j.cpc.2013.06.003
Phillips JC, 2005, J COMPUT CHEM, V26, P1781, DOI 10.1002/jcc.20289
PLIMPTON S, 1995, J COMPUT PHYS, V117, P1, DOI 10.1006/jcph.1995.1039
Pronk S, 2013, BIOINFORMATICS, V29, P845, DOI 10.1093/bioinformatics/btt055
Radu M, 2017, PHYS REV LETT, V118, DOI 10.1103/PhysRevLett.118.055702
Rapaport DC, 2011, COMPUT PHYS COMMUN, V182, P926, DOI 10.1016/j.cpc.2010.12.029
Rapaport DC, 2004, PHYS REV E, V70, DOI 10.1103/PhysRevE.70.051905
RAPAPORT DC, 1988, COMPUT PHYS REP, V9, P1, DOI 10.1016/0167-7977(88)90014-7
Rapaport DC, 2004, ART MOL DYNAMICS SIM
Rathgeber F, 2012, IEEE COMPUTER SOC, P1116
Razali A, 2017, SOFT MATTER, V13, P3230, DOI 10.1039/c6sm02221a
Reguly IZ, 2016, CONCURR COMP-PRACT E, V28, P557, DOI 10.1002/cpe.3621
Saunders W. R., 2017, ARXIV170801135
Smith W, 1996, J MOL GRAPHICS, V14, P136, DOI 10.1016/S0263-7855(96)00043-4
STEINHARDT PJ, 1983, PHYS REV B, V28, P784, DOI 10.1103/PhysRevB.28.784
Stone JE, 2007, J COMPUT CHEM, V28, P2618, DOI 10.1002/jcc.20829
Stukowski A, 2012, MODEL SIMUL MATER SC, V20, DOI 10.1088/0965-0393/20/3/035012
Todorov IT, 2006, J MATER CHEM, V16, P1911, DOI 10.1039/b517931a
VERLET L, 1967, PHYS REV, V159, P98, DOI 10.1103/PhysRev.159.98
Williams NR, 2015, J NUCL MATER, V458, P45, DOI 10.1016/j.jnucmat.2014.11.120
NR 52
TC 3
Z9 3
U1 0
U2 8
PU ELSEVIER
PI AMSTERDAM
PA RADARWEG 29, 1043 NX AMSTERDAM, NETHERLANDS
SN 0010-4655
EI 1879-2944
J9 COMPUT PHYS COMMUN
JI Comput. Phys. Commun.
PD MAR
PY 2018
VL 224
BP 119
EP 135
DI 10.1016/j.cpc.2017.11.006
PG 17
WC Computer Science, Interdisciplinary Applications; Physics, Mathematical
SC Computer Science; Physics
GA FV6XZ
UT WOS:000424726700010
DA 2021-04-21
ER
PT J
AU Parsons, RA
Pimont, F
Wells, L
Cohn, G
Jolly, WM
de Coligny, F
Rigolot, E
Dupuy, JL
Mell, W
Linn, RR
AF Parsons, Russell A.
Pimont, Francois
Wells, Lucas
Cohn, Greg
Jolly, W. Matt
de Coligny, Francois
Rigolot, Eric
Dupuy, Jean-Luc
Mell, William
Linn, Rodman R.
TI Modeling thinning effects on fire behavior with STANDFIRE
SO ANNALS OF FOREST SCIENCE
LA English
DT Article
DE Fuel treatments; Fire behavior; Modeling; Physics-based; WFDS; FIRETEC;
FuelManager
ID FOREST VEGETATION SIMULATOR; NORTHERN ROCKY-MOUNTAINS; FUEL TREATMENT
LONGEVITY; MIXED-CONIFER FOREST; BEETLE OUTBREAK; CROWN FIRE;
WIND-FLOWS; PROPAGATION; CANOPY; INCREASES
AB Key message We describe a modeling system that enables detailed, 3D fire simulations in forest fuels. Using data from three sites, we analyze thinning fuel treatments on fire behavior and fire effects and compare outputs with a more commonly used model.
Context Thinning is considered useful in altering fire behavior, reducing fire severity, and restoring resilient ecosystems. Yet, few tools currently exist that enable detailed analysis of such efforts.
Aims The study aims to describe and demonstrate a new modeling system. A second goal is to put its capabilities in context of previous work through comparisons with established models.
Methods The modeling system, built in Python and Java, uses data from a widely used forest model to develop spatially explicit fuel inputs to two 3D physics-based fire models. Using forest data from three sites in Montana, USA, we explore effects of thinning on fire behavior and fire effects and compare model outputs.
Results The study demonstrates new capabilities in assessing fire behavior and fire effects changes from thinning. While both models showed some increases in fire behavior relating to higher winds within the stand following thinning, results were quite different in terms of tree mortality. These different outcomes illustrate the need for continuing refinement of decision support tools for forest management.
Conclusion This system enables researchers and managers to use measured forest fuel data in dynamic, 3D fire simulations, improving capabilities for quantitative assessment of fuel treatments, and facilitating further refinement in physics-based fire modeling.
C1 [Parsons, Russell A.; Jolly, W. Matt] US Forest Serv, Rocky Mt Res Stn, Fire Sci Lab, 5775 W Highway 10, Missoula, MT 59801 USA.
[Pimont, Francois; Rigolot, Eric; Dupuy, Jean-Luc] INRA, UR Ecol Forets Mediterraneennes 629, Site Agroparc, F-84914 Avignon 9, France.
[Wells, Lucas] Oregon State Univ, Coll Forestry, Dept Forest Engn Resources & Management, 210 Snell Hall, Corvallis, OR 97331 USA.
[Cohn, Greg] Oregon State Univ, Coll Forestry, Dept Forest Ecosyst & Soc, 321 Richardson Hall, Corvallis, OR 97331 USA.
[de Coligny, Francois] INRA, UMR AMAP Bot & BioinforMat Architecture Plantes, Bd Lironde TA A-51-PS2, F-34398 Montpellier 5, France.
[Mell, William] US Forest Serv, Pacific Wildland Fire Sci Lab, 400 N 34th St,Suite 201, Seattle, WA 98103 USA.
[Linn, Rodman R.] Los Alamos Natl Lab, Environm Sci Div, Mail Stop D401, Los Alamos, NM 87544 USA.
RP Parsons, RA (corresponding author), US Forest Serv, Rocky Mt Res Stn, Fire Sci Lab, 5775 W Highway 10, Missoula, MT 59801 USA.
EM rparsons@fs.fed.us
FU Joint Fire Science Program of the US Department of Agriculture (USDA);
US Department of the Interior (USDI) [12-1-03-30]; USDA Forest Service
Research [13-IA-11221633-103]; Los Alamos National LaboratoryUnited
States Department of Energy (DOE)Los Alamos National Laboratory
FX This work was made possible by funding from the Joint Fire Science
Program of the US Department of Agriculture (USDA) and US Department of
the Interior (USDI), Project No. 12-1-03-30 (STANDFIRE), as well as from
USDA Forest Service Research (both Rocky Mountain Research Station and
Washington office) National Fire Plan Dollars, through Interagency
Agreements 13-IA-11221633-103 with Los Alamos National Laboratory.
CR Adams HD, 2009, P NATL ACAD SCI USA, V106, P7063, DOI 10.1073/pnas.0901438106
Almeida M, 2017, FIRE TECHNOL, V53, P553, DOI 10.1007/s10694-016-0591-5
Anderson H. E, 1982, INT122 USDA FOR SERV
Cary GJ, 2017, LANDSCAPE ECOL, V32, P1473, DOI 10.1007/s10980-016-0420-8
Clyatt KA, 2017, FOREST ECOL MANAG, V400, P587, DOI 10.1016/j.foreco.2017.06.021
COVINGTON WW, 1994, J FOREST, V92, P39
Crookston NL, 2005, COMPUT ELECTRON AGR, V49, P60, DOI 10.1016/j.compag.2005.02.003
Crotteau JS, 2016, INT J WILDLAND FIRE, V25, P633, DOI 10.1071/WF14223
Cruz MG, 2017, INT J WILDLAND FIRE, V26, P413, DOI 10.1071/WF16218
Cruz MG, 2010, INT J WILDLAND FIRE, V19, P377, DOI 10.1071/WF08132
Dufour-Kowalski S, 2012, ANN FOREST SCI, V69, P221, DOI 10.1007/s13595-011-0140-9
Fernandes PM, 2009, CAN J FOREST RES, V39, P2529, DOI 10.1139/X09-145
Forney GP, 2004, USERS GUIDE SMOKEVIE
Hoffman CM, 2015, AGR FOREST METEOROL, V204, P79, DOI 10.1016/j.agrformet.2015.01.018
Hood SM, 2007, INT J WILDLAND FIRE, V16, P679, DOI 10.1071/WF06122
Hood SM, 2016, ECOL APPL, V26, P1984, DOI 10.1002/eap.1363
Jimenez E, 2016, EUR J FOREST RES, V135, P675, DOI 10.1007/s10342-016-0963-x
Johnson MC, 2011, CAN J FOREST RES, V41, P1018, DOI [10.1139/X11-032, 10.1139/x11-032]
Jones KW, 2017, J ENVIRON MANAGE, V198, P66, DOI 10.1016/j.jenvman.2017.05.023
Kalies EL, 2016, FOREST ECOL MANAG, V375, P84, DOI 10.1016/j.foreco.2016.05.021
Keane RE, 2012, LANDSCAPE ECOL, V27, P1213, DOI 10.1007/s10980-012-9773-9
Larson AJ, 2012, FOREST ECOL MANAG, V267, P74, DOI 10.1016/j.foreco.2011.11.038
Linn R, 2005, INT J WILDLAND FIRE, V14, P37, DOI 10.1071/WF04043
Linn RR, 2013, AGR FOREST METEOROL, V173, P139, DOI 10.1016/j.agrformet.2012.11.007
Linn RR, 1997, THESIS LOS ALAMOS NA
Loudermilk EL, 2012, INT J WILDLAND FIRE, V21, P882, DOI 10.1071/WF10116
McGaughey R.J., 2004, STAND VISUALIZATION
Mell W, 2009, COMBUST FLAME, V156, P2023, DOI 10.1016/j.combustflame.2009.06.015
Moghaddas JJ, 2007, INT J WILDLAND FIRE, V16, P673, DOI 10.1071/WF06066
Noonan-Wright EK, 2014, FOREST SCI, V60, P231, DOI 10.5849/forsci.12-062
North MP, 2011, FOREST ECOL MANAG, V261, P1115, DOI 10.1016/j.foreco.2010.12.039
Omi PN, 2010, EFFECTIVENESS FUEL T
Parsons RA, 2017, LAND-BASEL, V6, DOI 10.3390/land6020043
Parsons RA, 2011, ECOL MODEL, V222, P679, DOI 10.1016/j.ecolmodel.2010.10.023
Pimont F, 2016, ENVIRON MODELL SOFTW, V80, P225, DOI 10.1016/j.envsoft.2016.03.003
Pimont F, 2015, REMOTE SENS-BASEL, V7, P7995, DOI 10.3390/rs70607995
Pimont F, 2011, ANN FOREST SCI, V68, P523, DOI 10.1007/s13595-011-0061-7
Pimont F, 2009, INT J WILDLAND FIRE, V18, P775, DOI 10.1071/WF07130
Rebain S A, 2015, FIRE FUELS EXTENSION, P403
Reinhardt E.D., 2003, RMRSGTR116 USDA FOR, P209
Reinhardt ED, 1997, INTGTR344 USDA FOR S, P65
Rothermel R.C., 1991, INT438 USDA FOR SERV, P46
Rothermel R.C, 1972, INT115 USDA FOR SERV, P40
RYAN KC, 1988, CAN J FOREST RES, V18, P1291, DOI 10.1139/x88-199
Scott J., 2005, RMRSGTR153 USDA FOR
Scott J.H., 2001, RMRSRP29 USDA FOR SE, P59
Stephens SL, 2012, FOREST ECOL MANAG, V285, P204, DOI 10.1016/j.foreco.2012.08.030
Van Wagner C.E., 1977, CAN J FOREST RES, V7, P23
Ziegler JP, 2017, FOREST ECOL MANAG, V386, P1, DOI 10.1016/j.foreco.2016.12.002
Ziska LH, 2005, GLOBAL CHANGE BIOL, V11, P1325, DOI 10.1111/j.1365-2486.2005.00992.x
NR 50
TC 8
Z9 8
U1 1
U2 12
PU SPRINGER FRANCE
PI PARIS
PA 22 RUE DE PALESTRO, PARIS, 75002, FRANCE
SN 1286-4560
EI 1297-966X
J9 ANN FOREST SCI
JI Ann. For. Sci.
PD MAR
PY 2018
VL 75
IS 1
AR 7
DI 10.1007/s13595-017-0686-2
PG 10
WC Forestry
SC Forestry
GA GC0QO
UT WOS:000429482800009
OA Green Published, Bronze
DA 2021-04-21
ER
PT J
AU Axani, SN
Frankiewicz, K
Conrad, JM
AF Axani, S. N.
Frankiewicz, K.
Conrad, J. M.
TI The CosmicWatch Desktop Muon Detector: a self-contained, pocket sized
particle detector
SO JOURNAL OF INSTRUMENTATION
LA English
DT Article
DE Scintillators, scintillation and light emission processes (solid, gas
and liquid scintillators); Solid state detectors; Data acquisition
circuits; Particle detectors
AB The CosmicWatch Desktop Muon Detector is a self-contained, hand-held cosmic ray muon detector that is valuable for astro/particle physics research applications and outreach. The material cost of each detector is under 100$ and it takes a novice student approximately four hours to build their first detector. The detectors are powered via a USB connection and the data can either be recorded directly to a computer or to a microSD card. Arduino- and Python-based software is provided to operate the detector and an online application to plot the data in real-time. In this paper, we describe the various design features, evaluate the performance, and illustrate the detectors capabilities by providing several example measurements.
C1 [Axani, S. N.; Conrad, J. M.] MIT, Dept Phys, 77 Massachusetts Av, Cambridge, MA 02139 USA.
[Frankiewicz, K.] Natl Ctr Nucl Res, Hoza 69, PL-00681 Warsaw, Poland.
RP Axani, SN (corresponding author), MIT, Dept Phys, 77 Massachusetts Av, Cambridge, MA 02139 USA.
EM saxani@mit.edu
OI Conrad, Janet/0000-0002-6393-0438
FU MIT seed funding [NSF-PHY-1505858]; National Science Centre,
PolandNational Science Centre, Poland [2015/17/N/ST2/04064]; Direct For
Mathematical & Physical ScienNational Science Foundation (NSF)NSF -
Directorate for Mathematical & Physical Sciences (MPS) [1505858] Funding
Source: National Science Foundation
FX This work was supported by grant NSF-PHY-1505858 (PI: Prof. Janet
Conrad), MIT seed funding, and funds from the National Science Centre,
Poland (2015/17/N/ST2/04064). The authors would like to thank SensL and
Fermilab, for donations that made the development of this project
possible, as well as P. Fisher at MIT and IceCube collaborators at
WIPAC, for their support in developing this as a high school and
undergraduate project.
CR Aarsten M.G., ARXIV14125106
Aguilar-Arevalo AA, 2009, NUCL INSTRUM METH A, V599, P28, DOI 10.1016/j.nima.2008.10.028
Axani SN, 2017, AM J PHYS, V85, P948, DOI 10.1119/1.5003806
Axani S.N., COSMICWATCH REPOSITO
Cowen DF, 2003, NUCL PHYS B-PROC SUP, V118, P371, DOI 10.1016/S0920-5632(03)01335-5
Fukuda S, 2003, NUCL INSTRUM METH A, V501, P418, DOI 10.1016/S0168-9002(03)00425-X
Olive KA, 2014, CHINESE PHYS C, V38, DOI 10.1088/1674-1137/38/9/090001
NR 7
TC 4
Z9 4
U1 0
U2 3
PU IOP PUBLISHING LTD
PI BRISTOL
PA TEMPLE CIRCUS, TEMPLE WAY, BRISTOL BS1 6BE, ENGLAND
SN 1748-0221
J9 J INSTRUM
JI J. Instrum.
PD MAR
PY 2018
VL 13
AR P03019
DI 10.1088/1748-0221/13/03/P03019
PG 11
WC Instruments & Instrumentation
SC Instruments & Instrumentation
GA GA3LM
UT WOS:000428230500002
DA 2021-04-21
ER
PT J
AU Yao, K
Herr, JE
Toth, DW
Mckintyre, R
Parkhill, J
AF Yao, Kun
Herr, John E.
Toth, David W.
Mckintyre, Ryker
Parkhill, John
TI The TensorMol-0.1 model chemistry: a neural network augmented with
long-range physics
SO CHEMICAL SCIENCE
LA English
DT Article
ID POTENTIAL-ENERGY SURFACES; FORCE-FIELD; MANY-BODY; ORGANIC
PHOTOVOLTAICS; DENSITY
AB Traditional force fields cannot model chemical reactivity, and suffer from low generality without re-fitting. Neural network potentials promise to address these problems, offering energies and forces with near ab initio accuracy at low cost. However a data-driven approach is naturally inefficient for long-range interatomic forces that have simple physical formulas. In this manuscript we construct a hybrid model chemistry consisting of a nearsighted neural network potential with screened long-range electrostatic and van der Waals physics. This trained potential, simply dubbed "TensorMol-0.1", is offered in an open-source Python package capable of many of the simulation types commonly used to study chemistry: geometry optimizations, harmonic spectra, open or periodic molecular dynamics, Monte Carlo, and nudged elastic band calculations. We describe the robustness and speed of the package, demonstrating its millihartree accuracy and scalability to tens-of-thousands of atoms on ordinary laptops. We demonstrate the performance of the model by reproducing vibrational spectra, and simulating the molecular dynamics of a protein. Our comparisons with electronic structure theory and experimental data demonstrate that neural network molecular dynamics is poised to become an important tool for molecular simulation, lowering the resource barrier to simulating chemistry.
C1 [Yao, Kun; Herr, John E.; Toth, David W.; Mckintyre, Ryker; Parkhill, John] Univ Notre Dame Lac, Dept Chem & Biochem, Notre Dame, IN 46556 USA.
RP Parkhill, J (corresponding author), Univ Notre Dame Lac, Dept Chem & Biochem, Notre Dame, IN 46556 USA.
EM john.parkhill@gmail.com
RI Yao, Kun/AAW-5046-2020
OI Yao, Kun/0000-0003-2032-7441; Toth, David/0000-0003-1396-6165
CR Artrith N, 2011, PHYS REV B, V83, DOI 10.1103/PhysRevB.83.153101
Barducci A, 2008, PHYS REV LETT, V100, DOI 10.1103/PhysRevLett.100.020603
Bartok AP, 2010, PHYS REV LETT, V104, DOI 10.1103/PhysRevLett.104.136403
Behler J, 2007, PHYS REV LETT, V98, DOI 10.1103/PhysRevLett.98.146401
Behler J, 2017, ANGEW CHEM INT EDIT, V56, P12828, DOI 10.1002/anie.201703114
Behler J, 2011, PHYS CHEM CHEM PHYS, V13, P17930, DOI 10.1039/c1cp21668f
Bereau T., 2017, ARXIV171005871
Brockherde F, 2017, NAT COMMUN, V8, DOI 10.1038/s41467-017-00839-3
Carpenter BK, 2018, J PHYS CHEM B, V122, P3230, DOI 10.1021/acs.jpcb.7b08707
Ceriotti M, 2014, COMPUT PHYS COMMUN, V185, P1019, DOI 10.1016/j.cpc.2013.10.027
Chai JD, 2008, PHYS CHEM CHEM PHYS, V10, P6615, DOI 10.1039/b810189b
Chmiela S, 2017, SCI ADV, V3, DOI 10.1126/sciadv.1603015
Clevert D.-A., 2015, ARXIV151107289
Conte R, 2015, J CHEM THEORY COMPUT, V11, P1631, DOI 10.1021/acs.jctc.5b00091
Cubuk ED, 2017, J CHEM PHYS, V147, DOI 10.1063/1.4990503
Deringer VL, 2017, PHYS REV B, V95, DOI 10.1103/PhysRevB.95.094203
Eastman P, 2017, PLOS COMPUT BIOL, V13, DOI 10.1371/journal.pcbi.1005659
Faber FA, 2017, J CHEM THEORY COMPUT, V13, P5255, DOI 10.1021/acs.jctc.7b00577
Fennell CJ, 2006, J CHEM PHYS, V124, DOI 10.1063/1.2206581
Fracchia F, 2018, J CHEM THEORY COMPUT, V14, P255, DOI 10.1021/acs.jctc.7b00779
Gastegger M, 2017, CHEM SCI, V8, P6924, DOI 10.1039/c7sc02267k
Ghiringhelli LM, 2017, NEW J PHYS, V19, DOI 10.1088/1367-2630/aa57bf
Gomez-Bombarelli R, 2016, ARXIV161002415
Grimme S, 2006, J COMPUT CHEM, V27, P1787, DOI 10.1002/jcc.20495
Grisa A., 2017, ARXIV170906757
Guimaraes GL, 2017, ARXIV170510843
Hachmann J, 2014, ENERG ENVIRON SCI, V7, P698, DOI 10.1039/c3ee42756k
Hachmann J, 2011, J PHYS CHEM LETT, V2, P2241, DOI 10.1021/jz200866s
Halgren TA, 1996, J COMPUT CHEM, V17, P553
Han JQ, 2018, COMMUN COMPUT PHYS, V23, P629, DOI 10.4208/cicp.OA-2017-0213
Handley CM, 2010, J PHYS CHEM A, V114, P3371, DOI 10.1021/jp9105585
Hansen K, 2015, J PHYS CHEM LETT, V6, P2326, DOI 10.1021/acs.jpclett.5b00831
Hase F, 2017, CHEM SCI, V8, P8419, DOI 10.1039/c7sc03542j
Henkelman G, 2000, J CHEM PHYS, V113, P9901, DOI 10.1063/1.1329672
Herr J. E., 2017, ARXIV171207240
Isayev O, 2017, NAT COMMUN, V8, DOI 10.1038/ncomms15679
Isayev O, 2015, CHEM MATER, V27, P735, DOI 10.1021/cm503507h
Janet JP, 2017, J PHYS CHEM A, V121, P8939, DOI 10.1021/acs.jpca.7b08750
Janet JP, 2017, CHEM SCI, V8, P5137, DOI 10.1039/c7sc01247k
Jinnouchi R, 2017, J PHYS CHEM LETT, V8, P4279, DOI 10.1021/acs.jpclett.7b02010
John ST, 2017, J PHYS CHEM B, V121, P10934, DOI 10.1021/acs.jpcb.7b09636
Khaliullin RZ, 2011, NAT MATER, V10, P693, DOI [10.1038/NMAT3078, 10.1038/nmat3078]
Khorshidi A, 2016, COMPUT PHYS COMMUN, V207, P310, DOI 10.1016/j.cpc.2016.05.010
Kim E, 2017, SCI DATA, V4, DOI 10.1038/sdata.2017.127
Kingma D.P., 2014, ARXIV 14126980, DOI DOI 10.1145/1830483.1830503
Kobayashi R, 2017, PHYS REV MATER, V1, DOI 10.1103/PhysRevMaterials.1.053604
Kolb B, 2017, SCI REP-UK, V7, DOI 10.1038/s41598-017-01251-z
Kruglov I, 2017, SCI REP-UK, V7, DOI 10.1038/s41598-017-08455-3
Li J., 2017, ARXIV170903741
Li J, 2015, J CHEM PHYS, V142, DOI 10.1063/1.4921412
Li L, 2016, PHYS REV B, V94, DOI 10.1103/PhysRevB.94.245129
Li L, 2016, INT J QUANTUM CHEM, V116, P819, DOI 10.1002/qua.25040
Li Y, 2017, J CHEM THEORY COMPUT, V13, P4492, DOI 10.1021/acs.jctc.7b00521
Lopez-Bezanilla A, 2014, PHYS REV B, V89, DOI 10.1103/PhysRevB.89.235411
Lubbers N., 2017, ARXIV171000017
Ma XF, 2015, J PHYS CHEM LETT, V6, P3528, DOI 10.1021/acs.jpclett.5b01660
Malshe M, 2010, J CHEM PHYS, V132, DOI 10.1063/1.3431624
Manzhos S, 2015, INT J QUANTUM CHEM, V115, P1012, DOI 10.1002/qua.24795
Manzhos S, 2009, COMPUT PHYS COMMUN, V180, P2002, DOI 10.1016/j.cpc.2009.05.022
Martin Abadi, 2015, TENSORFLOW LARGE SCA
McGibbon RT, 2017, J CHEM PHYS, V147, DOI 10.1063/1.4986081
Medders GR, 2015, J CHEM PHYS, V143, DOI 10.1063/1.4930194
Medders GR, 2013, J CHEM THEORY COMPUT, V9, P1103, DOI 10.1021/ct300913g
Mills K, 2017, PHYS REV A, V96, DOI 10.1103/PhysRevA.96.042113
Moberg DR, 2017, J PHYS CHEM LETT, V8, P2579, DOI 10.1021/acs.jpclett.7b01106
Mones L, 2016, J CHEM THEORY COMPUT, V12, P5100, DOI 10.1021/acs.jctc.6b00553
Morawietz T, 2013, J PHYS CHEM A, V117, P7356, DOI 10.1021/jp401225b
Morawietz T, 2012, J CHEM PHYS, V136, DOI 10.1063/1.3682557
Olivares-Amaya R, 2011, ENERG ENVIRON SCI, V4, P4849, DOI 10.1039/c1ee02056k
Ouyang R., 2017, ARXIV171003319
Peterson AA, 2016, J CHEM PHYS, V145, DOI 10.1063/1.4960708
Pilania G, 2013, SCI REP-UK, V3, DOI 10.1038/srep02810
Piquemal JP, 2017, J CHEM PHYS, V147, DOI 10.1063/1.5008887
Ramsundar B, 2017, J CHEM INF MODEL, V57, P2068, DOI 10.1021/acs.jcim.7b00146
Reddy SK, 2016, J CHEM PHYS, V145, DOI 10.1063/1.4967719
Riera M, 2017, J CHEM PHYS, V147, DOI 10.1063/1.4993213
Schutt KT, 2014, PHYS REV B, V89, DOI 10.1103/PhysRevB.89.205118
Schutt KT, 2017, NAT COMMUN, V8, DOI 10.1038/ncomms13890
Segler M., 2017, ARXIV170200020
Shakouri K, 2017, J PHYS CHEM LETT, V8, P2131, DOI 10.1021/acs.jpclett.7b00784
Shao KJ, 2016, J CHEM PHYS, V145, DOI 10.1063/1.4961454
Shao YH, 2015, MOL PHYS, V113, P184, DOI 10.1080/00268976.2014.952696
Smith JS, 2017, CHEM SCI, V8, P3192, DOI 10.1039/c6sc05720a
Smith JS, 2017, SCI DATA, V4, DOI 10.1038/sdata.2017.193
Snyder JC, 2013, J CHEM PHYS, V139, DOI 10.1063/1.4834075
Snyder JC, 2012, PHYS REV LETT, V108, DOI 10.1103/PhysRevLett.108.253002
Srivastava N, 2014, J MACH LEARN RES, V15, P1929
Sun YT, 2017, J PHYS CHEM LETT, V8, P3434, DOI 10.1021/acs.jpclett.7b01046
THOLE BT, 1981, CHEM PHYS, V59, P341, DOI 10.1016/0301-0104(81)85176-2
Timoshenko J, 2017, J PHYS CHEM LETT, V8, P5091, DOI 10.1021/acs.jpclett.7b02364
Tkatchenko A, 2012, PHYS REV LETT, V108, DOI [10.1103/PhysRevLett.108.058301, 10.1103/PhysRevLett.108.236402]
Ulissi ZW, 2017, ACS CATAL, V7, P6600, DOI 10.1021/acscatal.7b01648
Vu K, 2015, INT J QUANTUM CHEM, V115, P1115, DOI 10.1002/qua.24939
Wei JN, 2016, ACS CENTRAL SCI, V2, P725, DOI 10.1021/acscentsci.6b00219
Wu JH, 2017, J CHEM PHYS, V147, DOI 10.1063/1.5006882
Yao K, 2017, J PHYS CHEM LETT, V8, P2689, DOI 10.1021/acs.jpclett.7b01072
Yao K, 2017, J CHEM PHYS, V146, DOI 10.1063/1.4973380
Yao K, 2016, J CHEM THEORY COMPUT, V12, P1139, DOI 10.1021/acs.jctc.5b01011
Zhang ZJ, 2014, J CHEM PHYS, V141, DOI 10.1063/1.4897308
Zhou XX, 2017, ANAL CHEM, V89, P12690, DOI 10.1021/acs.analchem.7b02566
NR 100
TC 129
Z9 129
U1 9
U2 73
PU ROYAL SOC CHEMISTRY
PI CAMBRIDGE
PA THOMAS GRAHAM HOUSE, SCIENCE PARK, MILTON RD, CAMBRIDGE CB4 0WF, CAMBS,
ENGLAND
SN 2041-6520
EI 2041-6539
J9 CHEM SCI
JI Chem. Sci.
PD FEB 28
PY 2018
VL 9
IS 8
BP 2261
EP 2269
DI 10.1039/c7sc04934j
PG 9
WC Chemistry, Multidisciplinary
SC Chemistry
GA FY8CV
UT WOS:000427091500023
PM 29719699
OA DOAJ Gold, Green Published
DA 2021-04-21
ER
PT J
AU Tauscher, K
Rapetti, D
Burns, JO
Switzer, E
AF Tauscher, Keith
Rapetti, David
Burns, Jack O.
Switzer, Eric
TI Global 21 cm Signal Extraction from Foreground and Instrumental Effects.
I. Pattern Recognition Framework for Separation Using Training Sets
SO ASTROPHYSICAL JOURNAL
LA English
DT Article
DE dark ages, reionization, first stars; methods: data analysis; methods:
statistical
ID 21-CM SIGNAL; INFORMATION CRITERION; HIGH-REDSHIFT; COSMIC DAWN;
REIONIZATION; MODEL; EPOCH; COSMOLOGY; UNIVERSE; LIMIT
AB The sky-averaged (global) highly redshifted 21 cm spectrum from neutral hydrogen is expected to appear in the VHF range of similar to 20-200 MHz and its spectral shape and strength are determined by the heating properties of the first stars and black holes, by the nature and duration of reionization, and by the presence or absence of exotic physics. Measurements of the global signal would therefore provide us with a wealth of astrophysical and cosmological knowledge. However, the signal has not yet been detected because it must be seen through strong foregrounds weighted by a large beam, instrumental calibration errors, and ionospheric, ground, and radio-frequency-interference effects, which we collectively refer to as "systematics." Here, we present a signal extraction method for global signal experiments which uses Singular Value Decomposition of "training sets" to produce systematics basis functions specifically suited to each observation. Instead of requiring precise absolute knowledge of the systematics, our method effectively requires precise knowledge of how the systematics can vary. After calculating eigenmodes for the signal and systematics, we perform a weighted least square fit of the corresponding coefficients and select the number of modes to include by minimizing an information criterion. We compare the performance of the signal extraction when minimizing various information criteria and find that minimizing the Deviance Information Criterion most consistently yields unbiased fits. The methods used here are built into our widely applicable, publicly available Python package, pylinex, which analytically calculates constraints on signals and systematics from given data, errors, and training sets.
C1 [Tauscher, Keith; Rapetti, David; Burns, Jack O.] Univ Colorado, Ctr Astrophys & Space Astron, Dept Astrophys & Planetary Sci, Campus Box 391, Boulder, CO 80309 USA.
[Tauscher, Keith] Univ Colorado, Dept Phys, Boulder, CO 80309 USA.
[Rapetti, David] NASA, Ames Res Ctr, Moffett Field, CA 94035 USA.
[Switzer, Eric] NASA, Goddard Space Flight Ctr, Greenbelt, MD 20771 USA.
RP Tauscher, K (corresponding author), Univ Colorado, Ctr Astrophys & Space Astron, Dept Astrophys & Planetary Sci, Campus Box 391, Boulder, CO 80309 USA.; Tauscher, K (corresponding author), Univ Colorado, Dept Phys, Boulder, CO 80309 USA.
EM Keith.Tauscher@colorado.edu
OI BURNS, JACK/0000-0002-4468-2117
FU NASA Solar System Exploration Research Virtual Institute
[80ARC017M0006]; NASA's Ames Research Center [NNA09DB30A, NNX15AD20A,
NNX16AF59G]; NASA ATP grant [NNX15AK80G]; NASA Postdoctoral Program
Senior Fellowship at NASA's Ames Research Center; NASA High-End
Computing (HEC) Program through the NASA Advanced Supercomputing (NAS)
Division at Ames Research Center [SMD-16-7501]
FX We thank Raul Monsalve, Jordan Mirocha, Richard Bradley, Bang Nhan,
Licia Verde, and Nicholas Kern for useful discussions. This work was
directly supported by the NASA Solar System Exploration Research Virtual
Institute cooperative agreement number 80ARC017M0006. This work was also
supported by grants from NASA's Ames Research Center (NNA09DB30A,
NNX15AD20A, NNX16AF59G) and by a NASA ATP grant (NNX15AK80G). D.R. is
supported by a NASA Postdoctoral Program Senior Fellowship at NASA's
Ames Research Center, administered by the Universities Space Research
Association under contract with NASA. Resources supporting this work
were provided by the NASA High-End Computing (HEC) Program through the
NASA Advanced Supercomputing (NAS) Division at Ames Research Center
through the award SMD-16-7501.
CR AKAIKE H, 1974, IEEE T AUTOMAT CONTR, VAC19, P716, DOI 10.1109/TAC.1974.1100705
Ali ZS, 2015, ASTROPHYS J, V809, DOI 10.1088/0004-637X/809/1/61
Ando T, 2007, BIOMETRIKA, V94, P443, DOI 10.1093/biomet/asm017
Bernardi G, 2016, MON NOT R ASTRON SOC, V461, P2847, DOI 10.1093/mnras/stw1499
Bernardi G, 2015, ASTROPHYS J, V799, DOI 10.1088/0004-637X/799/1/90
Bowman JD, 2010, NATURE, V468, P796, DOI 10.1038/nature09601
Burns JO, 2017, ASTROPHYS J, V844, DOI 10.3847/1538-4357/aa77f4
Burns JO, 2012, ADV SPACE RES, V49, P433, DOI 10.1016/j.asr.2011.10.014
Chang TC, 2010, NATURE, V466, P463, DOI 10.1038/nature09187
Cohen A, 2017, MON NOT R ASTRON SOC, V472, P1915, DOI 10.1093/mnras/stx2065
DeBoer DR, 2017, PUBL ASTRON SOC PAC, V129, DOI 10.1088/1538-3873/129/974/045001
Dillon JS, 2015, PHYS REV D, V91, DOI 10.1103/PhysRevD.91.123011
Dillon JS, 2014, PHYS REV D, V89, DOI 10.1103/PhysRevD.89.023002
EWEN HI, 1951, NATURE, V168, P356, DOI 10.1038/168356a0
Furlanetto SR, 2006, PHYS REP, V433, P181, DOI 10.1016/j.physrep.2006.08.002
Guzman AE, 2011, ASTRON ASTROPHYS, V525, DOI 10.1051/0004-6361/200913628
Harker GJA, 2016, MON NOT R ASTRON SOC, V455, P3829, DOI 10.1093/mnras/stv2630
Haslam C. G. T., 1982, Astronomy & Astrophysics Supplement Series, V47, P1
Jacobs DC, 2015, ASTROPHYS J, V801, DOI 10.1088/0004-637X/801/1/51
Kern NS, 2017, ASTROPHYS J, V848, DOI 10.3847/1538-4357/aa8bb4
Leistedt B, 2014, MON NOT R ASTRON SOC, V444, P2, DOI 10.1093/mnras/stu1439
Liddle AR, 2007, MON NOT R ASTRON SOC, V377, pL74, DOI 10.1111/j.1745-3933.2007.00306.x
Loeb A, 2013, 1 GALAXIES UNIVERSE
Madau P, 1997, ASTROPHYS J, V475, P429, DOI 10.1086/303549
Mahesh N., 2014, ARXIV14062585
Mirocha J., 2017, ARXIV171002530
Mirocha J, 2017, MON NOT R ASTRON SOC, V464, P1365, DOI 10.1093/mnras/stw2412
Mirocha J, 2015, ASTROPHYS J, V813, DOI 10.1088/0004-637X/813/1/11
Monsalve RA, 2017, ASTROPHYS J, V847, DOI 10.3847/1538-4357/aa88d1
Monsalve RA, 2017, ASTROPHYS J, V835, DOI 10.3847/1538-4357/835/1/49
Morales MF, 2010, ANNU REV ASTRON ASTR, V48, P127, DOI 10.1146/annurev-astro-081309-130936
Nhan BD, 2017, ASTROPHYS J, V836, DOI 10.3847/1538-4357/836/1/90
Offringa A. R., 2015, PASA, V32, P8
Paciga G, 2013, MON NOT R ASTRON SOC, V433, P639, DOI 10.1093/mnras/stt753
Parsons AR, 2014, ASTROPHYS J, V788, DOI 10.1088/0004-637X/788/2/106
Patra N, 2013, EXP ASTRON, V36, P319, DOI 10.1007/s10686-013-9336-3
Porciani C, 2006, MON NOT R ASTRON SOC, V371, P1824, DOI 10.1111/j.1365-2966.2006.10813.x
Presley ME, 2015, ASTROPHYS J, V809, DOI 10.1088/0004-637X/809/1/18
Price D. C., 2017, ARXIV170909313
Pritchard JR, 2012, REP PROG PHYS, V75, DOI 10.1088/0034-4885/75/8/086901
RAO MS, 2017, APJ, V840, DOI DOI 10.3847/1538-4357/AA69BD
Schmit CJ, 2018, MON NOT R ASTRON SOC, V475, P1213, DOI 10.1093/mnras/stx3292
SCHWARZ G, 1978, ANN STAT, V6, P461, DOI 10.1214/aos/1176344136
Singh S, 2017, ASTROPHYS J LETT, V845, DOI 10.3847/2041-8213/aa831b
Singh S, 2015, ASTROPHYS J, V815, DOI 10.1088/0004-637X/815/2/88
Spiegelhalter DJ, 2014, J R STAT SOC B, V76, P485, DOI 10.1111/rssb.12062
Spiegelhalter DJ, 2002, J R STAT SOC B, V64, P583, DOI 10.1111/1467-9868.00353
Stern H.S., 2013, BAYESIAN DATA ANAL 3
Switzer ER, 2014, ASTROPHYS J, V793, DOI 10.1088/0004-637X/793/2/102
Vedantham HK, 2014, MON NOT R ASTRON SOC, V437, P1056, DOI 10.1093/mnras/stt1878
Voytek TC, 2014, ASTROPHYS J LETT, V782, DOI 10.1088/2041-8205/782/1/L9
NR 51
TC 14
Z9 14
U1 0
U2 1
PU IOP PUBLISHING LTD
PI BRISTOL
PA TEMPLE CIRCUS, TEMPLE WAY, BRISTOL BS1 6BE, ENGLAND
SN 0004-637X
EI 1538-4357
J9 ASTROPHYS J
JI Astrophys. J.
PD FEB 1
PY 2018
VL 853
IS 2
AR 187
DI 10.3847/1538-4357/aaa41f
PG 9
WC Astronomy & Astrophysics
SC Astronomy & Astrophysics
GA FV0NM
UT WOS:000424253400021
DA 2021-04-21
ER
PT J
AU Johnson, D
Huerta, EA
Haas, R
AF Johnson, Daniel
Huerta, E. A.
Haas, Roland
TI Python Open source Waveform ExtractoR (POWER): an open source, Python
package to monitor and post-process numerical relativity simulations
SO CLASSICAL AND QUANTUM GRAVITY
LA English
DT Article
DE gravitational waves; numerical relativity; computational astrophysics;
open source software
AB Numerical simulations of Einstein's field equations provide unique insights into the physics of compact objects moving at relativistic speeds, and which are driven by strong gravitational interactions. Numerical relativity has played a key role to firmly establish gravitational wave astrophysics as a new field of research, and it is now paving the way to establish whether gravitational wave radiation emitted from compact binary mergers is accompanied by electromagnetic and astroparticle counterparts. As numerical relativity continues to blend in with routine gravitational wave data analyses to validate the discovery of gravitational wave events, it is essential to develop open source tools to streamline these studies. Motivated by our own experience as users and developers of the open source, community software, the Einstein Toolkit, we present an open source, Python package that is ideally suited to monitor and post-process the data products of numerical relativity simulations, and compute the gravitational wave strain at future null infinity in high performance environments. We showcase the application of this new package to post-process a large numerical relativity catalog and extract higher-order waveform modes from numerical relativity simulations of eccentric binary black hole mergers and neutron star mergers. This new software fills a critical void in the arsenal of tools provided by the Einstein Toolkit consortium to the numerical relativity community.
C1 [Johnson, Daniel; Huerta, E. A.; Haas, Roland] Univ Illinois, NCSA, Urbana, IL 61801 USA.
[Johnson, Daniel] Univ Illinois, Dept Phys, Urbana, IL 61801 USA.
[Johnson, Daniel] Univ Illinois, Dept Comp Sci, Urbana, IL 61801 USA.
[Johnson, Daniel] Univ Illinois, Students Pushing Innovat SPIN Undergraduate Inter, Urbana, IL 61801 USA.
RP Johnson, D (corresponding author), Univ Illinois, NCSA, Urbana, IL 61801 USA.; Johnson, D (corresponding author), Univ Illinois, Dept Phys, Urbana, IL 61801 USA.; Johnson, D (corresponding author), Univ Illinois, Dept Comp Sci, Urbana, IL 61801 USA.; Johnson, D (corresponding author), Univ Illinois, Students Pushing Innovat SPIN Undergraduate Inter, Urbana, IL 61801 USA.
EM dsjohns2@illinois.edu
OI Huerta, Eliu/0000-0002-9682-3604; Johnson, Daniel/0000-0001-7717-5640
FU National Science FoundationNational Science Foundation (NSF)
[OCI-0725070, ACI-1238993]; State of Illinois; NSF SI2-SSI award
[OAC-1550514]; NCSA; SPIN (Students Pushing Innovation) Program at NCSA;
Direct For Computer & Info Scie & EnginrNational Science Foundation
(NSF)NSF - Directorate for Computer & Information Science & Engineering
(CISE) [1550514] Funding Source: National Science Foundation
FX This research is part of the Blue Waters sustained-petascale computing
project, which is supported by the National Science Foundation (awards
OCI-0725070 and ACI-1238993) and the State of Illinois. Blue Waters is a
joint effort of the University of Illinois at Urbana-Champaign and its
National Center for Supercomputing Applications (NCSA). The eccentric
numerical relativity simulations used in this article were generated
with the open source, community software, the Einstein Toolkit on the
Blue Waters petascale supercomputer and XSEDE (TG-PHY160053). This work
was partially supported by the NSF SI2-SSI award OAC-1550514. We
acknowledge support from the NCSA and the SPIN (Students Pushing
Innovation) Program at NCSA. We thank Ian Hinder and Barry Wardell for
the SimulationTools analysis package. Plots were generated with
Matplotlib [29]. POWER uses numpy [30] and scipy [31].
CR Abbott BP, 2016, PHYS REV LETT, V116, DOI 10.1103/PhysRevLett.116.061102
Abbott BP, 2016, PHYS REV LETT, V116, DOI 10.1103/PhysRevLett.116.241103
Abbott BP, 2017, PHYS REV LETT, V119, DOI 10.1103/PhysRevLett.119.141101
Abbott BP, 2017, PHYS REV LETT, V118, DOI 10.1103/PhysRevLett.118.221101
Abbott BP, 2017, CLASSICAL QUANT GRAV, V34, DOI 10.1088/1361-6382/aa6854
Abbott BP, 2016, PHYS REV D, V94, DOI 10.1103/PhysRevD.94.064035
Abbott BP, 2016, LIVING REV RELATIV, V19, DOI 10.1007/lrr-2016-1
Baker JG, 2006, PHYS REV LETT, V96, DOI 10.1103/PhysRevLett.96.111102
Baumgarte TW, 1999, PHYS REV D, V59, DOI 10.1103/PhysRevD.59.024007
Campanelli M, 2006, PHYS REV LETT, V96, DOI 10.1103/PhysRevLett.96.111101
Einstein A, 1915, SITZBER K PREUSS AKA, P844
Einstein Toolkit Consortium, 2017, THE EINST TOOLK
Etienne ZB, 2015, CLASSICAL QUANT GRAV, V32, DOI 10.1088/0264-9381/32/17/175009
Haas R, 2016, PHYS REV D, V93, DOI 10.1103/PhysRevD.93.124062
Hinder I, 2017, SIMULATION TOOLS IS
Hunter JD, 2007, COMPUT SCI ENG, V9, P90, DOI 10.1109/MCSE.2007.55
Jones E., 2001, SCIPY OPEN SOURCE SC
Khan S, 2016, PHYS REV D, V93, DOI 10.1103/PhysRevD.93.044007
Kidder LE, 2017, J COMPUT PHYS, V335, P84, DOI 10.1016/j.jcp.2016.12.059
Lehner L, 2014, ANNU REV ASTRON ASTR, V52, P661, DOI 10.1146/annurev-astro-081913-040031
Loffler F, 2012, CLASSICAL QUANT GRAV, V29, DOI 10.1088/0264-9381/29/11/115001
Mosta P, 2014, CLASSICAL QUANT GRAV, V31, DOI 10.1088/0264-9381/31/1/015005
NAKAMURA T, 1987, PROG THEOR PHYS SUPP, P1
Pretorius F, 2005, PHYS REV LETT, V95, DOI 10.1103/PhysRevLett.95.121101
Reisswig C, 2011, CLASSICAL QUANT GRAV, V28, DOI 10.1088/0264-9381/28/19/195015
Sathyaprakash BS, 2009, LIVING REV RELATIV, V12, DOI 10.12942/lrr-2009-2
SHIBATA M, 1995, PHYS REV D, V52, P5428, DOI 10.1103/PhysRevD.52.5428
Taracchini A, 2014, PHYS REV D, V89, DOI 10.1103/PhysRevD.89.061502
The HDF Group, 1997, HIER DAT FORM VERS 5
van der Walt S, 2011, COMPUT SCI ENG, V13, P22, DOI 10.1109/MCSE.2011.37
NR 30
TC 5
Z9 5
U1 0
U2 6
PU IOP PUBLISHING LTD
PI BRISTOL
PA TEMPLE CIRCUS, TEMPLE WAY, BRISTOL BS1 6BE, ENGLAND
SN 0264-9381
EI 1361-6382
J9 CLASSICAL QUANT GRAV
JI Class. Quantum Gravity
PD JAN 25
PY 2018
VL 35
IS 2
AR 027002
DI 10.1088/1361-6382/aa9cad
PG 10
WC Astronomy & Astrophysics; Quantum Science & Technology; Physics,
Multidisciplinary; Physics, Particles & Fields
SC Astronomy & Astrophysics; Physics
GA FQ5MF
UT WOS:000418404000001
DA 2021-04-21
ER
PT J
AU Hubber, DA
Rosotti, GP
Booth, RA
AF Hubber, D. A.
Rosotti, G. P.
Booth, R. A.
TI GANDALF - Graphical Astrophysics code for N-body Dynamics And Lagrangian
Fluids
SO MONTHLY NOTICES OF THE ROYAL ASTRONOMICAL SOCIETY
LA English
DT Article
DE hydrodynamics; methods: numerical
ID SMOOTHED PARTICLE HYDRODYNAMICS; SPH; SIMULATIONS; ACCRETION; DUST; GAS;
ALGORITHM; FLOW; IMPLEMENTATION; FRAGMENTATION
AB GANDALF is a new hydrodynamics and N-body dynamics code designed for investigating planet formation, star formation and star cluster problems. GANDALF is written in C++, parallelized with both OPENMP and MPI and contains a PYTHON library for analysis and visualization. The code has been written with a fully object-oriented approach to easily allowuser-defined implementations of physics modules or other algorithms. The code currently contains implementations of smoothed particle hydrodynamics, meshless finite-volume and collisional N-body schemes, but can easily be adapted to include additional particle schemes. We present in this paper the details of its implementation, results from the test suite, serial and parallel performance results and discuss the planned future development. The code is freely available as an open source project on the code-hosting website github at https://github.com/gandalfcode/gandalf and is available under the GPLv2 license.
C1 [Hubber, D. A.] Univ Sternwarte Munchen, Scheinerstr 1, D-81679 Munich, Germany.
[Hubber, D. A.] Excellence Cluster Universe, Boltzmannstr 2, D-85748 Garching, Germany.
[Rosotti, G. P.; Booth, R. A.] Inst Astron, Madingley Rd, Cambridge CB3 0HA, England.
RP Hubber, DA (corresponding author), Univ Sternwarte Munchen, Scheinerstr 1, D-81679 Munich, Germany.; Hubber, DA (corresponding author), Excellence Cluster Universe, Boltzmannstr 2, D-85748 Garching, Germany.
EM dhubber@usm.lmu.de
RI Rosotti, Giovanni P/O-4774-2018
OI Rosotti, Giovanni P/0000-0003-4853-5736; Booth,
Richard/0000-0002-0364-937X
FU DFG cluster of excellence 'Origin and Structure of the Universe'German
Research Foundation (DFG); Munich Institute for Astro-and Particle
Physics (MIAPP), DFG Projects [841797-4, 841798-2]; DISCSIM project -
European Research Council under ERC-ADG [341137]; BIS National
E-infrastructure capital grant [ST/K001590/1]; STFC capital grants
[ST/H008861/1, ST/H00887X/1]; STFC DiRAC Operations grant [ST/K00333X/1]
FX This research was supported by the DFG cluster of excellence 'Origin and
Structure of the Universe' including the Munich Institute for Astro-and
Particle Physics (MIAPP), DFG Projects 841797-4, 841798-2 (DAH and GPR),
the DISCSIM project, grant agreement 341137 funded by the European
Research Council under ERC-2013-ADG (GPR and RAB). Some development of
the code and simulations have been carried out on the computing
facilities of the Computational centre for Particle and Astrophysics
(C2PAP) and on the DiRAC Data Analytic system at the University of
Cambridge, operated by the University of Cambridge High Performance
Computing Service on behalf of the Science and Technology Facilities
Council (STFC) DiRAC HPC Facility (www.dirac.ac.uk); the equipment was
funded by BIS National E-infrastructure capital grant (ST/K001590/1),
STFC capital grants ST/H008861/1 and ST/H00887X/1, and STFC DiRAC
Operations grant ST/K00333X/1. We would like to thank the following
people for helpful discussions or for contributing code to the public
version, including Alexander Arth (discussions on IC generation), Scott
Balfour (ionizing radiation algorithms), Seamus Clarke (sink particle
algorithm refinements and various bug fixes), James Dale (ionizing
radiation algorithms) Franta Dinnbier (periodic gravity), Stefan Heigl
(assisting implementing the MFV schemes), Oliver Lomax (assisting
implementing the KD tree), Judith Ngoumou (assisting implementing the
MFV schemes), Margarita Petkova (parallelization and C2PAP support),
Paul Rohde (stellar feedback routines), SteffiWalch (supernova feedback
routines) and AnthonyWhitworth (discussions on trees and radiation
algorithms). We also thank the anonymous referee for helpful and
detailed comments which have improved the clarity and readability of
this paper.
CR Aarseth S. J., 2003, GRAVITATIONAL N BODY
AARSETH SJ, 1974, ASTRON ASTROPHYS, V37, P183
ARTYMOWICZ P, 1994, ASTROPHYS J, V421, P651, DOI 10.1086/173679
BARNES J, 1986, NATURE, V324, P446, DOI 10.1038/324446a0
BATE MR, 1995, MON NOT R ASTRON SOC, V277, P362, DOI 10.1093/mnras/277.2.362
Batten P, 1997, SIAM J SCI COMPUT, V18, P1553, DOI 10.1137/S1064827593260140
Binney J., 2008, GALACTIC DYNAMICS 2
Booth RA, 2015, MON NOT R ASTRON SOC, V452, P3932, DOI 10.1093/mnras/stv1486
BOSS AP, 1979, ASTROPHYS J, V234, P289, DOI 10.1086/157497
Cullen L, 2010, MON NOT R ASTRON SOC, V408, P669, DOI 10.1111/j.1365-2966.2010.17158.x
de Val-Borro M, 2007, ASTRON ASTROPHYS, V471, P1043, DOI 10.1051/0004-6361:20077169
de Val-Borro M, 2006, MON NOT R ASTRON SOC, V370, P529, DOI 10.1111/j.1365-2966.2006.10488.x
Deng HP, 2017, ASTROPHYS J, V847, DOI 10.3847/1538-4357/aa872b
Dipierro G, 2016, MON NOT R ASTRON SOC, V459, pL1, DOI 10.1093/mnrasl/slw032
FLEBBE O, 1994, ASTROPHYS J, V431, P754, DOI 10.1086/174526
Gaburov E, 2011, MON NOT R ASTRON SOC, V414, P129, DOI 10.1111/j.1365-2966.2011.18313.x
Gafton E, 2011, MON NOT R ASTRON SOC, V418, P770, DOI 10.1111/j.1365-2966.2011.19528.x
GINGOLD RA, 1977, MON NOT R ASTRON SOC, V181, P375, DOI 10.1093/mnras/181.3.375
Grassi T, 2014, MON NOT R ASTRON SOC, V439, P2386, DOI 10.1093/mnras/stu114
GRESHO PM, 1990, INT J NUMER METH FL, V11, P621, DOI 10.1002/fld.1650110510
Helm E., 1995, ADDISON WESLEY PROFE
HERNQUIST L, 1991, ASTROPHYS J SUPPL S, V75, P231, DOI 10.1086/191530
Hess S, 2010, MON NOT R ASTRON SOC, V406, P2289, DOI 10.1111/j.1365-2966.2010.16892.x
Hopkins PF, 2017, MON NOT R ASTRON SOC, V466, P3387, DOI 10.1093/mnras/stw3306
Hopkins PF, 2015, MON NOT R ASTRON SOC, V450, P53, DOI 10.1093/mnras/stv195
Hopkins PF, 2013, MON NOT R ASTRON SOC, V428, P2840, DOI 10.1093/mnras/sts210
Hubber D, 2016, ASCL1602015
Hubber DA, 2013, MON NOT R ASTRON SOC, V430, P1599, DOI 10.1093/mnras/sts694
Hubber DA, 2013, MON NOT R ASTRON SOC, V430, P3261, DOI 10.1093/mnras/stt128
Hubber D. A., 2011, A A, V529, P27, DOI DOI 10.1051/0004-6361/201014949
Hubber DA, 2006, ASTRON ASTROPHYS, V450, P881, DOI 10.1051/0004-6361:20054100
HUT P, 1995, ASTROPHYS J, V443, pL93, DOI 10.1086/187844
Inutsuka S, 2002, J COMPUT PHYS, V179, P238, DOI 10.1006/jcph.2002.7053
Kley W, 1999, MON NOT R ASTRON SOC, V303, P696, DOI 10.1046/j.1365-8711.1999.02198.x
Laibe G, 2012, MON NOT R ASTRON SOC, V420, P2345, DOI 10.1111/j.1365-2966.2011.20202.x
Laibe G, 2011, MON NOT R ASTRON SOC, V418, P1491, DOI 10.1111/j.1365-2966.2011.19291.x
Lanson N, 2008, SIAM J NUMER ANAL, V46, P1912, DOI 10.1137/S0036142903427718
Loren-Aguilar P, 2015, MON NOT R ASTRON SOC, V454, P4114, DOI 10.1093/mnras/stv2262
Lovelace RVE, 1999, ASTROPHYS J, V513, P805, DOI 10.1086/306900
LUCY LB, 1977, ASTRON J, V82, P1013, DOI 10.1086/112164
MAKINO J, 1992, PUBL ASTRON SOC JPN, V44, P141
Mignone A, 2007, J COMPUT PHYS, V225, P1427, DOI 10.1016/j.jcp.2007.01.033
MONAGHAN JJ, 1992, ANNU REV ASTRON ASTR, V30, P543, DOI 10.1146/annurev.aa.30.090192.002551
MONAGHAN JJ, 1985, ASTRON ASTROPHYS, V149, P135
Monaghan JJ, 1997, J COMPUT PHYS, V136, P298, DOI 10.1006/jcph.1997.5732
Morris JP, 1997, J COMPUT PHYS, V136, P41, DOI 10.1006/jcph.1997.5690
Morris JP, 1996, THESIS
Munoz DJ, 2013, MON NOT R ASTRON SOC, V428, P254, DOI 10.1093/mnras/sts015
Murray JR, 1996, MON NOT R ASTRON SOC, V279, P402, DOI 10.1093/mnras/279.2.402
Portegies Zwart S, 2001, MON NOT R ASTRON SOC, V321, P199, DOI 10.1046/j.1365-8711.2001.03976.x
Price DJ, 2007, MON NOT R ASTRON SOC, V374, P1347, DOI 10.1111/j.1365-2966.2006.11241.x
Price D. J., 2017, PASA
Price DJ, 2007, PUBL ASTRON SOC AUST, V24, P159, DOI 10.1071/AS07022
Price DJ, 2015, MON NOT R ASTRON SOC, V451, P813, DOI 10.1093/mnras/stv996
Price DJ, 2012, J COMPUT PHYS, V231, P759, DOI 10.1016/j.jcp.2010.12.011
Price DJ, 2008, J COMPUT PHYS, V227, P10040, DOI 10.1016/j.jcp.2008.08.011
Rosswog S, 2015, MON NOT R ASTRON SOC, V448, P3628, DOI 10.1093/mnras/stv225
Saitoh TR, 2013, ASTROPHYS J, V768, DOI 10.1088/0004-637X/768/1/44
Saitoh TR, 2009, ASTROPHYS J LETT, V697, pL99, DOI 10.1088/0004-637X/697/2/L99
Sijacki D, 2012, MON NOT R ASTRON SOC, V424, P2999, DOI 10.1111/j.1365-2966.2012.21466.x
Springel V, 2005, MON NOT R ASTRON SOC, V364, P1105, DOI 10.1111/j.1365-2966.2005.09655.x
Springel V, 2002, MON NOT R ASTRON SOC, V333, P649, DOI 10.1046/j.1365-8711.2002.05445.x
Springel V, 2010, MON NOT R ASTRON SOC, V401, P791, DOI 10.1111/j.1365-2966.2009.15715.x
Stone JM, 2008, ASTROPHYS J SUPPL S, V178, P137, DOI 10.1086/588755
Toro E. F., 1994, Shock Waves, V4, P25, DOI 10.1007/BF01414629
Toro E. F., 1997, RIEMANN SOLVERS NUME
VAN LEER B, 1979, J COMPUT PHYS, V32, P101, DOI 10.1016/0021-9991(79)90145-1
Wadsley JW, 2008, MON NOT R ASTRON SOC, V387, P427, DOI 10.1111/j.1365-2966.2008.13260.x
Wadsley JW, 2004, NEW ASTRON, V9, P137, DOI 10.1016/j.newast.2003.08.004
Wetzstein M, 2009, ASTROPHYS J SUPPL S, V184, P298, DOI 10.1088/0067-0049/184/2/298
WHITWORTH AP, 1995, ASTRON ASTROPHYS, V301, P929
Wunsch R., 2017, MNRAS
NR 72
TC 22
Z9 22
U1 0
U2 1
PU OXFORD UNIV PRESS
PI OXFORD
PA GREAT CLARENDON ST, OXFORD OX2 6DP, ENGLAND
SN 0035-8711
EI 1365-2966
J9 MON NOT R ASTRON SOC
JI Mon. Not. Roy. Astron. Soc.
PD JAN
PY 2018
VL 473
IS 2
BP 1603
EP 1632
DI 10.1093/mnras/stx2405
PG 30
WC Astronomy & Astrophysics
SC Astronomy & Astrophysics
GA FU3DS
UT WOS:000423731200015
DA 2021-04-21
ER
PT S
AU Chang, J
Gutsche, O
Mandrichenko, I
Pivarski, J
AF Chang, Jin
Gutsche, Oliver
Mandrichenko, Igor
Pivarski, James
GP IOP
TI Striped Data Server for Scalable Parallel Data Analysis
SO 18TH INTERNATIONAL WORKSHOP ON ADVANCED COMPUTING AND ANALYSIS
TECHNIQUES IN PHYSICS RESEARCH (ACAT2017)
SE Journal of Physics Conference Series
LA English
DT Proceedings Paper
CT 18th International Workshop on Advanced Computing and Analysis
Techniques in Physics Research (ACAT)
CY AUG 21-25, 2017
CL Univ Washington, Seattle, WA
HO Univ Washington
AB A columnar data representation is known to be an efficient way for data storage, specifically in cases when the analysis is often done based only on a small fragment of the available data structures. A data representation like Apache Parquet is a step forward from a columnar representation, which splits data horizontally to allow for easy parallelization of data analysis. Based on the general idea of columnar data storage, working on the FNAL LDRD Project FNAL-LDRD-2016-032, we have developed a striped data representation, which, we believe, is better suited to the needs of High Energy Physics data analysis. A traditional columnar approach allows for efficient data analysis of complex structures. While keeping all the benefits of columnar data representations, the striped mechanism goes further by enabling easy parallelization of computations without requiring special hardware. We will present an implementation and some performance characteristics of such a data representation mechanism using a distributed no-SQL database or a local file system, unified under the same API and data representation model. The representation is efficient and at the same time simple so that it allows for a common data model and APIs for wide range of underlying storage mechanisms such as distributed no-SQL databases and local file systems. Striped storage adopts Numpy arrays as its basic data representation format, which makes it easy and efficient to use in Python applications. The Striped Data Server is a web service, which allows to hide the server implementation details from the end user, easily exposes data to WAN users, and allows to utilize well known and developed data caching solutions to further increase data access efficiency. We are considering the Striped Data Server as the core of an enterprise scale data analysis platform for High Energy Physics and similar areas of data processing. We have been testing this architecture with a 2TB dataset from a CMS dark matter search and plan to expand it to multiple 100 TB or even PB scale. We will present the striped format, Striped Data Server architecture and performance test results.
EM ivm@fnal.gov
CR Chatrchyan S, 2008, J INSTRUM, V3, DOI 10.1088/1748-0221/3/08/S08004
Colbert S. C., 2011, COMPUTING SCI ENG, V3
NR 2
TC 0
Z9 0
U1 0
U2 0
PU IOP PUBLISHING LTD
PI BRISTOL
PA DIRAC HOUSE, TEMPLE BACK, BRISTOL BS1 6BE, ENGLAND
SN 1742-6588
EI 1742-6596
J9 J PHYS CONF SER
PY 2018
VL 1085
AR 042035
DI 10.1088/1742-6596/1085/4/042035
PG 6
WC Computer Science, Interdisciplinary Applications; Physics,
Multidisciplinary
SC Computer Science; Physics
GA BM7GU
UT WOS:000467872200101
OA Bronze
DA 2021-04-21
ER
PT S
AU Lange, DJ
AF Lange, David J.
GP IOP
TI Building a scalable python distribution for HEP data analysis
SO 18TH INTERNATIONAL WORKSHOP ON ADVANCED COMPUTING AND ANALYSIS
TECHNIQUES IN PHYSICS RESEARCH (ACAT2017)
SE Journal of Physics Conference Series
LA English
DT Proceedings Paper
CT 18th International Workshop on Advanced Computing and Analysis
Techniques in Physics Research (ACAT)
CY AUG 21-25, 2017
CL Univ Washington, Seattle, WA
HO Univ Washington
AB There are numerous approaches to building analysis applications across the high-energy physics community. Among them are Python-based, or at least Python-driven, analysis workflows. We aim to ease the adoption of a Python-based analysis toolkit by making it easier for non-expert users to gain access to Python tools for scientific analysis. Experimental software distributions and individual user analysis have quite different requirements. Distributions tend to worry most about stability, usability and reproducibility, while the users usually strive to be fast and nimble. We discuss how we built and now maintain a python distribution for analysis while satisfying requirements both a large software distribution (in our case, that of CMSSW) and user, or laptop, level analysis. We pursued the integration of tools used by the broader data science community as well as HEP developed (e.g., histogrammar, root_numpy) Python packages. We discuss concepts we investigated for package integration and testing, as well as issues we encountered through this process. Distribution and platform support are important topics. We discuss our approach and progress towards a sustainable infrastructure for supporting this Python stack for the CMS user community and for the broader HEP user community.
C1 [Lange, David J.] Princeton Univ, Dept Phys, Princeton, NJ 08544 USA.
RP Lange, DJ (corresponding author), Princeton Univ, Dept Phys, Princeton, NJ 08544 USA.
EM dlange@princeton.edu
CR Eulisse G, 2014, J PHYS CONF SER, V513, DOI 10.1088/1742-6596/513/5/052009
Jones C D, 2008, Journal of Physics: Conference Series, DOI 10.1088/1742-6596/119/3/032027
NR 2
TC 0
Z9 0
U1 0
U2 0
PU IOP PUBLISHING LTD
PI BRISTOL
PA DIRAC HOUSE, TEMPLE BACK, BRISTOL BS1 6BE, ENGLAND
SN 1742-6588
EI 1742-6596
J9 J PHYS CONF SER
PY 2018
VL 1085
AR 042041
DI 10.1088/1742-6596/1085/4/042041
PG 5
WC Computer Science, Interdisciplinary Applications; Physics,
Multidisciplinary
SC Computer Science; Physics
GA BM7GU
UT WOS:000467872200107
OA Bronze
DA 2021-04-21
ER
PT S
AU Pivarski, J
Lange, D
Jatuphattharachat, T
AF Pivarski, Jim
Lange, David
Jatuphattharachat, Thanat
GP IOP
TI Toward real-time data query systems in HEP
SO 18TH INTERNATIONAL WORKSHOP ON ADVANCED COMPUTING AND ANALYSIS
TECHNIQUES IN PHYSICS RESEARCH (ACAT2017)
SE Journal of Physics Conference Series
LA English
DT Proceedings Paper
CT 18th International Workshop on Advanced Computing and Analysis
Techniques in Physics Research (ACAT)
CY AUG 21-25, 2017
CL Univ Washington, Seattle, WA
HO Univ Washington
AB Exploratory data analysis tools must respond quickly to a user's questions, so that the answer to one question (e.g. a visualized histogram or fit) can influence the next. In some SQL-based query systems used in industry, even very large (petabyte) datasets can be summarized on a human timescale (seconds), employing techniques such as columnar data representation, caching, indexing, and code generation/JIT-compilation. This article describes progress toward realizing such a system for High Energy Physics (HEP), focusing on the intermediate problems of optimizing data access and calculations for "query sized" payloads, such as a single histogram or group of histograms, rather than large reconstruction or data-skimming jobs. These techniques include direct extraction of ROOT TBranches into Numpy arrays and compilation of Python analysis functions (rather than SQL) to be executed very quickly. We will also discuss the problem of caching and actively delivering jobs to worker nodes that have the necessary input data preloaded in cache. All of these pieces of the larger solution are available as standalone GitHub repositories, and could be used in current analyses.
C1 [Pivarski, Jim; Lange, David] Princeton Univ, Phys Dept, Princeton, NJ 08544 USA.
[Jatuphattharachat, Thanat] Comp Engn Chulalongkorn Univ, Krung Thep Maha Nakhon 10330, Thailand.
RP Pivarski, J (corresponding author), Princeton Univ, Phys Dept, Princeton, NJ 08544 USA.
EM pivarski@princeton.edu
FU National Science FoundationNational Science Foundation (NSF)
[ACI-1450377, PHY-1624356]
FX This work was supported by the National Science Foundation under grants
ACI-1450377 and PHY-1624356.
CR Bockelman B, 2017, J PHYS C SERIES
Bockelman B, 2017, ROOT BULKAPI FASTREA
Hausenblas M, 2013, BIG DATA, V1, P100, DOI 10.1089/big.2013.0011
Jatuphattharachat T, 2017, FEMTO MESOS
Pivarski J, 2017, FAST ACCESS COLUMNAR
Pivarski J, 2017, OAMAP TOOLSET COMPUT
Pivarski J, 2017, UPROOT MINIMALIST RO
The Apache Arrow team, 2016, AP ARR POW COL MEM A
1997, NUCL INSTRUM METH A, V389, P81, DOI DOI 10.1016/S0168-9002(97)00048-X
NR 9
TC 0
Z9 0
U1 0
U2 0
PU IOP PUBLISHING LTD
PI BRISTOL
PA DIRAC HOUSE, TEMPLE BACK, BRISTOL BS1 6BE, ENGLAND
SN 1742-6588
EI 1742-6596
J9 J PHYS CONF SER
PY 2018
VL 1085
AR 032044
DI 10.1088/1742-6596/1085/3/032044
PG 7
WC Computer Science, Interdisciplinary Applications; Physics,
Multidisciplinary
SC Computer Science; Physics
GA BM7GU
UT WOS:000467872200054
OA Bronze
DA 2021-04-21
ER
PT B
AU Barbuta, MG
Marcu, A
Slusanschi, EI
AF Barbuta, Mihail-Gabriel
Marcu, Aurelian
Slusanschi, Emil-Ioan
GP IEEE
TI Adaptive algorithm for detecting beam center in high-power laser
transport lines
SO 2018 IEEE CONFERENCE ON CONTROL TECHNOLOGY AND APPLICATIONS (CCTA)
LA English
DT Proceedings Paper
CT 2nd IEEE Conference on Control Technology and Applications (CCTA)
CY AUG 21-24, 2018
CL Copenhagen, DENMARK
SP IEEE, IEEE Control Syst Soc
AB High-power laser installations which require complex beam transport systems under vacuum also require a method to accurately align the beam(s) remotely. The alignment precision depends on the accuracy of the beam measurements, and multiple solutions have been adopted in current laser installations [1][2]. However, time constraints entail the implementation of automated alignment procedures using fast and accurate detection methods for a wide variety of capture types. The current paper proposes an adaptive template matching beam detection algorithm, implemented using Python and OpenCV, which has shown very good accuracy and performance. This is demonstrated using tests on sample captures from the Petawatt laser of the Center of Advanced Laser Technologies [3] at the National Institute for Laser, Plasma and Radiation Physics.
C1 [Barbuta, Mihail-Gabriel] Univ Politehn Bucuresti, Fac Appl Phys, Splaiul Independentei 313, Bucharest 060042, Romania.
[Marcu, Aurelian] Natl Inst Laser Plasma & Radiat Phys, Str Atomistilor 409, Magurele 077125, Romania.
[Slusanschi, Emil-Ioan] Univ Politehn Bucuresti, Fac Automat Control & Comp, Splaiul Independentei 313, Bucharest 060042, Romania.
RP Barbuta, MG (corresponding author), Univ Politehn Bucuresti, Fac Appl Phys, Splaiul Independentei 313, Bucharest 060042, Romania.
EM mihail.barbuta@protonmail.ch; aurelian.marcu@inflpr.ro;
slusanschi@cs.pub.ro
FU Romanian MCI under 'Sectoral Programme' [10-PS]
FX Dr. A. Marcu would like to acknowledge financial support from Romanian
MCI under 'Sectoral Programme', 10-PS project.
CR Awwal A. A. S., 2005, SPIE OPTICS PHOTONIC
Awwal AAS, 2009, OPT LASER TECHNOL, V41, P193, DOI 10.1016/j.optlastec.2008.05.008
Barbuta M. G., 2016, THESIS
Bradski G., 2008, LEARNING OPENCV
Candy J., 2004, P SPIE INT SOC OPTIC, P11
Douglas DH., 1973, CARTOGRAPHICA, V10, P112, DOI [DOI 10.3138/FM57-6770-U75U-7727, 10.3138/fm57-6770-u75u-7727]
Hou B, 2011, OPT ENG, V50, DOI 10.1117/1.3554379
Wilhelmsen K., 2007, P 2007 INT C ACC LAR, V02, P486
NR 8
TC 1
Z9 1
U1 0
U2 1
PU IEEE
PI NEW YORK
PA 345 E 47TH ST, NEW YORK, NY 10017 USA
BN 978-1-5386-7698-1
PY 2018
BP 115
EP 120
PG 6
WC Automation & Control Systems; Engineering, Electrical & Electronic
SC Automation & Control Systems; Engineering
GA BM2RZ
UT WOS:000461414700018
DA 2021-04-21
ER
PT J
AU Wilkinson, CJ
Mauro, YZ
Mauro, JC
AF Wilkinson, Collin J.
Mauro, Yihong Z.
Mauro, John C.
TI RelaxPy: Python code for modeling of glass relaxation behavior
SO SOFTWAREX
LA English
DT Article
DE Glass; Thermodynamics; Viscosity; Relaxation; Fictive temperature;
Kinetics
ID FICTIVE TEMPERATURE; CONSTRAINT THEORY
AB The degree of relaxation in any glass sample is a governing property in every property of the glass. It plays an important role in every major glass product commercially available, but has required individual groups to develop their own relaxation codes. RelaxPy is a Python-based script designed to be used in a Python interpreter or Linux terminal. Given an input temperature path and set of material properties including the nonequilibrium viscosity parameters, RelaxPy returns the evolution of the composite fictive temperature, viscosity, and relaxation time. Optionally, the software can also return the individual values of the fictive temperature components using a Prony series fit to approximate the stretched exponential relaxation form. RelaxPy aims to provide a flexible, open-source framework for glass relaxation modeling where new advances in glass physics can be easily incorporated and shared with the community. (c) 2018 The Authors. Published by Elsevier B.V. This is an open access article under the CC BY license (http://creativecommons.org/licenses/by/4.0/).
C1 [Wilkinson, Collin J.; Mauro, Yihong Z.; Mauro, John C.] Penn State Univ, Dept Mat Sci & Engn, University Pk, PA 16802 USA.
RP Wilkinson, CJ (corresponding author), Penn State Univ, Dept Mat Sci & Engn, University Pk, PA 16802 USA.
EM collin.wilkinson1123@gmail.com
OI Mauro, John/0000-0002-4319-3530
CR GRASSBERGER P, 1982, J CHEM PHYS, V77, P6281, DOI 10.1063/1.443832
Guo XJ, 2018, J AM CERAM SOC, V101, P1169, DOI 10.1111/jace.15272
Gupta PK, 2009, J CHEM PHYS, V130, DOI 10.1063/1.3077168
Kohlrausch R, 1854, POGG ANN PHYS CHEM, V91, P179, DOI DOI 10.1002/ANDP.18541670203
Mauro JC, 2018, PHYSICA A, V506, P284, DOI 10.1016/j.physa.2018.04.064
Mauro JC, 2016, CHEM MATER, V28, P4267, DOI 10.1021/acs.chemmater.6b01054
Mauro JC, 2014, J NON-CRYST SOLIDS, V396, P41, DOI 10.1016/j.jnoncrysol.2014.04.009
Mauro JC, 2011, AM CERAM SOC BULL, V90, P31
Mauro JC, 2009, J CHEM PHYS, V130, DOI 10.1063/1.3152432
Mauro JC, 2009, J AM CERAM SOC, V92, P75, DOI 10.1111/j.1551-2916.2008.02851.x
NARAYANASWAMY OS, 1971, J AM CERAM SOC, V54, P491, DOI 10.1111/j.1151-2916.1971.tb12186.x
Phillips JC, 2011, J NON-CRYST SOLIDS, V357, P3853, DOI 10.1016/j.jnoncrysol.2011.08.001
PHILLIPS JC, 1985, SOLID STATE COMMUN, V53, P699, DOI 10.1016/0038-1098(85)90381-3
PHILLIPS JC, 1994, J STAT PHYS, V77, P945, DOI 10.1007/BF02179472
RITLAND HN, 1956, J AM CERAM SOC, V39, P403, DOI 10.1111/j.1151-2916.1956.tb15613.x
TOOL AQ, 1946, J AM CERAM SOC, V29, P240, DOI 10.1111/j.1151-2916.1946.tb11592.x
Tool AQ, 1931, J AM CERAM SOC, V14, P276, DOI 10.1111/j.1151-2916.1931.tb16602.x
Welch RC, 2013, PHYS REV LETT, V110, DOI 10.1103/PhysRevLett.110.265901
Zanotto ED, 2017, J NON-CRYST SOLIDS, V471, P490, DOI 10.1016/j.jnoncrysol.2017.05.019
Zheng QJ, 2017, J AM CERAM SOC, V100, P6, DOI 10.1111/jace.14678
NR 20
TC 3
Z9 3
U1 0
U2 1
PU ELSEVIER
PI AMSTERDAM
PA RADARWEG 29, 1043 NX AMSTERDAM, NETHERLANDS
SN 2352-7110
J9 SOFTWAREX
JI SoftwareX
PD JAN-JUN
PY 2018
VL 7
BP 255
EP 258
DI 10.1016/j.softx.2018.07.008
PG 4
WC Computer Science, Software Engineering
SC Computer Science
GA HJ4JC
UT WOS:000457139300043
OA DOAJ Gold
DA 2021-04-21
ER
PT J
AU Silva, DJ
Amaral, JS
Amaral, VS
AF Silva, D. J.
Amaral, J. S.
Amaral, V. S.
TI Heatrapy: A flexible Python framework for computing dynamic heat
transfer processes involving caloric effects in 1.5D systems
SO SOFTWAREX
LA English
DT Article
DE Heat transfer; Thermodynamics; Caloric effect; Python
ID MAGNETOCALORIC MATERIALS; MAGNETIC REFRIGERATOR; CYCLES
AB Although the number of computational investigations of heat transfer processes involving non-conventional effects has been increasing for the past years, mainly due to the modeling of caloric systems such as magnetocaloric devices, an open source package dedicated to such dynamics is still unavailable. So far, all reported simulations of caloric systems have been run with new code without a good level of flexibility. We have developed a Python framework that makes the modeling of such systems easy to be computed. The related package, called heatrapy, can compute heat transfer processes inside and between thermal objects. Moreover, the thermal objects can be activated (magnetized, electrified, compressed) or deactivated at any time. A complete overview of the framework is presented and all the related physics are described. The package includes examples of two magnetocaloric systems, although it can be used in a variety of problems, including electrocaloric, barocaloric and elastocaloric systems. Additional features, such as the sweeping of the material activation or deactivation, the discontinuity of thermal objects, and the inclusion of cascade of materials are described. (c) 2018 The Authors. Published by Elsevier B.V. This is an open access article under the CC BY license (http://creativecommons.org/licenses/by/4.0/).
C1 [Silva, D. J.] Univ Aveiro, Dept Phys, P-3810193 Aveiro, Portugal.
Univ Aveiro, CICECO Aveiro Inst Mat, P-3810193 Aveiro, Portugal.
RP Silva, DJ (corresponding author), Univ Aveiro, Dept Phys, P-3810193 Aveiro, Portugal.
EM djsilva@ua.pt
RI Amaral, Vitor/M-9882-2019; Amaral, Vitor S/A-1570-2009; Amaral, J.
S./C-6354-2009; da Silva, Daniel Jose/L-6503-2014
OI Amaral, Vitor/0000-0003-3359-7133; Amaral, Vitor S/0000-0003-3359-7133;
Amaral, J. S./0000-0003-0488-9372; da Silva, Daniel
Jose/0000-0002-2660-4692
FU Smart Green Homes Project, Portugal [POCI-01-0247-FEDER-007678];
Portugal 2020 under the Competitiveness and Internationalization
Operational Program, Portugal; European Regional Development Fund,
Portugal; Project CICECO-Aveiro Institute of Materials, Portugal
[POCI-01-0145-FEDER-007679, UID/CT/5001/2013]; FCT/MEC, Portugal; FCT,
PortugalPortuguese Foundation for Science and Technology
[UID/CT/5001/2013, IF/01089/2015]
FX The present study was developed in the scope of the Smart Green Homes
Project, Portugal [POCI-01-0247-FEDER-007678], a co-promotion between
Bosch Termotecnologia S.A. and the University of Aveiro. It is financed
by Portugal 2020 under the Competitiveness and Internationalization
Operational Program, Portugal, and by the European Regional Development
Fund, Portugal. Project CICECO-Aveiro Institute of Materials, Portugal,
POCI-01-0145-FEDER-007679 (FCT, Portugal Ref. UID/CT/5001/2013),
financed by national funds through the FCT/MEC, Portugal and co-financed
by FEDER under the PT2020 Partnership Agreement is acknowledged. JSA
acknowledges FCT, Portugal IF/01089/2015 research grant.
CR [Anonymous], 2018, SOURCE PHYSPLOTLIB P
Aprea C, 2017, INT J HEAT TECHNOL, V35, pS383, DOI 10.18280/ijht.35Sp0152
Canesin FC, 2012, INT C MAGN REFR ROOM, P509
Ezan MA, 2017, INT J REFRIG, V75, P262, DOI 10.1016/j.ijrefrig.2016.12.018
Jabbari M, 2013, INT J REFRIG, V36, P2403, DOI 10.1016/j.ijrefrig.2013.05.002
Kamran MS, 2016, APPL THERM ENG, V102, P1126, DOI 10.1016/j.applthermaleng.2016.02.085
Kitanovski A, 2014, INT J REFRIG, V37, P28, DOI 10.1016/j.ijrefrig.2013.05.014
KITANOVSKI A, 2015, MAGNETOCALORIC ENERG
Kitanovski A, 2015, INT J REFRIG, V57, P288, DOI 10.1016/j.ijrefrig.2015.06.008
Landau RH, 2007, COMPUTATIONAL PHYS P
Lienhard J.H, 2017, HEAT TRANSFER TXB
Lloveras P, 2015, NAT COMMUN, V6, DOI 10.1038/ncomms9801
Luo D, 2017, ENERGY, V130, P500, DOI 10.1016/j.energy.2017.05.008
Ma N, 2017, SCI REP-UK, V7, DOI 10.1038/s41598-017-13515-9
Ma RJ, 2017, SCIENCE, V357, P1130, DOI 10.1126/science.aan5980
Manosa L, 2010, NAT MATER, V9, P478, DOI [10.1038/nmat2731, 10.1038/NMAT2731]
Moya X, 2014, NAT MATER, V13, P439, DOI [10.1038/NMAT3951, 10.1038/nmat3951]
Nielsen KK, 2011, INT J REFRIG, V34, P603, DOI 10.1016/j.ijrefrig.2010.12.026
Ozbolt M, 2014, INT J REFRIG, V40, P174, DOI 10.1016/j.ijrefrig.2013.11.007
Petersen TF, 2008, INT J REFRIG, V31, P432, DOI 10.1016/j.ijrefrig.2007.07.009
Petersen TF, 2008, J PHYS D APPL PHYS, V41, DOI 10.1088/0022-3727/41/10/105002
Qian SX, 2016, INT J REFRIG, V64, P1, DOI 10.1016/j.ijrefrig.2015.12.001
Scott JF, 2011, ANNU REV MATER RES, V41, P229, DOI 10.1146/annurev-matsci-062910-100341
Silva DJ, 2016, APPL THERM ENG, V99, P514, DOI 10.1016/j.applthermaleng.2016.01.026
Silva DJ, 2014, APPL ENERG, V113, P1149, DOI 10.1016/j.apenergy.2013.08.070
Silva DJ, 2012, APPL ENERG, V93, P570, DOI 10.1016/j.apenergy.2011.12.002
Thijssen J., 2007, COMPUTATIONAL PHYS, V2nd
You YH, 2017, INT J REFRIG, V79, P217, DOI 10.1016/j.ijrefrig.2017.04.014
NR 28
TC 5
Z9 5
U1 0
U2 2
PU ELSEVIER
PI AMSTERDAM
PA RADARWEG 29, 1043 NX AMSTERDAM, NETHERLANDS
SN 2352-7110
J9 SOFTWAREX
JI SoftwareX
PD JAN-JUN
PY 2018
VL 7
BP 373
EP 382
DI 10.1016/j.softx.2018.09.007
PG 10
WC Computer Science, Software Engineering
SC Computer Science
GA HJ4JC
UT WOS:000457139300062
OA DOAJ Gold
DA 2021-04-21
ER
PT S
AU Johnson, LC
Cummings, K
Drobilek, M
Johansson, E
Marino, J
Rampy, R
Richards, K
Rimmele, T
Sekulic, P
Woger, F
AF Johnson, Luke C.
Cummings, Keith
Drobilek, Mark
Johansson, Erik
Marino, Jose
Rampy, Rachel
Richards, Kit
Rimmele, Thomas
Sekulic, Predrag
Woger, Friedrich
BE Close, LM
Schreiber, L
Schmidt, D
TI Laboratory integration of the DKIST wavefront correction system
SO ADAPTIVE OPTICS SYSTEMS VI
SE Proceedings of SPIE
LA English
DT Proceedings Paper
CT Conference on Adaptive Optics Systems VI
CY JUN 10-15, 2018
CL Austin, TX
SP SPIE, 4D Technol, Andor Technol Ltd, Astron Consultants & Equipment, Inc, Giant Magellan Telescope, GPixel, Inc, Harris Corp, Mater Corp, Optimax Syst, Inc, Princeton Infrared Technologies, Symetrie, Teledyne Technologies Inc, Thirty Meter Telescope
DE adaptive optics; DKIST; project status; solar AO; solar physics;
integration; commissioning; laboratory testing
AB The Wavefront Correction (WFC) system for the Daniel K. Inouye Solar Telescope (DKIST) is in its final stages of laboratory integration. All optical, mechanical, and software components have been unit tested and installed and aligned in our laboratory testbed in Boulder, CO. We will verify all aspects of WFC system performance in the laboratory before disassembling and shipping it to Maui for final integration with the DKIST in early 2019.
The DKIST Adaptive Optics (AO) system contains a 1600-actuator deformable mirror, a correlating Shack-Hartmann wavefront sensor, a fast tip-tilt mirror, and an FPGA-based control system. Running at a nominal rate of 1975 Hz, the AO system will deliver diffraction-limited images to five of the DKIST science instruments simultaneously. The DKIST AO system is designed to achieve the diffraction limit (on-axis Strehl > 0.3) at wavelengths up to 500 nm in median daytime seeing (r(0) = 7 cm).
In addition to AO for diffraction-limited observing, the DKIST WFC system has a low-order wavefront sensor for sensing quasi-static wavefront errors, a context viewer for telescope pointing and image quality assessment, and an active optics engine. The active optics engine uses inputs from the low-order wavefront sensor and the AO system to actively correct for telescope misalignment. All routine alignment and calibration procedures are automated via motorized stages that can be controlled from Python scripts.
We present the current state of the WFC system as we prepare for final integration with the DKIST, including verification test design, system performance metrics, and laboratory test data.
C1 [Johnson, Luke C.; Drobilek, Mark; Johansson, Erik; Marino, Jose; Rampy, Rachel; Richards, Kit; Rimmele, Thomas; Woger, Friedrich] Natl Solar Observ, 3665 Discovery Dr, Boulder, CO 80303 USA.
[Cummings, Keith] Natl Solar Observ, 8 Kiopaa St Ste 201, Pukalani, HI USA.
[Sekulic, Predrag] Natl Solar Observ, 950 Cherry Ave, Tucson, AZ USA.
RP Johnson, LC (corresponding author), Natl Solar Observ, 3665 Discovery Dr, Boulder, CO 80303 USA.
EM ljohnson@nso.edu
FU National Science FoundationNational Science Foundation (NSF)
FX The Daniel K. Inouye Solar Telescope is a facility of the National Solar
Observatory (NSO). NSO is managed by the Association of Universities for
Research in Astronomy, Inc., and is funded by the National Science
Foundation. Any opinions, findings and conclusions or recommendations
expressed in this publication are those of the author(s) and do not
necessarily reflect the views of the National Science Foundation or the
Association of Universities for Research in Astronomy, Inc.
CR Bret S. B. W., 2004, P SOC PHOTO-OPT INS, V5496, P5496
BROWN TM, 1982, ASTRON ASTROPHYS, V116, P260
Craig S, 2016, PROC SPIE, V9911, DOI 10.1117/12.2233654
Johansson E., 2018, P SOC PHOTO-OPT INS, P10703
Johnson L. C., 2012, P SPIE, V8447
Johnson L. C., 2016, P SPIE INT SOC OPT E, V9909, P99090
Johnson L. C., 2012, P SPIE, V8444
Johnson LC, 2014, PROC SPIE, V9148, DOI 10.1117/12.2056990
Meimon S, 2015, OPT EXPRESS, V23, P27134, DOI 10.1364/OE.23.027134
Robinson D, 2004, IEEE T IMAGE PROCESS, V13, P1185, DOI 10.1109/TIP.2004.832923
WANG JY, 1978, J OPT SOC AM, V68, P78, DOI 10.1364/JOSA.68.000078
NR 11
TC 1
Z9 1
U1 0
U2 0
PU SPIE-INT SOC OPTICAL ENGINEERING
PI BELLINGHAM
PA 1000 20TH ST, PO BOX 10, BELLINGHAM, WA 98227-0010 USA
SN 0277-786X
EI 1996-756X
BN 978-1-5106-1960-9
J9 PROC SPIE
PY 2018
VL 10703
AR UNSP 107030F
DI 10.1117/12.2314012
PG 9
WC Optics
SC Optics
GA BL5UM
UT WOS:000452819300012
DA 2021-04-21
ER
PT S
AU Por, EH
Haffert, SY
Radhakrishnan, VM
Doelman, DS
van Kooten, M
Bos, SP
AF Por, Emiel H.
Haffert, Sebastiaan Y.
Radhakrishnan, Vikram M.
Doelman, David S.
van Kooten, Maaike
Bos, Steven P.
BE Close, LM
Schreiber, L
Schmidt, D
TI High Contrast Imaging for Python (HCIPy): an open-source adaptive optics
and coronagraph simulator
SO ADAPTIVE OPTICS SYSTEMS VI
SE Proceedings of SPIE
LA English
DT Proceedings Paper
CT Conference on Adaptive Optics Systems VI
CY JUN 10-15, 2018
CL Austin, TX
SP SPIE, 4D Technol, Andor Technol Ltd, Astron Consultants & Equipment, Inc, Giant Magellan Telescope, GPixel, Inc, Harris Corp, Mater Corp, Optimax Syst, Inc, Princeton Infrared Technologies, Symetrie, Teledyne Technologies Inc, Thirty Meter Telescope
DE HCIPy; Python; Simulations; High Contrast Imaging; Adaptive Optics;
Coronagraphy; Open source
ID WAVE-FRONT SENSOR; PLANET DETECTION; PHASE
AB HCIPy is a package written in Python for simulating the interplay between wavefront control and coronagraphic systems. By defining an element which merges values/coefficients with its sampling grid/modal basis into a single object called Field, this minimizes errors in writing the code and makes it clearer to read. HCIPy provides a monochromatic Wavefront and defines a Propagator that acts as the transformation between two wavefronts. In this way a Propagator acts as any physical part of the optical system, be it a piece of free space, a thin complex apodizer or a microlens array.
HCIPy contains Fraunhofer and Fresnel propagators through free space. It includes an implementation of a thin complex apodizer, which can modify the phase and/or amplitude of a wavefront, and forms the basis for more complicated optical elements. Included in HCIPy are wavefront errors (modal, power spectra), complex apertures (VLT, Keck or Subaru pupil), coronagraphs (Lyot, vortex or apodizing phase plate coronagraph), deformable mirrors, wavefront sensors (Shack-Hartmann, Pyramid, Zernike or phase-diversity wavefront sensor) and multi-layer atmospheric models including scintillation).
HCIPy aims to provide an easy-to-use, modular framework for wavefront control and coronagraphy on current and future telescopes, enabling rapid prototyping of the full high-contrast imaging system. Adaptive optics and coronagraphic systems can be easily extended to include more realistic physics. The package includes a complete documentation of all classes and functions, and is available as open-source software.
C1 [Por, Emiel H.; Haffert, Sebastiaan Y.; Radhakrishnan, Vikram M.; Doelman, David S.; van Kooten, Maaike; Bos, Steven P.] Leiden Univ, Leiden Observ, POB 9513, NL-2300 RA Leiden, Netherlands.
RP Por, EH (corresponding author), Leiden Univ, Leiden Observ, POB 9513, NL-2300 RA Leiden, Netherlands.
EM por@strw.leidenuniv.nl
OI Radhakrishnan, Vikram Mark/0000-0003-4122-4046; Doelman,
David/0000-0003-0695-0480; Por, Emiel/0000-0002-3961-083X
FU Netherlands Organisation for Scientific Research (NWO)Netherlands
Organization for Scientific Research (NWO); Sao Paulo Research
Foundation (FAPESP)Fundacao de Amparo a Pesquisa do Estado de Sao Paulo
(FAPESP)
FX EP acknowledges funding by The Netherlands Organisation for Scientific
Research (NWO) and the Sao Paulo Research Foundation (FAPESP).
CR Assemat F, 2006, OPT EXPRESS, V14, P988, DOI 10.1364/OE.14.000988
Basden A, 2010, APPL OPTICS, V49, P6354, DOI 10.1364/AO.49.006354
Baudoz P., 2005, P INT ASTRON UNION, V1, P553
Bloemhof EE, 2003, P SOC PHOTO-OPT INS, V5169, P309, DOI 10.1117/12.507245
Bos S. P., 2018, P SPIE
Bos SP, 2017, PROC SPIE, V10407, DOI 10.1117/12.2273341
Carlotti A, 2011, OPT EXPRESS, V19, P26796, DOI 10.1364/OE.19.026796
Cavarroc C, 2006, ASTRON ASTROPHYS, V447, P397, DOI 10.1051/0004-6361:20053916
Codona JL, 2004, ASTROPHYS J, V604, pL117, DOI 10.1086/383569
Foo G, 2005, OPT LETT, V30, P3308, DOI 10.1364/OL.30.003308
Frigo M, 2005, P IEEE, V93, P216, DOI 10.1109/JPROC.2004.840301
GONSALVES RA, 1982, OPT ENG, V21, P829, DOI 10.1117/12.7972989
Gonzalez CAG, 2017, ASTRON J, V154, DOI 10.3847/1538-3881/aa73d7
Gratadour D, 2014, PROC SPIE, V9148, DOI 10.1117/12.2056358
Gurobi Optimization I, 2016, GUROBI OPTIMIZER REF
Guyon O, 2006, ASTROPHYS J SUPPL S, V167, P81, DOI 10.1086/507630
Haffert SY, 2016, OPT EXPRESS, V24, P18986, DOI 10.1364/OE.24.018986
Hirsch M, 2013, PLOS ONE, V8, DOI 10.1371/journal.pone.0053671
Hunter JD, 2007, COMPUT SCI ENG, V9, P90, DOI 10.1109/MCSE.2007.55
Jones E., 2014, SCIPY OPEN SOURCE SC
Jovanovic N., 2018, P SPIE
Kenworthy MA, 2007, ASTROPHYS J, V660, P762, DOI 10.1086/513596
Konnik Mikhail V, 2014, ARXIV14124031
Krist JE, 2007, P SOC PHOTO-OPT INS, V6675, pP6750, DOI 10.1117/12.731179
Le Louarn M, 2006, PROC SPIE, V6272, pU1048, DOI 10.1117/12.670187
Mawet D, 2005, ASTROPHYS J, V633, P1191, DOI 10.1086/462409
Mawet D, 2013, ASTROPHYS J SUPPL S, V209, DOI 10.1088/0067-0049/209/1/7
Nickolls John, 2008, ACM Queue, V6, DOI 10.1145/1365490.1365500
Perrin MD, 2012, PROC SPIE, V8442, DOI 10.1117/12.925230
Por EH, 2017, PROC SPIE, V10400, DOI 10.1117/12.2274219
Radhakrishnan V. M., 2018, P SPIE
Ragazzoni R, 2002, OPT COMMUN, V208, P51, DOI 10.1016/S0030-4018(02)01580-8
Ragazzoni R, 1996, J MOD OPTIC, V43, P289, DOI 10.1080/095003496156165
Reeves A, 2016, PROC SPIE, V9909
Rigaut F., 2002, YAO ADAPTIVE OPTICS
Snik F., 2012, P SOC PHOTO-OPT INS, V8450, P84500
Soummer R, 2007, OPT EXPRESS, V15, P15935, DOI 10.1364/OE.15.015935
SPRAGUE RA, 1972, APPL OPTICS, V11, P1469, DOI 10.1364/AO.11.001469
Stone JE, 2010, COMPUT SCI ENG, V12, P66, DOI 10.1109/MCSE.2010.69
Taylor GI, 1938, PROC R SOC LON SER-A, V164, P0476, DOI 10.1098/rspa.1938.0032
The Astropy Collaboration, 2018, ARXIV E PRINTS
van der Walt S, 2011, COMPUT SCI ENG, V13, P22, DOI 10.1109/MCSE.2011.37
Wang J.J., 2015, ASTROPHYSICS SOURCE
Wilby MJ, 2017, ASTRON ASTROPHYS, V597, DOI 10.1051/0004-6361/201628628
Zernike F., 1935, Z TECHN PHYS, V16, P454
Zimmerman NT, 2016, J ASTRON TELESC INST, V2, DOI 10.1117/1.JATIS.2.1.011012
NR 46
TC 5
Z9 5
U1 0
U2 0
PU SPIE-INT SOC OPTICAL ENGINEERING
PI BELLINGHAM
PA 1000 20TH ST, PO BOX 10, BELLINGHAM, WA 98227-0010 USA
SN 0277-786X
EI 1996-756X
BN 978-1-5106-1960-9
J9 PROC SPIE
PY 2018
VL 10703
AR UNSP 1070342
DI 10.1117/12.2314407
PG 14
WC Optics
SC Optics
GA BL5UM
UT WOS:000452819300121
DA 2021-04-21
ER
PT S
AU Cieszewski, R
Pozniak, K
Romaniuk, R
Linczuk, M
AF Cieszewski, Radoslaw
Pozniak, Krzysztof
Romaniuk, Ryszard
Linczuk, Maciej
BE Romaniuk, RS
Linczuk, M
TI Widely parameterizable High-Level Synthesis
SO PHOTONICS APPLICATIONS IN ASTRONOMY, COMMUNICATIONS, INDUSTRY, AND
HIGH-ENERGY PHYSICS EXPERIMENTS 2018
SE Proceedings of SPIE
LA English
DT Proceedings Paper
CT SPIE-IEEE-PSP WILGA on Photonics Applications in Astronomy,
Communications, Industry, and High-Energy Physics Experiments
CY JUN 03-10, 2018
CL Wilga, POLAND
SP Warsaw Univ Technol, Inst Elect Syst, Fac Elect & Informat Technologies, Photon Soc Poland, Polish Acad Sci, Comm Elect & Telecommunicat, Accelerator Res & Innovat European Sci & Soc, Assoc Polish Elect Engineers, Polish Comm Optoelectron, EuroFus Collaborat, EuroFus Poland, SPIE, Polish Chapter, IEEE, Poland Sect, WILGA
DE High-Level Synthesis; graph partitioning; FPGA; Algorithmic Synthesis;
Behavioral Synthesis; Hot Plasma Physics Experiment; networkx
AB In recent years, HLS compilers are gaining increasing popularity. This popularity is due to the fact that FPGA chips can achieve higher computing power than traditional CPUs in fine-grained algorithms. The greatest development of compilers were in recent years. Both commercial and open solutions are being developed. The most difficult part of compilers are algorithms responsible for converting code from a high high level of abstraction to low. In commercial solutions, these algorithms are closed as a "black box" and open solutions have implemented rather simple algorithms. The article presents an alternative, open solution of a high-level synthesis compiler (HLS) implemented in Python with its algorithms. The compiler, based on Python's high-level functional description, generates a configuration that allows the creation of a given structure in the FPGA system during the synthesis process. The article describes the design methods, tools and implementation of the developed Python-VHDL compiler with examples of its use.
C1 [Cieszewski, Radoslaw; Pozniak, Krzysztof; Romaniuk, Ryszard; Linczuk, Maciej] Warsaw Univ Technol, Inst Elect Syst, Nowowiejska 15-19, PL-00665 Warsaw, Poland.
RP Cieszewski, R (corresponding author), Warsaw Univ Technol, Inst Elect Syst, Nowowiejska 15-19, PL-00665 Warsaw, Poland.
EM R.Cieszewski@stud.elka.pw.edu.pl
RI Romaniuk, Ryszard S/B-9140-2011; Pozniak, Krzysztof/AAO-7377-2020
OI Romaniuk, Ryszard S/0000-0002-5710-4041; Pozniak,
Krzysztof/0000-0001-5426-1423
CR Asanovic K, 2009, COMMUN ACM, V52, P56, DOI 10.1145/1562764.1562783
Berdychowski PP, 2010, PHOTONICS APPL ASTRO
Bowyer B., 2005, WHY WHAT ALGORITHMIC
Cieszewski Radoslaw, 2017, Elektronika, V58, P31, DOI 10.15199/13.2017.8.7
Cong J, 2011, IEEE T COMPUT AID D, V30, P473, DOI 10.1109/TCAD.2011.2110592
Coussy P., 2009, IEEE DESIGN TEST COM, V26
Gajski Daniel D., 2012, SPRINGER SCI BUSINES
Gajski DD, 1992, HIGH LEVEL SYNTHESIS, V34
Kolasinski P., 2007, PHOTONICS APPL ASTRO
Meredith M., 2004, EETIMES, P04
Wojenski A., 2017, MEASUREMENT AUTOMATI, V6, P223
Wojenski A., 2017, MULTICHANNEL MEASURE, V121, P1, DOI [10.1016/j.fusengdes.2017.04.134, DOI 10.1016/J.FUSENGDES.2017.04.134]
Wojenski AJ, 2015, NUCL INSTRUM METH B, V364, P49, DOI 10.1016/j.nimb.2015.06.022
Zabolotny WM, 2017, J INSTRUM, V12, DOI 10.1088/1748-0221/12/02/C02060
Zabolotny WM, 2017, J INSTRUM, V12, DOI 10.1088/1748-0221/12/01/C01050
Zabolotny WM, 2011, PHOTONICS APPL ASTRO
Zabolotny WM, 2010, PHOTONICS APPL ASTRO
Zabolotny WM, 2006, PROC SPIE, V6347, DOI 10.1117/12.714532
NR 18
TC 0
Z9 0
U1 0
U2 0
PU SPIE-INT SOC OPTICAL ENGINEERING
PI BELLINGHAM
PA 1000 20TH ST, PO BOX 10, BELLINGHAM, WA 98227-0010 USA
SN 0277-786X
EI 1996-756X
BN 978-1-5106-2204-3
J9 PROC SPIE
PY 2018
VL 10808
AR 108084D
DI 10.1117/12.2502153
PG 7
WC Optics
SC Optics
GA BL4TX
UT WOS:000450820000157
DA 2021-04-21
ER
PT S
AU Morra, G
AF Morra, Gabriele
BA Morra, G
BF Morra, G
TI Mechanics I: Kinematics
SO PYTHONIC GEODYNAMICS: IMPLEMENTATIONS FOR FAST COMPUTING
SE Lecture Notes in Earth System Sciences
LA English
DT Article; Book Chapter
AB It is shown here how to use Finite Differences to calculate displacement, velocity, and acceleration using a discretized approximation, that in a general undergraduate Physics course would be obtained by using derivatives and integrals. Both ways are illustrated (i) the calculation of the kinematic quantities from a known trajectory, and (ii) the calculation of the trajectory by knowing the acceleration field in the space. The possible errors that can be made are illustrated in detail, in particular how a small error in calculating a trajectory can amplify when solving for a long trajectory.
In this and in the following chapter, we will consider very simple problems that are normally encountered in a general Physics course, relative to Kinematic and Newtonian mechanics. We will focus on how all these problems can be solved by using a numerical approach only, without any analytical calculation. For many of the cases considered here, it is simply an overkill to use a computer to solve them, however because of their simplicity they are an excellent way to introduce an inexpert reader to new numerical techniques.
The strategies that we use here will be useful in the rest of the book to understand how to calculate the motion of particles in the Particles in Cell method. They will give us the opportunity to simply introduce Eulerian (mesh based) and Lagrangian (particle based) approach that are essential when solving problem in the more complex deformable media.
In this and the next chapter, we will initially assume that every object is rigid, and introduce only generically the concept of strain, i.e., the quantification of the deformation of the objects themselves. In fact while the velocity and the acceleration are the derivative of the displacement in time, the strain is the derivative of the displacement in space, and the strain rate both the derivative of the displacement in space and in time. With them, combined with the diffusion process, we can describe every process that happens in the solid interiors of a planet (the liquid core, as well as stars interiors, require also the treatment of the electromagnetic equations that go beyond the scope of this introductory text).
Overall the goal of this chapter is to start to familiarize with programming in Python and to understand the importance of the concepts of momentum and energy. In all the problems in this chapters only the time is discretized, meaning that the solutions are calculated for a time t and then for the next time step at t + Delta t, where Delta t can be constant (regular discretization), or vary. The discretization of the physical space, instead, will be introduced in Chap. 7 together with the equations that control the deformation of a stressed body.
C1 [Morra, Gabriele] Univ Louisiana Lafayette, Dept Phys, Lafayette, LA 70504 USA.
[Morra, Gabriele] Univ Louisiana Lafayette, Sch Geosci, Lafayette, LA 70504 USA.
RP Morra, G (corresponding author), Univ Louisiana Lafayette, Dept Phys, Lafayette, LA 70504 USA.; Morra, G (corresponding author), Univ Louisiana Lafayette, Sch Geosci, Lafayette, LA 70504 USA.
NR 0
TC 0
Z9 0
U1 0
U2 0
PU SPRINGER INTERNATIONAL PUBLISHING AG
PI CHAM
PA GEWERBESTRASSE 11, CHAM, CH-6330, SWITZERLAND
SN 2193-8571
EI 2193-858X
BN 978-3-319-55682-6; 978-3-319-55680-2
J9 LECT N EARTH SYST SC
PY 2018
BP 63
EP 75
DI 10.1007/978-3-319-55682-6_4
D2 10.1515/9783110317794
PG 13
WC Computer Science, Interdisciplinary Applications; Geosciences,
Multidisciplinary
SC Computer Science; Geology
GA BL3LQ
UT WOS:000449857100006
DA 2021-04-21
ER
PT J
AU Derouillat, J
Beck, A
Perez, F
Vinci, T
Chiaramello, M
Grassi, A
Fle, M
Bouchard, G
Plotnikov, I
Aunai, N
Dargent, J
Riconda, C
Grech, M
AF Derouillat, J.
Beck, A.
Perez, F.
Vinci, T.
Chiaramello, M.
Grassi, A.
Fle, M.
Bouchard, G.
Plotnikov, I.
Aunai, N.
Dargent, J.
Riconda, C.
Grech, M.
TI SMILEI: A collaborative, open-source, multi-purpose particle-in-cell
code for plasma simulation
SO COMPUTER PHYSICS COMMUNICATIONS
LA English
DT Article
DE Plasma kinetic simulation; Particle-In-Cell (PIC); High-performance
computing; Laser-plasma interaction; Astrophysical plasmas
ID STIMULATED SCATTERING; ENERGY-LOSS; LASER; ACCELERATION; LIGHT; BEAMS;
IONIZATION; COLLISIONS; ELECTRONS; RADIATION
AB SMILE! is a collaborative, open-source, object-oriented (C++) particle-in-cell code. To benefit from the latest advances in high-performance computing (HPC), SMILEI is co-developed by both physicists and HPC experts. The code's structures, capabilities, parallelization strategy and performances are discussed. Additional modules (e.g. to treat ionization or collisions), benchmarks and physics highlights are also presented. Multi-purpose and evolutive, SMILEI is applied today to a wide range of physics studies, from relativistic laser-plasma interaction to astrophysical plasmas.
Program summary
Program title: SMILEI (version 3.2)
Program Files doi: http://dx.doi.org/10.17632/gsn4x6mbrg.1
Licensing provisions: This version of the code is distributed under the GNU General Public License v3
Programming language: C++11, Python 2.7
Nature of the problem: The kinetic simulation of plasmas is at the center of various physics studies, from laser plasma interaction to astrophysics. To address today's challenges, a versatile simulation tool requires high-performance computing on massively parallel super-computers.
Solution method: The Vlasov-Maxwell system describing the self-consistent evolution of a collisionless plasma is solved using the Particle-In-Cell (PIC) method. Additional physics modules allow to account for additional effects such as collisions and/or ionization. A hybrid MPI-OpenMP strategy, based on a patch based super-decomposition, allows for efficient cache-use, dynamic load balancing and high-performance on massively parallel super-computers.
Additional comments: Repository https://github.com/SmileiPlC/Smilei
References: http://www.maisondelasimulation.fr/smilei (C) 2017 Published by Elsevier B.V.
C1 [Derouillat, J.] Univ Paris Saclay, UVSQ, Univ Paris Sud, CEA,Maison Simulat, F-91191 Gif Sur Yvette, France.
[Beck, A.] Ecole Polytech, CNRS, IN2P3, Lab Leprince Ringuet, F-91128 Palaiseau, France.
[Perez, F.; Vinci, T.; Grech, M.] Sorbonne Univ, UPMC Univ Paris 06, Univ Paris Saclay, Ecole Polytech,CNRS,CEA,Lab Utilisat Lasers Inten, F-91128 Palaiseau, France.
[Chiaramello, M.; Grassi, A.; Riconda, C.] Univ Paris Saclay, Sorbonne Univ, UPMC Univ Paris 06, CNRS,Ecole Polytech,CEA,Lab Utilisat Lasers Inten, F-75252 Paris 05, France.
[Grassi, A.] Univ Pisa, Dipartimento Fis Enrico Fermi, Largo Bruno Pontecorvo 3, I-56127 Pisa, Italy.
[Grassi, A.] Ist Nazl Ottica, CNR, Uos Adriano Gozzini, I-56127 Pisa, Italy.
[Fle, M.] Inst Dev Ressources Informat Sci, CNRS, I-56127 Pisa, Italy.
[Bouchard, G.] Univ Paris Saclay, CEN Saclay, DSM IRAMIS, CEA,Lasers Interact & Dynam Lab, F-91191 Gif Sur Yvette, France.
[Plotnikov, I.; Dargent, J.] Univ Toulouse, UPS OMP, Inst Rech Astrophys & Planetol, F-31400 Toulouse, France.
[Aunai, N.; Dargent, J.] Univ Paris Sud, UPMC, Ecole Polytech, CNRS,Lab Phys Plasmas, F-91128 Palaiseau, France.
RP Grech, M (corresponding author), Sorbonne Univ, UPMC Univ Paris 06, Univ Paris Saclay, Ecole Polytech,CNRS,CEA,Lab Utilisat Lasers Inten, F-91128 Palaiseau, France.
EM mickael.grech@polytechnique.edu
RI Grassi, Anna/AAG-9663-2019; Grech, Mickael/H-3587-2011
OI Grassi, Anna/0000-0003-3314-7060; Grech, Mickael/0000-0002-3351-0635;
Vinci, Tommaso/0000-0002-1595-1752
FU Investissements d'Avenir of the PALM LabExFrench National Research
Agency (ANR) [ANR-10-LABX-0039-PALM]; Plas@Par LabEx
[ANR-11-IDEX-0004-02]; Universite Franco-Italienne through the Vinci
program [C2-133]; ANRFrench National Research Agency (ANR)
[ANR-13-PDOC-0027]; ANR MACH projectFrench National Research Agency
(ANR) [ANR-14-CE33-0019 MACH]; GENCI-IDRIS Grands Challenges,
GENCI-IDRIS/TGCC [2016-x2016057678, 2017-x2016057678]; GENCI-CINES
[2016-c2016067484]
FX The authors are grateful to L. Gremillet, M. Lobet, R. Nuter and A.
Sgattoni for fruitful discussions, and Ph. Savoini for feedback on the
code. MG and GB thank F. Que're and H. Vincenti for sharing physics
insights. Financial support from the Investissements d'Avenir of the
PALM LabEx (ANR-10-LABX-0039-PALM, Junior Chair SimPLE) and from the
Plas@Par LabEx (ANR-11-IDEX-0004-02) are acknowledged. AG acknowledges
financial support from the Universite Franco-Italienne through the Vinci
program (Grant No. C2-133). NA and JDa thank the ANR (project
ANR-13-PDOC-0027) for funding their research. MG personally thanks the
collaboration federated around the ANR MACH project (ANR-14-CE33-0019
MACH). This work was performed using HPC resources from GENCI-IDRIS
Grands Challenges 2015, GENCI-IDRIS/TGCC (Grants 2016-x2016057678 and
2017-x2016057678), GENCI-CINES (Grant 2016-c2016067484) and GENCI-CINES
(Special allocation no t201604s020).
CR Ammosov M. V., 1986, Soviet Physics - JETP, V64, P1191
Andreev AA, 2006, PHYS PLASMAS, V13, DOI 10.1063/1.2201896
Arenberg JW, 2014, PROC SPIE, V9237, DOI 10.1117/12.2068336
Barucq H, 1997, ASYMPTOTIC ANAL, V15, P25
Beck A, 2016, NUCL INSTRUM METH A, V829, P418, DOI 10.1016/j.nima.2016.03.112
BERENGER JP, 1994, J COMPUT PHYS, V114, P185, DOI 10.1006/jcph.1994.1159
Birsall C. K., 1985, PLASMA PHYS VIA COMP
BLANDFORD RD, 1976, PHYS FLUIDS, V19, P1130, DOI 10.1063/1.861619
Blasi P, 2013, ASTRON ASTROPHYS REV, V21, DOI 10.1007/s00159-013-0070-7
Boris J., 1970, P 4 C NUM SIM PLASM, P3
Bret A, 2010, PHYS PLASMAS, V17, DOI 10.1063/1.3514586
Cassak PA, 2007, PHYS PLASMAS, V14, DOI 10.1063/1.2795630
Chen H, 2009, PHYS REV LETT, V102, DOI 10.1103/PhysRevLett.102.105001
Chiaramello M, 2016, PHYS REV LETT, V117, DOI 10.1103/PhysRevLett.117.235003
Chiaramello M, 2016, PHYS PLASMAS, V23, DOI 10.1063/1.4955322
COHEN BI, 1979, PHYS FLUIDS, V22, P1115, DOI 10.1063/1.862713
Cros B, 2014, NUCL INSTRUM METH A, V740, P27, DOI 10.1016/j.nima.2013.10.090
Decyk VK, 2011, COMPUT PHYS COMMUN, V182, P641, DOI 10.1016/j.cpc.2010.11.009
Di Piazza A, 2012, REV MOD PHYS, V84, P1177, DOI 10.1103/RevModPhys.84.1177
Esirkepov TZ, 2001, COMPUT PHYS COMMUN, V135, P144, DOI 10.1016/S0010-4655(00)00228-9
Faure J, 2004, NATURE, V431, P541, DOI 10.1038/nature02963
Flannery Brian P., 2007, NUMERICAL RECIPIES
Fonseca RA, 2002, LECT NOTES COMPUT SC, V2331, P342
FORSLUND DW, 1975, PHYS FLUIDS, V18, P1002, DOI 10.1063/1.861248
FRANKEL NE, 1979, PHYS REV A, V20, P2120, DOI 10.1103/PhysRevA.20.2120
Fuchs J, 2014, EUR PHYS J-SPEC TOP, V223, P1169, DOI 10.1140/epjst/e2014-02169-y
Geddes CGR, 2004, NATURE, V431, P538, DOI 10.1038/nature02900
Germaschewski K, 2016, J COMPUT PHYS, V318, P305, DOI 10.1016/j.jcp.2016.05.013
GODFREY BB, 1974, J COMPUT PHYS, V15, P504, DOI 10.1016/0021-9991(74)90076-X
Golovanov AA, 2017, PHYS PLASMAS, V24, DOI 10.1063/1.4996856
Grassi A, 2017, PHYS REV E, V96, DOI 10.1103/PhysRevE.96.033204
Grassi A, 2017, PHYS REV E, V95, DOI 10.1103/PhysRevE.95.023203
Greenwood AD, 2004, J COMPUT PHYS, V201, P665, DOI 10.1016/j.jcp.2004.06.021
Harlow F.H., 1956, TECHNICAL REPORT
Haugbolle T, 2013, PHYS PLASMAS, V20, DOI 10.1063/1.4811384
Haugbolle T, 2011, ASTROPHYS J LETT, V739, DOI 10.1088/2041-8205/739/2/L42
Hesse M, 2013, PHYS PLASMAS, V20, DOI 10.1063/1.4811467
HILBERT D., 1891, MATH ANN, V38, P459, DOI [10.1007/BF01199431, DOI 10.1007/BF01199431]
Huba J. D., 2013, NRL PLASMA FORMULARY
Huebl Axel, 2015, OPENPMD 1 0 0 META D
Kim YK, 2000, PHYS REV A, V62, DOI 10.1103/PhysRevA.62.052710
Kirk JG, 1999, J PHYS G NUCL PARTIC, V25, pR163, DOI 10.1088/0954-3899/25/8/201
Lancia L, 2016, PHYS REV LETT, V116, DOI 10.1103/PhysRevLett.116.075001
Lancia L, 2010, PHYS REV LETT, V104, DOI 10.1103/PhysRevLett.104.025001
Lehe R., 2014, THESIS
Lifschitz AF, 2009, J COMPUT PHYS, V228, P1803, DOI 10.1016/j.jcp.2008.11.017
Lobet M, 2016, J PHYS CONF SER, V688, DOI 10.1088/1742-6596/688/1/012058
Macchi A, 2013, REV MOD PHYS, V85, P751, DOI 10.1103/RevModPhys.85.751
Mangles SPD, 2004, NATURE, V431, P535, DOI 10.1038/nature02939
Mulser P, 1998, PHYS PLASMAS, V5, P4466, DOI 10.1063/1.873184
Nanbu K, 1997, PHYS REV E, V55, P4642, DOI 10.1103/PhysRevE.55.4642
Nanbu K, 1998, J COMPUT PHYS, V145, P639, DOI 10.1006/jcph.1998.6049
Nuter R, 2011, PHYS PLASMAS, V18, DOI 10.1063/1.3559494
Nuter R, 2016, J COMPUT PHYS, V305, P664, DOI 10.1016/j.jcp.2015.10.057
Nuter R, 2014, EUR PHYS J D, V68, DOI 10.1140/epjd/e2014-50162-y
PERELOMOV AM, 1966, SOV PHYS JETP-USSR, V23, P924
PERELOMOV AM, 1967, SOV PHYS JETP-USSR, V24, P207
Perez F, 2012, PHYS PLASMAS, V19, DOI 10.1063/1.4742167
Pukhov A, 2002, APPL PHYS B-LASERS O, V74, P355, DOI 10.1007/s003400200795
ROHRLICH F, 1954, PHYS REV, V93, P38, DOI 10.1103/PhysRev.93.38
Sarri G, 2015, NAT COMMUN, V6, DOI 10.1038/ncomms7747
Sironi L, 2013, ASTROPHYS J, V771, DOI 10.1088/0004-637X/771/1/54
Spitkovsky A, 2008, ASTROPHYS J LETT, V682, pL5, DOI 10.1086/590248
Spohn H., 1991, LARGE SCALE DYNAMICS
Stantchev G, 2008, J PARALLEL DISTR COM, V68, P1339, DOI 10.1016/j.jpdc.2008.05.009
STUART BC, 1995, PHYS REV LETT, V74, P2248, DOI 10.1103/PhysRevLett.74.2248
Taflove A., 2005, COMPUTATION ELECTROD
TAJIMA T, 1979, PHYS REV LETT, V43, P267, DOI 10.1103/PhysRevLett.43.267
Thaury C, 2010, J PHYS B-AT MOL OPT, V43, DOI 10.1088/0953-4075/43/21/213001
Thevenet M, 2016, NAT PHYS, V12, P355, DOI 10.1038/NPHYS3597
Thiele I, 2016, J COMPUT PHYS, V321, P1110, DOI 10.1016/j.jcp.2016.06.004
Trier Frederiksen J., 2015, PARTICLE CONTROL PHA
Umstadter D, 1996, PHYS REV LETT, V76, P2073, DOI 10.1103/PhysRevLett.76.2073
Vay JL, 2008, PHYS PLASMAS, V15, DOI 10.1063/1.2837054
Vay JL, 2011, J COMPUT PHYS, V230, P5908, DOI 10.1016/j.jcp.2011.04.003
Vincenti H, 2016, COMPUT PHYS COMMUN, V200, P147, DOI 10.1016/j.cpc.2015.11.009
Weber S, 2013, PHYS REV LETT, V111, DOI 10.1103/PhysRevLett.111.055004
WEIBEL ES, 1959, PHYS REV LETT, V2, P83, DOI 10.1103/PhysRevLett.2.83
Wilson R, 2016, PHYS PLASMAS, V23, DOI 10.1063/1.4943200
WRIGHT TP, 1975, PHYS REV A, V12, P686, DOI 10.1103/PhysRevA.12.686
Zenitani S, 2015, PHYS PLASMAS, V22, DOI 10.1063/1.4919383
NR 81
TC 70
Z9 71
U1 5
U2 38
PU ELSEVIER SCIENCE BV
PI AMSTERDAM
PA PO BOX 211, 1000 AE AMSTERDAM, NETHERLANDS
SN 0010-4655
EI 1879-2944
J9 COMPUT PHYS COMMUN
JI Comput. Phys. Commun.
PD JAN
PY 2018
VL 222
BP 351
EP 373
DI 10.1016/j.cpc.2017.09.024
PG 23
WC Computer Science, Interdisciplinary Applications; Physics, Mathematical
SC Computer Science; Physics
GA FR3MD
UT WOS:000418969900027
DA 2021-04-21
ER
PT J
AU Dercks, D
Desai, N
Kim, JS
Rolbiecki, K
Tattersall, J
Weber, T
AF Dercks, Daniel
Desai, Nishita
Kim, Jong Soo
Rolbiecki, Krzysztof
Tattersall, Jamie
Weber, Torsten
TI CheckMATE 2: From the model to the limit
SO COMPUTER PHYSICS COMMUNICATIONS
LA English
DT Article
DE Confidence limits; Monte Carlo; Detector simulation; Delphes; LHC;
Recasting; Beyond the Standard Model
ID TRANSVERSE-MOMENTUM; MEASURING MASSES; MISSING ENERGY; LHC; PHYSICS;
SEARCH; SUPERSYMMETRY; ELECTRONS; EVENTS; SQUARK
AB We present the latest developments to the CheckMATE program that allows models of new physics to be easily tested against the recent LHC data. To achieve this goal, the core of CheckMATE now contains over 60 LHC analyses of which 12 are from the 13 TeV run. The main new feature is that CheckMATE 2 now integrates the Monte Carlo event generation via MadGraph5_aMC@NLO and Pythia 8. This allows users to go directly from a SLHA file or UFO model to the result of whether a model is allowed or not. In addition, the integration of the event generation leads to a significant increase in the speed of the program. Many other improvements have also been made, including the possibility to now combine signal regions to give a total likelihood for a model.
Program summary
Program Title: CheckMATE
Program Files doi:http://dx.doLorg/10.17632/k4pnk5wrfm.1
Licensing provisions: GPLv3
Programming language: C++, Python
External routines/libraries: ROOT, Python, HepMC (optional) Pythia 8 (optional), Madgraph5_aMC@NLO (optional)
Subprograms used: Delphes
Nature of problem: The LHC experiments have performed a huge number of searches for new physics in the past few years. However the results can only be given for a few benchmark models out of the huge number that exist in the literature.
Solution method: CheckMATE is a program that automatically calculates limits for new physics models. The original version required the user to generate Monte Carlo events themselves before CheckMATE could be run but the new version now integrates this step. The simplest output of CheckMATE is whether the model is ruled out at 95% CLs or not. However, more complicated statistical metrics are also available, including the combination of many signal regions.
Restrictions: Only a subset of available experimental results have been implemented.
Additional comments:
CheckMATE is built upon the tools and hard work of many people. If CheckMATE is used in your publication it is extremely important that all of the following citations are included,
- Delphes 3 [1].
https://cp3.irmp.ucl.ac.be/projects/delphes
- FastJet [2,3].
http://fastjet.fr/
- Anti-k(t) jet algorithm [4].
- CLs prescription [5].
- All experimental analyses that were used to set limits in the study and if the analysis was implemented by non-CheckMATE authors, the relevant implementation reference.
- MadGraph5_aMC@NLO [6] if it is used to calculate the hard matrix element from within CheckMATE.
https://launchpad.net/mg5amcnlo
- Pythia 8.2 [7] if showering or matching is done from within CheckMATE.
http://home.thep.lu.se/-torbjorn/Pythia.html
- The Monte Carlo event generator that was used if. hepmc or. The files were generated externally.
- In analyses that use the m(T2) kinematical discriminant [8,9] we use the mt2_bisect library [10]. We also include the M-T2(bl) and M-T2(W) derivatives [11].
http://particle.physics.ucdavis.edu/hefti/projects/doku.php?id=wimpmass
https://sites.google.com/a/ucdavis.edu/mass/
- In analyses that use the M-cr family of kinematical discriminants we use the MctLib library that includes the following variables, Ma [12], M-cr corrected [13], Ma parallel and perpendicular [14].
https://mctlib.hepforge.org/
- In analyses that use topness variable we use the topness library [15].
https://github.com/michaelgraesser/topness
- Super-Razor [16] in analyses that use this variable.
[1]). de Favereau et al. [DELPHES 3 Collaboration], JHEP 1402 (2014) 057 [arXiv:1307.6346 [hep-ex]].
[2] M. Cacciari, G. P. Salam and G. Soyez, Eur. Phys.). C 72 (2012) 1896 [arXiv:1111.6097 [hep-ph]].
[3] M. Cacciari and G. P. Salam, Phys. Lett. B 641(2006) 57 [hep-ph/0512210].
[4] M. Cacciari, G. P. Salam and G. Soyez, JHEP 0804 (2008) 063 [arXiv:0802.1189 [hep-ph]].
[5] A. L. Read, J. Phys. G 28 (2002) 2693.
[6] J. Alwall et al., JHEP 1407 (2014) 079 [arXiv:1405.0301 [hep-ph]].
[7] T. Sjostrand et al., Comput. Phys. Commun. 191 (2015) 159 [arXiv:1410.3012 [hep-ph]].
[8] C. G. Lester and D. J. Summers, Phys. Lett. B 463 (1999) 99 [hep-ph/9906349].
[9] A. Barr, C. Lester and P. Stephens, J. Phys. G 29 (2003) 2343 [hep-ph/0304226].
[10] H. C. Cheng and Z. Han, JHEP 0812 (2008) 063 [arXiv:0810.5178 [hep-ph]].
[11]Y. Bai, H. C. Cheng, J. Gallicchio and). Gu, JHEP 1207 (2012) 110 [arXiv:1203.4813 [hep-ph]].
[12] D. R. Tovey, JHEP 0804 (2008) 034 [arXiv:0802.2879 [hep-ph]].
[13] G. Polesello and D. R. Tovey, JHEP 1003 (2010) 030 [arXiv:0910.0174 [hep-ph]].
[14] K. T. Matchev and M. Park, Phys. Rev. Lett. 107 (2011) 061801 [arXiv:0910.1584 [hep-ph]].
[15] M. L. Graesser and). Shelton, Phys. Rev. Lett. 111 (2013) no.12, 121802 [arXiv:1212.4495 [hep-ph]].
[16] M. R. Buckley, J. D. Lykken, C. Rogan and M. Spiropulu, Phys. Rev. D 89 (2014) no:5, 055020 [arXiv:1310.4827 [hep-ph]]. (C) 2017 Elsevier B.V. All rights reserved.
C1 [Dercks, Daniel] Univ Hamburg, Inst Theoret Phys 2, Lumper Chaussee 149, D-22761 Hamburg, Germany.
[Dercks, Daniel] Univ Bonn, Bethe Ctr Theoret Phys, Nussallee 12, D-53115 Bonn, Germany.
[Dercks, Daniel] Univ Bonn, Phys Inst, Nussallee 12, D-53115 Bonn, Germany.
[Desai, Nishita] Univ Montpellier, CNRS, L2C, UMR 5221, F-34090 Montpellier, France.
[Desai, Nishita] Univ Montpellier, CNRS, LUPM, UMR 5299, F-34090 Montpellier, France.
[Kim, Jong Soo] Inst for Basic Sci Korea, Ctr Theoret Phys Universe, Daejeon 34051, South Korea.
[Kim, Jong Soo] Univ Autonoma Madrid, Inst Fis Teor, Calle Nicolas Cabrera 13-15, E-28049 Madrid, Spain.
[Rolbiecki, Krzysztof] Univ Warsaw, Fac Phys, Pasteura 5, PL-02093 Warsaw, Poland.
[Tattersall, Jamie; Weber, Torsten] Rhein Westfal TH Aachen, Inst Theoret Particle Phys & Cosmol, D-52056 Aachen, Germany.
RP Kim, JS (corresponding author), Inst for Basic Sci Korea, Ctr Theoret Phys Universe, Daejeon 34051, South Korea.
EM daniel.dercks@desy.de; nishita.desai@umontpellier.fr;
jongsoo.kim@tu-dortmund.de; krzysztof.rolbiecki@fuw.edu.pl;
tattersall@physik.rwth-aachen.de; torsten.weber@rwth-aachen.de
RI , Krzysztof/R-3697-2019; Kim, Jong Soo/G-6307-2018
OI , Krzysztof/0000-0002-9645-9670; Kim, Jong Soo/0000-0002-1244-4181
FU BMBFFederal Ministry of Education & Research (BMBF) [00160200]; OCEVU
Labex [ANR-11-LABX-0060]; A*MIDEX project - French Government programme
"Investissements d'Avenir"French National Research Agency (ANR)
[ANR-11-IDEX-0001-02]; German Research Foundation (DFG) through the
Forschergruppe New Physics at the Large Hadron ColliderGerman Research
Foundation (DFG) [FOR 2239]; IBS [IBS-R018-D1]; MINECO, Spain
[FPA2013-44773-P]; Consolider-Ingenio CPANSpanish Government
[CSD2007-00042]; Spanish MINECO Centro de excelencia Severo Ochoa
Program [SEV-2012-0249]; National Science Centre (Poland)National
Science Centre, Poland [2015/19/D/ST2/03136]; Collaborative Research
Center of the DFG, "Particles, Strings, and the Early Universe" [SFB676]
FX The work has been supported by the BMBF grant 00160200. ND acknowledges
partial support of the OCEVU Labex (ANR-11-LABX-0060), the A*MIDEX
project (ANR-11-IDEX-0001-02) funded by the French Government programme
"Investissements d'Avenir" and the German Research Foundation (DFG)
through the Forschergruppe New Physics at the Large Hadron Collider (FOR
2239). The work of JSK was supported by IBS under the project code,
IBS-R018-D1 and was partially supported by the MINECO, Spain, under
contract FPA2013-44773-P; Consolider-Ingenio CPAN CSD2007-00042 and the
Spanish MINECO Centro de excelencia Severo Ochoa Program under grant
SEV-2012-0249. KR was supported by the National Science Centre (Poland)
under Grant 2015/19/D/ST2/03136 and the Collaborative Research Center
SFB676 of the DFG, "Particles, Strings, and the Early Universe".
CR Aaboud M, 2016, PHYS REV D, V94, DOI 10.1103/PhysRevD.94.052009
Aaboud M, 2016, PHYS REV D, V94, DOI 10.1103/PhysRevD.94.032005
Aaboud M, 2016, EUR PHYS J C, V76, DOI 10.1140/epjc/s10052-016-4184-8
Aaboud M, 2016, J HIGH ENERGY PHYS, DOI 10.1007/JHEP06(2016)059
Aad G, 2015, PHYS REV LETT, V114, DOI 10.1103/PhysRevLett.114.191803
Aad G, 2016, EUR PHYS J C, V76, DOI 10.1140/epjc/s10052-016-4397-x
Aad G, 2016, PHYS REV D, V94, DOI 10.1103/PhysRevD.94.032003
Aad G, 2016, EUR PHYS J C, V76, DOI 10.1140/epjc/s10052-016-4095-8
Aad G, 2016, EUR PHYS J C, V76, DOI 10.1140/epjc/s10052-016-4120-y
Aad G, 2015, EUR PHYS J C, V75, DOI 10.1140/epjc/s10052-015-3518-2
Aad G, 2014, J HIGH ENERGY PHYS, DOI 10.1007/JHEP11(2014)118
Aad G, 2015, PHYS REV D, V91, DOI 10.1103/PhysRevD.91.012008
Aad G, 2014, J HIGH ENERGY PHYS, DOI 10.1007/JHEP09(2014)176
Aad G, 2014, PHYS REV D, V90, DOI 10.1103/PhysRevD.90.052008
Aad G, 2014, J HIGH ENERGY PHYS, DOI 10.1007/JHEP06(2014)035
Aad G, 2014, J HIGH ENERGY PHYS, DOI 10.1007/JHEP04(2014)169
Aad G, 2013, J HIGH ENERGY PHYS, DOI 10.1007/JHEP10(2013)189
AAD G, 2014, J HIGH ENERGY PHYS
AAD G, 2014, EUR PHYS J C, V74
AAD G, 2014, J HIGH ENERGY PHYS
AAD G, 2015, EUR PHYS J C, V75
Aad G., 2016, J HIGH ENERGY PHYS, V08
Allanach BC, 2009, COMPUT PHYS COMMUN, V180, P8, DOI 10.1016/j.cpc.2008.08.004
Alloul A, 2014, COMPUT PHYS COMMUN, V185, P2250, DOI 10.1016/j.cpc.2014.04.012
Alves D, 2012, J PHYS G NUCL PARTIC, V39, DOI 10.1088/0954-3899/39/10/105005
Alwall J, 2007, COMPUT PHYS COMMUN, V176, P300, DOI 10.1016/j.cpc.2006.11.010
Alwall J, 2014, J HIGH ENERGY PHYS, DOI 10.1007/JHEP07(2014)079
Alwall J, 2009, PHYS REV D, V79, DOI 10.1103/PhysRevD.79.075020
[Anonymous], 2012, ATLASCONF2012147 CER
[Anonymous], 2014, ATLPHYSPUB2014010 CE
[Anonymous], 2013, ATLPHYSPUB2013009 CE
[Anonymous], 2013, ATLASCONF2013061 CER
[Anonymous], 2015, ATLASCONF2015082 CER
[Anonymous], 2013, ATLASCONF2013049 CER
[Anonymous], 2015, CMSPASSUS15011 CERN
[Anonymous], 2013, ATLPHYSPUB2013011 CE
[Anonymous], 2015, ATLPHYSPUB2015022 CE
[Anonymous], 2016, ATLASCONF2016013 CER
[Anonymous], 2012, ATLASCONF2012104 CER
[Anonymous], 2013, ATLPHYSPUB2013004 CE
[Anonymous], 2013, ATLASCONF2013024 CER
Arkani-Hamed N., 2007, ARXIVHEPPH0703088
ARNISON G, 1983, PHYS LETT B, V122, P103, DOI 10.1016/0370-2693(83)91177-2
ATLAS Collaboration, 2012, ATLASCONF2012040 CER
ATLAS Collaboration, 2012, ATLASCONF2012097 CER
ATLAS Collaboration, 2016, ATLASCONF2016076 CER
ATLAS Collaboration, 2012, ATLASCONF2012043 CER
ATLAS Collaboration, 2016, ATLASCONF2016024 CER
Baek S, 2016, J HIGH ENERGY PHYS, DOI 10.1007/JHEP10(2016)117
Bai Y, 2012, J HIGH ENERGY PHYS, DOI 10.1007/JHEP07(2012)110
Ball RD, 2013, NUCL PHYS B, V867, P244, DOI 10.1016/j.nuclphysb.2012.10.003
BANNER M, 1983, PHYS LETT B, V122, P476, DOI 10.1016/0370-2693(83)91605-2
Barducci D, 2015, COMPUT PHYS COMMUN, V197, P263, DOI 10.1016/j.cpc.2015.08.016
BARGER V, 1987, PHYS REV D, V36, P295, DOI 10.1103/PhysRevD.36.295
Barr A, 2003, J PHYS G NUCL PARTIC, V29, P2343, DOI 10.1088/0954-3899/29/10/304
Barr AJ, 2010, J PHYS G NUCL PARTIC, V37, DOI 10.1088/0954-3899/37/12/123001
Beenakker W, 1998, NUCL PHYS B, V515, P3, DOI 10.1016/S0550-3213(98)00014-5
Beenakker W, 1997, NUCL PHYS B, V492, P51, DOI 10.1016/S0550-3213(97)80027-2
Beenakker W, 2008, PHYS REV LETT, V100, DOI 10.1103/PhysRevLett.100.029901
Beenakker W, 1999, PHYS REV LETT, V83, P3780, DOI 10.1103/PhysRevLett.83.3780
Beenakker W, 2011, INT J MOD PHYS A, V26, P2637, DOI 10.1142/S0217751X11053560
Beenakker W, 2010, J HIGH ENERGY PHYS, DOI 10.1007/JHEP08(2010)098
Beenakker W, 2009, J HIGH ENERGY PHYS, DOI 10.1088/1126-6708/2009/12/041
Belyaev A, 2013, COMPUT PHYS COMMUN, V184, P1729, DOI 10.1016/j.cpc.2013.01.014
Brun R, 1997, NUCL INSTRUM METH A, V389, P81, DOI 10.1016/S0168-9002(97)00048-X
Buckley A, 2013, COMPUT PHYS COMMUN, V184, P2803, DOI 10.1016/j.cpc.2013.05.021
Buckley MR, 2014, PHYS REV D, V89, DOI 10.1103/PhysRevD.89.055020
Butterworth J. M., 2016, ARXIV 1606 05296
Cao JJ, 2015, J HIGH ENERGY PHYS, DOI 10.1007/JHEP06(2015)152
Caron S., 2014, EUR PHYS J C, V77, P257
Chatrchyan S, 2012, PHYS REV D, V85, DOI 10.1103/PhysRevD.85.012004
CHATRCHYAN S, 2013, EUR PHYS J C, V73
Chen CH, 2012, PHYS REV D, V85, DOI 10.1103/PhysRevD.85.034007
Cheng HC, 2008, J HIGH ENERGY PHYS, DOI 10.1088/1126-6708/2008/12/063
Christensen ND, 2009, COMPUT PHYS COMMUN, V180, P1614, DOI 10.1016/j.cpc.2009.02.018
Conte E, 2013, COMPUT PHYS COMMUN, V184, P222, DOI 10.1016/j.cpc.2012.09.009
Cranmer K., 2015, P 2011 EUR SCH HIGH, V2015, P267
Cranmer K, 2011, J HIGH ENERGY PHYS, DOI 10.1007/JHEP04(2011)038
de Favereau J, 2014, J HIGH ENERGY PHYS, DOI 10.1007/JHEP02(2014)057
Degrande C, 2012, COMPUT PHYS COMMUN, V183, P1201, DOI 10.1016/j.cpc.2012.01.022
Dobbs M, 2001, COMPUT PHYS COMMUN, V134, P41, DOI 10.1016/S0010-4655(00)00189-2
Dowell M., 1972, BIT (Nordisk Tidskrift for Informationsbehandling), V12, P503, DOI 10.1007/BF01932959
Drees M, 2015, COMPUT PHYS COMMUN, V187, P227, DOI 10.1016/j.cpc.2014.10.018
Dumont B, 2015, EUR PHYS J C, V75, DOI 10.1140/epjc/s10052-014-3242-3
Graesser ML, 2013, PHYS REV LETT, V111, DOI 10.1103/PhysRevLett.111.121802
Khachatryan V, 2015, J HIGH ENERGY PHYS, DOI 10.1007/JHEP06(2015)121
Khachatryan V, 2015, J HIGH ENERGY PHYS, DOI 10.1007/JHEP04(2015)124
Khachatryan V, 2015, PHYS REV D, V91, DOI 10.1103/PhysRevD.91.052018
Khachatryan V, 2011, PHYS LETT B, V698, P196, DOI 10.1016/j.physletb.2011.03.021
KHACHATRYAN V, 2015, EUR PHYS J C, V75
Kilian W, 2011, EUR PHYS J C, V71, DOI 10.1140/epjc/s10052-011-1742-y
Kim JS, 2015, COMPUT PHYS COMMUN, V196, P535, DOI 10.1016/j.cpc.2015.06.002
Kim JS, 2014, J HIGH ENERGY PHYS, DOI 10.1007/JHEP12(2014)010
Knuteson B., 2006, ARXIVHEPPH0602101
Kraml S., 2014, ARXIV14121745
Kraml S, 2014, EUR PHYS J C, V74, DOI 10.1140/epjc/s10052-014-2868-5
Kulesza A, 2009, PHYS REV D, V80, DOI 10.1103/PhysRevD.80.095004
Kulesza A, 2009, PHYS REV LETT, V102, DOI 10.1103/PhysRevLett.102.111802
Lester CG, 1999, PHYS LETT B, V463, P99, DOI 10.1016/S0370-2693(99)00945-4
LSPC Grenoble, 2014, MIN WORKSH REC ATLAS
Matchev KT, 2011, PHYS REV LETT, V107, DOI 10.1103/PhysRevLett.107.061801
Moretti M., 2001, ARXIVHEPPH0102195
Olive KA, 2014, CHINESE PHYS C, V38, DOI 10.1088/1674-1137/38/9/090001
Papucci M, 2014, EUR PHYS J C, V74, DOI 10.1140/epjc/s10052-014-3163-1
Plehn T, 2005, CZECH J PHYS, V55, pB213
Polesello G, 2010, J HIGH ENERGY PHYS, DOI 10.1007/JHEP03(2010)030
Randall L, 2008, PHYS REV LETT, V101, DOI 10.1103/PhysRevLett.101.221803
Read AL, 2002, J PHYS G NUCL PARTIC, V28, P2693, DOI 10.1088/0954-3899/28/10/313
Rogan C., 2010, ARXIV10062727
Semenov A, 2016, COMPUT PHYS COMMUN, V201, P167, DOI 10.1016/j.cpc.2016.01.003
Sjostrand T, 2008, COMPUT PHYS COMMUN, V178, P852, DOI 10.1016/j.cpc.2008.01.036
Sjostrand T, 2015, COMPUT PHYS COMMUN, V191, P159, DOI 10.1016/j.cpc.2015.01.024
Skands P, 2004, J HIGH ENERGY PHYS, DOI 10.1088/1126-6708/2004/07/036
SMITH J, 1983, PHYS REV LETT, V50, P1738, DOI 10.1103/PhysRevLett.50.1738
Spira M., 2002, P SUSY 02 DESY HAMB, P217
Staub F., 2015, ADV HIGH ENERGY PHYS, V2015, DOI [10.1155/2015/840780, DOI 10.1155/2015/840780]
Staub F, 2014, COMPUT PHYS COMMUN, V185, P1773, DOI 10.1016/j.cpc.2014.02.018
The ATLAS Collaboration, 2015, ATLASCONF2015004 CER
The ATLAS Collaboration, 2013, ATLASCONF2013089 CER
The CMS Collaboration, 2013, CMSPASSUS13013
TOVEY DR, 2008, JHEP, V804
van Neerven W., 1982, TECH REP
Wilks SS, 1938, ANN MATH STAT, V9, P60, DOI 10.1214/aoms/1177732360
NR 123
TC 84
Z9 86
U1 4
U2 15
PU ELSEVIER
PI AMSTERDAM
PA RADARWEG 29, 1043 NX AMSTERDAM, NETHERLANDS
SN 0010-4655
EI 1879-2944
J9 COMPUT PHYS COMMUN
JI Comput. Phys. Commun.
PD DEC
PY 2017
VL 221
BP 383
EP 418
DI 10.1016/j.cpc.2017.08.021
PG 36
WC Computer Science, Interdisciplinary Applications; Physics, Mathematical
SC Computer Science; Physics
GA FK3HS
UT WOS:000413376800030
DA 2021-04-21
ER
PT J
AU Renard, S
Holtz, A
Baylard, C
Banduch, M
AF Renard, Sebastien
Holtz, Andreas
Baylard, Christophe
Banduch, Martin
CA W7-X Team
TI Software tool solutions for the design of W7-X
SO FUSION ENGINEERING AND DESIGN
LA English
DT Article; Proceedings Paper
CT 29th Symposium on Fusion Technology (SOFT)
CY SEP 05-09, 2016
CL Prague, CZECH REPUBLIC
SP CAS, Inst Plasma Phys, Res Ctr Rez
DE W7-X; Digital mockup; SmarTeam; Concurrent engineering; Collision
analysis report; PDM
AB As an international research facility, the W7-X project hosts many types of components designed not only by the Max Planck Institute for Plasma Physics (IPP) teams, but also by external partners. At IPP the Design Engineering (DE) division is responsible for design and spatial integration of all the components within the Torus Hall. Two software tools, interfacing with Computer Aided Design (CAD) models, have been developed in-house to assist in the design and installation of components of W7-X. The first, the Digital MockUp (DMU) is a full scale 3D graphical representation of W7-X generated by a Python script using CATIA and information from the Product Data Management system (PDM), SmarTeam. The second, the Collision Analysis Report (CAR) is an Excel table listing the critical distances between a selection of components and their surroundings using the DMU and SmarTeam. SmarTeam along with the DMU and the CAR reduces the risk of design iteration, increases the efficiency of the designers and promotes the communication within the project teams. (C) 2017 The Authors. Published by Elsevier B.V.
C1 [Renard, Sebastien] CEA IRFM, F-13108 St Paul Les Durance, France.
[Holtz, Andreas; Banduch, Martin; W7-X Team] Max Planck Inst Plasma Phys, Teilinst Greifswald, Wendelsteinstr 1, D-17491 Greifswald, Germany.
[Baylard, Christophe] ITER Org, Route Vinon Verdon, F-13115 St Paul Les Durance, France.
RP Renard, S (corresponding author), CEA IRFM, F-13108 St Paul Les Durance, France.
EM Sebastien.renard@ipp.mpg.de
FU Euratom research and training programme [633053]
FX This work has been carried out within the framework of the EUROfusion
Consortium and has received funding from the Euratom research and
training programme 2014-2018 under grant agreement No. 633053. The views
and opinions expressed herein do not necessarily reflect those of the
European Commission.
CR Baylard C, 2009, FUSION ENG DES, V84, P435, DOI 10.1016/j.fusengdes.2008.12.035
Ksiazek I, 2011, NUKLEONIKA, V56, P155
NR 2
TC 1
Z9 1
U1 0
U2 0
PU ELSEVIER SCIENCE SA
PI LAUSANNE
PA PO BOX 564, 1001 LAUSANNE, SWITZERLAND
SN 0920-3796
EI 1873-7196
J9 FUSION ENG DES
JI Fusion Eng. Des.
PD NOV
PY 2017
VL 123
BP 133
EP 136
DI 10.1016/j.fusengdes.2017.04.027
PG 4
WC Nuclear Science & Technology
SC Nuclear Science & Technology
GA FR3UL
UT WOS:000418992000026
OA Other Gold
DA 2021-04-21
ER
PT J
AU Huber, V
Huber, A
Kinna, D
Matthews, G
Balboa, I
Capel, A
McCullen, P
Mertens, P
Sergienko, G
Silburn, S
Zastrow, KD
AF Huber, Valentina
Huber, Alexander
Kinna, David
Matthews, Guy
Balboa, Itziar
Capel, Adrian
McCullen, Paul
Mertens, Philippe
Sergienko, Gennady
Silburn, Scott
Zastrow, Klaus-Dieter
TI JUVIL: A new innovative software framework for data analysis of JET
imaging systems intended for the study of plastha physics and machine
operational safety
SO FUSION ENGINEERING AND DESIGN
LA English
DT Article; Proceedings Paper
CT 29th Symposium on Fusion Technology (SOFT)
CY SEP 05-09, 2016
CL Prague, CZECH REPUBLIC
SP CAS, Inst Plasma Phys, Res Ctr Rez
DE Imaging diagnostics; JUVIL software; Image processing; Data analysis
AB A new powerful software framework JUVIL (JET Users Video Imaging Library) has been developed and successfully installed at JET for fast data visualization and advanced analysis of all types of imaging data. The JUVIL framework is based on modular object -oriented components implemented in Python to simplify work with JET scientific data. It provides standard interfaces to access video data and post-processing, which are highly configurable and can be easily extended and adapted for new data formats and imaging cameras. One of the GUI components is the video player, widely used during the last JET campaign. It displays the video data for NIR/IR/VIS cameras and automatically carries out the post-processing (image rotation, data format conversion, scaling of non-interlaced fields to full frames). (C) 2017 The Authors. Published by Elsevier B.V.
C1 [Huber, Valentina] Forschungszentrum Julich, Supercomp Ctr, D-52425 Julich, Germany.
[Huber, Alexander; Mertens, Philippe; Sergienko, Gennady] Forschungszentrum Julich, Inst Energie & Klimaforsch Plasmaphys, Partner Trilateral Euregio Cluster TEC, D-52425 Julich, Germany.
[Kinna, David; Matthews, Guy; Balboa, Itziar; Capel, Adrian; McCullen, Paul; Silburn, Scott; Zastrow, Klaus-Dieter] Culham Sci Ctr, CCFE, Abingdon OX14 3DB, Oxon, England.
RP Huber, V (corresponding author), Forschungszentrum Julich, Supercomp Ctr, D-52425 Julich, Germany.
EM V.Huber@fz-juelich.de
RI Huber, Alexander/X-7394-2019; Sergienko, Gennady/W-8597-2019
OI Huber, Alexander/0000-0002-3558-8129; Sergienko,
Gennady/0000-0002-1539-4909
FU European Union's Horizon research and innovation program
FX This work has been carried out within the framework of the Contract for
the Operation of the JET Facilities and has received funding from the
European Union's Horizon 2020 research and innovation program. The views
and opinions expressed herein do not necessarily reflect those of the
European Commission.
CR Alvares D., 2012, PHYS REV ST ACCEL BE, V15
Arnoux G, 2012, REV SCI INSTRUM, V83, DOI 10.1063/1.4738742
Arnoux G., E4200707001 IET CDN
Balboa I, 2016, REV SCI INSTRUM, V87, DOI 10.1063/1.4960323
Capel A.J., H200505010 JET CDN
Devaux S., H201313050 JET CDN
Ford O., T1200503003 JET CDN
Huber A, 2012, REV SCI INSTRUM, V83, DOI 10.1063/1.4731759
Huber V, 2016, REV SCI INSTRUM, V87, DOI 10.1063/1.4959912
Jouve M., 2011, EFDAJETCP110601
Kinna D., H201010007 JET CDN
Kinna D., H201111 JET CDN
Martin V., 2011, FUSION ENG DES, V86
NR 13
TC 5
Z9 5
U1 0
U2 2
PU ELSEVIER SCIENCE SA
PI LAUSANNE
PA PO BOX 564, 1001 LAUSANNE, SWITZERLAND
SN 0920-3796
EI 1873-7196
J9 FUSION ENG DES
JI Fusion Eng. Des.
PD NOV
PY 2017
VL 123
BP 979
EP 985
DI 10.1016/j.fusengdes.2017.03.005
PG 7
WC Nuclear Science & Technology
SC Nuclear Science & Technology
GA FR3UL
UT WOS:000418992000206
OA Green Published, Other Gold
DA 2021-04-21
ER
PT J
AU Schillaci, MJ
AF Schillaci, Michael Jay
TI Perfectly Python
SO COMPUTING IN SCIENCE & ENGINEERING
LA English
DT Article
AB As some of my own research is in classical and post-Newtonian theories of gravity, I found the text's treatment of orbital motion to be of particular interest. The author does a wonderful job of presenting the basic physics and then discussing the importance of units and scales before introducing the Runge-Lenz vector.
C1 [Schillaci, Michael Jay] Harris Corp, Geospatial Syst Div, Melbourne, FL 32919 USA.
RP Schillaci, MJ (corresponding author), Harris Corp, Geospatial Syst Div, Melbourne, FL 32919 USA.
EM Michael.Schillaci98@gmail.com
NR 0
TC 0
Z9 0
U1 0
U2 3
PU IEEE COMPUTER SOC
PI LOS ALAMITOS
PA 10662 LOS VAQUEROS CIRCLE, PO BOX 3014, LOS ALAMITOS, CA 90720-1314 USA
SN 1521-9615
EI 1558-366X
J9 COMPUT SCI ENG
JI Comput. Sci. Eng.
PD NOV-DEC
PY 2017
VL 19
IS 6
BP 51
EP 53
PG 3
WC Computer Science, Interdisciplinary Applications
SC Computer Science
GA FL7AU
UT WOS:000414398800008
DA 2021-04-21
ER
PT J
AU Chekanov, SV
Pogrebnyak, I
Wilbern, D
AF Chekanov, S. V.
Pogrebnyak, I.
Wilbern, D.
TI Cross-platform validation and analysis environment for particle physics
SO COMPUTER PHYSICS COMMUNICATIONS
LA English
DT Article
DE Monte Carlo; Format; IO; Analysis software
AB A multi-platform validation and analysis framework for public Monte Carlo simulation for high-energy particle collisions is discussed. The front-end of this framework uses the Python programming language, while the back-end is written in Java, which provides a multi-platform environment that can be run from a web browser and can easily be deployed at the grid sites. The analysis package includes all major software tools used in high-energy physics, such as Lorentz vectors, jet algorithms, histogram packages, graphic canvases, and tools for providing data access. This multi-platform software suite, designed to minimize OS-specific maintenance and deployment time, is used for online validation of Monte Carlo event samples through a web interface. Published by Elsevier B.V.
C1 [Chekanov, S. V.; Pogrebnyak, I.] Argonne Natl Lab, HEP Div, 9700 S Cass Ave, Argonne, IL 60439 USA.
[Pogrebnyak, I.] Michigan State Univ, Dept Phys & Astron, 567 Wilson Rd, E Lansing, MI 48824 USA.
[Wilbern, D.] Northern Michigan Univ, 1401 Presque Isle Ave, Marquette, MI 49855 USA.
RP Chekanov, SV (corresponding author), Argonne Natl Lab, HEP Div, 9700 S Cass Ave, Argonne, IL 60439 USA.
EM chekanov@anl.gov; ivanp@msu.edu; dwilbern@nmu.edu
OI Chekanov, Sergei/0000-0001-7314-7247
FU National Science FoundationNational Science Foundation (NSF); U.S.
Department of Energy's Office of ScienceUnited States Department of
Energy (DOE); U.S. Department of Energy, Office of ScienceUnited States
Department of Energy (DOE) [DE-AC02-06CH11357]
FX This research was performed using resources provided by the Open Science
Grid, which is supported by the National Science Foundation and the U.S.
Department of Energy's Office of Science. We gratefully acknowledge the
computing resources provided on Blues, a highperformance computing
cluster operated by the Laboratory Computing Resource Center at Argonne
National Laboratory. Argonne National Laboratory's work was supported by
the U.S. Department of Energy, Office of Science under contract
DE-AC02-06CH11357.
CR [Anonymous], 2015, JAVA ANAL STUDIO PAR
AURENHAMMER F, 1991, ACM COMPUT SURV, V23, P345, DOI DOI 10.1145/116873.116880
Butterworth JM, 2003, COMPUT PHYS COMMUN, V153, P85, DOI 10.1016/S0010-4655(03)00156-5
Cacciari M, 2008, J HIGH ENERGY PHYS, DOI 10.1088/1126-6708/2008/04/063
Cacciari M, 2006, PHYS LETT B, V641, P57, DOI 10.1016/j.physletb.2006.08.037
Cacciari M, 2012, EUR PHYS J C, V72, DOI 10.1140/epjc/s10052-012-1896-2
Chekanov SV, 2015, ADV HIGH ENERGY PHYS, V2015, DOI 10.1155/2015/136093
Chekanov S. V., 2016, NUMERIC COMPUTATION
Chekanov S. V., 2013, COMPUT PHYS COMMUN, V185, P2629
Chekanov S. V., SNOWM 2013 EL P ECON
DMelt, 2015, COMP VIS ENV
Gaede F., 2003, CHEP03, P5
Graf NA, 2012, IEEE NUCL SCI CONF R, P1016
Salam G. P., 2006, LPTHE0602
NR 14
TC 2
Z9 2
U1 0
U2 1
PU ELSEVIER
PI AMSTERDAM
PA RADARWEG 29, 1043 NX AMSTERDAM, NETHERLANDS
SN 0010-4655
EI 1879-2944
J9 COMPUT PHYS COMMUN
JI Comput. Phys. Commun.
PD NOV
PY 2017
VL 220
BP 91
EP 96
DI 10.1016/j.cpc.2017.06.017
PG 6
WC Computer Science, Interdisciplinary Applications; Physics, Mathematical
SC Computer Science; Physics
GA FI8KU
UT WOS:000412252300008
DA 2021-04-21
ER
PT J
AU Sibalic, N
Pritchard, JD
Adams, CS
Weatherill, KJ
AF Sibalic, N.
Pritchard, J. D.
Adams, C. S.
Weatherill, K. J.
TI ARC: An open-source library for calculating properties of alkali Rydberg
atoms
SO COMPUTER PHYSICS COMMUNICATIONS
LA English
DT Article
DE Alkali atom; Matrix elements; Dipole-dipole interactions; Stark shift;
Forster resonances
ID QUANTUM DEFECTS; METAL ATOMS; DISPERSION COEFFICIENTS; WALL INTERACTION;
RB VAPOR; STATES; CESIUM; NP; NS; POLARIZABILITIES
AB We present an object-oriented Python library for the computation of properties of highly-excited Rydberg states of alkali atoms. These include single-body effects such as dipole matrix elements, excited-state lifetimes (radiative and black-body limited) and Stark maps of atoms in external electric fields, as well as two-atom interaction potentials accounting for dipole and quadrupole coupling effects valid at both long and short range for arbitrary placement of the atomic dipoles. The package is cross-referenced to precise measurements of atomic energy levels and features extensive documentation to facilitate rapid upgrade or expansion by users. This library has direct application in the field of quantum information and quantum optics which exploit the strong Rydberg dipolar interactions for two-qubit gates, robust atom-light interfaces and simulating quantum many-body physics, as well as the field of metrology using Rydberg atoms as precise microwave electrometers.
Program summary
Program Title: ARC: Alkali Rydberg Calculator
Program Files doi: http://dx.doi.org/10.17632/hm5n8w628c.1
Licensing provisions: BSD-3-Clause
Programming language: Python 2.7 or 3.5, with C extension
External Routines: NumPy [1], SciPy [1], Matplotlib [2]
Nature of problem: Calculating atomic properties of alkali atoms including lifetimes, energies, Stark shifts and dipole dipole interaction strengths using matrix elements evaluated from radial wavefunctions.
Solution method: Numerical integration of radial Schrodinger equation to obtain atomic wavefunctions, which are then used to evaluate dipole matrix elements. Properties are calculated using second order perturbation theory or exact diagonalisation of the interaction Hamiltonian, yielding results valid even at large external fields or small interatomic separation.
Restrictions: External electric field fixed to be parallel to quantisation axis.
Supplementary material: Detailed documentation (.html), and Jupyter notebook with examples and bench marking runs (.html and.ipynb).
[1] T.E. Oliphant, Comput. Sci. Eng. 9, 10 (2007). http://www.scipy.org/.
[2] J.D. Hunter, Comput. Sci. Eng. 9, 90 (2007). http://matplotlib.org/. (C) 2017 The Author(s). Published by Elsevier B.V.
C1 [Sibalic, N.; Adams, C. S.; Weatherill, K. J.] Univ Durham, Dept Phys, Joint Quantum Ctr JQC Durham Newcastle, South Rd, Durham DH1 3LE, England.
[Pritchard, J. D.] Univ Strathclyde, Dept Phys, SUPA, 107 Rottenrow East, Glasgow G4 0NG, Lanark, Scotland.
RP Sibalic, N (corresponding author), Univ Durham, Dept Phys, Joint Quantum Ctr JQC Durham Newcastle, South Rd, Durham DH1 3LE, England.
EM nikolasibalic@physics.org
RI Pritchard, Jonathan/AAB-3387-2020; Sibalic, Nikola/B-7622-2016;
Pritchard, Jonathan D/G-4339-2012; Adams, Charles S/C-8808-2015;
Weatherill, Kevin/A-6226-2012
OI Pritchard, Jonathan/0000-0003-2172-7340; Sibalic,
Nikola/0000-0001-9198-1630; Pritchard, Jonathan D/0000-0003-2172-7340;
Adams, Charles S/0000-0001-5602-2741; Weatherill,
Kevin/0000-0002-6577-116X
FU Durham University; EPSRCUK Research & Innovation (UKRI)Engineering &
Physical Sciences Research Council (EPSRC) [EP/N003527/1, EP/M013103/1];
FET-PROACT project "RySQ" [H2020-FETPROACT-2014-640378-RYSQ]; EPSRC
grant "Rydberg soft matter" [EP/M014398/1]; DSTL; Engineering and
Physical Sciences Research CouncilUK Research & Innovation
(UKRI)Engineering & Physical Sciences Research Council (EPSRC)
[EP/N003527/1, EP/M014398/1, EP/M013103/1] Funding Source: researchfish
FX We thank D. Whiting, J. Keaveney, H. Busche, P. Huillery, T. Billam, C.
Nicholas, I. Hughes, R. Potvliege and M. Jones for stimulating
discussions. This work is supported by Durham University, EPSRC grant
EP/N003527/1, FET-PROACT project "RySQ"
(H2020-FETPROACT-2014-640378-RYSQ), EPSRC grant "Rydberg soft matter"
(EP/M014398/1), DSTL and EPSRC grant EP/M013103/1. Data for the
presented examples can be generated by the 'Python note-book available
in Supplementary material [20]. In the final stages of this project we
have been informed by Sebastian Weber about a related efforts on C++
program for pair-state calculations [62].
CR Afrousheh K, 2006, PHYS REV A, V74, DOI 10.1103/PhysRevA.74.062712
Akulshin A, 2014, OPT LETT, V39, P845, DOI 10.1364/OL.39.000845
Akulshin AM, 2009, OPT EXPRESS, V17, P22861, DOI 10.1364/OE.17.022861
Ates C, 2013, PHYS REV LETT, V110, DOI 10.1103/PhysRevLett.110.213003
Ates C, 2007, PHYS REV A, V76, DOI 10.1103/PhysRevA.76.013413
Beterov II, 2015, PHYS REV A, V92, DOI 10.1103/PhysRevA.92.042710
Beterov II, 2009, PHYS REV A, V79, DOI 10.1103/PhysRevA.79.052504
BHATTI SA, 1981, PHYS REV A, V24, P161, DOI 10.1103/PhysRevA.24.161
Bloch D, 2005, ADV ATOM MOL OPT PHY, V50, P91, DOI 10.1016/S1049-250X(04)50003-4
Condon E.U., 1970, THEORY ATOMIC SPECTR
Courtade E, 2004, J PHYS B-AT MOL OPT, V37, P967, DOI 10.1088/0953-4075/37/5/002
Deiglmayr J, 2016, PHYS REV A, V93, DOI 10.1103/PhysRevA.93.013424
Deiglmayr J, 2014, PHYS REV LETT, V113, DOI 10.1103/PhysRevLett.113.193001
Derevianko A, 1999, PHYS REV LETT, V82, P3589, DOI 10.1103/PhysRevLett.82.3589
Firstenberg O, 2016, J PHYS B-AT MOL OPT, V49, DOI 10.1088/0953-4075/49/15/152003
Gaj A, 2014, NAT COMMUN, V5, DOI 10.1038/ncomms5546
GALLAGHER TF, 1982, PHYS REV A, V25, P1905, DOI 10.1103/PhysRevA.25.1905
Goldschmidt EA, 2015, PHYS REV A, V91, DOI 10.1103/PhysRevA.91.032518
GOY P, 1986, PHYS REV A, V34, P2889, DOI 10.1103/PhysRevA.34.2889
GOY P, 1982, PHYS REV A, V26, P2733, DOI 10.1103/PhysRevA.26.2733
Han JN, 2006, PHYS REV A, V74, DOI 10.1103/PhysRevA.74.054502
Hoening M, 2014, PHYS REV A, V90, DOI 10.1103/PhysRevA.90.021603
Javanainen J., COMPUT PHYS COMM
Johansson JR, 2013, COMPUT PHYS COMMUN, V184, P1234, DOI 10.1016/j.cpc.2012.11.019
Kiffner M., 2015, J PHYS B ATOM MOL PH, V49
Kondo JM, 2015, OPT LETT, V40, P5570, DOI 10.1364/OL.40.005570
Lee HG, 2013, PHYS REV A, V88, DOI 10.1103/PhysRevA.88.053427
Lesanovsky I, 2014, PHYS REV A, V90, DOI 10.1103/PhysRevA.90.011603
Li WH, 2003, PHYS REV A, V67, DOI 10.1103/PhysRevA.67.052502
LORENZEN CJ, 1983, PHYS SCRIPTA, V27, P300, DOI 10.1088/0031-8949/27/4/012
Maas DJ, 1999, PHYS REV A, V59, P1374, DOI 10.1103/PhysRevA.59.1374
Mack M, 2011, PHYS REV A, V83, DOI 10.1103/PhysRevA.83.052515
MARINESCU M, 1994, PHYS REV A, V49, P982, DOI 10.1103/PhysRevA.49.982
Moon HS, 2007, J OPT SOC AM B, V24, P2157, DOI 10.1364/JOSAB.24.002157
Murray C, 2016, ADV ATOM MOL OPT PHY, V65, P321, DOI 10.1016/bs.aamop.2016.04.005
Noumerov BV, 1923, MON NOT R ASTRON SOC, V84, P0592
Numerov B V, 1927, ASTRON NACHR, V230, P359, DOI DOI 10.1002/ASNA.19272301903
Oliphant TE, 2007, COMPUT SCI ENG, V9, P10, DOI 10.1109/MCSE.2007.58
Paredes-Barato D, 2014, PHYS REV LETT, V112, DOI 10.1103/PhysRevLett.112.040501
Pritchard JD, 2013, ANN REV COLD ATOMS, V1, P301
Ravets S, 2015, PHYS REV A, V92, DOI 10.1103/PhysRevA.92.020701
Roy R. J. L., 1974, CAN J PHYS, V52, P246
Ryabtsev II, 2010, PHYS REV LETT, V104, DOI 10.1103/PhysRevLett.104.073003
Saffman M, 2016, J PHYS B-AT MOL OPT, V49, DOI 10.1088/0953-4075/49/20/202001
Safronova MS, 2004, PHYS REV A, V69, DOI 10.1103/PhysRevA.69.022509
Safronova MS, 1999, PHYS REV A, V60, P4476, DOI 10.1103/PhysRevA.60.4476
Schauss P, 2015, SCIENCE, V347, P1455, DOI 10.1126/science.1258351
Sedlacek JA, 2012, NAT PHYS, V8, P819, DOI [10.1038/NPHYS2423, 10.1038/nphys2423]
Sevincli S, 2014, NEW J PHYS, V16, DOI 10.1088/1367-2630/16/12/123036
Sibalic N, 2016, PHYS REV A, V94, DOI 10.1103/PhysRevA.94.033840
Sibalic N, 2016, PHYS REV A, V94, DOI 10.1103/PhysRevA.94.011401
Sibalii N., 2016, INTRO RYDBERG ATOMS
Simons M. T., 2016, P SOC PHOTO-OPT INS, V9747, P1
Singer K, 2005, J PHYS B-AT MOL OPT, V38, pS295, DOI 10.1088/0953-4075/38/2/021
Sobelman II, 1979, ATOMIC SPECTRA RAD T
Sulham CV, 2010, APPL PHYS B-LASERS O, V101, P57, DOI 10.1007/s00340-010-4015-9
Tanasittikosol M, 2011, J PHYS B-AT MOL OPT, V44, DOI 10.1088/0953-4075/44/18/184020
THEODOSIOU CE, 1984, PHYS REV A, V30, P2881, DOI 10.1103/PhysRevA.30.2881
Urvoy A, 2015, PHYS REV LETT, V114, DOI 10.1103/PhysRevLett.114.203002
van Bijnen RMW, 2015, PHYS REV LETT, V114, DOI 10.1103/PhysRevLett.114.243002
Vogt T, 2006, PHYS REV LETT, V97, DOI 10.1103/PhysRevLett.97.083003
Wade CG, 2017, NAT PHOTONICS, V11, P40, DOI [10.1038/NPHOTON.2016.214, 10.1038/nphoton.2016.214]
WEBER KH, 1987, PHYS REV A, V35, P4650, DOI 10.1103/PhysRevA.35.4650
Weber S, 2017, J PHYS B-AT MOL OPT, V50, DOI 10.1088/1361-6455/aa743a
Whaley R. C., 1998, P 1998 ACM IEEE C SU, P38, DOI [DOI 10.1109/SC.1998.10004, 10.1109/SC.1998.10004]
Zentile MA, 2015, COMPUT PHYS COMMUN, V189, P162, DOI 10.1016/j.cpc.2014.11.023
ZIMMERMAN ML, 1979, PHYS REV A, V20, P2251, DOI 10.1103/PhysRevA.20.2251
NR 67
TC 57
Z9 60
U1 2
U2 17
PU ELSEVIER SCIENCE BV
PI AMSTERDAM
PA PO BOX 211, 1000 AE AMSTERDAM, NETHERLANDS
SN 0010-4655
EI 1879-2944
J9 COMPUT PHYS COMMUN
JI Comput. Phys. Commun.
PD NOV
PY 2017
VL 220
BP 319
EP 331
DI 10.1016/j.cpc.2017.06.015
PG 13
WC Computer Science, Interdisciplinary Applications; Physics, Mathematical
SC Computer Science; Physics
GA FI8KU
UT WOS:000412252300027
OA Green Accepted, Other Gold
DA 2021-04-21
ER
PT J
AU Trassinelli, M
AF Trassinelli, Martino
TI Bayesian data analysis tools for atomic physics
SO NUCLEAR INSTRUMENTS & METHODS IN PHYSICS RESEARCH SECTION B-BEAM
INTERACTIONS WITH MATERIALS AND ATOMS
LA English
DT Article; Proceedings Paper
CT 18th International Conference on the Physics of Highly Charged Ions
(HCI)
CY SEP 11-16, 2016
CL Jan Kochanowski Univ, Kielce, POLAND
SP Int Union Pure & Appl Phys, Dreebit, SPECS
HO Jan Kochanowski Univ
DE Bayesian data analysis; Atomic physics; Nested sampling; Model testing
ID INFERENCE; EFFICIENT; SPECTROSCOPY
AB We present an introduction to some concepts of Bayesian data analysis in the context of atomic physics. Starting from basic rules of probability, we present the Bayes' theorem and its applications. In particular we discuss about how to calculate simple and joint probability distributions and the Bayesian evidence, a model dependent quantity that allows to assign probabilities to different hypotheses from the analysis of a same data set. To give some practical examples, these methods are applied to two concrete cases. In the first example, the presence or not of a satellite line in an atomic spectrum is investigated. In the second example, we determine the most probable model among a set of possible profiles from the analysis of a statistically poor spectrum. We show also how to calculate the probability distribution of the main spectral component without having to determine uniquely the spectrum modeling. For these two studies, we implement the program Nested_fit to calculate the different probability distributions and other related quantities. Nested_fit is a Fortran90/Python code developed during the last years for analysis of atomic spectra. As indicated by the name, it is based on the nested algorithm, which is presented in details together with the program itself. (C) 2017 Elsevier B.V. All rights reserved.
C1 [Trassinelli, Martino] UPMC Univ Paris 06, Sorbonne Univ, CNRS, Inst NanoSci Paris, F-75005 Paris, France.
RP Trassinelli, M (corresponding author), UPMC Univ Paris 06, Sorbonne Univ, CNRS, Inst NanoSci Paris, F-75005 Paris, France.
EM trassinelli@insp.jussieu.fr
RI Trassinelli, Martino/M-5326-2016
OI Trassinelli, Martino/0000-0003-4414-1801
CR Abbott BP, 2016, PHYS REV LETT, V116, DOI 10.1103/PhysRevLett.116.241103
Abbott B.P., 2016, PHYS REV LETT, V116
AKAIKE H, 1974, IEEE T AUTOMAT CONTR, VAC19, P716, DOI 10.1109/TAC.1974.1100705
BAKER S, 1984, NUCL INSTRUM METH A, V221, P437, DOI 10.1016/0167-5087(84)90016-4
BALLENTINE LE, 1986, AM J PHYS, V54, P883, DOI 10.1119/1.14783
Barradas NP, 1999, THIN SOLID FILMS, V343, P31, DOI 10.1016/S0040-6090(98)01681-2
Bayes T., 1763, PHIL T ROY SOC LOND, V53, P370, DOI DOI 10.1098/RSTL.1763.0053
Bevington PR., 2003, DATA REDUCTION ERROR
Buchner J, 2016, STAT COMPUT, V26, P383, DOI 10.1007/s11222-014-9512-y
Calonico D, 2009, METROLOGIA, V46, P267, DOI 10.1088/0026-1394/46/3/014
Chopin N, 2010, BIOMETRIKA, V97, P741, DOI 10.1093/biomet/asq021
Cox R.T., 1961, ALGEBRA PROBABLE INF
COX RT, 1946, AM J PHYS, V14, P1, DOI 10.1119/1.1990764
Feroz F, 2008, MON NOT R ASTRON SOC, V384, P449, DOI 10.1111/j.1365-2966.2007.12353.x
Feroz F, 2009, MON NOT R ASTRON SOC, V398, P1601, DOI 10.1111/j.1365-2966.2009.14548.x
Feroz F, 2013, AIP CONF PROC, V1553, P106, DOI 10.1063/1.4819989
Gordon C, 2007, MON NOT R ASTRON SOC, V382, P1859, DOI 10.1111/j.1365-2966.2007.12707.x
JAMES F, 1975, COMPUT PHYS COMMUN, V10, P343, DOI 10.1016/0010-4655(75)90039-9
Jaynes Edwin T, 2003, PROBABILITY THEORY L
Jeffreys H., 1961, THEORY PROBABILITY
Kalambet Y, 2011, J CHEMOMETR, V25, P352, DOI 10.1002/cem.1343
KASS RE, 1995, J AM STAT ASSOC, V90, P773, DOI 10.1080/01621459.1995.10476572
KULLBACK S, 1951, ANN MATH STAT, V22, P79, DOI 10.1214/aoms/1177729694
Laplace P-S, 1825, ESSAI PHILOS PROBABI
Lewis A, 2002, PHYS REV D, V66, DOI 10.1103/PhysRevD.66.103511
MASSEY FJ, 1951, J AM STAT ASSOC, V46, P68, DOI 10.2307/2280095
Mooser A, 2013, PHYS REV LETT, V110, DOI 10.1103/PhysRevLett.110.140405
Mukherjee P, 2006, ASTROPHYS J, V638, pL51, DOI 10.1086/501068
Neyman J, 1933, PHILOS T R SOC LOND, V231, P289, DOI 10.1098/rsta.1933.0009
Patrignani C, 2016, CHINESE PHYS C, V40, DOI 10.1088/1674-1137/40/10/100001
Robert Christian P., 2013, MONTE CARLO STAT MET, DOI 10.1007/978-1-4757-4145-2
SHANNON CE, 1948, BELL SYST TECH J, V27, P623, DOI 10.1002/j.1538-7305.1948.tb00917.x
Sivia D.S., 2006, DATA ANAL BAYESIAN T
SIVIA DS, 1992, J CHEM PHYS, V96, P170, DOI 10.1063/1.462505
Skilling J, 2004, AIP CONF PROC, V735, P395
Skilling J, 2006, BAYESIAN ANAL, V1, P833, DOI 10.1214/06-BA127
Skilling J, 2012, AIP CONF PROC, V1443, P145, DOI 10.1063/1.3703630
Skilling J, 2009, AIP CONF PROC, V1193, P277, DOI 10.1063/1.3275625
Spiegelhalter DJ, 2014, J R STAT SOC B, V76, P485, DOI 10.1111/rssb.12062
Spiegelhalter DJ, 2002, J R STAT SOC B, V64, P583, DOI 10.1111/1467-9868.00353
Stockton JK, 2007, PHYS REV A, V76, DOI 10.1103/PhysRevA.76.033613
Theisen M., 2013, ANAL LINIENFORM RONT
Trassinelli M, 2016, EPJ WEB CONF, V130, DOI 10.1051/epjconf/201613001022
Trassinelli M, 2016, PHYS LETT B, V759, P583, DOI 10.1016/j.physletb.2016.06.025
Trassinelli M, 2009, EPL-EUROPHYS LETT, V87, DOI 10.1209/0295-5075/87/63001
Trotta R, 2008, CONTEMP PHYS, V49, P71, DOI 10.1080/00107510802066753
Veitch J, 2010, PHYS REV D, V81, DOI 10.1103/PhysRevD.81.062003
Wiebe N, 2016, PHYS REV LETT, V117, DOI 10.1103/PhysRevLett.117.010503
Yates F., 1934, JR STATIST SOC S, V1, P217, DOI DOI 10.2307/2983604
NR 49
TC 7
Z9 7
U1 0
U2 12
PU ELSEVIER
PI AMSTERDAM
PA RADARWEG 29, 1043 NX AMSTERDAM, NETHERLANDS
SN 0168-583X
EI 1872-9584
J9 NUCL INSTRUM METH B
JI Nucl. Instrum. Methods Phys. Res. Sect. B-Beam Interact. Mater. Atoms
PD OCT 1
PY 2017
VL 408
BP 301
EP 312
DI 10.1016/j.nimb.2017.05.030
PG 12
WC Instruments & Instrumentation; Nuclear Science & Technology; Physics,
Atomic, Molecular & Chemical; Physics, Nuclear
SC Instruments & Instrumentation; Nuclear Science & Technology; Physics
GA FI2MQ
UT WOS:000411773400067
DA 2021-04-21
ER
PT J
AU Kelly, DJ
Kelly, AE
Aviles, BN
Godfrey, AT
Salko, RK
Collins, BS
AF Kelly, Daniel J., III
Kelly, Ann E.
Aviles, Brian N.
Godfrey, Andrew T.
Salko, Robert K.
Collins, Benjamin S.
TI MC21/CTF and VERA multiphysics solutions to VERA core physics benchmark
progression problems 6 and 7
SO NUCLEAR ENGINEERING AND TECHNOLOGY
LA English
DT Article; Proceedings Paper
CT International Conference on Mathematics and Computational Methods
Applied to Nuclear Science and Engineering (M&C)
CY APR 16-20, 2017
CL SOUTH KOREA
DE CTF; MC21; MPACT; Multiphysics; VERA
AB The continuous energy Monte Carlo neutron transport code, MC21, was coupled to the CTF subchannel thermal-hydraulics code using a combination of Consortium for Advanced Simulation of Light Water Reactors (CASL) tools and in-house Python scripts. An MC21/CTF solution for VERA Core Physics Benchmark Progression Problem 6 demonstrated good agreement with MC21/COBRA-IE and VERA solutions. The MC21/CTF solution for VERA Core Physics Benchmark Progression Problem 7, Watts Bar Unit 1 at beginning of cycle hot full power equilibrium xenon conditions, is the first published coupled Monte Carlo neutronics/subchannel T-H solution for this problem. MC21/CTF predicted a critical boron concentration of 854.5 ppm, yielding a critical eigenvalue of 0.99994 +/- 6.8E-6 (95% confidence interval). Excellent agreement with a VERA solution of Problem 7 was also demonstrated for integral and local power and temperature parameters. (C) 2017 Korean Nuclear Society, Published by Elsevier Korea LLC. This is an open access article under the CC BY-NC-ND license.
C1 [Kelly, Daniel J., III; Kelly, Ann E.; Aviles, Brian N.] Bechtel Marine Prop Corp, POB 1072, Schenectady, NY 12301 USA.
[Godfrey, Andrew T.; Salko, Robert K.; Collins, Benjamin S.] Oak Ridge Natl Lab, 1 Bethel Valley Rd, Oak Ridge, TN 37830 USA.
RP Kelly, DJ (corresponding author), Bechtel Marine Prop Corp, POB 1072, Schenectady, NY 12301 USA.
EM daniel.kelly@unnpp.gov
FU DOEUnited States Department of Energy (DOE); Office of Science of the
U.S. Department of EnergyUnited States Department of Energy (DOE)
[DE-AC05-00OR22725]; Office of Nuclear Energy of the US Department of
Energy and the Nuclear Science User Facility [DE-AC07-05ID14517]
FX The Naval Nuclear Laboratory authors would like to thank the MC21 and
PUMA development teams for their support during this research. Work for
the Oak Ridge National Laboratory authors was funded by the
DOE-sponsored "Consortium for Advanced Simulation of Light Water
Reactors" (CASL) project, and ORNL research used resources of the Oak
Ridge Leadership Computing Facility at the Oak Ridge National
Laboratory, which is supported by the Office of Science of the U.S.
Department of Energy under Contract No. DE-AC05-00OR22725. CASL research
made use of the resources of the High Performance Computing Center at
Idaho National Laboratory, which is supported by the Office of Nuclear
Energy of the US Department of Energy and the Nuclear Science User
Facility under Contract No. DE-AC07-05ID14517.
CR AUMILLER D. L., 2015, P NURETH 16 CHIC ILL
Aviles B.N., 2017, PROGR NUCL ENERGY
Bennett A, 2016, ANN NUCL ENERGY, V96, P1, DOI 10.1016/j.anucene.2016.05.008
Daeubler M, 2015, ANN NUCL ENERGY, V83, P352, DOI 10.1016/j.anucene.2015.03.040
Ellis M, 2017, NUCL SCI ENG, V185, P184, DOI 10.13182/NSE16-26
Gill DF, 2017, NUCL SCI ENG, V185, P194, DOI 10.13182/NSE16-3
GLEASON J., 2008, ANS2008
GODFREY A.T., 2014, CASLU20120131004
Griesheimer DP, 2015, ANN NUCL ENERGY, V82, P29, DOI 10.1016/j.anucene.2014.08.020
Guilliard N., 2016, P PHYSOR 2016 SUN VA
Ivanov A, 2015, ANN NUCL ENERGY, V84, P204, DOI 10.1016/j.anucene.2014.12.030
Kelly D. J., 2010, P PHYSOR 2010 PITTSB
Kelly III D.J., 2014, P PHYSOR 2014 KYOT J
Kochunas B., 2015, CASLU20150155000
Kotlyar D, 2016, ANN NUCL ENERGY, V96, P61, DOI 10.1016/j.anucene.2016.05.032
Lee H., 2017, P M C 2017 JEJ REP K
Lemaire M., 2017, P M C JEJ REP KOR AP
Leppanen J, 2015, ANN NUCL ENERGY, V84, P55, DOI 10.1016/j.anucene.2014.10.014
Liu S., 2017, P M C JEJ REP KOR AP
Palmtag S, 2014, CASLU20140014002
Pecchia M, 2015, ANN NUCL ENERGY, V85, P271, DOI 10.1016/j.anucene.2015.05.023
Salko R., 2014, CASLU20140188000
Salko R., 2014, CTF THEORY MANUAL
Sieger M., 2015, CASLU20150042000
NR 24
TC 7
Z9 7
U1 0
U2 0
PU KOREAN NUCLEAR SOC
PI DAEJEON
PA NUTOPIA BLDG, 342-1 JANGDAE-DONG, DAEJEON, 305-308, SOUTH KOREA
SN 1738-5733
J9 NUCL ENG TECHNOL
JI Nucl. Eng. Technol.
PD SEP
PY 2017
VL 49
IS 6
SI SI
BP 1326
EP 1338
DI 10.1016/j.net.2017.07.016
PG 13
WC Nuclear Science & Technology
SC Nuclear Science & Technology
GA FL4TY
UT WOS:000414225600024
OA DOAJ Gold
DA 2021-04-21
ER
PT J
AU Schmieschek, S
Shamardin, L
Frijters, S
Kruger, T
Schiller, UD
Harting, J
Coveney, PV
AF Schmieschek, S.
Shamardin, L.
Frijters, S.
Kruger, T.
Schiller, U. D.
Harting, J.
Coveney, P. V.
TI LB3D: A parallel implementation of the Lattice-Boltzmann method for
simulation of interacting amphiphilic fluids
SO COMPUTER PHYSICS COMMUNICATIONS
LA English
DT Article
DE Lattice-Boltzmann method; High performance computing; Multiphase flow;
LBM; LB3D
ID BOUNDARY-CONDITIONS; MODEL; FLOW; EQUATION; SCIENCE; ARRAY; CODE
AB We introduce the lattice-Boltzmann code LB3D, version 7.1. Building on a parallel program and supporting tools which have enabled research utilising high performance computing resources for nearly two decades, LB3D version 7 provides a subset of the research code functionality as an open source project. Here, we describe the theoretical basis of the algorithm as well as computational aspects of the implementation. The software package is validated against simulations of meso-phases resulting from self-assembly in ternary fluid mixtures comprising immiscible and amphiphilic components such as water-oil-surfactant systems. The impact of the surfactant species on the dynamics of spinodal decomposition are tested and quantitative measurement of the permeability of a body centred cubic (BCC) model porous medium for a simple binary mixture is described. Single-core performance and scaling behaviour of the code are reported for simulations on current supercomputer architectures.
Program summary
Program Title: LB3D
Program Files doi: http://dx.doi.org/10.17632/9g9x2wr8z8.1
Licensing provisions: BSD 3-clause
Programming language: FORTRAN90, Python, C
Nature of problem: Solution of the hydrodynamics of single phase, binary immiscible and ternary amphiphilic fluids. Simulation of fluid mixtures comprising miscible and immiscible fluid components as well as amphiphilic species on the mesoscopic scale. Observable phenomena include self-organisation of mesoscopic complex fluid phases and fluid transport in porous media.
Solution method: Lattice-Boltzmann (lattice-Bhatnagar-Gross-Krook, LBGK) [1, 2, 3] method describing fluid dynamics in terms of the single particle velocity distribution function in a 3-dimensional discrete phase space (D3Q19) [4, 5, 6]. Multiphase interactions are modelled using a phenomenological pseudo potential approach [7, 8] with amphiphilic interactions utilising an additional dipole field [9, 10]. Solid boundaries are modelled using simple bounce-back boundary conditions and additional pseudo-potential wetting interactions [11].
Additional comments including Restrictions and Unusual features: The purpose of the release is the provision of a refactored minimal version of LB3D suitable as a starting point for the integration of additional features building on the parallel computation and 10 functionality.
[1] S. Succi, The Lattice Boltzmann Equation: For Fluid Dynamics and Beyond, Oxford University Press, 2001.
[2] B. Dunweg, A. Ladd, Lattice Boltzmann simulations of soft matter systems, Adv. Poly. Sci. 221 (2009) 89-166
[3] C. K. Aidun, J. R. Clausen, Lattice-Boltzmann Method for Complex Flows, Annual Review of Fluid Mechanics 42 (2010) 439.
[4] X. He, L.-S. Luo, A priori derivation of the lattice-Boltzmann equation, Phys. Rev. E 55 (1997) R6333.
[5] X. He, L-S. Luo, Theory of the lattice Boltzmann method: from the Boltzmann equation to the lattice Boltzmann equation, Phys. Rev. E 56.
[6] Y. H. Qian, D. D'Humieres, P. Lallemand, Lattice BGK Models for Navier Stokes Equation, Euro-physics Letters 17 (1992) 479.
[7] X. Shan, H. Chen, Lattice-Boltzmann model for simulating flows with multiple phases and components, Physical Review E 47 (1993) 1815.
[8] X. Shan, G. Doolen, Multicomponent lattice-Boltzmann model with interparticle interaction, Journal of Statistical Physics 81 (1995) 379.
[9] H. Chen, B. Boghosian, P.V. Coveney, M. Nekovee, A ternary lattice-Boltzmann model for amphiphilic fluids, Proceedings of the Royal Society of London A 456 (2000) 2043.
[10] M. Nekovee, P. V. Coveney, H. Chen, B. M. Boghosian, Lattice-Boltzmann model for interacting amphiphilic fluids, Phys. Rev. E 62 (2000) 8282.
[11] N. S. Marlys, H. Chen, Simulation of multicomponent fluids in complex three-dimensional geometries by the lattice-Boltzmann method, Phys. Rev. E 53 (1996) 743. (C) 2017 The Authors. Published by Elsevier B.V.
C1 [Schmieschek, S.; Shamardin, L.; Kruger, T.; Schiller, U. D.; Coveney, P. V.] UCL, Dept Chem, Ctr Computat Sci, 20 Gordon St, London WC1H 0AJ, England.
[Shamardin, L.] Google UK Ltd, 6 Pancras Sq, London N1C 4AG, England.
[Frijters, S.; Harting, J.] Eindhoven Univ Technol, Dept Appl Phys, POB 513, NL-5600 MB Eindhoven, Netherlands.
[Kruger, T.] Univ Edinburgh, Sch Engn, Kings Bldg,Mayfield Rd, Edinburgh EH9 3JL, Midlothian, Scotland.
[Schiller, U. D.] Clemson Univ, Dept Mat Sci & Engn, 161 Sirrine Hall, Clemson, SC 29634 USA.
[Harting, J.] Forschungszentrum Julich, Helmholtz Inst Erlangen Nurnberg Renewable Energy, Further Str 248, D-90429 Nurnberg, Germany.
RP Coveney, PV (corresponding author), UCL, Dept Chem, Ctr Computat Sci, 20 Gordon St, London WC1H 0AJ, England.
EM p.v.coveney@ucl.ac.uk
RI Harting, Jens/B-4884-2008; Schiller, Ulf D./A-2067-2015; Kruger,
Timm/N-1745-2014
OI Harting, Jens/0000-0002-9200-6623; Schiller, Ulf D./0000-0001-8941-1284;
Kruger, Timm/0000-0003-2934-2699; Shamardin, Lev/0000-0002-4392-6626
FU UK Consortium on Mesoscale Engineering Sciences (UKCOMES) under EPSRC
[EP/L00030X/1]; EU H ComPat project [223979]; Fujitsu Laboratories
Europe; Engineering and Physical Sciences Research CouncilUK Research &
Innovation (UKRI)Engineering & Physical Sciences Research Council
(EPSRC) [EP/L00030X/1] Funding Source: researchfish
FX Support from Fujitsu Laboratories Europe, from the UK Consortium on
Mesoscale Engineering Sciences (UKCOMES) under EPSRC Grant No.
EP/L00030X/1 and the EU H2020 ComPat project No. 223979 is acknowledged.
Our work also made use of the ARCHER UK National Supercomputing Service
(http://www.archer.ac.uk).
CR Aidun CK, 2010, ANNU REV FLUID MECH, V42, P439, DOI 10.1146/annurev-fluid-121108-145519
BHATNAGAR PL, 1954, PHYS REV, V94, P511, DOI 10.1103/PhysRev.94.511
Blake R. J., 2005, Scientific Programming, V13, P1
Boghosian BM, 2000, P ROY SOC A-MATH PHY, V456, P1431, DOI 10.1098/rspa.2000.0570
Breitmoser E., LB3D USER MANUAL
CAHN JW, 1962, ACTA METALL MATER, V10, P179, DOI 10.1016/0001-6160(62)90114-1
CAHN JW, 1961, ACTA METALL MATER, V9, P795, DOI 10.1016/0001-6160(61)90182-1
CHEKHLOV A, 1995, PHYS REV E, V51, pR2739, DOI 10.1103/PhysRevE.51.R2739
Chen HD, 2000, P ROY SOC A-MATH PHY, V456, P2043, DOI 10.1098/rspa.2000.0601
Chin J, 2003, CONTEMP PHYS, V44, P417, DOI 10.1080/00107510310001605046
Chin J, 2002, PHYS REV E, V66, DOI 10.1103/PhysRevE.66.016303
CHIN J, 2004, P UK E SCI ALL HANDS
Chin J, 2006, P ROY SOC A-MATH PHY, V462, P3575, DOI 10.1098/rspa.2006.1741
Chun B, 2007, PHYS REV E, V75, DOI 10.1103/PhysRevE.75.066705
CORNUBERT R, 1991, PHYSICA D, V47, P241, DOI 10.1016/0167-2789(91)90295-K
Coveney PV, 2010, ABSTR PAP AM CHEM S, V239
DEGENNES PG, 1980, J CHEM PHYS, V72, P4756, DOI 10.1063/1.439809
Desplat JC, 2001, COMPUT PHYS COMMUN, V134, P273, DOI 10.1016/S0010-4655(00)00205-8
Dunweg B, 2009, ADV POLYM SCI, V221, P89, DOI 10.1007/12_2008_4
Folk M., 2011, P EDBT ICDT 2011 WOR, P36, DOI DOI 10.1145/1966895.1966900
Ginzburg I, 2003, PHYS REV E, V68, DOI 10.1103/PhysRevE.68.066614
Giupponi G, 2006, MATH COMPUT SIMULAT, V72, P124, DOI 10.1016/j.matcom.2006.05.035
Gonzalez-Segredo N, 2006, PHYS REV E, V73, DOI 10.1103/PhysRevE.73.031503
Gonzalez-Segredo N, 2004, PHYS REV E, V69, DOI 10.1103/PhysRevE.69.061501
Gonzalez-Segredo N, 2003, PHYS REV E, V67, DOI 10.1103/PhysRevE.67.046304
Gonzalez-Segredo N., 2004, PHYS REV E, V6, P9
Gonzalez-Segredo N., 2004, EPL-EUROPHYS LETT, V6, P5
Groen D., 2011, JUL BLUE GEN P EXTR
Guo ZL, 2002, PHYS REV E, V65, DOI 10.1103/PhysRevE.65.046308
Harting J, 2005, PHILOS T R SOC A, V363, P1895, DOI 10.1098/rsta.2005.1618
Harting J, 2005, COMPUT PHYS COMMUN, V165, P97, DOI 10.1016/j.cpc.2004.10.001
Harting J, 2004, PHILOS T R SOC A, V362, P1703, DOI 10.1098/rsta.2004.1402
Harting J, 2007, PHYS REV E, V75, DOI 10.1103/PhysRevE.75.041504
Harting J, 2008, HIGH PERFORMANCE COMPUTING IN SCIENCE AND ENGINEERING '07, P457, DOI 10.1007/978-3-540-74739-0_31
HASIMOTO H, 1959, J FLUID MECH, V5, P317, DOI 10.1017/S0022112059000222
HDF Group, 2010, HIER DAT FORM VERS 5
He XY, 1998, PHYS REV E, V57, pR13, DOI 10.1103/PhysRevE.57.R13
He XY, 1997, PHYS REV E, V56, P6811, DOI 10.1103/PhysRevE.56.6811
He XY, 1997, PHYS REV E, V55, pR6333, DOI 10.1103/PhysRevE.55.R6333
He XY, 1997, J STAT PHYS, V87, P115, DOI 10.1007/BF02181482
Hecht M., 2010, J STAT MECH-THEORY E, V13, P1
Kutay ME, 2006, COMPUT GEOTECH, V33, P381, DOI 10.1016/j.compgeo.2006.08.002
Ladd AJC, 2001, J STAT PHYS, V104, P1191, DOI 10.1023/A:1010414013942
LAVALLEE P, 1991, PHYSICA D, V47, P233, DOI 10.1016/0167-2789(91)90294-J
Love PJ, 2001, PHYS REV E, V64, DOI 10.1103/PhysRevE.64.061302
Martys NS, 1996, PHYS REV E, V53, P743, DOI 10.1103/PhysRevE.53.743
Mattila K, 2009, J STAT MECH-THEORY E, DOI 10.1088/1742-5468/2009/06/P06015
Mazzeo MD, 2008, COMPUT PHYS COMMUN, V178, P894, DOI 10.1016/j.cpc.2008.02.013
Nekovee M, 2000, PHYS REV E, V62, P8282, DOI 10.1103/PhysRevE.62.8282
Pickles S. M., P WORKSH CAS STUD GR, V10
QIAN YH, 1992, EUROPHYS LETT, V17, P479, DOI 10.1209/0295-5075/17/6/001
Saksena RS, 2008, J PHYS CHEM B, V112, P2950, DOI 10.1021/jp0731506
Saksena RS, 2009, SOFT MATTER, V5, P4446, DOI 10.1039/b911884e
Saksena RS, 2009, P ROY SOC A-MATH PHY, V465, P1977, DOI 10.1098/rspa.2008.0479
Saksena RS, 2009, PHILOS T R SOC A, V367, P2557, DOI 10.1098/rsta.2009.0049
Saksena RS., 2008, P TER C 2008, V2008, P92
SANGANI AS, 1982, INT J MULTIPHAS FLOW, V8, P343, DOI 10.1016/0301-9322(82)90047-7
Schmieschek S, 2013, HIGH PERFORMANCE COMPUTING IN SCIENCE AND ENGINEERING '12: TRANSACTIONS OF THE HIGH PERFORMANCE COMPUTING CENTER, STUTTGART (HLRS) 2012, P39, DOI 10.1007/978-3-642-33374-3_5
Segredo NJ. Gonzalez, LATTICE BOLTZMANN LA
Shan XW, 2006, J FLUID MECH, V550, P413, DOI 10.1017/S0022112005008153
Shan XW, 1998, PHYS REV LETT, V80, P65, DOI 10.1103/PhysRevLett.80.65
SHAN XW, 1995, J STAT PHYS, V81, P379, DOI 10.1007/BF02179985
SHAN XW, 1993, PHYS REV E, V47, P1815, DOI 10.1103/PhysRevE.47.1815
Succi S., 2001, LATTICE BOLTZMANN EQ
Wolf-Gladrow D. A., 2000, LECT NOTES MATH
ZIEGLER DP, 1993, J STAT PHYS, V71, P1171, DOI 10.1007/BF01049965
Zou QS, 1997, PHYS FLUIDS, V9, P1591, DOI 10.1063/1.869307
NR 67
TC 18
Z9 19
U1 2
U2 38
PU ELSEVIER
PI AMSTERDAM
PA RADARWEG 29, 1043 NX AMSTERDAM, NETHERLANDS
SN 0010-4655
EI 1879-2944
J9 COMPUT PHYS COMMUN
JI Comput. Phys. Commun.
PD AUG
PY 2017
VL 217
BP 149
EP 161
DI 10.1016/j.cpc.2017.03.013
PG 13
WC Computer Science, Interdisciplinary Applications; Physics, Mathematical
SC Computer Science; Physics
GA EX3HN
UT WOS:000403123300014
OA Green Published, Other Gold
DA 2021-04-21
ER
PT J
AU Kitcher, ED
Chirayath, SS
AF Kitcher, Evans D.
Chirayath, Sunil S.
TI A neutron transport and thermal hydraulics coupling scheme to study
xenon induced power oscillations in a nuclear reactor
SO ANNALS OF NUCLEAR ENERGY
LA English
DT Article
DE Multi-physics coupling; Xenon induced power oscillations; Xenon
oscillation bench mark
ID CARLO BURNUP CALCULATIONS; MONTE-CARLO; NUMERICAL STABILITY
AB A multi-physics computational methodology to analyze xenon induced power oscillations in a nuclear reactor is developed and presented. The methodology development takes into account both neutron transport and thermal hydraulics behaviors and their inter-dependent feedbacks in a nuclear power reactor as a function of fuel burn-up. The methodology uses the Monte Carlo N-Particle radiation transport computational code (MCNP6) along with its fuel transmutation module (CINDER90), a semi-analytic single channel analysis tool for thermal hydraulics and the SIGACE code suite for temperature dependent neutron interaction cross section processing. A Python script developed couples the physics codes for full automation. The accuracy of the multi-physics computational methodology developed was verified through a benchmark calculation for the published core parameters of a startup test performed at Yonggwang Power Reactor Unit No. 3. The power axial offset and xenon axial offset parameters were calculated for this benchmark case and used to quantify the oscillatory behavior observed, the results of this benchmark study is also presented here. The results showed that the developed methodology was able to capture the underlying phenomena governing xenon induced power oscillations in a nuclear reactor. (C) 2017 Elsevier Ltd. All rights reserved.
C1 [Kitcher, Evans D.; Chirayath, Sunil S.] Texas A&M Univ, Dept Nucl Engn, College Stn, TX 77843 USA.
RP Chirayath, SS (corresponding author), Texas A&M Univ, Dept Nucl Engn, College Stn, TX 77843 USA.
EM sunilsc@tamu.edu
CR Dufek J, 2013, ANN NUCL ENERGY, V56, P34, DOI 10.1016/j.anucene.2013.01.018
Dufek J, 2009, NUCL SCI ENG, V162, P307, DOI 10.13182/NSE08-69TN
Guo ZP, 2013, NUCL ENG DES, V258, P144, DOI 10.1016/j.nucengdes.2013.01.013
IAPWS, 2007, REV REL IAPWS IND FO
Isotalo AE, 2013, ANN NUCL ENERGY, V60, P78, DOI 10.1016/j.anucene.2013.04.031
Jae Seung Song, 1999, Journal of the Korean Nuclear Society, V31, P80
Kitcher ED, 2016, ANN NUCL ENERGY, V97, P232, DOI 10.1016/j.anucene.2016.07.019
Kotlyar D, 2011, NUCL ENG DES, V241, P3777, DOI 10.1016/j.nucengdes.2011.07.028
Mckinney, 2014, LACP1400745
Pelowitz DB, 2008, MCNPX USERS MANUAL
Shan JQ, 2010, ANN NUCL ENERGY, V37, P58, DOI 10.1016/j.anucene.2009.09.016
Sharma A. R., 2005, SIGACE PACKAGE GENER
Sjenitzer BL, 2015, ANN NUCL ENERGY, V76, P27, DOI 10.1016/j.anucene.2014.09.018
Song JS, 1997, NUCL TECHNOL, V119, P105, DOI 10.13182/NT97-A35379
Turinsky PJ, 2012, NUCL ENG TECHNOL, V44, P103, DOI 10.5516/NET.01.2012.500
Wilson W B, 2007, MANUAL CINDER 90 VER, P07
Xi X, 2013, NUCL ENG DES, V258, P157, DOI 10.1016/j.nucengdes.2013.01.031
NR 17
TC 1
Z9 1
U1 0
U2 11
PU PERGAMON-ELSEVIER SCIENCE LTD
PI OXFORD
PA THE BOULEVARD, LANGFORD LANE, KIDLINGTON, OXFORD OX5 1GB, ENGLAND
SN 0306-4549
J9 ANN NUCL ENERGY
JI Ann. Nucl. Energy
PD AUG
PY 2017
VL 106
BP 64
EP 70
DI 10.1016/j.anucene.2017.03.045
PG 7
WC Nuclear Science & Technology
SC Nuclear Science & Technology
GA EU9RZ
UT WOS:000401378600005
DA 2021-04-21
ER
PT J
AU Dijkstra, YM
Brouwer, RL
Schuttelaars, HM
Schramkowski, GP
AF Dijkstra, Yoeri M.
Brouwer, Ronald L.
Schuttelaars, Henk M.
Schramkowski, George P.
TI The iFlow modelling framework v2.4: a modular idealized process-based
model for flow and transport in estuaries
SO GEOSCIENTIFIC MODEL DEVELOPMENT
LA English
DT Article
ID INDUCED RESIDUAL CURRENTS; TIDAL ASYMMETRY; RIVER FLOW; DYNAMICS;
MORPHODYNAMICS; BREADTH
AB The iFlow modelling framework is a width-averaged model for the systematic analysis of the water motion and sediment transport processes in estuaries and tidal rivers. The distinctive solution method, a mathematical perturbation method, used in the model allows for identification of the effect of individual physical processes on the water motion and sediment transport and study of the sensitivity of these processes to model parameters. This distinction between processes provides a unique tool for interpreting and explaining hydrodynamic interactions and sediment trapping. iFlow also includes a large number of options to configure the model geometry and multiple choices of turbulence and salinity models. Additionally, the model contains auxiliary components, including one that facilitates easy and fast sensitivity studies.
iFlow has a modular structure, which makes it easy to include, exclude or change individual model components, called modules. Depending on the required functionality for the application at hand, modules can be selected to construct anything from very simple quasi-linear models to rather complex models involving multiple non-linear interactions. This way, the model complexity can be adjusted to the application. Once the modules containing the required functionality are selected, the underlying model structure automatically ensures modules are called in the correct order. The model inserts iteration loops over groups of modules that are mutually dependent. iFlow also ensures a smooth coupling of modules using analytical and numerical solution methods. This way the model combines the speed and accuracy of analytical solutions with the versatility of numerical solution methods.
In this paper we present the modular structure, solution method and two examples of the use of iFlow. In the examples we present two case studies, of the Yangtze and Scheldt rivers, demonstrating how iFlow facilitates the analysis of model results, the understanding of the underlying physics and the testing of parameter sensitivity. A comparison of the model results to measurements shows a good qualitative agreement.
iFlow is written in Python and is available as open source code under the LGPL license.
C1 [Dijkstra, Yoeri M.; Brouwer, Ronald L.; Schuttelaars, Henk M.; Schramkowski, George P.] Delft Univ Technol, Delft Inst Appl Math, POB 5031, NL-2628 CD Delft, Netherlands.
[Brouwer, Ronald L.; Schramkowski, George P.] Flanders Hydraul Res, Berchemlei 115, B-2140 Antwerp, Belgium.
RP Dijkstra, YM (corresponding author), Delft Univ Technol, Delft Inst Appl Math, POB 5031, NL-2628 CD Delft, Netherlands.
EM y.m.dijkstra@tudelft.nl
RI Schuttelaars, Henk/AAH-9494-2020
OI Brouwer, Ronald/0000-0001-6154-3410; Dijkstra, Yoeri/0000-0003-0682-0969
FU VNSC of the "Agenda for the Future" scientific research program [3109
6925, 3110 6170]
FX The development of iFlow is funded by VNSC (http://www.vnsc.eu) through
contracts 3109 6925 and 3110 6170 of the "Agenda for the Future"
scientific research program that is aimed at a better understanding of
the Scheldt Estuary for improved policy and management.
CR Brouwer R. L., 2016, TECH REP
Burchard H, 2011, J PHYS OCEANOGR, V41, P548, DOI 10.1175/2010JPO4453.1
Cheng P, 2010, J PHYS OCEANOGR, V40, P2135, DOI 10.1175/2010JPO4314.1
Chernetsky AS, 2010, OCEAN DYNAM, V60, P1219, DOI 10.1007/s10236-010-0329-8
Cloern JE, 1996, REV GEOPHYS, V34, P127, DOI 10.1029/96RG00986
COLIJN F, 1982, NETH J SEA RES, V15, P196, DOI 10.1016/0077-7579(82)90004-7
de Jonge VN, 2014, ESTUAR COAST SHELF S, V139, P46, DOI 10.1016/j.ecss.2013.12.030
de Swart HE, 2009, ANNU REV FLUID MECH, V41, P203, DOI 10.1146/annurev.fluid.010908.165159
Dijkstra Y. M., 2014, THESIS
Dijkstra Y. M., 2016, WL2016R150391 FLAND
Dijkstra YM, 2017, J GEOPHYS RES-OCEANS, V122, P4217, DOI 10.1002/2016JC012379
Friedrichs C.T., 1996, BUOYANCY EFFECTS COA, P283, DOI [10.1029/CE053p0283, DOI 10.1029/CE053P0283]
FRIEDRICHS CT, 1994, J GEOPHYS RES-OCEANS, V99, P3321, DOI 10.1029/93JC03219
Friedrichs CT, 1998, PHYSICS OF ESTUARIES AND COASTAL SEAS, P315
Guo L, 2014, J GEOPHYS RES-EARTH, V119, P2315, DOI 10.1002/2014JF003110
Guo LC, 2016, J GEOPHYS RES-EARTH, V121, P1000, DOI 10.1002/2016JF003821
HANSEN DONALD V., 1965, J MAR RES, V23, P104
Huijts KMH, 2006, J GEOPHYS RES-OCEANS, V111, DOI 10.1029/2006JC003615
IANNIELLO JP, 1979, J PHYS OCEANOGR, V9, P962, DOI 10.1175/1520-0485(1979)009<0962:TIRCIE>2.0.CO;2
IANNIELLO JP, 1977, J MAR RES, V35, P755
Jones JE, 1996, ESTUAR COAST SHELF S, V42, P311, DOI 10.1006/ecss.1996.0022
MCCARTHY RK, 1993, TELLUS A, V45A, P325, DOI 10.1034/j.1600-0870.1993.00007.x
Murray AB., 2003, GEOPHYS MONOGR SER, P151, DOI DOI 10.1029/135GM11
Plancke Y., 2015, TECH REP
Postma H, 1954, ARCH NEERL ZOOL, P405, DOI [10.1163/036551654x00087, DOI 10.1163/036551654X00087]
PRANDLE D, 1982, CONT SHELF RES, V1, P191, DOI 10.1016/0278-4343(82)90004-8
Schramkowski G. P., 2016, TECH REP
Schramkowski G. P, 2015, TECH REP
Schramkowski GP, 2002, J GEOPHYS RES-OCEANS, V107, DOI 10.1029/2000JC000693
Schuttelaars HM, 2013, OCEAN COAST MANAGE, V79, P70, DOI 10.1016/j.ocecoaman.2012.05.009
Talke SA, 2009, ESTUAR COAST, V32, P602, DOI 10.1007/s12237-009-9171-y
Van Straaten L.M.J. U., 1957, GEOLOGIE MIJNB, P329
Warner JC, 2005, J GEOPHYS RES-OCEANS, V110, DOI 10.1029/2004JC002691
Wei XY, 2016, J PHYS OCEANOGR, V46, P1457, DOI 10.1175/JPO-D-15-0045.1
Winterwerp JC, 2013, OCEAN DYNAM, V63, P1279, DOI 10.1007/s10236-013-0662-9
NR 35
TC 12
Z9 12
U1 0
U2 4
PU COPERNICUS GESELLSCHAFT MBH
PI GOTTINGEN
PA BAHNHOFSALLEE 1E, GOTTINGEN, 37081, GERMANY
SN 1991-959X
EI 1991-9603
J9 GEOSCI MODEL DEV
JI Geosci. Model Dev.
PD JUL 14
PY 2017
VL 10
IS 7
BP 2691
EP 2713
DI 10.5194/gmd-10-2691-2017
PG 23
WC Geosciences, Multidisciplinary
SC Geology
GA FA7WJ
UT WOS:000405657800001
OA DOAJ Gold, Green Published
DA 2021-04-21
ER
PT J
AU Owsiak, M
Plociennik, M
Palak, B
Zok, T
Reux, C
Di Gallo, L
Kalupin, D
Johnson, T
Schneider, M
AF Owsiak, Michal
Plociennik, Marcin
Palak, Bartek
Zok, Tomasz
Reux, Cedric
Di Gallo, Luc
Kalupin, Denis
Johnson, Thomas
Schneider, Mireille
TI Running simultaneous Kepler sessions for the parallelization of
parametric scans and optimization studies applied to complex workflows
SO JOURNAL OF COMPUTATIONAL SCIENCE
LA English
DT Article; Proceedings Paper
CT 16th International Conference on Computational Science (ICCS) - Data
through the Computational Lens
CY JUN 06-08, 2016
CL San Diego, CA
DE Kepler project; Workflows; Parallel execution; Docker
AB In this paper we present an approach taken to run multiple Kepler sessions at the same time. This kind of execution is one of the requirements for Integrated Tokamak Modelling (ITM) platform developed by the Nuclear Fusion community within the context of EUROFusion project [1]. The platform is unique and original: it entails the development of a comprehensive and completely generic tokamak simulator including both the physics and the machine, which can be applied for any fusion device. All components are linked inside workflows. This approach allows complex coupling of various algorithms while at the same time provides consistency. Workflows are composed of Kepler and Ptolemy II elements as well as set of the native libraries written in various languages (Fortran, C, C++). In addition to that, there are Python based components that are used for visualization of results as well as for pre/post processing. At the bottom of all these components there is a database layer that may vary between software releases, and require different version of access libraries. The community is using a shared virtual research environment to prepare and execute workflows. All these constraints make running multiple Kepler sessions really challenging. However, ability to run numerous sessions in parallel is a must - to reduce computation time and to make it possible to run released codes while working with new software at the same time. In this paper we present our approach to solve this issue and examples that show its correctness. (C) 2016 Published by Elsevier B.V.
C1 [Owsiak, Michal; Plociennik, Marcin; Palak, Bartek; Zok, Tomasz] Poznan Supercomp & Networking Ctr IBCh PAS, Poznan, Poland.
[Reux, Cedric; Di Gallo, Luc] CEA, IRFM, F-13108 St Paul Les Durance, France.
[Kalupin, Denis] EUROfus Programme Management Unit, Boltzmannstr 2, D-85748 Garching, Germany.
[Johnson, Thomas] KTH, Fus Plasma Phys, EES, SE-10044 Stockholm, Sweden.
[Schneider, Mireille] ITER Org, St Paul Les Durance, France.
RP Owsiak, M (corresponding author), Poznan Supercomp & Networking Ctr IBCh PAS, Poznan, Poland.
EM michalo@man.poznan.pl; marcinp@man.poznan.pl; bartek@man.poznan.pl;
tzok@man.poznan.pl; cedric.reux@cea.fr; luc.digallo@cea.fr;
denis.kalupin@euro-fusion.org; johnso@kth.se;
mireille.mchneider@iter.org
RI Zok, Tomasz/M-1714-2014
OI Zok, Tomasz/0000-0003-4103-9238
FU Euratom research and training programme [633053]; Horizon Framework
Programme through INDIGO [RIA-653549]
FX This work has been carried out within the framework of the EUROfusion
Consortium and has received funding from the Euratom research and
training programme 2014-2018 under grant agreement No. 633053. Part of
this work has been co-funded by the Horizon 2020 Framework Programme
through the INDIGO-DataCloud Project, RIA-653549.
CR Abramson D., 2011, NIMROD K MASSIVELY P
Altintas Ilkay, 2012, P WORKSH DAT AN CLOU, P73
Di Gallo L., 2015, COMPUT PHYS COMMUN, V200, P1
Kalupin D, 2013, NUCL FUSION, V53, DOI 10.1088/0029-5515/53/12/123007
Kalupin D., 2015, P EPS LISB
Nguyen H. A., 2015, WORKWAYS INTERACTING
Owsiak M., 2015, 14 ITPA EN PART PHYS
Plociennik M., 2013, FUNDAM INF, V128, P1
Reux C., 2015, NUCL FUSION, V55
Schneider M., 2015, BENCHMARKING NEUTRAL
Schneider M., 2015, 15 ITPA EN PART PHYS
NR 11
TC 3
Z9 3
U1 0
U2 7
PU ELSEVIER SCIENCE BV
PI AMSTERDAM
PA PO BOX 211, 1000 AE AMSTERDAM, NETHERLANDS
SN 1877-7503
J9 J COMPUT SCI-NETH
JI J. Comput. Sci.
PD MAY
PY 2017
VL 20
BP 103
EP 111
DI 10.1016/j.jocs.2016.12.005
PG 9
WC Computer Science, Interdisciplinary Applications; Computer Science,
Theory & Methods
SC Computer Science
GA EX3HO
UT WOS:000403123400012
DA 2021-04-21
ER
PT J
AU Lyonnet, F
Schienbein, I
AF Lyonnet, F.
Schienbein, I.
TI PyR@TE 2: A Python tool for computing RGEs at two-loop
SO COMPUTER PHYSICS COMMUNICATIONS
LA English
DT Article
DE Renormalization group equations; Quantum field theory; Running coupling
constants; Model building; Physics beyond the Standard Model
ID RENORMALIZATION-GROUP EQUATIONS; SUSY MODEL
AB Renormalization group equations are an essential tool for the description of theories across different energy scales. Even though their expressions at two-loop for an arbitrary gauge field theory have been known for more than thirty years, deriving the full set of equations for a given model by hand is very challenging and prone to errors. To tackle this issue, we have introduced in Lyonnet et al. (2014) a Python tool called PyR@TE; Python Renormalization group equations @ Two-loop for Everyone. With PyR@TE, it is easy to implement a given Lagrangian and derive the complete set of two-loop RGEs for all the parameters of the theory. In this paper, we present the new version of this code, PyR@TE 2, which brings many new features and in particular it incorporates kinetic mixing when several U(1) gauge groups are involved. In addition, the group theory part has been greatly improved as we introduced a new Python module dubbed PyLie that deals with all the group theoretical aspects required for the calculation of the RGEs as well as providing very useful model building capabilities. This allows the use of any irreducible representation of the SU(n), SO(2n) and SO(2n + 1) groups. Furthermore, it is now possible to implement terms in the Lagrangian involving fields which can be contracted into gauge singlets in more than one way. As a byproduct, results for a popular model (SM + complex triplet) for which, to our knowledge, the complete set of two-loop RGEs has not been calculated before are presented in this paper. Finally, the two-loop RGEs for the anomalous dimension of the scalar and fermion fields have been implemented as well. It is now possible to export the coupled system of beta functions into a numerical C++ function, leading to a consequent speed up in solving them.
Program summary
Program title: PyR@TE 2
Program Files doi: http://dx.doi.org/10.17632/8h454kdd5n.1
Licensing provisions: GNU GPLv3
Programming language: Python
Nature of problem: Deriving the renormalization group equations for a general quantum field theory.
Solution method: Group theory, tensor algebra.
Dependencies: SymPy, PyYAML, NumPy, IPython, SciPy (C) 2016 Elsevier B.V. All rights reserved.
C1 [Lyonnet, F.] Southern Methodist Univ, Dallas, TX 75275 USA.
[Schienbein, I.] UJF Grenoble 1, Lab Phys Subat & Cosmol, CNRS, IN2P3,INPG, 53 Ave Martyrs, F-38026 Grenoble, France.
RP Lyonnet, F (corresponding author), Southern Methodist Univ, Dallas, TX 75275 USA.
EM florian.lyonnet@lpsc.in2p3.fr
FU U.S. Department of EnergyUnited States Department of Energy (DOE)
[DE-SC0010129]
FX We are grateful to Florian Staub, Renato Fonseca, Kristjan Kannike,
Helena Kolesova and Fred Olness for many useful discussions. F.L. would
like to thank Florian Staub for helping validating the implementation of
the kinetic mixing. F.L. is also grateful to Luigi Delle Rose for
providing insights on the implementation of the kinetic mixing and for
the help in resolving early discrepancies in the U(1)B-L
model. This work was also partially supported by the U.S. Department of
Energy under Grant No. DE-SC0010129.
CR Basso L, 2010, PHYS REV D, V82, DOI 10.1103/PhysRevD.82.055018
Coriano C, 2016, J HIGH ENERGY PHYS, P1, DOI 10.1007/JHEP02(2016)135
Datta A, 2013, PHYS REV D, V88, DOI 10.1103/PhysRevD.88.053011
DELAGUILA F, 1989, NUCL PHYS B, V312, P751
DELAGUILA F, 1988, NUCL PHYS B, V307, P571, DOI 10.1016/0550-3213(88)90265-9
Dubois P. F., 1996, COMPUT PHYS, V10
Feger R, 2015, COMPUT PHYS COMMUN, V192, P166, DOI 10.1016/j.cpc.2014.12.023
Fonseca RM, 2013, J PHYS CONF SER, V447, DOI 10.1088/1742-6596/447/1/012034
Fonseca RM, 2013, PHYS LETT B, V726, P882, DOI 10.1016/j.physletb.2013.09.042
Fonseca RM, 2012, COMPUT PHYS COMMUN, V183, P2298, DOI 10.1016/j.cpc.2012.05.017
Jones E., SCIPY OPEN SOURCE SC
Luo MX, 2003, PHYS REV D, V67, DOI 10.1103/PhysRevD.67.065019
Luo MX, 2003, PHYS LETT B, V555, P279, DOI 10.1016/S0370-2693(03)00076-5
Lyonnet F, 2014, COMPUT PHYS COMMUN, V185, P1130, DOI 10.1016/j.cpc.2013.12.002
Lyonnet F., IMPACT KINETIC MIXIN
Lyonnet F., DPF P
Perez F, 2007, COMPUT SCI ENG, V9, P21, DOI 10.1109/MCSE.2007.53
Rizzo TG, 1999, PHYS REV D, V59, DOI 10.1103/PhysRevD.59.015020
Sanderson C., 2010, TECHNICAL REPORT
Simonov K., PYYAML
Staub F, 2014, COMPUT PHYS COMMUN, V185, P1773, DOI 10.1016/j.cpc.2014.02.018
SymPy Development Team, SYMP PYTH LIB SYMB M
van Rossum G., 1991, CWI Q, V4, P283
Wingerter A, 2011, PHYS REV D, V84, DOI 10.1103/PhysRevD.84.095012
NR 24
TC 25
Z9 25
U1 0
U2 10
PU ELSEVIER SCIENCE BV
PI AMSTERDAM
PA PO BOX 211, 1000 AE AMSTERDAM, NETHERLANDS
SN 0010-4655
EI 1879-2944
J9 COMPUT PHYS COMMUN
JI Comput. Phys. Commun.
PD APR
PY 2017
VL 213
BP 181
EP 196
DI 10.1016/j.cpc.2016.12.003
PG 16
WC Computer Science, Interdisciplinary Applications; Physics, Mathematical
SC Computer Science; Physics
GA EK0QB
UT WOS:000393630800018
OA Bronze
DA 2021-04-21
ER
PT J
AU Huang, XY
Tsai, YLS
Yuan, Q
AF Huang, Xiaoyuan
Tsai, Yue-Lin Sming
Yuan, Qiang
TI LIKEDM: Likelihood calculator of dark matter detection
SO COMPUTER PHYSICS COMMUNICATIONS
LA English
DT Article
DE Dark matter; Statistics tools; Dark matter indirect detection
ID COSMIC-RAYS; DIFFUSION-MODEL; PROPAGATION; CONSTRAINTS; PARAMETERS;
CANDIDATES; GALAXY; GAMMA
AB With the large progress in searches for dark matter (DM) particles with indirect and direct methods, we develop a numerical tool that enables fast calculations of the likelihoods of specified DM particle models given a number of observational data, such as charged cosmic rays from space-borne experiments (e.g., PAMELA, AMS-02), gamma-rays from the Fermi space telescope, and underground direct detection experiments. The purpose of this tool - LIKEDM, likelihood calculator for dark matter detection - is to bridge the gap between a particle model of DM and the observational data. The intermediate steps between these two, including the astrophysical backgrounds, the propagation of charged particles, the analysis of Fermi gamma-ray data, as well as the DM velocity distribution and the nuclear form factor, have been dealt with in the code. We release the first version (v1.0) focusing on the constraints from indirect detection of DM with charged cosmic and gamma rays. Direct detection will be implemented in the next version. This manual describes the framework, usage, and related physics of the code.
Program summary
Program Title: LIKEDM
Program Files doi: http://dx.doi.org/10.17632/p93d3ksfvd.1
Licensing provisions: GPLv3
Programming language: FORTRAN 90 and Python
Nature of problem: Dealing with the intermediate steps between a dark matter model and data.
Solution method: Fast computation of the likelihood of a given dark matter model (defined by a mass, cross section or decay rate, and annihilation or decay yield spectrum), without digging into the details of cosmic-ray propagation, Fermi-LAT data analysis, or related astrophysical backgrounds. (C) 2017 Elsevier B.V. All rights reserved.
C1 [Huang, Xiaoyuan] Tech Univ Munich, Phys Dept T30d, James Franck Str, D-85748 Garching, Germany.
[Tsai, Yue-Lin Sming] Univ Tokyo, WPI, Kavli IPMU, Kashiwa, Chiba 2778583, Japan.
[Yuan, Qiang] Univ Massachusetts, Dept Astron, 710 North Pleasant St, Amherst, MA 01003 USA.
[Yuan, Qiang] Chinese Acad Sci, Purple Mt Observ, Key Lab Dark Matter & Space Astron, Nanjing 210008, Jiangsu, Peoples R China.
RP Tsai, YLS (corresponding author), Univ Tokyo, WPI, Kavli IPMU, Kashiwa, Chiba 2778583, Japan.
EM yue-lin.tsai@ipmu.jp
OI Tsai, Yue-Lin Sming/0000-0002-7275-8561
FU World Premier International Research Center Initiative (WPI), MEXT,
JapanMinistry of Education, Culture, Sports, Science and Technology,
Japan (MEXT); National Key Program for Research and Development
[2016YFA0400200]; Chinese Academy of SciencesChinese Academy of
Sciences; World Premier International Research Center Initiative (WPI),
MEXT, JapanMinistry of Education, Culture, Sports, Science and
Technology, Japan (MEXT); National Key Program for Research and
Development [2016YFA0400200]; Chinese Academy of SciencesChinese Academy
of Sciences
FX We thank Vincent Bonnivard who kindly provides the MCMC results about
the profile parameters of dSphs, and Andrew Fowlie and Shankha Banerjee
who carefully read and improve the presentation of the manuscript.
Y.S.T. was supported by World Premier International Research Center
Initiative (WPI), MEXT, Japan. Q.Y. was supported by the National Key
Program for Research and Development (No. 2016YFA0400200) and the 100
Talents program of Chinese Academy of Sciences.
CR Aad G, 2012, PHYS LETT B, V716, P1, DOI 10.1016/j.physletb.2012.08.020
Accardo L, 2014, PHYS REV LETT, V113, DOI 10.1103/PhysRevLett.113.121101
Acero F, 2015, ASTROPHYS J SUPPL S, V218, DOI 10.1088/0067-0049/218/2/23
Ackermann M, 2015, PHYS REV LETT, V115, DOI 10.1103/PhysRevLett.115.231301
Ackermann M, 2014, PHYS REV D, V89, DOI 10.1103/PhysRevD.89.042001
Ackermann M, 2012, ASTROPHYS J, V761, DOI 10.1088/0004-637X/761/2/91
Ackermann M, 2012, ASTROPHYS J, V750, DOI 10.1088/0004-637X/750/1/3
Ackermann M, 2011, PHYS REV LETT, V107, DOI 10.1103/PhysRevLett.107.241302
Adriani O, 2010, PHYS REV LETT, V105, DOI 10.1103/PhysRevLett.105.121101
Adriani O, 2009, NATURE, V458, P607, DOI 10.1038/nature07942
Aguilar M, 2014, PHYS REV LETT, V113, DOI 10.1103/PhysRevLett.113.221102
Aguilar M, 2014, PHYS REV LETT, V113, DOI 10.1103/PhysRevLett.113.121102
Aguilar M, 2013, PHYS REV LETT, V110, DOI 10.1103/PhysRevLett.110.141102
Ahnen ML, 2016, J COSMOL ASTROPART P, DOI 10.1088/1475-7516/2016/02/039
Akerib D. S., ARXIV13108214ASTROPH
Aprile E, 2012, PHYS REV LETT, V109, DOI 10.1103/PhysRevLett.109.181301
Arhrib A, 2014, J COSMOL ASTROPART P, DOI 10.1088/1475-7516/2014/06/030
BAHCALL JN, 1980, ASTROPHYS J SUPPL S, V44, P73, DOI 10.1086/190685
Bechtol K, 2015, ASTROPHYS J, V807, DOI 10.1088/0004-637X/807/1/50
Bergstrom L, 2013, PHYS REV LETT, V111, DOI 10.1103/PhysRevLett.111.171101
Bergstrom L, 2012, ANN PHYS-BERLIN, V524, P479, DOI 10.1002/andp.201200116
Bertone G., 2009, JCAP, V0903, P009
Bi XJ, 2013, FRONT PHYS-BEIJING, V8, P794, DOI 10.1007/s11467-013-0330-z
Bonnivard V, 2015, MON NOT R ASTRON SOC, V453, P849, DOI 10.1093/mnras/stv1601
Chatrchyan S, 2012, PHYS LETT B, V716, P30, DOI 10.1016/j.physletb.2012.08.021
Cirelli M, 2012, PRAMANA-J PHYS, V79, P1021, DOI 10.1007/s12043-012-0419-x
de Austri RR, 2006, J HIGH ENERGY PHYS, DOI 10.1088/1126-6708/2006/05/002
Drlica-Wagner A, 2015, ASTROPHYS J, V813, DOI 10.1088/0004-637X/813/2/109
Einasto J., 1965, T ASTROFIZICHESKOGO, V5, P87
Evoli C, 2008, J COSMOL ASTROPART P, DOI 10.1088/1475-7516/2008/10/018
Feng L, 2013, PHYS LETT B, V720, P1, DOI 10.1016/j.physletb.2013.01.060
Geringer-Sameth A, 2015, PHYS REV LETT, V115, DOI 10.1103/PhysRevLett.115.081101
Geringer-Sameth A, 2015, PHYS REV D, V91, DOI 10.1103/PhysRevD.91.083535
Girelli M., 2012, JCAP, V1210, pE0I
GLEESON LJ, 1968, ASTROPHYS J, V154, P1011, DOI 10.1086/149822
JAMES F, 1975, COMPUT PHYS COMMUN, V10, P343, DOI 10.1016/0010-4655(75)90039-9
Kamae T, 2007, ASTROPHYS J, V662, P779, DOI 10.1086/513602
Kamae T, 2006, ASTROPHYS J, V647, P692, DOI 10.1086/505189
Lewis A, 2002, PHYS REV D, V66, DOI 10.1103/PhysRevD.66.103511
Li S, 2016, PHYS REV D, V93, DOI 10.1103/PhysRevD.93.043518
Maurin D, 2001, ASTROPHYS J, V555, P585, DOI 10.1086/321496
Navarro JF, 1997, ASTROPHYS J, V490, P493, DOI 10.1086/304888
Putze A, 2010, ASTRON ASTROPHYS, V516, DOI 10.1051/0004-6361/201014010
SEO ES, 1994, ASTROPHYS J, V431, P705, DOI 10.1086/174520
SHEN CS, 1970, ASTROPHYS J, V162, pL181, DOI 10.1086/180650
Strong AW, 2007, ANNU REV NUCL PART S, V57, P285, DOI 10.1146/annurev.nucl.57.090506.123011
Strong AW, 1998, ASTROPHYS J, V509, P212, DOI 10.1086/306470
Tsai YLS, 2013, J COSMOL ASTROPART P, DOI 10.1088/1475-7516/2013/03/018
Xiao MJ, 2014, SCI CHINA PHYS MECH, V57, P2024, DOI 10.1007/s11433-014-5598-7
Yuan Q, 2015, ASTROPART PHYS, V60, P1, DOI 10.1016/j.astropartphys.2014.05.005
Yuan Q, 2013, PHYS LETT B, V727, P1, DOI 10.1016/j.physletb.2013.10.010
Yue Q, 2014, PHYS REV D, V90, DOI 10.1103/PhysRevD.90.091701
Zhao Y., ARXIV160102181ASTROP
NR 53
TC 22
Z9 22
U1 0
U2 11
PU ELSEVIER SCIENCE BV
PI AMSTERDAM
PA PO BOX 211, 1000 AE AMSTERDAM, NETHERLANDS
SN 0010-4655
EI 1879-2944
J9 COMPUT PHYS COMMUN
JI Comput. Phys. Commun.
PD APR
PY 2017
VL 213
BP 252
EP 263
DI 10.1016/j.cpc.2016.12.015
PG 12
WC Computer Science, Interdisciplinary Applications; Physics, Mathematical
SC Computer Science; Physics
GA EK0QB
UT WOS:000393630800024
DA 2021-04-21
ER
PT J
AU King, RN
Dykes, K
Graf, P
Hamlington, PE
AF King, Ryan N.
Dykes, Katherine
Graf, Peter
Hamlington, Peter E.
TI Optimization of wind plant layouts using an adjoint approach
SO WIND ENERGY SCIENCE
LA English
DT Article
ID COMPUTATIONAL FLUID-DYNAMICS; TURBINE WAKES; MODEL; FLOW; DESIGN; FARMS
AB Using adjoint optimization and three-dimensional steady-state Reynolds-averaged Navier-Stokes (RANS) simulations, we present a new gradient-based approach for optimally siting wind turbines within utilityscale wind plants. By solving the adjoint equations of the flow model, the gradients needed for optimization are found at a cost that is independent of the number of control variables, thereby permitting optimization of large wind plants with many turbine locations. Moreover, compared to the common approach of superimposing prescribed wake deficits onto linearized flow models, the computational efficiency of the adjoint approach allows the use of higher-fidelity RANS flow models which can capture nonlinear turbulent flow physics within a wind plant. The steady-state RANS flow model is implemented in the Python finite-element package FEniCS and the derivation and solution of the discrete adjoint equations are automated within the dolfin-adjoint framework. Gradient-based optimization of wind turbine locations is demonstrated for idealized test cases that reveal new optimization heuristics such as rotational symmetry, local speedups, and nonlinear wake curvature effects. Layout optimization is also demonstrated on more complex wind rose shapes, including a full annual energy production (AEP) layout optimization over 36 inflow directions and 5 wind speed bins.
C1 [King, Ryan N.; Hamlington, Peter E.] Univ Colorado, Boulder, CO 80309 USA.
[King, Ryan N.; Dykes, Katherine; Graf, Peter] Natl Renewable Energy Lab, Golden, CO 80401 USA.
RP King, RN (corresponding author), Univ Colorado, Boulder, CO 80309 USA.; King, RN (corresponding author), Natl Renewable Energy Lab, Golden, CO 80401 USA.
EM ryan.king@nrel.gov
RI Dykes, Katherine/A-9502-2012
OI Dykes, Katherine/0000-0002-5734-3122
FU Alliance Partner University Program [UGA-0-41026-70]; National Renewable
Energy LaboratoryUnited States Department of Energy (DOE)
FX This work was supported by award UGA-0-41026-70 through the Alliance
Partner University Program in partnership with the National Renewable
Energy Laboratory.
CR AINSLIE JF, 1988, J WIND ENG IND AEROD, V27, P213, DOI 10.1016/0167-6105(88)90037-2
Amestoy PR, 2000, COMPUT METHOD APPL M, V184, P501, DOI 10.1016/S0045-7825(99)00242-X
Barthelmie RJ, 2009, WIND ENERGY, V12, P431, DOI 10.1002/we.348
Boersma S, 2016, J PHYS CONF SER, V753, DOI 10.1088/1742-6596/753/3/032005
Bokharaie VS, 2016, WIND ENERGY SCI, V1, P311, DOI 10.5194/wes-1-311-2016
Brezzi F., 1991, MIXED HYBRID FINITE
Burton T, 2011, WIND ENERGY HDB
BYRD RH, 1995, SIAM J SCI COMPUT, V16, P1190, DOI 10.1137/0916069
Cabezon D, 2011, WIND ENERGY, V14, P909, DOI 10.1002/we.516
Calaf M, 2010, PHYS FLUIDS, V22, DOI 10.1063/1.3291077
Chen TY, 2011, EXP THERM FLUID SCI, V35, P565, DOI 10.1016/j.expthermflusci.2010.12.005
Churchfield MJ, 2012, J TURBUL, V13, P1, DOI 10.1080/14685248.2012.668191
Crespo A., 1999, WIND ENERGY, V2, P1, DOI [10.1002/(SICI)1099-1824(199901/03)2:1%C1::AID-WE16%E3.0.CO;2-7, DOI 10.1002/(SICI)1099-1824(199901/03)2:1%C1::AID-WE16%E3.0.CO;2-7, 10.1002/(SICI)1099-1824(199901/03)2:1,1::AID-WE16.3.0.CO;2-7, DOI 10.1002/(SICI)1099-1824(199901/03)2:1<1::AID-WE16>3.0.CO;2-7]
Donea J., 2003, FINITE ELEMENT METHO
Du Pont BL, 2012, J MECH DESIGN, V134, DOI 10.1115/1.4006997
Dykes K., 2014, AM I AERONAUTICS AST, DOI [10.2514/6.2014-1087, DOI 10.2514/6.2014-1087]
El Kasmi A, 2008, J WIND ENG IND AEROD, V96, P103, DOI 10.1016/j.jweia.2007.03.007
Farrell PE, 2013, SIAM J SCI COMPUT, V35, pC369, DOI 10.1137/120873558
Fitch AC, 2012, MON WEATHER REV, V140, P3017, DOI 10.1175/MWR-D-11-00352.1
Fleming PA, 2016, WIND ENERGY, V19, P329, DOI 10.1002/we.1836
Funke SW, 2014, RENEW ENERG, V63, P658, DOI 10.1016/j.renene.2013.09.031
Gebraad PMO, 2016, WIND ENERGY, V19, P95, DOI 10.1002/we.1822
Giannakoglou KC, 2008, OPTIMIZATION AND COMPUTATIONAL FLUID DYNAMICS, P79, DOI 10.1007/978-3-540-72153-6_4
Giles MB, 2000, FLOW TURBUL COMBUST, V65, P393, DOI 10.1023/A:1011430410075
Goit JP, 2016, ENERGIES, V9, DOI 10.3390/en9010029
Goit JP, 2015, J FLUID MECH, V768, DOI 10.1017/jfm.2015.70
Herbert-Acero JF, 2014, ENERGIES, V7, P6930, DOI 10.3390/en7116930
Heywood JG, 1996, INT J NUMER METH FL, V22, P325, DOI 10.1002/(SICI)1097-0363(19960315)22:5<325::AID-FLD307>3.0.CO;2-Y
Hinze M, 2009, MATH MODEL-THEOR APP, V23, P1, DOI 10.1007/978-1-4020-8839-1
Iungo G. V., 2016, 34th Wind Energy Symposium, P1
Jackson P. S., 1975, J ROY METEOR SOC, V101, P929, DOI [10.1002/qj.49710143015, DOI 10.1002/QJ.49710143015]
Jager D., 1996, NREL NATL WIND TECHN
Jameson A, 2003, AERODYNAMIC SHAPE OP
Jensen N.O., 1983, RIS M 2411 RIS NATL, P1
Jimenez A, 2007, J PHYS CONF SER, V75, DOI 10.1088/1742-6596/75/1/012041
King R., 2016, ADJOINT OPTIMIZATION, P2016, DOI [10.2514/6.2016-2199, DOI 10.2514/6.2016-2199]
Kusiak A, 2010, RENEW ENERG, V35, P685, DOI 10.1016/j.renene.2009.08.019
Kwong WY, 2012, PROCEEDINGS OF THE ASME INTERNATIONAL DESIGN ENGINEERING TECHNICAL CONFERENCES AND COMPUTERS AND INFORMATION IN ENGINEERING CONFERENCE 2012, VOL 3, PTS A AND B, P323
Larsen G. C., 2011, RISOR1805 EN
Logg A., 2012, LECT NOTES COMPUT SC, V84
Luchini P, 2014, ANNU REV FLUID MECH, V46, P493, DOI 10.1146/annurev-fluid-010313-141253
Marden JR, 2013, IEEE T CONTR SYST T, V21, P1207, DOI 10.1109/TCST.2013.2257780
Martins JRRA, 2013, AIAA J, V51, P2582, DOI 10.2514/1.J052184
Meyers J, 2012, WIND ENERGY, V15, P305, DOI 10.1002/we.469
Mirocha JD, 2014, J RENEW SUSTAIN ENER, V6, DOI 10.1063/1.4861061
Nocedal J., 1999, NUMERICAL OPTIMIZATI
Porte-Agel F, 2011, J WIND ENG IND AEROD, V99, P154, DOI 10.1016/j.jweia.2011.01.011
Sanderse B, 2011, WIND ENERGY, V14, P799, DOI 10.1002/we.458
Gonzalez JS, 2010, RENEW ENERG, V35, P1671, DOI 10.1016/j.renene.2010.01.010
van der Laan MP, 2015, WIND ENERGY, V18, P889, DOI 10.1002/we.1736
WALMSLEY JL, 1986, BOUND-LAY METEOROL, V36, P157, DOI 10.1007/BF00117466
Wilcox D. C., 2006, TURBULENCE MODELING
Wu YT, 2013, BOUND-LAY METEOROL, V146, P181, DOI 10.1007/s10546-012-9757-y
Wu YT, 2011, BOUND-LAY METEOROL, V138, P345, DOI 10.1007/s10546-010-9569-x
NR 54
TC 12
Z9 12
U1 0
U2 1
PU COPERNICUS GESELLSCHAFT MBH
PI GOTTINGEN
PA BAHNHOFSALLEE 1E, GOTTINGEN, 37081, GERMANY
SN 2366-7443
EI 2366-7451
J9 WIND ENERGY SCI
JI Wind Energy Sci.
PD MAR 10
PY 2017
VL 2
IS 1
BP 115
EP 131
DI 10.5194/wes-2-115-2017
PG 17
WC Green & Sustainable Science & Technology
SC Science & Technology - Other Topics
GA FL3MD
UT WOS:000414126300001
OA DOAJ Gold
DA 2021-04-21
ER
PT J
AU Betzler, BR
Powers, JJ
Worrall, A
AF Betzler, Benjamin R.
Powers, Jeffrey J.
Worrall, Andrew
TI Molten salt reactor neutronics and fuel cycle modeling and simulation
with SCALE
SO ANNALS OF NUCLEAR ENERGY
LA English
DT Article
DE Molten salt reactors; Fuel cycle; Depletion; Salt treatment; Salt
separations
ID BREEDER REACTOR; CORE PHYSICS; DYNAMICS; PERFORMANCE; SYSTEMS; CODE; MSR
AB Current interest in advanced nuclear energy and molten salt reactor (MSR) concepts has enhanced interest in building the tools necessary to analyze these systems. A Python script known as ChemTriton has been developed to simulate equilibrium MSR fuel cycle performance by modeling the changing isotopic composition of an irradiated fuel salt using SCALE for neutron transport and depletion calculations. Improved capabilities in ChemTriton include a generic geometry capable of modeling multi-zone and multi-fluid systems, enhanced time-dependent feed and separations, and a critical concentration search. Although more generally applicable, the capabilities developed to date art illustrated in this paper in three applied problems: (1) simulating the startup of a thorium-based MSR fuel cycle (a likely scenario requires the first of these MSRs to be started without available U-233); (2) determining the effect of the removal of different fission products on MSR operations; and (3) obtaining the equilibrium concentration of a mixed oxide light-water reactor fuel in a two-stage fuel cycle with a sodium fast reactor. The third problem is chosen to demonstrate versatility in an application to analyze the fuel cycle of a non-MSR system.
In the first application, the initial fuel salt compositions fueled with different sources of fissile material are made feasible after (1) removing the associated nonfissile actinides after much of the initial fissile isotopes have burned and (2) optimizing the thorium concentration to maintain a critical configuration without significantly reducing breeding capability. In the second application, noble metal, volatile gas, and rare earth element fission products are shown to have a strong negative effect on criticality in a uranium-fueled thermal-spectrum MSR; their removal significantly increases core lifetime (by 30%) and fuel utilization. In the third application, the fuel of a mixed-oxide light-water reactor approaches an equilibrium composition after 20 depletion steps, demonstrating the potential for the longer time scales required to achieve equilibrium for solid-fueled systems over liquid fuel systems. This time to equilibrium can be reduced by starting with an initial fuel composition closer to that of the equilibrium fuel, reducing the need to handle time-dependent fuel compositions. (C) 2016 Elsevier Ltd. All rights reserved.
C1 [Betzler, Benjamin R.; Powers, Jeffrey J.; Worrall, Andrew] Oak Ridge Natl Lab, Bldg 5700,Room J301,Mail Stop 6172, Oak Ridge, TN 37831 USA.
RP Betzler, BR (corresponding author), Oak Ridge Natl Lab, Bldg 5700,Room J301,Mail Stop 6172, Oak Ridge, TN 37831 USA.
EM betzlerbr@ornl.gov
OI Powers, Jeffrey/0000-0003-3653-3880
FU Fuel Cycles Options Campaign of the Fuel Cycle Technologies initiative
of the US Department of Energy Office of Nuclear Energy; US Department
of EnergyUnited States Department of Energy (DOE) [DE-AC05-00OR22725]
FX This work has been funded by the Fuel Cycles Options Campaign of the
Fuel Cycle Technologies initiative of the US Department of Energy Office
of Nuclear Energy. This manuscript has been authored by employees of Oak
Ridge National Laboratory, managed by UT-Battelle LLC under US
Department of Energy contract DE-AC05-00OR22725.
CR Ahmad A, 2015, ANN NUCL ENERGY, V75, P261, DOI 10.1016/j.anucene.2014.08.014
[Anonymous], 2013, LACP13000634 AL NAT
Aufiero M, 2013, J NUCL MATER, V441, P473, DOI 10.1016/j.jnucmat.2013.06.026
Aufiero M, 2014, ANN NUCL ENERGY, V65, P78, DOI 10.1016/j.anucene.2013.10.015
Bauman H.F., 1971, ORNLTM3359
Bays S. E., 2013, INLEXT132849
BETTIS ES, 1970, NUCL APPL TECHNOL, V8, P190, DOI 10.13182/NT70-A28625
Betzler B. R., 2016, P INT C PHYS 2016 SU
Bowman S.M., 2011, NUCL TECHNOL, V174
Brinton S., 2016, ADV NUCL IND
Brown NR, 2015, NUCL ENG DES, V289, P252, DOI 10.1016/j.nucengdes.2015.04.015
Brown NR, 2016, ANN NUCL ENERGY, V96, P88, DOI 10.1016/j.anucene.2016.05.027
Brown NR, 2016, NUCL TECHNOL, V194, P233, DOI 10.13182/NT15-40
Bulmer J.J., 1956, CF568204 OAK RIDG SC
Cammi A, 2012, NUCL ENG DES, V246, P12, DOI 10.1016/j.nucengdes.2011.08.002
Cheng MS, 2014, NUCL SCI TECH, V25
DeHart MD, 2011, NUCL TECHNOL, V174, P196, DOI 10.13182/NT174-196
Doligez X, 2014, ANN NUCL ENERGY, V64, P430, DOI 10.1016/j.anucene.2013.09.009
Duderstadt J. J., 1976, NUCL REACTOR ANAL
Engel J.R., 1980, CONCEPTUAL DESIGN CH
Feng B, 2016, ANN NUCL ENERGY, V94, P300, DOI 10.1016/j.anucene.2016.03.002
Fiorina C, 2014, ANN NUCL ENERGY, V64, P485, DOI 10.1016/j.anucene.2013.08.003
Fiorina C, 2013, PROG NUCL ENERG, V68, P153, DOI 10.1016/j.pnucene.2013.06.006
Gauld I. C., 2011, NUCL TECHNOL, V174
Gehin JC, 2016, NUCL TECHNOL, V194, P152, DOI 10.13182/NT15-124
Goluoglu S, 2011, NUCL TECHNOL, V174, P214, DOI 10.13182/NT10-124
Heidet Florent, 2015, Reviews of Accelerator Science and Technology, V8, P99, DOI 10.1142/S1793626815300066
Heuer D, 2014, ANN NUCL ENERGY, V64, P421, DOI 10.1016/j.anucene.2013.08.002
Heuer D., 2010, CONTRIBUTION A0115
IAEA Safeguards Glossary, INT NUCL VERIFICATIO, V3
Jeong Y, 2016, J NUCL SCI TECHNOL, V53, P529, DOI 10.1080/00223131.2015.1062812
Kee C. W., 1976, ORNLTM4210
KERLIN TW, 1971, NUCL TECHNOL, V10, P118, DOI 10.13182/NT71-A30920
Kophazi J, 2009, NUCL SCI ENG, V163, P118, DOI 10.13182/NSE163-118
Krepel J, 2008, NUCL TECHNOL, V164, P34, DOI 10.13182/NT08-A4006
Krepel J, 2007, ANN NUCL ENERGY, V34, P449, DOI 10.1016/j.anucene.2006.12.011
LUDWIG S.B., 1989, ORNLTM11018
MCNP, GEN M CARL N PART MC
Mourogov A, 2006, ENERG CONVERS MANAGE, V47, P2761, DOI 10.1016/j.enconman.2006.02.013
Nuttin A, 2005, PROG NUCL ENERG, V46, P77, DOI 10.1016/j.pnucene.2004.11.001
Park J, 2015, INT J ENERG RES, V39, P1673, DOI 10.1002/er.3371
Powers J.J., 2013, P INT C MATH COMP ME
Powers J. J., 2014, P INT C PHYS 2014 KY
Rearden B.T., 2016, ORNLTM200539
Rimpault G., 2002, P INT C PHYS 2002 SE
Robertson R.C., 1970, ORNL4528
Robertson R.C, 1971, ORNL4541
Sen S., 2013, MULTIRECYCLING PLUTO
Serp J, 2014, PROG NUCL ENERG, V77, P308, DOI 10.1016/j.pnucene.2014.02.014
SERPENT, 2011, PSG2 SERP M CARL REA
Sheu RJ, 2013, ANN NUCL ENERGY, V53, P1, DOI 10.1016/j.anucene.2012.10.017
Shi CB, 2016, NUCL ENG DES, V305, P378, DOI 10.1016/j.nucengdes.2016.05.034
SHIMAZU Y, 1978, J NUCL SCI TECHNOL, V15, P514, DOI 10.3327/jnst.15.514
Smith J., 1974, 956 AEEWR
Stauff NE, 2015, J NUCL SCI TECHNOL, V52, P1058, DOI 10.1080/00223131.2015.1032380
TAUBE M, 1974, ANN NUCL SCI ENG, V1, P277, DOI 10.1016/0302-2927(74)90045-2
Taube M., 1974, 249 INT REAKT WUR SC
Toppel BJ., 1983, ANL832
US DOE, 2015, GAT ACC INN NUCL
US DOE, 2016, GAT ACC INN NUCL
van Rossum Guido, 1995, CSR9526
Wigeland R., 2014, INLEXT1431465
Xu Z., 2002, IMPROVED MCNPORIGEN
Zhou JJ, 2015, NUCL ENG DES, V282, P93, DOI 10.1016/j.nucengdes.2014.11.026
NR 64
TC 21
Z9 23
U1 1
U2 40
PU PERGAMON-ELSEVIER SCIENCE LTD
PI OXFORD
PA THE BOULEVARD, LANGFORD LANE, KIDLINGTON, OXFORD OX5 1GB, ENGLAND
SN 0306-4549
J9 ANN NUCL ENERGY
JI Ann. Nucl. Energy
PD MAR
PY 2017
VL 101
BP 489
EP 503
DI 10.1016/j.anucene.2016.11.040
PG 15
WC Nuclear Science & Technology
SC Nuclear Science & Technology
GA EI8OU
UT WOS:000392767800051
DA 2021-04-21
ER
PT J
AU Liu, QB
Li, J
Liu, J
AF Liu, Qingbin
Li, Jiang
Liu, Jie
TI ParaView visualization of Abaqus output on the mechanical deformation of
complex microstructures
SO COMPUTERS & GEOSCIENCES
LA English
DT Article
DE Abaqus (R); Post-processing; Python script; ParaView; Parallel
visualization
ID MICROTOMOGRAPHY
AB Abaqus (R) is a popular software suite for finite element analysis. It delivers linear and nonlinear analyses of mechanical and fluid dynamics, includes multi-body system and multi-physics coupling. However, the visualization capability of Abaqus using its CAE module is limited. Models from microtomography have extremely complicated structures, and datasets of Abaqus output are huge, requiring a visualization tool more powerful than Abaqus/CAE. We convert Abaqus output into the XML-based VTK format by developing a Python script and then using ParaView to visualize the results. Such capabilities as volume rendering, tensor glyphs, superior animation and other filters allow ParaView to offer excellent visualizing manifestations. ParaView's parallel visualization makes it possible to visualize very big data. To support full parallel visualization, the Python script achieves data partitioning by reorganizing all nodes, elements and the corresponding results on those nodes and elements. The data partition scheme minimizes data redundancy and works efficiently. Given its good readability and extendibility, the script can be extended to the processing of more different problems in Abaqus. We share the script with Abaqus users on GitHub.
C1 [Liu, Qingbin] Peking Univ, Sch Earth & Space Sci, Beijing 100871, Peoples R China.
[Liu, Qingbin; Liu, Jie] Sun Yat Sen Univ, Sch Earth Sci & Geol Engn, Guangzhou 510275, Guangdong, Peoples R China.
[Li, Jiang] Natl Supercomp Ctr Guangzhou, Guangzhou 510006, Guangdong, Peoples R China.
[Liu, Jie] Guangdong Prov Key Lab Mineral Resources & Geol P, Guangzhou 510275, Guangdong, Peoples R China.
EM liujie86@mail.sysu.edu.cn
FU Fundamental Research Funds of China for the Central Universities
[14Igjc12]
FX We thank Andrew Squelch (Curtin University, Australia), Thomas Poulet
(Energy Flagship of CSIRO, Australia), and Guihua Shan and Zhenyu Ba
(Computer Network Information Center, Chinese Academy of Science) for
their help on the use of ParaView and Python. The work reported here was
partially supported by the 'Fundamental Research Funds of China (Grant
no. 14Igjc12) for the Central Universities'.
CR Arns CH, 2002, GEOPHYSICS, V67, P1396, DOI 10.1190/1.1512785
Ayachit U., 2015, PARAVIEW GUIDE PARAL, P276
Charleux L., 2016, ABAPY DOCUMENTATION
Dassault Systemes Simulia Corp, 2014, AB 6 14SCRIPT US GUI, P324
Hergert T, 2010, NAT GEOSCI, V3, P132, DOI 10.1038/NGEO739
Karrech A, 2011, J GEOPHYS RES-SOL EA, V116, DOI 10.1029/2010JB007501
Kitware Inc, 2010, THE VTK US GUID, P536
Kurfess D, 2009, COMPUT GEOSCI-UK, V35, P1959, DOI 10.1016/j.cageo.2008.10.019
Liu J, 2016, COMPUT GEOSCI-UK, V89, P107, DOI 10.1016/j.cageo.2016.01.014
Liu J, 2015, J EARTH SCI-CHINA, V26, P53, DOI 10.1007/s12583-015-0520-4
Liu J, 2014, GEOPHYS J INT, V198, P1319, DOI 10.1093/gji/ggu200
Regenauer-Lieb K, 2006, NATURE, V442, P67, DOI 10.1038/nature04868
Romano F, 2014, SCI REP-UK, V4, DOI 10.1038/srep05631
Rosenbaum G, 2010, J GEOPHYS RES-SOL EA, V115, DOI 10.1029/2009JB006696
Schrank CE, 2012, GEOCHEM GEOPHY GEOSY, V13, DOI 10.1029/2012GC004085
Wang HS, 2013, NAT GEOSCI, V6, P38, DOI [10.1038/ngeo1652, 10.1038/NGEO1652]
Zysset Philippe K, 2013, Bonekey Rep, V2, P386, DOI 10.1038/bonekey.2013.120
NR 17
TC 5
Z9 5
U1 4
U2 29
PU PERGAMON-ELSEVIER SCIENCE LTD
PI OXFORD
PA THE BOULEVARD, LANGFORD LANE, KIDLINGTON, OXFORD OX5 1GB, ENGLAND
SN 0098-3004
EI 1873-7803
J9 COMPUT GEOSCI-UK
JI Comput. Geosci.
PD FEB
PY 2017
VL 99
BP 135
EP 144
DI 10.1016/j.cageo.2016.11.008
PG 10
WC Computer Science, Interdisciplinary Applications; Geosciences,
Multidisciplinary
SC Computer Science; Geology
GA EI7PR
UT WOS:000392690900015
DA 2021-04-21
ER
PT J
AU Liu, HB
Chen, HC
Chen, K
Kierstead, J
Lanni, F
Takai, H
Jin, G
AF Liu, Hong-Bin
Chen, Hu-Cheng
Chen, Kai
Kierstead, James
Lanni, Francesco
Takai, Helio
Jin, Ge
TI Development of an ADC radiation tolerance characterization system for
the upgrade of the ATLAS LAr calorimeter
SO CHINESE PHYSICS C
LA English
DT Article
DE radiation tolerance characterization; high-speed multi-channel ADC;
total ionization dose; single event effect
AB ATLAS LAr calorimeter will undergo its Phase-I upgrade during the long shutdown (LS2) in 2018, and a new LAr Trigger Digitizer Board (LTDB) will be designed and installed. Several commercial-off-the-shelf (COTS) multi-channel high-speed ADCs have been selected as possible backups of the radiation tolerant ADC ASICs for the LTDB. To evaluate the radiation tolerance of these backup commercial ADCs, we developed an ADC radiation tolerance characterization system, which includes the ADC boards, data acquisition (DAQ) board, signal generator, external power supplies and a host computer. The ADC board is custom designed for different ADCs, with ADC drivers and clock distribution circuits integrated on board. The Xilinx ZC706 FPGA development board is used as a DAQ board. The data from the ADC are routed to the FPGA through the FMC (FPGA Mezzanine Card) connector, de-serialized and monitored by the FPGA, and then transmitted to the host computer through the Gigabit Ethernet. A software program has been developed with Python, and all the commands are sent to the DAQ board through Gigabit Ethernet by this program. Two ADC boards have been designed for the ADC, ADS52J90 from Texas Instruments and AD9249 from Analog Devices respectively. TID tests for both ADCs have been performed at BNL, and an SEE test for the ADS52J90 has been performed at Massachusetts General Hospital (MGH). Test results have been analyzed and presented. The test results demonstrate that this test system is very versatile, and works well for the radiation tolerance characterization of commercial multi-channel high-speed ADCs for the upgrade of the ATLAS LAr calorimeter. It is applicable to other collider physics experiments where radiation tolerance is required as well.
C1 [Liu, Hong-Bin; Jin, Ge] Univ Sci & Technol China, State Key Lab Particle Detect & Elect, Hefei 230026, Peoples R China.
[Liu, Hong-Bin; Jin, Ge] Univ Sci & Technol China, Dept Modern Phys, Hefei 230026, Peoples R China.
[Liu, Hong-Bin; Chen, Hu-Cheng; Chen, Kai; Kierstead, James; Lanni, Francesco; Takai, Helio] Brookhaven Natl Lab, Upton, NY 11973 USA.
RP Liu, HB (corresponding author), Univ Sci & Technol China, State Key Lab Particle Detect & Elect, Hefei 230026, Peoples R China.; Liu, HB (corresponding author), Univ Sci & Technol China, Dept Modern Phys, Hefei 230026, Peoples R China.; Liu, HB (corresponding author), Brookhaven Natl Lab, Upton, NY 11973 USA.
EM hliu2@bnl.gov
RI Liu, Hongbin/AAM-3489-2020; Chen, Kai/B-2271-2015
OI Chen, Kai/0000-0003-4936-3825; Chen, Hucheng/0000-0002-9936-0115
FU U.S. Department of EnergyUnited States Department of Energy (DOE)
[DE-SC001270]
FX Supported by the U.S. Department of Energy (DE-SC001270)
CR American National Standards Institute, 2010, FMC STAND
Analog Devices, 2013, AD9249 DAT
Cascio EW, 2003, 2003 IEEE RADIATION EFFECTS DATA WORKSHOP RECORD, P141, DOI 10.1109/REDW.2003.1281365
Chen K, 2015, J INSTRUM, V10, DOI 10.1088/1748-0221/10/08/P08009
Hu XY, 2014, NUCL SCI TECH, V25, DOI 10.13538/j.1001-8042/nst.25.060403
Sexton FW, 2003, IEEE T NUCL SCI, V50, P603, DOI 10.1109/TNS.2003.813137
Texas Instrumentation, 2015, ADS52J90 DAT
Xilinx Inc, 2015, ZC706 US GUID
NR 8
TC 0
Z9 0
U1 0
U2 8
PU IOP PUBLISHING LTD
PI BRISTOL
PA TEMPLE CIRCUS, TEMPLE WAY, BRISTOL BS1 6BE, ENGLAND
SN 1674-1137
EI 2058-6132
J9 CHINESE PHYS C
JI Chin. Phys. C
PD FEB
PY 2017
VL 41
IS 2
AR 026101
DI 10.1088/1674-1137/41/2/026101
PG 8
WC Physics, Nuclear; Physics, Particles & Fields
SC Physics
GA EO4CE
UT WOS:000396641300018
DA 2021-04-21
ER
PT J
AU Chilenski, MA
Faust, IC
Walk, JR
AF Chilenski, M. A.
Faust, I. C.
Walk, J. R.
TI eqtools. Modular, extensible, open-source, cross-machine Python tools
for working with magnetic equilibria
SO COMPUTER PHYSICS COMMUNICATIONS
LA English
DT Article
DE Plasma physics; Tokamaks; Magnetic equilibrium; Data analysis
ID ALCATOR C-MOD
AB As plasma physics research for fusion energy transitions to an increasing emphasis on cross-machine collaboration and numerical simulation, it becomes increasingly important that portable tools be developed to enable data from diverse sources to be analyzed in a consistent manner. This paper presents eqtools, a modular, extensible, open-source toolkit implemented in the Python programming language for handling magnetic equilibria and associated data from tokamaks. eqtools provides a single interface for working with magnetic equilibrium data, both for handling derived quantities and mapping between coordinate systems, extensible to function with data from different experiments, data formats, and magnetic reconstruction codes, replacing the diverse, non-portable solutions currently in use. Moreover, while the open-source Python programming language offers a number of advantages as a scripting language for research purposes, the lack of basic tokamak-specific functionality has impeded the adoption of the language for regular use. Implementing equilibrium-mapping tools in Python removes a substantial barrier to new development in and porting legacy code into Python. In this paper, we introduce the design of the eqtools package and detail the workflow for usage and expansion to additional devices. The implementation of a novel three-dimensional spline solution (in two spatial dimensions and in time) is also detailed. Finally, verification and benchmarking for accuracy and speed against existing tools are detailed. Wider deployment of these tools will enable efficient sharing of data and software between institutions and machines as well as self-consistent analysis of the shared data.
Program summary
Program title: eqtools
Catalogue identifier: AFBICv1_0
Program summary URL: http://cpc.cs.qub.ac.uk/summaries/AFBICv1_0.html Program obtainable from: CPC Program Library, Queen's University, Belfast, N. Ireland
Licensing provisions: GNU GPL v3
No. of lines in distributed program, including test data, etc.: 27204
No. of bytes in distributed program, including test data, etc.: 1217844
Distribution format: tar.gz
Programming language: Python, C.
Computer: PCs.
Operating system: Linux, Macintosh OS X, Microsoft Windows.
RAM: Several megabytes," depends on resolution of data
Classification: 19.4.
External routines: F2PY [1], matplotlib [2], MDSplus [3], NumPy [4], SciPy [5]
Nature of problem: Access to results from magnetic equilibrium reconstruction code, conversion between various coordinate systems tied to the magnetic equilibrium.
Solution method:
Data are stored in an object-oriented data structure with human-readable getter methods. Coordinates are converted using bivariate or trivariate splines.
Running time:
Coordinate transformations on a 66x66 point spatial grid take between 1 and 5 ms per time slice, depending on the transformation used and how many intermediate results have been stored. References:
[1] P. Peterson, F2PY: a tool for connecting Fortran and Python programs, International Journal of Computational Science and Engineering 4 (4) (2009) 296-305.
[2] J. D. Hunter, Matplotlib: A 2D graphics environment, Computing in Science and Engineering, 9 (3) (2007) 90-95.
[3] J. A. Stillerman, T. W. Fredian, K. A. Klare, G. Manduchi, MDSplus data acquisition system, Review of Scientific Instruments 68 (1) (1997) 939-942.
[4] S. van der Walt, S. C. Colbert and G. Varoquaux, The NumPy array: a structure for efficient numerical computation, Computing in Science and Engineering 13 (2) (2011) 22-30.
[5] E. Jones, T. Oliphant, P Peterson, et al., SciPy: Open source scientific tools for Python (2001-). (C) 2016 Elsevier B.V. All rights reserved.
C1 [Chilenski, M. A.; Faust, I. C.; Walk, J. R.] MIT, Plasma Sci & Fus Ctr, 77 Massachusetts Ave, Cambridge, MA 02139 USA.
[Walk, J. R.] Cinch Financial, 24 Sch St, Boston, MA 02108 USA.
RP Chilenski, MA (corresponding author), MIT, Plasma Sci & Fus Ctr, 77 Massachusetts Ave, Cambridge, MA 02139 USA.
EM markchil@mit.edu; faustian@mit.edu; jrwalk@mit.edu
OI Chilenski, Mark/0000-0002-3616-8484
FU U.S. Department of Energy, Office of Science, Office of Fusion Energy
SciencesUnited States Department of Energy (DOE) [DE-FC02-99ER54512];
U.S. Department of Energy Office of Science Graduate Research Fellowship
Program (DOE SCGF) by American Recovery and Reinvestment Act
[DE-AC05-06OR23100]
FX This material is based upon work conducted using the Alcator C-Mod
tokamak, a DOE Office of Science user facility. This material is based
upon work supported by the U.S. Department of Energy, Office of Science,
Office of Fusion Energy Sciences under Award Number DE-FC02-99ER54512.
This material is based upon work supported in part by the U.S.
Department of Energy Office of Science Graduate Research Fellowship
Program (DOE SCGF), made possible in part by the American Recovery and
Reinvestment Act of 2009, administered by ORISE-ORAU under contract
number DE-AC05-06OR23100.
CR [Anonymous], 2007, GNU GEN PUBLIC LICEN
Basse NP, 2007, FUSION SCI TECHNOL, V51, P476, DOI 10.13182/FST07-A1434
Chilenski MA, 2015, NUCL FUSION, V55, DOI 10.1088/0029-5515/55/2/023012
Chilenski M. A., 2015, EQTOOLS PYTHON PACKA
Chilenski M. A., 2015, PROFILETOOLS CLASSES
Chilenski M. A., 2015, EQTOOLS TOOLS INTERA
Chilenski M. A., 2015, EQTOOLS PYTHON TOOLS
Chilenski M. A., 2015, GPTOOLS GAUSSIAN PRO
Dierckx P., 1993, CURVE SURFACE FITTIN
Faust I., REV SCI INSTRUM, V85
Faust I. C., TOROIDAL RAD INVERSI
Granetz R. S., 1991, International School of Plasma Physics `Piero Caldirola' Diagnostics for Contemporary Fusion Experiments. Proceedings of the Workshop, P425
Hughes JW, 2003, REV SCI INSTRUM, V74, P1667, DOI 10.1063/1.1532764
Hughes JW, 2001, REV SCI INSTRUM, V72, P1107, DOI 10.1063/1.1319367
Jones E., 2001, SCIPY OPEN SOURCE SC
Kouatchou J, 2009, COMP PYTHON NUMPY MA
LAO LL, 1985, NUCL FUSION, V25, P1611, DOI 10.1088/0029-5515/25/11/007
Lekien F, 2005, INT J NUMER METH ENG, V63, P455, DOI 10.1002/nme.1296
Peterson P, 2009, INT J COMPUT SCI ENG, V4, P296, DOI 10.1504/IJCSE.2009.029165
Puget J. F., 2016, SPEED COMP C JULIA P
Stillerman JA, 1997, REV SCI INSTRUM, V68, P939, DOI 10.1063/1.1147719
van der Walt S, 2011, COMPUT SCI ENG, V13, P22, DOI 10.1109/MCSE.2011.37
NR 22
TC 3
Z9 3
U1 0
U2 11
PU ELSEVIER SCIENCE BV
PI AMSTERDAM
PA PO BOX 211, 1000 AE AMSTERDAM, NETHERLANDS
SN 0010-4655
EI 1879-2944
J9 COMPUT PHYS COMMUN
JI Comput. Phys. Commun.
PD JAN
PY 2017
VL 210
BP 155
EP 162
DI 10.1016/j.cpc.2016.09.011
PG 8
WC Computer Science, Interdisciplinary Applications; Physics, Mathematical
SC Computer Science; Physics
GA EF7KC
UT WOS:000390507500015
DA 2021-04-21
ER
PT S
AU Bos, SP
Haffert, SY
Keller, CU
AF Bos, Steven P.
Haffert, Sebastiaan Y.
Keller, Christoph U.
BE Shaw, JA
Snik, F
TI Rigorous vector wave propagation for arbitrary flat media
SO POLARIZATION SCIENCE AND REMOTE SENSING VIII
SE Proceedings of SPIE
LA English
DT Proceedings Paper
CT Conference on Polarization Science and Remote Sensing VIII
CY AUG 08-09, 2017
CL San Diego, CA
SP SPIE
DE Vector Wave Propagation; Polarization; Polarization Aberrations;
Simulations; Modal Methods
ID POLARIZATION ABERRATIONS; FORMULATION; OPTICS
AB Precise modelling of the (off-axis) point spread function (PSF) to identify geometrical and polarization aberrations is important for many optical systems. In order to characterise the PSF of the system in all Stokes parameters, an end-to-end simulation of the system has to be performed in which Maxwell's equations are rigorously solved. We present the first results of a python code that we are developing to perform multiscale end-to-end wave propagation simulations that include all relevant physics. Currently we can handle plane-parallel near-and far-field vector diffraction effects of propagating waves in homogeneous isotropic and anisotropic materials, refraction and reflection of flat parallel surfaces, interference effects in thin films and unpolarized light. We show that the code has a numerical precision on the order of similar to 10(16) for non-absorbing isotropic and anisotropic materials. For absorbing materials the precision is on the order of similar to 10(8). The capabilities of the code are demonstrated by simulating a converging beam reflecting from a flat aluminium mirror at normal incidence.
C1 [Bos, Steven P.; Haffert, Sebastiaan Y.; Keller, Christoph U.] Leiden Univ, Leiden Observ, POB 9513, NL-2300 RA Leiden, Netherlands.
RP Bos, SP (corresponding author), Leiden Univ, Leiden Observ, POB 9513, NL-2300 RA Leiden, Netherlands.
EM stevenbos@strw.leidenuniv.nl
CR Aiello A, 2009, PHYS REV A, V80, DOI 10.1103/PhysRevA.80.061801
Bass M., 2009, HDB OPTICS, VII
BERREMAN DW, 1972, J OPT SOC AM, V62, P502, DOI 10.1364/JOSA.62.000502
Bindel D, 2002, ACM T MATH SOFTWARE, V28, P206, DOI 10.1145/567806.567809
Born M., 2013, PRINCIPLES OPTICS EL
Breckinridge JB, 2015, PUBL ASTRON SOC PAC, V127, P445, DOI 10.1086/681280
Chipman R. A., 2015, SPIE OPTICAL ENG APP
FRANCIS J, 1961, COMPUT J, V4, P265, DOI 10.1093/comjnl/4.3.265
FRANCIS JGF, 1962, COMPUT J, V4, P332, DOI 10.1093/comjnl/4.4.332
Hansen E. W., 1988, Proceedings of the SPIE - The International Society for Optical Engineering, V891, P190
Hecht E., 2002, OPTICS
Kublanovskaya V. N., 1962, USSR COMP MATH MATH, V1, P637, DOI DOI 10.1016/0041-5553(63)90168-X
Lavrinenko A. V., 2015, NUMERICAL METHODS PH
Li LF, 1996, J OPT SOC AM A, V13, P1024, DOI 10.1364/JOSAA.13.001024
Lvovsky A. I., 2013, ENCY OPTICAL ENG, P1
McCall MW., 2014, BIREFRINGENT THIN FI
MCGUIRE JP, 1990, J OPT SOC AM A, V7, P1614, DOI 10.1364/JOSAA.7.001614
McLeod RR, 2014, ADV OPT PHOTONICS, V6, P368, DOI 10.1364/AOP.6.000368
McPeak KM, 2015, ACS PHOTONICS, V2, P326, DOI 10.1021/ph5004237
Melville DOS, 2011, J VAC SCI TECHNOL B, V29, DOI 10.1116/1.3662090
Merano M, 2007, OPT EXPRESS, V15, P15928, DOI 10.1364/OE.15.015928
Ruoff J, 2009, J MICRO-NANOLITH MEM, V8, DOI 10.1117/1.3173803
Semel M, 2003, ASTRON ASTROPHYS, V401, P1, DOI 10.1051/0004-6361:20021606
Soummer R, 2007, OPT EXPRESS, V15, P15935, DOI 10.1364/OE.15.015935
Wang TL, 2002, MATH COMPUT, V71, P1473
Weenink J. G., 2012, A A UNPUB
Wolf E., 2007, INTRO THEORY COHEREN
NR 27
TC 1
Z9 1
U1 0
U2 1
PU SPIE-INT SOC OPTICAL ENGINEERING
PI BELLINGHAM
PA 1000 20TH ST, PO BOX 10, BELLINGHAM, WA 98227-0010 USA
SN 0277-786X
EI 1996-756X
BN 978-1-5106-1272-3; 978-1-5106-1271-6
J9 PROC SPIE
PY 2017
VL 10407
AR UNSP 1040709
DI 10.1117/12.2273341
PG 19
WC Remote Sensing; Optics
SC Remote Sensing; Optics
GA BJ1XF
UT WOS:000418369900006
DA 2021-04-21
ER
PT B
AU Wilke, B
Semwal, SK
AF Wilke, Brian
Semwal, Sudhanshu K.
BE Claudio, AP
Bechmann, D
Braz, J
TI Generative Animation in a Physics Engine using Motion Captures
SO PROCEEDINGS OF THE 12TH INTERNATIONAL JOINT CONFERENCE ON COMPUTER
VISION, IMAGING AND COMPUTER GRAPHICS THEORY AND APPLICATIONS (VISIGRAPP
2017), VOL 1
LA English
DT Proceedings Paper
CT 12th International Joint Conference on Computer Vision, Imaging and
Computer Graphics Theory and Applications (VISIGRAPP)
CY FEB 27-MAR 01, 2017
CL Porto, PORTUGAL
SP Inst Syst & Technologies Informat, Control & Commun, ACM SIGGRAPH, AFIG, Eurographics
DE Articulated Motion; Motion Capture; Novel Application; Asymmetric
Scaling; Under Controlling
ID INTERPOLATION
AB Motion captures are an industry standard for producing high-quality, realistic animations. However, generating novel animations from motion captures remains a complex, non-trivial problem. Many techniques have been developed, including kinematics and manually solving the equations of motion. We present a new technique using a physics engine to generate novel animations. Motion captures are effectively simulated within a popular open-source physics engine, Bullet, and two generative techniques are applied. These generative techniques - asymmetric scaling and under-controlling - are shown to be simple and straight-forward. The techniques and methods were implemented in Python and C++, and show new promising avenues for generative animation using existing motion captures.
C1 [Wilke, Brian; Semwal, Sudhanshu K.] Univ Colorado, Dept Comp Sci, Colorado Springs, CO 80918 USA.
RP Wilke, B (corresponding author), Univ Colorado, Dept Comp Sci, Colorado Springs, CO 80918 USA.
CR Ashraf G, 2000, PROC GRAPH INTERF, P45
Baraff D., 1989, Computer Graphics, V23, P223
BARAFF D, 1991, COMP GRAPH, V25, P31
Beazley D. M, 2011, PLY PYTHON LEX YACC
Boulic R., 1990, Visual Computer, V6, P344, DOI 10.1007/BF01901021
Calvert T, 2003, CARNEGIE MELLON U MO
CALVERT TW, 1982, IEEE COMPUT GRAPH, V2, P41
Coumans E., 2012, BULLET 2 80 PHYS SDK
Evans C, 2011, YAML
Glardon P, 2004, ICOMPUTER GRAPHICS I
Hodgins J, 1996, ROB AUT 1996 P 1996, V4
Knuth D. E, 1998, IEEE COMPUT GRAPH, P232
Macchietto A, 2009, IACM T GRAPHICS, V28, P2009
Multon F, 1999, J VISUAL COMP ANIMAT, V10, P39
OpenGL, 2013, PYTH OP BIND
Rose C, 1998, IEEE COMPUT GRAPH, V18, P32, DOI 10.1109/38.708559
Ryan R, 1990, IADAMS MULTIBODY SYS, P361
Schreiner L. K. J., 2002, ACM SIGGRAPH EUR S C
SciPy, 2013, SCIPY V0 12 REF GUID
NR 19
TC 0
Z9 0
U1 1
U2 2
PU SCITEPRESS
PI SETUBAL
PA AV D MANUELL, 27A 2 ESQ, SETUBAL, 2910-595, PORTUGAL
BN 978-989-758-224-0
PY 2017
BP 250
EP 257
DI 10.5220/0006134702500257
PG 8
WC Computer Science, Artificial Intelligence; Computer Science, Software
Engineering; Imaging Science & Photographic Technology
SC Computer Science; Imaging Science & Photographic Technology
GA BK9SL
UT WOS:000444939100025
OA Other Gold
DA 2021-04-21
ER
PT B
AU Urcelay-Olabarria, I
Lazkoz, R
Urrestilla, J
Leonardo, A
Igartua, JM
AF Urcelay-Olabarria, Irene
Lazkoz, Ruth
Urrestilla, Jon
Leonardo, Aritz
Igartua, Josu M.
BE Escudeiro, P
Costagliola, G
Zvacek, S
Uhomoibhi, J
McLaren, BM
TI Jupyter Notebook as the Physics Experimental Laboratory's Logbook First
Approach
SO PROCEEDINGS OF THE 9TH INTERNATIONAL CONFERENCE ON COMPUTER SUPPORTED
EDUCATION (CSEDU), VOL 1
LA English
DT Proceedings Paper
CT 9th International Conference on Computer Supported Education (CSEDU)
CY APR 21-23, 2017
CL Porto, PORTUGAL
SP Inst Syst & Technologies Informat, Control & Commun, Int Soc Engn Educ, IEEE Portugal Sect, IEEE Portugal Educ Chapter
DE Jupyter Notebook; Virtual Laboratory; Python
ID LINEAR-REGRESSION ANALYSIS; LAB
AB In the Physics Degree it is of fundamental importance to practice in an Experimental Laboratory. The standard Laboratory Sessions consist of two main parts: data handling and data processing. The session should also have a prologue, where students get to know the underlaying theory of the practical session and an epilogue, where students present the results obtained and the difficulties encountered. The prologue and the epilogue naturally decouple from the work in the laboratory. Data processing, in most cases, is effectively decoupled from the work in the laboratory, as well. In this short paper we present a tool, the Jupyter Notebook, an electronic laboratory logbook, which conveniently facilitates the decoupling of the data handling and processing, but which merges almost completely into an electronic notebook the four parts of the laboratory practical session: theory, data, processing and presentation. But, interestingly, the notebook goes beyond that: it allows the students to explore the data in an interactive way (simulating variants), to acquire a deeper knowledge of the data (by digitally altering the experiment or simulating new ones), to propose new experiments, etcetera. We strongly believe that this tool can also motivate the students: the results are obtained interactively, immediately, visually, and they can be shared and even improved. Moreover, the laboratory sessions get optimized: simulations make the sessions be focused on obtaining data and in its variants.
C1 [Urcelay-Olabarria, Irene] Euskal Herriko Unibertsitatea UPV EHU, Bilboko Ingn Eskola, Fis Aplikatua Saila 1, Urkixo Zumarkalea Z-G, Bilbao, Spain.
[Lazkoz, Ruth; Urrestilla, Jon] Univ Basque Country UPV EHU, Dept Theoret Phys & Hist Sci, Bilbao 48040, Spain.
[Leonardo, Aritz; Igartua, Josu M.] Univ Basque Country UPV EHU, Fac Sci & Technol, Appl Phys Dept 2, B Sarriena S-N, Leioa, Bizkaia, Spain.
RP Urcelay-Olabarria, I (corresponding author), Euskal Herriko Unibertsitatea UPV EHU, Bilboko Ingn Eskola, Fis Aplikatua Saila 1, Urkixo Zumarkalea Z-G, Bilbao, Spain.
RI Igartua, Josu M./Z-5395-2019; Liceranzu, Aritz Leonardo/AAA-9337-2019;
Urcelay-Olabarria, Irene/H-9024-2015
OI Igartua, Josu M./0000-0001-7983-5331; Liceranzu, Aritz
Leonardo/0000-0002-5942-2270; Urcelay-Olabarria,
Irene/0000-0001-7228-8531
CR Bernhard J, 2003, PHYS LEARNING MICROC, P313
COLLINS LA, 1974, AM J PHYS, V42, P560, DOI 10.1119/1.1987777
Damyanov DS, 2015, EUR J PHYS, V36, DOI 10.1088/0143-0807/36/5/055047
Dounas-Frazer D, 2016, ARXIV E PRINTS
Eshach H, 2016, CAN J PHYS, V94, P1205, DOI 10.1139/cjp-2016-0308
Harms U, 2003, P 2 EUR C PHYS TEACH
HMURCIK L, 1989, AM J PHYS, V57, P135, DOI 10.1119/1.16110
Jupyter, 2017, JUP NOT
Ricci MLM, 2007, AM J PHYS, V75, P707, DOI 10.1119/1.2742400
OREAR J, 1991, AM J PHYS, V59, P87, DOI 10.1119/1.16706
Sassi E, 2001, PHYS TEACHER ED, P57
Stanley J. T, 2016, ARXIV E PRINTS
Vicentini E. S. E. M, 2008, CONNECTING RES PHYS, P1
Wilcox B. R, 2017, PHYS REV X
Wilcox BR, 2016, PHYS REV PHYS EDUC R, V12, DOI 10.1103/PhysRevPhysEducRes.12.020132
NR 15
TC 0
Z9 0
U1 0
U2 8
PU SCITEPRESS
PI SETUBAL
PA AV D MANUELL, 27A 2 ESQ, SETUBAL, 2910-595, PORTUGAL
BN 978-989-758-239-4
PY 2017
BP 458
EP 463
DI 10.5220/0006352104580463
PG 6
WC Computer Science, Interdisciplinary Applications; Education &
Educational Research; Education, Scientific Disciplines
SC Computer Science; Education & Educational Research
GA BK9IM
UT WOS:000444645500050
OA Other Gold
DA 2021-04-21
ER
PT S
AU Blandon, JS
Grisales, JP
Riascos, H
AF Blandon, J. S.
Grisales, J. P.
Riascos, H.
BE PerezTaborda, JA
TI Electrostatic plasma simulation by Particle-In-Cell method using
ANACONDA package
SO 5TH COLOMBIAN CONFERENCE OF ENGINEERING PHYSICS (V CNIF)
SE Journal of Physics Conference Series
LA English
DT Proceedings Paper
CT 5th Colombian Conference of Engineering Physics (V CNIF)
CY SEP 26-30, 2016
CL Medellin, COLOMBIA
SP EAFIT Univ, Natl Univ Colombia, Colombian Soc Engn Phys
AB Electrostatic plasma is the most representative and basic case in plasma physics field. One of its main characteristics is its ideal behavior, since it is assumed be in thermal equilibrium state. Through this assumption, it is possible to study various complex phenomena such as plasma oscillations, waves, instabilities or damping. Likewise, computational simulation of this specific plasma is the first step to analyze physics mechanisms on plasmas, which are not at equilibrium state, and hence plasma is not ideal. Particle-In-Cell (PIC) method is widely used because of its precision for this kind of cases. This work, presents PIC method implementation to simulate electrostatic plasma by Python, using ANACONDA packages. The code has been corroborated comparing previous theoretical results for three specific phenomena in cold plasmas: oscillations, Two-Stream instability (TSI) and Landau Damping(LD). Finally, parameters and results are discussed.
C1 [Blandon, J. S.; Grisales, J. P.; Riascos, H.] Univ Tecnol Pereira, Plasma Laser & Applicat Grp, Pereira, Colombia.
RP Blandon, JS (corresponding author), Univ Tecnol Pereira, Plasma Laser & Applicat Grp, Pereira, Colombia.
EM jsblandon@utp.edu.co; jpgrisales@utp.edu.co; hriascos@utp.edu.co
FU Plasma, Laser and Applications Group(GPLA); Universidad Tecnologica de
Pereira (UTP)
FX We would like to thanks to Henry Riascos Landazuri, director of the
Plasma, Laser and Applications Group(GPLA) and Universidad Tecnologica
de Pereira (UTP), for his support to develop this work. Also, we
acknowledge the resources given by SENA and Colciencias to do this
simulation project.
CR Analytics C, 2016, AN SOFTW DISTR
BIRDSALL CK, 1984, PLASMA PHYS VIA COMP
Fitzpatrick R, 2006, COMPUTATIONAL PHYS I
FORSLUND DW, 1985, SPACE SCI REV, V42, P3, DOI 10.1007/BF00218219
Francis FC, 1974, INTRO PLASMA PHYS
Gibbon P, 2014, ESPIC PY
Guasp J, 1976, ELEMENTOS TEORIA CIN
Lapenta G, 2006, CENTRUM PLASMA ASTRO
Lindman E. L., 1970, Journal of Computational Physics, V5, P13, DOI 10.1016/0021-9991(70)90049-5
Markidis S, 2016, PIC M
Markidis S, 2014, DISPERSION M
Markidis S, 2011, J COMPUT PHYS, V230, P7037, DOI 10.1016/j.jcp.2011.05.033
Martin D, 2007, ELECTROSTATIC PIC SI
Matsumoto H, 1985, COMPUTER SIMULATIONS
McMillan S, 2013, COMPUTATIONAL PHYS
MORSE RL, 1969, PHYS REV LETT, V23, P1087, DOI 10.1103/PhysRevLett.23.1087
WOOLFSON MM, 1999, INTRO COMPUTER SIMUL
NR 17
TC 2
Z9 2
U1 0
U2 1
PU IOP PUBLISHING LTD
PI BRISTOL
PA DIRAC HOUSE, TEMPLE BACK, BRISTOL BS1 6BE, ENGLAND
SN 1742-6588
EI 1742-6596
J9 J PHYS CONF SER
PY 2017
VL 850
AR 012007
DI 10.1088/1742-6596/850/1/012007
PG 8
WC Physics, Multidisciplinary
SC Physics
GA BK4YT
UT WOS:000437878400007
OA Bronze
DA 2021-04-21
ER
PT B
AU Sanjaya, WSM
Anggraeni, D
Zakaria, K
Juwardi, A
Munawwaroh, M
AF Sanjaya, W. S. Mada
Anggraeni, Dyah
Zakaria, Kiki
Juwardi, Atip
Munawwaroh, Madinatul
BE Wibowo, FW
TI The Design of Face Recognition and Tracking for Human-Robot Interaction
SO 2017 2ND INTERNATIONAL CONFERENCES ON INFORMATION TECHNOLOGY,
INFORMATION SYSTEMS AND ELECTRICAL ENGINEERING (ICITISEE): OPPORTUNITIES
AND CHALLENGES ON BIG DATA FUTURE INNOVATION
LA English
DT Proceedings Paper
CT 2nd International conferences on Information Technology, Information
Systems and Electrical Engineering (ICITISEE)
CY NOV 01-02, 2017
CL Yogyakarta, INDONESIA
SP Univ Amikom Yogyakarta,IEEE Student Branch, STMIK AMIKOM Purwokerto, Univ Gadjah Mada, IEEE Indonesia
DE Face Recognition; Social Robot; Arduino; Python 2.7; Human-Robot
Interaction; SyPEHUL
ID FACIAL EXPRESSION RECOGNITION
AB This paper discusses the development of Social Robot named SyPEHUL (System of Physic, Electronic, Humanoid Robot and Machine Learning) which can recognize and tracking human face. Face recognition and tracking process use Cascade Classification and LBPH (Local Binary Pattern Histogram) Face Recognizer method based on OpenCV library and Python 2.7. The social robot hardware based on Arduino microcontroller contains by 12 DoF (Degree of Freedom) motor servos to actuate robotic head and its face. The face recognition system has been implemented to Social Robot which can recognize and tracking human face and then mentioned the person name. The face recognition system of Social Robot result shows a good accuracy for Human-Robot Interaction.
C1 [Sanjaya, W. S. Mada; Anggraeni, Dyah; Zakaria, Kiki; Juwardi, Atip; Munawwaroh, Madinatul] UIN Sunan Gunung Djati, Fac Sci & Technol, Dept Phys, Bandung, Indonesia.
[Sanjaya, W. S. Mada; Anggraeni, Dyah; Zakaria, Kiki; Juwardi, Atip; Munawwaroh, Madinatul] CV Sanjaya Star Grp, Bolabot Techno Robot Inst, Bandung, Indonesia.
RP Sanjaya, WSM (corresponding author), UIN Sunan Gunung Djati, Fac Sci & Technol, Dept Phys, Bandung, Indonesia.; Sanjaya, WSM (corresponding author), CV Sanjaya Star Grp, Bolabot Techno Robot Inst, Bandung, Indonesia.
EM madasws@gmail.com
RI Sanjaya, W. S. Mada/S-3109-2019
OI Sanjaya, W. S. Mada/0000-0003-1963-193X
CR Boda R., 2016, ARPN J ENG APPL SCI, V11, P13472
Breazeal C, 2003, ROBOT AUTON SYST, V42, P167, DOI 10.1016/S0921-8890(02)00373-1
Buss M., 2006, INT C INT ROB SYST, P3113
Cid F, 2014, SENSORS-BASEL, V14, P7711, DOI 10.3390/s140507711
Dabhi M. K., 2016, INT J SCI RES, V5, P2015
Doroftei I., 2016, 7 INT C ADV CONC MEC, V147
Hajari P. V., 2015, INT J ADV RES COMPUT, V4, P232
Hedaoo S.V., 2014, INT J ENG TRENDS TEC, V8, P517
Hegel F, 2010, 2010 IEEE RO-MAN, P107, DOI 10.1109/ROMAN.2010.5598691
Kalas M. S., 2014, IJSCAI, V2, P41
Khan MI, 2009, INT J COMPUT SCI NET, V9, P300
Manjunatha R., 2017, INT RES J ENG TECHNO, V4, P437
Punitha A, 2013, INT J EMERGING TECHN, V3, P180
Rahim M.A., 2013, GLOBAL J COMPUTER SC, V13
Rajesham J., 2013, INT J SCI ENG RES, V4, P1540
Sadek NO, 2017, INT J ADV COMPUT SC, V8, P303
Sanjaya W. S. M., 2018, ICCSE, P1
Shan CF, 2009, IMAGE VISION COMPUT, V27, P803, DOI 10.1016/j.imavis.2008.08.005
Sharma N., 2016, INT J INNOVATIVE RES, V5, p10 357
Shayganfar M., 2012, INT C INT ROB SYST, V12
Shi Y., 2015, INT J COMPUTER TREND, V25, P54
Thakare Nita, 2016, INT J COMPUTER SCI M, V5, P74
Tikoo S., 2016, INT J COMPUTER SCI M, V5, P288
Vijay LHC, 2010, INT J COMPUTER THEOR, V2, P552
Viola P, 2004, INT J COMPUT VISION, V57, P137, DOI 10.1023/B:VISI.0000013087.49260.fb
Viola P., 2001, TECH REP
Vit P., 2016, INT J SIGNAL PROCESS, V9, P143, DOI DOI 10.14257/IJSIP.2016.9.2.13
Zhan C., 2008, INT J COMPUTER GAMES, V2008, P1
NR 28
TC 2
Z9 2
U1 0
U2 3
PU IEEE
PI NEW YORK
PA 345 E 47TH ST, NEW YORK, NY 10017 USA
BN 978-1-5386-0658-2
PY 2017
BP 315
EP 320
PG 6
WC Computer Science, Information Systems; Engineering, Electrical &
Electronic
SC Computer Science; Engineering
GA BJ6SX
UT WOS:000426942900060
DA 2021-04-21
ER
PT S
AU Chen, J
Cao, LZ
Zhao, CQ
Liu, ZY
AF Chen, Jun
Cao, Liangzhi
Zhao, Chuanqi
Liu, Zhouyu
BE Capriotti, L
Cao, L
Jimenez, G
TI Development of Subchannel Code SUBSC for high-fidelity multi-physics
coupling application
SO INTERNATIONAL YOUTH NUCLEAR CONGRESS 2016, IYNC2016
SE Energy Procedia
LA English
DT Proceedings Paper
CT 9th International Youth Nuclear Congress (IYNC)
CY JUL 24-30, 2016
CL Hangzhou, PEOPLES R CHINA
DE Subchannel; SUBSC; PSBT benchmark; OpenMC; Coupling
ID NEUTRON-TRANSPORT; SIMULATIONS; CHALLENGES; REACTORS
AB PWR core phenomena can be simulated and predicted more precisely and in more details with high-fidelity neutronics and thermal-hydraulics coupling calculation. An in-house subchannel code SUBSC was developed as the thermal-hydraulics solver. The steady-state bundle benchmark of PSBT benchmark was calculated to validate SUBSC; the results showed that the channel averaged quality provided by SUBSC agreed well with the measured data at various conditions. Then a coupling code written in python language was employed to couple SUBSC and the Monte Carlo code OpenMC. The coupling code SUBSC/OpenMC was applied for a typical PWR 3x3 pin cluster coupling calculation. The numerical results demonstrated that the subchannel code SUBSC developed in this paper is applicable for high-fidelity coupling calculation. (C) 2017 The Authors. Published by Elsevier Ltd.
C1 [Chen, Jun; Cao, Liangzhi; Liu, Zhouyu] Xi An Jiao Tong Univ, Sch Nucl Sci & Technol, Xian 710049, Shaanxi, Peoples R China.
[Zhao, Chuanqi] Nucl & Radiat Safety Ctr, Beijing 100082, Peoples R China.
RP Liu, ZY (corresponding author), Xi An Jiao Tong Univ, Sch Nucl Sci & Technol, Xian 710049, Shaanxi, Peoples R China.
EM zhouyuliu@mail.xjtu.edu.cn
FU National Natural Science Foundation of ChinaNational Natural Science
Foundation of China (NSFC) [11522544]
FX This work was supported by the Project 11522544 supported by National
Natural Science Foundation of China.
CR Brian N A, 2016, P ANS REACT PHYS TOP, P243
Clarno K.T., 2015, P ANS JOINT INT C MA
Daeubler M, 2015, ANN NUCL ENERGY, V83, P352, DOI 10.1016/j.anucene.2015.03.040
Ivanov K, 2007, ANN NUCL ENERGY, V34, P501, DOI 10.1016/j.anucene.2007.02.016
Jung YS, 2013, ANN NUCL ENERGY, V62, P357, DOI 10.1016/j.anucene.2013.06.031
Kochunas B., 2014, CASLU20140051000
Romano PK, 2013, ANN NUCL ENERGY, V51, P274, DOI 10.1016/j.anucene.2012.06.040
Rubin A.J., 2011, OECD NRC BENCHMARK B
Sanchez V, 2009, AM NUCL SOC INT C MA, P3472
Seker V, 2007, P JOINT INT TOP M MA
Short MP, 2013, J NUCL MATER, V443, P579, DOI 10.1016/j.jnucmat.2013.08.014
Turinsky PJ, 2016, J COMPUT PHYS, V313, P367, DOI 10.1016/j.jcp.2016.02.043
NR 12
TC 1
Z9 1
U1 1
U2 4
PU ELSEVIER SCIENCE BV
PI AMSTERDAM
PA SARA BURGERHARTSTRAAT 25, PO BOX 211, 1000 AE AMSTERDAM, NETHERLANDS
SN 1876-6102
J9 ENRGY PROCED
PY 2017
VL 127
BP 264
EP 274
DI 10.1016/j.egypro.2017.08.121
PG 11
WC Energy & Fuels; Nuclear Science & Technology
SC Energy & Fuels; Nuclear Science & Technology
GA BJ6OA
UT WOS:000426883700031
OA Other Gold
DA 2021-04-21
ER
PT S
AU Sweitzer, JC
AF Sweitzer, Justin C.
BE Burchell, MJ
TI Energy based distribution for multi-layer fragmentation
SO 14TH HYPERVELOCITY IMPACT SYMPOSIUM (HVIS 2017)
SE Procedia Engineering
LA English
DT Proceedings Paper
CT 14th Hypervelocity Impact Symposium (HVIS)
CY APR 24-28, 2017
CL Univ Kent, Canterbury Campus, Canterbury, ENGLAND
HO Univ Kent, Canterbury Campus
DE fragmentation; energy based; fracture
ID DYNAMIC FRAGMENTATION; SIZE DISTRIBUTIONS; CRACK-GROWTH; STATISTICS;
FRACTURE; SOLIDS
AB A closed form, energy based statistical model of dynamic fragmentation is presented with application to traditional and multi-layer processes. Modern fragmentation predictive tools generally rely on the Mott & Linfoot distribution(1) alongside the Mott physics-based(2) average fragment size. Multi-layer fragmentation sleeves have been observed to produce larger fragments than those predicted by the Mott physics-based model(3). The experiments that indicate this effect were performed as a part of Insensitive Munitions (IM) efforts to protect munitions from unintentional initiation by adding a barrier layer to the fragmenting sleeve. The present model is developed with the intent of application to this class of multi-layer devices.
The energy-based(1-9) extensions to Mott's theory, meant to reconcile the effect of energy absorption during crack formation, were investigated as a starting point for the present model. The present model is formulated by building the energy-based theory of Kipp & Grady(9). Modification to the Kipp & Grady theory is made by closing the calculation of average fragment size through the introduction of crack velocity. The crack velocity is used to determine the time required for a fracture to proceed to completion, leading to the average distance a tensile release wave propagates on the formation interval. Multi-layer effects are treated through a method of statistical mixtures.
This paper will discuss the model formulation, to include the physical basis and supporting calculations for crack velocity. The model has been integrated with the ALE3D(10) hydrocode through the VisIt Python(11-13) interface, and the implementation will be briefly summarized Application to fragmentation of a uniformly expanding cylinder and impact fragmentation of flat plates will be presented with supporting experimental results. (C) 2017 The Authors. Published by Elsevier Ltd.
C1 [Sweitzer, Justin C.] Pract Energet Res LLC, 7500 Mem Pkwy SW, Huntsville, AL 35803 USA.
RP Sweitzer, JC (corresponding author), Pract Energet Res LLC, 7500 Mem Pkwy SW, Huntsville, AL 35803 USA.
EM justin.sweitzer@per-hq.com
FU Presto Foundation
FX Initial development of the fragmentation model was funded in part under
a grant from the Presto Foundation. Spectra Technologies, LLC and
Practical Energetics Research, LLC provided early validation tests and
test articles. Further development was also made possible by Practical
Energetics Research, LLC.
CR [Anonymous], 2013, VISIT PYTHON INTERFA
Childs H., 2013, VISIT END USER TOOL
Daniels A., 2007, 2007 INS MUN EN MAT
Dehn J., 1981, PROBABILITY FORMULAS
Dehn J. T., 1980, TERMINAL EFFECTIVENE
DULANEY EN, 1960, J APPL PHYS, V31, P2233, DOI 10.1063/1.1735529
Elek P, 2009, FME TRANS, V37, P129
Engelkemier D., 2012, AM PIMS NO PIMS COMP
FREUND LB, 1985, J MECH PHYS SOLIDS, V33, P169, DOI 10.1016/0022-5096(85)90029-8
FREUND LB, 1986, ENG FRACT MECH, V23, P119, DOI 10.1016/0013-7944(86)90181-5
Gao Y., 2008, INT C NAN NAN, P25
GLENN LA, 1986, J APPL PHYS, V59, P1379, DOI 10.1063/1.336532
Gold V. M., 2001, METHOD PREDICTING FR, V21
Gold V. M., 2009, EFFECT EXPLOSIVE DET, P1, DOI [10.1016/0734-743X(88)90011-5, DOI 10.1016/0734-743X(88)90011-5]
Gold V. M., 2007, PROGRAM
Gold VM, 2008, ENG FRACT MECH, V75, P275, DOI 10.1016/j.engfracmech.2007.02.025
Grady D., 2007, FRAGMENTATION RINGS
Grady DE, 2003, INT J IMPACT ENG, V29, P293, DOI 10.1016/j.ijimpeng.2003.09.026
GRADY DE, 1985, J APPL PHYS, V58, P1210, DOI 10.1063/1.336139
GRADY DE, 1981, J GEOPHYS RES, V86, P1047, DOI 10.1029/JB086iB02p01047
GRADY DE, 1990, J APPL PHYS, V68, P6099, DOI 10.1063/1.347188
GRADY DE, 1982, J APPL PHYS, V53, P322, DOI 10.1063/1.329934
Grady DE, 2015, J APPL PHYS, V117, DOI 10.1063/1.4918603
Grady DE, 2010, INT J FRACTURE, V163, P85, DOI 10.1007/s10704-009-9418-4
KIPP ME, 1985, J MECH PHYS SOLIDS, V33, P399, DOI 10.1016/0022-5096(85)90036-5
Li YL, 2015, COMP MATER SCI, V104, P212, DOI 10.1016/j.commatsci.2015.04.011
Mott N. F., 1948, ENGINEERING, V165
MOTT NF, 1947, PROC R SOC LON SER-A, V189, P300, DOI 10.1098/rspa.1947.0042
Mott NF., 1943, THEORY FRAGMENTATION
Nichols A., 2007, USERS MANUAL ALE3D A
Peterson NR, 2015, PROCEDIA ENGINEER, V103, P475, DOI 10.1016/j.proeng.2015.04.062
Pichugin A., 2008, PEOPLE BRUNEL AC UK, V2, P1
Rayleigh L, 1885, P LOND MATH SOC, Vs1-17, P4, DOI [10.1112/plms/s1-17.1.4, DOI 10.1112/PLMS/S1-17.1.4]
Sweitzer J. C., 2016, HIGH VELOCITY IMPACT
Sweitzer J. C., 2016, JOINT CLASS BOMBS WA, P1
van Rossum Jr G, 2011, PYTHON LANGUAGE REFE
Zhou F, 2006, APPL PHYS LETT, V88, DOI 10.1063/1.2216892
NR 37
TC 0
Z9 0
U1 0
U2 1
PU ELSEVIER SCIENCE BV
PI AMSTERDAM
PA SARA BURGERHARTSTRAAT 25, PO BOX 211, 1000 AE AMSTERDAM, NETHERLANDS
SN 1877-7058
J9 PROCEDIA ENGINEER
PY 2017
VL 204
BP 162
EP 169
DI 10.1016/j.proeng.2017.09.769
PG 8
WC Engineering, Mechanical
SC Engineering
GA BJ6AA
UT WOS:000426435000018
OA Other Gold
DA 2021-04-21
ER
PT S
AU Morgan, R
Turmon, M
Delacroix, C
Savransky, D
Garrett, D
Lowrance, P
Liu, XC
Nunez, P
AF Morgan, Rhonda
Turmon, Michael
Delacroix, Christian
Savransky, Dmitry
Garrett, Daniel
Lowrance, Patrick
Liu, Xiang Cate
Nunez, Paul
BE Shaklan, S
TI ExEP Yield Modeling Tool and Validation Test Results
SO TECHNIQUES AND INSTRUMENTATION FOR DETECTION OF EXOPLANETS VIII
SE Proceedings of SPIE
LA English
DT Proceedings Paper
CT Conference on Techniques and Instrumentation for Detection of Exoplanets
VIII
CY AUG 08-10, 2017
CL San Diego, CA
SP SPIE
DE exoplanets; yield modeling; software testing; EXOSIMS; Design Reference
Mission simulation; high contrast imaging; coronagraph; starshade
AB EXOSIMS is an open-source simulation tool for parametric modeling of the detection yield and characterization of exoplanets. EXOSIMS has been adopted by the Exoplanet Exploration Programs Standards Definition and Evaluation Team (ExSDET) as a common mechanism for comparison of exoplanet mission concept studies. To ensure trustworthiness of the tool, we developed a validation test plan that leverages the Python-language unit-test framework, utilizes integration tests for selected module interactions, and performs end-to-end crossvalidation with other yield tools. This paper presents the test methods and results, with the physics-based tests such as photometry and integration time calculation treated in detail and the functional tests treated summarily.
The test case utilized a 4m unobscured telescope with an idealized coronagraph and an exoplanet population from the IPAC radial velocity (RV) exoplanet catalog. The known RV planets were set at quadrature to allow deterministic validation of the calculation of physical parameters, such as working angle, photon counts and integration time. The observing keepout region was tested by generating plots and movies of the targets and the keepout zone over a year. Although the keepout integration test required the interpretation of a user, the test revealed problems in the L2 halo orbit and the parameterization of keepout applied to some solar system bodies, which the development team was able to address. The validation testing of EXOSIMS was performed iteratively with the developers of EXOSIMS and resulted in a more robust, stable, and trustworthy tool that the exoplanet community can use to simulate exoplanet direct-detection missions from probe class, to WFIRST, up to large mission concepts such as HabEx and LUVOIR.
C1 [Morgan, Rhonda; Turmon, Michael; Nunez, Paul] CALTECH, Jet Prop Lab, Pasadena, CA 91125 USA.
[Delacroix, Christian; Savransky, Dmitry; Garrett, Daniel] Cornell Univ, Space Imaging & Opt Syst Lab, Ithaca, NY 14853 USA.
[Delacroix, Christian; Savransky, Dmitry] Cornell Univ, Carl Sagan Inst, Ithaca, NY 14853 USA.
[Lowrance, Patrick; Liu, Xiang Cate] CALTECH, IPAC, Pasadena, CA 91125 USA.
RP Morgan, R (corresponding author), 4800 Oak Grove Dr, Pasadena, CA 91109 USA.
EM Rhonda.Morgan@jpl.nasa.gov
RI Savransky, Dmitry/M-1298-2014
OI Savransky, Dmitry/0000-0002-8711-7206; Lowrance,
Patrick/0000-0001-8014-0270; Garrett, Daniel/0000-0002-4252-7017
FU National Aeronautics and Space AdministrationNational Aeronautics &
Space Administration (NASA); U.S. Government sponsorship; Exoplanet
Exploration Program office; NASA funds
FX Copyright 2017. All rights reserved. The research was carried out at the
Jet Propulsion Laboratory, California Institute of Technology, under a
contract with the National Aeronautics and Space Administration. U.S.
Government sponsorship acknowledged; this work was partially supported
by the Exoplanet Exploration Program office, and NASA funds to Cornell.
CR Cahoy KL, 2010, ASTROPHYS J, V724, P189, DOI 10.1088/0004-637X/724/1/189
Delacroix C., 2017, EXOPLANET OPEN SOURC
Delacroix C, 2016, PROC SPIE, V9911, DOI 10.1117/12.2233913
Fortney JJ, 2007, ASTROPHYS J, V668, P1267, DOI 10.1086/521435
Fortney JJ, 2007, ASTROPHYS J, V659, P1661, DOI 10.1086/512120
Garrett D., 2016, AAS M ABSTRACTS, V227
Leinert C, 1998, ASTRON ASTROPHYS SUP, V127, P1, DOI 10.1051/aas:1998105
Morgan R., 2016, AAS M ABSTRACTS, V227
NASA Navigation and Ancillary Information Facility, 2017, SPICE OBS GEOM SYST
Nemati B., 2014, P SOC PHOTO-OPT INS, V9143, P9143
Savransky D., 2017, EXOSIMS EXOPLANET OP
Savransky D, 2016, J ASTRON TELESC INST, V2, DOI 10.1117/1.JATIS.2.1.011006
Stark C, 2017, COMMUNICATION
Stark CC, 2015, ASTROPHYS J, V808, DOI 10.1088/0004-637X/808/2/149
Stark CC, 2014, ASTROPHYS J, V795, DOI 10.1088/0004-637X/795/2/122
Traub WA, 2016, J ASTRON TELESC INST, V2, DOI 10.1117/1.JATIS.2.1.011020
NR 16
TC 0
Z9 0
U1 0
U2 1
PU SPIE-INT SOC OPTICAL ENGINEERING
PI BELLINGHAM
PA 1000 20TH ST, PO BOX 10, BELLINGHAM, WA 98227-0010 USA
SN 0277-786X
EI 1996-756X
BN 978-1-5106-1258-7; 978-1-5106-1257-0
J9 PROC SPIE
PY 2017
VL 10400
AR UNSP 104001K
DI 10.1117/12.2274468
PG 14
WC Astronomy & Astrophysics; Instruments & Instrumentation; Physics,
Applied
SC Astronomy & Astrophysics; Instruments & Instrumentation; Physics
GA BJ3MH
UT WOS:000423870900043
OA Green Accepted
DA 2021-04-21
ER
PT S
AU Li, L
Yan, H
Xu, W
Yu, DT
Heroux, A
Lee, WK
Campbell, SI
Chu, YS
AF Li, Li
Yan, Hanfei
Xu, Wei
Yu, Dantong
Heroux, Annie
Lee, Wah-Keat
Campbell, Stuart I.
Chu, Yong S.
BE Lai, B
Somogyi, A
TI PyXRF: Python-Based X-ray Fluorescence Analysis Package
SO X-RAY NANOIMAGING: INSTRUMENTS AND METHODS III
SE Proceedings of SPIE
LA English
DT Proceedings Paper
CT Conference on X-Ray Nanoimaging - Instruments and Methods III
CY AUG 07-08, 2017
CL San Diego, CA
SP SPIE, XIA LLC
DE X-ray fluorescence; quantitative analysis
ID SPECTRA
AB We developed a python-based fluorescence analysis package (PyXRF) at the National Synchrotron Light Source II (NSLS-II) for the X-ray fluorescence-microscopy beamlines, including Hard X-ray Nanoprobe (HXN), and Submicron Resolution X-ray Spectroscopy (SRX). This package contains a high-level fitting engine, a comprehensive commandline/GUI design, rigorous physics calculations, and a visualization interface. PyXRF offers a method of automatically finding elements, so that users do not need to spend extra time selecting elements manually. Moreover, PyXRF provides a convenient and interactive way of adjusting fitting parameters with physical constraints. This will help us perform quantitative analysis, and find an appropriate initial guess for fitting. Furthermore, we also create an advanced mode for expert users to construct their own fitting strategies with a full control of each fitting parameter. PyXRF runs single-pixel fitting at a fast speed, which opens up the possibilities of viewing the results of fitting in real time during experiments. A convenient I/O interface was designed to obtain data directly from NSLS-II's experimental database. PyXRF is under open-source development and designed to be an integral part of NSLS-II's scientific computation library.
C1 [Li, Li; Yan, Hanfei; Heroux, Annie; Lee, Wah-Keat; Campbell, Stuart I.; Chu, Yong S.] Brookhaven Natl Lab, Natl Synchrotron Light Source 2, Upton, NY 11973 USA.
[Xu, Wei; Yu, Dantong] Brookhaven Natl Lab, Computat Sci Initiat, Upton, NY 11973 USA.
RP Li, L (corresponding author), Brookhaven Natl Lab, Natl Synchrotron Light Source 2, Upton, NY 11973 USA.
EM lili@bnl.gov
RI Campbell, Stuart I/A-8485-2010; Campbell, Stuart/ABA-6344-2020; Yan,
Hanfei/F-7993-2011
OI Campbell, Stuart I/0000-0001-7079-0878; Campbell,
Stuart/0000-0001-7079-0878; Yan, Hanfei/0000-0001-6824-0367
FU DOE Office of ScienceUnited States Department of Energy (DOE)
[DE-SC0012704]
FX This research used the Hard X-ray Nanoprobe (HXN) Beamline (3-ID) of the
National Synchrotron Light Source II, a U.S. Department of Energy (DOE)
Office of Science User Facility operated for the DOE Office of Science
by Brookhaven National Laboratory under Contract No. DE-SC0012704. The
authors acknowledge the support from all the group members at Data
Acquisition, Management and Analysis group at NSLS-II of BNL. We
especially thank Eric Dill, Kenneth Lauer, Thomas Caswell, Arman Arkilic
and Daniel Allan for useful discussions on GUI development and data I/O.
We thank Stefan Vogt at Advanced Photon Source of Argonne National
Laboratory for fruitful discussions at the early stage of this project.
We thank Yao Shun at Computational Science Initiative of BNL for
contributions to this work. We also thank Garth Williams, Yu-chen Karen
Chen-Wiegart and Juergen Thieme at SRX of NSLS-II for all the comments
on, and suggestions for this work. We acknowledge Mingyuan Ge, Wen Hu,
Xiaojing Huang and Sebastian Kalbfleisch for experimental support at the
HXN beamline for producing the XRF maps used for this work. We are
deeply grateful to Hirofumi Sumi and Toshio Suzuki from Materials and
Chemistry Department at National Institute of Advanced Industrial
Science and Technology in Japan for giving us permission to use their
microscopy images of a Ni-GDC sample, taken at the HXN beamline of the
NSLS-II.
CR Alfeld M, 2014, J PHYS CONF SER, V499, DOI 10.1088/1742-6596/499/1/012013
Chang C., 2013, P 10 INT C EXP EM TE, DOI [10.1109/CEWIT.2013.6713744, DOI 10.1109/CEWIT.2013.6713744]
Da Silva JC, 2017, OPTICA, V4, P492, DOI 10.1364/OPTICA.4.000492
Denizer B, 2014, MATER SCI FORUM, V772, P39, DOI 10.4028/www.scientific.net/MSF.772.39
FARROW R, 1995, NUCL INSTRUM METH B, V97, P567, DOI 10.1016/0168-583X(94)00370-X
Huang XJ, 2013, SCI REP-UK, V3, DOI 10.1038/srep03562
Lawson C.L., 1995, SOLVING LEAST SQUARE
Lerotic M, 2004, ULTRAMICROSCOPY, V100, P35, DOI 10.1016/j.ultramic.2004.01.008
Levenberg K., 1944, Quarterly of Applied Mathematics, V2, P164
MARQUARDT DW, 1963, J SOC IND APPL MATH, V11, P431, DOI 10.1137/0111030
Miqueles EX, 2010, PHYS MED BIOL, V55, P1007, DOI 10.1088/0031-9155/55/4/007
Paunesku T, 2006, J CELL BIOCHEM, V99, P1489, DOI 10.1002/jcb.21047
RYAN CG, 1988, NUCL INSTRUM METH B, V34, P396, DOI 10.1016/0168-583X(88)90063-8
Schoonjans T, 2011, SPECTROCHIM ACTA B, V66, P776, DOI 10.1016/j.sab.2011.09.011
Schroer CG, 2001, APPL PHYS LETT, V79, P1912, DOI 10.1063/1.1402643
Sole VA, 2007, SPECTROCHIM ACTA B, V62, P63, DOI 10.1016/j.sab.2006.12.002
Stone SS, 2008, J PARALLEL DISTR COM, V68, P1307, DOI 10.1016/j.jpdc.2008.05.013
Tsuji K., 2004, XRAY SPECTROMETRY RE
Van Grieken R., 2001, HDB XRAY SPECTROMETR
Vogt S, 2003, J PHYS IV, V104, P635, DOI 10.1051/jp4:20030160
Wu CQ, 2015, IEEE T CLOUD COMPUT, V3, P169, DOI 10.1109/TCC.2014.2358220
Wu HR, 2012, J PHYS D APPL PHYS, V45, DOI 10.1088/0022-3727/45/24/242001
Yan HF, 2016, SCI REP-UK, V6, DOI 10.1038/srep20112
NR 23
TC 16
Z9 16
U1 1
U2 6
PU SPIE-INT SOC OPTICAL ENGINEERING
PI BELLINGHAM
PA 1000 20TH ST, PO BOX 10, BELLINGHAM, WA 98227-0010 USA
SN 0277-786X
EI 1996-756X
BN 978-1-5106-1236-5; 978-1-5106-1235-8
J9 PROC SPIE
PY 2017
VL 10389
AR UNSP 103890U
DI 10.1117/12.2272585
PG 8
WC Nanoscience & Nanotechnology; Instruments & Instrumentation; Optics;
Physics, Applied
SC Science & Technology - Other Topics; Instruments & Instrumentation;
Optics; Physics
GA BJ0ZW
UT WOS:000417335200008
DA 2021-04-21
ER
PT S
AU Ghalila, H
Ammar, A
Varadharajan, S
Majdi, Y
Zghal, M
Lahmar, S
Lakshminarayanan, V
AF Ghalila, H.
Ammar, A.
Varadharajan, S.
Majdi, Y.
Zghal, M.
Lahmar, S.
Lakshminarayanan, V.
BE Liu, X
Zhang, XC
TI Optics Simulations: A Python Workshop
SO 14TH CONFERENCE ON EDUCATION AND TRAINING IN OPTICS AND PHOTONICS (ETOP
2017)
SE Proceedings of SPIE
LA English
DT Proceedings Paper
CT 14th Conference on Education and Training in Optics and Photonics (ETOP)
CY MAY 29-31, 2017
CL Hangzhou, PEOPLES R CHINA
SP Int Commiss Opt, IEEE Photon Soc, Opt Soc, SPIE, Chinese Opt Soc, Chinese Natl Steering Comm Opt & Photon, Opt Soc Zhejiang Province, Zhejiang Univ, Fac Informat Technol, Zhejiang Univ, Coll Opt Sci & Engn, State Key Lab Modern Opt Instrumentat
DE Active learning; Optics simulations; Python programming language;
Numerical experiments
AB Numerical simulations allow teachers and students to indirectly perform sophisticated experiments that cannot be realizable otherwise due to cost and other constraints. During the past few decades there has been an explosion in the development of numerical tools concurrently with open source environments such as Python software. This availability of open source software offers an incredible opportunity for advancing teaching methodologies as well as in research. More specifically it is possible to correlate theoretical knowledge with experimental measurements using "virtual" experiments. We have been working on the development of numerical simulation tools using the Python program package and we have concentrated on geometric and physical optics simulations. The advantage of doing hands-on numerical experiments is that it allows the student learner to be an active participant in the pedagogical/learning process rather than playing a passive role as in the traditional lecture format. Even in laboratory classes because of constraints of space, lack of equipment and often-large numbers of students, many students play a passive role since they work in groups of 3 or more students. Furthermore these new tools help students get a handle on numerical methods as well simulations and impart a "feel" for the physics under investigation.
C1 [Ghalila, H.; Ammar, A.; Majdi, Y.; Lahmar, S.] Univ Tunis El Manar, Lab Spect Atom Mol & Applicat, Fac Sci Tunis, Tunis, Tunisia.
[Ghalila, H.; Ammar, A.; Majdi, Y.; Zghal, M.; Lahmar, S.] Soc Tunisienne Opt, Tunis, Tunisia.
[Lakshminarayanan, V.] Univ Waterloo, Sch Optometry & Vis Sci, Waterloo, ON N2L 3G1, Canada.
[Varadharajan, S.] LV Prasad Eye Inst, Brien Holden Inst Optometry & Vis Sci, Hyderabad, Andhra Prades, India.
[Varadharajan, S.] LV Prasad Eye Inst, Prof Brien Holden Eye Res Ctr, Hyderabad, Andhra Prades, India.
[Zghal, M.] Univ Carthage, Engn Sch Commun Tunis SupCom, GreSCom Lab, Ghazala Technopk, Ariana 2083, Tunisia.
RP Lakshminarayanan, V (corresponding author), Univ Waterloo, Sch Optometry & Vis Sci, Waterloo, ON N2L 3G1, Canada.
RI ; Lakshminarayanan, Vasudevan/L-6055-2018
OI GHALILA, Hassen/0000-0001-5105-3389; Lakshminarayanan,
Vasudevan/0000-0002-3473-1245
CR Alarcon M., ACTIVE LEARNING OPTI
Alarcon M., ETOP 2010
Alarcon M., ETOP 2005
Ammar A, 2015, PROC SPIE, V9793, DOI 10.1117/12.2223072
Ben Lakhdar Z., ACTIVE LEARNING PHYS
Carnicer A., 2005, Proceedings of the SPIE, V9664, DOI 10.1117/12.2207725
Eylon B., 1996, J SCI EDUC TECHNOL, V5, P93, DOI DOI 10.1007/BF01575150
Foley John T., 2003, Proceedings of the SPIE, V9663, DOI 10.1117/12.2207350
Ghalila H, 2014, PROC SPIE, V9289, DOI 10.1117/12.2070776
Hunter JD, 2007, COMPUT SCI ENG, V9, P90, DOI 10.1109/MCSE.2007.55
Lakshminarayanan V., 2015, PHOTONICS SPECTRA
Lakshminarayanan V., 2015, OPTICS TUTORIALS PYT
Lakshminarayanan V., 2017, UNDERSTANDING OPTICS
Lakshminarayanan V, 2011, PROC SPIE, V8065, DOI 10.1117/12.889508
Zghal M., ETOP 2009
NR 15
TC 2
Z9 2
U1 0
U2 4
PU SPIE-INT SOC OPTICAL ENGINEERING
PI BELLINGHAM
PA 1000 20TH ST, PO BOX 10, BELLINGHAM, WA 98227-0010 USA
SN 0277-786X
EI 1996-756X
BN 978-1-5106-1382-9; 978-1-5106-1381-2
J9 PROC SPIE
PY 2017
VL 10452
AR UNSP 1045218
DI 10.1117/12.2268377
PN 1
PG 9
WC Education, Scientific Disciplines; Optics
SC Education & Educational Research; Optics
GA BI3TK
UT WOS:000411458900032
OA Bronze
DA 2021-04-21
ER
PT B
AU McIntire, MG
Keshavarzi, E
Tumer, IY
Hoyle, C
AF McIntire, Matthew G.
Keshavarzi, Elham
Tumer, Irem Y.
Hoyle, Christopher
GP ASME
TI FUNCTIONAL MODELS WITH INHERENT BEHAVIOR: TOWARDS A FRAMEWORK FOR SAFETY
ANALYSIS EARLY IN THE DESIGN OF COMPLEX SYSTEMS
SO PROCEEDINGS OF THE ASME INTERNATIONAL MECHANICAL ENGINEERING CONGRESS
AND EXPOSITION, 2016, VOL. 11
LA English
DT Proceedings Paper
CT ASME International Mechanical Engineering Congress and Exposition
(IMECE2016)
CY NOV 11-17, 2016
CL Phoenix, AZ
SP Amer Soc Mech Engineers
AB This paper represents a step toward a more complete framework of safety analysis early in the design process, specifically during functional modeling. This would be especially useful when designing in a new domain, where many functions have yet to be solved, or for a problem where the functional architecture space is large. In order to effectively analyze the inherent safety of a design only described by its functions and flows, we require some way to simulate it.
As an already-available function failure reasoning tool, Function Failure Identification and Propagation (FFIP) utilizes two distinct system models: a behavioral model, and a functional model. The behavioral model simulates system component behavior, and PEEP maps specific component behaviors to functions in the functional model. We have created a new function-failure reasoning method which generalizes failure behavior directly to functions, by which the engineer can create functional models to simulate the functional failure propagations a system may experience early in the design process without a separate behavioral model.
We give each basis-defined function-flow element a pre-defined behavior consisting of nominal and failure operational modes, and the resultant effect each mode has on its functions connected flows. Flows are represented by a two-variable object reminiscent of a bond from bond graphs: the state of each flow is represented by an effort variable and a flow-rate variable. The functional model may be thought of as a bond graph where each functional element is a state machine. Users can quickly describe functional models with consistent behavior by constructing their models as Python NetworkX graph objects, so that they may quickly model multiple functional architectures of their proposed system. We are implementing the method in Python to be used in conjunction with other function-failure analysis tools.
We also introduce a new method for the inclusion of time in a state machine model, so that dynamic systems may be modeled as fast-evaluating state machines. State machines have no inherent representation of time, while physics-based models simulate along repetitive time steps. We use a more middle-ground pseudo time approach. State transitions may impose a time delay once all of their connected flow conditions are met. Once the entire system model has reached steady state in a timeless sense, the
C1 [McIntire, Matthew G.; Keshavarzi, Elham; Tumer, Irem Y.; Hoyle, Christopher] Oregon State Univ, Mech Engn, Corvallis, OR 97331 USA.
RP Hoyle, C (corresponding author), Oregon State Univ, Mech Engn, Corvallis, OR 97331 USA.
EM mcintima@oregonstate.edu; keshavae@oregonstate.edu;
irem.tumer@oregonstate.edu; chris.hoyle@oregonstate.edu
FU National Science FoundationNational Science Foundation (NSF)
[CMMI-1363509, CMMI-1363349]
FX This research is supported by the National Science Foundation award
numbers CMMI-1363509 and CMMI-1363349. Any opinions or findings of this
work are the responsibility of the authors, and do not necessarily
reflect the views of the sponsors or collaborators.
CR Backman B., 2000, ICASE SERIES RISK BA
Choi K., 2001, ICASE SERIES RISK BA
Coatanea E, 2011, J MECH DESIGN, V133, DOI 10.1115/1.4005230
Defense D, 1980, MIL1629A
Directorate E. S. M., 2005, TECH REP
Grantham-Lough K., 2008, J IND SYSTEMS ENG, V2, P126
Greenfield MA, 2001, SCI TECH, V102, P153
Hirtz J, 2002, RES ENG DES, V13, P65, DOI 10.1007/s00163-001-0008-3
Jensen D., 2009, ASME 2009 INT DES EN, P1033
Jensen DC., 2008, ASME 2008 INT MECH E, P283
Krus D., 2007, ASME 2007 INT DES EN, P407
Kurtoglu, 2009, DESIGN ELECT POWER S
Kurtoglu T, 2010, RES ENG DES, V21, P209, DOI 10.1007/s00163-010-0086-1
Kurtoglu T, 2008, J MECH DESIGN, V130, DOI 10.1115/1.2885181
Lough KG, 2009, J ENG DESIGN, V20, P155, DOI 10.1080/09544820701684271
Mahadevan S., 2003, TECH REP
Minhas R, 2014, P 10 INT MOD C
Papakonstantinou N., 2011, ASME 2011 INT DES EN, P1045
regulatory Commission U. N, 1981, FAULT TREE HDB
Sierla S, 2012, MECHATRONICS, V22, P137, DOI 10.1016/j.mechatronics.2012.01.003
Smith N, 2003, J SPACECRAFT ROCKETS, V40, P411, DOI 10.2514/2.3961
Stone RB, 2005, RES ENG DES, V16, P96, DOI [10.1007/s00163-005-0005-z, 10.1007/s00163-005-0005-Z]
Stone RB, 2005, J MECH DESIGN, V127, P397, DOI 10.1115/1.1862678
Tumer IY, 2011, IEEE T COMPUT, V60, P1072, DOI 10.1109/TC.2010.245
Venkatasubramanian V, 2000, COMPUT CHEM ENG, V24, P2291, DOI 10.1016/S0098-1354(00)00573-1
Zang T., 2002, NASA TM
NR 26
TC 0
Z9 0
U1 0
U2 1
PU AMER SOC MECHANICAL ENGINEERS
PI NEW YORK
PA THREE PARK AVENUE, NEW YORK, NY 10016-5990 USA
BN 978-0-7918-5065-7
PY 2017
AR V011T15A035
PG 8
WC Engineering, Mechanical
SC Engineering
GA BH5FW
UT WOS:000400879300035
DA 2021-04-21
ER
PT S
AU Campos-Rozo, JI
Dominguez, SV
AF Campos-Rozo, J. I.
Dominguez, S. Vargas
BE Dominguez, SV
Kosovichev, AG
Antolin, P
Harra, L
TI A Python-based interface to examine motions in time series of solar
images
SO FINE STRUCTURE AND DYNAMICS OF THE SOLAR ATMOSPHERE
SE IAU Symposium Proceedings Series
LA English
DT Proceedings Paper
CT 327th Symposium of the International-Astronomical-Union (IAU) on Fine
Structure and Dynamics of the Solar Atmosphere
CY OCT 09-14, 2016
CL Cartagena de Indias, COLOMBIA
SP Int Astron Union
DE GUI; Solar Physics; Python; Sunpy; LCT
AB Python is considered to be a mature programming language, besides of being widely accepted as an engaging option for scientific analysis in multiple areas, as will be presented in this work for the particular case of solar physics research. SunPy is an open-source library based on Python that has been recently developed to furnish software tools to solar data analysis and visualization. In this work we present a graphical user interface (GUI) based on Python and Qt to effectively compute proper motions for the analysis of time series of solar data. This user-friendly computing interface, that is intended to be incorporated to the Sunpy library, uses a local correlation tracking technique and some extra tools that allows the selection of different parameters to calculate, vizualize and analyze vector velocity fields of solar data, i.e. time series of solar filtergrams and magnetograms.
C1 [Campos-Rozo, J. I.; Dominguez, S. Vargas] Univ Nacl Colombia, Observ Astron Nacl, Bogota, Colombia.
RP Campos-Rozo, JI (corresponding author), Univ Nacl Colombia, Observ Astron Nacl, Bogota, Colombia.
EM jicamposr@unal.edu.co; svargasd@unal.edu.co
OI Vargas Dominguez, Santiago/0000-0002-5999-4842
CR Campos Rozo J. I., 2014, CEAB, V38, P67
Hunter JD, 2007, COMPUT SCI ENG, V9, P90, DOI 10.1109/MCSE.2007.55
Jones E., 2001, SCIPY OPEN SOURCE SC
LANGTANGEN HP, 2004, PYTHON SCRIPTING COM
NOVEMBER LJ, 1988, ASTROPHYS J, V333, P427, DOI 10.1086/166758
Robitaille TP, 2013, ASTRON ASTROPHYS, V558, DOI 10.1051/0004-6361/201322068
SunPy Community, 2015, APJ COMP SCI DISC, V8
NR 7
TC 0
Z9 0
U1 2
U2 4
PU CAMBRIDGE UNIV PRESS
PI CAMBRIDGE
PA THE PITT BUILDING, TRUMPINGTON ST, CAMBRIDGE CB2 1RP, CAMBS, ENGLAND
SN 1743-9213
EI 1743-9221
BN 978-1-10717-004-9
J9 IAU SYMP P SERIES
JI IAU Symposium Proc. Series
PY 2017
VL 12
IS S327
BP 25
EP 27
DI 10.1017/S1743921317003568
PG 3
WC Astronomy & Astrophysics
SC Astronomy & Astrophysics
GA BL7NR
UT WOS:000455235900004
DA 2021-04-21
ER
PT J
AU Lambert, N
Matsuzaki, Y
Kakuyanagi, K
Ishida, N
Saito, S
Nori, F
AF Lambert, Neill
Matsuzaki, Yuichiro
Kakuyanagi, Kosuke
Ishida, Natsuko
Saito, Shiro
Nori, Franco
TI Superradiance with an ensemble of superconducting flux qubits
SO PHYSICAL REVIEW B
LA English
DT Article
ID OPEN QUANTUM-SYSTEMS; DICKE SUPERRADIANCE; ARTIFICIAL ATOMS; PYTHON
FRAMEWORK; PHASE-TRANSITION; METAMATERIALS; RADIATION; CIRCUITS;
DYNAMICS; CAVITY
AB Superconducting flux qubits are a promising candidate for realizing quantum information processing and quantum simulations. Such devices behave like artificial atoms, with the advantage that one can easily tune the "atoms" internal properties. Here, by harnessing this flexibility, we propose a technique to minimize the inhomogeneous broadening of a large ensemble of flux qubits by tuning only the external flux. In addition, as an example of many-body physics in such an ensemble, we show how to observe superradiance, and its quadratic scaling with ensemble size, using a tailored microwave control pulse that takes advantage of the inhomogeneous broadening itself to excite only a subensemble of the qubits. Our scheme opens up an approach to using superconducting circuits to explore the properties of quantum many-body systems.
C1 [Lambert, Neill; Ishida, Natsuko; Nori, Franco] RIKEN, CEMS, Wako, Saitama 3510198, Japan.
[Matsuzaki, Yuichiro; Kakuyanagi, Kosuke; Saito, Shiro] NTT Corp, NTT Basic Res Labs, 3-1 Morinosato Wakamiya, Atsugi, Kanagawa 2430198, Japan.
[Nori, Franco] Univ Michigan, Dept Phys, Ann Arbor, MI 48109 USA.
RP Lambert, N (corresponding author), RIKEN, CEMS, Wako, Saitama 3510198, Japan.
EM nwlambert@riken.jp; matsuzaki.yuichiro@lab.ntt.co.jp
RI Nori, Franco/B-1222-2009; Saito, Shiro/C-1848-2018; Lambert, Neill
W/B-4998-2009; Matsuzaki, Yuichiro/M-7347-2017
OI Nori, Franco/0000-0003-3682-7432;
FU JSPS KAKENHIMinistry of Education, Culture, Sports, Science and
Technology, Japan (MEXT)Japan Society for the Promotion of
ScienceGrants-in-Aid for Scientific Research (KAKENHI) [15K17732,
25220601]; Commissioned Research of NICT [158]; MEXT KAKENHIMinistry of
Education, Culture, Sports, Science and Technology, Japan (MEXT)Japan
Society for the Promotion of ScienceGrants-in-Aid for Scientific
Research (KAKENHI) [15H05870]; Sir John Templeton Foundation; RIKEN
iTHES Project; MURI Center for Dynamic Magneto-Optics via the AFOSR
[FA9550-14-1-0040]; IMPACT program of JST, CREST; Grants-in-Aid for
Scientific ResearchMinistry of Education, Culture, Sports, Science and
Technology, Japan (MEXT)Japan Society for the Promotion of
ScienceGrants-in-Aid for Scientific Research (KAKENHI) [15K17732,
15H05870, 15H02118] Funding Source: KAKEN
FX This work was supported by JSPS KAKENHI Grant No. 15K17732, JSPS KAKENHI
Grant No. 25220601, the Commissioned Research No. 158 of NICT, and MEXT
KAKENHI Grant No. 15H05870. F.N. and N.L. acknowledge support from the
Sir John Templeton Foundation. F.N. acknowledges support from the RIKEN
iTHES Project, the MURI Center for Dynamic Magneto-Optics via the AFOSR
Award No. FA9550-14-1-0040, the IMPACT program of JST, CREST, and a
Grant-in-Aid for Scientific Research (A). N.L. and Y.M. contributed
equally to this work.
CR Amsuss R, 2011, PHYS REV LETT, V107, DOI 10.1103/PhysRevLett.107.060502
Bartels B, 2013, PHYS REV A, V88, DOI 10.1103/PhysRevA.88.052315
Bennett SD, 2013, PHYS REV LETT, V110, DOI 10.1103/PhysRevLett.110.156402
Buluta I, 2011, REP PROG PHYS, V74, DOI 10.1088/0034-4885/74/10/104401
Buluta I, 2009, SCIENCE, V326, P108, DOI 10.1126/science.1177838
Bylander J, 2011, NAT PHYS, V7, P565, DOI [10.1038/NPHYS1994, 10.1038/nphys1994]
Chen Z., ARXIV160201584
Clarke J, 2008, NATURE, V453, P1031, DOI 10.1038/nature07128
Delanty M, 2011, NEW J PHYS, V13, DOI 10.1088/1367-2630/13/5/053032
DeVoe RG, 1996, PHYS REV LETT, V76, P2049, DOI 10.1103/PhysRevLett.76.2049
Diniz I, 2011, PHYS REV A, V84, DOI 10.1103/PhysRevA.84.063810
Emary C, 2003, PHYS REV E, V67, DOI 10.1103/PhysRevE.67.066203
Eschner J, 2001, NATURE, V413, P495, DOI 10.1038/35097017
Filipp S, 2011, PHYS REV A, V83, DOI 10.1103/PhysRevA.83.063827
Forn-Diaz P, 2017, NAT PHYS, V13, P39, DOI [10.1038/nphys3905, 10.1038/NPHYS3905]
Georgescu IM, 2014, REV MOD PHYS, V86, P153, DOI 10.1103/RevModPhys.86.153
GROSS M, 1976, PHYS REV LETT, V36, P1035, DOI 10.1103/PhysRevLett.36.1035
HEPP K, 1973, ANN PHYS-NEW YORK, V76, P360, DOI 10.1016/0003-4916(73)90039-0
Imamoglu A, 2009, PHYS REV LETT, V102, DOI 10.1103/PhysRevLett.102.083602
Johansson JR, 2013, COMPUT PHYS COMMUN, V184, P1234, DOI 10.1016/j.cpc.2012.11.019
Johansson JR, 2012, COMPUT PHYS COMMUN, V183, P1760, DOI 10.1016/j.cpc.2012.02.021
Julsgaard B, 2013, PHYS REV LETT, V110, DOI 10.1103/PhysRevLett.110.250503
Kakuyanagi K, 2016, PHYS REV LETT, V117, DOI 10.1103/PhysRevLett.117.210503
Keaveney J, 2012, PHYS REV LETT, V108, DOI 10.1103/PhysRevLett.108.173601
KITAGAWA M, 1993, PHYS REV A, V47, P5138, DOI 10.1103/PhysRevA.47.5138
Knee GC, 2016, NAT COMMUN, V7, DOI 10.1038/ncomms13253
Kubo Y, 2012, PHYS REV B, V86, DOI 10.1103/PhysRevB.86.064514
Kubo Y, 2012, PHYS REV A, V85, DOI 10.1103/PhysRevA.85.012333
Kubo Y, 2011, PHYS REV LETT, V107, DOI 10.1103/PhysRevLett.107.220501
Kubo Y, 2010, PHYS REV LETT, V105, DOI 10.1103/PhysRevLett.105.140502
Lambert N, 2004, PHYS REV LETT, V92, DOI 10.1103/PhysRevLett.92.073602
Lambert N, 2016, PHYS REV A, V94, DOI 10.1103/PhysRevA.94.012105
Lambert N, 2009, PHYS REV B, V80, DOI 10.1103/PhysRevB.80.165308
Ma J, 2011, PHYS REP, V509, P89, DOI 10.1016/j.physrep.2011.08.003
Macha P, 2014, NAT COMMUN, V5, DOI 10.1038/ncomms6146
Marcos D, 2010, PHYS REV LETT, V105, DOI 10.1103/PhysRevLett.105.210501
Matsuzaki Y, 2012, PHYS REV B, V86, DOI 10.1103/PhysRevB.86.184501
Meiser D, 2010, PHYS REV A, V81, DOI 10.1103/PhysRevA.81.033847
Mlynek JA, 2014, NAT COMMUN, V5, DOI 10.1038/ncomms6186
Nobauer T, 2015, PHYS REV LETT, V115, DOI 10.1103/PhysRevLett.115.190801
Orlando TP, 1999, PHYS REV B, V60, P15398, DOI 10.1103/PhysRevB.60.15398
RAIMOND JM, 1982, PHYS REV LETT, V49, P117, DOI 10.1103/PhysRevLett.49.117
Rakhmanov AL, 2008, PHYS REV B, V77, DOI 10.1103/PhysRevB.77.144507
Rohlsberger R, 2010, SCIENCE, V328, P1248, DOI 10.1126/science.1187770
Roof SJ, 2016, PHYS REV LETT, V117, DOI 10.1103/PhysRevLett.117.073003
Saito S, 2013, PHYS REV LETT, V111, DOI 10.1103/PhysRevLett.111.107008
Scheibner M, 2007, NAT PHYS, V3, P106, DOI 10.1038/nphys494
Schuster DI, 2010, PHYS REV LETT, V105, DOI 10.1103/PhysRevLett.105.140501
SKRIBANO.N, 1973, PHYS REV LETT, V30, P309, DOI 10.1103/PhysRevLett.30.309
Soukoulis CM, 2011, NAT PHOTONICS, V5, P523, DOI [10.1038/NPHOTON.2011.154, 10.1038/nphoton.2011.154]
Tanaka T, 2015, PHYS REV LETT, V115, DOI 10.1103/PhysRevLett.115.170801
Temnov VV, 2005, PHYS REV LETT, V95, DOI 10.1103/PhysRevLett.95.243602
Twamley J, 2010, PHYS REV B, V81, DOI 10.1103/PhysRevB.81.241202
van Loo AF, 2013, SCIENCE, V342, P1494, DOI 10.1126/science.1244324
WANG YK, 1973, PHYS REV A, V7, P831, DOI 10.1103/PhysRevA.7.831
Wesenberg JH, 2009, PHYS REV LETT, V103, DOI 10.1103/PhysRevLett.103.070502
Wu H, 2010, PHYS REV LETT, V105, DOI 10.1103/PhysRevLett.105.140503
Xiang ZL, 2013, REV MOD PHYS, V85, P623, DOI 10.1103/RevModPhys.85.623
Yoshihara F, 2017, NAT PHYS, V13, P44, DOI [10.1038/NPHYS3906, 10.1038/nphys3906]
Yoshihara F, 2014, PHYS REV B, V89, DOI 10.1103/PhysRevB.89.020503
You JQ, 2011, NATURE, V474, P589, DOI 10.1038/nature10122
Zhang J., ARXIV14078536
Zheludev NI, 2012, NAT MATER, V11, P917, DOI [10.1038/NMAT3431, 10.1038/nmat3431]
Zhu XB, 2014, NAT COMMUN, V5, DOI 10.1038/ncomms4524
Zhu XB, 2011, NATURE, V478, P221, DOI 10.1038/nature10462
NR 65
TC 19
Z9 19
U1 0
U2 18
PU AMER PHYSICAL SOC
PI COLLEGE PK
PA ONE PHYSICS ELLIPSE, COLLEGE PK, MD 20740-3844 USA
SN 2469-9950
EI 2469-9969
J9 PHYS REV B
JI Phys. Rev. B
PD DEC 15
PY 2016
VL 94
IS 22
AR 224510
DI 10.1103/PhysRevB.94.224510
PG 8
WC Materials Science, Multidisciplinary; Physics, Applied; Physics,
Condensed Matter
SC Materials Science; Physics
GA EF3UV
UT WOS:000390251100002
OA Bronze
DA 2021-04-21
ER
PT J
AU Sugandhi, R
Swamy, R
Khirwadkar, S
AF Sugandhi, Ritesh
Swamy, Rajamannar
Khirwadkar, Samir
TI Use of EPICS and Python technology for the development of a
computational toolkit for high heat flux testing of plasma facing
components
SO FUSION ENGINEERING AND DESIGN
LA English
DT Article; Proceedings Paper
CT 10th IAEA Technical Meeting on Control, Data Acquisition, and Remote
Participation for Fusion Research
CY APR 20-24, 2015
CL Inst Plasma Res, Ahmedabad, INDIA
SP IAEA
HO Inst Plasma Res
DE Critical heat flux; Plasma facing component; Numerical optimization;
EPICS; Python
AB The high heat flux testing and characterization of the divertor and first wall components are a challenging engineering problem of a tokamak. These components are subject to steady state and transient heat load of high magnitude. Therefore, the accurate prediction and control of the cooling parameters is crucial to prevent burnout. The prediction of the cooling parameters is based on the numerical solution of the critical heat flux (CHF) model. In a test facility for high heat flux testing of plasma facing components (PFC), the integration of computations and experimental control is an essential requirement. Experimental physics and industrial control system (EPICS) provides powerful tools for steering controls, data simulation, hardware interfacing and wider usability. Python provides an open source alternative for numerical computations and scripting. We have integrated these two open source technologies to develop a graphical software for a typical high heat flux experiment. The implementation uses EPICS based tools namely IOC (I/O controller) server, control system studio (CSS) and Python based tools namely Numpy, Scipy, Matplotlib and NOSE. EPICS and Python are integrated using PyEpics library. This toolkit is currently under operation at high heat flux test facility at Institute for Plasma Research (IPR) and is also useful for the experimental labs working in the similar research areas. The paper reports the software architectural design, implementation tools and rationale for their selection, test and validation. (C) 2016 Elsevier B.V. All rights reserved.
C1 [Sugandhi, Ritesh; Swamy, Rajamannar; Khirwadkar, Samir] Inst Plasma Res, Gandhinagar 382428, India.
RP Sugandhi, R (corresponding author), Inst Plasma Res, Gandhinagar 382428, India.
EM ritesh@ipr.res.in; rajamannar@ipr.res.in; sameer@ipr.res.in
RI Sugandhi, Ritesh/J-2132-2019
OI Sugandhi, Ritesh/0000-0003-1319-7888
CR [Anonymous], 2016, CONTROL SYSTEM STUDI
[Anonymous], 2016, NOSE TEST FRAMEWORK
[Anonymous], 2016, EPICS HARDWARE SUPPO
[Anonymous], 2016, THEMOPHYSICAL PROPER
Baek W.-P., 1997, J KOREAN NUCL SOC, V29, P348
Boscary J, 1999, INT J HEAT MASS TRAN, V42, P287, DOI 10.1016/S0017-9310(98)00108-2
Khirwadkar SS, 2011, FUSION ENG DES, V86, P1736, DOI 10.1016/j.fusengdes.2011.01.111
Marshall TD, 2001, FUSION TECHNOL, V39, P849, DOI 10.13182/FST01-A11963345
Patil Y, 2013, J NUCL MATER, V437, P326, DOI 10.1016/j.jnucmat.2013.02.014
Powell MJD, 1994, ADV OPTIMIZATION NUM, P51, DOI DOI 10.1007/978-94-015-8330-5_4
Singh K.P., 2011, FUSION ENG DES, V86, P1741
Tong L.S., 1975, PHENOMENOLOGICAL STU, P75
NR 12
TC 1
Z9 3
U1 0
U2 13
PU ELSEVIER SCIENCE SA
PI LAUSANNE
PA PO BOX 564, 1001 LAUSANNE, SWITZERLAND
SN 0920-3796
EI 1873-7196
J9 FUSION ENG DES
JI Fusion Eng. Des.
PD NOV 15
PY 2016
VL 112
BP 783
EP 787
DI 10.1016/j.fusengdes.2016.04.036
PG 5
WC Nuclear Science & Technology
SC Nuclear Science & Technology
GA EC1AV
UT WOS:000387836800114
DA 2021-04-21
ER
PT J
AU Dexter, J
AF Dexter, Jason
TI A public code for general relativistic, polarised radiative transfer
around spinning black holes
SO MONTHLY NOTICES OF THE ROYAL ASTRONOMICAL SOCIETY
LA English
DT Article
DE accretion, accretion discs; black hole physics; radiative transfer;
relativistic processes; Galaxy: centre; galaxies: jets
ID SAGITTARIUS A-ASTERISK; THIN ACCRETION DISK; MONTE-CARLO CODE;
SYNCHROTRON-RADIATION; SGR-A; GALACTIC-CENTER; TRANSFER EQUATIONS;
MAGNETIC-FIELD; KERR SPACETIME; EVENT-HORIZON
AB Ray tracing radiative transfer is a powerful method for comparing theoretical models of black hole accretion flows and jets with observations. We present a public code, GRTRANS, for carrying out such calculations in the Kerr metric, including the full treatment of polarised radiative transfer and parallel transport along geodesics. The code is written in FORTRAN 90 and efficiently parallelises with OPENMP, and the full code and several components have PYTHON interfaces. We describe several tests which are used for verifiying the code, and we compare the results for polarised thin accretion disc and semi-analytic jet problems with those from the literature as examples of its use. Along the way, we provide accurate fitting functions for polarised synchrotron emission and transfer coefficients from thermal and power-law distribution functions, and compare results from numerical integration and quadrature solutions of the polarised radiative transfer equations. We also show that all transfer coefficients can play an important role in predicted images and polarisation maps of the Galactic centre black hole, Sgr A*, at submillimetre wavelengths.
C1 [Dexter, Jason] Max Planck Inst Extraterr Phys, Giessenbachstr 1, D-85748 Garching, Germany.
RP Dexter, J (corresponding author), Max Planck Inst Extraterr Phys, Giessenbachstr 1, D-85748 Garching, Germany.
EM jdexter@mpe.mpg.de
OI Dexter, Jason/0000-0003-3903-0373
FU International Space Science Institute; Sofja Kovalevskaja Award from the
Alexander von Humboldt Foundation of GermanyAlexander von Humboldt
Foundation
FX JD thanks S. Alwin Mao for significant contributions to the development
and testing of the code presented here, J. Davelaar, A. Pandya, and M.
Moscibrodzka for helpful feedback on the code and manuscript, C. Gammie
for useful discussions, the anonymous referee for constructive comments,
and the International Space Science Institute for hospitality and
support. This work was supported by a Sofja Kovalevskaja Award from the
Alexander von Humboldt Foundation of Germany.
CR Abramowitz M., 1970, HDB MATH FUNCTIONS F
Agol E, 2000, ASTROPHYS J, V528, P161, DOI 10.1086/308177
Agol E., 1997, THESIS
Akiyama K, 2015, ASTROPHYS J, V807, DOI 10.1088/0004-637X/807/2/150
BALBUS SA, 1991, ASTROPHYS J, V376, P214, DOI 10.1086/170270
BARDEEN JM, 1972, ASTROPHYS J, V178, P347, DOI 10.1086/151796
Beckwith K, 2008, MON NOT R ASTRON SOC, V390, P21, DOI 10.1111/j.1365-2966.2008.13710.x
BLUMENTHAL GR, 1970, REV MOD PHYS, V42, P237, DOI 10.1103/RevModPhys.42.237
Bower GC, 2015, ASTROPHYS J, V802, DOI 10.1088/0004-637X/802/1/69
Broderick A, 2004, MON NOT R ASTRON SOC, V349, P994, DOI 10.1111/j.1365-2966.2004.07582.x
Broderick A. E, 2004, THESIS
Broderick AE, 2006, ASTROPHYS J, V636, pL109, DOI 10.1086/500008
Broderick AE, 2005, MON NOT R ASTRON SOC, V363, P353, DOI 10.1111/j.1365-2966.2005.09458.x
Broderick AE, 2009, ASTROPHYS J, V697, P1164, DOI 10.1088/0004-637X/697/2/1164
Broderick AE, 2009, ASTROPHYS J, V697, P45, DOI 10.1088/0004-637X/697/1/45
Bromley BC, 2001, ASTROPHYS J, V555, pL83, DOI 10.1086/322862
Chan CK, 2015, ASTROPHYS J, V799, DOI 10.1088/0004-637X/799/1/1
Chandrasekhar, 1983, MATH THEORY BLACK HO
Chandrasekhar S, 1950, RAD TRANSFER
Chen B, 2015, ASTROPHYS J SUPPL S, V218, DOI 10.1088/0067-0049/218/1/4
CONNORS PA, 1980, ASTROPHYS J, V235, P224, DOI 10.1086/157627
CONNORS PA, 1977, NATURE, V269, P128, DOI 10.1038/269128a0
CUNNINGHAM CT, 1975, ASTROPHYS J, V202, P788, DOI 10.1086/154033
Dauser T, 2010, MON NOT R ASTRON SOC, V409, P1534, DOI 10.1111/j.1365-2966.2010.17393.x
Davis SW, 2006, ASTROPHYS J SUPPL S, V164, P530, DOI 10.1086/503549
De Villiers JP, 2003, ASTROPHYS J, V589, P458, DOI 10.1086/373949
DEGL'INNOCENTI EL, 1985, SOL PHYS, V97, P239, DOI 10.1007/BF00165988
Dexter J, 2012, J PHYS CONF SER, V372, DOI 10.1088/1742-6596/372/1/012023
Dexter J, 2011, THESIS
Dexter J, 2012, MON NOT R ASTRON SOC, V426, pL71, DOI 10.1111/j.1745-3933.2012.01328.x
Dexter J, 2012, MON NOT R ASTRON SOC, V421, P1517, DOI 10.1111/j.1365-2966.2012.20409.x
Dexter J, 2011, ASTROPHYS J, V730, DOI 10.1088/0004-637X/730/1/36
Dexter J, 2011, ASTROPHYS J LETT, V727, DOI 10.1088/2041-8205/727/1/L24
Dexter J, 2010, ASTROPHYS J, V717, P1092, DOI 10.1088/0004-637X/717/2/1092
Dexter J, 2009, ASTROPHYS J LETT, V703, pL142, DOI 10.1088/0004-637X/703/2/L142
Dexter J, 2009, ASTROPHYS J, V696, P1616, DOI 10.1088/0004-637X/696/2/1616
Doeleman S, 2009, ASTRO2010 ASTRONOMY, V2010, P68
Doeleman SS, 2012, SCIENCE, V338, P355, DOI 10.1126/science.1224768
Dolence JC, 2009, ASTROPHYS J SUPPL S, V184, P387, DOI 10.1088/0067-0049/184/2/387
Eisenhauer F., 2008, P SPIE C SER, V7013, P2
Falcke H, 1998, ASTROPHYS J, V499, P731, DOI 10.1086/305687
Font JA, 1999, MON NOT R ASTRON SOC, V305, P920, DOI 10.1046/j.1365-8711.1999.02459.x
Gammie CF, 2003, ASTROPHYS J, V589, P444, DOI 10.1086/374594
Gammie CF, 2012, ASTROPHYS J, V752, DOI 10.1088/0004-637X/752/2/123
GINZBURG VL, 1969, ANNU REV ASTRON ASTR, V7, P375, DOI 10.1146/annurev.aa.07.090169.002111
GINZBURG VL, 1965, ANNU REV ASTRON ASTR, V3, P297, DOI 10.1146/annurev.aa.03.090165.001501
Gold R., 2016, ARXIV E PRINTS
Hindmarsh A.C., 1983, SCI COMPUT, P55
Huang L, 2011, MON NOT R ASTRON SOC, V416, P2574, DOI 10.1111/j.1365-2966.2011.19207.x
Huang L, 2009, ASTROPHYS J, V703, P557, DOI 10.1088/0004-637X/703/1/557
Johnson MD, 2015, SCIENCE, V350, P1242, DOI 10.1126/science.aac7087
JONES TW, 1977, ASTROPHYS J, V214, P522, DOI 10.1086/155278
JONES TW, 1979, ASTROPHYS J, V228, P268, DOI 10.1086/156843
Krolik JH, 2005, ASTROPHYS J, V622, P1008, DOI 10.1086/427932
Kulkarni A. K., 2011, MNRAS, P620
LEGG MPC, 1968, ASTROPHYS J, V154, P499, DOI 10.1086/149777
Li LX, 2005, ASTROPHYS J SUPPL S, V157, P335, DOI 10.1086/428089
LUMINET JP, 1979, ASTRON ASTROPHYS, V75, P228
Mahadevan R, 1996, ASTROPHYS J, V465, P327, DOI 10.1086/177422
Mao, 2016, IN PRESS
Melrose D.B, 1980, ARXIV E PRINTS, V2
MELROSE DB, 1971, ASTROPHYS SPACE SCI, V12, P172, DOI 10.1007/BF00656148
Melrose DB, 1997, J PLASMA PHYS, V58, P735, DOI 10.1017/S0022377897006284
MELROSE DB, 1980, PLASMA ASTROPHYSICS, V1
MICHEL FC, 1972, ASTROPHYS SPACE SCI, V15, P153, DOI 10.1007/BF00649949
Moscibrodzka M., 2015, ARXIV E PRINTS
Moscibrodzka M, 2009, ASTROPHYS J, V706, P497, DOI 10.1088/0004-637X/706/1/497
Noble SC, 2006, ASTROPHYS J, V641, P626, DOI 10.1086/500349
Noble SC, 2007, CLASSICAL QUANT GRAV, V24, pS259, DOI 10.1088/0264-9381/24/12/S17
Noble SC, 2011, ASTROPHYS J, V743, DOI 10.1088/0004-637X/743/2/115
Noble SC, 2009, ASTROPHYS J, V703, P964, DOI 10.1088/0004-637X/703/1/964
PAGE DN, 1974, ASTROPHYS J, V191, P499, DOI 10.1086/152990
Pandya A., 2016, ARXIV E PRINTS
RAUCH KP, 1994, ASTROPHYS J, V421, P46, DOI 10.1086/173625
REES DE, 1989, ASTROPHYS J, V339, P1093, DOI 10.1086/167364
Rybicki G.B., 1979, RAD PROCESSES ASTROP
SAZONOV VN, 1969, SOV ASTRON, V13, P396
Schnittman JD, 2006, ASTROPHYS J, V651, P1031, DOI 10.1086/507421
Schnittman JD, 2013, ASTROPHYS J, V777, DOI 10.1088/0004-637X/777/1/11
Schnittman JD, 2009, ASTROPHYS J, V701, P1175, DOI 10.1088/0004-637X/701/2/1175
SHAKURA NI, 1973, ASTRON ASTROPHYS, V24, P337
SHAPIRO SL, 1973, ASTROPHYS J, V185, P69, DOI 10.1086/152396
SHAPIRO SL, 1973, ASTROPHYS J, V180, P531, DOI 10.1086/151982
Shcherbakov RV, 2008, ASTROPHYS J, V688, P695, DOI 10.1086/592326
Shcherbakov RV, 2012, ASTROPHYS J, V755, DOI 10.1088/0004-637X/755/2/133
Shcherbakov RV, 2011, MON NOT R ASTRON SOC, V410, P1052, DOI 10.1111/j.1365-2966.2010.17502.x
Sobolev V. V., 1963, TREATISE RAD TRANSFE
VIERGUTZ SU, 1993, ASTRON ASTROPHYS, V272, P355
Vincent FH, 2011, CLASSICAL QUANT GRAV, V28, DOI 10.1088/0264-9381/28/22/225011
WALKER M, 1970, COMMUN MATH PHYS, V18, P265, DOI 10.1007/BF01649445
WESTFOLD KC, 1959, ASTROPHYS J, V130, P241, DOI 10.1086/146713
Yuan F, 2003, ASTROPHYS J, V598, P301, DOI 10.1086/378716
NR 92
TC 57
Z9 57
U1 0
U2 1
PU OXFORD UNIV PRESS
PI OXFORD
PA GREAT CLARENDON ST, OXFORD OX2 6DP, ENGLAND
SN 0035-8711
EI 1365-2966
J9 MON NOT R ASTRON SOC
JI Mon. Not. Roy. Astron. Soc.
PD OCT 11
PY 2016
VL 462
IS 1
BP 115
EP 136
DI 10.1093/mnras/stw1526
PG 22
WC Astronomy & Astrophysics
SC Astronomy & Astrophysics
GA DW3BI
UT WOS:000383516700034
OA Bronze
DA 2021-04-21
ER
PT J
AU Kaczmarczyk, J
Weimer, H
Lemeshko, M
AF Kaczmarczyk, J.
Weimer, H.
Lemeshko, M.
TI Dissipative preparation of antiferromagnetic order in the Fermi-Hubbard
model
SO NEW JOURNAL OF PHYSICS
LA English
DT Article
DE Hubbard model; ultracold gases; antiferromagnetic phase; lattice fermion
models; dissipative preparation
ID OPEN QUANTUM-SYSTEMS; OPTICAL LATTICE; ULTRACOLD FERMIONS;
HEISENBERG-MODEL; PYTHON FRAMEWORK; GROUND-STATE; TRAPPED IONS; GASES;
ATOMS; DYNAMICS
AB The Fermi-Hubbard model is one of the key models of condensed matter physics, which holds a potential for explaining the mystery of high-temperature superconductivity. Recent progress in ultracold atoms in optical lattices has paved the way to studying the model's phase diagram using the tools of quantum simulation, which emerged as a promising alternative to the numerical calculations plagued by the infamous sign problem. However, the temperatures achieved using elaborate laser cooling protocols so far have been too high to show the appearance of antiferromagnetic (AF) and superconducting quantum phases directly. In this work, we demonstrate that using the machinery of dissipative quantum state engineering, one can observe the emergence of the AF order in the Fermi-Hubbard model with fermions in optical lattices. The core of the approach is to add incoherent laser scattering in such a way that the AF state emerges as the dark state of the driven-dissipative dynamics. The proposed controlled dissipation channels described in this work are straightforward to add to already existing experimental setups.
C1 [Kaczmarczyk, J.; Lemeshko, M.] IST Austria, Campus 1, A-3400 Klosterneuburg, Austria.
[Weimer, H.] Leibniz Univ Hannover, Inst Theoret Phys, Appelstr 2, D-30167 Hannover, Germany.
RP Kaczmarczyk, J (corresponding author), IST Austria, Campus 1, A-3400 Klosterneuburg, Austria.
EM jan.kaczmarczyk@ist.ac.at; mikhail.lemeshko@ist.ac.at
RI Weimer, Hendrik/AAP-1707-2020
OI Weimer, Hendrik/0000-0002-4551-377X; Kaczmarczyk,
Jan/0000-0002-1629-3675
FU People Programme (Marie Curie Actions) of the European Union's Seventh
Framework Programme (FP7) under REA grant [291734]; Volkswagen
FoundationVolkswagen; DFGGerman Research Foundation (DFG)European
Commission [SFB 1227]
FX We acknowledge stimulating discussions with Ken Brown, Tommaso Calarco,
Andrew Daley, Suzanne McEndoo, Tobias Osborne, Cindy Regal, Luis Santos,
Michal Tomza, and Martin Zwierlein. The work was supported by the People
Programme (Marie Curie Actions) of the European Union's Seventh
Framework Programme (FP7/2007-2013) under REA grant agreement no.
[291734], by the Volkswagen Foundation, and by DFG within SFB 1227
(DQ-mat).
CR Aikawa K, 2014, PHYS REV LETT, V113, DOI 10.1103/PhysRevLett.113.263201
Alharbi AF, 2010, PHYS REV A, V82, DOI 10.1103/PhysRevA.82.054103
AZZOUZ M, 1993, PHYS REV B, V48, P6136, DOI 10.1103/PhysRevB.48.6136
Barreiro JT, 2011, NATURE, V470, P486, DOI 10.1038/nature09801
Barreiro JT, 2010, NAT PHYS, V6, P943, DOI [10.1038/nphys1781, 10.1038/NPHYS1781]
Bermudez A, 2013, PHYS REV LETT, V110, DOI 10.1103/PhysRevLett.110.110502
Bernier JS, 2014, PHYS REV B, V90, DOI 10.1103/PhysRevB.90.205125
Bernier JS, 2009, PHYS REV A, V79, DOI 10.1103/PhysRevA.79.061601
Bloch I, 2008, REV MOD PHYS, V80, P885, DOI 10.1103/RevModPhys.80.885
Bohn JL, 1999, PHYS REV A, V59, P3660, DOI 10.1103/PhysRevA.59.3660
Budich JC, 2015, PHYS REV A, V91, DOI 10.1103/PhysRevA.91.042117
CARMICHAEL HJ, 1993, PHYS REV LETT, V70, P2273, DOI 10.1103/PhysRevLett.70.2273
Carr AW, 2013, PHYS REV LETT, V111, DOI 10.1103/PhysRevLett.111.033607
Chan CK, 2015, PHYS REV A, V91, DOI 10.1103/PhysRevA.91.051601
Cheuk LW, 2015, PHYS REV LETT, V114, DOI 10.1103/PhysRevLett.114.193001
Chin C, 2010, REV MOD PHYS, V82, P1225, DOI 10.1103/RevModPhys.82.1225
Chin JK, 2006, NATURE, V443, P961, DOI 10.1038/nature05224
Colome-Tatche M, 2011, NEW J PHYS, V13, DOI 10.1088/1367-2630/13/11/113021
DALIBARD J, 1992, PHYS REV LETT, V68, P580, DOI 10.1103/PhysRevLett.68.580
Derzhko O, 2003, PHYSICA A, V320, P407, DOI 10.1016/S0378-4371(02)01595-9
Derzhko O., 2001, Journal of Physical Studies, V5, P49
Diehl S, 2010, PHYS REV LETT, V105, DOI 10.1103/PhysRevLett.105.227001
Diehl S, 2008, NAT PHYS, V4, P878, DOI 10.1038/nphys1073
Diehl S, 2011, NAT PHYS, V7, P971, DOI [10.1038/nphys2106, 10.1038/NPHYS2106]
Diehl S, 2010, PHYS REV LETT, V105, DOI 10.1103/PhysRevLett.105.015702
Dutta O, 2015, REP PROG PHYS, V78, DOI 10.1088/0034-4885/78/6/066001
Dzhioev AA, 2015, J PHYS A-MATH THEOR, V48, DOI 10.1088/1751-8113/48/1/015004
Edge G J A, 2015, PHYS REV A, V92
Esslinger T, 2010, ANNU REV CONDEN MA P, V1, P129, DOI 10.1146/annurev-conmatphys-070909-104059
Finazzi S, 2015, PHYS REV LETT, V115, DOI 10.1103/PhysRevLett.115.080604
FRADKIN E, 1989, PHYS REV LETT, V63, P322, DOI 10.1103/PhysRevLett.63.322
Georgescu IM, 2014, REV MOD PHYS, V86, P153, DOI 10.1103/RevModPhys.86.153
Gommers R, 2005, PHYS REV LETT, V95, DOI 10.1103/PhysRevLett.95.073003
Greif D, 2015, PHYS REV LETT, V115, DOI 10.1103/PhysRevLett.115.260401
Greif D, 2013, SCIENCE, V340, P1307, DOI 10.1126/science.1236362
Haller E, 2015, NAT PHYS, V11, P738, DOI [10.1038/nphys3403, 10.1038/NPHYS3403]
Hart RA, 2015, NATURE, V519, P211, DOI 10.1038/nature14223
Heidrich-Meisner F, 2009, PHYS REV A, V80, DOI 10.1103/PhysRevA.80.041603
Ho TL, 2009, P NATL ACAD SCI USA, V106, P6916, DOI 10.1073/pnas.0809862105
Jin JS, 2013, PHYS REV LETT, V110, DOI 10.1103/PhysRevLett.110.163605
Jordens R, 2008, NATURE, V455, P204, DOI 10.1038/nature07244
Johansson JR, 2013, COMPUT PHYS COMMUN, V184, P1234, DOI 10.1016/j.cpc.2012.11.019
Johansson JR, 2012, COMPUT PHYS COMMUN, V183, P1760, DOI 10.1016/j.cpc.2012.02.021
Jotzu G, 2014, NATURE, V515, P237, DOI 10.1038/nature13915
Keimer B, 2015, NATURE, V518, P179, DOI 10.1038/nature14165
Kordas G, 2012, EPL-EUROPHYS LETT, V100, DOI 10.1209/0295-5075/100/30007
Kraus B, 2008, PHYS REV A, V78, DOI 10.1103/PhysRevA.78.042307
Krauser JS, 2012, NAT PHYS, V8, P813, DOI [10.1038/nphys2409, 10.1038/NPHYS2409]
Krauter H, 2011, PHYS REV LETT, V107, DOI 10.1103/PhysRevLett.107.080503
Le Boite A, 2013, PHYS REV LETT, V110, DOI 10.1103/PhysRevLett.110.233601
Le Hur K, 2009, ANN PHYS-NEW YORK, V324, P1452, DOI 10.1016/j.aop.2009.02.004
Lee TE, 2013, PHYS REV LETT, V110, DOI 10.1103/PhysRevLett.110.257204
Lee TE, 2011, PHYS REV A, V84, DOI 10.1103/PhysRevA.84.031402
Lemeshko M, 2013, NAT COMMUN, V4, DOI 10.1038/ncomms3230
Lemeshko M, 2012, PHYS REV LETT, V109, DOI 10.1103/PhysRevLett.109.035301
Lewenstein M, 2007, ADV PHYS, V56, P243, DOI 10.1080/00018730701223200
Lin Y, 2013, NATURE, V504, P415, DOI 10.1038/nature12801
Liu WV, 2004, PHYS REV A, V70, DOI 10.1103/PhysRevA.70.033603
Lu MW, 2012, PHYS REV LETT, V108, DOI 10.1103/PhysRevLett.108.215301
Lucia A, 2015, PHYS REV A, V91, DOI 10.1103/PhysRevA.91.040302
Mazzucchi G, 2016, SCI REP-UK, V6, DOI 10.1038/srep31196
Medley P, 2011, PHYS REV LETT, V106, DOI 10.1103/PhysRevLett.106.195301
MOLMER K, 1993, J OPT SOC AM B, V10, P524, DOI 10.1364/JOSAB.10.000524
Murmann S, 2015, PHYS REV LETT, V115, DOI 10.1103/PhysRevLett.115.215301
Naylor B, 2015, PHYS REV A, V91, DOI 10.1103/PhysRevA.91.011603
Otterbach J, 2014, PHYS REV LETT, V113, DOI 10.1103/PhysRevLett.113.070401
Overbeck VR, 2016, PHYS REV A, V93, DOI 10.1103/PhysRevA.93.012106
Parsons MF, 2015, PHYS REV LETT, V114, DOI 10.1103/PhysRevLett.114.213002
Prosen T, 2008, NEW J PHYS, V10, DOI 10.1088/1367-2630/10/4/043026
Rao DDB, 2013, PHYS REV LETT, V111, DOI 10.1103/PhysRevLett.111.033606
Regal CA, 2004, PHYS REV LETT, V92, DOI 10.1103/PhysRevLett.92.040403
Schindler P, 2013, NAT PHYS, V9, P361, DOI [10.1038/NPHYS2630, 10.1038/nphys2630]
Schneider U, 2008, SCIENCE, V322, P1520, DOI 10.1126/science.1165449
Shankar S, 2013, NATURE, V504, P419, DOI 10.1038/nature12802
Sieberer LM, 2013, PHYS REV LETT, V110, DOI 10.1103/PhysRevLett.110.195301
Taie S, 2010, PHYS REV LETT, V105, DOI 10.1103/PhysRevLett.105.190401
Tomadin A, 2011, PHYS REV A, V83, DOI 10.1103/PhysRevA.83.013611
Uehlinger T, 2013, PHYS REV LETT, V111, DOI 10.1103/PhysRevLett.111.185307
Verstraete F, 2009, NAT PHYS, V5, P633, DOI 10.1038/NPHYS1342
WANG YR, 1991, PHYS REV B, V43, P3786, DOI 10.1103/PhysRevB.43.3786
Watanabe G, 2012, PHYS REV A, V85, DOI 10.1103/PhysRevA.85.023604
Weimer H, 2015, PHYS REV A, V91, DOI 10.1103/PhysRevA.91.063401
Weimer H, 2015, PHYS REV LETT, V114, DOI 10.1103/PhysRevLett.114.040402
Weimer H, 2013, MOL PHYS, V111, P1753, DOI 10.1080/00268976.2013.789567
Weimer H, 2010, NAT PHYS, V6, P382, DOI 10.1038/NPHYS1614
Williams JR, 2010, PHYS REV A, V82, DOI 10.1103/PhysRevA.82.011610
Witthaut D, 2008, PHYS REV LETT, V101, DOI 10.1103/PhysRevLett.101.200402
Yi W, 2012, NEW J PHYS, V14, DOI 10.1088/1367-2630/14/5/055002
NR 88
TC 9
Z9 9
U1 0
U2 5
PU IOP PUBLISHING LTD
PI BRISTOL
PA TEMPLE CIRCUS, TEMPLE WAY, BRISTOL BS1 6BE, ENGLAND
SN 1367-2630
J9 NEW J PHYS
JI New J. Phys.
PD SEP 22
PY 2016
VL 18
AR 093042
DI 10.1088/1367-2630/18/9/093042
PG 10
WC Physics, Multidisciplinary
SC Physics
GA DZ0GY
UT WOS:000385516800002
OA DOAJ Gold, Green Published
DA 2021-04-21
ER
PT J
AU Hocker, D
Kosut, R
Rabitz, H
AF Hocker, David
Kosut, Robert
Rabitz, Herschel
TI PEET: a Matlab tool for estimating physical gate errors in quantum
information processing systems
SO QUANTUM INFORMATION PROCESSING
LA English
DT Article
DE Quantum computation; Quantum simulation; Quantum control; Open quantum
system; Decoherence; Matlab
ID PYTHON FRAMEWORK; DYNAMICS; QUTIP
AB A Physical Error Estimation Tool (PEET) is introduced in Matlab for predicting physical gate errors of quantum information processing (QIP) operations by constructing and then simulating gate sequences for a wide variety of user-defined, Hamiltonian-based physical systems. PEET is designed to accommodate the interdisciplinary needs of quantum computing design by assessing gate performance for users familiar with the underlying physics of QIP, as well as those interested in higher-level computing operations. The structure of PEET separates the bulk of the physical details of a system into Gate objects, while the construction of quantum computing gate operations are contained in GateSequence objects. Gate errors are estimated by Monte Carlo sampling of noisy gate operations. The main utility of PEET, though, is the implementation of QuantumControl methods that act to generate and then test gate sequence and pulse-shaping techniques for QIP performance. This work details the structure of PEET and gives instructive examples for its operation.
C1 [Hocker, David; Rabitz, Herschel] Princeton Univ, Dept Chem, Princeton, NJ 08544 USA.
[Kosut, Robert] SC Solut Inc, 1261 Oakmead Pkwy, Sunnyvale, CA 94085 USA.
RP Hocker, D (corresponding author), Princeton Univ, Dept Chem, Princeton, NJ 08544 USA.
EM dhocker@princeton.edu
FU National Science Foundation Graduate Research Fellowship ProgramNational
Science Foundation (NSF) [DGE 1148900]; National Science
FoundationNational Science Foundation (NSF) [CHE-1058644]; ARO-MURIMURI
[W911NF-11-1-2068]; Intelligence Advanced Research Projects Activity
(IARPA) via Department of Interior National Business Center
[D11PC20165]; Division Of ChemistryNational Science Foundation (NSF)NSF
- Directorate for Mathematical & Physical Sciences (MPS) [1464569]
Funding Source: National Science Foundation
FX This material is based upon work supported by the (D.H.) National
Science Foundation Graduate Research Fellowship Program under Grant No.
(DGE 1148900), (H.R.for the basic concepts) National Science Foundation
(CHE-1058644) and (R.K.) ARO-MURI (W911NF-11-1-2068). This work was also
supported by the (R.K.) (H.R. for the illustrations) Intelligence
Advanced Research Projects Activity (IARPA) via Department of Interior
National Business Center Contract No. D11PC20165. The US Government is
authorized to reproduce and distribute reprints for Governmental
purposes notwithstanding any copyright annotation thereon. The views and
conclusions contained herein are those of the authors and should not be
interpreted as necessarily representing the official policies or
endorsements, either expressed or implied, of IARPA, DoI/NBC, or the US
Government.
CR Brif C, 2010, NEW J PHYS, V12, DOI 10.1088/1367-2630/12/7/075008
Brockwell P, 2002, INTRO TIME SERIES FO
Bruer H.-P., 2002, THEORY OPEN QUANTUM
Clark CR, 2009, PHYS REV A, V79, DOI 10.1103/PhysRevA.79.062314
Dawson CM, 2006, QUANTUM INFORM COMPU, V6, P81
Devitt SJ, 2013, NAT COMMUN, V4, DOI 10.1038/ncomms3524
Green A.S., CORR
Grover L.K., 1996, P 28 ACM S THEOR COM, V212, P212, DOI DOI 10.1145/237814.237866
Ho TS, 2009, PHYS REV A, V79, DOI 10.1103/PhysRevA.79.013422
Hocker D., QUANT INF PROC UNPUB
Hocker D, 2014, PHYS REV A, V90, DOI 10.1103/PhysRevA.90.062309
Hsieh M, 2009, J CHEM PHYS, V130, DOI 10.1063/1.2981796
Hsieh MH, 2008, PHYS REV A, V78, DOI 10.1103/PhysRevA.78.042306
Iglin S., 2013, GRTHEORY 2004 MATLAB
Johansson JR, 2013, COMPUT PHYS COMMUN, V184, P1234, DOI 10.1016/j.cpc.2012.11.019
Johansson JR, 2012, COMPUT PHYS COMMUN, V183, P1760, DOI 10.1016/j.cpc.2012.02.021
Jones NC, 2012, PHYS REV X, V2, DOI 10.1103/PhysRevX.2.031007
Kabytayev C, 2014, PHYS REV A, V90, DOI 10.1103/PhysRevA.90.012316
Khodjasteh K, 2012, PHYS REV A, V86, DOI 10.1103/PhysRevA.86.042329
Khodjasteh K, 2010, PHYS REV LETT, V104, DOI 10.1103/PhysRevLett.104.090501
Khodjasteh K, 2009, PHYS REV A, V80, DOI 10.1103/PhysRevA.80.032314
Khodjasteh K, 2009, PHYS REV LETT, V102, DOI 10.1103/PhysRevLett.102.080501
Kosu RL, 2013, PHYS REV A, V88, DOI 10.1103/PhysRevA.88.052326
Merrill J. T., 2014, PROGR COMPENSATING P
Moore KW, 2011, PHYS REV A, V83, DOI 10.1103/PhysRevA.83.012326
Nielsen M. A., 2000, QUANTUM COMPUTATION
Quantiki.org, 2015, LIST QC SIM
Rabitz H, 2006, PHYS REV A, V74, DOI 10.1103/PhysRevA.74.012721
Rabitz H, 2005, PHYS REV A, V72, DOI 10.1103/PhysRevA.72.052337
Rabitz HA, 2004, SCIENCE, V303, P1998, DOI 10.1126/science.1093384
Rothman A, 2005, PHYS REV A, V72, DOI 10.1103/PhysRevA.72.023416
Rothman A, 2006, PHYS REV A, V73, DOI 10.1103/PhysRevA.73.053401
Saffman M, 2005, PHYS REV A, V72, DOI 10.1103/PhysRevA.72.022347
Saffman M, 2010, REV MOD PHYS, V82, P2313, DOI 10.1103/RevModPhys.82.2313
Schwabl F, 2002, QUANTUM MECH
Shor PW, 1997, SIAM J COMPUT, V26, P1484, DOI 10.1137/S0097539795293172
Sorber L., 2011, KRON MATLAB CENTRAL
Stark H., 1986, PROBABILITY RANDOM P
Suchara M, 2013, ARXIV13122316
SUZUKI M, 1992, PHYS LETT A, V165, P387, DOI 10.1016/0375-9601(92)90335-J
Tibbetts KWM, 2012, PHYS REV A, V86, DOI 10.1103/PhysRevA.86.062309
Viola L, 1998, PHYS REV A, V58, P2733, DOI 10.1103/PhysRevA.58.2733
Viola L, 1999, PHYS REV LETT, V82, P2417, DOI 10.1103/PhysRevLett.82.2417
NR 43
TC 2
Z9 2
U1 2
U2 5
PU SPRINGER
PI NEW YORK
PA 233 SPRING ST, NEW YORK, NY 10013 USA
SN 1570-0755
EI 1573-1332
J9 QUANTUM INF PROCESS
JI Quantum Inf. Process.
PD SEP
PY 2016
VL 15
IS 9
BP 3489
EP 3518
DI 10.1007/s11128-016-1337-5
PG 30
WC Quantum Science & Technology; Physics, Multidisciplinary; Physics,
Mathematical
SC Physics
GA DU4DN
UT WOS:000382162400001
DA 2021-04-21
ER
PT J
AU Coelho, RCV
Neumann, RF
AF Coelho, Rodrigo C. V.
Neumann, Rodrigo F.
TI Fluid dynamics in porous media with Sailfish
SO EUROPEAN JOURNAL OF PHYSICS
LA English
DT Article
DE fluid dynamics; computational physics; porous media; statistical physics
ID LATTICE-BOLTZMANN; FLOW; PERMEABILITY; SIMULATION; FORM
AB In this work we show the application of Sailfish to the study of fluid dynamics in porous media. Sailfish is an open-source software based on the lattice-Boltzmann method. This application of computational fluid dynamics is of particular interest to the oil and gas industry and the subject could be a starting point for an undergraduate or graduate student in physics or engineering. We built artificial samples of porous media with different porosities and used Sailfish to simulate the fluid flow through them in order to calculate their permeability and tortuosity. We also present a simple way to obtain the specific superficial area of porous media using Python libraries. To contextualise these concepts, we analyse the applicability of the Kozeny-Carman equation, which is a well-known permeability-porosity relation, to our artificial samples.
C1 [Coelho, Rodrigo C. V.] Univ Fed Rio de Janeiro UFRJ, Inst Fis, Caixa Postal 68528, BR-21941972 Rio De Janeiro, Brazil.
[Coelho, Rodrigo C. V.; Neumann, Rodrigo F.] IBM Res, Ave Pasteur 138 & 146, BR-22290240 Rio De Janeiro, Brazil.
RP Coelho, RCV (corresponding author), Univ Fed Rio de Janeiro UFRJ, Inst Fis, Caixa Postal 68528, BR-21941972 Rio De Janeiro, Brazil.; Coelho, RCV (corresponding author), IBM Res, Ave Pasteur 138 & 146, BR-22290240 Rio De Janeiro, Brazil.
EM rcvcoelho@if.ufrj.br; rneumann@br.ibm.com
RI Ferreira, Rodrigo Neumann Barros/D-8111-2013; Coelho, Rodrigo C
V/N-9867-2018
OI Ferreira, Rodrigo Neumann Barros/0000-0003-4435-0507; Coelho, Rodrigo C
V/0000-0001-8904-0778
FU CNPqNational Council for Scientific and Technological Development (CNPq)
FX RCVC thanks CNPq for financial support.
CR Andra H, 2013, COMPUT GEOSCI-UK, V50, P33, DOI 10.1016/j.cageo.2012.09.008
Andra H, 2013, COMPUT GEOSCI-UK, V50, P25, DOI 10.1016/j.cageo.2012.09.005
BHATNAGAR PL, 1954, PHYS REV, V94, P511, DOI 10.1103/PhysRev.94.511
Carman PC, 1997, CHEM ENG RES DES, V75, pS32, DOI 10.1016/S0263-8762(97)80003-2
Clennell M. B., 1997, GEOL SOC SPEC PUBL, V122, P299
Coelho RCV, 2014, PHYS REV E, V89, DOI 10.1103/PhysRevE.89.043302
Darcy H., 1969, THEORY GROUND WATER, P287
Deane A., 2006, PARALLEL COMPUTATION
Geller S, 2006, COMPUT FLUIDS, V35, P888, DOI 10.1016/j.compfluid.2005.08.009
Huang HB, 2011, COMPUT MATH APPL, V61, P3606, DOI 10.1016/j.camwa.2010.06.034
Januszewski M, 2014, COMPUT PHYS COMMUN, V185, P2350, DOI 10.1016/j.cpc.2014.04.018
Jenkins DR, 2013, J CHEM PHYS, V138, DOI 10.1063/1.4790691
Kaviany M., 1995, PRINCIPLES HEAT TRAN
Khabbazi AE, 2013, COMPUT FLUIDS, V75, P35, DOI 10.1016/j.compfluid.2013.01.008
Klockner A, 2012, PARALLEL COMPUT, V38, P157, DOI 10.1016/j.parco.2011.09.001
Koponen A, 1996, PHYS REV E, V54, P406, DOI 10.1103/PhysRevE.54.406
Koponen A, 1997, PHYS REV E, V56, P3319, DOI 10.1103/PhysRevE.56.3319
Kozeny J., 1927, Sitzungsberichte der Akademie der Wissenschaften in Wien, Mathematisch-Naturwissenschaftliche Klasse, V136, P271
Kremer GM, 2010, INTERACT MECH MATH, P1, DOI 10.1007/978-3-642-11696-4
Lowell S., 2012, PARTICLE TECHNOLOGY
MASSAIOLI F, 2002, 4 EUR WORKSH OPENMP
Matyka M, 2012, AIP CONF PROC, V1453, P17, DOI 10.1063/1.4711147
MCNAMARA GR, 1988, PHYS REV LETT, V61, P2332, DOI 10.1103/PhysRevLett.61.2332
Mohamad AA, 2011, LATTICE BOLTZMANN METHOD: FUNDAMENTALS AND ENGINEERING APPLICATIONS WITH COMPUTER CODES, P1, DOI 10.1007/978-0-85729-455-5
Mourzenko V, 2008, PHYS REV E, V77, DOI 10.1103/PhysRevE.77.066306
Pan CX, 2006, COMPUT FLUIDS, V35, P898, DOI 10.1016/j.compfluid.2005.03.008
Philippi PC, 2006, PHYS REV E, V73, DOI 10.1103/PhysRevE.73.056702
Rabbani A, 2014, J PETROL SCI ENG, V123, P164, DOI 10.1016/j.petrol.2014.08.020
Ren B, 2016, COAST ENG, V107, P14, DOI 10.1016/j.coastaleng.2015.10.004
Sheidegger AE, 1974, PHYS FLOW POROUS MED
Suvachittanont S, 1996, ADV POWDER TECHNOL, V7, P91, DOI 10.1016/S0921-8831(08)60504-X
Tolke J, 2008, INT J COMPUT FLUID D, V22, P443, DOI 10.1080/10618560802238275
WHITAKER S, 1986, TRANSPORT POROUS MED, V1, P3, DOI 10.1007/BF01036523
Xu P, 2008, ADV WATER RESOUR, V31, P74, DOI 10.1016/j.advwatres.2007.06.003
Yazdchi K, 2011, VALIDITY CARMAN KOZE
NR 35
TC 5
Z9 5
U1 0
U2 18
PU IOP PUBLISHING LTD
PI BRISTOL
PA TEMPLE CIRCUS, TEMPLE WAY, BRISTOL BS1 6BE, ENGLAND
SN 0143-0807
EI 1361-6404
J9 EUR J PHYS
JI Eur. J. Phys.
PD SEP
PY 2016
VL 37
IS 5
AR 055102
DI 10.1088/0143-0807/37/5/055102
PG 13
WC Education, Scientific Disciplines; Physics, Multidisciplinary
SC Education & Educational Research; Physics
GA DT9KV
UT WOS:000381819000011
DA 2021-04-21
ER
PT J
AU Roberts, JA
Langston, M
Nichols, D
Schlaikjer, E
Schlaikjer, G
Bindra, H
AF Roberts, Jeremy A.
Langston, Max
Nichols, Daniel
Schlaikjer, Eric
Schlaikjer, Graham
Bindra, Hitesh
TI A nonlinear reactivity method with application to accident-tolerant
fuels
SO ANNALS OF NUCLEAR ENERGY
LA English
DT Article
DE Nonlinear reactivity model; Nodal parameter model; Coupled physics;
Accident-tolerant fuel; Educational tools
AB A nonlinear reactivity model with thermal-hydraulic feedback is presented for analysis of PWR fuel with various cladding materials. The simplified, batch-wise neutronic model requires batch reactivities and migration areas as predetermined functions of burnup, temperature (fuel and coolant), and any design parameters having neutronic significance (e.g., enrichment or cladding composition). Here, such functions are found by using symbolic regression, a unique approach that finds both a functional form and any model coefficients simultaneously. Core thermal-hydraulics are modeled using single-channel, axially-averaged values. The models were implemented in an open-source, Python-based tool, used here to the analyze fuel with Zr-based cladding having outer, protective layers of either FeCrAl or SiC, two materials proposed for use in next-generation, accident-tolerant fuel. (C) 2016 Elsevier Ltd. All rights reserved.
C1 [Roberts, Jeremy A.; Langston, Max; Nichols, Daniel; Schlaikjer, Eric; Schlaikjer, Graham; Bindra, Hitesh] Kansas State Univ, Dept Mech & Nucl Engn, Manhattan, KS 66506 USA.
RP Roberts, JA (corresponding author), 3002 Rathbone Hall, Manhattan, KS 66506 USA.
EM jaroberts@ksu.edu
FU U.S. Department of Energy via Idaho National Laboratory
[DE-AC07-05ID14517]; KSU's Department of Mechanical and Nuclear
Engineering Undergraduate Research Program
FX The material presented is based upon work supported in part by the U.S.
Department of Energy via Idaho National Laboratory under Prime Contract
No. DE-AC07-05ID14517. In addition, the work of M.L., D.N., E.S., and
E.G. was further supported by KSU's Department of Mechanical and Nuclear
Engineering Undergraduate Research Program.
CR BRAGG-SITTON S. M., 2015, NUCL NEWS
Cisneros AT, 2013, NUCL TECHNOL, V183, P331, DOI 10.13182/NT13-A19422
Driscoll M.J., 1990, AM NUCL SOC
George NM, 2015, ANN NUCL ENERGY, V75, P703, DOI 10.1016/j.anucene.2014.09.005
Hales J., 2013, INLEXT1329930
Knott D., 2010, HDB NUCL ENG, P913, DOI DOI 10.1007/978-0-387-98149-9_9
Machiels A., 2011, BENCHMARKS QUANTIFYI
Muller E., 2007, P INT C NUCL EN NEW
Roberts J., 2015, NRM COUPLED NEUTRONI
Schmidt M., 2014, EUREQA VERSION 0 98
Schmidt M, 2009, SCIENCE, V324, P81, DOI 10.1126/science.1165893
Stacey W.M., 2007, NUCL REACTOR PHYS
Stedwell M.J., 1964, TECHNICAL REPORT
TODREAS N., 2011, NUCL SYSTEMS
Tombakoglu Y., 2002, P INT C NUCL EN NEW
Wu X., 2015, ANN NUCL ENERGY
Wu X, 2014, INLEXT1432591
Xu Y., 2006, TECHNICAL REPORT
NR 18
TC 2
Z9 2
U1 0
U2 8
PU PERGAMON-ELSEVIER SCIENCE LTD
PI OXFORD
PA THE BOULEVARD, LANGFORD LANE, KIDLINGTON, OXFORD OX5 1GB, ENGLAND
SN 0306-4549
J9 ANN NUCL ENERGY
JI Ann. Nucl. Energy
PD AUG
PY 2016
VL 94
BP 581
EP 588
DI 10.1016/j.anucene.2016.03.021
PG 8
WC Nuclear Science & Technology
SC Nuclear Science & Technology
GA DN7BZ
UT WOS:000377231600063
DA 2021-04-21
ER
PT J
AU Aichhorn, M
Pourovskii, L
Seth, P
Vildosola, V
Zingl, M
Peil, OE
Deng, XY
Mravlje, J
Kraberger, GJ
Martins, C
Ferrero, M
Parcollet, O
AF Aichhorn, Markus
Pourovskii, Leonid
Seth, Priyanka
Vildosola, Veronica
Zingl, Manuel
Peil, Oleg E.
Deng, Xiaoyu
Mravlje, Jernej
Kraberger, Gernot J.
Martins, Cyril
Ferrero, Michel
Parcollet, Olivier
TI TRIQS/DFTTools: A TRIQS application for ab initio calculations of
correlated materials
SO COMPUTER PHYSICS COMMUNICATIONS
LA English
DT Article
DE Many-body physics; Strongly-correlated electrons; Dynamical mean-field
theory; ab-initio calculations
ID 3D(1)-CORRELATED METAL CA1-XSRXVO3; ELECTRONIC-STRUCTURE CALCULATIONS;
OPTICAL-PROPERTIES; WANNIER-FUNCTIONS; BANDWIDTH CONTROL; SYSTEMS;
SPECTRA; SOLVER
AB We present the TRIQS/DFTTools package, an application based on the TRIQS library that connects this toolbox to realistic materials calculations based on density functional theory (DFT). In particular, TRIQS/DFTTools together with TRIQS allows an efficient implementation of DFT plus dynamical mean field theory (DMFT) calculations. It supplies tools and methods to construct Wannier functions and to perform the DMFT self-consistency cycle in this basis set. Post-processing tools, such as band-structure plotting or the calculation of transport properties are also implemented. The package comes with a fully charge self-consistent interface to the Wien2k band structure code, as well as a generic interface that allows to use TRIQS/DFTTools together with a large variety of DFT codes. It is distributed under the GNU General Public License (GPLv3).
Program summary
Program title: TRIQS/DFTTools
Project homepage: https://triqs.ipht.cnrs.fr/applications/dft_tools
Catalogue identifier: AFAF_v1_0
Program summary URL: http://cpc.cs.qub.ac.uk/summaries/AFAF_v1_0.html
Program obtainable from: CPC Program Library, Queen's University, Belfast, N. Ireland
Licensing provisions: GNU General Public License, version 3np
No. of lines in distributed program, including test data, etc.: 164018
No. of bytes in distributed program, including test data, etc.: 4916969
Distribution format: tar.gz
Programming language: Fortran/Python.
Computer: Any architecture with suitable compilers including PCs and clusters.
Operating system: Unix, Linux, OSX.
RAM: Highly problem dependent
Classification: 6.5, 7.3, 7.7, 7.9.
External routines: TRIQS, cmake
Nature of problem: Setting up state-of-the-art methods for an ab initio description of correlated systems from scratch requires a lot of code development. In order to make these calculations possible for a larger community there is need for high-level methods that allow the construction of DFT+DMFT calculations in a modular and efficient way.
Solution method: We present a Fortran/Python open-source computational library that provides high-level abstractions and modules for the combination of DFT with many-body methods, in particular the dynamical mean-field theory. It allows the user to perform fully-fledged DFF+DMFT calculations using simple and short Python scripts.
Running time: Tests take less than a minute; otherwise highly problem dependent. (C) 2016 Elsevier B.V. All rights reserved.
C1 [Aichhorn, Markus; Zingl, Manuel; Kraberger, Gernot J.] NAWI Graz, TU Graz, Inst Theoret & Computat Phys, Petersgasse 16, A-8010 Graz, Austria.
[Pourovskii, Leonid; Seth, Priyanka; Ferrero, Michel] Univ Paris Saclay, Ecole Polytech, CNRS, Ctr Phys Theor, F-91128 Palaiseau, France.
[Pourovskii, Leonid; Ferrero, Michel] Coll France, 11 Pl Marcelin Berthelot, F-75005 Paris, France.
[Seth, Priyanka; Parcollet, Olivier] CEA, CNRS, UMR CNRS 3681, Inst Phys Theor IPhT, F-91191 Gif Sur Yvette, France.
[Vildosola, Veronica] CNEA, GlyA, Dept Fis Mat Condensada, RA-1650 San Martin, Buenos Aires, Argentina.
[Vildosola, Veronica] Consejo Nacl Invest Cient & Tecn, RA-1033 Buenos Aires, DF, Argentina.
[Peil, Oleg E.] Univ Geneva, Dept Quantum Matter Phys, 24 Quai Ernest Ansermet, CH-1211 Geneva 4, Switzerland.
[Deng, Xiaoyu] Rutgers State Univ, Dept Phys & Astron, Piscataway, NJ 08854 USA.
[Mravlje, Jernej] Jozef Stefan Inst, Jamova 39, Ljubljana, Slovenia.
[Martins, Cyril] CNRS, IRSAMC, UMR 5626, Lab Chim & Phys Quant, 118 Route Narbonne, F-31062 Toulouse, France.
[Martins, Cyril] Univ Toulouse UPS, 118 Route Narbonne, F-31062 Toulouse, France.
RP Aichhorn, M (corresponding author), NAWI Graz, TU Graz, Inst Theoret & Computat Phys, Petersgasse 16, A-8010 Graz, Austria.
EM aichhorn@tugraz.at; leonid@cpht.polytechnique.fr; priyanka.seth@cea.fr;
vildosol@tandar.cnea.gov.ar; manuel.zingl@tugraz.at; oleg.peil@unige.ch;
xiaoyu.deng@gmail.com; jernej.mravlje@ijs.si; gkraberger@tugraz.at;
cyril.martins@irsamc.ups-tlse.fr; michel.ferrero@polytechnique.edu;
olivier.parcollet@cea.fr
RI Peil, Oleg/N-4346-2018; Parcollet, Olivier/C-2340-2008; Ferrero,
Michel/Q-3628-2019; Parcollet, Olivier/AAE-2863-2021; Aichhorn,
Markus/L-5872-2013; Pourovskii, Leonid V./F-1764-2015
OI Peil, Oleg/0000-0001-9828-4483; Parcollet, Olivier/0000-0002-0389-2660;
Aichhorn, Markus/0000-0003-1034-5187; Pourovskii, Leonid
V./0000-0003-4003-3539; Ferrero, Michel/0000-0003-1882-2881; Zingl,
Manuel/0000-0002-1890-4812
FU Austrian Science FundAustrian Science Fund (FWF) [Y746, P26220, F04103];
Ministry of Education and Science of the Russian Federation in the
framework of Increase Competitiveness Program of NUST MISTS
[K3-2015-038]; Swiss National Science FoundationSwiss National Science
Foundation (SNSF)European Commission; ERCEuropean Research Council
(ERC)European Commission [617196, 278472]; Slovenian Research Agency
(ARRS)Slovenian Research Agency - Slovenia [P1-0044]; [ECOS-A13E04];
Austrian Science Fund (FWF)Austrian Science Fund (FWF) [P 26220] Funding
Source: researchfish
FX We gratefully acknowledge discussions, comments, and feedback from the
user community, and ongoing collaborations with A. Georges and S.
Biermann. M. Aichhorn, M. Zingl, and G. J. Kraberger acknowledge
financial support from the Austrian Science Fund (Y746, P26220, F04103),
and great hospitality at College de France, Ecole Polytechnique, and CEA
Saclay. L. Pourovskii acknowledges the financial support of the Ministry
of Education and Science of the Russian Federation in the framework of
Increase Competitiveness Program of NUST MISTS (No. K3-2015-038). L.
Pourovskii and V. Vildosola acknowledge financial support from
ECOS-A13E04. O. E. Peil acknowledges support from the Swiss National
Science Foundation (program NCCR-MARVEL). P. Seth acknowledges support
from ERC Grant No. 617196-CorrelMat. O. Parcollet and P. Seth
acknowledge support from ERC Grant No. 278472-MottMetals. J. Mravlje
acknowledges discussions with J. Tomczak and support of Slovenian
Research Agency (ARRS) under Program P1-0044.
CR Aichhorn M, 2011, PHYS REV B, V84, DOI 10.1103/PhysRevB.84.054529
Aichhorn M, 2009, PHYS REV B, V80, DOI 10.1103/PhysRevB.80.085101
Amadon B, 2012, J PHYS-CONDENS MAT, V24, DOI 10.1088/0953-8984/24/7/075604
Ambrosch-Draxl C, 2006, COMPUT PHYS COMMUN, V175, P1, DOI 10.1016/j.cpc.2006.03.005
Andersen OK, 2000, PHYS REV B, V62, P16219, DOI 10.1103/PhysRevB.62.R16219
Anisimov VI, 2005, PHYS REV B, V71, DOI 10.1103/PhysRevB.71.125119
Anisimov VI, 1997, J PHYS-CONDENS MAT, V9, P7359, DOI 10.1088/0953-8984/9/35/010
Anisimov VI, 1997, J PHYS-CONDENS MAT, V9, P767, DOI 10.1088/0953-8984/9/4/002
Assmann E, 2016, COMPUT PHYS COMMUN, V202, P1, DOI 10.1016/j.cpc.2015.12.010
Beach K. S. D., ARXIV0403055
Blaha P., 2001, WIEN2K AUGMENTED PLA
Dang HT, 2015, PHYS REV LETT, V115, DOI 10.1103/PhysRevLett.115.107003
DOUGIER P, 1975, J SOLID STATE CHEM, V14, P247, DOI 10.1016/0022-4596(75)90029-8
Georges A, 1996, REV MOD PHYS, V68, P13, DOI 10.1103/RevModPhys.68.13
Gull E, 2011, REV MOD PHYS, V83, P349, DOI 10.1103/RevModPhys.83.349
Haule K, 2007, PHYS REV B, V75, DOI 10.1103/PhysRevB.75.155113
Haule K, 2010, PHYS REV B, V81, DOI 10.1103/PhysRevB.81.195107
Held K, 2007, ADV PHYS, V56, P829, DOI 10.1080/00018730701619647
Imada M, 1998, REV MOD PHYS, V70, P1039, DOI 10.1103/RevModPhys.70.1039
Inoue IH, 1998, PHYS REV B, V58, P4372, DOI 10.1103/PhysRevB.58.4372
Jarrell M, 1996, PHYS REP, V269, P133, DOI 10.1016/0370-1573(95)00074-7
KHURANA A, 1990, PHYS REV LETT, V64, P1990, DOI 10.1103/PhysRevLett.64.1990
Kokalj A, 2003, COMP MATER SCI, V28, P155, DOI 10.1016/S0927-0256(03)00104-6
Korotin D, 2008, EUR PHYS J B, V65, P91, DOI 10.1140/epjb/e2008-00326-3
Kotliar G, 2006, REV MOD PHYS, V78, P865, DOI 10.1103/RevModPhys.78.865
Kresse G, 1996, PHYS REV B, V54, P11169, DOI 10.1103/PhysRevB.54.11169
Kresse G, 1999, PHYS REV B, V59, P1758, DOI 10.1103/PhysRevB.59.1758
Kunes J, 2010, COMPUT PHYS COMMUN, V181, P1888, DOI 10.1016/j.cpc.2010.08.005
Lechermann F, 2006, PHYS REV B, V74, DOI 10.1103/PhysRevB.74.125120
Makino H, 1998, PHYS REV B, V58, P4384, DOI 10.1103/PhysRevB.58.4384
Martins C, 2011, PHYS REV LETT, V107, DOI 10.1103/PhysRevLett.107.266404
Marzari N, 1997, PHYS REV B, V56, P12847, DOI 10.1103/PhysRevB.56.12847
Mravlje J, 2011, PHYS REV LETT, V106, DOI 10.1103/PhysRevLett.106.096401
Parcollet O, 2015, COMPUT PHYS COMMUN, V196, P398, DOI 10.1016/j.cpc.2015.04.023
Park H, 2014, PHYS REV B, V90, DOI 10.1103/PhysRevB.90.235103
Pchelkina ZV, 2007, PHYS REV B, V75, DOI 10.1103/PhysRevB.75.035122
Pourovskii LV, 2007, PHYS REV B, V76, DOI 10.1103/PhysRevB.76.235101
Rubtsov AN, 2005, PHYS REV B, V72, DOI 10.1103/PhysRevB.72.035122
Seth P, 2016, COMPUT PHYS COMMUN, V200, P274, DOI 10.1016/j.cpc.2015.10.023
Singh DJ, 1994, PLANE WAVES PSEUDOPO
Souza I, 2002, PHYS REV B, V65, DOI 10.1103/PhysRevB.65.035109
Werner P, 2006, PHYS REV B, V74, DOI 10.1103/PhysRevB.74.155107
Werner P, 2006, PHYS REV LETT, V97, DOI 10.1103/PhysRevLett.97.076405
NR 43
TC 57
Z9 57
U1 1
U2 29
PU ELSEVIER SCIENCE BV
PI AMSTERDAM
PA PO BOX 211, 1000 AE AMSTERDAM, NETHERLANDS
SN 0010-4655
EI 1879-2944
J9 COMPUT PHYS COMMUN
JI Comput. Phys. Commun.
PD JUL
PY 2016
VL 204
BP 200
EP 208
DI 10.1016/j.cpc.2016.03.014
PG 9
WC Computer Science, Interdisciplinary Applications; Physics, Mathematical
SC Computer Science; Physics
GA DN7BW
UT WOS:000377231300021
OA Green Accepted
DA 2021-04-21
ER
PT J
AU Jeong, Y
Park, J
Lee, HC
Lee, D
AF Jeong, Yongjin
Park, Jinsu
Lee, Hyun Chul
Lee, Deokjung
TI Equilibrium core design methods for molten salt breeder reactor based on
two-cell model
SO JOURNAL OF NUCLEAR SCIENCE AND TECHNOLOGY
LA English
DT Article
DE MCNP; online processing; molten-salt; MSR; unit-cell
AB Two unit-cell-based core design methods are presented for a molten salt breeder reactor (MSBR) equilibrium core with online reprocessing and refueling: a single-cell method and a two-cell method. The single-cell method adopts a representative single unit cell which has the same fuel-to-moderator volume ratio as the average value of an MSBR core which actually consists of two zones with different ratios. The two-cell method uses two representative unit cells, one for each zone, with each zone having the appropriate fuel-to-moderator ratio. It is demonstrated that the two-cell-based method is able to catch the neutron physics of spectral interaction of the two zones with different neutron energy spectra, whereas the single-cell method cannot accurately predict the breeding ratio nor the resonance escape probability of the MSBR core. A new code system was established using MCNP6/PYTHON script language for modeling of the online reprocessing of molten fuel, and the depletion and online refueling of the MSBR core.
C1 [Jeong, Yongjin; Park, Jinsu; Lee, Deokjung] Ulsan Natl Inst Sci & Technol, School Mech & Nucl Engn, UNIST Gil 50, Ulsan 689798, South Korea.
[Lee, Hyun Chul] Korea Atom Energy Res Inst, 150 Deokjin Dong, Taejon 305353, South Korea.
RP Lee, D (corresponding author), Ulsan Natl Inst Sci & Technol, School Mech & Nucl Engn, UNIST Gil 50, Ulsan 689798, South Korea.
EM deokjung@unist.ac.kr
RI Lee, Deokjung/R-7496-2019
OI Lee, Deokjung/0000-0002-3935-5058
FU National Research Foundation of Korea (NRF) - Korea government (MSIP);
Future Challenge Research Fund of UNIST (Ulsan National Institute of
Science and Technology) [1.140018.01]
FX This work was partially supported by National Research Foundation of
Korea (NRF) grant funded by the Korea government (MSIP). This work was
partially supported by the 2014 Future Challenge Research Fund [Project
no. 1.140018.01] of UNIST (Ulsan National Institute of Science and
Technology).
CR BETTIS ES, 1957, NUCL SCI ENG, V2, P804, DOI 10.13182/NSE57-A35495
HAUBENREICH PN, 1970, NUCL APPL TECHNOL, V8, P118, DOI 10.13182/NT8-2-118
Lewis EE, 2008, FUNDAMENTALS OF NUCLEAR REACTOR PHYSICS, P1
Park J, INT J ENERG RES
Pelowitz DB, 2013, LACP1300634 LOS AL N
Powers JJ, 2013, P M C MAY 5 9 SUN VA
Robertson R.C, 1971, ORNL4541
NR 7
TC 6
Z9 6
U1 0
U2 12
PU TAYLOR & FRANCIS LTD
PI ABINGDON
PA 2-4 PARK SQUARE, MILTON PARK, ABINGDON OR14 4RN, OXON, ENGLAND
SN 0022-3131
EI 1881-1248
J9 J NUCL SCI TECHNOL
JI J. Nucl. Sci. Technol.
PD APR 2
PY 2016
VL 53
IS 4
BP 529
EP 536
DI 10.1080/00223131.2015.1062812
PG 8
WC Nuclear Science & Technology
SC Nuclear Science & Technology
GA DE9SL
UT WOS:000370979000009
OA Green Published
DA 2021-04-21
ER
PT J
AU Grieco, S
Nyanteh, YD
Masson, PJ
AF Grieco, Salvatore
Nyanteh, Yaw D.
Masson, Philippe J.
TI Monte Carlo Design Space Exploration of Superconducting Wind Generator
Using MgB2 and YBCO Conductors
SO IEEE TRANSACTIONS ON APPLIED SUPERCONDUCTIVITY
LA English
DT Article
DE Finite-element analysis; high-temperature superconductivity; MgB2; Monte
Carlo design space exploration; wind turbine generators; YBCO
AB Considering the global outcry over climate change and the need for increased penetration of clean forms of energy in ever-increasing aspects of the modern economic sphere, wind power installations, particularly offshore, are expected to rise in the next decades. However, the main obstacle to this trend will be represented by the high installation costs. In order to reduce these costs, the wind turbines generators require to be manufactured to yield higher torque density with lower material input in terms of weight. One way to pursue this requirement is to employ high-temperature superconductor (HTS) technology for the rotor windings and/or the stator windings, making it possible to design cheaper generators for 10-MW applications. This work considers the impact of HTS conductors in wind turbine generators to complement material research work at the University of Houston HTS material research work. Four topologies of generators were investigated: two partially superconducting generators employing YBCO and MgB2 as conductor for the rotor windings and two fully superconducting generators employing MgB2. The electromagnetic physics governing the machine was simulated by finite-element analysis software, i.e., FlexPDE; then, a numerical analysis of thousands of models was realized by Python. An exploration of the results was carried out making use of an optimum criterion considering a simple aggregation of the generator mass active, the copper, ac stator windings losses, and the amount of superconductor needed. Trends of these optimization criteria with reference to design parameters are shown in detailed plots. According to the specific aims of the designer, the investigation provided interesting clues regarding, among others, the number of pair of poles and the amount of superconductor employed characterizing the best models.
C1 [Grieco, Salvatore] Univ Bologna, I-40126 Bologna, Italy.
[Nyanteh, Yaw D.; Masson, Philippe J.] Univ Houston, Houston, TX 77004 USA.
RP Nyanteh, YD (corresponding author), Univ Houston, Houston, TX 77004 USA.
EM nyantehyaw@gmail.com
CR Bumby J. R., 1983, SUPERCONDUCTING ROTA
Elliott D, 2002, 2002 IEEE POWER ENGINEERING SOCIETY WINTER MEETING, VOLS 1 AND 2, CONFERENCE PROCEEDINGS, P346, DOI 10.1109/PESW.2002.985013
Kalsi SS., 2011, APPL HIGH TEMPERATUR, V1st edn
Nyanteh Y., 2014, P IEEE POW EN SOC GE, P1
Snitchler G, 2011, IEEE T APPL SUPERCON, V21, P1089, DOI 10.1109/TASC.2010.2100341
Umashankar S., 2011, P IEEE IICPE IND, P1
NR 6
TC 3
Z9 3
U1 1
U2 17
PU IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
PI PISCATAWAY
PA 445 HOES LANE, PISCATAWAY, NJ 08855-4141 USA
SN 1051-8223
EI 1558-2515
J9 IEEE T APPL SUPERCON
JI IEEE Trans. Appl. Supercond.
PD APR
PY 2016
VL 26
IS 3
AR 5206405
DI 10.1109/TASC.2016.2524563
PG 5
WC Engineering, Electrical & Electronic; Physics, Applied
SC Engineering; Physics
GA DO6RD
UT WOS:000377910100001
DA 2021-04-21
ER
PT J
AU von Asseldonk, D
Erdmann, M
Fischer, R
Glaser, C
Muller, G
Quast, T
Rieger, M
Urban, M
AF von Asseldonk, D.
Erdmann, M.
Fischer, R.
Glaser, C.
Mueller, G.
Quast, T.
Rieger, M.
Urban, M.
TI The VISPA Internet Platform for Students
SO NUCLEAR AND PARTICLE PHYSICS PROCEEDINGS
LA English
DT Proceedings Paper
CT 37th International Conference on High Energy Physics (ICHEP)
CY JUL 02-09, 2014
CL Valencia, SPAIN
SP Int Union Pure & Appl Phys, Sect C11
DE university; teaching; online; physics education; internet-based
education; cooperative learning
AB The VISPA internet platform enables users to remotely run Python scripts and view resulting plots or inspect their output data. With a standard web browser as the only user requirement on the client-side, the system becomes suitable for blended learning approaches for university physics students. VISPA was used in two consecutive years each by approx. 100 third year physics students at the RWTH Aachen University for their homework assignments. For example, in one exercise students gained a deeper understanding of Einsteins mass-energy relation by analyzing experimental data of electron-positron pairs revealing J/Psi and Z particles. Because the students were free to choose their working hours, only few users accessed the platform simultaneously. The positive feedback from students and the stability of the platform lead to further development of the concept. This year, students accessed the platform in parallel while they analyzed the data recorded by demonstrated experiments live in the lecture hall. The platform is based on experience in the development of professional analysis tools. It combines core technologies from previous projects: an object-oriented C++ library, a modular data-driven analysis flow, and visual analysis steering. We present the platform and discuss its benefits in the context of teaching based on surveys that are conducted each semester.
C1 [von Asseldonk, D.; Erdmann, M.; Fischer, R.; Glaser, C.; Mueller, G.; Quast, T.; Rieger, M.; Urban, M.] Rhein Westfal TH Aachen, Phys Inst A 3, Aachen, Germany.
RP Fischer, R (corresponding author), Rhein Westfal TH Aachen, Phys Inst A 3, Aachen, Germany.
EM rfischer@physik.rwth-aachen.de
OI Erdmann, Martin/0000-0002-1653-1303
CR Bender H., 2002, BLENDED LEARNING EFF
Erdmann M, 2014, EUR J PHYS, V35, DOI 10.1088/0143-0807/35/3/035018
NR 2
TC 1
Z9 1
U1 0
U2 1
PU ELSEVIER SCIENCE BV
PI AMSTERDAM
PA PO BOX 211, 1000 AE AMSTERDAM, NETHERLANDS
SN 2405-6014
EI 1873-3832
J9 NUCL PART PHYS P
JI Nucl. Part. Phys. Proc.
PD APR-JUN
PY 2016
VL 273
BP 2581
EP 2583
DI 10.1016/j.nuclphysbps.2015.09.466
PG 3
WC Physics, Particles & Fields
SC Physics
GA EF4KW
UT WOS:000390295200461
OA Green Published, Other Gold
DA 2021-04-21
ER
PT J
AU Cirio, M
De Liberato, S
Lambert, N
Nori, F
AF Cirio, Mauro
De Liberato, Simone
Lambert, Neill
Nori, Franco
TI Ground State Electroluminescence
SO PHYSICAL REVIEW LETTERS
LA English
DT Article
ID CIRCUIT QUANTUM ELECTRODYNAMICS; ULTRASTRONG-COUPLING REGIME;
LIGHT-MATTER INTERACTION; SUPERCONDUCTING CIRCUITS; PYTHON FRAMEWORK;
ATOM; SYSTEMS; RESONATOR; DYNAMICS; VACUUM
AB Electroluminescence, the emission of light in the presence of an electric current, provides information on the allowed electronic transitions of a given system. It is commonly used to investigate the physics of strongly coupled light-matter systems, whose eigenfrequencies are split by the strong coupling with the photonic field of a cavity. Here we show that, together with the usual electroluminescence, systems in the ultrastrong light-matter coupling regime emit a uniquely quantum radiation when a flow of current is driven through them. While standard electroluminescence relies on the population of excited states followed by spontaneous emission, the process we describe herein extracts bound photons from the dressed ground state and it has peculiar features that unequivocally distinguish it from usual electroluminescence.
C1 [Cirio, Mauro] RIKEN, Interdisciplinary Theoret Sci Res Grp iTHES, 2-1 Hirosawa, Wako, Saitama 3510198, Japan.
[De Liberato, Simone] Univ Southampton, Sch Phys & Astron, Southampton SO17 1BJ, Hants, England.
[Lambert, Neill; Nori, Franco] RIKEN, CEMS, Wako, Saitama 3510198, Japan.
[Nori, Franco] Univ Michigan, Dept Phys, Ann Arbor, MI 48109 USA.
RP Cirio, M (corresponding author), RIKEN, Interdisciplinary Theoret Sci Res Grp iTHES, 2-1 Hirosawa, Wako, Saitama 3510198, Japan.
RI Lambert, Neill W/B-4998-2009; Nori, Franco/B-1222-2009
OI Nori, Franco/0000-0003-3682-7432
FU RIKENiTHES Project; MURI Center for Dynamic Magneto-Optics via the AFOSR
[FA9550-14-1-0040]; IMPACT program of JST; Canon Foundation in
EuropeCanon Foundation; RIKEN iTHES program; FY Incentive Research
Project; Engineering and Physical Sciences Research Council (EPSRC)UK
Research & Innovation (UKRI)Engineering & Physical Sciences Research
Council (EPSRC) [EP/L020335/1]; Engineering and Physical Sciences
Research CouncilUK Research & Innovation (UKRI)Engineering & Physical
Sciences Research Council (EPSRC) [EP/L020335/1] Funding Source:
researchfish
FX The authors thank L. Garziano, S. Savasta, R. Stassi, and A.
Stockklauser for useful and inspirational discussions. This work is
partially supported by the RIKENiTHES Project, the MURI Center for
Dynamic Magneto-Optics via the AFOSR Award No. FA9550-14-1-0040, the
IMPACT program of JST, and a Grant-in-Aid for Scientific Research (A).
M. C. is supported by the Canon Foundation in Europe and the RIKEN iTHES
program. N. L. is partially supported by the FY2015 Incentive Research
Project. S. D. L. is a Royal Society Research Fellow. S. D. L.
acknowledges support from the Engineering and Physical Sciences Research
Council (EPSRC), Research Grant No. EP/L020335/1.
CR Agnesi A, 2009, J PHYS CONF SER, V161, DOI 10.1088/1742-6596/161/1/012028
Anappara AA, 2009, PHYS REV B, V79, DOI 10.1103/PhysRevB.79.201303
Ashhab S, 2009, NEW J PHYS, V11, DOI 10.1088/1367-2630/11/2/023030
Askenazi B, 2014, NEW J PHYS, V16, DOI 10.1088/1367-2630/16/4/043029
Astafiev O, 2007, NATURE, V449, P588, DOI 10.1038/nature06141
Auer A, 2012, PHYS REV B, V85, DOI 10.1103/PhysRevB.85.235140
Baksic A, 2013, PHYS REV A, V87, DOI 10.1103/PhysRevA.87.023813
Bamba M., ARXIV14103912
Baust A., ARXIV14127372
Beaudoin F, 2011, PHYS REV A, V84, DOI 10.1103/PhysRevA.84.043832
Braak D, 2011, PHYS REV LETT, V107, DOI 10.1103/PhysRevLett.107.100401
Breuer HP., 2002, THEORY OPEN QUANTUM
Carusotto I, 2012, PHYS REV A, V85, DOI 10.1103/PhysRevA.85.023805
Ciuti C, 2005, PHYS REV B, V72, DOI 10.1103/PhysRevB.72.115303
Cwik J. A., ARXIV150608974
De Liberato S, 2009, PHYS REV A, V80, DOI 10.1103/PhysRevA.80.053810
De Liberato S, 2007, PHYS REV LETT, V98, DOI 10.1103/PhysRevLett.98.103602
De Liberato S, 2015, PHYS REV B, V92, DOI 10.1103/PhysRevB.92.125433
De Liberato S, 2014, PHYS REV A, V89, DOI 10.1103/PhysRevA.89.017801
De Liberato S, 2014, PHYS REV LETT, V112, DOI 10.1103/PhysRevLett.112.016401
De Liberato S, 2008, PHYS REV B, V77, DOI 10.1103/PhysRevB.77.155321
De Liberato S, 2009, PHYS REV B, V79, DOI 10.1103/PhysRevB.79.075317
Delbecq MR, 2013, NAT COMMUN, V4, DOI 10.1038/ncomms2407
Delbecq MR, 2011, PHYS REV LETT, V107, DOI 10.1103/PhysRevLett.107.256804
Deng GW, 2015, NANO LETT, V15, P6620, DOI 10.1021/acs.nanolett.5b02400
Devoret M, 2007, ANN PHYS-BERLIN, V16, P767, DOI 10.1002/andp.200710261
Dodonov VV, 2006, J PHYS B-AT MOL OPT, V39, pS749, DOI 10.1088/0953-4075/39/15/S20
Faccio D, 2011, EPL-EUROPHYS LETT, V96, DOI 10.1209/0295-5075/96/24006
Feist J, 2015, PHYS REV LETT, V114, DOI 10.1103/PhysRevLett.114.196402
Frey T, 2012, PHYS REV B, V86, DOI 10.1103/PhysRevB.86.115303
Frey T, 2012, PHYS REV LETT, V108, DOI 10.1103/PhysRevLett.108.046807
Galego J, 2015, PHYS REV X, V5, DOI 10.1103/PhysRevX.5.041022
Gambino S, 2014, ACS PHOTONICS, V1, P1042, DOI 10.1021/ph500266d
Garcia-Ripoll JJ, 2015, SCI REP-UK, V5, DOI 10.1038/srep16055
Garziano L, 2014, PHYS REV A, V90, DOI 10.1103/PhysRevA.90.043817
Garziano L, 2013, PHYS REV A, V88, DOI 10.1103/PhysRevA.88.063829
Garziano L, 2015, PHYS REV A, V92, DOI 10.1103/PhysRevA.92.063830
Geiser M, 2012, APPL PHYS LETT, V101, DOI 10.1063/1.4757611
Geiser M, 2012, PHYS REV LETT, V108, DOI 10.1103/PhysRevLett.108.106402
Goryachev M, 2014, PHYS REV APPL, V2, DOI 10.1103/PhysRevApplied.2.054002
Gubbin CR, 2014, APPL PHYS LETT, V104, DOI 10.1063/1.4871271
Gunter G, 2009, NATURE, V458, P178, DOI 10.1038/nature07838
Hauss J, 2008, PHYS REV LETT, V100, DOI 10.1103/PhysRevLett.100.037003
HUTCHISON JA, 2012, ANGEW CHEM, V124, P1624, DOI DOI 10.1002/ANGE.201107033
Hutchison JA, 2013, ADV MATER, V25, P2481, DOI 10.1002/adma.201203682
Johansson JR, 2013, PHYS REV A, V87, DOI 10.1103/PhysRevA.87.043804
Johansson JR, 2013, COMPUT PHYS COMMUN, V184, P1234, DOI 10.1016/j.cpc.2012.11.019
Johansson JR, 2012, COMPUT PHYS COMMUN, V183, P1760, DOI 10.1016/j.cpc.2012.02.021
Johansson JR, 2010, PHYS REV A, V82, DOI 10.1103/PhysRevA.82.052509
Johansson JR, 2009, PHYS REV LETT, V103, DOI 10.1103/PhysRevLett.103.147003
Jouy P, 2010, PHYS REV B, V82, DOI 10.1103/PhysRevB.82.045322
Khalifa AA, 2008, APPL PHYS LETT, V92, DOI 10.1063/1.2844860
Lambert N, 2004, PHYS REV LETT, V92, DOI 10.1103/PhysRevLett.92.073602
Lambert N, 2015, PHYS REV LETT, V115, DOI 10.1103/PhysRevLett.115.216803
Lambert N, 2013, EPL-EUROPHYS LETT, V103, DOI 10.1209/0295-5075/103/17005
Lambrecht A, 1996, PHYS REV LETT, V77, P615, DOI 10.1103/PhysRevLett.77.615
Liu YY, 2015, SCIENCE, V347, P285, DOI 10.1126/science.aaa2501
Liu YY, 2014, PHYS REV LETT, V113, DOI 10.1103/PhysRevLett.113.036801
Lodden GH, 2011, APPL PHYS LETT, V98, DOI 10.1063/1.3599058
Lolli J, 2015, PHYS REV LETT, V114, DOI 10.1103/PhysRevLett.114.183601
Maissen C, 2014, PHYS REV B, V90, DOI 10.1103/PhysRevB.90.205309
McKeever J, 2003, NATURE, V425, P268, DOI 10.1038/nature01974
MU Y, 1992, PHYS REV A, V46, P5944, DOI 10.1103/PhysRevA.46.5944
Muravev VM, 2011, PHYS REV B, V83, DOI 10.1103/PhysRevB.83.075309
Nataf P, 2010, NAT COMMUN, V1, DOI 10.1038/ncomms1069
Nation PD, 2012, REV MOD PHYS, V84, P1, DOI 10.1103/RevModPhys.84.1
Niemczyk T, 2010, NAT PHYS, V6, P772, DOI 10.1038/NPHYS1730
Orgiu E, 2015, NAT MATER, V14, P1123, DOI [10.1038/NMAT4392, 10.1038/nmat4392]
Petersson KD, 2012, NATURE, V490, P380, DOI 10.1038/nature11559
Porer M, 2012, PHYS REV B, V85, DOI 10.1103/PhysRevB.85.081302
Ridolfo A, 2012, PHYS REV LETT, V109, DOI 10.1103/PhysRevLett.109.193602
Rodrigues DA, 2007, PHYS REV LETT, V98, DOI 10.1103/PhysRevLett.98.067204
Sapienza L, 2008, PHYS REV LETT, V100, DOI 10.1103/PhysRevLett.100.136806
Scalari G, 2012, SCIENCE, V335, P1323, DOI 10.1126/science.1216022
Schwartz T, 2011, PHYS REV LETT, V106, DOI 10.1103/PhysRevLett.106.196405
Stassi R, 2013, PHYS REV LETT, V110, DOI 10.1103/PhysRevLett.110.243601
Stassi R., ARXIV150909064
Stassi R, 2015, PHYS REV A, V92, DOI 10.1103/PhysRevA.92.013830
Stockklauser A, 2015, PHYS REV LETT, V115, DOI 10.1103/PhysRevLett.115.046802
Toida H, 2013, PHYS REV LETT, V110, DOI 10.1103/PhysRevLett.110.066802
Tsintzos SI, 2008, NATURE, V453, P372, DOI 10.1038/nature06979
Viennot JJ, 2014, PHYS REV B, V89, DOI 10.1103/PhysRevB.89.165404
Wallraff A, 2013, PHYS REV LETT, V111, DOI 10.1103/PhysRevLett.111.249701
Wilson CM, 2011, NATURE, V479, P376, DOI 10.1038/nature10561
Xiang ZL, 2013, REV MOD PHYS, V85, P623, DOI 10.1103/RevModPhys.85.623
You JQ, 2007, PHYS REV B, V75, DOI 10.1103/PhysRevB.75.104516
You JQ, 2011, NATURE, V474, P589, DOI 10.1038/nature10122
You JQ, 2005, PHYS TODAY, V58, P42, DOI 10.1063/1.2155757
Zhang XF, 2014, PHYS REV LETT, V113, DOI 10.1103/PhysRevLett.113.156401
NR 89
TC 44
Z9 44
U1 0
U2 40
PU AMER PHYSICAL SOC
PI COLLEGE PK
PA ONE PHYSICS ELLIPSE, COLLEGE PK, MD 20740-3844 USA
SN 0031-9007
EI 1079-7114
J9 PHYS REV LETT
JI Phys. Rev. Lett.
PD MAR 17
PY 2016
VL 116
IS 11
AR 113601
DI 10.1103/PhysRevLett.116.113601
PG 7
WC Physics, Multidisciplinary
SC Physics
GA DG9VU
UT WOS:000372432700007
PM 27035302
OA Bronze, Green Accepted
DA 2021-04-21
ER
PT J
AU Spitznagel, B
Pritchett, PR
Messina, TC
Goadrich, M
Rodriguez, J
AF Spitznagel, Benjamin
Pritchett, Paige R.
Messina, Troy C.
Goadrich, Mark
Rodriguez, Juan
TI An undergraduate laboratory activity on molecular dynamics simulations
SO BIOCHEMISTRY AND MOLECULAR BIOLOGY EDUCATION
LA English
DT Article
DE molecular dynamics; simulation; modeling; biophysics;
structure-function; curricular enhancement; undergraduate laboratory
activity
ID PROTEINS
AB Vision and Change [AAAS, 2011] outlines a blueprint for modernizing biology education by addressing conceptual understanding of key concepts, such as the relationship between structure and function. The document also highlights skills necessary for student success in 21st century Biology, such as the use of modeling and simulation. Here we describe a laboratory activity that allows students to investigate the dynamic nature of protein structure and function through the use of a modeling technique known as molecular dynamics (MD). The activity takes place over two lab periods that are 3 hr each. The first lab period unpacks the basic approach behind MD simulations, beginning with the kinematic equations that all bioscience students learn in an introductory physics course. During this period students are taught rudimentary programming skills in Python while guided through simple modeling exercises that lead up to the simulation of the motion of a single atom. In the second lab period students extend concepts learned in the first period to develop skills in the use of expert MD software. Here students simulate and analyze changes in protein conformation resulting from temperature change, solvation, and phosphorylation. The article will describe how these activities can be carried out using free software packages, including Abalone and VMD/NAMD. (c) 2016 by The International Union of Biochemistry and Molecular Biology, 44:130-139, 2016.
C1 [Spitznagel, Benjamin; Rodriguez, Juan] St Louis Coll Pharm, Dept Basic Sci, St Louis, MO 63110 USA.
[Pritchett, Paige R.] Centenary Coll Louisiana, Dept Biophys, Shreveport, LA 71104 USA.
[Messina, Troy C.] Berea Coll, Dept Phys, Berea, KY 40404 USA.
[Goadrich, Mark] Hendrix Coll, Dept Math & Comp Sci, Conway, AR 72032 USA.
RP Rodriguez, J (corresponding author), St Louis Coll Pharm, Dept Basic Sci, St Louis, MO 63110 USA.
EM juan.rodriguez@stlcop.edu
FU Creative Teaching Fund grant from St. Louis College of Pharmacy
FX This project was supported in part by a Creative Teaching Fund grant
from the St. Louis College of Pharmacy awarded to B.S. and J.R.
CR ALDER BJ, 1959, J CHEM PHYS, V31, P459, DOI 10.1063/1.1730376
American Association for the Advancement of Science, 2011, VIS CANG UND BIOL ED
Chiang H, 2013, BIOCHEM MOL BIOL EDU, V41, P402, DOI 10.1002/bmb.20737
Cochran AG, 2001, P NATL ACAD SCI USA, V98, P5578, DOI 10.1073/pnas.091100898
Comer J, 2014, J CHEM THEORY COMPUT, V10, P5276, DOI 10.1021/ct500874p
Durrant JD, 2011, BMC BIOL, V9, DOI 10.1186/1741-7007-9-71
Henzler-Wildman K, 2007, NATURE, V450, P964, DOI 10.1038/nature06522
Honda S, 2004, STRUCTURE, V12, P1507, DOI 10.1016/j.str.2004.05.022
Humphrey W, 1996, J MOL GRAPH MODEL, V14, P33, DOI 10.1016/0263-7855(96)00018-5
KOSHLAND DE, 1966, BIOCHEMISTRY-US, V5, P365, DOI 10.1021/bi00865a047
MONOD J, 1965, J MOL BIOL, V12, P88, DOI 10.1016/S0022-2836(65)80285-6
Pelaez NJ, 2005, ADV PHYSIOL EDUC, V29, P172, DOI 10.1152/advan.00022.2004
Phillips JC, 2005, J COMPUT CHEM, V26, P1781, DOI 10.1002/jcc.20289
SWOPE WC, 1982, J CHEM PHYS, V76, P637, DOI 10.1063/1.442716
NR 14
TC 3
Z9 3
U1 0
U2 17
PU WILEY
PI HOBOKEN
PA 111 RIVER ST, HOBOKEN 07030-5774, NJ USA
SN 1470-8175
EI 1539-3429
J9 BIOCHEM MOL BIOL EDU
JI Biochem. Mol. Biol. Educ.
PD MAR-APR
PY 2016
VL 44
IS 2
BP 130
EP 139
DI 10.1002/bmb.20939
PG 10
WC Biochemistry & Molecular Biology; Education, Scientific Disciplines
SC Biochemistry & Molecular Biology; Education & Educational Research
GA DH7XT
UT WOS:000373008100004
PM 26751047
OA Bronze
DA 2021-04-21
ER
PT J
AU Moon, C
Yoshinuma, M
Emoto, M
Ida, K
AF Moon, Chanho
Yoshinuma, Mikirou
Emoto, Masahiko
Ida, Katsumi
TI MyView2, a new visualization software tool for analysis of LHD data
SO FUSION ENGINEERING AND DESIGN
LA English
DT Article
DE LHD; MyView2; Python; Data visualization software; Portable structure
ID REMOTE PARTICIPATION; TECHNOLOGY
AB The Large Helical Device (LHD) at the National Institute for Fusion Science (NIFS) is the world's largest superconducting helical fusion device, providing a scientific research center to elucidate important physics research such as plasma transport, turbulence dynamics, and other topics. Furthermore, many types of advanced diagnostic devices are used to measure the confinement plasma characteristics, and these valuable physical data are registered over the 131,000 discharges in the LHD database. However, it is difficult to investigate the experimental data even though much physical data has been registered. In order to improve the efficiency for investigating plasma physics in LHD, we have developed a new data visualization software, MyView2, which consists of Python-based modules that can be easily set up and updated. MyView2 provides immediate access to experimental results, cross-shot analysis, and a collaboration point for scientific research. In particular, the MyView2 software is a portable structure for making viewable LHD experimental data in on- and off-site web servers, which is a capability not previously available in any general use tool. We will also discuss the benefits of using the MyView2 software for in-depth analysis of LHD experimental data. (C) 2016 Elsevier B.V. All rights reserved.
C1 [Moon, Chanho; Yoshinuma, Mikirou; Emoto, Masahiko; Ida, Katsumi] Natl Inst Fus Sci, 322-6 Oroshi, Toki, Gifu 5095292, Japan.
RP Moon, C (corresponding author), Natl Inst Fus Sci, 322-6 Oroshi, Toki, Gifu 5095292, Japan.
EM moon@nifs.ac.jp
RI MOON, Chanho/AAJ-1235-2021; Ida, Katsumi/E-4731-2016
OI MOON, Chanho/0000-0002-0929-774X; Ida, Katsumi/0000-0002-0585-4561
FU JSPS JapanMinistry of Education, Culture, Sports, Science and
Technology, Japan (MEXT)Japan Society for the Promotion of Science
[15H02336]
FX The authors acknowledge all the members of the LHD Experiment Group for
their assistance. This work is supported by a Grant-in-Aid for
Scientific Research (No. 15H02336) of JSPS Japan.
CR Bracco G, 1999, FUSION ENG DES, V43, P425, DOI 10.1016/S0920-3796(98)00414-1
Davis WM, 2012, FUSION ENG DES, V87, P2229, DOI 10.1016/j.fusengdes.2012.04.013
Emoto M, 2006, FUSION ENG DES, V81, P2051, DOI 10.1016/j.fusengdes.2006.04.004
Emoto M, 2006, FUSION ENG DES, V81, P2019, DOI 10.1016/j.fusengdes.2006.04.030
Emoto M, 2014, FUSION ENG DES, V89, P758, DOI 10.1016/j.fusengdes.2014.02.014
Emoto M, 2010, FUSION ENG DES, V85, P622, DOI 10.1016/j.fusengdes.2010.03.065
Emoto M, 2002, FUSION ENG DES, V60, P367, DOI 10.1016/S0920-3796(02)00034-0
Emoto M, 2012, FUSION ENG DES, V87, P2076, DOI 10.1016/j.fusengdes.2012.02.127
Forrest RA, 2001, FUSION ENG DES, V54, P387, DOI 10.1016/S0920-3796(00)00557-3
Hunter JD, 2007, COMPUT SCI ENG, V9, P90, DOI 10.1109/MCSE.2007.55
Lunt T, 2010, NUCL INSTRUM METH A, V623, P812, DOI 10.1016/j.nima.2010.04.150
Moon C., 2015, J PLASMA FUSION RES, V10
Motojima O, 2003, NUCL FUSION, V43, P1674, DOI 10.1088/0029-5515/43/12/013
Nagayama Y, 2012, FUSION ENG DES, V87, P2218, DOI 10.1016/j.fusengdes.2012.09.012
Schachter J, 2000, FUSION ENG DES, V48, P91, DOI 10.1016/S0920-3796(00)00139-3
Suzuki C, 2013, PLASMA PHYS CONTR F, V55, DOI 10.1088/0741-3335/55/1/014016
NR 16
TC 2
Z9 2
U1 0
U2 7
PU ELSEVIER SCIENCE SA
PI LAUSANNE
PA PO BOX 564, 1001 LAUSANNE, SWITZERLAND
SN 0920-3796
EI 1873-7196
J9 FUSION ENG DES
JI Fusion Eng. Des.
PD MAR
PY 2016
VL 104
BP 56
EP 60
DI 10.1016/j.fusengdes.2016.01.076
PG 5
WC Nuclear Science & Technology
SC Nuclear Science & Technology
GA DH3GI
UT WOS:000372675100008
DA 2021-04-21
ER
PT J
AU Seth, P
Krivenko, I
Ferrero, M
Parcollet, O
AF Seth, Priyanka
Krivenko, Igor
Ferrero, Michel
Parcollet, Olivier
TI TRIQS/CTHYB: A continuous-time quantum Monte Carlo hybridisation
expansion solver for quantum impurity problems
SO COMPUTER PHYSICS COMMUNICATIONS
LA English
DT Article
DE Many-body physics; Impurity solvers; Strongly-correlated systems; DMFT;
Monte Carlo; C plus; Python
ID MEAN-FIELD THEORY; SYSTEMS; MODELS
AB We present TRIQS/CTHYB, a state-of-the art open-source implementation of the continuous-time hybridisation expansion quantum impurity solver of the TRIQS package. This code is mainly designed to be used with the TRIQS library in order to solve the self-consistent quantum impurity problem in a multi-orbital dynamical mean field theory approach to strongly-correlated electrons, in particular in the context of realistic electronic structure calculations. It is implemented in C++ for efficiency and is provided with a high-level Python interface. The code ships with a new partitioning algorithm that divides the local Hilbert space without any user knowledge of the symmetries and quantum numbers of the Hamiltonian. Furthermore, we implement higher-order configuration moves and show that such moves are necessary to ensure ergodicity of the Monte Carlo in common Hamiltonians even without symmetry-breaking.
Program summary
Program title: TRIQS/CTHYB
Catalogue identifier: AEYU_v1_0
Program summary URL: http://cpc.cs.qub.ac.uk/summaries/AEYU_v1_0.html
Program obtainable from: CPC Program Library, Queen's University, Belfast, N. Ireland.
Licensing provisions: GNU General Public Licence (GPLv3)
No. of lines in distributed program, including test data, etc.: 159,017
No. of bytes in distributed program, including test data, etc.: 10,215,893
Distribution format: tar.gz
Programming language: C++/Python.
Computer: Any architecture with suitable compilers including PCs and clusters.
Operating system: Unix, Linux, OSX.
RAM: Highly problem-dependent
Classification: 7.3, 4.4.
External routines: TRIQS, cmake.
Nature of problem: Accurate solvers for quantum impurity problems are needed in condensed matter theory.
Solution method: We present an efficient C++/Python open -source implementation of a continuous-time hybridisation expansion solver.
Running time: Tests take less than a minute. Otherwise it is highly problem dependent (from minutes to several days). (C) 2015 Elsevier B.V. All rights reserved.
C1 [Seth, Priyanka; Ferrero, Michel] Ecole Polytech, CNRS, F-91128 Palaiseau, France.
[Seth, Priyanka; Parcollet, Olivier] CEA, CNRS, UMR 3681, IPhT, F-91191 Gif Sur Yvette, France.
[Krivenko, Igor] Univ Hamburg, Inst Theoret Phys 1, Jungiusstr 9, D-20355 Hamburg, Germany.
[Ferrero, Michel] Coll France, 11 Pl Marcelin Berthelot, F-75005 Paris, France.
RP Seth, P (corresponding author), CEA, CNRS, UMR 3681, IPhT, F-91191 Gif Sur Yvette, France.
EM priyanka.seth@cea.fr; ikrivenk@physnet.uni-hamburg.de;
michel.ferrero@polytechnique.edu; olivier.parcollet@cea.fr
RI Ferrero, Michel/Q-3628-2019; Parcollet, Olivier/C-2340-2008; Parcollet,
Olivier/AAE-2863-2021
OI Parcollet, Olivier/0000-0002-0389-2660; Ferrero,
Michel/0000-0003-1882-2881; Seth, Priyanka/0000-0001-8280-8430
FU ERCEuropean Research Council (ERC)European Commission
[278472-MottMetals, 617196-CorrelMat]; Deutsche
ForschungsgemeinschaftGerman Research Foundation (DFG) [SFB 668-A3]
FX We thank S. Biermann, E. Gull and P. Werner for useful discussions, and
M. Aichhorn, B. Amadon, T. Ayral, P. Delange, P. Hansmann, M. Harland,
L. Pourovskii, W. Rowe, M. Zingl for their feedback on the code. The
TRIQS and TRILIS/CTHYB projects are supported by the ERC Grant No.
278472-MottMetals. I. K. acknowledges support from Deutsche
Forschungsgemeinschaft via Project SFB 668-A3. P. S. acknowledges
support from ERC Grant No. 617196-CorrelMat.
CR Antipov A.E., 2015, POMEROL 1 1
Augustinsky P, 2013, COMPUT PHYS COMMUN, V184, P2119, DOI 10.1016/j.cpc.2013.04.005
Boehnke L, 2011, PHYS REV B, V84, DOI 10.1103/PhysRevB.84.075145
Georges A, 1996, REV MOD PHYS, V68, P13, DOI 10.1103/RevModPhys.68.13
Gull E, 2008, EPL-EUROPHYS LETT, V82, DOI 10.1209/0295-5075/82/57003
Gull E., 2008, THESIS ETH ZURICH
Gull E, 2011, REV MOD PHYS, V83, P349, DOI 10.1103/RevModPhys.83.349
Haule K, 2007, PHYS REV B, V75, DOI 10.1103/PhysRevB.75.155113
Huang L, 2015, COMPUT PHYS COMMUN, V195, P140, DOI 10.1016/j.cpc.2015.04.020
Kotliar G, 2006, REV MOD PHYS, V78, P865, DOI 10.1103/RevModPhys.78.865
Maier T, 2005, REV MOD PHYS, V77, P1027, DOI 10.1103/RevModPhys.77.1027
Parcollet O, 2015, COMPUT PHYS COMMUN, V196, P398, DOI 10.1016/j.cpc.2015.04.023
Parragh N, 2012, PHYS REV B, V86, DOI 10.1103/PhysRevB.86.155158
Rubtsov AN, 2012, ANN PHYS-NEW YORK, V327, P1320, DOI 10.1016/j.aop.2012.01.002
Rubtsov AN, 2008, PHYS REV B, V77, DOI 10.1103/PhysRevB.77.033101
Rubtsov AN, 2005, PHYS REV B, V72, DOI 10.1103/PhysRevB.72.035122
Sedgewick R., ALGORITHMS 1
Sedgewick R., 2008, LEFT LEANING R UNPUB
Sedgewick R., 2011, ALGORITHMS
Semon P, 2014, PHYS REV B, V90, DOI 10.1103/PhysRevB.90.075149
Semon P, 2014, PHYS REV B, V89, DOI 10.1103/PhysRevB.89.165113
TARJAN RE, 1975, J ACM, V22, P215, DOI 10.1145/321879.321884
Toschi A, 2007, PHYS REV B, V75, DOI 10.1103/PhysRevB.75.045118
Werner P, 2006, PHYS REV B, V74, DOI 10.1103/PhysRevB.74.155107
Werner P, 2006, PHYS REV LETT, V97, DOI 10.1103/PhysRevLett.97.076405
Yee C.-H., 2012, THESIS RUTGERS U
NR 26
TC 99
Z9 99
U1 2
U2 30
PU ELSEVIER SCIENCE BV
PI AMSTERDAM
PA PO BOX 211, 1000 AE AMSTERDAM, NETHERLANDS
SN 0010-4655
EI 1879-2944
J9 COMPUT PHYS COMMUN
JI Comput. Phys. Commun.
PD MAR
PY 2016
VL 200
BP 274
EP 284
DI 10.1016/j.cpc.2015.10.023
PG 11
WC Computer Science, Interdisciplinary Applications; Physics, Mathematical
SC Computer Science; Physics
GA DC8EE
UT WOS:000369451900024
DA 2021-04-21
ER
PT J
AU Lin, JYY
Smith, HL
Granroth, GE
Abernathy, DL
Lumsden, MD
Winn, B
Aczel, AA
Aivazis, M
Fultz, B
AF Lin, Jiao Y. Y.
Smith, Hillary L.
Granroth, Garrett E.
Abernathy, Douglas L.
Lumsden, Mark D.
Winn, Barry
Aczel, Adam A.
Aivazis, Michael
Fultz, Brent
TI MCViNE - An object oriented Monte Carlo neutron ray tracing simulation
package
SO NUCLEAR INSTRUMENTS & METHODS IN PHYSICS RESEARCH SECTION A-ACCELERATORS
SPECTROMETERS DETECTORS AND ASSOCIATED EQUIPMENT
LA English
DT Article
DE Neutron scattering; Monte Carlo simulation; Ray-tracing; Inelastic;
Spectrometry
ID EUROPEAN SPALLATION SOURCE; SCATTERING EXPERIMENTS; INSTRUMENT;
RESOLUTION; MCSTAS; GUIDE; SPECTROMETER; PERFORMANCE; VANADIUM; PROGRAM
AB MCViNE (Monte-Carlo Virtual Neutron Experiment) is an open-source Monte Carlo (MC) neutron ray tracing software for performing computer modeling and simulations that mirror real neutron scattering experiments. We exploited the close similarity between how instrument components are designed and operated and how such components can be modeled in software. For example we used object oriented programming concepts for representing neutron scatterers and detector systems, and recursive algorithms for implementing multiple scattering. Combining these features together in MCViNE allows one to handle sophisticated neutron scattering problems in modern instruments, including, for example, neutron detection by complex detector systems, and single and multiple scattering events in a variety of samples and sample environments. In addition, MCViNE can use simulation components from linear-chain-based MC ray tracing packages which facilitates porting instrument models from those codes. Furthermore it allows for components written solely in Python, which expedites prototyping of new components. These developments have enabled detailed simulations of neutron scattering experiments, with non-trivial samples, for time-of-flight inelastic instruments at the Spallation Neutron Source. Examples of such simulations for powder and single-crystal samples with various scattering kernels, including kernels for phonon and magnon scattering, are presented. With simulations that closely reproduce experimental results, scattering mechanisms can be turned on and off to determine how they contribute to the measured scattering intensities, improving our understanding of the underlying physics. (C) 2015 Elsevier B.V. All rights reserved.
C1 [Lin, Jiao Y. Y.; Aivazis, Michael] CALTECH, Caltech Ctr Adv Comp Res, Pasadena, CA 91125 USA.
[Lin, Jiao Y. Y.; Smith, Hillary L.; Fultz, Brent] CALTECH, Dept Appl Phys & Mat Sci, Pasadena, CA 91125 USA.
[Lin, Jiao Y. Y.; Granroth, Garrett E.] Oak Ridge Natl Lab, Neutron Data Anal & Visualizat Div, Oak Ridge, TN 37831 USA.
[Abernathy, Douglas L.; Lumsden, Mark D.; Winn, Barry; Aczel, Adam A.] Oak Ridge Natl Lab, Quantum Condensed Matter Div, Oak Ridge, TN USA.
RP Lin, JYY (corresponding author), CALTECH, Caltech Ctr Adv Comp Res, Pasadena, CA 91125 USA.; Fultz, B (corresponding author), CALTECH, Dept Appl Phys & Mat Sci, Pasadena, CA 91125 USA.; Granroth, GE (corresponding author), Oak Ridge Natl Lab, Neutron Data Anal & Visualizat Div, Oak Ridge, TN 37831 USA.
EM linjiao@ornl.gov; granrothge@ornl.gov; btf@caltech.edu
RI Lumsden, Mark/F-5366-2012; Lin, Jiao/A-2529-2016; Abernathy, Douglas
L/A-3038-2012; Granroth, Garrett E/G-3576-2012; BL18, ARCS/A-3000-2012;
Aczel, Adam/A-6247-2016
OI Lumsden, Mark/0000-0002-5472-9660; Lin, Jiao/0000-0001-9233-0100;
Abernathy, Douglas L/0000-0002-3533-003X; Granroth, Garrett
E/0000-0002-7583-8778; Aczel, Adam/0000-0003-1964-1943; Smith,
Hillary/0000-0001-6155-7812
FU NSFNational Science Foundation (NSF) [DMR-0520547]; U. S. Department of
Energy, Office of Basic Energy SciencesUnited States Department of
Energy (DOE); Scientific User Facilities DivisionUnited States
Department of Energy (DOE)
FX The development of the MCViNE software was begun by J.Y.Y.L. under the
DANSE project supported by the NSF award DMR-0520547. The research on
simulations of experiments in the ARCS, SEQUOIA, and HYSPEC instruments
was supported by the U. S. Department of Energy, Office of Basic Energy
Sciences. G.E.G., A. A.A., D.L.A., M.D.L., B.A. were fully supported,
J.Y.Y.L. and H.L.S. partially supported by the Scientific User
Facilities Division. We thank M. E. Hagen, A. Payzant, and P. Willendrup
for stimulating discussions. We also thank L. Li and A. Dementsov for
developing the powder diffraction scattering kernel for MCViNE, A. Fang
for building MCViNE adaptations of some McStas components, and M. Reuter
and S. Campbell for updating the MANTID code to read in the Monte Carlo
generated data.
CR Abernathy DL, 2012, REV SCI INSTRUM, V83, DOI 10.1063/1.3680104
Aczel AA, 2012, NAT COMMUN, V3, DOI 10.1038/ncomms2117
Alianelli L, 2004, NUCL INSTRUM METH A, V529, P231, DOI 10.1016/j.nima.2004.04.161
Arnold O, 2014, NUCL INSTRUM METH A, V764, P156, DOI 10.1016/j.nima.2014.07.029
Bertelsen M, 2013, NUCL INSTRUM METH A, V729, P387, DOI 10.1016/j.nima.2013.07.062
BISCHOFF FG, 1970, THESIS RENSSELAER PO
Boin M, 2011, J APPL CRYSTALLOGR, V44, P1040, DOI 10.1107/S0021889811025970
Boin M, 2012, J APPL CRYSTALLOGR, V45, P603, DOI 10.1107/S0021889812016056
Brown P.J., 1983, INT TABLES CRYSTALLO, P391
COLELLA R, 1970, PHYS REV B, V1, P3913, DOI 10.1103/PhysRevB.1.3913
COPLEY JRD, 1986, COMPUT PHYS COMMUN, V40, P337, DOI 10.1016/0010-4655(86)90118-9
Daemen L.L., SPIE P, V3771
Dederichs P., 1981, PHONON STATES ALLOYS, V1
Delaire O, 2011, P NATL ACAD SCI USA, V108, P4725, DOI 10.1073/pnas.1014869108
Ehlers G, 2011, REV SCI INSTRUM, V82, DOI 10.1063/1.3626935
Farhi E, 2014, J NEUTRON RES, V17, P63, DOI 10.3233/JNR-130007
Farhi E, 2009, J COMPUT PHYS, V228, P5251, DOI 10.1016/j.jcp.2009.04.006
Farhi E., 2011, COLLECTION SFN, V12, P303
Friemel G, 2013, J PHYS CONF SER, V449, DOI 10.1088/1742-6596/449/1/012016
Gamma E., 1995, DESIGN PATTERNS ELEM
Ghali S, 2008, INTRO GEOMETRIC COMP
GILAT G, 1966, PHYS REV, V143, P487, DOI 10.1103/PhysRev.143.487
Granroth GE, 2006, PHYSICA B, V385-86, P1104, DOI 10.1016/j.physb.2006.05.379
Granroth GE, 2010, J PHYS CONF SER, V251, DOI 10.1088/1742-6596/251/1/012058
Granroth G.E., 2003, P ICANS 16, P289
Granroth GE, 2007, J NEUTRON RES, V15, P91, DOI 10.1080/10238160601046092
GUPTA OP, 1978, NUOVO CIMENTO B, V45, P255, DOI 10.1007/BF02894684
HAGEN M, COMMUNICATION
Hahn SE, 2014, PHYS REV B, V89, DOI 10.1103/PhysRevB.89.014420
Houben A, 2012, NUCL INSTRUM METH A, V680, P124, DOI 10.1016/j.nima.2012.03.015
Hugouvieux V, 2004, PHYSICA B, V350, P151, DOI 10.1016/j.physb.2004.04.015
Hugouvieux V, 2007, PHYS REV B, V75, DOI 10.1103/PhysRevB.75.104208
Izaola Z, 2010, J PHYS CONF SER, V251, DOI 10.1088/1742-6596/251/1/012064
Klinkby EB, 2014, J PHYS CONF SER, V528, DOI 10.1088/1742-6596/528/1/012032
Kresch M, 2008, PHYS REV B, V77, DOI 10.1103/PhysRevB.77.024301
Kynde S, 2014, NUCL INSTRUM METH A, V764, P133, DOI 10.1016/j.nima.2014.06.084
Lee WT, 2002, APPL PHYS A-MATER, V74, pS1502, DOI 10.1007/s003390201723
Lefmann K, 2008, J NEUTRON RES, V16, P97, DOI 10.1080/10238160902819684
Lefmann K., 1999, NEUTRON NEWS, V10, P20, DOI DOI 10.1080/10448639908233684
Li CW, 2014, PHYS REV LETT, V112, DOI 10.1103/PhysRevLett.112.175501
Lin JYY, 2014, PHYS REV B, V89, DOI 10.1103/PhysRevB.89.144302
Lumsden MD, 2006, PHYS REV B, V74, DOI 10.1103/PhysRevB.74.214424
Maradudin A., 1971, THEORY LATTICE DYNAM, V2nd ed.
MASON TE, SPALLATION NEUTRON S
Niedziela J.L., HIGH TEMPERATU UNPUB
Perring T.G., 1991, HIGH ENERGY MAGNETIC
Plumb KW, 2014, PHYS REV B, V89, DOI 10.1103/PhysRevB.89.180410
Proffen T, 1997, J APPL CRYSTALLOGR, V30, P171, DOI 10.1107/S002188989600934X
Proffen T, 1999, J APPL CRYSTALLOGR, V32, P838, DOI [10.1107/S0021889899004860, DOI 10.1107/S0021889899004860]
Prokhnenko O, 2014, NUCL INSTRUM METH A, V764, P30, DOI 10.1016/j.nima.2014.07.013
Riedel RA, 2012, NUCL INSTRUM METH A, V664, P366, DOI 10.1016/j.nima.2011.08.038
Saroun J, 1997, PHYSICA B, V234, P1102, DOI 10.1016/S0921-4526(97)00037-9
Sears VF, 1992, NEUTRON NEWS, V3, P26, DOI [10.1080/10448639208218770, DOI 10.1080/10448639208218770]
Shirane G., 2002, NEUTRON SCATTERING T
Skoulatos M, 2011, NUCL INSTRUM METH A, V647, P100, DOI 10.1016/j.nima.2011.05.037
Stone MB, 2014, REV SCI INSTRUM, V85, DOI 10.1063/1.4870050
Tang XL, 2010, PHYS REV B, V82, DOI 10.1103/PhysRevB.82.184301
Taylor J., B AM PHYS SOC, V57
Tregenna-Piggott PLW, 2008, J NEUTRON RES, V16, P13, DOI 10.1080/10238160802348446
Udby L, 2011, NUCL INSTRUM METH A, V634, pS138, DOI 10.1016/j.nima.2010.06.235
Walters AC, 2009, NAT PHYS, V5, P867, DOI 10.1038/NPHYS1405
Weber F, 2012, PHYS REV LETT, V109, DOI 10.1103/PhysRevLett.109.057001
Wildes AR, 2002, APPL PHYS A-MATER, V74, pS1452, DOI 10.1007/s003390101243
Willendrup P.K., COMPONENT MANUAL NEU
Willendrup P, 2004, PHYSICA B, V350, pE735, DOI 10.1016/j.physb.2004.03.193
Willendrup PK, 2011, NUCL INSTRUM METH A, V634, pS150, DOI 10.1016/j.nima.2010.06.212
Winn B., EPJ WEB C
Wuttke J, 2000, PHYS REV E, V62, P6531, DOI 10.1103/PhysRevE.62.6531
Young R., IUCR MONOGRAPH CRYST, V5
Zsigmond G., 2002, NEUTRON NEWS, V13, P11, DOI [10.1080/10448630208218488, DOI 10.1080/10448630208218488]
NR 70
TC 22
Z9 22
U1 0
U2 27
PU ELSEVIER SCIENCE BV
PI AMSTERDAM
PA PO BOX 211, 1000 AE AMSTERDAM, NETHERLANDS
SN 0168-9002
EI 1872-9576
J9 NUCL INSTRUM METH A
JI Nucl. Instrum. Methods Phys. Res. Sect. A-Accel. Spectrom. Dect. Assoc.
Equip.
PD FEB 21
PY 2016
VL 810
BP 86
EP 99
DI 10.1016/j.nima.2015.11.118
PG 14
WC Instruments & Instrumentation; Nuclear Science & Technology; Physics,
Nuclear; Physics, Particles & Fields
SC Instruments & Instrumentation; Nuclear Science & Technology; Physics
GA DB6NY
UT WOS:000368632900015
OA Bronze, Green Accepted
DA 2021-04-21
ER
PT J
AU Shen, C
Qiu, Z
Song, HC
Bernhard, J
Bass, S
Heinz, U
AF Shen, Chun
Qiu, Zhi
Song, Huichao
Bernhard, Jonah
Bass, Steffen
Heinz, Ulrich
TI The iEBE-VISHNU code package for relativistic heavy-ion collisions
SO COMPUTER PHYSICS COMMUNICATIONS
LA English
DT Article
DE Relativistic heavy-ion collision; Relativistic viscous hydrodynamics;
Quark-gluon plasma
ID CHARGED-PARTICLE MULTIPLICITY; COLOR GLASS CONDENSATE;
EQUATION-OF-STATE; PB COLLISIONS; DYNAMICS; FLOW; DEPENDENCE; MODEL;
HYDRODYNAMICS; FIELD
AB The iEBE-VISHNU code package performs event-by-event simulations for relativistic heavy-ion collisions using a hybrid approach based on (2 + 1)-dimensional viscous hydrodynamics coupled to a hadronic cascade model. We present the detailed model implementation, accompanied by some numerical code tests for the package. iEBE-VISHNU forms the core of a general theoretical framework for model-data comparisons through large scale Monte-Carlo simulations. A numerical interface between the hydrodynamically evolving medium and thermal photon radiation is also discussed. This interface is more generally designed for calculations of all kinds of rare probes that are coupled to the temperature and flow velocity evolution of the bulk medium, such as jet energy loss and heavy quark diffusion.
Program summary
Program title: iEBE-VISHNU
Catalogue identifier: AEYA_v1_0
Program summary URL: http://cpc.cs.qub.ac.uk/summaries/AEYA_v1_0.html
Program obtainable from: CPC Program Library, Queen's University, Belfast, N. Ireland
Licensing provisions: Standard CPC licence, http://cpc.cs.qub.ac.uk/licence/licence.html
No. of lines in distributed program, including test data, etc.: 5257939
No. of bytes in distributed program, including test data, etc.: 262822421
Distribution format: tar.gz
Programming language: Fortran, C++, python, bash, SQLite.
Computer: Laptop, desktop, cluster.
Operating system: Tested on GNU/Linux Ubuntu 12.04 x64, Red Hat Linux 6, Mac OS X 10.8+.
RAM: 2G bytes
Classification: 17.11, 17.16, 17.20.
External routines: GNU Scientific Library (GSL), HDF5 (Fortran and C++ enabled), Numpy
Nature of problem: Relativistic heavy-ion collisions are tiny in size (V approximate to 10(-42) m(3)) and live in a flash (similar to 5 x 10(-23) s). It is impossible to use external probes to study the properties of the quark-gluon plasma (QGP), a novel state of matter created during the collisions. Experiments can only measure the momentum information of stable hadrons, who are the remnants of the collisions. In order to extract the thermal and transport properties of the QGP one needs to rely on Monte-Carlo event-by-event model simulations, which reverse-engineer the experimental measurements to the early time dynamics of the relativistic heavy-ion collisions.
Solution method: Relativistic heavy-ion collisions contain multiple stages of evolution. The physics that governs each stage is implemented into individual code components. A general driver script glues all the modular packages as a whole to perform large-scale Monte-Carlo simulations. The final results are stored into SQLite database, which supports standard querying for massive data analysis. By tuning transport coefficients of the QGP as free parameters, e.g. the specific shear viscosity eta/s, we can constrain various transport properties of the QGP through model-data comparisons.
Additional comments: !!!!! The distribution file for this program is over 260 Mbytes and therefore is not delivered directly when download or Email is requested. Instead a html file giving details of how the program can be obtained is sent. !!!!!
Running time: The following running time is tested on a laptop computer with a 2.4 GHz Intel Core i5 CPU, 4 GB memory. All the C++ and Fortran codes are compiled with the GNU Compiler Collection (GCC) 4.9.2 and -03 optimization (Table 1). (C) 2015 Elsevier B.V. All rights reserved.
C1 [Shen, Chun; Qiu, Zhi; Song, Huichao; Heinz, Ulrich] Ohio State Univ, Dept Phys, Columbus, OH 43210 USA.
[Shen, Chun] McGill Univ, Dept Phys, Montreal, PQ H3A 2T8, Canada.
[Song, Huichao] Peking Univ, Dept Phys, Beijing 100871, Peoples R China.
[Song, Huichao] Peking Univ, State Key Lab Nucl Phys & Technol, Beijing 100871, Peoples R China.
[Bernhard, Jonah; Bass, Steffen] Duke Univ, Dept Phys, Durham, NC 27708 USA.
RP Shen, C (corresponding author), Ohio State Univ, Dept Phys, 174 W 18th Ave, Columbus, OH 43210 USA.
EM chunshen@physics.mcgill.ca; heinz@mps.ohio-state.edu
RI Bass, Steffen A./AAB-9800-2020
OI Bass, Steffen A./0000-0002-9451-0954; Heinz, Ulrich/0000-0003-3941-7789;
Shen, Chun/0000-0002-6677-4784; Bernhard, Jonah/0000-0003-2959-954X
FU U.S. Department of Energy, Office of Science, Office of Nuclear
PhysicsUnited States Department of Energy (DOE) [DE-SC0004286,
DE-FG02-05ER41367, DE-SC0004104]; Natural Sciences and Engineering
Research Council of CanadaNatural Sciences and Engineering Research
Council of Canada (NSERC)CGIAR
FX This work was supported in part by the U.S. Department of Energy, Office
of Science, Office of Nuclear Physics, under Award Nos. DE-SC0004286,
DE-FG02-05ER41367, and (within the framework of the JET Collaboration)
DE-SC0004104, and in part by the Natural Sciences and Engineering
Research Council of Canada. We gratefully acknowledge important
contributions by Scott Moreland during the early stages of the
development of the superMC module.
CR Abelev B, 2014, PHYS REV C, V90, DOI 10.1103/PhysRevC.90.054901
AICHELIN J, 1988, PHYS REV C, V37, P2451, DOI 10.1103/PhysRevC.37.2451
Akamatsu Y, 2014, J COMPUT PHYS, V256, P34, DOI 10.1016/j.jcp.2013.08.047
Albacete J. L., ARXIV10115161
Albacete JL, 2013, NUCL PHYS A, V897, P1, DOI 10.1016/j.nuclphysa.2012.09.012
Alver B., ARXIV08054411
ANSORGE RE, 1989, Z PHYS C PART FIELDS, V43, P357
Back BB, 2002, PHYS REV C, V65, DOI 10.1103/PhysRevC.65.061901
Barnett RM, 1996, PHYS REV D, V54, P1, DOI 10.1103/PhysRevD.54.1
Bass SA, 1998, PROG PART NUCL PHYS, V41, P255, DOI 10.1016/S0146-6410(98)00058-1
Bazavov A, 2014, PHYS REV D, V90, DOI 10.1103/PhysRevD.90.094503
Belkacem M, 1998, PHYS REV C, V58, P1727, DOI 10.1103/PhysRevC.58.1727
Bernhard JE, 2015, PHYS REV C, V91, DOI 10.1103/PhysRevC.91.054910
Bleicher M, 1999, J PHYS G NUCL PARTIC, V25, P1859, DOI 10.1088/0954-3899/25/9/308
Borsanyi S, 2014, PHYS LETT B, V730, P99, DOI 10.1016/j.physletb.2014.01.007
Bozek P, 2013, PHYS REV C, V88, DOI 10.1103/PhysRevC.88.014903
Bozek P, 2012, PHYS REV C, V85, DOI 10.1103/PhysRevC.85.034901
Bozek P, 2012, PHYS REV C, V85, DOI 10.1103/PhysRevC.85.014911
Bozek P, 2010, PHYS REV C, V81, DOI 10.1103/PhysRevC.81.034909
Broniowski W, 2009, COMPUT PHYS COMMUN, V180, P69, DOI 10.1016/j.cpc.2008.07.016
Chatrchyan S, 2014, J HIGH ENERGY PHYS, DOI 10.1007/JHEP02(2014)088
Chatrchyan S, 2013, PHYS LETT B, V724, P213, DOI 10.1016/j.physletb.2013.06.028
Chojnacki M, 2007, ACTA PHYS POL B, V38, P3249
DANIELEWICZ P, 1991, NUCL PHYS A, V533, P712, DOI 10.1016/0375-9474(91)90541-D
Del Zanna L, 2013, EUR PHYS J C, V73, DOI 10.1140/epjc/s10052-013-2524-5
Dusling K, 2008, PHYS REV C, V77, DOI 10.1103/PhysRevC.77.034905
Dusling K, 2011, NUCL PHYS A, V850, P69, DOI 10.1016/j.nuclphysa.2010.11.009
Epelbaum T, 2013, PHYS REV LETT, V111, DOI 10.1103/PhysRevLett.111.232301
Filip P, 2008, PHYS ATOM NUCL+, V71, P1609, DOI 10.1134/S1063778808090172
Filip P, 2009, PHYS REV C, V80, DOI 10.1103/PhysRevC.80.054903
Gale C, 2013, PHYS REV LETT, V110, DOI 10.1103/PhysRevLett.110.012302
Goldschmidt A., ARXIV150703910
Goldschmidt A., ARXIV150200603
Gubser SS, 2011, NUCL PHYS B, V846, P469, DOI 10.1016/j.nuclphysb.2011.01.012
Gubser SS, 2010, PHYS REV D, V82, DOI 10.1103/PhysRevD.82.085027
Habich M, 2015, EUR PHYS J C, V75, DOI 10.1140/epjc/s10052-014-3206-7
Heinz, ARXIVNUCLTH0305084
Heinz U, 2014, NUCL PHYS A, V932, P310, DOI 10.1016/j.nuclphysa.2014.07.020
Heinz U, 2013, J PHYS CONF SER, V455, DOI 10.1088/1742-6596/455/1/012044
Heinz U, 2013, PHYS REV C, V87, DOI 10.1103/PhysRevC.87.034913
Heinz U, 2011, PHYS REV C, V84, DOI 10.1103/PhysRevC.84.054905
Heinz U, 2008, J PHYS G NUCL PARTIC, V35, DOI 10.1088/0954-3899/35/10/104126
Hirano T, 2013, PROG PART NUCL PHYS, V70, P108, DOI 10.1016/j.ppnp.2013.02.002
Hirano T, 2004, NUCL PHYS A, V743, P305, DOI 10.1016/j.nuclphysa.2004.08.003
Hirano T, 2002, PHYS REV C, V66, DOI 10.1103/PhysRevC.66.054905
Hirano T, 2009, PHYS REV C, V79, DOI 10.1103/PhysRevC.79.064904
Holopainen H, 2011, PHYS REV C, V83, DOI 10.1103/PhysRevC.83.034901
Huovinen P, 2010, NUCL PHYS A, V837, P26, DOI 10.1016/j.nuclphysa.2010.02.015
Karpenko I, 2014, COMPUT PHYS COMMUN, V185, P3016, DOI 10.1016/j.cpc.2014.07.010
Khachatryan V, 2011, J HIGH ENERGY PHYS, DOI 10.1007/JHEP01(2011)079
Kharzeev D, 2005, PHYS REV C, V71, DOI 10.1103/PhysRevC.71.054903
Kharzeev D, 2005, NUCL PHYS A, V747, P609, DOI 10.1016/j.nuclphysa.2004.10.018
Kharzeev D, 2001, PHYS LETT B, V507, P121, DOI 10.1016/S0370-2693(01)00457-9
Kovchegov Y. V, 2012, CAMBRIDGE MONOGRAPHS
KRUSE H, 1985, PHYS REV C, V31, P1770, DOI 10.1103/PhysRevC.31.1770
Liu J, 2015, PHYS REV C, V91, DOI 10.1103/PhysRevC.91.064906
Loizides C., ARXIV14082549
Luzum M, 2009, PHYS REV C, V79, DOI 10.1103/PhysRevC.79.039903
Luzum M, 2008, PHYS REV C, V78, DOI 10.1103/PhysRevC.78.034915
Marrochio H, 2015, PHYS REV C, V91, DOI 10.1103/PhysRevC.91.014903
Molnar E, 2014, PHYS REV C, V90, DOI 10.1103/PhysRevC.90.044904
Niemi H, 2012, PHYS REV C, V86, DOI 10.1103/PhysRevC.86.014909
Niemi H., ARXIV14047327
Noronha-Hostler J, 2013, PHYS REV C, V88, DOI 10.1103/PhysRevC.88.044916
Pang LG, 2015, PHYS REV D, V91, DOI 10.1103/PhysRevD.91.074027
Pang LG, 2012, PHYS REV C, V86, DOI 10.1103/PhysRevC.86.024911
Plumberg C, 2015, PHYS REV C, V91, DOI 10.1103/PhysRevC.91.054905
Qiu Z, 2012, PHYS LETT B, V707, P151, DOI 10.1016/j.physletb.2011.12.041
Ryu S., ARXIV150201675
Schenke B, 2012, PHYS REV LETT, V108, DOI 10.1103/PhysRevLett.108.252301
Schenke B, 2011, PHYS REV LETT, V106, DOI 10.1103/PhysRevLett.106.042301
Shen C., 2013, P 6 INT C HARD EL PR
Shen C., ARXIV150407989
Shen C, 2015, PHYS REV C, V92, DOI 10.1103/PhysRevC.92.014901
Shen C, 2015, PHYS REV C, V91, DOI 10.1103/PhysRevC.91.024908
Shen C, 2014, NUCL PHYS A, V931, P675, DOI 10.1016/j.nuclphysa.2014.08.030
Shen C, 2014, PHYS REV C, V89, DOI 10.1103/PhysRevC.89.044910
Shen C, 2010, PHYS REV C, V82, DOI 10.1103/PhysRevC.82.054904
Song HC, 2008, PHYS REV C, V77, DOI 10.1103/PhysRevC.77.064901
Song HC, 2011, PHYS REV C, V83, DOI 10.1103/PhysRevC.83.024912
Song HC, 2014, PHYS REV C, V89, DOI 10.1103/PhysRevC.89.034919
Song HC, 2008, PHYS REV C, V78, DOI 10.1103/PhysRevC.78.024902
SORGE H, 1989, ANN PHYS-NEW YORK, V192, P266, DOI 10.1016/0003-4916(89)90136-X
YARIV Y, 1979, PHYS REV C, V20, P2227, DOI 10.1103/PhysRevC.20.2227
NR 84
TC 170
Z9 175
U1 1
U2 27
PU ELSEVIER
PI AMSTERDAM
PA RADARWEG 29, 1043 NX AMSTERDAM, NETHERLANDS
SN 0010-4655
EI 1879-2944
J9 COMPUT PHYS COMMUN
JI Comput. Phys. Commun.
PD FEB
PY 2016
VL 199
BP 61
EP 85
DI 10.1016/j.cpc.2015.08.039
PG 25
WC Computer Science, Interdisciplinary Applications; Physics, Mathematical
SC Computer Science; Physics
GA CZ4ZZ
UT WOS:000367113200009
OA Bronze
DA 2021-04-21
ER
PT S
AU Pelupessy, I
van Werkhoven, B
van Elteren, A
Viebahn, J
Candy, A
Zwart, SP
Dijkstra, H
AF Pelupessy, Inti
van Werkhoven, Ben
van Elteren, Arjen
Viebahn, Jan
Candy, Adam
Zwart, Simon Portegies
Dijkstra, Henk
GP IEEE
TI OMUSE: Oceanographic Multipurpose Software Environment
SO PROCEEDINGS OF THE 2016 IEEE 12TH INTERNATIONAL CONFERENCE ON E-SCIENCE
(E-SCIENCE)
SE Proceeding IEEE International Conference on e-Science (e-Science)
LA English
DT Proceedings Paper
CT 12th IEEE International Conference on e-Science (e-Science)
CY OCT 23-27, 2016
CL Baltimore, MD
SP IEEE, Inst Date Intens Engn & Sci, Microsoft, Gordon & Betty Moore Fdn, Alfred P Sloan Fdn
ID OCEAN; EDDIES
AB This talk will give a brief introduction to OMUSE, the Oceanographic Multipurpose Software Environment, which is currently being developed. OMUSE is a Python framework that provides high-level object-oriented interfaces to existing or newly developed numerical ocean simulation codes, simplifying their use and development. In this way, OMUSE facilitates the efficient design of numerical experiments that combine ocean models representing different physics or spanning different ranges of physical scales, for example coupling a global open ocean simulation with a regional coastal ocean model. OMUSE enables its users to write high-level Python scripts that describe simulations. The functionality provided by OMUSE takes care of the low-level integration with the code and deploying simulations on high-performance computing resources, allowing its users to focus on the physics of the simulation. We give an overview of the design of OMUSE and the modules and model components currently included. In particular, we will discuss the process of creating a new OMUSE interface to an existing code, and explain how OMUSE keeps track of the internal state of a running simulation. In addition, we will discuss the grid data types and grid remapping functionality that OMUSE provides. We also give an example of performing online data analysis on a running simulation, which is becoming increasingly important as models simulate a broader range of scales, generating large datasets that cannot be fully stored for offline analysis.
C1 [Pelupessy, Inti; Viebahn, Jan; Dijkstra, Henk] Inst Marine & Atmospher Res Utrecht, Utrecht, Netherlands.
[van Werkhoven, Ben] Netherlands eSci Ctr, Amsterdam, Netherlands.
[Pelupessy, Inti; van Elteren, Arjen; Zwart, Simon Portegies] Leiden Univ, Leiden Observ, Leiden, Netherlands.
[Candy, Adam] Delft Univ Technol, Delft, Netherlands.
RP Pelupessy, I (corresponding author), Inst Marine & Atmospher Res Utrecht, Utrecht, Netherlands.; Pelupessy, I (corresponding author), Leiden Univ, Leiden Observ, Leiden, Netherlands.
OI Candy, Adam/0000-0002-0132-6833
FU Netherlands eScience Center [027.013.701]
FX The ABC-MUSE project is funded by Netherlands eScience Center (file
number 027.013.701, 2013-2016).
CR CDO, 2015, CLIM DAT OP
Drost N, 2012, IEEE SYM PARA DISTR, P150, DOI 10.1109/IPDPSW.2012.14
Gregersen JB, 2007, J HYDROINFORM, V9, P175, DOI 10.2166/hydro.2007.023
Griffies SM, 2015, J CLIMATE, V28, P952, DOI 10.1175/JCLI-D-14-00353.1
Hill C, 2004, COMPUT SCI ENG, V6, P18, DOI 10.1109/MCISE.2004.1255817
Jones PW, 1999, MON WEATHER REV, V127, P2204, DOI 10.1175/1520-0493(1999)127<2204:FASOCR>2.0.CO;2
Larson J, 2005, INT J HIGH PERFORM C, V19, P277, DOI 10.1177/1094342005056115
Le Bars D, 2012, J PHYS OCEANOGR, V42, P1158, DOI 10.1175/JPO-D-11-0119.1
Maltrud M, 2010, ENVIRON FLUID MECH, V10, P275, DOI 10.1007/s10652-009-9154-3
Mason E, 2014, J ATMOS OCEAN TECH, V31, P1181, DOI 10.1175/JTECH-D-14-00019.1
Peckham SD, 2013, COMPUT GEOSCI-UK, V53, P3, DOI 10.1016/j.cageo.2012.04.002
Pelupessy FI, 2013, ASTRON ASTROPHYS, V557, DOI 10.1051/0004-6361/201321252
Seinstra F. J., 2011, GRIDS CLOUDS VIRTUAL, P167
Smith RD, 2010, LAUR1001853 LOS AL N
Stocker T.F, 2013, CLIMATE CHANGE 2013
Valcke S, 2013, GEOSCI MODEL DEV, V6, P373, DOI 10.5194/gmd-6-373-2013
van Werkhoven B, 2014, GEOSCI MODEL DEV, V7, P267, DOI 10.5194/gmd-7-267-2014
Viebahn J, 2014, INT J BIFURCAT CHAOS, V24, DOI 10.1142/S0218127414300079
Viebahn J, 2010, OCEAN MODEL, V34, P150, DOI 10.1016/j.ocemod.2010.05.005
Zijlema M, 2010, COAST ENG, V57, P267, DOI 10.1016/j.coastaleng.2009.10.011
Zwart SFP, 2013, COMPUT PHYS COMMUN, V184, P456, DOI 10.1016/j.cpc.2012.09.024
NR 21
TC 1
Z9 1
U1 0
U2 0
PU IEEE
PI NEW YORK
PA 345 E 47TH ST, NEW YORK, NY 10017 USA
SN 2325-372X
BN 978-1-5090-4273-9
J9 P IEEE INT C E-SCI
PY 2016
BP 399
EP 399
PG 1
WC Computer Science, Interdisciplinary Applications; Computer Science,
Theory & Methods
SC Computer Science
GA BI1CU
UT WOS:000405564400046
DA 2021-04-21
ER
PT B
AU Telenta, M
Kos, L
Akers, R
AF Telenta, M.
Kos, L.
Akers, Robert
CA EUROfusion MST1 Team
BE Snoj, L
Lengar, I
TI CAD Data Storage and Access in IDAM
SO 25TH INTERNATIONAL CONFERENCE NUCLEAR ENERGY FOR NEW EUROPE, (NENE 2016)
LA English
DT Proceedings Paper
CT 25th International Conference Nuclear Energy for New Europe (NENE)
CY SEP 05-08, 2016
CL Portoroz, SLOVENIA
SP NEK, GEN Energija, elmont, SIPRO Inzeniring, Inst Nucl Technol, Westinghouse, NUMIP, ELES, GEN I, ENCONET d o o, IDOM, SFA, QTECHNA, LKB, EiMV, APoSS, Nukel, ZEL EN, European Nucl Soc
AB Integrated Data Access Management (IDAM) is a data access tool for analysis, visualisation, and modelling. It is developed at CCFE for MAST-U data access. Data access is based on data objects from within the files. IDAM is also put capable. Metadata from raw and analysed files is written to the IDAM database. The IDAM server uses a plugin architecture for each data resource type. The goal of the presented work is to build a workflow which will access and eventually store CAD data through IDAM. CAD data in IDAM has a single source, i.e. it is stored in a single location as CATIA files. A first step is to build a data resource which will include metadata and a catalogue design. This step includes categorising the resources and recording them to the IDAM database. The data resource will provide CAD data in a STEP format for two workflows. The first workflow is an engineering one, in which more complex 3D models are required in STEP format to be read by COMSOL and ANSYS and perform, for example, electro-magnetic numerical simulations. The second workflow is a scientific one, in which 2D axisymmetric section-cuts are performed on CAD models with different levels of detail. The section cut is performed for a specific angle phi from the horizontal axis. These section-cuts are then converted/exported by a python plug-in to VTK/XML and used by the equilibrium physics code, EFIT++ developed by General Atomic enhanced at CCFE.. Attention is given to the definition of a structure in the STEP file in order to locate different components needed for the code. Generally, it is better to have one file to ensure the data provenance of the workflow. Better efficiency could be achieved with a zero-copy approach type movement, such that no unnecessary copying of the STEP file is done. The second step is to develop an IDAM server plugin to get/put metadata for CAD data objects into an object store. This will serve the data through the plug-in. The IDAM storage includes collection and cataloguing of the metadata during the CAD data handling. This ensures that the CAD data provenance tracking and capture are together with other objects available in the IDAM.
[GRAPHICS]
.
C1 [Telenta, M.; Kos, L.] Univ Ljubljana, Fac Mech Engn, Askerceva 6, Ljubljana 1000, Slovenia.
[Akers, Robert; EUROfusion MST1 Team] Culham Sci Ctr, CCFE, Abingdon OX14 3DB, Oxon, England.
RP Telenta, M (corresponding author), Univ Ljubljana, Fac Mech Engn, Askerceva 6, Ljubljana 1000, Slovenia.
EM marijo.telenta@lecad.fs.uni-lj.si; leon.kos@lecad.fs.uni-lj.si;
rob.akers@ukaea.uk
FU Euratom research and training programme [633053, AWP15-EEG-JSI/Telenta]
FX This work has been carried out within the framework of the EUROfusion
Consortium and has received funding from the Euratom research and
training programme 2014-2018 under grant agreement No. 633053, task
agreement AWP15-EEG-JSI/Telenta. The views and opinions expressed herein
do not necessarily reflect those of the European Commission. Also, the
authors would like to acknowledge Ivan Lupelli, David. G. Muir, and
Jason Hess from CCFE for helpful discussions.
CR Firdaouss M, 2013, J NUCL MATER, V438, pS536, DOI 10.1016/j.jnucmat.2013.01.111
Lupelli I, 2015, FUSION ENG DES, V96-97, P835, DOI 10.1016/j.fusengdes.2015.04.016
Muir DG, 2008, FUSION ENG DES, V83, P406, DOI 10.1016/j.fusengdes.2007.12.018
Muir D. G., 2015, DATA ACCESS ASPIRATI
Muir D. G., 2015, ITER IMAS AND IDAM
NR 5
TC 0
Z9 0
U1 1
U2 1
PU NUCLEAR SOCIETY SLOVENIA
PI 1001 LJUBLJANA
PA JAMOVA 39, 1001 LJUBLJANA, SLOVENIA
BN 978-961-6207-40-9
PY 2016
PG 8
WC Nuclear Science & Technology
SC Nuclear Science & Technology
GA BH2RT
UT WOS:000399192400065
DA 2021-04-21
ER
PT S
AU Slupski, P
Wymyslowski, A
Czarczynski, W
AF Slupski, Piotr
Wymyslowski, Artur
Czarczynski, Wojciech
BE Swatowska, B
Maziarz, W
Pisarkiewicz, T
Kucewicz, W
TI Electromagnetic field patterning or crystal light
SO ELECTRON TECHNOLOGY CONFERENCE 2016
SE Proceedings of SPIE
LA English
DT Proceedings Paper
CT 12th Conference on Electron Technology (ELTE)
CY SEP 11-14, 2016
CL Wisla, POLAND
SP AGH Univ Sci & Technol, Fac Comp Sci, Elect, Dept Elect, Fitech Sp z o o, COMEF Sci & Res Equipment Sp z o o
DE photon; patterning; QED; photonics; vacuum; gyrotron; vircator
ID GENERATION; VIRCATOR
AB Using the orbital angular momentum of light for the development of a vortex interferometer, the underlying physics requires microwave/RF models,1 as well as quantum mechanics for light1,2 and fluid flow for semiconductor devices.3,4 The combination of the aforementioned physical models yields simulations and results such as optical lattices,1 or an Inverse Farday effect.5 The latter is explained as the absorption of optical angular momentum, generating extremely high instantenous magnetic fields due to radiation friction. An algorithmic reduction across the computational methods used in microwaves, lasers, quantum optics and holography is performed in order to explain electromagnetic field interactions in a single computational framework. This work presents a computational model for photon-electron interactions, being a simplified gauge theory described using differentials or disturbances (photons) instead of integrals or fields. The model is based on treating the Z-axis variables as a Laplace fluid with spatial harmonics, and the XY plane as Maxwell's equations on boundaries. The result is a unified, coherent, graphical computational method of describing the photon qualitatively, quantitatively and with proportion. The model relies on five variables and is described using two equations, which use emitted power, cavity wavelength, input frequency, phase and time. Phase is treated as a rotated physical dimension under gauge theory of Feynmann's QED. In essence, this model allows the electromagnetic field to be treated with it's specific crystallography. The model itself is described in Python programming language.
C1 [Slupski, Piotr; Wymyslowski, Artur] Wroclaw Univ Technol, Microsyst Elect & Photon, Wroclaw, Poland.
[Czarczynski, Wojciech] Wroclaw Univ Technol, Elect Fac, Wroclaw, Poland.
RP Slupski, P (corresponding author), Wroclaw Univ Technol, Microsyst Elect & Photon, Wroclaw, Poland.
EM piotr.slupski@pwr.edu.pl; artur.wymyslowski@pwr.edu.pl
CR Alyokhin B. V., 1994, PLASMA SCI IEEE T, V22, P945
Baryshevsky V, 2013, IEEE T PLASMA SCI, V41, P2712, DOI 10.1109/TPS.2013.2275022
Czarczynski W., 1971, LAMMU MIKROFALOWE
Elfsberg M., 2007, EXPT STUDIES ANODE C, P484
GABOR D, 1948, NATURE, V161, P777, DOI 10.1038/161777a0
GERCHBERG RW, 1972, OPTIK, V35, P237
Ghai DP, 2009, OPT COMMUN, V282, P2692, DOI 10.1016/j.optcom.2009.04.032
Gillmour A. J., 2013, KLYSTRONS TRAVELING, V53
Jinseo L., 2004, J KOREAN PHYS SOC, V45
Kurtsiefer C, 2000, PHYS REV LETT, V85, P290, DOI 10.1103/PhysRevLett.85.290
Liseykina TV, 2016, NEW J PHYS, V18, DOI 10.1088/1367-2630/18/7/072001
Moll PJW, 2016, SCIENCE, V351, P1061, DOI 10.1126/science.aac8385
Munk B. A., 2004, FREQUENCY SELECTIVE
Olson H.F., 1943, DYNAMICAL ANALOGIES
Schultz H., 1993, ELECT BEAM WELDING
Schungel E, 2016, J PHYS D APPL PHYS, V49, DOI 10.1088/0022-3727/49/26/265203
SULLIVAN DJ, 1983, IEEE T NUCL SCI, V30, P3426, DOI 10.1109/TNS.1983.4336679
Van Mechelen T, 2016, OPTICA, V3, P118, DOI 10.1364/OPTICA.3.000118
Xiao H., 2016, 160501315 ARXIV
Yang S., 2015, SCI REPORTS, P2045
NR 20
TC 0
Z9 0
U1 0
U2 5
PU SPIE-INT SOC OPTICAL ENGINEERING
PI BELLINGHAM
PA 1000 20TH ST, PO BOX 10, BELLINGHAM, WA 98227-0010 USA
SN 0277-786X
BN 978-1-5106-0843-6; 978-1-5106-0844-3
J9 PROC SPIE
PY 2016
VL 10175
AR UNSP 101750X
DI 10.1117/12.2260445
PG 6
WC Engineering, Electrical & Electronic; Optics
SC Engineering; Optics
GA BG7NB
UT WOS:000391493400033
DA 2021-04-21
ER
PT S
AU Cieszewski, R
Linczuk, M
AF Cieszewski, Radoslaw
Linczuk, Maciej
BE Romaniuk, RS
TI RPython High-Level Synthesis
SO PHOTONICS APPLICATIONS IN ASTRONOMY, COMMUNICATIONS, INDUSTRY, AND
HIGH-ENERGY PHYSICS EXPERIMENTS 2016
SE Proceedings of SPIE
LA English
DT Proceedings Paper
CT Conference on Photonics Applications in Astronomy, Communications,
Industry, and High-Energy Physics Experiments
CY MAY 29-JUN 06, 2016
CL Wilga, POLAND
SP SPIE, Warsaw Univ Technol, Fac Elect & Informat Technologies, Inst Elect Syst, Photon Soc Poland, Polish Acad Sci, Comm Elect & Telecommunicat, Enhanced European Coordinat Accelerator R&D, Assoc Polish Elect Engineers, Polish Comm Optoelectron, EuroFus Collaborat, EuroFus Poland
DE High-Level Synthesis; Rpython; FPGA; Compiler; Algorithmic Synthesis;
Behavioral Synthesis; Hot Plasma Physics Experiment; Python
AB The development of FPGA technology and the increasing complexity of applications in recent decades have forced compilers to move to higher abstraction levels. Compilers interprets an algorithmic description of a desired behavior written in High-Level Languages (HLLs) and translate it to Hardware Description Languages (HDLs). This paper presents a RPython based High-Level synthesis (HLS) compiler. The compiler get the configuration parameters and map RPython program to VHDL. Then, VHDL code can be used to program FPGA chips. In comparison of other technologies usage, FPGAs have the potential to achieve far greater performance than software as a result of omitting the fetch-decode-execute operations of General Purpose Processors (GPUs), and introduce more parallel computation. This can be exploited by utilizing many resources at the same time. Creating parallel algorithms computed with FPGAs in pure HDL is difficult and time consuming. Implementation time can be greatly reduced with High-Level Synthesis compiler. This article describes design methodologies and tools, implementation and first results of created VHDL backend for RPython compiler.
C1 [Cieszewski, Radoslaw; Linczuk, Maciej] Warsaw Univ Technol, Inst Elect Syst, Nowowiejska 15-19, PL-00665 Warsaw, Poland.
RP Cieszewski, R (corresponding author), Warsaw Univ Technol, Inst Elect Syst, Nowowiejska 15-19, PL-00665 Warsaw, Poland.
EM R.Cieszewski@stud.elka.pw.edu.pl
CR Asanovic K, 2009, COMMUN ACM, V52, P56, DOI 10.1145/1562764.1562783
Babb J., 1999, Seventh Annual IEEE Symposium on Field-Programmable Custom Computing Machines (Cat. No.PR00375), P70, DOI 10.1109/FPGA.1999.803669
Berdychowski PP, 2010, PHOTONICS APPL ASTRO
Bowyer B., 2005, EETIMES
Bujnowski K., 2007, PHOTONICS APPL ASTRO
Cieszewski R., 2015, PHOTONICS APPL ASTRO, V9662
Cong J, 2011, IEEE T COMPUT AID D, V30, P473, DOI 10.1109/TCAD.2011.2110592
Coussy P., 2008, HIGH LEVEL SYNTHESIS
Coussy P, 2009, IEEE DES TEST COMPUT, V26, P8, DOI 10.1109/MDT.2009.69
GAJSKI DD, 1994, IEEE DES TEST COMPUT, V11, P44, DOI 10.1109/54.329454
Gajski DD, 1992, HIGH LEVEL SYNTHESIS, V34
Gokhale M, 1997, 5TH ANNUAL IEEE SYMPOSIUM ON FIELD-PROGRAMMABLE CUSTOM COMPUTING MACHINES, P165, DOI 10.1109/FPGA.1997.624616
Kolasinski P., 2007, PHOTONICS APPL ASTRO
Liang Y, 2012, J ELECTR COMPUT ENG, V2012, DOI 10.1155/2012/649057
Meredith M., 2004, EETIMES, P04
Philippe C., 2008, EURASIP J EMBEDDED S, V2008
Pozniak K., 2013, PHOTONICS APPL ASTRO, V8903
Zabolotny W. M., 2011, PHOTONICS APPL ASTRO
Zabolotny WM, 2003, P SOC PHOTO-OPT INS, V5125, P223, DOI 10.1117/12.531581
Zabolotny WM, 2010, PHOTONICS APPL ASTRO
Zabolotny WM, 2011, PROC SPIE, V8008, DOI 10.1117/12.905281
Zabolotny WM, 2006, PROC SPIE, V6347, DOI 10.1117/12.714532
NR 22
TC 0
Z9 0
U1 0
U2 1
PU SPIE-INT SOC OPTICAL ENGINEERING
PI BELLINGHAM
PA 1000 20TH ST, PO BOX 10, BELLINGHAM, WA 98227-0010 USA
SN 0277-786X
EI 1996-756X
BN 978-1-5106-0485-8
J9 PROC SPIE
PY 2016
VL 10031
AR UNSP 100314O
DI 10.1117/12.2249143
PG 6
WC Astronomy & Astrophysics; Engineering, Electrical & Electronic; Optics;
Physics, Particles & Fields
SC Astronomy & Astrophysics; Engineering; Optics; Physics
GA BF9PJ
UT WOS:000385793100168
DA 2021-04-21
ER
PT B
AU Sachan, UGPS
Rajan, SS
Malhotra, S
Satyamurthy, P
AF Sachan, Udai Giri Pratap Singh
Rajan, S. Sundar
Malhotra, Sanjay
Satyamurthy, P.
GP IEEE
TI Quench Analysis, Detection & Protection of 5-Tesla Superconducting
Solenoid Magnet using Numerical Methods
SO 2016 IEEE FIRST INTERNATIONAL CONFERENCE ON CONTROL, MEASUREMENT AND
INSTRUMENTATION (CMI)
LA English
DT Proceedings Paper
CT 1st IEEE International Conference on Control, Measurement and
Instrumentation (CMI)
CY JAN 08-10, 2016
CL Jadavpur Univ, Kolkata, INDIA
SP IEEE, IEEE Instrumentat & Measurement Soc, IEEE Joint CSS IMS Kolkata Chapter
HO Jadavpur Univ
DE Superconducting Solenoid Magnet (SC Magnet); Quench; Degradation;
Training; Minimum Quench Energy; Quench propagation velocity; Residual
Resistivity Ratio (RRR); Finite Element Method (FEM)
AB Superconducting (SC) magnets are used in accelerators, high energy physics, material science studies, modalities such as MRI etc. Bhabha Atomic Research Centre in India is constructing a superconducting solenoid magnet for corrosion and Magneto hydro dynamic studies related to development of Lead Lithium cooled ceramic breeder (LLCB). The complete electro-magnet will be maintained at 4.2 K. A sudden irrevocable transition to normal state of SC magnet's operating point is known as quench. During normal operation, the magnet will be storing 2.6 MJ of energy which needs to be dissipated rapidly in the form of heat energy at the time of quench. A quench though not wished to occur is part of normal operation of magnet and has to be explicitly considered while magnet designing for the safety. Uncontrolled quench is catastrophic in nature which may even lead to melt down of windings, punching holes through insulation etc. The possible reasons for quench are lack of stability (design mistakes), transients, conductor movement, resin cracking etc. A quench protection program is written in COMSOL MULTIPHYSICS along with non-linear resistivity module implemented in PYTHON which attempts to estimate the quench parameters for 5 Tesla SC Magnet. This paper discusses the intrinsic quench behavior along with quench parameters (thermal stability limit of SC magnet in terms of MQE, quench propagation velocity, layer voltages) of the SC magnet.
C1 [Sachan, Udai Giri Pratap Singh; Rajan, S. Sundar; Malhotra, Sanjay; Satyamurthy, P.] Bhabha Atom Res Ctr, Bombay 400085, Maharashtra, India.
RP Sachan, UGPS (corresponding author), Bhabha Atom Res Ctr, Bombay 400085, Maharashtra, India.
CR Bottura L., 2010, IEEE T APPL SUPERCON, V10
Bruzzone P, 2004, PHYSICA C, V401, P7, DOI 10.1016/j.physc.2003.09.005
Denz R, 2006, IEEE T APPL SUPERCON, V16, P1725, DOI 10.1109/TASC.2005.864258
Giulio M., 2011, REV ROXIES MAT PROPE
Kim S.-W., 00041 TD DT FERMILAB
Marquardt E.D., 2000, CRYOGENIC MAT PROPER
Marscin E., 2002, TD02031 FNAL
Mints R.G., 1993, IEEE T APPL SUPERCON, V3
SundarRajan S., 2014, ISDEIV
Tinkham M., 1975, INTRO SUPERCONDUCTIV
Wilson M., 1987, SUPERCONDUCTING MAGN
NR 11
TC 0
Z9 0
U1 1
U2 3
PU IEEE
PI NEW YORK
PA 345 E 47TH ST, NEW YORK, NY 10017 USA
BN 978-1-4799-1769-3
PY 2016
BP 156
EP 161
PG 6
WC Automation & Control Systems; Engineering, Electrical & Electronic;
Instruments & Instrumentation
SC Automation & Control Systems; Engineering; Instruments & Instrumentation
GA BF7ZH
UT WOS:000384644400032
DA 2021-04-21
ER
PT B
AU Tapaninen, O
Myohanen, P
Majanen, M
Sitomaniemi, A
Olkkonen, J
Hildenbrand, V
Gielen, AWJ
Mackenzie, FV
Barink, M
Smilauer, V
Patzak, B
AF Tapaninen, Olli
Myohanen, Petri
Majanen, Mikko
Sitomaniemi, Aila
Olkkonen, Juuso
Hildenbrand, Volker
Gielen, Alexander W. J.
Mackenzie, Fidel Valega
Barink, Marco
Smilauer, Vit
Patzak, Borek
GP IEEE
TI Optical and thermal simulation chain for LED package
SO 2016 17TH INTERNATIONAL CONFERENCE ON THERMAL, MECHANICAL AND
MULTI-PHYSICS SIMULATION AND EXPERIMENTS IN MICROELECTRONICS AND
MICROSYSTEMS (EUROSIME)
LA English
DT Proceedings Paper
CT 17th International Conference on Thermal, Mechanical and Multi-Physics
Simulation and Experiments in Microelectronics and Microsystems
(EuroSimE)
CY APR 18-20, 2016
CL Montpellier, FRANCE
ID LIGHT-EMITTING-DIODES; PHOSPHOR
AB This paper presents a test case for coupling two physical aspects of an LED, optical and thermal, using specific simulation models coupled through an open source platform for distributed multi-physics modelling. The glue code for coupling is written with Python programming language including routines to interface specific simulation models. This approach can also be used for any other software. The main optical simulations are performed with an open source ray tracer software and the main thermal simulations are performed with Comsol Multiphysics. We show how to connect a Mie theory based scattering calculator with the ray tracer. Simulation results are compared to measured samples. The total radiant power emitted by the modelled LED is shown to be up to 3% consistent with the measurements.
C1 [Tapaninen, Olli; Myohanen, Petri; Majanen, Mikko; Sitomaniemi, Aila; Olkkonen, Juuso] VTT Tech Res Ctr Finland Ltd, Kaitovayla 1, Oulu 90571, Finland.
[Hildenbrand, Volker] Philips Lighting Solut, High Tech Campus 44, NL-5656 AE Eindhoven, Netherlands.
[Gielen, Alexander W. J.; Mackenzie, Fidel Valega; Barink, Marco] TNO Tech Sci, Rondom 1, NL-5612 AP Eindhoven, Netherlands.
[Smilauer, Vit; Patzak, Borek] Czech Tech Univ, Thakurova 7, Prague 16629 6, Czech Republic.
RP Tapaninen, O (corresponding author), VTT Tech Res Ctr Finland Ltd, Kaitovayla 1, Oulu 90571, Finland.
EM olli.tapaninen@vtt.fi
RI Smilauer, Vit/F-2080-2017; Patzak, Borek/E-2472-2013
OI Patzak, Borek/0000-0002-3373-9333
CR Alexeev A., 2016, THERM MECH MULT SIM
[Anonymous], 2007, GNU LESSER GEN PUBLI
de Jong I., 2016, PYTHON REMOTE OBJECT
Geveci Berk, 2013, PERFORMANCE OPEN SOU
Lin CC, 2011, J PHYS CHEM LETT, V2, P1268, DOI 10.1021/jz2002452
Liu ZY, 2010, APPL OPTICS, V49, P247, DOI 10.1364/AO.49.000247
Patzak B, 2013, ADV ENG SOFTW, V60-61, P89, DOI 10.1016/j.advengsoft.2012.09.005
Patzak B., 2015, MULTIPHYSICS INTEGRA
Saad Y, 2000, J COMPUT APPL MATH, V123, P1, DOI 10.1016/S0377-0427(00)00412-X
Ye HY, 2014, APPL THERM ENG, V63, P588, DOI 10.1016/j.applthermaleng.2013.11.058
Yuan C, 2013, INT J HEAT MASS TRAN, V56, P206, DOI 10.1016/j.ijheatmasstransfer.2012.09.053
NR 11
TC 6
Z9 6
U1 0
U2 5
PU IEEE
PI NEW YORK
PA 345 E 47TH ST, NEW YORK, NY 10017 USA
BN 978-1-5090-2106-2
PY 2016
PG 6
WC Engineering, Electrical & Electronic; Physics, Applied
SC Engineering; Physics
GA BF4WM
UT WOS:000381743700057
DA 2021-04-21
ER
PT J
AU Baehr, S
Sander, O
Heck, M
Feindt, M
Becker, J
AF Baehr, S.
Sander, O.
Heck, M.
Feindt, M.
Becker, J.
TI A framework for porting the NeuroBayes machine learning algorithm to
FPGAs
SO JOURNAL OF INSTRUMENTATION
LA English
DT Article; Proceedings Paper
CT Topical Workshop on Electronics for Particle Physics (TWEPP)
CY SEP 28-OCT 02, 2015
CL Lisbon, PORTUGAL
DE Online farms and online filtering; Hardware and accelerator control
systems; Data acquisition concepts; Pattern recognition, cluster
finding, calibration and fitting methods
ID II PIXEL DETECTOR; REDUCTION SYSTEM
AB The NeuroBayes machine learning algorithm is deployed for online data reduction at the pixel detector of Belle II. In order to test, characterize and easily adapt its implementation on FPGAs, a framework was developed. Within the framework an HDL model, written in python using MyHDL, is used for fast exploration of possible configurations. Under usage of input data from physics simulations figures of merit like throughput, accuracy and resource demand of the implementation are evaluated in a fast and flexible way. Functional validation is supported by usage of unit tests and HDL simulation for chosen configurations.
C1 [Baehr, S.; Sander, O.; Becker, J.] Karlsruhe Inst Technol ITIV, Engesserstr 5, D-76021 Karlsruhe, Germany.
[Heck, M.; Feindt, M.] Karlsruhe Inst Technol IEKP, Karlsruhe, Germany.
RP Baehr, S (corresponding author), Karlsruhe Inst Technol ITIV, Engesserstr 5, D-76021 Karlsruhe, Germany.
EM Steffen.baehr@kit.edu
OI Becker, Jurgen/0000-0002-5082-5487
CR Abe T., ARXIV10110352
[Anonymous], 2012, AMBA AXI ACE PROT SP
Feindt M, 2011, NUCL INSTRUM METH A, V654, P432, DOI 10.1016/j.nima.2011.06.008
Furletov S, 2012, J INSTRUM, V7, DOI 10.1088/1748-0221/7/01/C01014
Gessler T, 2015, IEEE T NUCL SCI, V62, P1149, DOI 10.1109/TNS.2015.2414713
KEMMER J, 1987, NUCL INSTRUM METH A, V253, P365, DOI 10.1016/0168-9002(87)90518-3
Knopf J, 2011, J INSTRUM, V6, DOI 10.1088/1748-0221/6/01/C01085
Lemarenko M, 2012, J INSTRUM, V7, DOI 10.1088/1748-0221/7/01/C01069
Levit D., 2014, IEEE REAL TIM C, P1
Levit D., 2013, IEEE NUCL SCI S, P1
Mentor Graphics, MOD US MAN 10 2A
Moll A, 2011, J PHYS CONF SER, V331, DOI 10.1088/1742-6596/331/3/032024
Spruck B, 2013, IEEE T NUCL SCI, V60, P3709, DOI 10.1109/TNS.2013.2281571
NR 13
TC 1
Z9 1
U1 0
U2 3
PU IOP PUBLISHING LTD
PI BRISTOL
PA TEMPLE CIRCUS, TEMPLE WAY, BRISTOL BS1 6BE, ENGLAND
SN 1748-0221
J9 J INSTRUM
JI J. Instrum.
PD JAN
PY 2016
VL 11
AR C01058
DI 10.1088/1748-0221/11/01/C01058
PG 10
WC Instruments & Instrumentation
SC Instruments & Instrumentation
GA DF6MM
UT WOS:000371469800058
DA 2021-04-21
ER
PT S
AU Owsiak, M
Plociennik, M
Palak, B
Zok, T
Reux, C
Di Gallo, L
Kalupin, D
Johnson, T
Schneider, M
AF Owsiak, Michal
Plociennik, Marcin
Palak, Bartek
Zok, Tomasz
Reux, Cedric
Di Gallo, Luc
Kalupin, Denis
Johnson, Thomas
Schneider, Mireille
BE Altintas, I
Norman, M
Dongarra, J
Krzhizhanovskaya, VV
Lees, M
Sloot, PMA
TI Running simultaneous Kepler sessions for the parallelization of
parametric scans and optimization studies applied to complex workflows
SO INTERNATIONAL CONFERENCE ON COMPUTATIONAL SCIENCE 2016 (ICCS 2016)
SE Procedia Computer Science
LA English
DT Proceedings Paper
CT 16th Annual International Conference on Computational Science (ICCS)
CY JUN 06-08, 2016
CL Univ Calif, San Diego Supercomputer Ctr, San Diego, CA
SP Elsevier, Univ Amsterdam, NTU Singapore, Univ Tennessee
HO Univ Calif, San Diego Supercomputer Ctr
DE Kepler; parallel execution; parametric scan
AB In this paper we present an approach taken to run multiple Kepler sessions at the same time. This kind of execution is one of the requirements for Integrated Tokamak Modelling platform developed by the Nuclear Fusion community within the context of EUROFusion project [2]. The platform is unique and original: it entails the development of a comprehensive and completely generic tokamak simulator including both the physics and the machine, which can be applied for any fusion device. All components are linked inside workflows. This approach allows complex coupling of various algorithms while at the same time provides consistency. Workflows are composed of Kepler and Ptolemy II elements as well as set of the native libraries written in various languages (Fortran, C, C++). In addition to that, there are Python based components that are used for visualization of results as well as for pre/post processing. At the bottom of all these components there is a database layer that may vary between software releases, and require different version of access libraries. The community is using shared virtual research environment to prepare and execute workflows. All these constraints make running multiple Kepler sessions really challenging. However, ability to run numerous sessions in parallel is a must - to reduce computation time and to make it possible to run released codes while working with new software at the same time. In this paper we present our approach to solve this issue and examples that show its correctness.
C1 [Owsiak, Michal; Plociennik, Marcin; Palak, Bartek; Zok, Tomasz] PAS, IBCh, Poznan Supercomp & Networking Ctr, Poznan, Poland.
[Reux, Cedric; Di Gallo, Luc; Schneider, Mireille] CEA, IRFM, F-13108 St Paul Les Durance, France.
[Kalupin, Denis] EUROfus Programme Management Unit, Boltzmannstr 2, D-85748 Garching, Germany.
[Johnson, Thomas] KTH, EES, Fus Plasma Phys, SE-10044 Stockholm, Sweden.
RP Owsiak, M (corresponding author), PAS, IBCh, Poznan Supercomp & Networking Ctr, Poznan, Poland.
EM michalo@man.poznan.pl; marcinp@man.poznan.pl; bartek@man.poznan.pl;
tzok@man.poznan.pl; cedric.reux@cea.fr; luc.digallo@cea.fr;
denis.kalupin@euro-fusion.org; johnso@kth.se; mireille.schneider@cea.fr
RI Zok, Tomasz/M-1714-2014
OI Zok, Tomasz/0000-0003-4103-9238
FU Euratom research and training programme [633053]
FX This work has been carried out within the framework of the EUROfusion
Consortium and has received funding from the Euratom research and
training programme 2014-2018 under grant agreement No 633053. The views
and opinions expressed herein do not necessarily reflect those of the
European Commission.
CR Asunta O., 2015, 14 ITPA EN PART PHYS
Di Gallo L., 2015, COMPUT PHYS COMMUN, P1
Kalupin D, 2013, NUCL FUSION, V53, DOI 10.1088/0029-5515/53/12/123007
Kalupin D., 2015, P EPS LISB
Plociennik M., 2013, FUNDAM INF, V128, P1
Reux C., 2015, NUCL FUSION, V55, P7
Schneider M., 2015, BENCHMARKING NEUTRAL
Schneider M., 2015, 15 ITPA EN PART PHYS
NR 8
TC 0
Z9 0
U1 0
U2 0
PU ELSEVIER
PI AMSTERDAM
PA Radarweg 29, PO Box 211, AMSTERDAM, NETHERLANDS
SN 1877-0509
J9 PROCEDIA COMPUT SCI
PY 2016
VL 80
BP 690
EP 699
DI 10.1016/j.procs.2016.05.362
PG 10
WC Computer Science, Theory & Methods; Mathematics, Applied
SC Computer Science; Mathematics
GA BQ2GK
UT WOS:000579452200063
OA Other Gold
DA 2021-04-21
ER
PT J
AU Maurer, V
AF Maurer, Vinzenz
TI T3PS v1.0: Tool for Parallel Processing in Parameter Scans
SO COMPUTER PHYSICS COMMUNICATIONS
LA English
DT Article
DE Parameter scans; Parallelization; Multiprocessing; Optimization; Monte
Carlo
ID PROGRAM; SPHENO; FLAVOR
AB T3PS is a program that can be used to quickly design and perform parameter scans while easily taking advantage of the multi-core architecture of current processors. It takes an easy to read and write parameter scan definition file format as input. Based on the parameter ranges and other options contained therein, it distributes the calculation of the parameter space over multiple processes and possibly computers. The derived data is saved in a plain text file format readable by most plotting software. The supported scanning strategies include: grid scan, random scan, Markov Chain Monte Carlo, numerical optimization. Several example parameter scans are shown and compared with results in the literature.
Program summary
Program title: T3PS
Catalogue identifier: AEXZ_v1_0
Program summary URL: http://cpc.cs.qub.ac.uk/summaries/AEXZ_v1_0.html
Program obtainable from: CPC Program Library, Queen's University, Belfast, N. Ireland
Licensing provisions: GNU General Public License v3
No. of lines in distributed program, including test data, etc.: 291799
No. of bytes in distributed program, including test data, etc.: 14864680
Distribution format: tar.gz
Programming language: Python 2.
Computer: PC running under Linux, should run in every Unix environment.
Operating system: Linux, Unix.
Has the code been vectorized or parallelized?: Parallelized
Classification: 4.9, 4.12, 4.13, 6.5.
External routines: Python 2.7; micrOMEGAs, SciPy, SoftSUSY, SPheno (in optional examples)
Nature of problem: While current processor architecture firmly goes the way of parallelization even on desktop computers, programs commonly used for parameter scans in physics often lack the capability to take advantage of this. While it is possible to change the source code of some programs, it may not be feasible for every program still in use. Fortunately, current operating systems routinely make use of multiple processor cores already, if multiple processes are running at the same time. The easiest way to make use of this using shell scripts and background tasks or similar, however, turns the problem into an organizational one, as the calculation will run asynchronously. This poses the problem of merging the different data sets into a final complete one and/or makes it hard to implement more advanced scan strategies.
Solution method: The parameter scan definition is read in from a simple plain text format. The subdivisions of the parameter scan are then distributed to concurrently running sub-processes, which run the code doing the actual calculation. These sub-processes naturally take advantage of the multiprocessing architecture of modern operating systems without the need for the user to change any code.
Restrictions: The program is not supported under Python 3.
Unusual features: The parallelization is not restricted to the local computer and can be extended to use remote computers as well with little effort.
Running time: Largely dependent on user input; examples take about 5 s, 30 min, I s, 10 s, 30 s, 100 min, 100 min, 5 min, 5 min in order of appearance in the text (i.e. QuickStart, ScalarDM, 3x ChargedLeptons, 4 x HiggsMass) on an Intel Core i7-3770. (C) 2015 Elsevier B.V. All rights reserved.
C1 [Maurer, Vinzenz] Univ Basel, Dept Phys, CH-4056 Basel, Switzerland.
RP Maurer, V (corresponding author), Univ Basel, Dept Phys, Klingelbergstr 82, CH-4056 Basel, Switzerland.
EM vinzenz.maurer@unibas.ch
FU Swiss National Science FoundationSwiss National Science Foundation
(SNSF)European Commission
FX This work is supported by the Swiss National Science Foundation. We
thank Stefan Antusch, Ivo de Medeiros Varzielas, Oliver Fischer,
Christian Gross and Constantin Sluka for useful discussions.
CR Akerib DS, 2014, PHYS REV LETT, V112, DOI 10.1103/PhysRevLett.112.091303
Allanach BC, 2002, COMPUT PHYS COMMUN, V143, P305, DOI 10.1016/S0010-4655(01)00460-X
[Anonymous], 2008, MATH VERS 7 0
Antusch S, 2013, J HIGH ENERGY PHYS, DOI 10.1007/JHEP11(2013)115
Arbey A, 2012, PHYS LETT B, V708, P162, DOI 10.1016/j.physletb.2012.01.053
Bechtle P, 2014, EUR PHYS J C, V74, DOI 10.1140/epjc/s10052-013-2693-2
Belanger G, 2014, COMPUT PHYS COMMUN, V185, P960, DOI 10.1016/j.cpc.2013.10.016
Belanger G, 2002, COMPUT PHYS COMMUN, V149, P103, DOI 10.1016/S0010-4655(02)00596-9
Belanger G, 2012, J COSMOL ASTROPART P, DOI 10.1088/1475-7516/2012/04/010
Brooks SP, 1998, STAT COMPUT, V8, P319, DOI 10.1023/A:1008820505350
Gonzalez-Garcia MC, 2012, J HIGH ENERGY PHYS, DOI 10.1007/JHEP12(2012)123
HASTINGS WK, 1970, BIOMETRIKA, V57, P97, DOI 10.2307/2334940
Mahmoudi F, 2012, COMPUT PHYS COMMUN, V183, P285, DOI 10.1016/j.cpc.2011.10.006
MCDONALD J, 1994, PHYS REV D, V50, P3637, DOI 10.1103/PhysRevD.50.3637
METROPOLIS N, 1953, J CHEM PHYS, V21, P1087, DOI 10.1063/1.1699114
Olive KA, 2014, CHINESE PHYS C, V38, DOI 10.1088/1674-1137/38/9/090001
Porod W, 2003, COMPUT PHYS COMMUN, V153, P275, DOI 10.1016/S0010-4655(03)00222-4
Porod W, 2012, COMPUT PHYS COMMUN, V183, P2458, DOI 10.1016/j.cpc.2012.05.021
Python Software Foundation, 2014, CONFIGPARSER CONF FI
Skands P., 2004, JHEP-Journal of High Energy Physics, V2004, DOI 10.1088/1126-6708/2004/07/036
Storn R, 1996, 1996 BIENNIAL CONFERENCE OF THE NORTH AMERICAN FUZZY INFORMATION PROCESSING SOCIETY - NAFIPS, P519, DOI 10.1109/NAFIPS.1996.534789
Storn R, 1997, J GLOBAL OPTIM, V11, P341, DOI 10.1023/A:1008202821328
Williams T., GNUPLOT 4 4 INTERACT
NR 23
TC 2
Z9 2
U1 1
U2 8
PU ELSEVIER
PI AMSTERDAM
PA RADARWEG 29, 1043 NX AMSTERDAM, NETHERLANDS
SN 0010-4655
EI 1879-2944
J9 COMPUT PHYS COMMUN
JI Comput. Phys. Commun.
PD JAN
PY 2016
VL 198
BP 195
EP 215
DI 10.1016/j.cpc.2015.08.032
PG 21
WC Computer Science, Interdisciplinary Applications; Physics, Mathematical
SC Computer Science; Physics
GA CX0EX
UT WOS:000365370800018
DA 2021-04-21
ER
PT J
AU Shao, HS
AF Shao, Hua-Sheng
TI HELAC-Onia 2.0: An upgraded matrix-element and event generator for heavy
quarkonium physics
SO COMPUTER PHYSICS COMMUNICATIONS
LA English
DT Article
DE General, high energy physics and computing; Phase space and event
simulation; Quantum chromodynamics; Lattice gauge theory
ID MONTE-CARLO APPROACH; P(P)OVER-BAR COLLISIONS; RADIATIVE-CORRECTIONS;
PHASE-SPACE; J/PSI; SCATTERING; UPSILON; MASS; PSI
AB We present an upgraded version (denoted as version 2.0) of the program HELAC-ONIA for the automated computation of heavy-quarkonium helicity amplitudes within non-relativistic QCD framework. The new code has been designed to include many new and useful features for practical phenomenological simulations. It is designed for job submissions under cluster environment for parallel computations via PYTHON scripts. We have interfaced HELAC-ONIA to the parton shower Monte Carlo programs PYTHIA 8 and QEDPS to take into account the parton-shower effects. Moreover, the decay module guarantees that the program can perform the spin-entangled (cascade-)decay of heavy quarkonium after its generation. We have also implemented a reweighting method to automatically estimate the uncertainties from renormalization and/or factorization scales as well as parton-distribution functions to weighted or unweighted events. A further update is the possibility to generate one-dimensional or two-dimensional plots encoded in the analysis files on the fly. Some dedicated examples are given at the end of the writeup.
Program summary
Program title: HELAC-Onia 2.0
Catalogue identifier: AEPR_v2_0
Program summary URL: http://cpc.cs.qub.ac.uk/summaries/AEPR_v2_0.html
Program obtainable from: CPC Program Library, Queen's University, Belfast, N. Ireland
Licensing provisions: Standard CPC licence, http://cpc.cs.qub.ac.uk/licence/licence.html
No. of lines in distributed program, including test data, etc.: 1513282
No. of bytes in distributed program, including test data, etc.: 17036140
Distribution format: tar.gz
Programming language: Python, Fortran 77, Fortran 90, C++.
Operating system: Unix-like platform.
Classification: 4.4, 11.1, 11.2, 11.5.
Catalogue identifier of previous version: AEPR_v1_0
Journal reference of previous version: Comput. Phys. Comm. 184(2013)2562
External routines: LHAPDF
Does the new version supersede the previous version?: Yes
Nature of problem: Heavy quarkonium production processes provide an important way to investigate QCD in its poorly known non-perturbative regime. Its production mechanism has attracted extensive interests from the high-energy physics community in decades. The qualitative and quantitative description of heavy-quarkonium production requires complex perturbative computations for high-multiplicity processes in the framework of the well established non-relativistic effective theory, NRQCD, and reliable Monte Carlo simulations to reproduce the collider environment.
Solution method: Based on a recursion relation, the program is able to calculate the helicity amplitudes of the high-multiplicity heavy- quarkonium-production processes. Several modules are also designed for dedicated simulations:
1. The code has been interfaced with the parton shower Monte Carlo programs;
2. A decay module to let heavy quarkonia decay with correct spin-correlations has been implemented;
3. The code estimates the theoretical uncertainties and analyzes the generated events on the fly;
4. The code is compliant with multi-threading/multi-core usage or cluster processors.
Reasons for new version: Improved and expanded functionalities.
Summary of revisions: Many new features were added and several important bugs were fixed. The new features extend the range of the physical applications. With the new interface, it also helps to improve the user-friendliness of the program. Running time: It depends on the process to be calculated and the required accuracy. (C) 2015 Elsevier B.V. All rights reserved.
C1 [Shao, Hua-Sheng] CERN, PH Dept, TH Unit, CH-1211 Geneva 23, Switzerland.
RP Shao, HS (corresponding author), CERN, PH Dept, TH Unit, CH-1211 Geneva 23, Switzerland.
EM huasheng.shao@cern.ch
OI Shao, Huasheng/0000-0002-4158-0668
FU ERCEuropean Research Council (ERC)European Commission [291377]
FX I thank Jean-Philippe Lansberg for motivating me to improve the tool by
its applications on several relevant physics projects, and for
proofreading the manuscript. I am grateful to Chole Gray, Darren Price,
Barbara Trzeciak for the feedback on using the program. Finally, I would
also like to thank the authors of MADGRApH5_AMC@NLO to collaborate on
the amazing MADGRApHs_AMC@NLO project, from which I indeed learned a lot
on how to improve the user friendliness of HELAC-ONIA. This work was
supported by ERC grant 291377 "LHCtheory: Theoretical predictions and
analyses of LHC physics: advancing the precision frontier".
CR Aad G, 2014, J HIGH ENERGY PHYS, P1, DOI 10.1007/JHEP05(2014)071
Aaij R, 2012, EUR PHYS J C, V72, DOI 10.1140/epjc/s10052-012-2100-4
Aaltonen T, 2009, PHYS REV D, V80, DOI 10.1103/PhysRevD.80.031103
Abe F, 1997, PHYS REV LETT, V79, P578, DOI 10.1103/PhysRevLett.79.578
ABE F, 1992, PHYS REV LETT, V69, P3704, DOI 10.1103/PhysRevLett.69.3704
Abe F, 1997, PHYS REV LETT, V79, P572, DOI 10.1103/PhysRevLett.79.572
ALTARELLI G, 1977, NUCL PHYS B, V126, P298, DOI 10.1016/0550-3213(77)90384-4
Alwall J, 2008, EUR PHYS J C, V53, P473, DOI 10.1140/epjc/s10052-007-0490-5
Alwall J, 2007, COMPUT PHYS COMMUN, V176, P300, DOI 10.1016/j.cpc.2006.11.010
Alwall J, 2014, J HIGH ENERGY PHYS, DOI 10.1007/JHEP07(2014)079
Alwall J, 2007, J HIGH ENERGY PHYS, DOI 10.1088/1126-6708/2007/09/028
Alwall J, 2011, J HIGH ENERGY PHYS, DOI 10.1007/JHEP06(2011)128
Andronic A., HEAVY FLAVOUR QUARKO
Artoisenet P, 2008, PHYS REV LETT, V101, DOI 10.1103/PhysRevLett.101.152001
Artoisenet P, 2008, J HIGH ENERGY PHYS, DOI 10.1088/1126-6708/2008/02/102
Artuso M, 2009, PHYS REV D, V80, DOI 10.1103/PhysRevD.80.112003
Bahr M, 2008, EUR PHYS J C, V58, P639, DOI 10.1140/epjc/s10052-008-0798-9
Bellm J., HERWIG 2 7 RELEASE N
BERENDS FA, 1988, NUCL PHYS B, V306, P759, DOI 10.1016/0550-3213(88)90442-7
BERGER EL, 1981, PHYS REV D, V23, P1521, DOI 10.1103/PhysRevD.23.1521
Bodwin G, 2013, PHYS REV D, V88, DOI 10.1103/PhysRevD.88.053003
Bodwin GT, 2014, PHYS REV LETT, V113, DOI 10.1103/PhysRevLett.113.022001
BODWIN GT, 1995, PHYS REV D, V51, P1125, DOI 10.1103/PhysRevD.51.1125
BOOS E, HEPPH0109068
Brambilla N, 2011, EUR PHYS J C, V71, DOI 10.1140/epjc/s10052-010-1534-9
Brambilla N, 2002, PHYS REV D, V65, DOI 10.1103/PhysRevD.65.034001
BRAMBILLA N, HEPPH0412158 QUARK W
Brodsky SJ, 2013, PHYS REP, V522, P239, DOI 10.1016/j.physrep.2012.10.001
BUCHMULLER W, 1981, PHYS REV D, V24, P132, DOI 10.1103/PhysRevD.24.132
Butenschoen M, 2015, PHYS REV LETT, V114, DOI 10.1103/PhysRevLett.114.092004
Butenschoen M, 2012, PHYS REV LETT, V108, DOI 10.1103/PhysRevLett.108.172002
Butenschoen M, 2011, PHYS REV D, V84, DOI 10.1103/PhysRevD.84.051501
Butenschon M, 2011, PHYS REV LETT, V106, DOI 10.1103/PhysRevLett.106.022003
Cacciari M, 2012, EUR PHYS J C, V72, DOI 10.1140/epjc/s10052-012-1896-2
Cafarella A, 2009, COMPUT PHYS COMMUN, V180, P1941, DOI 10.1016/j.cpc.2009.04.023
Campbell J, 2007, PHYS REV LETT, V98, DOI 10.1103/PhysRevLett.98.252002
Campbell JM, 2010, NUCL PHYS B-PROC SUP, V205-06, P10, DOI 10.1016/j.nuclphysbps.2010.08.011
Chao KT, 2012, PHYS REV LETT, V108, DOI 10.1103/PhysRevLett.108.242004
Chatrchyan S, 2012, J HIGH ENERGY PHYS, DOI 10.1007/JHEP02(2012)011
Corcella G, 2001, J HIGH ENERGY PHYS, DOI 10.1088/1126-6708/2001/01/010
Corcella G., HEPPH0210213
den Dunnen WJ, 2014, PHYS REV LETT, V112, DOI 10.1103/PhysRevLett.112.212001
Dobbs M, 2001, COMPUT PHYS COMMUN, V134, P41, DOI 10.1016/S0010-4655(00)00189-2
Dokshitzer Y. L., 1977, SOV PHYS JETP, V46, P641, DOI DOI 10.1016/0550-3213(77)90384-4
Drees M, 1996, PHYS REV LETT, V77, P4142, DOI 10.1103/PhysRevLett.77.4142
DYSON FJ, 1949, PHYS REV, V75, P1736, DOI 10.1103/PhysRev.75.1736
EICHTEN EJ, 1995, PHYS REV D, V52, P1726, DOI 10.1103/PhysRevD.52.1726
Faccioli P, 2014, PHYS LETT B, V736, P98, DOI 10.1016/j.physletb.2014.07.006
Ferreiro EG, 2013, PHYS REV C, V88, DOI 10.1103/PhysRevC.88.047901
Ferreiro EG, 2009, PHYS LETT B, V680, P50, DOI 10.1016/j.physletb.2009.07.076
Frederix R., 2012, JHEP, V1202, P099
FUJIMOTO J, 1994, PROG THEOR PHYS, V91, P333, DOI 10.1143/PTP.91.333
FUJIMOTO J, 1993, PROG THEOR PHYS, V90, P177, DOI 10.1143/PTP.90.177
Gaunt JR, 2010, J HIGH ENERGY PHYS, DOI 10.1007/JHEP03(2010)005
GLOVER EWN, 1988, Z PHYS C PART FIELDS, V38, P473, DOI 10.1007/BF01584398
Gong B, 2008, PHYS REV LETT, V100, DOI 10.1103/PhysRevLett.100.232001
Gong B, 2013, PHYS REV LETT, V110, DOI 10.1103/PhysRevLett.110.042002
GRIBOV VN, 1972, SOV J NUCL PHYS+, V15, P438
H.F.A. Group, WINT 2014 UPD UN TRI
Han H., ETAC PRODUCTION LHC
Han H., GAMMA NS CHIB NP PRO
JAMES F, 1975, COMPUT PHYS COMMUN, V10, P343, DOI 10.1016/0010-4655(75)90039-9
Jamin M, 1997, NUCL PHYS B, V507, P334, DOI 10.1016/S0550-3213(97)00558-0
Kanaki A, 2000, COMPUT PHYS COMMUN, V132, P306, DOI 10.1016/S0010-4655(00)00151-X
KANAKI A, HEPPH0012004
KATO K, 1989, PHYS REV D, V39, P156, DOI 10.1103/PhysRevD.39.156
Khachatryan V., 2014, JHEP, V1409
Kharchilava A, 2000, PHYS LETT B, V476, P73, DOI 10.1016/S0370-2693(00)00120-9
KLEISS R, 1986, COMPUT PHYS COMMUN, V40, P359, DOI 10.1016/0010-4655(86)90119-0
Kom CH, 2011, PHYS REV LETT, V107, DOI 10.1103/PhysRevLett.107.082002
Lange DJ, 2001, NUCL INSTRUM METH A, V462, P152, DOI 10.1016/S0168-9002(01)00089-4
Lansberg J.-P., DOUBLE QUARKONIUM PR
Lansberg J.-P., J PSI PAIR PRODUCTIO
Lansberg JP, 2013, PHYS REV LETT, V111, DOI 10.1103/PhysRevLett.111.122001
LEPAGE GP, 1978, J COMPUT PHYS, V27, P192
Ma YQ, 2014, PHYS REV LETT, V113, DOI 10.1103/PhysRevLett.113.192301
Ma YQ, 2011, PHYS REV D, V84, DOI 10.1103/PhysRevD.84.114001
Ma YQ, 2011, PHYS REV D, V83, DOI 10.1103/PhysRevD.83.111503
Ma YQ, 2011, PHYS REV LETT, V106, DOI 10.1103/PhysRevLett.106.042002
Maltoni F, 2004, PHYS REV D, V70, DOI 10.1103/PhysRevD.70.054014
Maltoni F, 2003, PHYS REV D, V67, DOI 10.1103/PhysRevD.67.014026
Mangano ML, 2007, J HIGH ENERGY PHYS, DOI 10.1088/1126-6708/2007/01/013
Martin AD, 2009, EUR PHYS J C, V63, P189, DOI 10.1140/epjc/s10052-009-1072-5
MARTIN AD, 1988, PHYS REV D, V37, P1161, DOI 10.1103/PhysRevD.37.1161
Massacrier L., FEASIBILITY STUDIES
Mocsy A, 2013, INT J MOD PHYS A, V28, DOI 10.1142/S0217751X13400125
Mrenna S, 2004, J HIGH ENERGY PHYS, DOI 10.1088/1126-6708/2004/05/040
Munehisa T, 1996, PROG THEOR PHYS, V95, P375, DOI 10.1143/PTP.95.375
Nason P., MINT COMPUTER PROGRA
Papadopoulos CG, 2001, COMPUT PHYS COMMUN, V137, P247, DOI 10.1016/S0010-4655(01)00163-1
PAPADOPOULOS CG, HEPPH0606320
Plehn T, 2012, PHYS REV D, V85, DOI 10.1103/PhysRevD.85.034029
Plehn T, 2010, J HIGH ENERGY PHYS, DOI 10.1007/JHEP10(2010)078
Plehn T, 2010, PHYS REV LETT, V104, DOI 10.1103/PhysRevLett.104.111801
Pumplin J, 2002, J HIGH ENERGY PHYS, DOI 10.1088/1126-6708/2002/07/012
Ryd A., EVTGEN MONTE CARLO G
SCHWINGER J, 1951, P NATL ACAD SCI USA, V37, P452, DOI 10.1073/pnas.37.7.452
SCHWINGER J, 1951, P NATL ACAD SCI USA, V37, P455, DOI 10.1073/pnas.37.7.455
Shao H.-S., PROBING HEAVY QUARKO
Shao HS, 2015, J HIGH ENERGY PHYS, DOI 10.1007/JHEP05(2015)103
Shao HS, 2014, PHYS REV D, V90, DOI 10.1103/PhysRevD.90.014002
Shao HS, 2014, PHYS REV LETT, V112, DOI 10.1103/PhysRevLett.112.182003
Shao HS, 2014, J HIGH ENERGY PHYS, DOI 10.1007/JHEP04(2014)182
Shao HS, 2013, COMPUT PHYS COMMUN, V184, P2562, DOI 10.1016/j.cpc.2013.05.023
Sjostrand T, 2008, COMPUT PHYS COMMUN, V178, P852, DOI 10.1016/j.cpc.2008.01.036
Sjostrand T, 2006, J HIGH ENERGY PHYS, DOI 10.1088/1126-6708/2006/05/026
Sjostrand T, 2015, COMPUT PHYS COMMUN, V191, P159, DOI 10.1016/j.cpc.2015.01.024
Sun P, 2013, PHYS REV D, V88, DOI 10.1103/PhysRevD.88.054008
Tao J., PRODUCTION GAMMAGAMM
THOOFT G, 1974, NUCL PHYS B, VB 72, P461, DOI 10.1016/0550-3213(74)90154-0
WHALLEY MR, HEPPH0508110
Zhang HF, 2015, PHYS REV LETT, V114, DOI 10.1103/PhysRevLett.114.092006
Zhang Y.-J., LEPTONIC DECAY UPSIL
NR 113
TC 57
Z9 59
U1 0
U2 9
PU ELSEVIER
PI AMSTERDAM
PA RADARWEG 29, 1043 NX AMSTERDAM, NETHERLANDS
SN 0010-4655
EI 1879-2944
J9 COMPUT PHYS COMMUN
JI Comput. Phys. Commun.
PD JAN
PY 2016
VL 198
BP 238
EP 259
DI 10.1016/j.cpc.2015.09.011
PG 22
WC Computer Science, Interdisciplinary Applications; Physics, Mathematical
SC Computer Science; Physics
GA CX0EX
UT WOS:000365370800022
DA 2021-04-21
ER
PT J
AU Manhart, M
Kion-Crosby, W
Morozov, AV
AF Manhart, Michael
Kion-Crosby, Willow
Morozov, Alexandre V.
TI Path statistics, memory, and coarse-graining of continuous-time random
walks on networks
SO JOURNAL OF CHEMICAL PHYSICS
LA English
DT Article
ID DIFFUSION; DYNAMICS; EVOLUTION; LATTICES; FLUX
AB Continuous-time random walks (CTRWs) on discrete state spaces, ranging from regular lattices to complex networks, are ubiquitous across physics, chemistry, and biology. Models with coarse-grained states (for example, those employed in studies of molecular kinetics) or spatial disorder can give rise to memory and non-exponential distributions of waiting times and first-passage statistics. However, existing methods for analyzing CTRWs on complex energy landscapes do not address these effects. Here we use statistical mechanics of the nonequilibrium path ensemble to characterize first-passage CTRWs on networks with arbitrary connectivity, energy landscape, and waiting time distributions. Our approach can be applied to calculating higher moments (beyond the mean) of path length, time, and action, as well as statistics of any conservative or non-conservative force along a path. For homogeneous networks, we derive exact relations between length and time moments, quantifying the validity of approximating a continuous-time process with its discrete-time projection. For more general models, we obtain recursion relations, reminiscent of transfer matrix and exact enumeration techniques, to efficiently calculate path statistics numerically. We have implemented our algorithm in PathMAN (Path Matrix Algorithm for Networks), a Python script that users can apply to their model of choice. We demonstrate the algorithm on a few representative examples which underscore the importance of non-exponential distributions, memory, and coarse-graining in CTRWs. (C) 2015 AIP Publishing LLC.
C1 [Manhart, Michael; Kion-Crosby, Willow; Morozov, Alexandre V.] Rutgers State Univ, Dept Phys & Astron, POB 849, Piscataway, NJ 08854 USA.
[Manhart, Michael] Harvard Univ, Dept Chem & Chem Biol, Cambridge, MA 02138 USA.
RP Manhart, M (corresponding author), Rutgers State Univ, Dept Phys & Astron, POB 849, Piscataway, NJ 08854 USA.
EM mmanhart@fas.harvard.edu; morozov@physics.rutgers.edu
RI Morozov, Alexandre/E-1984-2016
OI Morozov, Alexandre/0000-0003-2598-7000; Manhart,
Michael/0000-0003-3791-9056
FU NIHUnited States Department of Health & Human ServicesNational
Institutes of Health (NIH) - USA [F32 GM116217]; Alfred P. Sloan
Research FellowshipAlfred P. Sloan Foundation; NATIONAL INSTITUTE OF
GENERAL MEDICAL SCIENCESUnited States Department of Health & Human
ServicesNational Institutes of Health (NIH) - USANIH National Institute
of General Medical Sciences (NIGMS) [F32GM116217, F32GM116217,
F32GM116217, F32GM116217] Funding Source: NIH RePORTER
FX We thank Pavel Khromov and William Jacobs for careful reading of the
manuscript and helpful comments. M.M. was supported by NIH under Award
No. F32 GM116217 and A.V.M. was supported by an Alfred P. Sloan Research
Fellowship.
CR Albert R, 2002, REV MOD PHYS, V74, P47, DOI 10.1103/RevModPhys.74.47
Angelani L, 1998, PHYS REV LETT, V81, P4648, DOI 10.1103/PhysRevLett.81.4648
ARGYRAKIS P, 1995, PHYS REV E, V52, P3623, DOI 10.1103/PhysRevE.52.3623
Bel G, 2010, PHYS BIOL, V7, DOI 10.1088/1478-3975/7/1/016003
ben-Avraham D., 2000, DIFFUSION REACTIONS
Bollt EM, 2005, NEW J PHYS, V7, DOI 10.1088/1367-2630/7/1/026
CAMPBELL IA, 1988, PHYS REV B, V37, P3825, DOI 10.1103/PhysRevB.37.3825
Comtet L., 1974, ADV COMBINATORICS AR
Condamin S, 2007, NATURE, V450, P77, DOI 10.1038/nature06201
CURTIS CW, 1984, LINEAR ALGEBRA INTRO
Enver T, 2009, CELL STEM CELL, V4, P387, DOI 10.1016/j.stem.2009.04.011
Filyukov A. A., 1967, J ENG PHYS THERMOPH+, V13, P416, DOI DOI 10.1007/BF00828961
Flomenbom O, 2007, PHYS REV E, V76, DOI 10.1103/PhysRevE.76.041101
Flomenbom O, 2005, PHYS REV LETT, V95, DOI 10.1103/PhysRevLett.95.098105
Flomenbom O, 2005, P NATL ACAD SCI USA, V102, P2368, DOI 10.1073/pnas.0409039102
Flomenbom O, 2007, J CHEM PHYS, V127, DOI 10.1063/1.2743969
Gallos LK, 2007, P NATL ACAD SCI USA, V104, P7746, DOI 10.1073/pnas.0700250104
Goel N.S., 1974, STOCHASTIC MODELS BI
Harland B, 2007, J CHEM PHYS, V127, DOI 10.1063/1.2775439
HARRISON PG, 2002, SIGMETRICS PERFORM E, V30, P77
HAUS JW, 1987, PHYS REP, V150, P263, DOI 10.1016/0370-1573(87)90005-6
HUNTER JJ, 1969, ADV APPL PROBAB, V1, P188
Iomin A, 2006, PHYS REV E, V73, DOI 10.1103/PhysRevE.73.061918
Jones E., 2001, SCIPY OPEN SOURCE SC
Lane TJ, 2011, J AM CHEM SOC, V133, P18413, DOI 10.1021/ja207470h
Lang AH, 2014, PLOS COMPUT BIOL, V10, DOI 10.1371/journal.pcbi.1003734
MAJID I, 1984, PHYS REV B, V30, P1626, DOI 10.1103/PhysRevB.30.1626
Manhart M, 2014, 1 PASSAGE PHENOMENA
Manhart M, 2015, P NATL ACAD SCI USA, V112, P1797, DOI 10.1073/pnas.1415895112
Manhart M, 2013, PHYS REV LETT, V111, DOI 10.1103/PhysRevLett.111.088102
Metzner P, 2009, MULTISCALE MODEL SIM, V7, P1192, DOI 10.1137/070699500
MONTROLL EW, 1965, J MATH PHYS, V6, P167, DOI 10.1063/1.1704269
Mustonen V, 2010, P NATL ACAD SCI USA, V107, P4248, DOI 10.1073/pnas.0907953107
Noe F, 2009, P NATL ACAD SCI USA, V106, P19011, DOI 10.1073/pnas.0905466106
Press W. H., 1992, NUMERICAL RECIPES C
Prinz JH, 2011, J CHEM PHYS, V134, DOI 10.1063/1.3565032
Redner S., 2001, GUIDE 1 PASSAGE PROC
Reuveni S, 2014, P NATL ACAD SCI USA, V111, P4391, DOI 10.1073/pnas.1318122111
Sabelko J, 1999, P NATL ACAD SCI USA, V96, P6031, DOI 10.1073/pnas.96.11.6031
Stuart A., 1994, KENDALLS ADV THEORY, VI
Sun SX, 2006, PHYS REV LETT, V96, DOI 10.1103/PhysRevLett.96.210602
Wang J, 2008, P NATL ACAD SCI USA, V105, P12271, DOI 10.1073/pnas.0800579105
Weinreich DM, 2006, SCIENCE, V312, P111, DOI 10.1126/science.1123539
WEISS GH, 1987, PHILOS MAG B, V56, P941, DOI 10.1080/13642818708215329
Weiss GH., 1994, ASPECTS APPL RANDOM
Yang H, 2003, SCIENCE, V302, P262, DOI 10.1126/science.1086911
YAO DD, 1985, J APPL PROBAB, V22, P939, DOI 10.2307/3213962
Yeomans J. M., 1992, STAT MECH PHASE TRAN
NR 48
TC 5
Z9 5
U1 0
U2 11
PU AMER INST PHYSICS
PI MELVILLE
PA 1305 WALT WHITMAN RD, STE 300, MELVILLE, NY 11747-4501 USA
SN 0021-9606
EI 1089-7690
J9 J CHEM PHYS
JI J. Chem. Phys.
PD DEC 7
PY 2015
VL 143
IS 21
AR 214106
DI 10.1063/1.4935968
PG 22
WC Chemistry, Physical; Physics, Atomic, Molecular & Chemical
SC Chemistry; Physics
GA DD3EZ
UT WOS:000369806000008
PM 26646868
OA Green Published
DA 2021-04-21
ER
PT J
AU Cockett, R
Kang, S
Heagy, LJ
Pidlisecky, A
Oldenburg, DW
AF Cockett, Rowan
Kang, Seogi
Heagy, Lindsey J.
Pidlisecky, Adam
Oldenburg, Douglas W.
TI SIMPEG: An open source framework for simulation and gradient based
parameter estimation in geophysical applications
SO COMPUTERS & GEOSCIENCES
LA English
DT Review
DE Geophysics; Numerical modeling; Inversion; Electromagnetics;
Sensitivities; Object-oriented programming
ID GENERALIZED CROSS-VALIDATION; 3-D INVERSION; JOINT INVERSION; SYSTEM;
PYTHON; MESH; WAVE
AB Inverse modeling is a powerful tool for extracting information about the subsurface from geophysical data. Geophysical inverse problems are inherently multidisciplinary, requiring elements from the relevant physics, numerical simulation, and optimization, as well as knowledge of the geologic setting, and a comprehension of the interplay between all of these elements. The development and advancement of inversion methodologies can be enabled by a framework that supports experimentation, is flexible and extensible, and allows the knowledge generated to be captured and shared. The goal of this paper is to propose a framework that supports many different types of geophysical forward simulations and deterministic inverse problems. Additionally, we provide an open source implementation of this framework in Python called SIMPEG (Simulation and Parameter Estimation in Geophysics, http://simpeg.xyz). Included in SIMPEG are staggered grid, mimetic finite volume discretizations on a number of structured and semi-structured meshes, convex optimization programs, inversion routines, model parameterizations, useful utility codes, and interfaces to standard numerical solver packages. The framework and implementation are modular, allowing the user to explore, experiment with, and iterate over a variety of approaches to the inverse problem. SIMPEG provides an extensible, documented, and well-tested framework for inverting many types of geophysical data and thereby helping to answer questions in geoscience applications. Throughout the paper we use a generic direct current resistivity problem to illustrate the framework and functionality of SIMPEG. (C) 2015 The Authors. Published by Elsevier Ltd.
C1 [Cockett, Rowan; Kang, Seogi; Heagy, Lindsey J.; Oldenburg, Douglas W.] Univ British Columbia, Geophys Invers Facil, Vancouver, BC V5Z 1M9, Canada.
[Pidlisecky, Adam] Univ Calgary, Calgary, AB T2N 1N4, Canada.
RP Cockett, R (corresponding author), Univ British Columbia, Geophys Invers Facil, Vancouver, BC V5Z 1M9, Canada.
EM rcockett@eos.ubc.ca
OI Cockett, Rowan/0000-0002-7859-8394; Heagy, Lindsey/0000-0002-1551-5926;
KANG, SEOGI/0000-0002-9963-936X
FU Vanier Canada Graduate Scholarships Program; University of British
Columbia [NSERC 22R47082]; University of Calgary (NSERC Discovery Grant)
FX We would like to thank Dr. Eldad Haber for his invaluable guidance and
advice in the development process of our framework and SIMPEG
implementation. Dr. Dave Marchant, Dr. Lars Ruthotto, Luz Angelica
Caudillo-Mata, Gudni Rosenkjaer, and Dr. Brendan Smithyman have
contributed to the SIMPEG code base and are always willing to talk
through problems and ideas. The funding for this work is provided
through the Vanier Canada Graduate Scholarships Program, and Grants
through The University of British Columbia (NSERC 22R47082) and the
University of Calgary (NSERC Discovery Grant for A. Pidlisecky). We also
acknowledge and thank three reviewers, Peter Lelievre, Thomas Hansen,
and an anonymous reviewer for their thoughtful and constructive comments
on this paper.
CR CLAERBOUT JF, 1973, GEOPHYSICS, V38, P826, DOI 10.1190/1.1440378
CONSTABLE SC, 1987, GEOPHYSICS, V52, P289, DOI 10.1190/1.1442303
Doetsch J, 2010, GEOPHYSICS, V75, pG53, DOI 10.1190/1.3496476
Ekblom H., 1973, BIT (Nordisk Tidskrift for Informationsbehandling), V13, P292, DOI 10.1007/BF01951940
Farquharson CG, 2004, GEOPHYS J INT, V156, P411, DOI 10.1111/j.1365-246X.2004.02190.x
Farquharson CG, 1998, GEOPHYS J INT, V134, P213, DOI 10.1046/j.1365-246x.1998.00555.x
Feller J., 2000, P 21 INT C INF SYST, P58
Fomel S, 2009, COMPUT SCI ENG, V11, P5, DOI 10.1109/MCSE.2009.14
Fullagar Peter K, 2008, Leading Edge, V27, P98, DOI 10.1190/1.2831686
Gao GZ, 2012, GEOPHYSICS, V77, pWA3, DOI [10.1190/geo2011-0157.1, 10.1190/GEO2011-0157.1]
Golub GH, 1997, J COMPUT GRAPH STAT, V6, P1, DOI 10.2307/1390722
GOLUB GH, 1979, TECHNOMETRICS, V21, P215, DOI 10.1080/00401706.1979.10489751
Haber E, 2000, INVERSE PROBL, V16, P1263, DOI 10.1088/0266-5611/16/5/309
Haber E, 2000, COMPUTAT GEOSCI, V4, P41, DOI 10.1023/A:1011599530422
Haber E, 2014, INVERSE PROBL, V30, DOI 10.1088/0266-5611/30/5/055011
Haber E, 1997, INVERSE PROBL, V13, P63, DOI 10.1088/0266-5611/13/1/006
Haber E., 2015, COMPUTATIONAL METHOD
Haber E, 2007, J COMPUT PHYS, V223, P783, DOI 10.1016/j.jcp.2006.10.012
Hansen P.C, 1998, MATH MODEL COMPUT, DOI 10.1137/1.9780898719697
HANSEN PC, 1992, SIAM REV, V34, P561, DOI 10.1137/1034115
Hansen TM, 2013, COMPUT GEOSCI-UK, V52, P481, DOI 10.1016/j.cageo.2012.10.001
Harbaugh AW, 2005, US GEOLOGICAL SURVEY
Heagy L.J., 2015, MODELLING ELECTROMAG, P2
Heagy L. J., 2014, PARAMETRIZED INVERSI
Hewett R., 2013, PYSIT PYTHON SEISMIC
Holscher E, 2010, READ THE DOCS
Holtham E., 2010, 3 DIMENSIONAL INVERS, P655
Hunter JD, 2007, COMPUT SCI ENG, V9, P90, DOI 10.1109/MCSE.2007.55
Hyman J, 2002, COMPUTAT GEOSCI, V6, P333, DOI 10.1023/A:1021282912658
Hyman JM, 1999, J COMPUT PHYS, V151, P881, DOI 10.1006/jcph.1999.6225
Jones E., 2001, SCIPY OPEN SOURCE SC
Kalderimis J., 2011, TRAVIS CI GMBH
Kang S., 2014, RECOVERING INDUCED P, P1785
Kang S., 2015, SEG TECHN PROGR, P5000, DOI [10.1190/segam201 5- 5930379.1., DOI 10.1190/SEGAM2015-5930379.1]
KANG ST, 2015, ADV MATER SCI ENG, V2015, P1, DOI DOI 10.1155/2015/308725
Kelbert A, 2014, COMPUT GEOSCI-UK, V66, P40, DOI 10.1016/j.cageo.2014.01.010
Lelievre PG, 2009, EXPLOR GEOPHYS, V40, P334, DOI 10.1071/EG09012
Li MK, 2010, GEOPHYS PROSPECT, V58, P455, DOI 10.1111/j.1365-2478.2009.0824.x
Li YG, 1998, GEOPHYSICS, V63, P109, DOI 10.1190/1.1444302
Li YG, 2000, GEOPHYSICS, V65, P540, DOI 10.1190/1.1444749
Li YG, 1996, GEOPHYSICS, V61, P394, DOI 10.1190/1.1443968
Li YG, 2000, GEOPHYSICS, V65, P148, DOI 10.1190/1.1444705
Li YG, 2007, GEOPHYSICS, V72, pWA51, DOI 10.1190/1.2432262
Lin JWB, 2012, B AM METEOROL SOC, V93, P1823, DOI 10.1175/BAMS-D-12-00148.1
LINES LR, 1988, GEOPHYSICS, V53, P8, DOI 10.1190/1.1442403
Merwin N., 2015, COVERALLS
Nocedal J., 1999, NUMERICAL OPTIMIZATI
Oldenburg D.W., 2005, NEAR SURFACE GEOPHYS, P89, DOI [DOI 10.1190/1.9781560801719, 10.1190/1.9781560801719.ch5]
OLDENBURG DW, 1984, IEEE T GEOSCI REMOTE, V22, P665, DOI 10.1109/TGRS.1984.6499187
Oliphant TE, 2007, COMPUT SCI ENG, V9, P10, DOI 10.1109/MCSE.2007.58
Ollivier-Gooch C, 2002, J COMPUT PHYS, V181, P729, DOI 10.1006/jcph.2002.7159
Parker R.L.R.L., 1994, GEOPHYS INVERSE THEO
PARKER RL, 1977, ANNU REV EARTH PL SC, V5, P35, DOI 10.1146/annurev.ea.05.050177.000343
Perez F, 2007, COMPUT SCI ENG, V9, P21, DOI 10.1109/MCSE.2007.53
Pidlisecky A, 2007, GEOPHYSICS, V72, pH1, DOI 10.1190/1.2402499
Pidlisecky A, 2011, GEOPHYS J INT, V187, P214, DOI 10.1111/j.1365-246X.2011.05131.x
Robitaille TP, 2013, ASTRON ASTROPHYS, V558, DOI 10.1051/0004-6361/201322068
Schmidt Michael, 2002, BRIT ATLANTIC WORLD, pxix
Strong D, 2003, INVERSE PROBL, V19, pS165, DOI 10.1088/0266-5611/19/6/059
TARANTOLA A, 1982, REV GEOPHYS, V20, P219, DOI 10.1029/RG020i002p00219
Tarantula A., 2005, SOC IND APPL MATH
Tikhonov A.N., 1977, SOLUTION ILL POSED P
Uieda Leonardo, 2014, FATIANDO TERRA PYTHO
Van Rossum G., 1995, PYTHON REFERENCE MAN
Wahba Grace, 1990, SPLINE MODELS OBSERV, V59
Wilson G, 2014, PLOS BIOL, V12, DOI 10.1371/journal.pbio.1001745
Yang DK, 2014, GEOPHYS J INT, V196, P1492, DOI 10.1093/gji/ggt465
NR 67
TC 52
Z9 53
U1 0
U2 20
PU PERGAMON-ELSEVIER SCIENCE LTD
PI OXFORD
PA THE BOULEVARD, LANGFORD LANE, KIDLINGTON, OXFORD OX5 1GB, ENGLAND
SN 0098-3004
EI 1873-7803
J9 COMPUT GEOSCI-UK
JI Comput. Geosci.
PD DEC
PY 2015
VL 85
BP 142
EP 154
DI 10.1016/j.cageo.2015.09.015
PN A
PG 13
WC Computer Science, Interdisciplinary Applications; Geosciences,
Multidisciplinary
SC Computer Science; Geology
GA CW5QI
UT WOS:000365051100015
OA Other Gold
DA 2021-04-21
ER
PT J
AU Alwall, J
Duhr, C
Fuks, B
Mattelaer, O
Ozturk, DG
Shen, CH
AF Alwall, Johan
Duhr, Claude
Fuks, Benjamin
Mattelaer, Olivier
Oeztuerk, Deniz Gizem
Shen, Chia-Hsien
TI Computing decay rates for new physics theories with FEYNRULES and
MADGRAPH 5_AMC@NLO
SO COMPUTER PHYSICS COMMUNICATIONS
LA English
DT Article
DE Model building
ID ONE-LOOP CALCULATIONS; CALCULATING 2-BODY; PROGRAM PACKAGE; PARTICLE
DECAYS; FORTRAN CODE; FEYNARTS; INTERFACE; MASSES; LEVEL
AB We present new features of the FEYNRULES and MADGRAPH 5_AMC@NLO programs for the automatic computation of decay widths that consistently include channels of arbitrary final-state multiplicity. The implementations are generic enough so that they can be used in the framework of any quantum field theory, possibly including higher-dimensional operators. We extend at the same time the conventions of the Universal FEYNRULES Output (or UFO) format to include decay tables and information on the total widths. We finally provide a set of representative examples of the usage of the new functions of the different codes in the framework of the Standard Model, the Higgs Effective Field Theory, the Strongly Interacting Light Higgs model and the Minimal Supersymmetric Standard Model and compare the results to available literature and programs for validation purposes.
Program summary
Program title: MadWidth
Catalogue identifier: AEXY_v1_0
Program summary URL: http://cpc.cs.qub.ac.uk/summaries/AEXY_v1_0.html
Program obtainable from: CPC Program Library, Queen's University, Belfast, N. Ireland
Licensing provisions: Standard CPC licence, http://cpc.cs.qub.ac.uk/licence/licence.html
No. of lines in distributed program, including test data, etc.: 1854258
No. of bytes in distributed program, including test data, etc.: 21136244
Distribution format: tar.gz
Programming language: Mathematica and Python.
Computer: Platforms on which Mathematica and Python are available.
Operating system: Operating systems on which Mathematica and Python are available.
Classification: 11.1, 11.6.
External routines: FeynRules 2.0 or higher, MadGraph5_aMC@NLO 2.2 or higher.
Nature of problem: The program is a module for the FeynRules and MadGraph5_aMC@NLO packages that allows the computation of tree-level decay widths for arbitrary BSM models. The module consists of two parts:
1. A FeynRules part, which allows one to compute analytically all tree-level two-body decay rates and to output them in the UFO format.
2. A MadGraph5_aMC@NLO part, which allows the numerical computation of many-body decay rates.
Solution method:
1. For the FeynRules part, the analytic expressions for the three-point vertices can be squared to obtain analytic formulas for two-body decay rates.
2. For the MadGraph5_aMC@NLO part, MadGraph is used to generate all Feynman diagrams contributing to the decay, and diagrams that correspond to cascade decays are removed.
Restrictions: Mathematica version 7 to 9. As the package is a module relying on FeynRules and MadGraph5_aMC@NLO all restrictions of these packages apply.
Running time: The computation of the Feynman rules from a Lagrangian, as well as the computation of the decay rates, varies with the complexity of the model, and runs from a few seconds to several minutes. See Section 5 of the present manuscript for more information. (C) 2015 Elsevier B.V. All rights reserved.
C1 [Alwall, Johan; Shen, Chia-Hsien] Natl Taiwan Univ, Dept Phys, Taipei 10617, Taiwan.
[Duhr, Claude] Univ Durham, Inst Particle Phys Phenomenol, Durham DH1 3LE, England.
[Fuks, Benjamin] CERN, Div Theory, Dept Phys, CH-1211 Geneva 23, Switzerland.
[Fuks, Benjamin] Univ Strasbourg, Dept Rech Subatom, Inst Pluridisciplinaire Hubert Curien, CNRS,IN2P3, F-67037 Strasbourg, France.
[Mattelaer, Olivier] Catholic Univ Louvain, Ctr Cosmol Particle Phys & Phenomenol, B-1347 Louvain, Belgium.
[Oeztuerk, Deniz Gizem] Univ Zurich, Inst Theoret Phys, CH-8057 Zurich, Switzerland.
[Shen, Chia-Hsien] CALTECH, Pasadena, CA 91125 USA.
RP Duhr, C (corresponding author), Univ Durham, Inst Particle Phys Phenomenol, Durham DH1 3LE, England.
EM claude.duhr@cern.ch
OI Fuks, Benjamin/0000-0002-0041-0566; Duhr, Claude/0000-0001-5820-3570
FU French ANRFrench National Research Agency (ANR) [12 JS05 002 01];
Theory-LHC-France initiative of the CNRS/IN2P3; DOEUnited States
Department of Energy (DOE) [DE-SC0010255]; Theoretische Forschungen auf
dem Gebiet der Elementarteilchen (SNF); Research Executive Agency (REA)
of the European Union [PITN-GA-2010-264564]; MCnetITN FP7 Marie Curie
Initial Training Network [PITN-GA-2012-315877]; IISN "MadGraph"
conventionFonds de la Recherche Scientifique - FNRS [4.4511.10]; IISN
"Fundamental interactions" conventionFonds de la Recherche Scientifique
- FNRS [4.4517.08]; Belgian Federal Science Policy Office through the
Interuniversity Attraction PoleBelgian Federal Science Policy Office
[P7/37]
FX We want to thank C. Degrande and F. Maltoni for useful discussions on
this project and D. Goncalves Netto and K. Mawatari for their
constructive bug reports. OM wants to thank the IPPP center for its
hospitality during the time of this project. BF was supported in part by
the French ANR 12 JS05 002 01 BATS@LHC and by the Theory-LHC-France
initiative of the CNRS/IN2P3. CHS is supported by the DOE under grant
number DE-SC0010255. DGO is supported by Theoretische Forschungen auf
dem Gebiet der Elementarteilchen (SNF). OM is 'Chercheur scientifique
logistique postdoctoral F.R.S.-FNRS', Belgium. This work was partly
supported by the Research Executive Agency (REA) of the European Union
under the Grant Agreement number PITN-GA-2010-264564 (LHCPhenoNet), by
MCnetITN FP7 Marie Curie Initial Training Network PITN-GA-2012-315877,
by the IISN "MadGraph" convention 4.4511.10, by the IISN "Fundamental
interactions" convention 4.4517.08, and in part by the Belgian Federal
Science Policy Office through the Interuniversity Attraction Pole P7/37.
CR Agrawal S., ARXIV11120124, V7
Allanach BC, 2002, COMPUT PHYS COMMUN, V143, P305, DOI 10.1016/S0010-4655(01)00460-X
Allanach BC, 2002, EUR PHYS J C, V25, P113, DOI 10.1007/s10052-002-0949-3
Alloul A, 2014, COMPUT PHYS COMMUN, V185, P2250, DOI 10.1016/j.cpc.2014.04.012
Alloul A, 2013, EUR PHYS J C, V73, DOI 10.1140/epjc/s10052-013-2325-x
Alwall J, 2014, J HIGH ENERGY PHYS, DOI 10.1007/JHEP07(2014)079
Alwall J, 2008, AIP CONF PROC, V1078, P84, DOI 10.1063/1.3052056
Alwall J, 2011, J HIGH ENERGY PHYS, DOI 10.1007/JHEP06(2011)128
Artoisenet P, 2013, J HIGH ENERGY PHYS, DOI 10.1007/JHEP03(2013)015
Artoisenet P, 2010, J HIGH ENERGY PHYS, DOI 10.1007/JHEP12(2010)068
Bahr M, 2008, EUR PHYS J C, V58, P639, DOI 10.1140/epjc/s10052-008-0798-9
Baglio J, 2014, COMPUT PHYS COMMUN, V185, P3372, DOI 10.1016/j.cpc.2014.08.005
Bellm J., ARXIV13106877
Belyaev A, 2013, COMPUT PHYS COMMUN, V184, P1729, DOI 10.1016/j.cpc.2013.01.014
Boos E, 2004, NUCL INSTRUM METH A, V534, P250, DOI 10.1016/j.nima.2004.07.096
Bozzi G, 2007, NUCL PHYS B, V787, P1, DOI 10.1016/j.nuclphysb.2007.05.031
BYCKLING E, 1969, NUCL PHYS B, VB 9, P568, DOI 10.1016/0550-3213(69)90271-5
Christensen ND, 2013, EUR PHYS J C, V73, DOI 10.1140/epjc/s10052-013-2580-x
Christensen N, 2011, EUR PHYS J C, V71, DOI 10.1140/epjc/s10052-011-1541-5
Christensen ND, 2012, EUR PHYS J C, V72, DOI 10.1140/epjc/s10052-012-1990-5
Christensen ND, 2009, COMPUT PHYS COMMUN, V180, P1614, DOI 10.1016/j.cpc.2009.02.018
Contino R, 2013, J HIGH ENERGY PHYS, DOI 10.1007/JHEP07(2013)035
Cullen G, 2012, EUR PHYS J C, V72, DOI 10.1140/epjc/s10052-012-1889-1
Das D, 2012, COMPUT PHYS COMMUN, V183, P774, DOI 10.1016/j.cpc.2011.11.021
Degrande C, 2012, COMPUT PHYS COMMUN, V183, P1201, DOI 10.1016/j.cpc.2012.01.022
Djouadi A, 1998, COMPUT PHYS COMMUN, V108, P56, DOI 10.1016/S0010-4655(97)00123-9
Duhr C, 2011, COMPUT PHYS COMMUN, V182, P2404, DOI 10.1016/j.cpc.2011.06.009
Eriksson D, 2010, COMPUT PHYS COMMUN, V181, P189, DOI 10.1016/j.cpc.2009.09.011
Frederix R, 2009, J HIGH ENERGY PHYS, DOI 10.1088/1126-6708/2009/10/003
Frisch W, 2011, COMPUT PHYS COMMUN, V182, P2219, DOI 10.1016/j.cpc.2011.05.007
Fuks B, 2012, INT J MOD PHYS A, V27, DOI 10.1142/S0217751X12300074
Giudice GF, 2007, J HIGH ENERGY PHYS, DOI 10.1088/1126-6708/2007/06/045
Gleisberg T, 2004, J HIGH ENERGY PHYS
Gleisberg T, 2009, J HIGH ENERGY PHYS, DOI 10.1088/1126-6708/2009/02/007
Hahn T, 2008, COMPUT PHYS COMMUN, V178, P217, DOI 10.1016/j.cpc.2007.09.004
Hahn T, 1999, COMPUT PHYS COMMUN, V118, P153, DOI 10.1016/S0010-4655(98)00173-8
Hahn T, 2006, NUCL PHYS B-PROC SUP, V160, P101, DOI 10.1016/j.nuclphysbps.2006.09.035
Hahn T, 2001, COMPUT PHYS COMMUN, V140, P418, DOI 10.1016/S0010-4655(01)00290-9
Hahn T., 2008, POS ACAT, P121
Heinemeyer S, 2000, COMPUT PHYS COMMUN, V124, P76, DOI 10.1016/S0010-4655(99)00364-1
Hirschi V, 2011, J HIGH ENERGY PHYS, DOI 10.1007/JHEP05(2011)044
Hlucha H, 2012, COMPUT PHYS COMMUN, V183, P2307, DOI 10.1016/j.cpc.2012.05.022
James F., MONTE CARLO PHASE SP
Kilian W, 2011, EUR PHYS J C, V71, DOI 10.1140/epjc/s10052-011-1742-y
Klasen M, 2003, INT J MOD PHYS C, V14, P1273, DOI 10.1142/S012918310300539X
KNIEHL BA, 1995, Z PHYS C PART FIELDS, V69, P77, DOI 10.1007/s002880050007
Maltoni Fabio, 2003, JHEP, V02
MEADE P, ARXIVHEPPH0703031, P82004
Moretti M., ARXIVHEPPH0102195
Muhlleitner MM, 2007, ACTA PHYS POL B, V38, P635
Muhlleitner M, 2005, COMPUT PHYS COMMUN, V168, P46, DOI 10.1016/j.cpc.2005.01.012
Porod W, 2003, COMPUT PHYS COMMUN, V153, P275, DOI 10.1016/S0010-4655(03)00222-4
Pukhov A., ARXIVHEPPH9908288
Pukhov A., ARXIVHEPPH0412191
Shifman M.A., 1979, SOV J NUCL PHYS, V30, P711
NR 55
TC 55
Z9 55
U1 0
U2 2
PU ELSEVIER
PI AMSTERDAM
PA RADARWEG 29, 1043 NX AMSTERDAM, NETHERLANDS
SN 0010-4655
EI 1879-2944
J9 COMPUT PHYS COMMUN
JI Comput. Phys. Commun.
PD DEC
PY 2015
VL 197
BP 312
EP 323
DI 10.1016/j.cpc.2015.08.031
PG 12
WC Computer Science, Interdisciplinary Applications; Physics, Mathematical
SC Computer Science; Physics
GA CT6JO
UT WOS:000362919500031
OA Bronze, Green Accepted
DA 2021-04-21
ER
PT J
AU Nelson, D
Pillepich, A
Genel, S
Vogelsberger, M
Springel, V
Torrey, P
Rodriguez-Gomez, V
Sijackif, D
Snyder, GF
Griffen, B
Marinacci, F
Blecha, L
Sales, L
Xu, D
Hernquist, L
AF Nelson, D.
Pillepich, A.
Genel, S.
Vogelsberger, M.
Springel, V.
Torrey, P.
Rodriguez-Gomez, V.
Sijackif, D.
Snyder, G. F.
Griffen, B.
Marinacci, F.
Blecha, L.
Sales, L.
Xu, D.
Hernquist, L.
TI The illustris simulation: Public data release
SO ASTRONOMY AND COMPUTING
LA English
DT Article
DE Methods: data analysis; Methods: numerical; Galaxies: formation;
Galaxies: evolution; Data management systems; Data access methods
ID MOVING-MESH COSMOLOGY; DIGITAL SKY SURVEY; MASSIVE BLACK-HOLES;
HYDRODYNAMICAL SIMULATIONS; RADIATIVE FEEDBACK; SATELLITE GALAXIES;
NEUTRAL HYDROGEN; IA SUPERNOVAE; STELLAR; DARK
AB We present the full public release of all data from the Illustris simulation project. Illustris is a suite of large volume, cosmological hydrodynamical simulations run with the moving-mesh code AREPO and including a comprehensive set of physical models critical for following the formation and evolution of galaxies across cosmic time. Each simulates a volume of (106.5 Mpc)(3) and self-consistently evolves five different types of resolution elements from a starting redshift of z = 127 to the present day, z = 0. These components are: dark matter particles, gas cells, passive gas tracers, stars and stellar wind particles, and supermassive black holes. This data release includes the snapshots at all 136 available redshifts, halo and subhalo catalogs at each snapshot, and two distinct merger trees. Six primary realizations of the Illustris volume are released, including the flagship Illustris-1 run. These include three resolution levels with the fiducial "full" baryonic physics model, and a dark matter only analog for each. In addition, we provide four distinct, high time resolution, smaller volume "subboxes". The total data volume is 265 TB, including 800 full volume snapshots and similar to 30,000 subbox snapshots. We describe the released data products as well as tools we have developed for their analysis. All data may be directly downloaded in its native HDF5 format. Additionally, we release a comprehensive, web-based API which allows programmatic access to search and data processing tasks. In both cases we provide example scripts and a getting-started guide in several languages: currently, IDL, Python, and Matlab. This paper addresses scientific issues relevant for the interpretation of the simulations, serves as a pointer to published and on-line documentation of the project, describes planned future additional data releases, and discusses technical aspects of the release. (C) 2015 Elsevier B.V. All rights reserved.
C1 [Nelson, D.; Pillepich, A.; Genel, S.; Rodriguez-Gomez, V.; Hernquist, L.] Harvard Smithsonian Ctr Astrophys, 60 Garden St, Cambridge, MA 02138 USA.
[Genel, S.] Columbia Univ, Dept Astron, New York, NY 10027 USA.
[Vogelsberger, M.; Torrey, P.; Griffen, B.; Marinacci, F.] MIT, Dept Phys, Kavli Inst Astrophys & Space Res, Cambridge, MA 02139 USA.
[Springel, V.] Heidelberg Inst Theoret Studies, D-69118 Heidelberg, Germany.
[Springel, V.] Heidelberg Univ, Zentrum Astron, ARI, D-69120 Heidelberg, Germany.
[Sijackif, D.] Univ Cambridge, Inst Astron, Cambridge CB3 0HA, England.
[Sijackif, D.] Univ Cambridge, Kavli Inst Cosmol, Cambridge CB3 0HA, England.
[Torrey, P.] CALTECH, TAPIR, Pasadena, CA 91125 USA.
[Snyder, G. F.] Space Telescope Sci Inst, Baltimore, MD 21218 USA.
[Sales, L.] Univ Calif Riverside, Dept Phys & Astron, Riverside, CA 92521 USA.
[Blecha, L.] Univ Maryland, Dept Astron, College Pk, MD 20742 USA.
[Blecha, L.] Univ Maryland, Joint Space Sci Inst, College Pk, MD 20742 USA.
RP Nelson, D (corresponding author), Harvard Smithsonian Ctr Astrophys, 60 Garden St, Cambridge, MA 02138 USA.
EM dnelson@cfa.harvard.edu
OI Lang, Simon/0000-0002-6267-652X; Pillepich,
Annalisa/0000-0003-1065-9274; Torrey, Paul/0000-0002-5653-0786;
Springel, Volker/0000-0001-5976-4599; Lang, Simon/0000-0002-5901-5800;
Sales, Laura V./0000-0002-3790-720X; Rodriguez-Gomez,
Vicente/0000-0002-9495-0079; Genel, Shy/0000-0002-3185-1540;
/0000-0003-3816-7028; Lang, Simon/0000-0001-5931-2322; Lang,
Simon/0000-0002-4052-1623; Nelson, Dylan/0000-0001-8421-5890
FU HST [HST-AR-13897, HST-AR-12856.01-A, HST-AR-13887.004-A, 12856, 13887];
NASA through Hubble FellowshipNational Aeronautics & Space
Administration (NASA) [HST-HF2-51341 001-A]; STScISpace Telescope
Science Institute; NASANational Aeronautics & Space Administration
(NASA) [NAS5-26555]; European Research Council under ERC-StG
grantEuropean Research Council (ERC) [EXAGAL-308037]; DFG Priority
Program SPPEXA through project EXAMAG; NASA ATP Grant [NNX14AH35G]; NASA
through Einstein Fellowship [PF2-130093]; NASA grantNational Aeronautics
& Space Administration (NASA) [NNX12AC67G]; NSFNational Science
Foundation (NSF) [AST-1312095]; Science and Technology Facilities
CouncilUK Research & Innovation (UKRI)Science & Technology Facilities
Council (STFC) [ST/L000725/1] Funding Source: researchfish
FX DN would like to thank Research Computing and the Odyssey cluster at
Harvard University for significant computational resources. AP
acknowledges support from the HST grant HST-AR-13897. SG acknowledges
support provided by NASA through Hubble Fellowship grant HST-HF2-51341
001-A awarded by the STScI, which is operated by the Association of
Universities for Research in Astronomy, Inc., for NASA, under contract
NAS5-26555. VS acknowledges support by the European Research Council
under ERC-StG grant EXAGAL-308037, and by the DFG Priority Program
SPPEXA through project EXAMAG. PT acknowledges support from NASA ATP
Grant NNX14AH35G. GS acknowledges support from HST grants
HST-AR-12856.01-A and HST-AR-13887.004-A. Funding for HST programs
#12856 and #13887 is provided by NASA through grants from STScI. LB
acknowledges support provided by NASA through Einstein Fellowship grant
PF2-130093. LH acknowledges support from NASA grant NNX12AC67G and NSF
grant AST-1312095. The authors would like to thank many people for
contributing to analysis and understanding of the Illustris simulations
and their results: Andreas Bauer, Simeon Bird, Akos Bogdan, Aaron Bray,
Eddie Chua, Benjamin Cook, Chris Hayward, Rahul Kannan, Luke Kelley,
Cristina Popa, Kevin Schaal, Martin Sparre, Joshua Suresh, Sarah
Wellons.
CR Barro G, 2013, ASTROPHYS J, V765, DOI 10.1088/0004-637X/765/2/104
Bauer A, 2015, MON NOT R ASTRON SOC, V453, P3593, DOI 10.1093/mnras/stv1893
Behroozi PS, 2013, ASTROPHYS J, V763, DOI 10.1088/0004-637X/763/1/18
Bernyk M., 2014, ARXIV14035270
Bertin E, 2015, ASTRON COMPUT, V10, P43, DOI 10.1016/j.ascom.2014.12.006
Bird S, 2015, MON NOT R ASTRON SOC, V447, P1834, DOI 10.1093/mnras/stu2542
Bird S, 2014, MON NOT R ASTRON SOC, V445, P2313, DOI 10.1093/mnras/stu1923
Bird S, 2013, MON NOT R ASTRON SOC, V429, P3341, DOI 10.1093/mnras/sts590
Blecha L., 2015, ARXIV150801524
Bogdan A., 2015, ARXIV150301107
Boylan-Kolchin M, 2009, MON NOT R ASTRON SOC, V398, P1150, DOI 10.1111/j.1365-2966.2009.15191.x
Brammer GB, 2012, ASTROPHYS J SUPPL S, V200, DOI 10.1088/0067-0049/200/2/13
Bruzual G, 2003, MON NOT R ASTRON SOC, V344, P1000, DOI 10.1046/j.1365-8711.2003.06897.x
Bryan GL, 1998, ASTROPHYS J, V495, P80, DOI 10.1086/305262
BUSER R, 1978, ASTRON ASTROPHYS, V62, P411
Byna S., 2012, P INT C HIGH PERF CO
CEN R, 1992, ASTROPHYS J SUPPL S, V78, P341, DOI 10.1086/191630
Chabrier G, 2003, PUBL ASTRON SOC PAC, V115, P763, DOI 10.1086/376392
Chou JR, 2011, IEEE INT C CL COMP, P455, DOI 10.1109/CLUSTER.2011.86
Ciotti L, 2007, ASTROPHYS J, V665, P1038, DOI 10.1086/519833
Crocce M, 2010, MON NOT R ASTRON SOC, V403, P1353, DOI 10.1111/j.1365-2966.2009.16194.x
Dahlen T, 2004, ASTROPHYS J, V613, P189, DOI 10.1086/422899
Dark Energy Survey Collaboration, 2005, ARXIVASTROPH0510346
DAVIS M, 1985, ASTROPHYS J, V292, P371, DOI 10.1086/163168
Davis M., 2003, SPIE, V4834, P161, DOI DOI 10.1117/12.457897
De Lucia G, 2007, MON NOT R ASTRON SOC, V375, P2, DOI 10.1111/j.1365-2966.2006.11287.x
Di Matteo T, 2005, NATURE, V433, P604, DOI 10.1038/nature03335
Di Matteo T, 2008, ASTROPHYS J, V676, P33, DOI 10.1086/524921
Dubois Y, 2014, MON NOT R ASTRON SOC, V444, P1453, DOI 10.1093/mnras/stu1227
Ferland GJ, 1998, PUBL ASTRON SOC PAC, V110, P761, DOI 10.1086/316190
Fielding R.T., 2000, AAI9980887
Genel S., 2015, ARXIV150301117
Genel S, 2014, MON NOT R ASTRON SOC, V445, P175, DOI 10.1093/mnras/stu1654
Genel S, 2013, MON NOT R ASTRON SOC, V435, P1426, DOI 10.1093/mnras/stt1383
Giguere CAF, 2009, ASTROPHYS J, V703, P1416, DOI 10.1088/0004-637X/703/2/1416
Gray J., 2002, ARXIVCS02022014
Greggio L, 2005, ASTRON ASTROPHYS, V441, P1055, DOI 10.1051/0004-6361:20052926
Grogin NA, 2011, ASTROPHYS J SUPPL S, V197, DOI 10.1088/0067-0049/197/2/35
Guo Q, 2011, MON NOT R ASTRON SOC, V413, P101, DOI 10.1111/j.1365-2966.2010.18114.x
Hahn O, 2010, MON NOT R ASTRON SOC, V405, P274, DOI 10.1111/j.1365-2966.2010.16494.x
HERNQUIST L, 1989, ASTROPHYS J SUPPL S, V70, P419, DOI 10.1086/191344
Karakas AI, 2010, MON NOT R ASTRON SOC, V403, P1413, DOI 10.1111/j.1365-2966.2009.16198.x
KATZ N, 1992, ASTROPHYS J, V399, pL109, DOI 10.1086/186619
Katz N, 1996, ASTROPHYS J SUPPL S, V105, P19, DOI 10.1086/192305
Keres D, 2012, MON NOT R ASTRON SOC, V425, P2027, DOI 10.1111/j.1365-2966.2012.21548.x
Khandai N., 2014, ARXIV14020888
Kim J, 2011, J KOREAN ASTRON SOC, V44, P217, DOI 10.5303/JKAS.2011.44.6.217
Klypin AA, 2011, ASTROPHYS J, V740, DOI 10.1088/0004-637X/740/2/102
Lemson Gerard, 2011, Scientific and Statistical Database Management. Proceedings 23rd International Conference, SSDBM 2011, P509, DOI 10.1007/978-3-642-22351-8_34
Lemson G., 2009, MEMORIE SOC ASTRONOM, V80, P342
Lemson G., 2014, ARXIV14024744
Lemson G, 2006, ASP C SER, V351, P212
Lemson G., 2006, ARXIVASTROPH0608019
Li N, 2008, COMPUT SCI ENG, V10, P18, DOI 10.1109/MCSE.2008.6
Lotz JM, 2004, ASTRON J, V128, P163, DOI 10.1086/421849
LSST Science Collaboration, 2009, ARXIV09120201 LSST S
Maoz D, 2012, MON NOT R ASTRON SOC, V426, P3282, DOI 10.1111/j.1365-2966.2012.21871.x
Marinacci F, 2014, MON NOT R ASTRON SOC, V437, P1750, DOI 10.1093/mnras/stt2003
Matteucci F, 2006, MON NOT R ASTRON SOC, V372, P265, DOI 10.1111/j.1365-2966.2006.10848.x
Nelson D, 2015, MON NOT R ASTRON SOC, V448, P59, DOI 10.1093/mnras/stv017
Nelson D, 2013, MON NOT R ASTRON SOC, V429, P3353, DOI 10.1093/mnras/sts595
Okamoto T, 2010, MON NOT R ASTRON SOC, V406, P208, DOI 10.1111/j.1365-2966.2010.16690.x
Oppenheimer BD, 2008, MON NOT R ASTRON SOC, V387, P577, DOI 10.1111/j.1365-2966.2008.13280.x
Oppenheimer BD, 2006, MON NOT R ASTRON SOC, V373, P1265, DOI 10.1111/j.1365-2966.2006.10989.x
Overzier R, 2013, MON NOT R ASTRON SOC, V428, P778, DOI 10.1093/mnras/sts076
Perez F, 2007, COMPUT SCI ENG, V9, P21, DOI 10.1109/MCSE.2007.53
Pillepich A, 2014, MON NOT R ASTRON SOC, V444, P237, DOI 10.1093/mnras/stu1408
Portinari L, 1998, ASTRON ASTROPHYS, V334, P505
PRESS WH, 1974, ASTROPHYS J, V187, P425, DOI 10.1086/152650
Puchwein E, 2013, MON NOT R ASTRON SOC, V428, P2966, DOI 10.1093/mnras/sts243
Rahmati A, 2015, MON NOT R ASTRON SOC, V452, P2034, DOI 10.1093/mnras/stv1414
Rahmati A, 2013, MON NOT R ASTRON SOC, V430, P2427, DOI 10.1093/mnras/stt066
Rasera Y, 2010, AIP CONF PROC, V1241, P1134, DOI 10.1063/1.3462610
Riebe K, 2013, ASTRON NACHR, V334, P691, DOI 10.1002/asna.201211900
Rodriguez-Gomez V, 2015, MON NOT R ASTRON SOC, V449, P49, DOI 10.1093/mnras/stv264
Sales LV, 2015, MON NOT R ASTRON SOC, V447, pL6, DOI 10.1093/mnrasl/slu173
Sazonov SY, 2005, MON NOT R ASTRON SOC, V358, P168, DOI 10.1111/j.1365-2966.2005.08763.x
Schaal K, 2015, MON NOT R ASTRON SOC, V446, P3992, DOI 10.1093/mnras/stu2386
Schaller M., 2014, ARXIV14098617, DOI [10.1093/mnras/stv1067, DOI 10.1093/MNRAS/STV1067]
Schaye J, 2015, MON NOT R ASTRON SOC, V446, P521, DOI 10.1093/mnras/stu2058
Sijacki D., 2014, ARXIV14086842, DOI [10.1093/mnras/stv1340, DOI 10.1093/MNRAS/STV1340]
Sijacki D, 2007, MON NOT R ASTRON SOC, V380, P877, DOI 10.1111/j.1365-2966.2007.12153.x
Sijacki D, 2012, MON NOT R ASTRON SOC, V424, P2999, DOI 10.1111/j.1365-2966.2012.21466.x
Sijacki D, 2009, MON NOT R ASTRON SOC, V400, P100, DOI 10.1111/j.1365-2966.2009.15452.x
Skillman S. W., 2014, ARXIV14072600
Smith B, 2008, MON NOT R ASTRON SOC, V385, P1443, DOI [10.1111/j.1365-2966.2008.12922.x, 10.1111/J.1365-2966.2008.12922.x]
Snyder G. F., 2015, ARXIV150207747
Sparre M, 2015, MON NOT R ASTRON SOC, V447, P3548, DOI 10.1093/mnras/stu2713
Springel V, 2005, NATURE, V435, P629, DOI 10.1038/nature03597
Springel V, 2005, MON NOT R ASTRON SOC, V364, P1105, DOI 10.1111/j.1365-2966.2005.09655.x
Springel V, 2003, MON NOT R ASTRON SOC, V339, P289, DOI 10.1046/j.1365-8711.2003.06206.x
Springel V, 2005, MON NOT R ASTRON SOC, V361, P776, DOI 10.1111/j.1365-2966.2005.09238.x
Springel V, 2008, MON NOT R ASTRON SOC, V391, P1685, DOI 10.1111/j.1365-2966.2008.14066.x
Springel V, 2001, MON NOT R ASTRON SOC, V328, P726, DOI 10.1046/j.1365-8711.2001.04912.x
Springel V, 2001, NEW ASTRON, V6, P79, DOI 10.1016/S1384-1076(01)00042-2
Springel V, 2010, MON NOT R ASTRON SOC, V401, P791, DOI 10.1111/j.1365-2966.2009.15715.x
Srisawat C, 2013, MON NOT R ASTRON SOC, V436, P150, DOI 10.1093/mnras/stt1545
Stoughton C, 2002, ASTRON J, V123, P485, DOI 10.1086/324741
Suresh J, 2015, MON NOT R ASTRON SOC, V448, P895, DOI 10.1093/mnras/stu2762
Szalay A.S., 2002, ARXIVCS0202013
Szalay AS, 2002, PROC SPIE, V4836, P333, DOI 10.1117/12.461427
Szalay AS, 2000, ASTR SOC P, V216, P405
Thielemann FK, 2003, NUCL PHYS A, V718, p139C, DOI 10.1016/S0375-9474(03)00704-8
Torrey P, 2015, MON NOT R ASTRON SOC, V447, P2753, DOI 10.1093/mnras/stu2592
Torrey P, 2014, MON NOT R ASTRON SOC, V438, P1985, DOI 10.1093/mnras/stt2295
Torrey P, 2012, MON NOT R ASTRON SOC, V427, P2224, DOI 10.1111/j.1365-2966.2012.22082.x
Travaglio C, 2004, ASTRON ASTROPHYS, V425, P1029, DOI 10.1051/0004-6361:20041108
Vogelsberger M, 2014, NATURE, V509, P177, DOI 10.1038/nature13316
Vogelsberger M, 2014, MON NOT R ASTRON SOC, V444, P1518, DOI 10.1093/mnras/stu1536
Vogelsberger M, 2013, MON NOT R ASTRON SOC, V436, P3031, DOI 10.1093/mnras/stt1789
Vogelsberger M, 2012, MON NOT R ASTRON SOC, V425, P3024, DOI 10.1111/j.1365-2966.2012.21590.x
Wang Y, 2013, IEEE ACM INT SYMP, P335, DOI 10.1109/CCGrid.2013.9
Wellons S, 2015, MON NOT R ASTRON SOC, V449, P361, DOI 10.1093/mnras/stv303
Wiersma RPC, 2009, MON NOT R ASTRON SOC, V399, P574, DOI 10.1111/j.1365-2966.2009.15331.x
Wiersma RPC, 2009, MON NOT R ASTRON SOC, V393, P99, DOI 10.1111/j.1365-2966.2008.14191.x
Xu D., 2015, ARXIV150707937
Yepes G, 1997, MON NOT R ASTRON SOC, V284, P235, DOI 10.1093/mnras/284.1.235
York DG, 2000, ASTRON J, V120, P1579, DOI 10.1086/301513
Yu QJ, 2002, MON NOT R ASTRON SOC, V335, P965, DOI 10.1046/j.1365-8711.2002.05532.x
NR 119
TC 264
Z9 267
U1 0
U2 11
PU ELSEVIER SCIENCE BV
PI AMSTERDAM
PA PO BOX 211, 1000 AE AMSTERDAM, NETHERLANDS
SN 2213-1337
EI 2213-1345
J9 ASTRON COMPUT
JI Astron. Comput.
PD NOV
PY 2015
VL 13
BP 12
EP 37
DI 10.1016/j.ascom.2015.09.003
PG 26
WC Astronomy & Astrophysics; Computer Science, Interdisciplinary
Applications
SC Astronomy & Astrophysics; Computer Science
GA DE2MA
UT WOS:000370460200002
OA Green Published, Green Accepted
DA 2021-04-21
ER
PT J
AU Kim, JS
Schmeier, D
Tattersall, J
Rolbiecki, K
AF Kim, Jong Soo
Schmeier, Daniel
Tattersall, Jamie
Rolbiecki, Krzysztof
TI A framework to create customised LHC analyses within CheckMATE
SO COMPUTER PHYSICS COMMUNICATIONS
LA English
DT Article
DE Analysis; Detector simulation; Delphes; ATLAS; CMS; Tool
ID TRANSVERSE ENERGY; MEASURING MASSES; MISSING ENERGY; ELECTRONS
AB CheckMATE is a framework that allows the user to conveniently test simulated BSM physics events against current LHC data in order to derive exclusion limits. For this purpose, the data runs through a detector simulation and is then processed by a user chosen selection of experimental analyses. These analyses are all defined by signal regions that can be compared to the experimental data with a multitude of statistical tools.
Due to the large and continuously growing number of experimental analyses available, users may quickly find themselves in the situation that the study they are particularly interested in has not (yet) been implemented officially into the CheckMATE framework. However, the code includes a rather simple framework to allow users to add new analyses on their own. This document serves as a guide to this.
In addition, CheckMATE serves as a powerful tool for testing and implementing new search strategies. To aid this process, many tools are included to allow a rapid prototyping of new analyses.
Website: http://checkmate.hepforge.org/
Program summary
Program title: CheckMATE, AnalysisManager
Catalogue identifier: AEUT_v1_1
Program summary URL: http://cpc.cs.qub.ac.uk/summaries/AEUT_vl_1.html
Program obtainable from: CPC Program Library, Queen's University, Belfast, N. Ireland
Licensing provisions: Standard CPC licence, http://cpc.cs.qub.ac.uk/licence/licence.html
No. of lines in distributed program, including test data, etc.: 181436
No. of bytes in distributed program, including test data, etc.: 2169369
Distribution format: tar.gz
Programming language: C++, Python.
Computer: PC, Mac.
Operating system: Linux, Mac OS.
Catalogue identifier of previous version: AEUT v1_0
Journal reference of previous version: Comput. Phys. Comm. 187(2015)227 Classification: 11.9.
External routines: ROOT, Python, Delphes (included with the distribution)
Does the new version supersede the previous version?: Yes
Nature of problem: The LHC has delivered a wealth of new data that is now being analysed. Both ATLAS and CMS have performed many searches for new physics that theorists are eager to test their model against.
However, tuning the detector simulations, understanding the particular analysis details and interpreting the results can be a tedious and repetitive task. Furthermore, new analyses are being constantly published by the experiments and might be not yet included in the official CheckMATE distribution. Solution method: The AnalysisManager within CheckMATE framework allows the user to easily include new experimental analyses as they are published by the collaborations. Furthermore, completely novel analyses can be designed and added by the user in order to test models at higher centre-of-mass energy and/or luminosity.
Reasons for new version: New features, bug fixes, additional validated analyses.
Summary of revisions: New kinematic variables M_CT, M_T2b1, m_T, alpha_T, razor; internal likelihood calculation; missing energy smearing; efficiency tables; validated tau-tagging; improved AnalysisManager and code structure; new analyses; bug fixes.
Restrictions: Only a subset of available experimental results have been implemented.
Additional comments: Checkmate is built upon the tools and hard work of many people. If Checkmate is used in your publication it is extremely important that all of the following citations are included,
Delphes 3 [1].
FastJet [2,3].
Anti-k(t) jet algorithm [4].
CL, prescription [5].
In analyses that use the M-T2 kinematical discriminant we use the Oxbridge Kinetics Library [6,7] and the algorithm developed by Cheng and Han [8] which also includes the M(T2)(b)l variable [9].
In analyses that use the M-CT. family of kinematical discriminants we use MctLib [10,11] which also includes the M-CT perpendicular to and Mall variables [12].
All experimental analyses that were used to set limits in the study.
The Monte Carlo event generator that was used.
Running time: The running time scales about linearly with the number of input events provided by the user. The detector simulation/analysis of 20000 events needs about 50s/ls for a single core calculation on an Intel Core i5-3470 with 3.2 GHz and 8 GB RAM. (C) 2015 Elsevier B.V. All rights reserved.
C1 [Kim, Jong Soo; Rolbiecki, Krzysztof] Inst Fis Teor UAM CSIC, Madrid, Spain.
[Schmeier, Daniel] Univ Bonn, Inst Phys, Bonn, Germany.
[Schmeier, Daniel] Univ Bonn, Bethe Ctr Theoret Phys, Bonn, Germany.
[Tattersall, Jamie] Heidelberg Univ, Inst Theoret Phys, Heidelberg, Germany.
[Rolbiecki, Krzysztof] Uniwersytet Warszawski, Inst Fizyki Teoretycznej, Warsaw, Poland.
RP Rolbiecki, K (corresponding author), Inst Fis Teor UAM CSIC, Madrid, Spain.
EM jong.kim@csic.es; daschm@th.physik.uni-bonn.de;
j.tattersall@thphys.uni-heidelberg.de; rolbiecki.krzysztof@csic.es
RI Rolbiecki, Krzysztof/A-5402-2017; , Krzysztof/R-3697-2019; Kim, Jong
Soo/G-6307-2018
OI Rolbiecki, Krzysztof/0000-0002-9645-9670; ,
Krzysztof/0000-0002-9645-9670; Kim, Jong Soo/0000-0002-1244-4181
FU MINECO, Spain [FPA2013-44773-P]; Consolider-Ingenio CPANSpanish
Government [CSD2007-00042]; Spanish MINECO Centro de excelencia Severo
Ochoa Program [SEV-2012-0249]; JAE-Doc programme
FX We would all like to thank Sabine Kraml and LPSC Grenoble for support
and hospitality while part of this manuscript was prepared. We would
also like to thank Liang Shang for the careful reading of our
manuscript. J.T. would like to thank Herbi Dreiner and Bonn University
for additional kind support. The work of J.S. Kim and K. Rolbiecki has
been partially supported by the MINECO, Spain, under contract
FPA2013-44773-P; Consolider-Ingenio CPAN CSD2007-00042 and the Spanish
MINECO Centro de excelencia Severo Ochoa Program under grant
SEV-2012-0249. K. Rolbiecki was also supported by JAE-Doc programme.
CR Aad G, 2014, PHYS REV D, V90, DOI 10.1103/PhysRevD.90.052008
Aad G, 2014, J HIGH ENERGY PHYS, DOI 10.1007/JHEP04(2014)169
Allanach BC, 2009, COMPUT PHYS COMMUN, V180, P8, DOI 10.1016/j.cpc.2008.08.004
Alwall J, 2014, J HIGH ENERGY PHYS, DOI 10.1007/JHEP07(2014)079
[Anonymous], 2013, ATLASCONF2013047 CER
ARNISON G, 1983, PHYS LETT B, V122, P103, DOI 10.1016/0370-2693(83)91177-2
Bahr M, 2008, EUR PHYS J C, V58, P639, DOI 10.1140/epjc/s10052-008-0798-9
Bai Y, 2012, J HIGH ENERGY PHYS, DOI 10.1007/JHEP07(2012)110
BANNER M, 1983, PHYS LETT B, V122, P476, DOI 10.1016/0370-2693(83)91605-2
BARGER V, 1987, PHYS REV D, V36, P295, DOI 10.1103/PhysRevD.36.295
Barr A, 2003, J PHYS G NUCL PARTIC, V29, P2343, DOI 10.1088/0954-3899/29/10/304
Barr AJ, 2010, J PHYS G NUCL PARTIC, V37, DOI 10.1088/0954-3899/37/12/123001
Beenakker W, 1999, PHYS REV LETT, V83, P3780, DOI 10.1103/PhysRevLett.83.3780
Beenakker W., HEPPH9611232
Boehm C., 2014, J COSMOL ASTROPART P, V1405, P009
BUCKLEY A, ARXIV10030694
Cacciari M, 2008, J HIGH ENERGY PHYS, DOI 10.1088/1126-6708/2008/04/063
Cacciari M, 2006, PHYS LETT B, V641, P57, DOI 10.1016/j.physletb.2006.08.037
Cacciari M, 2012, EUR PHYS J C, V72, DOI 10.1140/epjc/s10052-012-1896-2
Calibbi L., ARXIV14105730
Cao JJ, 2014, J HIGH ENERGY PHYS, DOI 10.1007/JHEP05(2014)056
Chatrchyan S, 2012, PHYS REV D, V85, DOI 10.1103/PhysRevD.85.012004
Cheng HC, 2008, J HIGH ENERGY PHYS, DOI 10.1088/1126-6708/2008/12/063
Conte E, 2014, EUR PHYS J C, V74, DOI 10.1140/epjc/s10052-014-3103-0
Conte E, 2013, COMPUT PHYS COMMUN, V184, P222, DOI 10.1016/j.cpc.2012.09.009
de Favereau J., ARXIV13076346
de Favereau J., 2013, DELPHES 3 MODULAR FR
Drees M, 2012, PHYS REV D, V86, DOI 10.1103/PhysRevD.86.035024
Drees M., ARXIV13122591
Dumont B., ARXIV14073278
Gleisberg T, 2009, J HIGH ENERGY PHYS, DOI 10.1088/1126-6708/2009/02/007
Holdom B, 2014, PHYS REV D, V90, DOI 10.1103/PhysRevD.90.013015
Kaminska A, 2014, J HIGH ENERGY PHYS, DOI 10.1007/JHEP06(2014)153
Khachatryan V, 2011, PHYS LETT B, V698, P196, DOI 10.1016/j.physletb.2011.03.021
Khachatryan V., ARXIV15020030
Kim J.S., ARXIV14060858
Kim JS, 2015, EUR PHYS J C, V75, DOI 10.1140/epjc/s10052-015-3281-4
Lai HL, 2010, PHYS REV D, V82, DOI 10.1103/PhysRevD.82.074024
Lester CG, 1999, PHYS LETT B, V463, P99, DOI 10.1016/S0370-2693(99)00945-4
Matchev KT, 2011, PHYS REV LETT, V107, DOI 10.1103/PhysRevLett.107.061801
Polesello G, 2010, J HIGH ENERGY PHYS, DOI 10.1007/JHEP03(2010)030
Randall L, 2008, PHYS REV LETT, V101, DOI 10.1103/PhysRevLett.101.221803
Read AL, 2002, J PHYS G NUCL PARTIC, V28, P2693, DOI 10.1088/0954-3899/28/10/313
Rogan C., ARXIV10062727
Sjostrand T, 2006, J HIGH ENERGY PHYS, DOI 10.1088/1126-6708/2006/05/026
Skands P, 2004, J HIGH ENERGY PHYS, DOI 10.1088/1126-6708/2004/07/036
SMITH J, 1983, PHYS REV LETT, V50, P1738, DOI 10.1103/PhysRevLett.50.1738
Tovey DR, 2008, J HIGH ENERGY PHYS, DOI 10.1088/1126-6708/2008/04/034
TOVEY DR, 2008, JHEP, V804
van Neerven W., LEPTON JET EVENTS SI
NR 50
TC 47
Z9 48
U1 0
U2 7
PU ELSEVIER
PI AMSTERDAM
PA RADARWEG 29, 1043 NX AMSTERDAM, NETHERLANDS
SN 0010-4655
EI 1879-2944
J9 COMPUT PHYS COMMUN
JI Comput. Phys. Commun.
PD NOV
PY 2015
VL 196
BP 535
EP 562
DI 10.1016/j.cpc.2015.06.002
PG 28
WC Computer Science, Interdisciplinary Applications; Physics, Mathematical
SC Computer Science; Physics
GA CT1ZX
UT WOS:000362602900049
DA 2021-04-21
ER
PT J
AU Parcollet, O
Ferrero, M
Ayral, T
Hafermann, H
Krivenko, I
Messio, L
Seth, P
AF Parcollet, Olivier
Ferrero, Michel
Ayral, Thomas
Hafermann, Hartmut
Krivenko, Igor
Messio, Laura
Seth, Priyanka
TI TRIQS: A toolbox for research on interacting quantum systems
SO COMPUTER PHYSICS COMMUNICATIONS
LA English
DT Article
DE Many-body physics; Strongly-correlated systems; DMFT; Monte Carlo; ab
initio calculations; C plus; Python
ID MEAN-FIELD THEORY; IMPURITY MODELS; MONTE-CARLO
AB We present the TRIQS library, a Toolbox for Research on Interacting Quantum Systems. It is an open-source, computational physics library providing a framework for the quick development of applications in the field of many-body quantum physics, and in particular, strongly-correlated electronic systems. It supplies components to develop codes in a modern, concise and efficient way: e.g. Green's function containers, a generic Monte Carlo class, and simple interfaces to HDF5. TRIQS is a C++/Python library that can be used from either language. It is distributed under the GNU General Public License (GPLv3). State-of-the-art applications based on the library, such as modern quantum many-body solvers and interfaces between density-functional-theory codes and dynamical mean-field theory (DMFT) codes are distributed along with it.
Program summary
Program title: TRIQS
Catalogue identifier: AEWR_v1_0
Program summary URL: http://cpc.cs.qub.ac.uk/summaries/AEWR_v1_0.html
Program obtainable from: CPC Program Library, Queen's University, Belfast, N. Ireland
Licensing provisions: GNU General Public License (GPLv3)
No. of lines in distributed program, including test data, etc.: 93228
No. of bytes in distributed program, including test data, etc.: 2979367
Distribution format: tar.gz
Programming language: C++/Python.
Computer: Any architecture with suitable compilers including PCs and clusters.
Operating system: Unix, Linux, OSX.
RAM: Highly problem-dependent
Classification: 7.3, 20.
External routines: cmake, mpi, boost, FFTW, GMP, BIAS, LAPACK, HDF5, NumPy, SciPy, h5py, mpi4py, mako.
Nature of problem: Need for a modern programming framework to quickly write simple, efficient and higher-level code applicable to the studies of strongly-correlated electron systems.
Solution method: We present a C++/Python open-source computational library that provides high-level abstractions for common objects and various tools in the field of quantum many-body physics, thus forming a framework for developing applications.
Running time: Tests take less than a minute. Otherwise it is highly problem dependent (from minutes to several days). (C) 2015 Elsevier B.V. All rights reserved.
C1 [Parcollet, Olivier; Ayral, Thomas; Hafermann, Hartmut; Messio, Laura] CEA, CNRS, Inst Phys Theor IPhT, F-91191 Gif Sur Yvette, France.
[Ferrero, Michel; Ayral, Thomas; Seth, Priyanka] Ecole Polytech, CNRS, Ctr Phys Theor, F-91128 Palaiseau, France.
[Krivenko, Igor] Univ Hamburg, Inst Theoret Phys 1, D-20355 Hamburg, Germany.
[Messio, Laura] Univ Paris 06, CNRS, UMR 7600, LPTMC, F-75252 Paris, France.
RP Parcollet, O (corresponding author), CEA, CNRS, Inst Phys Theor IPhT, F-91191 Gif Sur Yvette, France.
EM olivier.parcollet@cea.fr; michel.ferrero@polytechnique.edu;
thomas.ayral@polytechnique.edu; hartmut.hafermann@cea.fr;
ikrivenk@physnet.uni-hamburg.de; messio@lptmc.jussieu.fr;
priyanka.seth@polytechnique.edu
RI Ayral, Thomas/AAC-3789-2019; Laura, Messio/S-2083-2018; Parcollet,
Olivier/AAE-2863-2021; Ferrero, Michel/Q-3628-2019; Parcollet,
Olivier/C-2340-2008
OI Ayral, Thomas/0000-0003-0960-4065; Laura, Messio/0000-0002-4048-9838;
Parcollet, Olivier/0000-0002-0389-2660; Ferrero,
Michel/0000-0003-1882-2881
FU ERCEuropean Research Council (ERC)European Commission
[278472-MottMetals, 617196-CorrelMat]; Deutsche
ForschungsgemeinschaftGerman Research Foundation (DFG) [SFB 668-A3]
FX The TRIQS project is supported by the ERC Grant No. 278472-MottMetals.
We acknowledge contributions to the library and feedbacks from M.
Aichhorn, A. Antipov, L. Boehnke, L. Pourovskii, as well as feedback
from our user community. I.K. acknowledges support from Deutsche
Forschungsgemeinschaft via Project SFB 668-A3. P.S. acknowledges support
from ERC Grant No. 617196-CorrelMat.
CR [Anonymous], 1949, ANN MATH STAT, V20, P620
Bauer B, 2011, J STAT MECH-THEORY E, DOI 10.1088/1742-5468/2011/05/P05001
Blackford LS, 2002, ACM T MATH SOFTWARE, V28, P135, DOI 10.1145/567806.567807
Georges A, 1996, REV MOD PHYS, V68, P13, DOI 10.1103/RevModPhys.68.13
Gull E, 2008, EPL-EUROPHYS LETT, V82, DOI 10.1209/0295-5075/82/57003
Gull E, 2011, REV MOD PHYS, V83, P349, DOI 10.1103/RevModPhys.83.349
Huang L., ARXIV14097573
Kotliar G, 2006, REV MOD PHYS, V78, P865, DOI 10.1103/RevModPhys.78.865
Lauchli AM, 2009, PHYS REV B, V80, DOI 10.1103/PhysRevB.80.235117
Lawson C. L., 1979, ACM Transactions on Mathematical Software, V5, P324, DOI 10.1145/355841.355848
Maier T, 2005, REV MOD PHYS, V77, P1027, DOI 10.1103/RevModPhys.77.1027
Perez F, 2007, COMPUT SCI ENG, V9, P21, DOI 10.1109/MCSE.2007.53
Rubtsov AN, 2008, PHYS REV B, V77, DOI 10.1103/PhysRevB.77.033101
Rubtsov AN, 2005, PHYS REV B, V72, DOI 10.1103/PhysRevB.72.035122
Schollwock U, 2005, REV MOD PHYS, V77, P259, DOI 10.1103/RevModPhys.77.259
SHERMAN J, 1950, ANN MATH STAT, V21, P124, DOI 10.1214/aoms/1177729893
Toschi A, 2007, PHYS REV B, V75, DOI 10.1103/PhysRevB.75.045118
van Loon EGCP, 2014, PHYS REV B, V90, DOI 10.1103/PhysRevB.90.235135
Werner P, 2006, PHYS REV LETT, V97, DOI 10.1103/PhysRevLett.97.076405
NR 19
TC 156
Z9 156
U1 1
U2 20
PU ELSEVIER SCIENCE BV
PI AMSTERDAM
PA PO BOX 211, 1000 AE AMSTERDAM, NETHERLANDS
SN 0010-4655
EI 1879-2944
J9 COMPUT PHYS COMMUN
JI Comput. Phys. Commun.
PD NOV
PY 2015
VL 196
BP 398
EP 415
DI 10.1016/j.cpc.2015.04.023
PG 18
WC Computer Science, Interdisciplinary Applications; Physics, Mathematical
SC Computer Science; Physics
GA CT1ZX
UT WOS:000362602900038
DA 2021-04-21
ER
PT J
AU Wiebusch, M
AF Wiebusch, Martin
TI HEPMath 1.4: A mathematica package for semi-automatic computations in
high energy physics
SO COMPUTER PHYSICS COMMUNICATIONS
LA English
DT Article
DE High energy physics; Feynman amplitudes; Tensor algebra; Code
generation; Python
ID AUTOMATIC-GENERATION; FEYNMAN-RULES; FEYNARTS; FORMCALC
AB This article introduces the Mathematica package HEPMath which provides a number of utilities and algorithms for High Energy Physics computations in Mathematica. Its functionality is similar to packages like FormCalc or FeynCalc, but it takes a more complete and extensible approach to implementing common High Energy Physics notations in the Mathematica language, in particular those related to tensors and index contractions. It also provides a more flexible method for the generation of numerical code which is based on new features for C code generation in Mathematica. In particular it can automatically generate Python extension modules which make the compiled functions callable from Python, thus eliminating the need to write any code in a low-level language like C or Fortran. It also contains seamless interfaces to LHAPDF, FeynArts, and LoopTools.
Program summary
Program title: HEPMath
Catalogue identifier: AEWU_v1_0
Program summary URL: http://cpc.cs.qub.ac.uk/summaries/AEWU_v1_0.html
Program obtainable from: CPC Program Library, Queen's University, Belfast, N. Ireland
Licensing provisions: Standard CPC licence, http://cpc.cs.qub.ac.uk/licence/licence.html
No. of lines in distributed program, including test data, etc.: 27360
No. of bytes in distributed program, including test data, etc.: 668749
Distribution format: tar.gz
Programming language: Mathematica, C and python.
Computer: Workstation. Operating system: Linux.
Classification: 11.1, 5, 4.4.
External routines: FeynArts (optional), LoopTools (optional), LHAPDF (optional)
Nature of problem:
Automatisation of (Feynman diagrammatic) computations in High Energy Physics, representation and manipulation of tensors with symbolic indices in the Mathematica language, generation of numerical code and interface to Python.
Solution method:
A Mathematica package which provides functions to construct and manipulate tensor expressions in Mathematica and interface to other popular tools in High Energy Physics.
Unusual features:
A code generation method which uses Mathematica's byte code compiler (Compile) rather than CForm/FortranForm and the automatic generation of Python extension modules.
Running time:
The examples provided only take a few seconds to run. (C) 2015 Elsevier B.V. All rights reserved.
C1 Univ Durham, Dept Phys, Inst Particle Phys Phenomenol, Durham CH1 3LE, England.
RP Wiebusch, M (corresponding author), Univ Durham, Dept Phys, Inst Particle Phys Phenomenol, Durham CH1 3LE, England.
EM martin.wiebusch@durham.ac.uk
CR Alioli S, 2010, J HIGH ENERGY PHYS, DOI 10.1007/JHEP06(2010)043
Alloul A, 2014, COMPUT PHYS COMMUN, V185, P2250, DOI 10.1016/j.cpc.2014.04.012
Alwall J, 2014, J HIGH ENERGY PHYS, DOI 10.1007/JHEP07(2014)079
Alwall J, 2007, J HIGH ENERGY PHYS, DOI 10.1088/1126-6708/2007/09/028
Belyaev A, 2013, COMPUT PHYS COMMUN, V184, P1729, DOI 10.1016/j.cpc.2013.01.014
Bern Z, 2014, J PHYS CONF SER, V523, DOI 10.1088/1742-6596/523/1/012051
Boos E, 2004, NUCL INSTRUM METH A, V534, P250, DOI 10.1016/j.nima.2004.07.096
Cafarella A, 2009, COMPUT PHYS COMMUN, V180, P1941, DOI 10.1016/j.cpc.2009.04.023
Cascioli F, 2012, PHYS REV LETT, V108, DOI 10.1103/PhysRevLett.108.111601
Christensen N, 2011, EUR PHYS J C, V71, DOI 10.1140/epjc/s10052-011-1541-5
Christensen ND, 2009, COMPUT PHYS COMMUN, V180, P1614, DOI 10.1016/j.cpc.2009.02.018
Ghosh D., ARXIV14112029HEPPH
Gleisberg T, 2009, J HIGH ENERGY PHYS, DOI 10.1088/1126-6708/2009/02/007
Hahn T, 2008, COMPUT PHYS COMMUN, V178, P217, DOI 10.1016/j.cpc.2007.09.004
Hahn T, 1999, COMPUT PHYS COMMUN, V118, P153, DOI 10.1016/S0010-4655(98)00173-8
Hahn T, 2006, NUCL PHYS B-PROC SUP, V160, P101, DOI 10.1016/j.nuclphysbps.2006.09.035
Hahn T, 2004, NUCL PHYS B-PROC SUP, V135, P333, DOI 10.1016/j.nuclphysbps.2004.09.018
Hahn T, 2003, NUCL PHYS B-PROC SUP, V116, P363, DOI 10.1016/S0920-5632(03)80200-1
Hahn T, 2001, COMPUT PHYS COMMUN, V140, P418, DOI 10.1016/S0010-4655(01)00290-9
Hahn T., 2005, ECONF C, P0604
Hahn T, 2006, NUCL PHYS B-PROC SUP, V157, P236, DOI 10.1016/j.nuclphysbps.2006.03.026
Hirschi V., 2011, JHEP, V1105
JAMIN M, 1993, COMPUT PHYS COMMUN, V74, P265, DOI 10.1016/0010-4655(93)90097-V
Kanaki A, 2000, COMPUT PHYS COMMUN, V132, P306, DOI 10.1016/S0010-4655(00)00151-X
Kilian W, 2011, EUR PHYS J C, V71, DOI 10.1140/epjc/s10052-011-1742-y
Krauss F, 2002, J HIGH ENERGY PHYS
KUBLBECK J, 1990, COMPUT PHYS COMMUN, V60, P165, DOI 10.1016/0010-4655(90)90001-H
MERTIG R, 1991, COMPUT PHYS COMMUN, V64, P345, DOI 10.1016/0010-4655(91)90130-D
Moretti M., ARXIVHEPPH0102195HEP
Semenov AV, 2009, COMPUT PHYS COMMUN, V180, P431, DOI 10.1016/j.cpc.2008.10.012
Semenov AV, 1997, NUCL INSTRUM METH A, V389, P293, DOI 10.1016/S0168-9002(97)00096-X
Staub F, 2014, COMPUT PHYS COMMUN, V185, P1773, DOI 10.1016/j.cpc.2014.02.018
Staub F, 2010, COMPUT PHYS COMMUN, V181, P1077, DOI 10.1016/j.cpc.2010.01.011
STELZER T, 1994, COMPUT PHYS COMMUN, V81, P357, DOI 10.1016/0010-4655(94)90084-1
Vermaseren J. A. M., ARXIVMATHPH0010025MA
Yuasa F, 2000, PROG THEOR PHYS SUPP, P18
NR 36
TC 11
Z9 11
U1 0
U2 8
PU ELSEVIER SCIENCE BV
PI AMSTERDAM
PA PO BOX 211, 1000 AE AMSTERDAM, NETHERLANDS
SN 0010-4655
EI 1879-2944
J9 COMPUT PHYS COMMUN
JI Comput. Phys. Commun.
PD OCT
PY 2015
VL 195
BP 172
EP 190
DI 10.1016/j.cpc.2015.04.022
PG 19
WC Computer Science, Interdisciplinary Applications; Physics, Mathematical
SC Computer Science; Physics
GA CM5SY
UT WOS:000357750100017
DA 2021-04-21
ER
PT J
AU Bernon, J
Dumont, B
AF Bernon, Jeremy
Dumont, Beranger
TI Lilith: a tool for constraining new physics from Higgs measurements
SO EUROPEAN PHYSICAL JOURNAL C
LA English
DT Article
ID STANDARD MODEL; CP PROPERTIES; BOSON; COUPLINGS; DECAYS; MASS
AB The properties of the observed Higgs boson with mass around 125 GeV can be affected in a variety of ways by new physics beyond the Standard Model (SM). The wealth of experimental results, targeting the different combinations for the production and decay of a Higgs boson, makes it a non-trivial task to assess the patibility of a non-SM-like Higgs boson with all available results. In this paper we present Lilith, a new public tool for constraining new physics from signal strength measurements performed at the LHC and the Tevatron. Lilith is a Python library that can also be used in C and C++/ROOT programs. The Higgs likelihood is based on experimental results stored in an easily extensible XML database, and is evaluated from the user input, given in XML format in terms of reduced couplings or signal strengths. The results of Lilith can be used to constrain a wide class of new physics scenarios.
C1 [Bernon, Jeremy; Dumont, Beranger] Univ Grenoble Alpes, Lab Phys Subatom & Cosmol, CNRS, IN2P3, F-38026 Grenoble, France.
[Dumont, Beranger] Inst for Basic Sci Korea, Ctr Theoret Phys Universe, Taejon 305811, South Korea.
RP Bernon, J (corresponding author), Univ Grenoble Alpes, Lab Phys Subatom & Cosmol, CNRS, IN2P3, 53 Ave Martyrs, F-38026 Grenoble, France.
EM bernon@lpsc.in2p3.fr; dum33@ibs.re.kr
FU ANRFrench National Research Agency (ANR); Investissements d'avenir,
Labex ENIGMASSFrench National Research Agency (ANR); IBSCentre National
de la Recherche Scientifique (CNRS) [IBS-R018-D1]; CTPU-IBS
FX We are deeply indebted to Sabine Kraml for her support at all stages of
the project and for comments on the manuscript. We would also like to
thank Genevieve Belanger, Ulrich Ellwanger, John F. Gunion and Sabine
Kraml for the fruitful collaboration on Higgs coupling fits in 2012 and
2013, from which the idea of Lilith originated. This work was supported
in part by the ANR project DMAstroLHC, the "Investissements d'avenir,
Labex ENIGMASS", and by the IBS under Project Code IBS-R018-D1. JB
thanks the CTPU-IBS for hospitality and financial support for a research
stay during which this work was finished.
CR Aad G, 2015, PHYS REV D, V92, DOI 10.1103/PhysRevD.92.012006
Aad G, 2015, J HIGH ENERGY PHYS, DOI 10.1007/JHEP04(2015)117
Aad G, 2015, J HIGH ENERGY PHYS, DOI 10.1007/JHEP01(2015)069
Aad G, 2014, PHYS REV D, V90, DOI 10.1103/PhysRevD.90.112015
Aad G, 2014, PHYS REV LETT, V112, DOI 10.1103/PhysRevLett.112.201802
Aad G, 2013, PHYS LETT B, V726, P88, DOI 10.1016/j.physletb.2013.08.010
Aad G, 2012, PHYS LETT B, V716, P1, DOI 10.1016/j.physletb.2012.08.020
Aad G., 2015, PHYS REV D, V91
Aaltonen T, 2013, PHYS REV D, V88, DOI 10.1103/PhysRevD.88.052014
[Anonymous], 2014, ATLASCONF2014009 CER
[Anonymous], 2014, ATLASCONF2014011 CER
[Anonymous], 2011, CMSNOTE2011005 CERN
[Anonymous], 2014, ATLASCONF2014010 CER
Arnold K, 2009, COMPUT PHYS COMMUN, V180, P1661, DOI 10.1016/j.cpc.2009.03.006
ATLAS Collab, 2013, JHEP UNPUB
ATLAS Collaboration, DAT FIG 7 MEAS HIGGS, DOI [10.7484/INSPIREHEP.DATA.A78C.HK44, DOI 10.7484/INSPIREHEP.DATA.A78C.HK44]
Banerjee S, 2014, PHYS REV D, V89, DOI 10.1103/PhysRevD.89.053010
Bechtle P, 2014, J HIGH ENERGY PHYS, DOI 10.1007/JHEP11(2014)039
Bechtle P, 2014, EUR PHYS J C, V74, DOI 10.1140/epjc/s10052-013-2711-4
Belanger G, 2013, PHYS REV D, V88, DOI 10.1103/PhysRevD.88.075008
Belanger G, 2013, J HIGH ENERGY PHYS, DOI 10.1007/JHEP02(2013)053
Belanger G, 2014, PHYS REV D, V89, DOI 10.1103/PhysRevD.89.095023
Belanger G, 2013, PHYS LETT B, V726, P773, DOI 10.1016/j.physletb.2013.09.059
Belyaev A, 2014, PHYS REV D, V90, DOI 10.1103/PhysRevD.90.035012
Berge S, 2014, EUR PHYS J C, V74, DOI 10.1140/epjc/s10052-014-3164-0
Bergstrom J, 2015, PHYS REV D, V91, DOI 10.1103/PhysRevD.91.075008
Bernon J, 2014, PHYS REV D, V90, DOI 10.1103/PhysRevD.90.071301
Boudjema F., ARXIV13075865
Brooijmans G., 2013, ARXIV14051617
Cacciapaglia G, 2013, J HIGH ENERGY PHYS, DOI 10.1007/JHEP03(2013)029
Cao JJ, 2014, J HIGH ENERGY PHYS, DOI 10.1007/JHEP01(2014)150
Carena M, 2012, J HIGH ENERGY PHYS, DOI 10.1007/JHEP07(2012)175
Chatrchyan S, 2014, EUR PHYS J C, V74, DOI 10.1140/epjc/s10052-014-2980-6
Chatrchyan S, 2014, PHYS REV D, V89, DOI 10.1103/PhysRevD.89.092007
Chatrchyan S, 2014, PHYS REV D, V89, DOI 10.1103/PhysRevD.89.012003
Chatrchyan S, 2014, J HIGH ENERGY PHYS, DOI 10.1007/JHEP01(2014)096
Chatrchyan S, 2012, PHYS LETT B, V716, P30, DOI 10.1016/j.physletb.2012.08.021
Cheung K., ARXIV150103552
Cheung K, 2014, PHYS REV D, V90, DOI 10.1103/PhysRevD.90.095009
Cheung K, 2014, J HIGH ENERGY PHYS, DOI 10.1007/JHEP01(2014)085
Choi S, 2013, J HIGH ENERGY PHYS, DOI 10.1007/JHEP10(2013)225
Ciuchini M., ARXIV14106940
CMS Collaboration, 2014, NAT PHYS, V10, P557, DOI 10.1038/NPHYS3005
Cranmer K, 2015, PHYS REV D, V91, DOI 10.1103/PhysRevD.91.054032
David A., ARXIV12090040 LHG HI
de Blas J., ARXIV14104204
Demartin F, 2014, EUR PHYS J C, V74, DOI 10.1140/epjc/s10052-014-3065-2
Djouadi A, 1998, COMPUT PHYS COMMUN, V108, P56, DOI 10.1016/S0010-4655(97)00123-9
Djouadi A, 2008, PHYS REP, V459, P1, DOI 10.1016/j.physrep.2007.10.005
Djouadi A, 2013, EUR PHYS J C, V73, DOI 10.1140/epjc/s10052-013-2512-9
Dumont B, 2014, PHYS REV D, V89, DOI 10.1103/PhysRevD.89.055018
Dumont B, 2014, PHYS REV D, V90, DOI 10.1103/PhysRevD.90.035021
Dumont B, 2013, J HIGH ENERGY PHYS, DOI 10.1007/JHEP07(2013)065
Ellis J, 2014, J HIGH ENERGY PHYS, DOI 10.1007/JHEP07(2014)036
Ellis J, 2013, J HIGH ENERGY PHYS, DOI 10.1007/JHEP06(2013)103
Endo M, 2014, J HIGH ENERGY PHYS, DOI 10.1007/JHEP04(2014)139
Falkowski A, 2013, J HIGH ENERGY PHYS, DOI 10.1007/JHEP11(2013)111
Fan JJ, 2014, J HIGH ENERGY PHYS, DOI 10.1007/JHEP06(2014)031
Frederix R, 2011, PHYS LETT B, V701, P427, DOI 10.1016/j.physletb.2011.06.012
Giardino PP, 2014, J HIGH ENERGY PHYS, DOI 10.1007/JHEP05(2014)046
Godbole R., ARXIVHEPPH0404024
Hunter JD, 2007, COMPUT SCI ENG, V9, P90, DOI 10.1109/MCSE.2007.55
JAMES F, 1975, COMPUT PHYS COMMUN, V10, P343, DOI 10.1016/0010-4655(75)90039-9
Khachatryan V, 2015, EUR PHYS J C, V75, DOI 10.1140/epjc/s10052-015-3351-7
Khachatryan V, 2014, EUR PHYS J C, V74, DOI 10.1140/epjc/s10052-014-3076-z
Khachatryan V., 2014, JHEP, V1409
Kitahara T, 2013, J HIGH ENERGY PHYS, DOI 10.1007/JHEP05(2013)035
Kraml S., ARXIVHEPPH0608079
LHC Higgs Cross Section Working Group, 2013, CERN2013004, DOI [10.5170/CERN-2013-004, DOI 10.5170/CERN-2013-004]
Lopez-Val D, 2013, J HIGH ENERGY PHYS, DOI 10.1007/JHEP10(2013)134
Martin AD, 2009, EUR PHYS J C, V63, P189, DOI 10.1140/epjc/s10052-009-1072-5
Robens T, 2015, EUR PHYS J C, V75, DOI 10.1140/epjc/s10052-015-3323-y
Skands P, 2004, J HIGH ENERGY PHYS, DOI 10.1088/1126-6708/2004/07/036
SPIRA M, ARXIVHEPPH9510347
Wang L, 2014, J HIGH ENERGY PHYS, DOI 10.1007/JHEP04(2014)128
NR 75
TC 37
Z9 37
U1 0
U2 8
PU SPRINGER
PI NEW YORK
PA 233 SPRING ST, NEW YORK, NY 10013 USA
SN 1434-6044
EI 1434-6052
J9 EUR PHYS J C
JI Eur. Phys. J. C
PD SEP 21
PY 2015
VL 75
IS 9
AR 440
DI 10.1140/epjc/s10052-015-3645-9
PG 30
WC Physics, Particles & Fields
SC Physics
GA CS0CI
UT WOS:000361724400001
OA DOAJ Gold
DA 2021-04-21
ER
PT J
AU Swingler, K
AF Swingler, K.
TI Soil Physics with Python
SO EUROPEAN JOURNAL OF SOIL SCIENCE
LA English
DT Book Review
CR Bittelli M., 2015, SOIL PHYS PYTHON
NR 1
TC 0
Z9 0
U1 0
U2 21
PU WILEY
PI HOBOKEN
PA 111 RIVER ST, HOBOKEN 07030-5774, NJ USA
SN 1351-0754
EI 1365-2389
J9 EUR J SOIL SCI
JI Eur. J. Soil Sci.
PD SEP
PY 2015
VL 66
IS 5
BP 963
EP 963
DI 10.1111/ejss.12287
PG 1
WC Soil Science
SC Agriculture
GA CR2UU
UT WOS:000361187000016
DA 2021-04-21
ER
PT J
AU Kumar, R
Kudinov, P
Bechta, S
Curnier, F
Marques, M
AF Kumar, Ranjan
Kudinov, Pavel
Bechta, Sevostian
Curnier, Florence
Marques, Michel
TI Dynamic Hybrid Reliability Studies of a Decay Heat Removal System
SO INTERNATIONAL JOURNAL OF RELIABILITY QUALITY & SAFETY ENGINEERING
LA English
DT Article
DE Probabilistic safety assessment; dynamic reliability; decay heat removal
system; stochastic hybrid automata; piecewise deterministic Markov
process
AB Some critical safety systems exhibit the characteristics of hybrid stochastic class whose performance depends on the dynamic interactions of deterministic variables of physical phenomena and probabilistic variables of system failures. However, conventional probabilistic safety assessment (PSA) method involves static event and linked fault tree analysis and does not capture the dynamic interactions of such hybrid stochastic systems. Additionally, the existing dynamic PSA methods do not consider any repair possibility of some failed components during safety assessment. To address these issues, this paper presents a dynamic hybrid reliability assessment scheme for performance studies of repairable nuclear safety systems during a mission time. This scheme combines the features of reliability block diagram (RBD) for system compositions and partial differential equations for system physics using a customized stochastic hybrid automata tool implemented on Python platform. A case study of decay heat removal (DHR) systems has been performed using the introduced scheme. The impacts of failure rates and repair rates on sodium temperature evolution over a mission time have been analyzed. The results provide useful safety insights in mission safety tests of DHR systems. In sum, this work advances the dynamic safety assessment approach for complex system designs including nuclear power plants.
C1 [Kumar, Ranjan; Kudinov, Pavel; Bechta, Sevostian] KTH Royal Inst Technol, Nucl Power Safety, SE-10691 Stockholm, Sweden.
[Curnier, Florence; Marques, Michel] CEN Cadarache, DEN CAD DER SESI, F-13108 St Paul Les Durance, France.
RP Kumar, R (corresponding author), KTH Royal Inst Technol, Nucl Power Safety, SE-10691 Stockholm, Sweden.
EM rankum@kth.se; pkudinov@kth.se; bechta@kth.se; florence.curnier@cea.fr;
michel.marques@cea.fr
OI Kudinov, Pavel/0000-0002-0683-9136; Bechta,
Sevostian/0000-0001-7816-8442
CR ACOSTA C, 1993, RELIAB ENG SYST SAFE, V41, P135, DOI 10.1016/0951-8320(93)90027-V
Aldemir T, 2013, ANN NUCL ENERGY, V52, P113, DOI 10.1016/j.anucene.2012.08.001
Amendola G. R., 1984, DYLAM 1 SOFTWARE PAC
[Anonymous], 1991, IAEATECDOC626
Babykina G., 2013, ANN C EUR SAF REL AM
Bouissou M., 2013, 4 IFAC WORKSH DEP CO, V4
Bouissou M., 2002, P 6 PROB SAF ASS MAN
Catalyurek U, 2010, RELIAB ENG SYST SAFE, V95, P278, DOI 10.1016/j.ress.2009.10.008
Chraibi H., 2013, PSAM TOP C TOK, P14
Curnier F., 2014, P INT C ADV NUCL POW
DAVIS MHA, 1984, J ROY STAT SOC B MET, V46, P353
DEVOOGHT J, 1992, NUCL SCI ENG, V111, P229, DOI 10.13182/NSE92-A23937
Fetcher C. D., 1992, RELAP5 MOD3 USER GUI
GARRETT CJ, 1995, IEEE T SYST MAN CYB, V25, P824, DOI 10.1109/21.376495
Gauthe P., 2013, P FRONT RES 2013 PAR
Goebel R, 2009, IEEE CONTR SYST MAG, V29, P28, DOI 10.1109/MCS.2008.931718
Hsueh KS, 1996, RELIAB ENG SYST SAFE, V52, P297, DOI 10.1016/0951-8320(95)00140-9
Kloos M, 2006, NUCL SCI ENG, V153, P137, DOI 10.13182/NSE06-A2601
Labeau PE, 2000, RELIAB ENG SYST SAFE, V68, P219, DOI 10.1016/S0951-8320(00)00017-X
Lin Y. H., 2014, P EUR SAF REL WROCL
MARSAN MA, 1995, MODELING GENERALIZED
Marseguerra M, 1998, MATH COMPUT SIMULAT, V47, P371, DOI 10.1016/S0378-4754(98)00112-8
MATSUOKA T, 1988, NUCL SCI ENG, V98, P64, DOI 10.13182/NSE88-A23526
Nayak AK, 2014, FRONT ENERGY RES, DOI 10.3389/fenrg.2014.00040
Castaneda GAP, 2011, P I MECH ENG O-J RIS, V225, P28, DOI 10.1177/1748006XJRR312
Saez M., 2013, P FRONT RES 2013 PAR
Swaminathan S, 1999, RELIAB ENG SYST SAFE, V65, P103, DOI 10.1016/S0951-8320(98)00092-1
U.S. Nuclear Regulatory Commission, 1975, WASH1400 US NUCL REG
Verlinden S, 2012, RELIAB ENG SYST SAFE, V101, P35, DOI 10.1016/j.ress.2012.01.004
Young M.F., 2005, MELCOR COMPUTER CODE, VVolume 1
Zhang HY, 2008, P I MECH ENG H, V222, P583, DOI 10.1243/09544119JEIM346
NR 31
TC 1
Z9 1
U1 0
U2 1
PU WORLD SCIENTIFIC PUBL CO PTE LTD
PI SINGAPORE
PA 5 TOH TUCK LINK, SINGAPORE 596224, SINGAPORE
SN 0218-5393
EI 1793-6446
J9 INT J RELIAB QUAL SA
JI Int. J. Reliab. Qual. Saf. Eng.
PD AUG
PY 2015
VL 22
IS 4
AR 1550020
DI 10.1142/S0218539315500205
PG 17
WC Engineering, Multidisciplinary
SC Engineering
GA V0P0R
UT WOS:000216429200005
DA 2021-04-21
ER
PT J
AU Wittek, P
AF Wittek, Peter
TI Algorithm 950: Ncpol2sdpa-Sparse Semidefinite Programming Relaxations
for Polynomial Optimization Problems of Noncommuting Variables
SO ACM TRANSACTIONS ON MATHEMATICAL SOFTWARE
LA English
DT Article
DE Design; Algorithms; Performance; Semidefinite programming; noncommuting
variables; ground state energy; quantum correlations
ID GLOBAL OPTIMIZATION; MOMENTS
AB A hierarchy of semidefinite programming (SDP) relaxations approximates the global optimum of polynomial optimization problems of noncommuting variables. Generating the relaxation, however, is a computationally demanding task, and only problems of commuting variables have efficient generators. We develop an implementation for problems of noncommuting variables that creates the relaxation to be solved by SDPA-a high-performance solver that runs in a distributed environment. We further exploit the inherent sparsity of optimization problems in quantum physics to reduce the complexity of the resulting relaxations. Constrained problems with a relaxation of order two may contain up to a hundred variables. The implementation is available in Python. The tool helps solve such as finding the ground state energy or testing quantum correlations.
C1 Univ Boras, Swedish Sch Lib & Informat Sci, S-50190 Boras, Sweden.
RP Wittek, P (corresponding author), Univ Boras, Swedish Sch Lib & Informat Sci, Allegatan 1, S-50190 Boras, Sweden.
EM peter@peterwittek.com
FU European CommissionEuropean CommissionEuropean Commission Joint Research
Centre [FP7-601138 PERICLES]; AWS in Education Machine Learning Grant
award; Red Espanola de Supercomputacion [FI-2013-1-0008, FI-2013-3-0004]
FX This work was supported by the European Commission 7th Framework
Programme under Grant Agreement Number FP7-601138 PERICLES, by the AWS
in Education Machine Learning Grant award, and by the Red Espanola de
Supercomputacion grants number FI-2013-1-0008 and FI-2013-3-0004.
CR Cafuta K, 2011, OPTIM METHOD SOFTW, V26, P363, DOI 10.1080/10556788.2010.544312
Cimpric J, 2010, J MATH ANAL APPL, V369, P443, DOI 10.1016/j.jmaa.2010.03.045
Fujisawa K., 2012, P SC 12 INT C HIGH P
Helton J., 2012, NCALGEBRA MATH PACKA
Henrion D, 2003, ACM T MATH SOFTWARE, V29, P165, DOI 10.1145/779359.779363
Henrion D, 2009, OPTIM METHOD SOFTW, V24, P761, DOI 10.1080/10556780802699201
Joyner D., 2012, ACM COMMUN COMPUT AL, V45, P225, DOI [10.1145/2110170.2110185, DOI 10.1145/2110170.2110185]
Lasserre JB, 2001, SIAM J OPTIMIZ, V11, P796, DOI 10.1137/S1052623400366802
Lofberg J., 2004, COMP AID CONTR SYST, P284, DOI DOI 10.1109/CACSD.2004.1393890
Navascues M, 2013, NEW J PHYS, V15, DOI 10.1088/1367-2630/15/2/023026
Navascues M, 2012, INT SER OPER RES MAN, V166, P601, DOI 10.1007/978-1-4614-0769-0_21
Navascues M, 2008, NEW J PHYS, V10, DOI 10.1088/1367-2630/10/7/073013
Pironio S, 2010, SIAM J OPTIMIZ, V20, P2157, DOI 10.1137/090760155
Sagnol G., 2012, 1248 ZUS I BERL
Vandenberghe L, 1996, SIAM REV, V38, P49, DOI 10.1137/1038003
Waki H, 2008, ACM T MATH SOFTWARE, V35, DOI 10.1145/1377612.1377619
Yamashita M, 2003, PARALLEL COMPUT, V29, P1053, DOI 10.1016/S0167-8191(03)00087-5
NR 17
TC 21
Z9 22
U1 0
U2 2
PU ASSOC COMPUTING MACHINERY
PI NEW YORK
PA 2 PENN PLAZA, STE 701, NEW YORK, NY 10121-0701 USA
SN 0098-3500
EI 1557-7295
J9 ACM T MATH SOFTWARE
JI ACM Trans. Math. Softw.
PD JUN
PY 2015
VL 41
IS 3
AR 21
DI 10.1145/2699464
PG 12
WC Computer Science, Software Engineering; Mathematics, Applied
SC Computer Science; Mathematics
GA CJ7JJ
UT WOS:000355670800009
DA 2021-04-21
ER
PT J
AU Overholt, KJ
Ezekoye, OA
AF Overholt, Kristopher J.
Ezekoye, Ofodike A.
TI Quantitative Testing of Fire Scenario Hypotheses: A Bayesian Inference
Approach
SO FIRE TECHNOLOGY
LA English
DT Article
DE Fire investigation; Hypothesis testing; Bayesian inference; Uncertainty
quantification
ID PYROLYSIS; PYTHON; MODELS
AB Fire models are routinely used to evaluate life safety aspects of building design projects and are being used more often in fire and arson investigations as well as reconstructions of firefighter line-of-duty deaths and injuries. A fire within a compartment effectively leaves behind a record of fire activity and history (i.e., fire signatures). Fire and arson investigators can utilize these fire signatures in the determination of cause and origin during fire reconstruction exercises. Researchers conducting fire experiments can utilize this record of fire activity to better understand the underlying physics. In all of these applications, the heat release rate and location of a fire are important parameters that govern the evolution of thermal conditions within a fire compartment. These input parameters can be a large source of uncertainty in fire models, especially in scenarios in which experimental data or detailed information on fire behavior are not available. A methodology is sought to estimate the amount of certainty (or degree of belief) in the input parameters for hypothesized scenarios. To address this issue, an inversion framework was applied to scenarios that have relevance in fire scene reconstructions. Rather than using point estimates of input parameters, a statistical inversion framework based on the Bayesian inference approach was used to calculate probability distributions of input parameters. These probability distributions contain uncertainty information about the input parameters and can be propagated through fire models to obtain uncertainty information about predicted quantities of interest. The Bayesian inference approach was applied to various fire problems using different models: empirical correlations, zone models, and computational fluid dynamics fire models. Example applications include the estimation of steady-state fire sizes in a compartment and the location of a fire.
C1 [Overholt, Kristopher J.] NIST, Gaithersburg, MD 20899 USA.
[Ezekoye, Ofodike A.] Univ Texas Austin, Dept Mech Engn, Austin, TX 78712 USA.
RP Ezekoye, OA (corresponding author), Univ Texas Austin, Dept Mech Engn, Austin, TX 78712 USA.
EM dezekoye@mail.utexas.edu
OI Ezekoye, Ofodike/0000-0002-8135-696X
CR Andrieu C, 2003, MACH LEARN, V50, P5, DOI 10.1023/A:1020281327116
[Anonymous], 2011, 921 NFPA
Babrauskas V, 2005, FIRE SAFETY J, V40, P528, DOI 10.1016/j.firesaf.2005.05.006
Bal N, 2013, FIRE SAFETY J, V61, P36, DOI 10.1016/j.firesaf.2013.08.015
Bayes T., 1763, PHIL T ROY SOC LOND, V53, P370, DOI DOI 10.1098/RSTL.1763.0053
Bolstad W.M., 2010, UNDERSTANDING COMPUT
Chaos M, 2011, P COMBUST INST, V33, P2599, DOI 10.1016/j.proci.2010.07.018
Cowlard A, 2010, FIRE TECHNOL, V46, P719, DOI 10.1007/s10694-008-0069-1
Drysdale D., 2002, INTRO FIRE DYNAMICS
Fleury R, 2010, THESIS U CANTERBURY
Fonnesbeck C, 2011, SCIPY 2011 C AUST TU
Gelman A, 2003, BAYESIAN DATA ANAL
Holladay KL, 2011, P 13 ANN C COMP GEN, P655
Hunter JD, 2007, COMPUT SCI ENG, V9, P90, DOI 10.1109/MCSE.2007.55
Jahn W, 2012, ADV ENG SOFTW, V47, P114, DOI 10.1016/j.advengsoft.2011.12.005
Jones E., 2001, SCIPY OPEN SOURCE SC
Kirk P, 1997, KIRKS FIRE INVESTIGA
Koo SH, 2010, FIRE SAFETY J, V45, P193, DOI 10.1016/j.firesaf.2010.02.003
Kruse C, 2013, SOC STUD SCI, V43, P657, DOI 10.1177/0306312712472572
Lautenberger C., 2011, FIRE SAFETY SCI, V10, P751, DOI [10.3801/IAFSS.FSS.10-751, DOI 10.3801/IAFSS.FSS.10-751]
Lautenberger C, 2006, FIRE SAFETY J, V41, P204, DOI 10.1016/j.firesaf.2005.12.004
Lautenberger C, 2009, FIRE SAFETY J, V44, P819, DOI 10.1016/j.firesaf.2009.03.011
Marquis DM, 2013, COMBUST FLAME, V160, P818, DOI 10.1016/j.combustflame.2012.12.008
McGrattan K., 2013, FIRE DYNAMICS SIMULA, V6th ed.
McGrattan K, 2011, METROLOGIA, V48, P173, DOI 10.1088/0026-1394/48/3/011
Miki K, 2013, COMBUST FLAME, V160, P861, DOI 10.1016/j.combustflame.2013.01.020
Oliphant TE, 2007, COMPUT SCI ENG, V9, P10, DOI 10.1109/MCSE.2007.58
Oliphant Travis E., 2006, A GUIDE TO NUMPY, V1
Overholt KJ, 2013, THESIS U TEXAS AUSTI
Overholt KJ, 2012, FIRE TECHNOL, V48, P893, DOI 10.1007/s10694-011-0250-9
Patil A, 2010, J STAT SOFTW, V35, P1
Peacock R. D., 2005, SPECIAL PUBLICATION, V1041
Petrovich WP, 1998, FIRE INVESTIGATORS H
Redsicker DR, 1997, PRACTICAL FIRE ARSON
Rein G, 2006, COMBUST FLAME, V146, P95, DOI 10.1016/j.combustflame.2006.04.013
Steckler KD, 1982, 822520 NBSIR
Wang JB, 2005, INT J HEAT MASS TRAN, V48, P15, DOI 10.1016/j.ijheatmasstransfer.2004.08.009
Yuen K.V., 2010, BAYESIAN METHODS STR
NR 38
TC 13
Z9 14
U1 1
U2 16
PU SPRINGER
PI DORDRECHT
PA VAN GODEWIJCKSTRAAT 30, 3311 GZ DORDRECHT, NETHERLANDS
SN 0015-2684
EI 1572-8099
J9 FIRE TECHNOL
JI Fire Technol.
PD MAR
PY 2015
VL 51
IS 2
BP 335
EP 367
DI 10.1007/s10694-013-0384-z
PG 33
WC Engineering, Multidisciplinary; Materials Science, Multidisciplinary
SC Engineering; Materials Science
GA CC4WR
UT WOS:000350356300009
DA 2021-04-21
ER
PT J
AU Tygier, S
Appleby, RB
Garland, JM
Hock, K
Owen, H
Kelliher, DJ
Sheehy, SL
AF Tygier, S.
Appleby, R. B.
Garland, J. M.
Hock, K.
Owen, H.
Kelliher, D. J.
Sheehy, S. L.
TI The PyZgoubi framework and the simulation of dynamic aperture in
fixed-field alternating-gradient accelerators
SO NUCLEAR INSTRUMENTS & METHODS IN PHYSICS RESEARCH SECTION A-ACCELERATORS
SPECTROMETERS DETECTORS AND ASSOCIATED EQUIPMENT
LA English
DT Article
DE Accelerator physics; FFAG; Dynamic aperture
ID TRACKING
AB We present PyZgoubi, a framework that has been developed based on the tracking engine Zgoubi to model, optimise and visualise the dynamics in particle accelerators, especially fixed-field alternating-gradient (FFAG) accelerators. We show that PyZgoubi abstracts Zgoubi by wrapping it in an easy-to-use Python framework in order to allow simple construction, parameterisation, visualisation and optimisation of FFAG accelerator lattices, Its object oriented design gives it the flexibility and extensibility required for current novel FFAG design. We apply PyZgoubi to two example FFAGs; this includes determining the dynamic aperture of the PAMELA medical FFAG in the presence of magnet misalignments, and illustrating how PyZgoubi may be used to optimise FFAGs. We also discuss a robust definition of dynamic aperture in an FFAG and show its implementation in PyZgoubi. (C) 2014 The Authors. Published by Elsevier B.V.
C1 [Tygier, S.; Appleby, R. B.; Garland, J. M.; Owen, H.] Univ Manchester, Cockcroft Accelerator Grp, Manchester M13 9PL, Lancs, England.
[Hock, K.] Univ Liverpool, Liverpool L69 3BX, Merseyside, England.
[Kelliher, D. J.; Sheehy, S. L.] STFC Rutherford Appleton Lab, Didcot OX11 0QX, Oxon, England.
RP Tygier, S (corresponding author), Univ Manchester, Cockcroft Accelerator Grp, Manchester M13 9PL, Lancs, England.
EM sam.tygier@hep.manchester.ac.uk; robert.appleby@manchester.ac.uk
RI Owen, Hywel/AAH-4337-2020; Appleby, Robert/A-3224-2016; Sheehy, Suzie
L/C-3304-2013
OI Owen, Hywel/0000-0001-5028-2841; Sheehy, Suzie L/0000-0002-7653-7205;
Tygier, Sam/0000-0002-7495-8655; Kelliher, David/0000-0001-9583-7804
FU STFCUK Research & Innovation (UKRI)Science & Technology Facilities
Council (STFC) [ST/K002503/1]; Science and Technology Facilities
CouncilUK Research & Innovation (UKRI)Science & Technology Facilities
Council (STFC) [John Adams Institute, ST/G008531/1, ST/K002503/1,
ST/J002011/1 John Adams Inst, ST/K001582/1, ST/J002011/1, ST/G008248/1]
Funding Source: researchfish
FX We wish to thank Francois Meot for all his help with understanding the
Zgoubi code. We would also like to thank Ken Peach for his help in
checking the PAMELA results. Additionally we would like to thank Scott
Berg for his great help in understanding many theoretical concepts in
beam dynamics and lattice design. Research supported by STFC Grant
number ST/K002503/1 "Racetrack FFAGs for medical, PRISM and energy
applications".
CR Aiba M., 2000, P EPAC, V581
Barlow R, 2010, NUCL INSTRUM METH A, V624, P1, DOI 10.1016/j.nima.2010.08.109
Berg J., 2010, P IPAC, P4235
Berg JS, 2008, NUCL INSTRUM METH A, V596, P276, DOI 10.1016/j.nima.2008.08.068
Berg JS, 2003, PROCEEDINGS OF THE 2003 PARTICLE ACCELERATOR CONFERENCE, VOLS 1-5, P3413
Borland M, 2000, LS287 ADV PHOT SOURC
Craddock M., 2004, REBIRTH FFAG
Enge H. A., 1964, REV SCI INSTRUM, V35, P278
Fourrier J, 2008, NUCL INSTRUM METH A, V589, P133, DOI 10.1016/j.nima.2008.01.082
Giovannozzi M, 1998, PHYS REV E, V57, P3432, DOI 10.1103/PhysRevE.57.3432
Giovannozzi M., 1996, PART ACCEL, V56, P195
Iselin F.C., MAD PROGRAM PHYS MET
Johnstone C, 2003, NUCL INSTRUM METH A, V503, P445, DOI 10.1016/S0168-9002(03)00997-5
Johnstone C., 1999, Proceedings of the 1999 Particle Accelerator Conference (Cat. No.99CH36366), P3068, DOI 10.1109/PAC.1999.792155
Kolomenskii caron A.A., 1957, Zhurnal Eksperimental'noi i Teoreticheskoi Fiziki, V33, P298
Lemuet F, 2005, NUCL INSTRUM METH A, V547, P638, DOI 10.1016/j.nima.2005.03.156
Machida S, 2003, PROCEEDINGS OF THE 2003 PARTICLE ACCELERATOR CONFERENCE, VOLS 1-5, P3452
Machida S, 2012, NAT PHYS, V8, P243, DOI [10.1038/NPHYS2179, 10.1038/nphys2179]
Meol F., 2006, P EPAC, P2308
Meot F., 2007, BEAM DYNAMICS NEWSLE, V43, P44
Meot F., 2014, NUCL INSTRUMENTS M A
Meot F., 2014, ZGOUBI
Meot F., 2013, C P IPAC13
Meot F., 2012, BNL987262012IR
Muratori B.D., ARXIV14041762
OHKAWA C, 1953, P ANN M JPS
Owen H, 2014, CONTEMP PHYS, V55, P55, DOI 10.1080/00107514.2014.891313
Peach KJ, 2013, PHYS REV SPEC TOP-AC, V16, DOI 10.1103/PhysRevSTAB.16.030101
RUBBIA C, 1993, CERNAT9347
Schmidt F., 2005, MAD X USERS GUIDE
Sheehy S., 2009, P PAC09
Sheehy S. L., 2010, THESIS U OXFORD
Sheehy SL, 2010, PHYS REV SPEC TOP-AC, V13, DOI 10.1103/PhysRevSTAB.13.040101
Symon K., 1954, MURA043
SYMON KR, 1956, PHYS REV, V103, P1837, DOI 10.1103/PhysRev.103.1837
Tanigaki M., 2005, P PAC05, P350
Tygier S., 2014, PYZGOUBI
Tygier S., 2011, THESIS U MANCHESTER
Witte H, 2012, IEEE T APPL SUPERCON, V22, DOI 10.1109/TASC.2012.2186135
NR 39
TC 3
Z9 3
U1 0
U2 0
PU ELSEVIER
PI AMSTERDAM
PA RADARWEG 29, 1043 NX AMSTERDAM, NETHERLANDS
SN 0168-9002
EI 1872-9576
J9 NUCL INSTRUM METH A
JI Nucl. Instrum. Methods Phys. Res. Sect. A-Accel. Spectrom. Dect. Assoc.
Equip.
PD MAR 1
PY 2015
VL 775
BP 15
EP 26
DI 10.1016/j.nima.2014.11.067
PG 12
WC Instruments & Instrumentation; Nuclear Science & Technology; Physics,
Nuclear; Physics, Particles & Fields
SC Instruments & Instrumentation; Nuclear Science & Technology; Physics
GA AZ2CA
UT WOS:000348040900004
OA Other Gold
DA 2021-04-21
ER
PT J
AU Gopalakrishnan, M
Guhr, M
AF Gopalakrishnan, Maithreyi
Guehr, Markus
TI A low-cost mirror mount control system for optics setups
SO AMERICAN JOURNAL OF PHYSICS
LA English
DT Article
ID FREE-ELECTRON LASER; OPERATION; ARDUINO
AB We describe a flexible, simple to build, low-cost, and computer-controlled optical mirror actuator system, developed for undergraduate research laboratories. Geared motors for hobby robotics are controlled by an Arduino microcontroller in combination with an H bridge to finely position mirror mount actuators. We present a graphical user interface based on the Python script language. The price of the fully controlled actuator system is only a small fraction of the price of a commercial system. It can be quickly implemented due to the use of open-hardware electronics. We discuss the performance of the system and give an outlook for future expansions and use in advanced optical setups. (C) 2015 American Association of Physics Teachers.
C1 [Gopalakrishnan, Maithreyi; Guehr, Markus] SLAC Natl Accelerator Lab, PULSE Inst, Menlo Pk, CA 94025 USA.
[Gopalakrishnan, Maithreyi] Univ Colorado, Boulder, CO 80309 USA.
RP Gopalakrishnan, M (corresponding author), SLAC Natl Accelerator Lab, PULSE Inst, 2575 Sand Hill Rd, Menlo Pk, CA 94025 USA.
EM mguehr@stanford.edu
RI Young, Michelle A/N-7228-2017; Guehr, Markus MG/B-7446-2015; Guehr,
Markus/X-2744-2019
OI Guehr, Markus MG/0000-0002-9111-8981;
FU Office of Science, U. S. Department of EnergyUnited States Department of
Energy (DOE); AMOS program within the Chemical Sciences, Geosciences,
and Biosciences Division of the Office of Basic Energy Sciences, Office
of Science, U.S. Department of EnergyUnited States Department of Energy
(DOE); Office of Science Early Career Research Program through the
Office of Basic Energy Sciences, U.S. Department of EnergyUnited States
Department of Energy (DOE)
FX M. Gopalakrishnan would also like to thank the science undergraduate
laboratory internship (SULI) program at SLAC National Accelerator
Laboratory sponsored by the Office of Science, U. S. Department of
Energy. This work was supported by the AMOS program within the Chemical
Sciences, Geosciences, and Biosciences Division of the Office of Basic
Energy Sciences, Office of Science, U.S. Department of Energy. M. Guhr
acknowledges funding via the Office of Science Early Career Research
Program through the Office of Basic Energy Sciences, U.S. Department of
Energy. We acknowledge fruitful discussions with B. K. McFarland and
Matthew Ware.
CR Ackermann W, 2007, NAT PHOTONICS, V1, P336, DOI 10.1038/nphoton.2007.76
Anderson M, 2004, AM J PHYS, V72, P1347, DOI 10.1119/1.1737398
Anzalone GC, 2013, SENSORS-BASEL, V13, P5338, DOI 10.3390/s130405338
Emma P, 2010, NAT PHOTONICS, V4, P641, DOI [10.1038/nphoton.2010.176, 10.1038/NPHOTON.2010.176]
Jobbins MM, 2012, REV SCI INSTRUM, V83, DOI 10.1063/1.3695001
March AM, 2011, REV SCI INSTRUM, V82, DOI 10.1063/1.3615245
Pearce JM, 2012, SCIENCE, V337, P1303, DOI 10.1126/science.1228183
Pile D, 2011, NAT PHOTONICS, V5, P456, DOI 10.1038/nphoton.2011.178
Severance C, 2014, COMPUTER, V47, P11, DOI 10.1109/MC.2014.19
Severance C, 2013, COMPUTER, V46, P14, DOI 10.1109/MC.2013.349
Storz R, 1998, OPT LETT, V23, P1031, DOI 10.1364/OL.23.001031
Teikari P, 2012, J NEUROSCI METH, V211, P227, DOI 10.1016/j.jneumeth.2012.09.012
Texas Instruments, 1995, SN754410 QUADR HALF
NR 13
TC 9
Z9 9
U1 0
U2 18
PU AMER ASSOC PHYSICS TEACHERS AMER INST PHYSICS
PI MELVILLE
PA STE 1 NO 1, 2 HUNTINGTON QUADRANGLE, MELVILLE, NY 11747-4502 USA
SN 0002-9505
EI 1943-2909
J9 AM J PHYS
JI Am. J. Phys.
PD FEB
PY 2015
VL 83
IS 2
BP 186
EP 190
DI 10.1119/1.4895343
PG 5
WC Education, Scientific Disciplines; Physics, Multidisciplinary
SC Education & Educational Research; Physics
GA AZ7CX
UT WOS:000348377000015
OA Bronze
DA 2021-04-21
ER
PT J
AU Drees, M
Dreiner, HK
Kim, JS
Schmeier, D
Tattersall, J
AF Drees, Manuel
Dreiner, Herbert K.
Kim, Jong Soo
Schmeier, Daniel
Tattersall, Jamie
TI CheckMATE: Confronting your favourite new physics model with LHC data
SO COMPUTER PHYSICS COMMUNICATIONS
LA English
DT Article
DE Analysis; Confidence limits; Monte Carlo; Detector simulation; Delphes;
ROOT; LHC 12.60.-i
ID HADRON COLLIDERS; MEASURING MASSES; SUPERSYMMETRY; HIERARCHY; SEARCH;
ENERGY; SQUARK
AB In the first three years of running, the LHC has delivered a wealth of new data that is now being analysed. With over 20 fb(-1) of integrated luminosity, both ATLAS and CMS have performed many searches for new physics that theorists are eager to test their model against. However, tuning the detector simulations, understanding the particular analysis details and interpreting the results can be a tedious task. CheckmATE (Check Models At Terascale Energies) is a program package which accepts simulated event files in many formats for any model. The program then determines whether the model is excluded or not at 95% C.L. by comparing to many recent experimental analyses. Furthermore the program can calculate confidence limits and provide detailed information about signal regions of interest. It is simple to use and the program structure allows for easy extensions to upcoming LHC results in the future.
Program summary
Program title: CheckMATE Catalogue identifier: AEUT_v1_0
Program summary URL: http://cpc.cs.qub.ac.uk/summaries/AEUT_v1_0.html
Program obtainable from: CPC Program Library, Queen's University, Belfast, N. Ireland
Licensing provisions: Standard CPC licence, http://cpc.cs.qub.ac.uk/licence/licence.html
No. of lines in distributed program, including test data, etc.: 179960
No. of bytes in distributed program, including test data, etc.: 6089336
Distribution format: tar.gz Programming language: C++, Python.
Computer: PC, Mac. Operating system: Linux, Mac OS.
RAM: Bytes
Classification: 11.9.
External routines: ROOT, Python, Delphes (included with the distribution)
Nature of problem:
The LHC has delivered a wealth of new data that is now being analysed. Both ATLAS and CMS have performed many searches for new physics that theorists are eager to test their model against. However, tuning the detector simulations, understanding the particular analysis details and interpreting the results can be a tedious and repetitive task.
Solution method:
CheckMATE is a program package which accepts simulated event files in many formats for any model. The program then determines whether the model is excluded or not at 95% C.L. by comparing to many recent experimental analyses. Furthermore the program can calculate confidence limits and provide detailed information about signal regions of interest. It is simple to use and the program structure allows for easy extensions to upcoming LHC results in the future.
Restrictions:
Only a subset of available experimental results have been implemented.
Additional comments:
Checkmate is built upon the tools and hard work of many people. If Checkmate is used in your publication it is extremely important that all of the following citations are included,
Delphes 3 [1].
FastJet [2, 3].
Anti-kt jet algorithm [4].
CL s prescription [5].
In analyses that use the MT2 kinematical discriminant we use the Oxbridge Kinetics Library [6, 7] and the algorithm developed by Cheng and Han [8].
All experimental analyses that were used to set limits in the study.
The Monte Carlo event generator that was used.
Running time:
The running time scales about linearly with the number of input events provided by the user. The detector simulation/analysis of 20000 events needs about 50 s/1 s for a single core calculation on an Intel Core i5-3470 with 3.2 GHz and 8 GB RAM. (C) 2014 Elsevier B.V. All rights reserved.
C1 [Drees, Manuel; Dreiner, Herbert K.; Schmeier, Daniel; Tattersall, Jamie] Univ Bonn, Bethe Ctr Theoret Phys, D-53115 Bonn, Germany.
[Drees, Manuel; Dreiner, Herbert K.; Schmeier, Daniel; Tattersall, Jamie] Univ Bonn, Inst Phys, D-53115 Bonn, Germany.
[Kim, Jong Soo] Univ Adelaide, ARC Ctr Excellence Particle Phys Terascale, Sch Chem & Phys, Adelaide, SA 5005, Australia.
[Kim, Jong Soo] Univ Autonoma Madrid, Inst Fis Teor, E-28049 Madrid, Spain.
[Tattersall, Jamie] Heidelberg Univ, Inst Theoret Phys, D-69120 Heidelberg, Germany.
RP Schmeier, D (corresponding author), Univ Bonn, Bethe Ctr Theoret Phys, Nussallee 12, D-53115 Bonn, Germany.
EM drees@th.physik.uni-bonn.de; dreiner@th.physik.uni-bonn.de;
jong.kim@csic.es; daschm@th.physik.uni-bonn.de;
j.tattersall@thphys.uni-heidelberg.de
RI Kim, Jong Soo/G-6307-2018
OI Kim, Jong Soo/0000-0002-1244-4181
CR Aad G, 2012, PHYS REV D, V86, DOI 10.1103/PhysRevD.86.072004
Aad G, 2012, EUR PHYS J C, V72, DOI 10.1140/epjc/s10052-012-1909-1
AAD G, ARXIV09010512
Aad G., ARXIV13082631
Allanach BC, 2002, COMPUT PHYS COMMUN, V143, P305, DOI 10.1016/S0010-4655(01)00460-X
Alloul A., ARXIV13101921
Alves D, 2012, J PHYS G NUCL PARTIC, V39, DOI 10.1088/0954-3899/39/10/105005
Alwall J, 2011, J HIGH ENERGY PHYS, DOI 10.1007/JHEP06(2011)128
[Anonymous], 2012, ATLASCONF2012097 CER
[Anonymous], 2013, ATLASCONF2013064 CER
[Anonymous], 2011, ATLASCONF2011152 CER
[Anonymous], 2013, ATLCOMPHYS20131287 C
[Anonymous], 2012, ATLASCONF2012039 CER
[Anonymous], 2013, ATLASCONF2013024 CER
[Anonymous], 2012, ATLASCONF2012043 CER
[Anonymous], 2013, ATLASCONF2013062 CER
[Anonymous], 2013, ATLASCONF2013089 CER
[Anonymous], 2011, ATLASCONF2011102 CER
[Anonymous], 2013, CMSPASEXO12048 CERN
[Anonymous], 2012, ATLASCONF2012104 CER
[Anonymous], 2011, ATLASCONF2011063 CER
Arkani-Hamed N, 1998, PHYS LETT B, V429, P263, DOI 10.1016/S0370-2693(98)00466-3
Bahr M, 2008, EUR PHYS J C, V58, P639, DOI 10.1140/epjc/s10052-008-0798-9
Barr A, 2003, J PHYS G NUCL PARTIC, V29, P2343, DOI 10.1088/0954-3899/29/10/304
Beenakker W, 1998, NUCL PHYS B, V515, P3, DOI 10.1016/S0550-3213(98)00014-5
Beenakker W, 1997, NUCL PHYS B, V492, P51, DOI 10.1016/S0550-3213(97)80027-2
Beenakker W, 1999, PHYS REV LETT, V83, P3780, DOI 10.1103/PhysRevLett.83.3780
Beenakker W, 2011, INT J MOD PHYS A, V26, P2637, DOI 10.1142/S0217751X11053560
Beenakker W, 2010, J HIGH ENERGY PHYS, DOI 10.1007/JHEP08(2010)098
Beenakker W, 2009, J HIGH ENERGY PHYS, DOI 10.1088/1126-6708/2009/12/041
Belyaev A, 2013, COMPUT PHYS COMMUN, V184, P1729, DOI 10.1016/j.cpc.2013.01.014
Bertone G, 2005, PHYS REP, V405, P279, DOI 10.1016/j.physrep.2004.08.031
Brun R, 1997, NUCL INSTRUM METH A, V389, P81, DOI 10.1016/S0168-9002(97)00048-X
BUCKLEY A, ARXIV10030694
Cacciari M, 2008, J HIGH ENERGY PHYS, DOI 10.1088/1126-6708/2008/04/063
Cacciari M, 2006, PHYS LETT B, V641, P57, DOI 10.1016/j.physletb.2006.08.037
Cacciari M, 2012, EUR PHYS J C, V72, DOI 10.1140/epjc/s10052-012-1896-2
Catani S, 2001, J HIGH ENERGY PHYS
Chatrchyan S., ARXIV13032985
Cheng HC, 2008, J HIGH ENERGY PHYS, DOI 10.1088/1126-6708/2008/12/063
Conte E, 2013, COMPUT PHYS COMMUN, V184, P222, DOI 10.1016/j.cpc.2012.09.009
de Favereau J., ARXIV13076346
Dobbs M, 2001, COMPUT PHYS COMMUN, V134, P41, DOI 10.1016/S0010-4655(00)00189-2
Dreiner H, 2013, PHYS REV D, V87, DOI 10.1103/PhysRevD.87.035006
Dreiner HK, 2012, EPL-EUROPHYS LETT, V99, DOI 10.1209/0295-5075/99/61001
Gavin R, 2011, COMPUT PHYS COMMUN, V182, P2388, DOI 10.1016/j.cpc.2011.06.008
Giudice GF, 1999, PHYS REP, V322, P419, DOI 10.1016/S0370-1573(99)00042-3
Gleisberg T, 2009, J HIGH ENERGY PHYS, DOI 10.1088/1126-6708/2009/02/007
Goodman J, 2010, PHYS REV D, V82, DOI 10.1103/PhysRevD.82.116010
HABER HE, 1985, PHYS REP, V117, P75, DOI 10.1016/0370-1573(85)90051-1
Knowles I., ARXIVHEPPH9601212
Kulesza A, 2009, PHYS REV D, V80, DOI 10.1103/PhysRevD.80.095004
Kulesza A, 2009, PHYS REV LETT, V102, DOI 10.1103/PhysRevLett.102.111802
Lester CG, 1999, PHYS LETT B, V463, P99, DOI 10.1016/S0370-2693(99)00945-4
Mangano ML, 2007, J HIGH ENERGY PHYS, DOI 10.1088/1126-6708/2007/01/013
Melnikov K, 2006, PHYS REV D, V74, DOI 10.1103/PhysRevD.74.114017
NILLES HP, 1984, PHYS REP, V110, P1, DOI 10.1016/0370-1573(84)90008-5
Randall L, 1999, PHYS REV LETT, V83, P3370, DOI 10.1103/PhysRevLett.83.3370
Read AL, 2002, J PHYS G NUCL PARTIC, V28, P2693, DOI 10.1088/0954-3899/28/10/313
Rogan C., ARXIV10062727
Salvucci A, 2012, EPJ WEB CONF, V28, DOI 10.1051/epjconf/20122812039
Sjostrand T, 2008, COMPUT PHYS COMMUN, V178, P852, DOI 10.1016/j.cpc.2008.01.036
Sjostrand T, 2006, J HIGH ENERGY PHYS, DOI 10.1088/1126-6708/2006/05/026
Staub F., ARXIV13097223
SUSSKIND L, 1979, PHYS REV D, V20, P2619, DOI 10.1103/PhysRevD.20.2619
WEINBERG S, 1976, PHYS REV D, V13, P974, DOI 10.1103/PhysRevD.13.974
NR 66
TC 175
Z9 177
U1 0
U2 16
PU ELSEVIER
PI AMSTERDAM
PA RADARWEG 29, 1043 NX AMSTERDAM, NETHERLANDS
SN 0010-4655
EI 1879-2944
J9 COMPUT PHYS COMMUN
JI Comput. Phys. Commun.
PD FEB
PY 2015
VL 187
BP 227
EP 265
DI 10.1016/j.cpc.2014.10.018
PG 39
WC Computer Science, Interdisciplinary Applications; Physics, Mathematical
SC Computer Science; Physics
GA AX5GH
UT WOS:000346954200024
DA 2021-04-21
ER
PT B
AU Joshi, R
Jadav, HM
Kulkarni, SV
AF Joshi, Ramesh
Jadav, H. M.
Kulkarni, S. V.
BE Mauri, JL
Thampi, SM
Wozniak, M
Marques, O
Krishnaswamy, D
Sahni, S
Callegari, C
Takagi, H
Bojkovic, ZS
Vinod, M
Prasad, NR
Calero, JMA
Rodrigues, J
Que, XY
Meghanathan, N
Sandhu, R
Au, E
TI Interfacing ICRH DAC system with WEB
SO 2015 INTERNATIONAL CONFERENCE ON ADVANCES IN COMPUTING, COMMUNICATIONS
AND INFORMATICS (ICACCI)
LA English
DT Proceedings Paper
CT International Conference on Advances in Computing, Communications and
Informatics ICACCI
CY AUG 10-13, 2015
CL SCMS Grp of Inst, Aluva, INDIA
SP SCMS Sch of Engn & Technol, IEEE Commun Soc, IEEE SMC, acm
HO SCMS Grp of Inst
DE Data acquisition and control; EPICS; MODBUS; Ion cyclotron resonance
heating; Control system studio; PLC; CSS; WebOPI
AB HTML5 [1] has been recently evolution in web technologies which enables user to create web based control system user interfaces (UI). These user interfaces can work on cross browser and cross device compatible. Control system studio (CSS) Operator Interfaces (OPI) allow user to create user interfaces with drag and drop fashion. These developed interfaces developed in CSS BOY[2] can be seamlessly display in web browsers without any modification in original OPI file using WebOPI [3]. For this purpose, WebOPI was implemented by SNS as a web-based system using Ajax (asynchronous JavaScript and XML) with Experimental physics and instrumentation control system (EPICS) [4]. On the other hand, it uses generic Python/ JavaScript and a generic communication mechanism between the web browser and web server. This interface uses the epics channel access gateway in glue with OPI which enables monitor and control process EPICS variables with different widgets. Apache Tomcat web server has been used to deploy application.
Programmable logic controller (PLC) based data acquisition and control (DAC) system has been developed for 45.6 MHz, 100 kW Ion Cyclotron Resonance Heating (ICRH) system using EPICS and MODBUS. It can monitor and control 32 analog inputs, 16 digital inputs, 16 analog outputs and 16 digital outputs using MODBUS protocol. Several python embedded as well as external script have been used in design of the control system software. WebOPI provides seamless interface between with local CSS OPI and EPICS process variables using channel access gateway. Multiple web browser based client can communicate with single instance of user interface simultaneously. This paper introduced WebOPI that facilitate the goal of bringing control system UIs to the web.
C1 [Joshi, Ramesh; Jadav, H. M.; Kulkarni, S. V.] Inst Plasma Res, Bhat 382428, Gandhinagar, India.
RP Joshi, R (corresponding author), Inst Plasma Res, Bhat 382428, Gandhinagar, India.
EM rjoshi@ipr.res.in
CR [Anonymous], 2000, ENG DESIGN REPORT ED
Chen X.H., 2011, P 2011 PART ACC C NE
Chen Y.K., 2010, IPAC 2010 1 INT PART, P2719
Joshi Ramesh, 2013, C P SPRING
Kasemir K. U., 2011, P ICALPECS 2011 GREN
Touchard D., 2008, PCAPAC 2008 INT WORK, P113
Wu X., 2013, IPAC 2013 P 4 INT PA, P3167
Yoon J. C., 2005, P PART ACC C MAY, P3100
NR 8
TC 0
Z9 0
U1 0
U2 0
PU IEEE
PI NEW YORK
PA 345 E 47TH ST, NEW YORK, NY 10017 USA
BN 978-1-4799-8792-4
PY 2015
BP 1170
EP 1173
PG 4
WC Computer Science, Theory & Methods; Engineering, Electrical & Electronic
SC Computer Science; Engineering
GA BF2KP
UT WOS:000380475900195
DA 2021-04-21
ER
PT B
AU Sawant, NP
Riley, CP
Venskus, A
Vassilev, DH
Wale, JD
Wearing, E
Michaelides, AM
Topping, PJ
Matharu, H
AF Sawant, Nikhil P.
Riley, Christopher P.
Venskus, Arturas
Vassilev, Danail H.
Wale, John D.
Wearing, Eddie
Michaelides, Alex M.
Topping, Philip J.
Matharu, Herminder
BE Schrefler, B
Onate, E
Papadrakakis, M
TI MULTIPHYSICS SIMULATION TOOLS FOR DESIGNING MOTORS FOR TRACTION
APPLICATIONS IN HYBRID AND ELECTRIC VEHICLES
SO Coupled Problems in Science and Engineering VI
LA English
DT Proceedings Paper
CT 6th International Conference on Computational Methods for Coupled
Problems in Science and Engineering (COUPLED PROBLEMS)
CY MAY 18-20, 2015
CL Venice, ITALY
SP Univ Padova, Dept Civil Environm & Architectural Engn, Tech Univ Catalonia, Int Centre Numerical Methods Engn, Natl Tech Univ Athens
DE Multiphysics; motors; electric vehicle; Opera; python; simulation
AB Motor manufacturers are facing a difficult challenge in designing traction motors for the latest generation of hybrid and all-electric vehicles. The efficiency with which these motors can perform is critical, as it impacts on the vehicle range and battery life. Many of the issues involved in the motor design have a complex nature which requires multiple fields of physics such as electromagnetics (EM), mechanics and thermal analysis. All these physics are usually interdependent and have to be considered collectively in order to obtain optimal performance for a particular scenario.
This paper presents a multiphysics simulation tool that was implemented to address this situation. The Opera FEA software suite [1] was developed to include a multiphysics analysis that can link several EM, thermal and stress analyses. Opera's Machines Environment (parameterised template software for designing motors and generators) has been extended to allow easy setup of coupled multiphysics analyses such as EM to thermal and EM to stress. In order to further facilitate the coupling of different analyses, a link to the Python programming language was embedded in Opera FEA software. The embedded Python facility offers options to perform certain post-processing operations during the solving stage and hence allow data transfer between different stages of the multiphysics analysis. It also extends Opera's capabilities to interact with other FEA software.
C1 [Sawant, Nikhil P.; Riley, Christopher P.; Venskus, Arturas; Vassilev, Danail H.] Cobham Tech Serv, Network House, Langford Locks OX5 1LH, Kidlington, England.
[Wale, John D.; Wearing, Eddie] Ricardo UK Ltd, Midlands Tech Ctr, Radford Semele CV31 1FQ, Leamington Spa, England.
[Michaelides, Alex M.; Topping, Philip J.; Matharu, Herminder] Jaguar Land Rover Ltd, Coventry CV3 4LF, W Midlands, England.
RP Sawant, NP (corresponding author), Cobham Tech Serv, Network House, Langford Locks OX5 1LH, Kidlington, England.
EM vectorfields.info@cobham.com; electric.drives@ricardo.com;
hmathar2@jaguarlandrover.com
CR [Anonymous], 2007, ANN EN OUTL 2007 PRO
Cobham Technical Services, OP EL FIN EL SOFTW S
Ghosh B., 1991, HDB SEQUENTIAL ANAL
Granjon P, 2014, CUSUM ALGORITHM SMAL
Killick R, 2012, J AM STAT ASSOC, V107, P1590, DOI 10.1080/01621459.2012.737745
Pullen K.R., COMMUNIUCATION
Riley C P, 2014, PEMD
Wald A., 1947, SEQUENTIAL ANAL
NR 8
TC 0
Z9 0
U1 0
U2 0
PU INT CENTER NUMERICAL METHODS ENGINEERING
PI 08034 BARCELONA
PA GRAN CAPITAN, S-N, CAMPUS NORTE UPC, MODULO C1, 08034 BARCELONA, SPAIN
BN 978-84-943928-3-2
PY 2015
BP 428
EP 439
PG 12
WC Engineering, Multidisciplinary; Mathematics, Interdisciplinary
Applications
SC Engineering; Mathematics
GA BF3WZ
UT WOS:000380588500037
DA 2021-04-21
ER
PT S
AU Frank, M
Gaede, F
Nikiforou, N
Petric, M
Sailer, A
AF Frank, M.
Gaede, F.
Nikiforou, N.
Petric, M.
Sailer, A.
GP IOP
TI DDG4 A Simulation Framework based on the DD4hep Detector Description
Toolkit
SO 21ST INTERNATIONAL CONFERENCE ON COMPUTING IN HIGH ENERGY AND NUCLEAR
PHYSICS (CHEP2015), PARTS 1-9
SE Journal of Physics Conference Series
LA English
DT Proceedings Paper
CT 21st International Conference on Computing in High Energy and Nuclear
Physics (CHEP)
CY APR 13-17, 2015
CL Okinawa, JAPAN
ID ROOT
AB The detector description is an essential component that has to be used to analyse and simulate data resulting from particle collisions in high energy physics experiments. Based on the DD4hep detector description toolkit a flexible and data driven simulation framework was designed using the Geant4 tool-kit. We present this framework and describe the guiding requirements and the architectural design, which was strongly driven by ease of use. The goal was, given an existing detector description, to simulate the detector response to particle collisions in high energy physics experiments with minimal effort, but not impose restrictions to support enhanced or improved behaviour.
Starting from the ROOT based geometry implementation used by DD4hep an automatic conversion mechanism to Geant4 was developed. The physics response and the mechanism to input particle data from generators was highly formalized and can be instantiated on demand using known factory patterns. A palette of components to model the detector response is provided by default, but improved or more sophisticated components may easily be added using the factory pattern. Only the final configuration of the instantiated components has to be provided by end-users using either C++ or python scripting or an XML based description.
C1 [Frank, M.; Nikiforou, N.; Petric, M.; Sailer, A.] CERN, CH-1211 Geneva 23, Switzerland.
[Gaede, F.] DESY, D-22607 Hamburg, Germany.
RP Frank, M (corresponding author), CERN, CH-1211 Geneva 23, Switzerland.
EM Markus.Frank@cern.ch
OI Nikiforou, Nikiforos/0000-0003-1267-7740
CR Agostinelli S, 2003, NUCL INSTRUM METH A, V506, P250, DOI 10.1016/S0168-9002(03)01368-8
Aihara H., 2009, ARXIV09110006
Brun R, 1997, NUCL INSTRUM METH A, V389, P81, DOI 10.1016/S0168-9002(97)00048-X
Brun R, 2003, NUCL INSTRUM METH A, V502, P676, DOI 10.1016/S0168-9002(03)00541-2
Frank M., 2013, INT C COMP HIGH EN N
Gaede Frank, 2003, INT C COMP HIGH EN N
LHCb Collaboration, 2003030 CERN LHCC LH
Ponce S., 2003, INT C COMP HIGH EN N
Sailer A., 2015, INT C COMP HIGH EN N
The ILD Concept Group, 2009, INT LARG DET LETT IN
NR 10
TC 1
Z9 1
U1 0
U2 0
PU IOP PUBLISHING LTD
PI BRISTOL
PA DIRAC HOUSE, TEMPLE BACK, BRISTOL BS1 6BE, ENGLAND
SN 1742-6588
J9 J PHYS CONF SER
PY 2015
VL 664
AR 072017
DI 10.1088/1742-6596/664/7/072017
PG 8
WC Physics, Nuclear; Physics, Particles & Fields
SC Physics
GA BE4TH
UT WOS:000372140602075
OA Other Gold
DA 2021-04-21
ER
PT S
AU Likhomanenko, T
Rogozhnikov, A
Baranov, A
Khairullin, E
Ustyuzhanin, A
AF Likhomanenko, Tatiana
Rogozhnikov, Alex
Baranov, Alexander
Khairullin, Egor
Ustyuzhanin, Andrey
GP IOP
TI Reproducible Experiment Platform
SO 21ST INTERNATIONAL CONFERENCE ON COMPUTING IN HIGH ENERGY AND NUCLEAR
PHYSICS (CHEP2015), PARTS 1-9
SE Journal of Physics Conference Series
LA English
DT Proceedings Paper
CT 21st International Conference on Computing in High Energy and Nuclear
Physics (CHEP)
CY APR 13-17, 2015
CL Okinawa, JAPAN
AB Data analysis in fundamental sciences nowadays is an essential process that pushes frontiers of our knowledge and leads to new discoveries. At the same time we can see that complexity of those analyses increases fast due to a) enormous volumes of datasets being analyzed, b) variety of techniques and algorithms one have to check inside a single analysis, c) distributed nature of research teams that requires special communication media for knowledge and information exchange between individual researchers. There is a lot of resemblance between techniques and problems arising in the areas of industrial information retrieval and particle physics. To address those problems we propose Reproducible Experiment Platform (REP), a software infrastructure to support collaborative ecosystem for computational science. It is a Python based solution for research teams that allows running computational experiments on shared datasets, obtaining repeatable results, and consistent comparisons of the obtained results. We present some key features of REP based on case studies which include trigger optimization and physics analysis studies at the LHCb experiment.
C1 [Likhomanenko, Tatiana; Rogozhnikov, Alex; Baranov, Alexander; Khairullin, Egor; Ustyuzhanin, Andrey] YSDA, Moscow, Russia.
[Likhomanenko, Tatiana; Rogozhnikov, Alex; Ustyuzhanin, Andrey] Natl Res Univ Higher Sch Econ HSE, Moscow, Russia.
[Likhomanenko, Tatiana; Ustyuzhanin, Andrey] NRC Kurchatov Inst, St Petersburg, Russia.
[Khairullin, Egor; Ustyuzhanin, Andrey] Moscow Inst Phys & Technol, Moscow, Russia.
RP Likhomanenko, T (corresponding author), YSDA, Moscow, Russia.
EM axelr@yandex-team.ru; antares@yandex-team.ru
RI Ustyuzhanin, Andrey E/K-7189-2013
OI Ustyuzhanin, Andrey E/0000-0001-7865-2357
CR Chacon S., 2014, PRO GIT, V2nd
Gulin A., 2011, P LEARN RANK CHALL, P63
Hoecker A., 2007, POS ACAT2007
Pedregosa F, 2011, J MACH LEARN RES, V12, P2825
Rogozhnikov A, 2015, J INSTRUM, V10, DOI 10.1088/1748-0221/10/03/T03002
NR 5
TC 0
Z9 0
U1 0
U2 0
PU IOP PUBLISHING LTD
PI BRISTOL
PA DIRAC HOUSE, TEMPLE BACK, BRISTOL BS1 6BE, ENGLAND
SN 1742-6588
EI 1742-6596
J9 J PHYS CONF SER
PY 2015
VL 664
AR 052022
DI 10.1088/1742-6596/664/5/052022
PG 6
WC Physics, Nuclear; Physics, Particles & Fields
SC Physics
GA BE4TH
UT WOS:000372140601069
OA Bronze
DA 2021-04-21
ER
PT S
AU Tamsett, M
Group, C
AF Tamsett, M.
Group, C.
GP IOP
TI The NOvA software testing framework
SO 21ST INTERNATIONAL CONFERENCE ON COMPUTING IN HIGH ENERGY AND NUCLEAR
PHYSICS (CHEP2015), PARTS 1-9
SE Journal of Physics Conference Series
LA English
DT Proceedings Paper
CT 21st International Conference on Computing in High Energy and Nuclear
Physics (CHEP)
CY APR 13-17, 2015
CL Okinawa, JAPAN
AB The NOvA experiment at Fermilab is a long-baseline neutrino experiment designed to study v, appearance in a v1 beam. NOvA has already produced more than one million Monte Carlo and detector generated files amounting to more than 1 PB in size. This data is divided between a number of parallel streams such as far and near detector beam spills, cosmic ray backgrounds, a number of data-driven triggers and over 20 different Monte Carlo configurations. Each of these data streams must be processed through the appropriate steps of the rapidly evolving, multi-tiered, interdependent NOvA software framework. In total there are greater than 12 individual software tiers, each of which performs a different function and can be configured differently depending on the input stream. In order to regularly test and validate that all of these software stages are working correctly NOvA has designed a powerful, modular testing framework that enables detailed validation and benchmarking to be performed in a fast, efficient and accessible way with minimal expert knowledge. The core of this system is a novel series of python modules which wrap, monitor and handle the underlying C++ software framework and then report the results to a slick front-end web-based interface. This interface utilises modern, cross-platform, visualisation libraries to render the test results in a meaningful way. They are fast and flexible, allowing for the easy addition of new tests and datasets. In total upwards of 14 individual streams are regularly tested amounting to over 70 individual software processes, producing over 25 GB of output files. The rigour enforced through this flexible testing framework enables NOvA to rapidly verify configurations, results and software and thus ensure that data is available for physics analysis in a timely and robust manner.
C1 [Tamsett, M.] Univ Sussex, Falmer, E Sussex, England.
[Group, C.] Univ Virginia, Charlottesville, VA USA.
RP Tamsett, M (corresponding author), Univ Sussex, Falmer, E Sussex, England.
EM m.tamsett@sussex.ac.uk
CR Ayres D. S., FERMILAB DESIGN 2007
Brun R, 1997, NUCL INSTRUM METH A, V389, P81, DOI 10.1016/S0168-9002(97)00048-X
Green C., 2012, J PHYS C SER, V396
Hagmann C, 2009, LLNLUCRLTM229233
NR 4
TC 0
Z9 0
U1 0
U2 0
PU IOP PUBLISHING LTD
PI BRISTOL
PA DIRAC HOUSE, TEMPLE BACK, BRISTOL BS1 6BE, ENGLAND
SN 1742-6588
J9 J PHYS CONF SER
PY 2015
VL 664
AR 062062
DI 10.1088/1742-6596/664/6/062062
PG 6
WC Physics, Nuclear; Physics, Particles & Fields
SC Physics
GA BE4TH
UT WOS:000372140602052
OA Bronze
DA 2021-04-21
ER
PT S
AU Cieszewski, R
Romaniuk, R
Pozniak, K
Linczuk, M
AF Cieszewski, Radoslaw
Romaniuk, Ryszard
Pozniak, Krzysztof
Linczuk, Maciej
BE Romaniuk, RS
TI Algorithmic synthesis using Python compiler
SO PHOTONICS APPLICATIONS IN ASTRONOMY, COMMUNICATIONS, INDUSTRY, AND
HIGH-ENERGY PHYSICS EXPERIMENTS 2015
SE Proceedings of SPIE
LA English
DT Proceedings Paper
CT Conference on Photonics Applications in Astronomy, Communications,
Industry, and High-Energy Physics Experiments
CY MAY 25-31, 2015
CL Wilga, POLAND
SP Warsaw Univ Technol, Fac Elect & Informat Technologies, Inst Elect Syst, Photon Soc Poland, SPIE Europe, Polish Acad Sci, Comm Elect & Telecommunicat, Enhanced European Coordinat Accelerator R & D, IEEE Poland Sect, Assoc Polish Elect Engineers, Polish Comm Optoelectron, EuroFus Collaborat, EuroFus Poland
DE FPGA; Algorithmic Synthesis; High-Level Synthesis; Behavioral Synthesis;
Hot Plasma Physics Experiment; Python; Compiler
ID HIGH-LEVEL SYNTHESIS
AB This paper presents a python to VHDL compiler. The compiler interprets an algorithmic description of a desired behavior written in Python and translate it to VHDL. FPGA combines many benefits of both software and ASIC implementations. Like software, the programmed circuit is flexible, and can be reconfigured over the lifetime of the system. FPGAs have the potential to achieve far greater performance than software as a result of bypassing the fetch-decode-execute operations of traditional processors. and possibly exploiting a greater level of parallelism. This can be achived by using many computational resources at the same time. Creating parallel programs implemented in FPGAs in pure HDL is difficult and time consuming. Using higher level of abstraction and High-Level Synthesis compiler implementation time can be reduced. The compiler has been implemented using the Python language. This article describes design, implementation and results of created tools.
C1 [Cieszewski, Radoslaw; Romaniuk, Ryszard; Pozniak, Krzysztof; Linczuk, Maciej] Warsaw Univ Technol, Inst Elect Syst, Nowowiejska 15-19, PL-00665 Warsaw, Poland.
RP Cieszewski, R (corresponding author), Warsaw Univ Technol, Inst Elect Syst, Nowowiejska 15-19, PL-00665 Warsaw, Poland.
EM R.Cieszewski@stud.elka.pw.edu.pl
RI Romaniuk, Ryszard S/B-9140-2011; Pozniak, Krzysztof/AAO-7377-2020
OI Romaniuk, Ryszard S/0000-0002-5710-4041; Pozniak,
Krzysztof/0000-0001-5426-1423
CR Asanovic K, 2009, COMMUN ACM, V52, P56, DOI 10.1145/1562764.1562783
Babb J., 1999, Seventh Annual IEEE Symposium on Field-Programmable Custom Computing Machines (Cat. No.PR00375), P70, DOI 10.1109/FPGA.1999.803669
Berdychowski PP, 2010, PHOTONICS APPL ASTRO
Bowyer B., 2005, EETIMES
Bujnowski K., 2007, PHOTONICS APPL ASTRO
Cieszewski R, 2014, PROC SPIE, V9290, DOI 10.1117/12.2075988
Cong J, 2011, IEEE T COMPUT AID D, V30, P473, DOI 10.1109/TCAD.2011.2110592
Coussy P., 2008, HIGH LEVEL SYNTHESIS
GAJSKI DD, 1994, IEEE DES TEST COMPUT, V11, P44, DOI 10.1109/54.329454
Gajski DD, 1992, HIGH LEVEL SYNTHESIS, V34
Gokhale M, 1997, 5TH ANNUAL IEEE SYMPOSIUM ON FIELD-PROGRAMMABLE CUSTOM COMPUTING MACHINES, P165, DOI 10.1109/FPGA.1997.624616
Kolasinski P., 2007, PHOTONICS APPL ASTRO
Liang Y, 2012, J ELECTR COMPUT ENG, V2012, DOI 10.1155/2012/649057
Meredith M., 2004, EETIMES, P04
Philippe C., 2008, EURASIP J EMBEDDED S, V2008
Zabolotny W. M., 2011, PHOTONICS APPL ASTRO
Zabolotny WM, 2003, P SOC PHOTO-OPT INS, V5125, P223, DOI 10.1117/12.531581
Zabolotny WM, 2010, PHOTONICS APPL ASTRO
Zabolotny WM, 2011, PROC SPIE, V8008, DOI 10.1117/12.905281
Zabolotny WM, 2006, PROC SPIE, V6347, DOI 10.1117/12.714532
NR 20
TC 0
Z9 0
U1 0
U2 1
PU SPIE-INT SOC OPTICAL ENGINEERING
PI BELLINGHAM
PA 1000 20TH ST, PO BOX 10, BELLINGHAM, WA 98227-0010 USA
SN 0277-786X
EI 1996-756X
BN 978-1-62841-880-4
J9 PROC SPIE
PY 2015
VL 9662
AR 96623J
DI 10.1117/12.2205609
PG 8
WC Optics; Physics, Applied
SC Optics; Physics
GA BD7MA
UT WOS:000363279000125
DA 2021-04-21
ER
PT S
AU Conte, E
Dumont, B
Fuks, B
Schmitt, T
AF Conte, Eric
Dumont, Beranger
Fuks, Benjamin
Schmitt, Thibaut
GP IOP
TI New features of MADANALYSIS 5 for analysis design and reinterpretation
SO 16TH INTERNATIONAL WORKSHOP ON ADVANCED COMPUTING AND ANALYSIS
TECHNIQUES IN PHYSICS RESEARCH (ACAT2014)
SE Journal of Physics Conference Series
LA English
DT Proceedings Paper
CT International Workshop on Advanced Computing and Analysis Techniques in
Physics Research (ACAT)
CY SEP 01-05, 2014
CL Czech Tech Univ, Fac Civil Engn, Prague, CZECH REPUBLIC
SP Western Digital, Brookhaven Natl Lab, Hewlett Packard, DataDirect Networks, M Comp, Bright Comp, Huawei, PDV Systemhaus
HO Czech Tech Univ, Fac Civil Engn
AB We present MADANALYSIS 5, an analysis package dedicated to phenomenological studies of simulated collisions occurring in high-energy physics experiments. Within this framework, users are invited, through a user-friendly PYTHON interpreter, to implement physics analyses in a very simple manner. A C++ code is then automatically generated, compiled and executed. Very recently, the expert mode of the program has been extended so that analyses with multiple signal/control regions can be handled. Additional observables have also been included, and an interface to several fast detector simulation packages has been developed, one of them being a tune of the DELPHES 3 software. As a result, a recasting of existing ATLAS and CMS analyses can be achieved straightforwardly.
C1 [Conte, Eric] Univ Haute Alsace, GRPHE, IUT Colmar, F-68008 Colmar, France.
[Dumont, Beranger] Ctr Theoret Phys Universe, IBS, Taejon 305811, South Korea.
[Dumont, Beranger] Univ Grenoble Alpes, LPSC, CNRS, IN2P3, F-38026 Grenoble, France.
[Fuks, Benjamin] CERN, PH TH, CH-1211 Geneva 23, Switzerland.
[Fuks, Benjamin; Schmitt, Thibaut] Univ Strasbourg, CNRS, IN2P3, Inst Pluridisciplinaire Hubert Curien,Dept Rech S, F-67037 Strasbourg, France.
RP Conte, E (corresponding author), Univ Haute Alsace, GRPHE, IUT Colmar, 34 Rue Grillenbreit BP 50568, F-68008 Colmar, France.
EM eric.conte@iphc.cnrs.fr; beranger.dumont@lpsc.in2p3.fr; fuks@cern.ch;
thibaut.schmitt@iphc.cnrs.fr
OI Fuks, Benjamin/0000-0002-0041-0566
CR Alloul A, 2013, JHEP, V1310, P033
Alwall J, 2007, COMPUT PHYS COMMUN, V176, P300, DOI 10.1016/j.cpc.2006.11.010
Brun R, 1997, NUCL INSTRUM METH A, V389, P81, DOI 10.1016/S0168-9002(97)00048-X
Cacciari M, 2012, EUR PHYS J C, V72, DOI 10.1140/epjc/s10052-012-1896-2
Chatrchyan S, 2013, PHYS REV D, V88, DOI 10.1103/PhysRevD.88.052017
CMS Collaboration, 2014, JHEP
Conte E, 2014, PREPRINT
Conte E, 2013, COMPUT PHYS COMMUN, V184, P222, DOI 10.1016/j.cpc.2012.09.009
de Favereau J, 2014, J HIGH ENERGY PHYS, DOI 10.1007/JHEP02(2014)057
Dobbs M, 2001, COMPUT PHYS COMMUN, V134, P41, DOI 10.1016/S0010-4655(00)00189-2
Dumont B, 2014, PREPRINT
Kraml S, 2012, EUR PHYS J C, V72, DOI 10.1140/epjc/s10052-012-1976-3
NR 12
TC 1
Z9 1
U1 0
U2 3
PU IOP PUBLISHING LTD
PI BRISTOL
PA DIRAC HOUSE, TEMPLE BACK, BRISTOL BS1 6BE, ENGLAND
SN 1742-6588
EI 1742-6596
J9 J PHYS CONF SER
PY 2015
VL 608
AR 012054
DI 10.1088/1742-6596/608/1/012054
PG 6
WC Computer Science, Interdisciplinary Applications; Physics,
Multidisciplinary
SC Computer Science; Physics
GA BD1PA
UT WOS:000358218000054
OA Bronze
DA 2021-04-21
ER
PT S
AU Roundy, D
Krebs, EJ
Schulte, JB
Mulder, GS
AF Roundy, David
Krebs, Eric J.
Schulte, Jeff B.
Mulder, Greg S.
GP IEEE
TI Look ma, no templates! Problem-based learning of computational physics
for novice programmers
SO FRONTIERS IN EDUCATION CONFERENCE (FIE), 2015
SE Frontiers in Education Conference
LA English
DT Proceedings Paper
CT 45th Annual Frontiers in Education Conference (FIE)
CY OCT 21-24, 2015
CL El Paso, TX
SP IEEE Educ Soc, IEEE Comp Soc, ASEE Educ Res & Methods Div, New Mexico State Univ, Univ Texas El Paso, Hewlett Packard, VentureWell, Markkula Ctr Appl Eth, IEEE
ID LANGUAGE; PYTHON
AB We present a problem-based approach to teach computational physics to junior-level students with a variety of programming skill levels, many of whom have only 10 weeks of prior programming experience. The students solve tasks, relating to their current class topics, which are mathematically challenging but computationally tractable. The tasks are chosen to develop and reinforce student understanding of traditional physics content. We provide students with minimal code examples (47 lines of python code in a one-year lab sequence) and no textbook, and require students to search the internet for help to achieve the tasks assigned. This is effective through the use of a pair-programming pedagogy, when students are given tasks at a suitable level. This pedagogical approach should be transferable to any mathematical discipline for which computation is appropriate. Because student effort is focused towards accomplishing tasks within their discipline, they gain the practical skills needed to use computation effectively. Because we give students no templates or example code, they are forced to develop the skill of learning a programming language, and debugging code. This gives students confidence, and prepares them to learn other programming languages in their future career: a most important skill!
C1 [Roundy, David; Krebs, Eric J.; Schulte, Jeff B.] Oregon State Univ, Dept Phys, Corvallis, OR 97331 USA.
[Mulder, Greg S.] Linn Benton Community Coll, Dept Phys Sci, Albany, OR 97321 USA.
RP Roundy, D (corresponding author), Oregon State Univ, Dept Phys, Corvallis, OR 97331 USA.
EM roundyd@physics.oregonstate.edu; mulderg@linnbenton.edu
RI Jolugbo, Olajide/D-3216-2017; Roundy, David/AAG-2356-2019
OI Jolugbo, Olajide/0000-0001-6512-4117; Roundy, David/0000-0001-5287-5472
CR Agarwal K., 2008, J COMPUTING SCI COLL, V23, P49
Backer A, 2007, COMPUT SCI ENG, V9, P30, DOI 10.1109/MCSE.2007.48
Borcherds PH, 2007, COMPUT PHYS COMMUN, V177, P199, DOI 10.1016/j.cpc.2007.02.019
Caballero MD, 2014, PHYS TEACH, V52, P38, DOI 10.1119/1.4849153
Caballero MD, 2012, PHYS REV SPEC TOP-PH, V8, DOI 10.1103/PhysRevSTPER.8.020106
Chabay R, 2008, AM J PHYS, V76, P307, DOI 10.1119/1.2835054
Hunter JD, 2007, COMPUT SCI ENG, V9, P90, DOI 10.1109/MCSE.2007.55
Jones E., 2001, SCIPY OPEN SOURCE SC
Landau R, 2006, COMPUT SCI ENG, V8, P22, DOI 10.1109/MCSE.2006.85
Mannila L, 2006, COMPUT SCI EDUC, V16, P211, DOI 10.1080/08993400600912384
Manogue CA, 2003, PHYS TODAY, V56, P53, DOI 10.1063/1.1620835
McDowell C, 2006, COMMUN ACM, V49, P90, DOI 10.1145/1145287.1145293
Millman KJ, 2011, COMPUT SCI ENG, V13, P9, DOI 10.1109/MCSE.2011.36
Perez F., 2010, COMPUTING SCI ENG
Perkins K, 2006, PHYS TEACH, V44, P18, DOI DOI 10.1119/1.2150754
Press W. H., 2007, NUMERICAL RECIPES AR
Sander L. M., 2013, EQUILIBRIUM STAT PHY
Scherer D, 2000, COMPUT SCI ENG, V2, P56, DOI 10.1109/5992.877397
Spencer RL, 2005, AM J PHYS, V73, P151, DOI 10.1119/1.1842751
Swendsen R H, 2012, INTRO STAT MECH THER
Werner L. L., 2004, J ED RESOURCES COMPU, V4, P4
Wieman CE, 2006, NAT PHYS, V2, P290, DOI 10.1038/nphys283
Williams L, 2003, 2003 INTERNATIONAL SYMPOSIUM ON EMPIRICAL SOFTWARE ENGINEERING, PROCEEDINGS, P143, DOI 10.1109/ISESE.2003.1237973
Williams L., 2002, COMPUTER SCI ED, V12, P197, DOI DOI 10.1076/CSED.12.3.197.8618
NR 24
TC 0
Z9 0
U1 0
U2 1
PU IEEE
PI NEW YORK
PA 345 E 47TH ST, NEW YORK, NY 10017 USA
SN 0190-5848
BN 978-1-4799-8454-1
J9 PROC FRONT EDUC CONF
PY 2015
BP 792
EP 796
PG 5
WC Education, Scientific Disciplines; Engineering, Electrical & Electronic
SC Education & Educational Research; Engineering
GA BE4GO
UT WOS:000371705200134
DA 2021-04-21
ER
PT B
AU Bittelli, B
Campbell, GS
Tomei, F
AF Bittelli, B
Campbell, GS
Tomei, F
TI Soil Physics with Python: Transport in the Soil-Plant-Atmosphere System
SO SOIL PHYSICS WITH PYTHON: TRANSPORT IN THE SOIL-PLANT-ATMOSPHERE SYSTEM
LA English
DT Book
ID PARTICLE-SIZE DISTRIBUTION; GAS-DIFFUSION COEFFICIENT;
LONG-WAVE-RADIATION; WATER-CONTENT; HYDRAULIC CONDUCTIVITY; LIQUID
WATER; MODEL; VAPOR; FLOW; AIR
CR Abramowitz M., 1970, HDB MATH FUNCTIONS
Acutis M, 2003, EUR J AGRON, V18, P373, DOI 10.1016/S1161-0301(02)00128-4
Allen T, 1981, PARTICLE SIZE MEASUR
BALL BC, 1981, J SOIL SCI, V32, P483, DOI 10.1111/j.1365-2389.1981.tb01724.x
Barone V., 2009, ENRICO FERMI ATOMI N, P110
Baveye P., 1997, FRACTALS SOIL SCI
Bear J., 1972, DYNAMICS FLUIDS PORO
Beazley D., 2006, PYTHON ESSENTIAL REF
Bechtold M, 2011, WATER RESOUR RES, V47, DOI 10.1029/2010WR010267
Bird R.B., 2007, TRANSPORT PHENOMENA
Bittelli M, 2004, WATER RESOUR RES, V40, DOI 10.1029/2003WR002343
Bittelli M, 1999, SOIL SCI SOC AM J, V63, P782, DOI 10.2136/sssaj1999.634782x
Bittelli M, 2008, J HYDROL, V362, P191, DOI 10.1016/j.jhydrol.2008.08.014
Bittelli M, 2008, GEODERMA, V143, P133, DOI 10.1016/j.geoderma.2007.10.022
Bittelli M, 2012, GEOMORPHOLOGY, V173, P161, DOI 10.1016/j.geomorph.2012.06.006
Bittelli M, 2010, ADV WATER RESOUR, V33, P106, DOI 10.1016/j.advwatres.2009.10.013
BRESLER E, 1973, WATER RESOUR RES, V9, P975, DOI 10.1029/WR009i004p00975
Bresler E., 1982, SALINE SODIC SOILS P
BRISTOW KL, 1984, SOIL SCI SOC AM J, V48, P266, DOI 10.2136/sssaj1984.03615995004800020007x
BRISTOW KL, 1984, AGR FOREST METEOROL, V31, P159, DOI 10.1016/0168-1923(84)90017-0
BRUTSAERT W, 1975, WATER RESOUR RES, V11, P742, DOI 10.1029/WR011i005p00742
BUCHAN GD, 1993, SOIL SCI SOC AM J, V57, P901, DOI 10.2136/sssaj1993.03615995005700040004x
BUCK AL, 1981, J APPL METEOROL, V20, P1527, DOI 10.1175/1520-0450(1981)020<1527:NEFCVP>2.0.CO;2
Burden R.L., 1997, NUMERICAL ANAL
Burdine N. T., 1953, PET T AIME, V198, P71
BURGESS TM, 1980, J SOIL SCI, V31, P315, DOI 10.1111/j.1365-2389.1980.tb02084.x
Campbell G., 1998, INTRO ENV BIOPHYSICS
Campbell G. S., 1985, SOIL PHYS BASIC TRAN
Campbell G.S., 1977, INTRO ENV BIOPHYSICS
CAMPBELL GS, 1994, SOIL SCI, V158, P307, DOI 10.1097/00010694-199411000-00001
CAMPBELL GS, 1974, SOIL SCI, V117, P311, DOI 10.1097/00010694-197406000-00001
Campbell Scientific, 2006, CR10X MEAS CONTR DAT
CARLSON BC, 1972, AM MATH MON, V79, P615, DOI 10.2307/2317088
Carminati A, 2012, VADOSE ZONE J, V11, DOI 10.2136/vzj2011.0106
Carminati A, 2011, VADOSE ZONE J, V10, P988, DOI 10.2136/vzj2010.0113
Carminati A, 2010, PLANT SOIL, V332, P163, DOI 10.1007/s11104-010-0283-8
Carslaw H., 1959, CONDUCTION HEAT SOLI, V2nd edn
Cavazza L., 1981, FISICA TERRENO AGRAR
Cheng S. W., 2012, DELAUNEY MESH GENERA
CHILDS EC, 1950, PROC R SOC LON SER-A, V201, P392, DOI 10.1098/rspa.1950.0068
Cignoni P, 1998, COMPUT AIDED DESIGN, V30, P333, DOI 10.1016/S0010-4485(97)00082-1
CLINE RG, 1976, ECOLOGY, V57, P367, DOI 10.2307/1934826
CONTE SD, 1972, ELEMENTARY NUMERICAL
CORAK SJ, 1987, PLANT PHYSIOL, V84, P582, DOI 10.1104/pp.84.3.582
COWAN I. R., 1965, J APPL ECOL, V2, P221, DOI 10.2307/2401706
CURRIE JA, 1965, J SOIL SCI, V16, P279, DOI 10.1111/j.1365-2389.1965.tb01439.x
Cussler E. L, 1997, DIFFUSION MASS TRANS
Dagan G., 2005, SUBSURFACE FLOW TRAN
Dal Ferro N, 2012, SOIL TILL RES, V119, P13, DOI 10.1016/j.still.2011.12.001
Darcy D, 1856, FONTAINES VILLE DIJO
Deresiewicz H., 1958, ADV APPL MECH, V5, P233, DOI DOI 10.1016/S0065-2156(08)70021-8
DeVries D.A., 1963, PHYS PLANT ENV, P210, DOI DOI 10.12691/AEES-2-2-1.
DiGiammarco P, 1996, J HYDROL, V175, P267, DOI 10.1016/0022-1694(95)02855-2
Douglas DH., 1973, CARTOGRAPHICA, V10, P112, DOI [DOI 10.3138/FM57-6770-U75U-7727, 10.3138/fm57-6770-u75u-7727]
ESRI, 2011, ARCG DESKT REL 10
Flerchinger GN, 1996, WATER RESOUR RES, V32, P2539, DOI 10.1029/96WR01240
Flury M., 2002, METHODS SOIL ANAL 4, P1323, DOI [DOI 10.2136/SSSABOOKSER5.4.C55, DOI 10.2136/SSSAB00KSER5.4.C55]
GARDNER W. R., 1958, SOIL SCI, V85, P228, DOI 10.1097/00010694-195804000-00006
GARDNER W. R., 1960, SOIL SCI, V89, P63, DOI 10.1097/00010694-196002000-00001
Gee G.W., 2002, METHODS SOIL ANAL PA, P255, DOI [10.2136/sssabookser5.4.c121, DOI 10.2136/SSSABOOKSER5.4.C12, DOI 10.2136/SSSAB00KSER5.4.C12]
Gee GW, 1986, METHODS SOIL ANAL, V9, P383, DOI DOI 10.2136/SSSAB00KSER5.1.2ED.C15
Green WH, 1911, J AGR SCI, V4, P1, DOI 10.1017/S0021859600001441
GRISMER ME, 1987, SOIL SCI, V144, P233, DOI 10.1097/00010694-198709000-00010
GUGGENHEIM EA, 1945, J CHEM PHYS, V13, P253, DOI 10.1063/1.1724033
HALLIKAINEN MT, 1985, IEEE T GEOSCI REMOTE, V23, P25, DOI 10.1109/TGRS.1985.289497
Hantel M., 2005, OBSERVED GLOBAL CLIM
Harrison L. P., 1963, HUMIDITY MOISTURE, V3
Hasted J. B., 1973, AQUEOUS DIELECTRICS
International Society of Soil Science ISSS, 1927, COMM ON INT C SOIL S
Ippisch O, 2006, ADV WATER RESOUR, V29, P1780, DOI 10.1016/j.advwatres.2005.12.011
IUPAC, 1972, MAN SYMB TERM
Iwamatsu M, 1996, J COLLOID INTERF SCI, V182, P400, DOI 10.1006/jcis.1996.0480
Johnson W. M., 1960, SOIL SCI, V12, P1
Jury W.A., 1990, TRANSFER FUNCTIONS S
Kleinbaum D., 2008, APPL REGRESSION ANAL
Kroener E, 2014, WATER RESOUR RES, V50, P6479, DOI 10.1002/2013WR014756
Kroener E, 2014, APPL THERM ENG, V70, P510, DOI 10.1016/j.applthermaleng.2014.05.033
Lai D. C, 2009, LINEAR ALGEBRA ITS A
LAI SH, 1976, SOIL SCI SOC AM J, V40, P3, DOI 10.2136/sssaj1976.03615995004000010006x
Langtangen H. P, 2009, PYTHON SCRIPTING COM
LEDIEU J, 1986, J HYDROL, V88, P319, DOI 10.1016/0022-1694(86)90097-1
LEE J, 1991, INT J GEOGR INF SYST, V5, P267, DOI 10.1080/02693799108927855
Levenberg K., 1944, Quarterly of Applied Mathematics, V2, P164
Lund L.J., 1992, P INT WORKSH IND MET, P317
Lutz M., 2009, LEARNING PYTHON
Malicki MA, 1996, EUR J SOIL SCI, V47, P357, DOI 10.1111/j.1365-2389.1996.tb01409.x
Mandelbrot B. B., 1975, OBJECTS FRACTALS FOR
MARQUARDT DW, 1963, J SOC IND APPL MATH, V11, P431, DOI 10.1137/0111030
MARSHALL TJ, 1959, J SOIL SCI, V10, P79, DOI 10.1111/j.1365-2389.1959.tb00667.x
MARSHALL TJ, 1958, J SOIL SCI, V9, P1, DOI 10.1111/j.1365-2389.1958.tb01892.x
Martelli A, 2006, PYTHON IN A NUTSHELL
MILLER EE, 1956, J APPL PHYS, V27, P324, DOI 10.1063/1.1722370
Monteith J.L., 1964, 19 S SOC EXP BIOL, V19, P205
Morton K. W., 1994, NUMERICAL SOLUTION P
MUALEM Y, 1976, WATER RESOUR RES, V12, P513, DOI 10.1029/WR012i003p00513
Nellis G, 2009, HEAT TRANSFER-BOOK, P1
Norman J. M., 1983, Advances in Irrigation, V2, P155
ONG SK, 1991, WATER RES, V25, P29, DOI 10.1016/0043-1354(91)90095-8
Or D, 2013, VADOSE ZONE J, V12, DOI 10.2136/vzj2012.0163
Pachepsky Y., 2000, FRACTALS SOIL SCI
Panofsky H.A., 1964, STRUCTURE ATMOSPHERI
PAPENDICK RI, 1980, WATER POTENTIAL RELA, P1
PASSIOURA JB, 1972, AUST J AGR RES, V23, P745, DOI 10.1071/AR9720745
Patankar S. V, 1992, NUMERICAL HEAT TRANS
Penman HL, 1940, J AGR SCI, V30, P437, DOI 10.1017/S0021859600048164
PHILIP J. R., 1957, TRANS AMER GEOPHYS UNION, V38, P222
PHILIP J. R., 1957, SOIL SCI, V83, P345, DOI 10.1097/00010694-195705000-00002
Pieri L, 2007, J HYDROL, V336, P84, DOI 10.1016/j.jhydrol.2006.12.014
Pieri L, 2006, GEODERMA, V135, P118, DOI 10.1016/j.geoderma.2005.11.009
Pistocchi A, 2003, P IT M AGR BOL IT
Poling B. E., 2000, PROPERTIES GASES LIQ, V5th
Posadas AND, 2003, SOIL SCI SOC AM J, V67, P1361, DOI 10.2136/sssaj2003.1361
Press WH, 1992, NUMERICAL RECIPES AR
PRITCHARD DT, 1982, J SOIL SCI, V33, P175, DOI 10.1111/j.1365-2389.1982.tb01757.x
PURCELL WR, 1949, T AM I MIN MET ENG, V186, P39
Raju GG., 2003, DIELECTRICS ELECT FI
Ramer Urs, 1972, COMPUT GRAPHICS IMAG, V1, P244, DOI [10.1016/S0146-664X(72) 80017-0, DOI 10.1016/S0146-664X(72)80017-0]
Ramo S., 1994, FIELDS WAVES COMMUNI
Rawls W. J., 1992, P INT WORKSH IND MET
RICHARDS LA, 1948, SOIL SCI, V66, P105, DOI 10.1097/00010694-194808000-00003
ROBIN MJL, 1993, WATER RESOUR RES, V29, P2385, DOI 10.1029/93WR00386
ROBINSON RA, 1965, ELECT SOLUTIONS
ROTH K, 1990, WATER RESOUR RES, V26, P2267, DOI 10.1029/WR026i010p02267
ROTH K, 1995, WATER RESOUR RES, V31, P2127, DOI 10.1029/95WR00946
RUSSO D, 1981, SOIL SCI SOC AM J, V45, P682, DOI 10.2136/sssaj1981.03615995004500040002x
RUSSO D, 1982, SOIL SCI SOC AM J, V46, P20, DOI 10.2136/sssaj1982.03615995004600010004x
SALLAM A, 1984, SOIL SCI SOC AM J, V48, P3, DOI 10.2136/sssaj1984.03615995004800010001x
Santamarina J.C., 2001, SOILS WAVES PARTICUL
Schaap MG, 2001, J HYDROL, V251, P163, DOI 10.1016/S0022-1694(01)00466-8
Scheidegger A. E., 1960, PHYS FLOW POROUS MED
SCHOLANDER PF, 1965, SCIENCE, V148, P339, DOI 10.1126/science.148.3668.339
Seaman J. C., 2012, VADOSE ZONE J, P3
SHIOZAWA S, 1991, SOIL SCI, V152, P427, DOI 10.1097/00010694-199112000-00004
Simnek J., 2005, HYDRUS 1D SOFTWARE P
SIMUNEK J, 1994, 136 USDA US SAL LAB
Slocum Terry A, 2009, THEMATIC CARTOGRAPHY
Smith O. L., 1991, SOIL BIOL BIOCHEM, V11, P585
*SOIL SURV DIV STA, 1993, US DEP AGR HDB, V18
Taina IA, 2008, CAN J SOIL SCI, V88, P1, DOI 10.4141/CJSS06027
TAYLOR STERLING A., 1960, SOIL SCI SOC AMER PROC, V24, P243
Tomei F., 2005, THESIS U BOLOGNA ITA
Tongyai M. L. C., 1977, THESIS WASHINGTON ST
TOPP GC, 1980, WATER RESOUR RES, V16, P574, DOI 10.1029/WR016i003p00574
Tuller M, 2005, WATER RESOUR RES, V41, DOI 10.1029/2005WR004142
Turcotte D. L., 1997, FRACTALS CHAOS GEOLO
U.S. Department of Agriculture USDA, 1975, SOIL SURV
UNSWORTH MH, 1975, Q J ROY METEOR SOC, V101, P13, DOI 10.1002/qj.49710142703
van Genuchten MT, 1991, EPA, V600, P2, DOI DOI 10.1002/9781118616871
van Kampen N. G., 1981, STOCHASTIC APPROACHE
VANDEGRIEND AA, 1994, WATER RESOUR RES, V30, P181, DOI 10.1029/93WR02747
VANDEPOL RM, 1977, SOIL SCI SOC AM J, V41, P10, DOI 10.2136/sssaj1977.03615995004100010008x
VANGENUCHTEN MT, 1980, SOIL SCI SOC AM J, V44, P892, DOI 10.2136/sssaj1980.03615995004400050002x
Vogel HJ, 2010, COMPUT GEOSCI-UK, V36, P1236, DOI 10.1016/j.cageo.2010.03.007
Vogel HJ, 2002, LECT NOTES PHYS, V600, P75
Warrick A. W., 1980, APPL SOIL PHYS, P224
WEBSTER R, 1977, QUANTITATIVE NUMERIC
NR 156
TC 1
Z9 1
U1 0
U2 3
PU OXFORD UNIV PRESS
PI NEW YORK
PA 198 MADISON AVENUE, NEW YORK, NY 10016 USA
BN 978-0-19-968309-3
PY 2015
BP 1
EP 449
DI 10.1093/acprof:oso/9780199683093.001.0001
PG 449
WC Computer Science, Interdisciplinary Applications; Soil Science
SC Computer Science; Agriculture
GA BH2PB
UT WOS:000399093300019
DA 2021-04-21
ER
PT B
AU Bittelli, M
Campbell, GS
Tomei, F
AF Bittelli, Marco
Campbell, Gaylon S.
Tomei, Fausto
BA Bittelli, B
Campbell, GS
Tomei, F
BF Bittelli, B
Campbell, GS
Tomei, F
TI Soil Physics with Python Transport in the Soil-Plant-Atmosphere System
Introduction
SO SOIL PHYSICS WITH PYTHON: TRANSPORT IN THE SOIL-PLANT-ATMOSPHERE SYSTEM
LA English
DT Editorial Material; Book Chapter
C1 [Bittelli, Marco] Univ Bologna, Bologna, Italy.
[Campbell, Gaylon S.] Decagon Devices Inc, Pullman, WA USA.
[Tomei, Fausto] Reg Agcy Environm Protect, Emilia Romagna, Italy.
RP Bittelli, M (corresponding author), Univ Bologna, Bologna, Italy.
NR 0
TC 0
Z9 0
U1 0
U2 1
PU OXFORD UNIV PRESS
PI NEW YORK
PA 198 MADISON AVENUE, NEW YORK, NY 10016 USA
BN 978-0-19-968309-3
PY 2015
BP 1
EP 2
D2 10.1093/acprof:oso/9780199683093.001.0001
PG 2
WC Computer Science, Interdisciplinary Applications; Soil Science
SC Computer Science; Agriculture
GA BH2PB
UT WOS:000399093300002
DA 2021-04-21
ER
PT B
AU Bittelli, M
Campbell, GS
Tomei, F
AF Bittelli, Marco
Campbell, Gaylon S.
Tomei, Fausto
BA Bittelli, B
Campbell, GS
Tomei, F
BF Bittelli, B
Campbell, GS
Tomei, F
TI Soil Physics with Python Transport in the Soil-Plant-Atmosphere System
Preface
SO SOIL PHYSICS WITH PYTHON: TRANSPORT IN THE SOIL-PLANT-ATMOSPHERE SYSTEM
LA English
DT Editorial Material; Book Chapter
C1 [Bittelli, Marco] Univ Bologna, Bologna, Italy.
[Campbell, Gaylon S.] Decagon Devices Inc, Pullman, WA USA.
[Tomei, Fausto] Reg Agcy Environm Protect, Emilia Romagna, Italy.
RP Bittelli, M (corresponding author), Univ Bologna, Bologna, Italy.
NR 0
TC 0
Z9 0
U1 0
U2 0
PU OXFORD UNIV PRESS
PI NEW YORK
PA 198 MADISON AVENUE, NEW YORK, NY 10016 USA
BN 978-0-19-968309-3
PY 2015
BP V
EP +
D2 10.1093/acprof:oso/9780199683093.001.0001
PG 9
WC Computer Science, Interdisciplinary Applications; Soil Science
SC Computer Science; Agriculture
GA BH2PB
UT WOS:000399093300001
DA 2021-04-21
ER
PT J
AU Antusch, S
Cefalia, F
Nolde, D
Orani, S
AF Antusch, Stefan
Cefalia, Francesco
Nolde, David
Orani, Stefano
TI False vacuum energy dominated inflation with large r and the importance
of k(S)
SO JOURNAL OF COSMOLOGY AND ASTROPARTICLE PHYSICS
LA English
DT Article
DE inflation; physics of the early universe; cosmological phase transitions
ID PARTICLE PHYSICS MODELS; HYBRID
AB We investigate to which extent and under which circumstances false vacuum energy (V-0) dominated slow-roll inflation is compatible with a large tensor-to-scalar ratio r = O(0.1), as indicated by the recent BICEP2 measurement. With V-0 we refer to a constant contribution to the inflaton potential, present before a phase transition takes place and absent in the true vacuum of the theory, like e.g. in hybrid inflation. Based on model-independent considerations, we derive an upper bound on the possible amount of V-0 domination and highlight the importance of higher-order runnings of the scalar spectral index (beyond alpha(s)) in order to realise scenarios of V-0 dominated inflation. We study the conditions for V-0 domination explicitly with an inflaton potential reconstruction around the inflaton field value 50 e-folds before the end of inflation, taking into account the present observational data. To this end, we provide the up-to-date parameter constraints within ACDM + r + alpha(s) + K-s using the cosmological parameter estimation code Monte Python together with the Boltzmann code CLASS.
C1 [Antusch, Stefan; Cefalia, Francesco; Nolde, David; Orani, Stefano] Univ Basel, Dept Phys, CH-4056 Basel, Switzerland.
[Antusch, Stefan] Max Planck Inst Phys & Astrophys, Werner Heisenberg Inst, D-80805 Munich, Germany.
RP Antusch, S (corresponding author), Univ Basel, Dept Phys, Klingelbergstr 82, CH-4056 Basel, Switzerland.
EM stefan.antusch@unibas.ch; f.cefala@unibas.ch; david.nolde@unibas.ch;
stefano.orani@unibas.ch
FU Swiss National Science FoundationSwiss National Science Foundation
(SNSF)European Commission
FX This work was supported by the Swiss National Science Foundation. F.C.
and D.N. thank Benjamin Audren, Julien Lesgourgues and Thomas Tram for
the introduction to CLASS and Monte Python during the "Tools for
Cosmology" workshop in Geneva. We also thank Vinzenz Maurer for helpful
discussions.
CR Abazajian KN, 2014, J COSMOL ASTROPART P, DOI 10.1088/1475-7516/2014/08/053
Ade PAR, 2014, ASTRON ASTROPHYS, V571, DOI 10.1051/0004-6361/201321569
Ade PAR, 2014, PHYS REV LETT, V112, DOI 10.1103/PhysRevLett.112.241101
Anderson L., ARXIV13124877INSPIRE
Aravind A, 2014, J COSMOL ASTROPART P, DOI 10.1088/1475-7516/2014/08/058
Ashoorioon A, 2014, PHYS LETT B, V737, P98, DOI 10.1016/j.physletb.2014.08.038
Audren B, 2013, J COSMOL ASTROPART P, DOI 10.1088/1475-7516/2013/02/001
Barranco L, 2014, PHYS REV D, V90, DOI 10.1103/PhysRevD.90.063007
Ben-Dayan I, 2010, J COSMOL ASTROPART P, DOI 10.1088/1475-7516/2010/09/007
Blas D, 2011, J COSMOL ASTROPART P, DOI 10.1088/1475-7516/2011/07/034
Bonvin C, 2014, PHYS REV LETT, V112, DOI 10.1103/PhysRevLett.112.191303
Bramante J, 2014, PHYS REV D, V90, DOI 10.1103/PhysRevD.90.023530
Brummer F., ARXIV14054868INSPIRE
Carrillo-Gonzalez M, 2014, PHYS LETT B, V734, P345, DOI 10.1016/j.physletb.2014.05.062
Choi KY, 2014, PHYS REV D, V90, DOI 10.1103/PhysRevD.90.023536
Collins H., ARXIV14034592INSPIRE
Cook JL, 2012, PHYS REV D, V86, DOI 10.1103/PhysRevD.86.069901
Cook JL, 2012, PHYS REV D, V85, DOI 10.1103/PhysRevD.85.023534
Das S., ARXIV14060857INSPIRE
Dodelson S, 2002, PHYS REV D, V65, DOI 10.1103/PhysRevD.65.101301
Dvorkin C, 2010, PHYS REV D, V81, DOI 10.1103/PhysRevD.81.023518
Flauger R, 2014, J COSMOL ASTROPART P, DOI 10.1088/1475-7516/2014/08/039
Hotchkiss S, 2012, J COSMOL ASTROPART P, DOI 10.1088/1475-7516/2012/02/008
Hui L, 2002, PHYS REV D, V65, DOI 10.1103/PhysRevD.65.103507
Josan AS, 2009, PHYS REV D, V79, DOI 10.1103/PhysRevD.79.103520
Kobayashi T., ARXIV14043102INSPIRE
Lesgourgues J., ARXIV11042932INSPIRE
Li H., ARXIV14040238INSPIRE
LIDDLE AR, 1994, PHYS REV D, V50, P7222, DOI 10.1103/PhysRevD.50.7222
Lidsey JE, 1997, REV MOD PHYS, V69, P373, DOI 10.1103/RevModPhys.69.373
Lizarraga J, 2014, PHYS REV LETT, V112, DOI 10.1103/PhysRevLett.112.171301
Lyth DH, 1999, PHYS REP, V314, P1, DOI 10.1016/S0370-1573(98)00128-8
Ma Y.-Z., ARXIV14034585INSPIRE
Martin J., 2014, PHYS DARK U IN PRESS
Mazumdara A, 2011, PHYS REP, V497, P85, DOI 10.1016/j.physrep.2010.08.001
Mortonson M. J., ARXIV14055857INSPIRE
Moss A, 2014, PHYS REV LETT, V112, DOI 10.1103/PhysRevLett.112.171302
Mukohyama S, 2014, J COSMOL ASTROPART P, DOI 10.1088/1475-7516/2014/08/036
Pallis C, 2014, PHYS LETT B, V736, P261, DOI 10.1016/j.physletb.2014.07.031
Senatore L, 2014, J COSMOL ASTROPART P, DOI 10.1088/1475-7516/2014/08/016
Stewart ED, 2002, PHYS REV D, V65, DOI 10.1103/PhysRevD.65.103508
Wu FQ, 2014, SCI CHINA PHYS MECH, V57, P1449, DOI 10.1007/s11433-014-5516-z
NR 42
TC 3
Z9 3
U1 0
U2 0
PU IOP PUBLISHING LTD
PI BRISTOL
PA TEMPLE CIRCUS, TEMPLE WAY, BRISTOL BS1 6BE, ENGLAND
SN 1475-7516
J9 J COSMOL ASTROPART P
JI J. Cosmol. Astropart. Phys.
PD OCT
PY 2014
IS 10
AR 015
DI 10.1088/1475-7516/2014/10/015
PG 17
WC Astronomy & Astrophysics; Physics, Particles & Fields
SC Astronomy & Astrophysics; Physics
GA AW0OA
UT WOS:000345990800016
OA Other Gold
DA 2021-04-21
ER
PT J
AU Chekanov, SV
May, E
Strand, K
Van Gemmeren, P
AF Chekanov, S. V.
May, E.
Strand, K.
Van Gemmeren, P.
TI ProMC: Input-output data format for HEP applications using varint
encoding
SO COMPUTER PHYSICS COMMUNICATIONS
LA English
DT Article
DE Data; Format; IO; Input-output; LHC
AB A new data format for Monte Carlo (MC) events, or any structural data, including experimental data, is discussed. The format is designed to store data in a compact binary form using variable-size integer encoding as implemented in the Google's Protocol Buffers package. This approach is implemented in the PRoMC library which produces smaller file sizes for MC records compared to the existing input-output libraries used in high-energy physics (HEP). Other important features of the proposed format are a separation of abstract data layouts from concrete programming implementations, self-description and random access. Data stored in PRoMC files can be written, read and manipulated in a number of programming languages, such C++, JAVA, FORTRAN and PYTHON. Published by Elsevier B.V.
C1 [Chekanov, S. V.; May, E.; Van Gemmeren, P.] Argonne Natl Lab, HEP Div, Argonne, IL 60439 USA.
[Strand, K.] Winona State Univ, Dept Phys, Winona, MN 55987 USA.
RP Chekanov, SV (corresponding author), Argonne Natl Lab, HEP Div, 9700 S Cass Ave, Argonne, IL 60439 USA.
EM chekanov@anl.gov
OI Chekanov, Sergei/0000-0001-7314-7247; Strand, Kyle/0000-0003-4556-2974;
van Gemmeren, Peter/0000-0002-7227-4006
FU US Department of Energy Office of Science laboratoryUnited States
Department of Energy (DOE) [DE-AC02-06CH11357]; Office of Science of the
US Department of EnergyUnited States Department of Energy (DOE)
[DE-AC02-06CH11357]
FX One of us (S.C.) would like to thank J. Proudfoot for a discussion. The
submitted manuscript has been created by UChicago Argonne, LLC, Operator
of Argonne National Laboratory ("Argonne"). Argonne, a US Department of
Energy Office of Science laboratory, is operated under Contract No.
DE-AC02-06CH11357. This research used resources of the Argonne
Leadership Computing Facility at Argonne National Laboratory, which is
supported by the Office of Science of the US Department of Energy under
contract DE-AC02-06CH11357.
CR Alwall J, 2007, COMPUT PHYS COMMUN, V176, P300, DOI 10.1016/j.cpc.2006.11.010
Antcheva I, 2009, COMPUT PHYS COMMUN, V180, P2499, DOI 10.1016/j.cpc.2009.08.005
Belov S, 2010, COMPUT PHYS COMMUN, V181, P1758, DOI 10.1016/j.cpc.2010.06.026
Chekanov S., 2014, HEPSIM REPORSITORY P
Chekanov S., 2013, SNOW1300090
Chekanov SV, 2010, SCIENTIFIC DATA ANALYSIS USING JYTHON SCRIPTING AND JAVA, P1, DOI 10.1007/978-1-84996-287-2
de Favereau J., 2013, ARXIV 1307 6346
Dobbs M., HEPMC USER MANUAL
Ebke J, 2012, J PHYS CONF SER, V396, DOI 10.1088/1742-6596/396/2/022012
Garren L., STDHEP COMMON OUTPUT
Google, 2008, PROT BUFF GOOGL DAT
Johnson A., 1996, JAVA BASED ANAL ENV
Ovyn S, 2009, TECH REP
Sjostrand T, 2008, COMPUT PHYS COMMUN, V178, P852, DOI 10.1016/j.cpc.2008.01.036
Sjostrand T, 2006, J HIGH ENERGY PHYS, DOI 10.1088/1126-6708/2006/05/026
Zipios, 2013, LIB READ WRIT ZIP FI
NR 16
TC 9
Z9 9
U1 0
U2 2
PU ELSEVIER SCIENCE BV
PI AMSTERDAM
PA PO BOX 211, 1000 AE AMSTERDAM, NETHERLANDS
SN 0010-4655
EI 1879-2944
J9 COMPUT PHYS COMMUN
JI Comput. Phys. Commun.
PD OCT
PY 2014
VL 185
IS 10
BP 2629
EP 2635
DI 10.1016/j.cpc.2014.06.016
PG 7
WC Computer Science, Interdisciplinary Applications; Physics, Mathematical
SC Computer Science; Physics
GA AN1KC
UT WOS:000340340200026
DA 2021-04-21
ER
PT J
AU Sandner, R
Vukics, A
AF Sandner, Raimar
Vukics, Andras
TI C++QEDv2 Milestone 10: A C++/Python application-programming framework
for simulating open quantum dynamics
SO COMPUTER PHYSICS COMMUNICATIONS
LA English
DT Article
DE Composite quantum systems; Open quantum systems; Quantum optics; Master
equation; Quantum trajectories; Cavity quantum electrodynamics;
Multi-array; Compile-time algorithms
AB The v2 Milestone 10 release of C++QED is primarily a feature release, which also corrects some problems of the previous release, especially as regards the build system. The adoption of C++11 features has led to many simplifications in the codebase. A full doxygen-based API manual [1] is now provided together with updated user guides. A largely automated, versatile new testsuite directed both towards computational and physics features allows for quickly spotting arising errors. The states of trajectories are now savable and recoverable with full binary precision, allowing for trajectory continuation regardless of evolution method (single/ensemble Monte Carlo wave-function or Master equation trajectory). As the main new feature, the framework now presents Python bindings to the highest-level programming interface, so that actual simulations for given composite quantum systems can now be performed from Python.
New version program summary
Program title: C++QED
Catalogue identifier: AELU_v2_0
Program summary URL: http://cpc.cs.qub.ac.uk/summaries/AELU_v2_0.html
Program obtainable from: CPC Program Library, Queen's University, Belfast, N. Ireland
Licensing provisions: yes
No. of lines in distributed program, including test data, etc.: 492422
No. of bytes in distributed program, including test data, etc.: 8070987
Distribution format: tar.gz
Programming language: C++/Python.
Computer: i386-i686, x86 64.
Operating system: In principle cross-platform, as yet tested only on UNIX-like systems (including Mac OS X).
RAM: The framework itself takes about 60MB, which is fully shared. The additional memory taken by the program which defines the actual physical system (script) is typically less than 1MB. The memory storing the actual data scales with the system dimension for state-vector manipulations, and the square of the dimension for density-operator manipulations. This might easily be GBs, and often the memory of the machine limits the size of the simulated system.
Classification: 4.3, 4.13, 6.2.
External routines: Boost C++ libraries, GNU Scientific Library, Blitz++, FLENS, NumPy, SciPy
Catalogue identifier of previous version: AELU_v1_0
Journal reference of previous version: Comput. Phys. Comm. 183 (2012) 1381
Does the new version supersede the previous version?: Yes
Nature of problem:
Definition of (open) composite quantum systems out of elementary building blocks [2,3]. Manipulation of such systems, with emphasis on dynamical simulations such as Master-equation evolution [4] and Monte Carlo wave-function simulation [5].
Solution method:
Master equation, Monte Carlo wave-function method
Reasons for new version:
The new version is mainly a feature release, but it does correct some problems of the previous version, especially as regards the build system.
Summary of revisions:
We give an example for a typical Python script implementing the ring-cavity system presented in Sec. 3.3 of Ref. [2]:
[GRAPHICS]
Restrictions:
Total dimensionality of the system. Master equation few thousands. Monte Carlo wave-function trajectory several millions.
Unusual features: Because of the heavy use of compile-time algorithms, compilation of programs written in the framework may take a long time and much memory (up to several GBs).
Additional comments:
The framework is not a program, but provides and implements an application-programming interface for developing simulations in the indicated problem domain.
We use several C++11 features which limits the range of supported compilers (g++ 4.7, clang++ 3.1)
Documentation, http://cppqed.sourceforge.net/
Running time:
Depending on the magnitude of the problem, can vary from a few seconds to weeks. (C) 2014 Elsevier B.V. All rights reserved.
C1 [Sandner, Raimar] Univ Innsbruck, Inst Theoret Phys, A-6020 Innsbruck, Austria.
[Vukics, Andras] Hungarian Acad Sci, Wigner Res Ctr, Inst Solid State Phys & Opt, H-1525 Budapest, Hungary.
RP Vukics, A (corresponding author), Hungarian Acad Sci, Wigner Res Ctr, Inst Solid State Phys & Opt, POB 49, H-1525 Budapest, Hungary.
EM raimar.sandner@uibk.ac.at; vukics.andras@wigner.mta.hu
FU EU FP7 (ITN) [CCQED-264666]; Hungarian National Office for Research and
TechnologyNational Office for Research and Technology [ERC_HU_09
OPTOMECH]; Hungarian Academy of Sciences (Lendulet Program)
[LP2011-016]; Janos Bolyai Research Scholarship of the Hungarian Academy
of SciencesHungarian Academy of Sciences; Stiftung Aktion
Osterreich-Ungarn [86ou17]
FX This work was supported by the EU FP7 (ITN, CCQED-264666), the Hungarian
National Office for Research and Technology under the contract ERC_HU_09
OPTOMECH, the Hungarian Academy of Sciences (Lendulet Program,
LP2011-016), the Janos Bolyai Research Scholarship of the Hungarian
Academy of Sciences, and the Stiftung Aktion Osterreich-Ungarn (86ou17).
CR Carmichael H., 1993, OPEN SYSTEMS APPROAC
DALIBARD J, 1992, PHYS REV LETT, V68, P580, DOI 10.1103/PhysRevLett.68.580
Vukics A, 2007, EUR PHYS J D, V44, P585, DOI 10.1140/epjd/e2007-00210-x
Vukics A, 2012, COMPUT PHYS COMMUN, V183, P1381, DOI 10.1016/j.cpc.2012.02.004
NR 4
TC 5
Z9 5
U1 1
U2 14
PU ELSEVIER SCIENCE BV
PI AMSTERDAM
PA PO BOX 211, 1000 AE AMSTERDAM, NETHERLANDS
SN 0010-4655
EI 1879-2944
J9 COMPUT PHYS COMMUN
JI Comput. Phys. Commun.
PD SEP
PY 2014
VL 185
IS 9
BP 2380
EP 2382
DI 10.1016/j.cpc.2014.04.011
PG 3
WC Computer Science, Interdisciplinary Applications; Physics, Mathematical
SC Computer Science; Physics
GA AK7LD
UT WOS:000338608900006
DA 2021-04-21
ER
PT J
AU van Elteren, A
Pelupessy, I
Zwart, SP
AF van Elteren, Arjen
Pelupessy, Inti
Zwart, Simon Portegies
TI Multi-scale and multi-domain computational astrophysics
SO PHILOSOPHICAL TRANSACTIONS OF THE ROYAL SOCIETY A-MATHEMATICAL PHYSICAL
AND ENGINEERING SCIENCES
LA English
DT Article
DE multi-scale; multi-physics; astrophysics
ID EVOLUTION; CODE; MASS; CLUSTERS; MODELS
AB Astronomical phenomena are governed by processes on all spatial and temporal scales, ranging from days to the age of the Universe (13.8 Gyr) as well as from kilometre size up to the size of the Universe. This enormous range in scales is contrived, but as long as there is a physical connection between the smallest and largest scales it is important to be able to resolve them all, and for the study of many astronomical phenomena this governance is present. Although covering all these scales is a challenge for numerical modellers, the most challenging aspect is the equally broad and complex range in physics, and the way in which these processes propagate through all scales. In our recent effort to cover all scales and all relevant physical processes on these scales, we have designed the Astrophysics Multipurpose Software Environment (AMUSE). AMUSE is a Python-based framework with production quality community codes and provides a specialized environment to connect this plethora of solvers to a homogeneous problem-solving environment.
C1 [van Elteren, Arjen; Pelupessy, Inti; Zwart, Simon Portegies] Leiden Univ, Leiden Observ, NL-2300 RA Leiden, Netherlands.
RP Zwart, SP (corresponding author), Leiden Univ, Leiden Observ, POB 9513, NL-2300 RA Leiden, Netherlands.
EM spz@strw.leidenuniv.nl
FU Interuniversity Attraction Poles ProgrammeBelgian Federal Science Policy
Office; Belgian Science Policy OfficeBelgian Federal Science Policy
Office [IAP P7/08 CHARM]; Netherlands Research Council NWONetherlands
Organization for Scientific Research (NWO) [643.200.503, 639.073.803,
614.061.608]; Netherlands Research School for Astronomy (NOVA)
FX This research has partially been funded by the Interuniversity
Attraction Poles Programme initiated by the Belgian Science Policy
Office (IAP P7/08 CHARM), and is supported by the Netherlands Research
Council NWO (grant nos. 643.200.503, 639.073.803 and 614.061.608) and by
The Netherlands Research School for Astronomy (NOVA).
CR Aarseth SJ, 1999, PUBL ASTRON SOC PAC, V111, P1333, DOI 10.1086/316455
Altay G, 2008, MON NOT R ASTRON SOC, V386, P1931, DOI 10.1111/j.1365-2966.2008.13212.x
Bedorf J, 2012, J COMPUT PHYS, V231, P2825, DOI 10.1016/j.jcp.2011.12.024
Chambers JE, 1999, MON NOT R ASTRON SOC, V304, P793, DOI 10.1046/j.1365-8711.1999.02379.x
EGGLETON PP, 1971, MON NOT R ASTRON SOC, V151, P351, DOI 10.1093/mnras/151.3.351
Fujii M, 2007, PUBL ASTRON SOC JPN, V59, P1095, DOI 10.1093/pasj/59.6.1095
Glebbeek E, 2008, ASTRON ASTROPHYS, V488, P1007, DOI 10.1051/0004-6361:200809930
Harfst S, 2008, ASTRON NACHR, V329, P885, DOI 10.1002/asna.200811058
Harfst S, 2007, NEW ASTRON, V12, P357, DOI 10.1016/j.newast.2006.11.003
HEGGIE DC, 1986, LECT NOTES PHYS, V267
HERNQUIST L, 1989, ASTROPHYS J SUPPL S, V70, P419, DOI 10.1086/191344
Hurley JR, 2000, MON NOT R ASTRON SOC, V315, P543, DOI 10.1046/j.1365-8711.2000.03426.x
Keppens R, 2012, J COMPUT PHYS, V231, P718, DOI 10.1016/j.jcp.2011.01.020
MELLEMA G, 1991, ASTRON ASTROPHYS, V252, P718
Mikkola S, 2008, ASTRON J, V135, P2398, DOI 10.1088/0004-6256/135/6/2398
Paardekooper JP, 2010, ASTRON ASTROPHYS, V515, DOI 10.1051/0004-6361/200913821
Paxton B, 2011, ASTROPHYS J SUPPL S, V192, DOI 10.1088/0067-0049/192/1/3
Pelupessy FI, 2012, MON NOT R ASTRON SOC, V420, P1503, DOI 10.1111/j.1365-2966.2011.20137.x
Portegies Zwart S, 2009, NEW ASTRON, V14, P369, DOI 10.1016/j.newast.2008.10.006
Springel V, 2005, MON NOT R ASTRON SOC, V364, P1105, DOI 10.1111/j.1365-2966.2005.09655.x
Stone JM, 2008, ASTROPHYS J SUPPL S, V178, P137, DOI 10.1086/588755
Zwart SFP, 1996, ASTRON ASTROPHYS, V309, P179
Zwart SFP, 2013, COMPUT PHYS COMMUN, V184, P456, DOI 10.1016/j.cpc.2012.09.024
NR 23
TC 5
Z9 5
U1 0
U2 2
PU ROYAL SOC
PI LONDON
PA 6-9 CARLTON HOUSE TERRACE, LONDON SW1Y 5AG, ENGLAND
SN 1364-503X
EI 1471-2962
J9 PHILOS T R SOC A
JI Philos. Trans. R. Soc. A-Math. Phys. Eng. Sci.
PD AUG 6
PY 2014
VL 372
IS 2021
SI SI
AR 20130385
DI 10.1098/rsta.2013.0385
PG 7
WC Multidisciplinary Sciences
SC Science & Technology - Other Topics
GA AL0VH
UT WOS:000338844500007
PM 24982254
OA Bronze
DA 2021-04-21
ER
PT J
AU Solis, C
Chavez-Lomeli, E
Ortiz, ME
Huerta, A
Andrade, E
Barrios, E
AF Solis, C.
Chavez-Lomeli, E.
Ortiz, M. E.
Huerta, A.
Andrade, E.
Barrios, E.
TI A new AMS facility in Mexico
SO NUCLEAR INSTRUMENTS & METHODS IN PHYSICS RESEARCH SECTION B-BEAM
INTERACTIONS WITH MATERIALS AND ATOMS
LA English
DT Article; Proceedings Paper
CT 11th European Conference on Accelerators in Applied Research and
Technology
CT 11th European Conference on Accelerators in Applied Research and
Technology
CY SEP 08-13, 2013
CY SEP 08-13, 2013
CL Namur, BELGIUM
CL Namur, BELGIUM
DE Accelerator Mass Spectrometry; Radiocarbon; Cosmogenic isotopes;
Radionuclides
AB A new Accelerator Mass Spectrometry system has been installed at the Institute of Physics of the National Autonomous University of Mexico (UNAM). A sample preparation chemistry laboratory equipped with computer controlled graphitization equipment (AGEIII) has also been established. Together both facilities constitute the LEMA (Laboratorio de Espectrometria de Masas con Aceleradores) first of its kind in Mexico. High sensitivity characterization of the concentration in a sample of C-14 as well as Be-10, Al-26, I-129 and Pu are now possible. Since the demand for C-14 dating is far more abundant, a data analysis program was developed in the cross-platform programming language Python in order to calculate radiocarbon age. Results from installation, acceptance tests and the first results of C-14 analyses of reference materials prepared in our own facility are presented. (C) 2014 Elsevier B.V. All rights reserved.
C1 [Solis, C.; Chavez-Lomeli, E.; Ortiz, M. E.; Huerta, A.; Andrade, E.; Barrios, E.] Univ Nacl Autonoma Mexico, Inst Fis, LEMA, Mexico City, DF, Mexico.
RP Solis, C (corresponding author), Univ Nacl Autonoma Mexico, Inst Fis, LEMA, Ap Po 20-364, Mexico City, DF, Mexico.
EM corina@fisica.unam.mx
RI Vargas, Jose Eduardo Barrios/I-8872-2016; Lomeli, Efrain R.
Chavez/AAS-3720-2020
OI Vargas, Jose Eduardo Barrios/0000-0002-6880-8941; SOLIS,
CORINA/0000-0003-0337-2398
FU CONACYT-UNAMConsejo Nacional de Ciencia y Tecnologia
(CONACyT)Universidad Nacional Autonoma de Mexico [123128]; DGAPA
[IG100313]
FX This work was supported by CONACYT-UNAM under project number 123128 and
by DGAPA project IG100313. Corina Solis acknowledges ETH Zurich (L.
Wacker and A. Synal) for kindly providing the reference samples for
acceptance tests; Dr. L. Wacker for the AMS training on the use of the
AGEIII, sample preparation and data analysis; Dr. I. Hajdas (ETH) for
the discussions about 14C analysis. We appreciate L. Wacker
and J. Santos' (CAN, Seville) help and advice regarding the data
analysis. Special mention goes to High Voltage Engineering Europe and
their engineers Hans van Bergen and Santino Liau for their determination
to produce a system that performs beyond contracted specifications.
CR Christl M, 2013, NUCL INSTRUM METH B, V294, P29, DOI 10.1016/j.nimb.2012.03.004
Klein M, 2013, RADIOCARBON, V55, P224, DOI 10.1017/S0033822200057325
LOYD DH, 1991, RADIOCARBON, V33, P297, DOI 10.1017/S0033822200040327
Nemec M, 2010, RADIOCARBON, V52, P1380
Nishiizumi K, 2004, NUCL INSTRUM METH B, V223, P388, DOI 10.1016/j.nimb.2004.04.075
Nishiizumi K, 2007, NUCL INSTRUM METH B, V258, P403, DOI 10.1016/j.nimb.2007.01.297
STUIVER M, 1977, RADIOCARBON, V19, P355, DOI 10.1017/S0033822200003672
Suter M, 2000, NUCL INSTRUM METH B, V172, P144, DOI 10.1016/S0168-583X(00)00359-1
Wacker L, 2010, NUCL INSTRUM METH B, V268, P931, DOI 10.1016/j.nimb.2009.10.067
Wacker L, 2010, NUCL INSTRUM METH B, V268, P976, DOI 10.1016/j.nimb.2009.10.078
NR 10
TC 17
Z9 17
U1 0
U2 7
PU ELSEVIER SCIENCE BV
PI AMSTERDAM
PA PO BOX 211, 1000 AE AMSTERDAM, NETHERLANDS
SN 0168-583X
EI 1872-9584
J9 NUCL INSTRUM METH B
JI Nucl. Instrum. Methods Phys. Res. Sect. B-Beam Interact. Mater. Atoms
PD JUL 15
PY 2014
VL 331
BP 233
EP 237
DI 10.1016/j.nimb.2014.02.015
PG 5
WC Instruments & Instrumentation; Nuclear Science & Technology; Physics,
Atomic, Molecular & Chemical; Physics, Nuclear
SC Instruments & Instrumentation; Nuclear Science & Technology; Physics
GA AL4ZZ
UT WOS:000339144600047
DA 2021-04-21
ER
PT J
AU Field, SE
Galley, CR
Hesthaven, JS
Kaye, J
Tiglio, M
AF Field, Scott E.
Galley, Chad R.
Hesthaven, Jan S.
Kaye, Jason
Tiglio, Manuel
TI Fast Prediction and Evaluation of Gravitational Waveforms Using
Surrogate Models
SO PHYSICAL REVIEW X
LA English
DT Article
ID REDUCED BASIS METHOD; GRAM-SCHMIDT ORTHOGONALIZATION;
PARTIAL-DIFFERENTIAL-EQUATIONS; EMPIRICAL INTERPOLATION; GREEDY
ALGORITHMS; COALESCENCE; RECOIL
AB We propose a solution to the problem of quickly and accurately predicting gravitational waveforms within any given physical model. The method is relevant for both real-time applications and more traditional scenarios where the generation of waveforms using standard methods can be prohibitively expensive. Our approach is based on three offline steps resulting in an accurate reduced order model in both parameter and physical dimensions that can be used as a surrogate for the true or fiducial waveform family. First, a set of m parameter values is determined using a greedy algorithm from which a reduced basis representation is constructed. Second, these m parameters induce the selection of m time values for interpolating a waveform time series using an empirical interpolant that is built for the fiducial waveform family. Third, a fit in the parameter dimension is performed for the waveform's value at each of these m times. The cost of predicting L waveform time samples for a generic parameter choice is of order O(mL + mc(fit)) online operations, where c(fit) denotes the fitting function operation count and, typically, m << L. The result is a compact, computationally efficient, and accurate surrogate model that retains the original physics of the fiducial waveform family while also being fast to evaluate. We generate accurate surrogate models for effective-one-body waveforms of nonspinning binary black hole coalescences with durations as long as 10(5) M, mass ratios from 1 to 10, and for multiple spherical harmonic modes. We find that these surrogates are more than 3 orders of magnitude faster to evaluate as compared to the cost of generating effective-one-body waveforms in standard ways. Surrogate model building for other waveform families and models follows the same steps and has the same low computational online scaling cost. For expensive numerical simulations of binary black hole coalescences, we thus anticipate extremely large speedups in generating new waveforms with a surrogate. As waveform generation is one of the dominant costs in parameter estimation algorithms and parameter space exploration, surrogate models offer a new and practical way to dramatically accelerate such studies without impacting accuracy. Surrogates built in this paper, as well as others, are available from GWSurrogate, a publicly available python package.
C1 [Field, Scott E.] Univ Maryland, Maryland Ctr Fundamental Phys, Joint Space Sci Inst, Dept Phys, College Pk, MD 20742 USA.
[Galley, Chad R.] CALTECH, Pasadena, CA 91125 USA.
[Hesthaven, Jan S.] Ecole Polytech Fed Lausanne, EPFL SB MATHICSE, CH-1015 Lausanne, Switzerland.
[Kaye, Jason] Brown Univ, Div Appl Math, Providence, RI 02912 USA.
[Tiglio, Manuel] Univ Calif San Diego, Ctr Astrophys & Space Sci, La Jolla, CA 92093 USA.
RP Field, SE (corresponding author), Univ Maryland, Maryland Ctr Fundamental Phys, Joint Space Sci Inst, Dept Phys, College Pk, MD 20742 USA.
RI Hesthaven, Jan S/A-7602-2009
OI Hesthaven, Jan S/0000-0001-8074-1586
FU NSFNational Science Foundation (NSF) [PHY-1208861, PHY-1316424,
PHY-1005632, PHY-1068881]; CAREERNational Science Foundation (NSF)
[PHY-0956189]; Direct For Mathematical & Physical ScienNational Science
Foundation (NSF)NSF - Directorate for Mathematical & Physical Sciences
(MPS) [1208861, 1068881, 0956189] Funding Source: National Science
Foundation; Division Of PhysicsNational Science Foundation (NSF)NSF -
Directorate for Mathematical & Physical Sciences (MPS) [0956189,
1208861, 1500818] Funding Source: National Science Foundation
FX We thank Frank Herrmann and Evan Ochsner for help during this project,
including some software tools, as well as Yi Pan, Alessandra Buonnano,
and Collin Capano for helpful discussions about the EOB model and its
generation using the LAL code. We thank Michael Purrer for comments on a
previous version of the paper. This work was supported in part by NSF
Grants No. PHY-1208861, No. PHY-1316424, and No. PHY-1005632 to the
University of Maryland and by NSF Grant No. PHY-1068881 and CAREER Grant
No. PHY-0956189 to the California Institute of Technology.
CR Aasi J, 2013, PHYS REV D, V87, DOI 10.1103/PhysRevD.87.022002
Abadie J, 2011, PHYS REV D, V83, DOI 10.1103/PhysRevD.83.122005
Ajith P, 2007, CLASSICAL QUANT GRAV, V24, pS689, DOI 10.1088/0264-9381/24/19/S31
Ajith P, 2012, CLASSICAL QUANT GRAV, V29, DOI 10.1088/0264-9381/29/12/124001
Antil H, 2013, J SCI COMPUT, V57, P604, DOI 10.1007/s10915-013-9722-z
Barrault M, 2004, CR MATH, V339, P667, DOI 10.1016/j.crma.2004.08.006
Binev P, 2011, SIAM J MATH ANAL, V43, P1457, DOI 10.1137/100795772
Brown DA, 2012, PHYS REV D, V86, DOI 10.1103/PhysRevD.86.084017
Buchman LT, 2012, PHYS REV D, V86, DOI 10.1103/PhysRevD.86.084033
Buonanno A, 1999, PHYS REV D, V59, DOI 10.1103/PhysRevD.59.084006
Buonanno A, 2009, PHYS REV D, V79, DOI 10.1103/PhysRevD.79.124028
Campanelli M, 2007, PHYS REV LETT, V98, DOI 10.1103/PhysRevLett.98.231102
Campanelli M, 2007, ASTROPHYS J, V659, pL5, DOI 10.1086/516712
Canizares P, 2013, PHYS REV D, V87, DOI 10.1103/PhysRevD.87.124005
Cannon K, 2013, PHYS REV D, V87, DOI 10.1103/PhysRevD.87.044008
Cannon K, 2012, PHYS REV D, V85, DOI 10.1103/PhysRevD.85.081504
Cannon K, 2012, ASTROPHYS J, V748, DOI 10.1088/0004-637X/748/2/136
Cannon K, 2011, PHYS REV D, V84, DOI 10.1103/PhysRevD.84.084003
Cannon K, 2010, PHYS REV D, V82, DOI 10.1103/PhysRevD.82.044025
Caudill S, 2012, CLASSICAL QUANT GRAV, V29, DOI 10.1088/0264-9381/29/9/095016
Chaturantabut S, 2010, SIAM J SCI COMPUT, V32, P2737, DOI 10.1137/090766498
Cohen A., ARXIV11114422
Damour T, 2013, PHYS REV D, V87, DOI 10.1103/PhysRevD.87.084035
Damour T, 2011, FUND THEOR PHYS, V162, P211, DOI 10.1007/978-90-481-3015-3_7
DeVore R, 2013, CONSTR APPROX, V37, P455, DOI 10.1007/s00365-013-9186-2
Dietterich TG, 2000, LECT NOTES COMPUT SC, V1857, P1, DOI 10.1007/3-540-45014-9_1
Eftang JL, 2012, J SCI COMPUT, V51, P28, DOI 10.1007/s10915-011-9494-2
Eftang JL, 2012, INT J NUMER METH ENG, V90, P412, DOI 10.1002/nme.3327
Eftang JL, 2011, MATH COMP MODEL DYN, V17, P395, DOI 10.1080/13873954.2011.547670
EPPERSON JF, 1987, AM MATH MON, V94, P329, DOI 10.2307/2323093
Fares M, 2011, J COMPUT PHYS, V230, P5532, DOI 10.1016/j.jcp.2011.03.023
Field SE, 2012, PHYS REV D, V86, DOI 10.1103/PhysRevD.86.084046
Field SE, 2011, PHYS REV LETT, V106, DOI 10.1103/PhysRevLett.106.221102
Galley CR, 2010, CLASSICAL QUANT GRAV, V27, DOI 10.1088/0264-9381/27/24/245007
Giraud L, 2005, NUMER MATH, V101, P87, DOI [10.1007/s00211-005-0615-4, 10.1007/s0211-005-0615-4]
Gonzalez JA, 2007, PHYS REV LETT, V98, DOI 10.1103/PhysRevLett.98.231101
Gonzalez JA, 2007, PHYS REV LETT, V98, DOI 10.1103/PhysRevLett.98.091101
Hannam M., ARXIV13083271
Herrmann F, 2007, PHYS REV D, V76, DOI 10.1103/PhysRevD.76.084032
Herrmann F, 2007, CLASSICAL QUANT GRAV, V24, pS33, DOI 10.1088/0264-9381/24/12/S04
Herrmann F, 2007, ASTROPHYS J, V661, P430, DOI 10.1086/513603
Hesthaven JS, 2002, J COMPUT PHYS, V181, P186, DOI 10.1006/jcph.2002.7118
Hinder I, 2014, CLASSICAL QUANT GRAV, V31, DOI 10.1088/0264-9381/31/2/025012
Horner W, 1819, PHILOS T ROY SOC LON, V109, P308
Karniadakis GE, 2005, NUMERICAL MATH SCI C
Kaye J., 2012, THESIS BROWN U
Koppitz M, 2007, PHYS REV LETT, V99, DOI 10.1103/PhysRevLett.99.041102
Lodha SK, 1997, MATH COMPUT, V66, P1521, DOI 10.1090/S0025-5718-97-00862-4
Lousto CO, 2009, PHYS REV D, V79, DOI 10.1103/PhysRevD.79.064018
Maday Y, 2009, COMMUN PUR APPL ANAL, V8, P383, DOI 10.3934/cpaa.2009.8.383
Mroue AH, 2013, PHYS REV LETT, V111, DOI 10.1103/PhysRevLett.111.241104
Pan Y, 2014, PHYS REV D, V89, DOI 10.1103/PhysRevD.89.084006
Pan Y, 2011, PHYS REV D, V84, DOI 10.1103/PhysRevD.84.124052
Pan Y, 2010, PHYS REV D, V81, DOI 10.1103/PhysRevD.81.084041
Pekowsky L, 2013, PHYS REV D, V88, DOI 10.1103/PhysRevD.88.024040
Pollney D, 2007, PHYS REV D, V76, DOI 10.1103/PhysRevD.76.124002
Purrer M., ARXIV14024146
Quarteroni A, 2011, J MATH IND, V1, DOI 10.1186/2190-5983-1-3
Rezzolla L, 2010, PHYS REV LETT, V104, DOI 10.1103/PhysRevLett.104.221101
RUHE A, 1983, LINEAR ALGEBRA APPL, V52-3, P591
Santamaria L, 2010, PHYS REV D, V82, DOI 10.1103/PhysRevD.82.064016
Smith RJE, 2013, PHYS REV D, V87, DOI 10.1103/PhysRevD.87.122002
Stairs Ingrid H, 2003, Living Rev Relativ, V6, P5, DOI 10.12942/lrr-2003-5
Sturani R, 2010, J PHYS CONF SER, V243, DOI 10.1088/1742-6596/243/1/012007
Szyld DB, 2006, NUMER ALGORITHMS, V42, P309, DOI 10.1007/s11075-006-9046-2
Taracchini A, 2012, PHYS REV D, V86, DOI 10.1103/PhysRevD.86.024011
TAYLOR JM, 1978, P ROY SOC EDINB A, V80, P45, DOI 10.1017/S030821050001012X
Will CM, 2006, LIVING REV RELATIV, V9, DOI 10.12942/lrr-2006-3
Xu JC, 2003, NUMER MATH, V94, P195, DOI 10.1007/s002110100308
NR 69
TC 77
Z9 77
U1 0
U2 7
PU AMER PHYSICAL SOC
PI COLLEGE PK
PA ONE PHYSICS ELLIPSE, COLLEGE PK, MD 20740-3844 USA
SN 2160-3308
J9 PHYS REV X
JI Phys. Rev. X
PD JUL 14
PY 2014
VL 4
IS 3
AR 031006
DI 10.1103/PhysRevX.4.031006
PG 21
WC Physics, Multidisciplinary
SC Physics
GA AO3PX
UT WOS:000341246700002
OA DOAJ Gold, Green Accepted
DA 2021-04-21
ER
PT J
AU Miller, BT
Singh, RP
Schalk, V
Pevzner, Y
Sun, JJ
Miller, CS
Boresch, S
Ichiye, T
Brooks, BR
Woodcock, HL
AF Miller, Benjamin T.
Singh, Rishi P.
Schalk, Vinushka
Pevzner, Yuri
Sun, Jingjun
Miller, Carrie S.
Boresch, Stefan
Ichiye, Toshiko
Brooks, Bernard R.
Woodcock, H. Lee, III
TI Web-Based Computational Chemistry Education with CHARMMing I: Lessons
and Tutorial
SO PLOS COMPUTATIONAL BIOLOGY
LA English
DT Article
ID GENERAL FORCE-FIELD; MOLECULAR-DYNAMICS; ENZYMATIC-REACTIONS; PROTEIN;
SIMULATIONS; QUANTUM; SYSTEMS; AUTOMATION; RESOLUTION; KINETICS
AB This article describes the development, implementation, and use of web-based "lessons'' to introduce students and other newcomers to computer simulations of biological macromolecules. These lessons, i.e., interactive step-by-step instructions for performing common molecular simulation tasks, are integrated into the collaboratively developed CHARMM INterface and Graphics (CHARMMing) web user interface (http://www.charmming.org). Several lessons have already been developed with new ones easily added via a provided Python script. In addition to CHARMMing's new lessons functionality, web-based graphical capabilities have been overhauled and are fully compatible with modern mobile web browsers (e. g., phones and tablets), allowing easy integration of these advanced simulation techniques into course-work. Finally, one of the primary objections to web-based systems like CHARMMing has been that "point and click'' simulation set-up does little to teach the user about the underlying physics, biology, and computational methods being applied. In response to this criticism, we have developed a freely available tutorial to bridge the gap between graphical simulation setup and the technical knowledge necessary to perform simulations without user interface assistance.
C1 [Miller, Benjamin T.; Singh, Rishi P.; Sun, Jingjun; Brooks, Bernard R.] NHLBI, Lab Computat Biol, Bethesda, MD 20892 USA.
[Schalk, Vinushka] New Coll Florida, Dept Nat Sci, Sarasota, FL USA.
[Pevzner, Yuri; Woodcock, H. Lee, III] Univ S Florida, Dept Chem, Tampa, FL 33620 USA.
[Miller, Carrie S.; Ichiye, Toshiko] Georgetown Univ, Dept Chem, Washington, DC 20057 USA.
[Boresch, Stefan] Univ Vienna, Fac Chem, Dept Computat Biol Chem, Vienna, Austria.
RP Miller, BT (corresponding author), NHLBI, Lab Computat Biol, Bldg 10, Bethesda, MD 20892 USA.
EM btmiller@nhlbi.nih.gov; hlw@usf.edu
RI Boresch, Stefan/F-3467-2014; Woodcock, Henry/M-6255-2019
OI Boresch, Stefan/0000-0002-2793-6656; Woodcock,
Henry/0000-0003-3539-273X; Miller, Benjamin/0000-0003-1647-0122
FU National Heart, Lung, and Blood Institute, NIHUnited States Department
of Health & Human ServicesNational Institutes of Health (NIH) - USANIH
National Heart Lung & Blood Institute (NHLBI); NIHUnited States
Department of Health & Human ServicesNational Institutes of Health (NIH)
- USA [1K22HL088341-01A1]; University of South Florida; National Science
FoundationNational Science Foundation (NSF) [CHE-1158267]; McGowan
Foundation; Division Of ChemistryNational Science Foundation (NSF)NSF -
Directorate for Mathematical & Physical Sciences (MPS) [1158267] Funding
Source: National Science Foundation; NATIONAL HEART, LUNG, AND BLOOD
INSTITUTEUnited States Department of Health & Human ServicesNational
Institutes of Health (NIH) - USANIH National Heart Lung & Blood
Institute (NHLBI) [ZIHHL001052, ZIHHL001052, ZIHHL001052, ZIHHL001052,
ZIHHL001052, ZIHHL001052, K22HL088341, ZIHHL001052, ZIHHL001052,
K22HL088341, ZIHHL001052, ZIHHL001052, K22HL088341] Funding Source: NIH
RePORTER
FX This research was supported in part by the Intramural Research Program
of the National Heart, Lung, and Blood Institute, NIH. HLW3 would like
to acknowledge NIH (1K22HL088341-01A1) and the University of South
Florida (start-up) for funding. TI gratefully acknowledges the support
of the National Science Foundation (CHE-1158267) and the McGowan
Foundation. The funders had no role in study design, data collection and
analysis, decision to publish, or preparation of the manuscript.
CR Acevedo O, 2010, ACCOUNTS CHEM RES, V43, P142, DOI 10.1021/ar900171c
Allen M. P., 1989, COMPUTER SIMULATION
Baker NA, 2001, P NATL ACAD SCI USA, V98, P10037, DOI 10.1073/pnas.181342398
Berman HM, 2000, NUCLEIC ACIDS RES, V28, P235, DOI 10.1093/nar/28.1.235
Bogusz S, 1998, J CHEM PHYS, V108, P7070, DOI 10.1063/1.476320
Bond PJ, 2006, J AM CHEM SOC, V128, P2697, DOI 10.1021/ja0569104
Brooks BR, 2009, J COMPUT CHEM, V30, P1545, DOI 10.1002/jcc.21287
Burkholder PR, 2008, J CHEM EDUC, V85, P1071, DOI 10.1021/ed085p1071
DARDEN T, 1993, J CHEM PHYS, V98, P10089, DOI 10.1063/1.464397
Department of Homeland Security: USCERT, 2013, AL TA13 010A OR JAV
DILL KA, 2002, MOL DRIVING FORCES S
Dill KA, 2008, ANNU REV BIOPHYS, V37, P289, DOI 10.1146/annurev.biophys.37.092707.153558
FIELD MJ, 1990, J COMPUT CHEM, V11, P700, DOI 10.1002/jcc.540110605
Frenkel D., 2007, UNDERSTANDING MOL SI
Garcia-Viloca M, 2004, SCIENCE, V303, P186, DOI 10.1126/science.1088172
Hanson RM, 2013, ISR J CHEM, V53, P207, DOI 10.1002/ijch.201300024
Humphrey W, 1996, J MOL GRAPH MODEL, V14, P33, DOI 10.1016/0263-7855(96)00018-5
Ichiye T, 2014, CHEM 573 COMPUTATION
Im YJ, 2005, NATURE, V437, P154, DOI 10.1038/nature03923
Jo S, 2008, J COMPUT CHEM, V29, P1859, DOI 10.1002/jcc.20945
Kessel A., 2010, INTRO PROTEINS STRUC
Klauda JB, 2010, J PHYS CHEM B, V114, P7830, DOI 10.1021/jp101759q
Klimov DK, 2000, P NATL ACAD SCI USA, V97, P2544, DOI 10.1073/pnas.97.6.2544
Leach A.R., 2001, MOL MODELLING PRINCI
Marrink SJ, 2004, J PHYS CHEM B, V108, P750, DOI 10.1021/jp036508g
Martin NH, 1998, J CHEM EDUC, V75, P241, DOI 10.1021/ed075p241
Miller BT, 2008, J CHEM INF MODEL, V48, P1920, DOI 10.1021/ci800133b
Nakane T, 2012, GLMOL MOL VIEWER WEB
Nelson R, 2005, NATURE, V435, P773, DOI 10.1038/nature03680
Nussinov R, 2013, SIGN 2013 NOB PRIZ C
Paniagua JC, 2008, J CHEM EDUC, V85, P1288, DOI 10.1021/ed085p1288
Perrin BS, 2010, PROTEINS, V78, P2798, DOI 10.1002/prot.22794
Piana S, 2013, J PHYS CHEM B, V117, P12935, DOI 10.1021/jp4020993
Piana S, 2013, P NATL ACAD SCI USA, V110, P5915, DOI 10.1073/pnas.1218321110
Piana S, 2012, P NATL ACAD SCI USA, V109, P17845, DOI 10.1073/pnas.1201811109
Schmidt J, 2014, WEBMO VERSION 14 0
Schrodinger, 2015, PYMOL MOL GRAPHICS S
Senn HM, 2009, ANGEW CHEM INT EDIT, V48, P1198, DOI 10.1002/anie.200802019
Shao Y, 2006, PHYS CHEM CHEM PHYS, V8, P3172, DOI 10.1039/b517914a
Sherwood P, 2008, CURR OPIN STRUC BIOL, V18, P630, DOI 10.1016/j.sbi.2008.07.003
Singh RP, 2009, PROTEINS, V75, P468, DOI 10.1002/prot.22263
SINGH UC, 1986, J COMPUT CHEM, V7, P718, DOI 10.1002/jcc.540070604
STEINBACH PJ, 1994, J COMPUT CHEM, V15, P667, DOI 10.1002/jcc.540150702
Suhre K, 2004, NUCLEIC ACIDS RES, V32, pW610, DOI 10.1093/nar/gkh368
TEETER MM, 1993, J MOL BIOL, V230, P292, DOI 10.1006/jmbi.1993.1143
Vanommeslaeghe K, 2012, J CHEM INF MODEL, V52, P3155, DOI 10.1021/ci3003649
Vanommeslaeghe K, 2012, J CHEM INF MODEL, V52, P3144, DOI 10.1021/ci300363c
Vanommeslaeghe K, 2010, J COMPUT CHEM, V31, P671, DOI 10.1002/jcc.21367
VIJAYKUMAR S, 1987, J MOL BIOL, V194, P531, DOI 10.1016/0022-2836(87)90679-6
WARSHEL A, 1976, J MOL BIOL, V103, P227, DOI 10.1016/0022-2836(76)90311-9
Willighagen E., 2007, NAT P, DOI [10. 1038/npre. 2007. 50. 1, DOI 10.1038/NPRE.2007.50.1, 10.1038/npre.2007.50.1]
Woodcock HL, 2007, J COMPUT CHEM, V28, P1485, DOI 10.1002/jcc.20587
Woods research group Complex Carboyhdrate Research Center University of Georgia, 2005, GLYC BIOM BUILD
Wu XW, 2003, CHEM PHYS LETT, V381, P512, DOI 10.1016/j.cplett.2003.10.013
Zheng GH, 2009, NUCLEIC ACIDS RES, V37, pW240, DOI 10.1093/nar/gkp358
Zheng W, 2007, AD ENM WEB SERVER
NR 56
TC 8
Z9 8
U1 0
U2 23
PU PUBLIC LIBRARY SCIENCE
PI SAN FRANCISCO
PA 1160 BATTERY STREET, STE 100, SAN FRANCISCO, CA 94111 USA
EI 1553-7358
J9 PLOS COMPUT BIOL
JI PLoS Comput. Biol.
PD JUL
PY 2014
VL 10
IS 7
AR e1003719
DI 10.1371/journal.pcbi.1003719
PG 7
WC Biochemical Research Methods; Mathematical & Computational Biology
SC Biochemistry & Molecular Biology; Mathematical & Computational Biology
GA AM5IO
UT WOS:000339890900033
PM 25057988
OA DOAJ Gold, Green Published
DA 2021-04-21
ER
PT J
AU Li, XB
Lei, YL
Vangheluwe, H
Wang, WP
Li, Q
AF Li, Xiaobo
Lei, Yonglin
Vangheluwe, Hans
Wang, Weiping
Li, Qun
TI Domain-specific decision modelling and statistical analysis for combat
system effectiveness simulation
SO JOURNAL OF STATISTICAL COMPUTATION AND SIMULATION
LA English
DT Article
DE decision modelling; domain-specific modelling; effectiveness simulation;
Bayesian networks; parameter estimation; Bayesian analysis
ID UNCERTAINTY
AB Combat system effectiveness simulation (CoSES) needs to model both the physical aspect (i.e. physics modelling) and intelligent aspect (i.e. decision modelling) of combat systems. Combat platform decision-making has several characteristics such as cognition, diversity, agility, uncertainty and higher abstraction level, which bring tough challenges for decision model design, implementation and optimization. In this paper, we propose a domain-specific modelling approach which develops friendly modelling environments for model design, we design code generation mechanisms to transform domain-specific decision models to Python code which is supported by a Python script framework to implement decision models and we present a Bayesian network-based statistical analysis method on simulation output data to optimize the decision model. The case study shows that the proposed modelling and optimization approach effectively supports CoSES with decision models of higher efficiency and increased effectiveness.
C1 [Li, Xiaobo; Lei, Yonglin; Wang, Weiping; Li, Qun] Natl Univ Def Technol, Inst Simulat Engn, Changsha 410073, Hunan, Peoples R China.
[Li, Xiaobo; Vangheluwe, Hans] Univ Antwerp, Dept Math & Comp Sci, B-2020 Antwerp, Belgium.
RP Li, XB (corresponding author), Natl Univ Def Technol, Inst Simulat Engn, Changsha 410073, Hunan, Peoples R China.
EM lixiaobo.nudt@gmail.com
RI Vangheluwe, Hans/H-9884-2016
OI Vangheluwe, Hans/0000-0003-2079-6643
FU National Natural Science Foundation of ChinaNational Natural Science
Foundation of China (NSFC) [61273198, 91024015, 61074107, 60974073,
60974074, 71031007]
FX We thank the anonymous reviewers from both Journal of Statistical
Computation and Simulation and AsiaSim 2012 conference for their
valuable suggestions. We are grateful to the discussions with Dr. Lei
Wang, Professor Qi Liu and Professor Jing Feng on Bayesian decision
analysis. The work presented in this paper is partly supported by the
National Natural Science Foundation of China (Nos. 61273198, 91024015,
61074107, 60974073, 60974074 and 71031007).
CR Andradottir S, 2000, SIMULAT PRACT THEORY, V8, P253, DOI 10.1016/S0928-4869(00)00025-2
Chick SE, 2004, PROCEEDINGS OF THE 2004 WINTER SIMULATION CONFERENCE, VOLS 1 AND 2, P89
Chick SE, 1997, PROCEEDINGS OF THE 1997 WINTER SIMULATION CONFERENCE, P253, DOI 10.1145/268437.268488
Chick SE, 2006, PROCEEDINGS OF THE 2006 WINTER SIMULATION CONFERENCE, VOLS 1-5, P96, DOI 10.1109/WSC.2006.323042
Davis PK., 1998, EXPT MULTIRESOLUTION
Ferayorni A. E., 2007, P 2007 SUMM COMP SIM, P297
France R., 2005, SOFTW SYST MODEL, V4, P1, DOI DOI 10.1007/S10270-005-0078-1
Hemingway G, 2011, SIMULATION, V88, P217
Hora SC, 1997, J STAT COMPUT SIM, V57, P175, DOI 10.1080/00949659708811807
Jiang XM, 2013, J STAT COMPUT SIM, V83, P1829, DOI 10.1080/00949655.2012.672572
Li Qun, 2010, SIMULATION MODEL POR
Li XB, 2011, P SUMM COMP, P210
Merrick JRW, 2009, DECIS ANAL, V6, P222, DOI 10.1287/deca.1090.0151
Merrick JRW, 2005, RISK ANAL, V25, P731, DOI 10.1111/j.1539-6924.2005.00616.x
Mittal S, 2011, SIMUL SERIES, V43, P256
Modarres M, 1999, RELIAB ENG SYST SAFE, V64, P181, DOI 10.1016/S0951-8320(98)00062-3
Pereira LN, 2010, J STAT COMPUT SIM, V80, P713, DOI 10.1080/00949650902766860
Poropudas J, 2007, PROCEEDINGS OF THE 2007 WINTER SIMULATION CONFERENCE, VOLS 1-5, P1349
Poropudas J, 2010, WINT SIMUL C PROC, P935, DOI 10.1109/WSC.2010.5679098
Seo KM, 2011, J DEF MODEL SIMUL-AP, V8, P157, DOI 10.1177/1548512910390245
Son MJ, 2012, EXPERT SYST APPL, V39, P12992, DOI 10.1016/j.eswa.2012.05.099
Son MJ, 2010, ADV ENG SOFTW, V41, P506, DOI 10.1016/j.advengsoft.2009.10.009
Sprinkle J. B., 2010, P 2007 INT DAGST C M, P57
US Army Space and Missile Defense Command, EADSIM EX SUMM
Vanderbilt University, 2010, GME MAN US GUID GEN
Verbraeck A, 2008, 2008 WINTER SIMULATION CONFERENCE, VOLS 1-5, P923, DOI 10.1109/WSC.2008.4736158
Wang Lei, 2008, Journal of System Simulation, V20, P6519
Washburn A, 2009, INT SER OPER RES MAN, V134, P1, DOI 10.1007/978-1-4419-0790-5_1
NR 28
TC 2
Z9 2
U1 0
U2 30
PU TAYLOR & FRANCIS LTD
PI ABINGDON
PA 2-4 PARK SQUARE, MILTON PARK, ABINGDON OR14 4RN, OXON, ENGLAND
SN 0094-9655
EI 1563-5163
J9 J STAT COMPUT SIM
JI J. Stat. Comput. Simul.
PD JUN 3
PY 2014
VL 84
IS 6
SI SI
BP 1261
EP 1279
DI 10.1080/00949655.2013.797421
PG 19
WC Computer Science, Interdisciplinary Applications; Statistics &
Probability
SC Computer Science; Mathematics
GA 303WF
UT WOS:000330705200008
DA 2021-04-21
ER
PT J
AU de Buyl, P
AF de Buyl, Pierre
TI The vmf90 program for the numerical resolution of the Vlasov equation
for mean-field systems
SO COMPUTER PHYSICS COMMUNICATIONS
LA English
DT Article
DE Vlasov equation; Hamiltonian Mean-Field model; Single wave model
ID LONG-RANGE INTERACTIONS; RELAXATION; DYNAMICS; MODEL
AB The numerical resolution of the Vlasov equation provides complementary information with respect to analytical studies and forms an important research tool in domains such as plasma physics. The study of mean-field models for systems with long-range interactions is another field in which the Vlasov equation plays an important role. We present the vmf90 program that performs numerical simulations of the Vlasov equation for this class of mean-field models with the semi-Lagrangian method.
Program summary
Program title: vmf90
Catalogue identifier: AESO_v1_0
Program summary URL: http://cpc.cs.qub.ac.uk/summaries/AESO_v1_0.html
Program obtainable from: CPC Program Library, Queen's University, Belfast, N. Ireland
Licensing provisions: GNU General Public License, version 3
No. of lines in distributed program, including test data, etc.: 7794
No. of bytes in distributed program, including test data, etc.: 58 851
Distribution format: tar.gz
Programming language: Fortran 95.
Computer: Single CPU computer.
Operating system: No specific operating system, the program is tested under Linux and OS X.
RAM: About 5 M bytes
Classification: 1.5, 19.8, 19.13, 23.
External routines: HDF5 for the code (tested with HDF5 v1.8.8 and above). Python, NumPy, h5py and Matplotlib for analysis.
Nature of problem:
Numerical resolution of the Vlasov equation for mean-field models (Hamiltonian Mean-Field model and Single Wave model).
Solution method:
The equation is solved with the semi-Lagrangian method and cubic spline interpolation.
Running time:
The examples provided with the program take 1 m 30 for the Hamiltonian-Mean Field model and 10 m for the Single Wave model, on an Intel Core i7 CPU @ 3.33 GHz. Increasing the number of grid points or the number of time steps increases the running time. (C) 2014 Elsevier B.V. All rights reserved.
C1 [de Buyl, Pierre] Univ Libre Bruxelles, Ctr Nonlinear Phenomena & Complex Syst, B-1050 Brussels, Belgium.
RP de Buyl, P (corresponding author), Katholieke Univ Leuven, Dept Chem, Celestijnenlaan 200F, B-3001 Heverlee, Belgium.
EM pdebuyl@ulb.ac.be
OI de Buyl, Pierre/0000-0002-6640-6463
CR ANTONI M, 1995, PHYS REV E, V52, P2361, DOI 10.1103/PhysRevE.52.2361
Antoniazzi A, 2007, PHYS REV LETT, V98, DOI 10.1103/PhysRevLett.98.150602
Bachelard R, 2010, J STAT MECH-THEORY E, DOI 10.1088/1742-5468/2010/06/P06009
Bouchet F, 2010, PHYSICA A, V389, P4389, DOI 10.1016/j.physa.2010.02.024
Campa A, 2009, PHYS REP, V480, P57, DOI 10.1016/j.physrep.2009.07.001
Chavanis PH, 2006, EUR PHYS J B, V53, P487, DOI 10.1140/epjb/e2006-00405-5
Chavanis PH, 2006, PHYSICA A, V361, P81, DOI 10.1016/j.physa.2005.06.088
CHENG CZ, 1976, J COMPUT PHYS, V22, P330, DOI 10.1016/0021-9991(76)90053-X
Collette A., 2008, HDF5 PYTHON
de Buyl P., 2014, COMP PHYS COMMUN
de Buyl P., PHYS REV ST ACCEL BE, V12
de Buyl P, 2014, COMPUT PHYS COMMUN, V185, P1546, DOI 10.1016/j.cpc.2014.01.018
de Buyl P, 2013, PHYS REV E, V87, DOI 10.1103/PhysRevE.87.042110
de Buyl P, 2012, CENT EUR J PHYS, V10, P652, DOI 10.2478/s11534-012-0010-6
de Buyl P, 2011, PHYS REV E, V84, DOI 10.1103/PhysRevE.84.061151
de Buyl P, 2011, PHYS REV E, V84, DOI 10.1103/PhysRevE.84.061139
de Buyl P, 2011, PHILOS T R SOC A, V369, P439, DOI 10.1098/rsta.2010.0251
de Buyl P, 2010, COMMUN NONLINEAR SCI, V15, P2133, DOI 10.1016/j.cnsns.2009.08.020
Decyk VK, 2008, COMPUT PHYS COMMUN, V178, P611, DOI 10.1016/j.cpc.2007.11.013
Decyk VK, 1998, COMPUT PHYS COMMUN, V115, P9, DOI 10.1016/S0010-4655(98)00101-5
Elskens Y., 2003, MICROSCOPIC DYNAMICS
McCormack D., 2009, SCI SOFTWARE DEV FOR
Ogawa S, 2012, PHYS REV E, V85, DOI 10.1103/PhysRevE.85.061115
Oliphant TE, 2007, COMPUT SCI ENG, V9, P10, DOI 10.1109/MCSE.2007.58
ONEIL TM, 1971, PHYS FLUIDS, V14, P1204, DOI 10.1063/1.1693587
Sonnendrucker E, 1999, J COMPUT PHYS, V149, P201, DOI 10.1006/jcph.1998.6148
VANHEESCH D, 1997, DOXYGEN
NR 27
TC 2
Z9 2
U1 0
U2 5
PU ELSEVIER SCIENCE BV
PI AMSTERDAM
PA PO BOX 211, 1000 AE AMSTERDAM, NETHERLANDS
SN 0010-4655
EI 1879-2944
J9 COMPUT PHYS COMMUN
JI Comput. Phys. Commun.
PD JUN
PY 2014
VL 185
IS 6
BP 1822
EP 1827
DI 10.1016/j.cpc.2014.03.004
PG 6
WC Computer Science, Interdisciplinary Applications; Physics, Mathematical
SC Computer Science; Physics
GA AJ8CD
UT WOS:000337929400026
OA Green Accepted
DA 2021-04-21
ER
PT J
AU Boyd, W
Shaner, S
Li, LL
Forget, B
Smith, K
AF Boyd, William
Shaner, Samuel
Li, Lulu
Forget, Benoit
Smith, Kord
TI The OpenMOC method of characteristics neutral particle transport code
SO ANNALS OF NUCLEAR ENERGY
LA English
DT Article
DE Method of characteristics; Neutron transport; Criticality; High
performance computing; Nonlinear diffusion acceleration; Open source
AB The method of characteristics (MOC) is a numerical integration technique for partial differential equations, and has seen widespread use for reactor physics lattice calculations. The exponential growth in computing power has finally brought the possibility for high-fidelity full core MOC calculations within reach. The OpenMOC code is being developed at the Massachusetts Institute of Technology to investigate algorithmic acceleration techniques and parallel algorithms for MOC. OpenMOC is a free, open source code written using modern software languages such as C/C++ and CUDA with an emphasis on extensible design principles for code developers and an easy to use Python interface for code users. The present work describes the OpenMOC code and illustrates its ability to model large problems accurately and efficiently. (C) 2014 Elsevier Ltd. All rights reserved.
C1 [Boyd, William; Shaner, Samuel; Li, Lulu; Forget, Benoit; Smith, Kord] MIT, Dept Nucl Sci & Engn, Cambridge, MA 02139 USA.
RP Boyd, W (corresponding author), MIT, Dept Nucl Sci & Engn, 77 Massachusetts Ave,Bldg 24, Cambridge, MA 02139 USA.
EM wboyd@mit.edu; shaner@mit.edu; lululi@mit.edu; bforget@mit.edu;
kord@mit.edu
FU Office of Advanced Scientific Computing Research, Office of Science, US
Department of EnergyUnited States Department of Energy (DOE)
[DE-AC02-06CH11357]; National Science FoundationNational Science
Foundation (NSF) [1122374]
FX The first author was supported by the National Science Foundation
Graduate Research Fellowship under Grant No. 1122374 in addition to the
DOE's Center for Exascale Simulation of Advanced Reactors (CESAR). The
second author is a recipient of the DOE Office of Nuclear Energy's
Nuclear Energy University Programs Fellowship, and the third author is a
Studsvik Scandpower Graduate Fellow. This work was also partially
supported by the Office of Advanced Scientific Computing Research,
Office of Science, US Department of Energy, under Contract
DE-AC02-06CH11357.
CR Askew J.R., 1972, 1108 AAEWM UK AT EN
Aviles B.N., 1993, DEV VARIABLE TIME ST, P425
Beazley DM, 2003, FUTURE GENER COMP SY, V19, P599, DOI 10.1016/S0167-739X(02)00171-1
Boyd W., 2013, P INT C MATH COMP ME
Boyd W., 2014, P PHYSOR KYOT UNPUB
Boyd W., 2014, THESIS MIT
Cho J.Y., 2002, J KOREAN NUCL SOC, V34, P250
Ferrer R., 2012, P PHYSOR KNOXV TN US
Hunter JD, 2007, COMPUT SCI ENG, V9, P90, DOI 10.1109/MCSE.2007.55
Koranne S., 2011, HDB OPEN SOURCE TOOL, P191, DOI DOI 10.1007/978-1-4419-7719-9_10
Lax D., 2014, P PHYSOR KYOT UNPUB
Lewis E. E., 2003, BENCHMARK SPECIFICAT
Li L., 2013, THESIS MIT
Moore G. E., 1965, ELECTRONICS, V38, P114, DOI DOI 10.1109/JPROC.1998.658762
NVIDIA, 2013, NVIDIA CUDA C PROGR
OpenMP Architecture Review Board, 2013, OPENMP APPL PROGR IN
Romano PK, 2013, ANN NUCL ENERGY, V51, P274, DOI 10.1016/j.anucene.2012.06.040
Sanner MF, 1999, J MOL GRAPH MODEL, V17, P57
Smith K.S., 1983, T AM NUCL SOC, V44
Smith KS, 2002, P PHYSOR SEOUL S KOR
Yamamoto A, 2007, J NUCL SCI TECHNOL, V44, P129, DOI 10.3327/jnst.44.129
Zhong Z, 2008, NUCL SCI ENG, V158, P289, DOI 10.13182/NSE06-24TN
NR 22
TC 60
Z9 63
U1 0
U2 13
PU PERGAMON-ELSEVIER SCIENCE LTD
PI OXFORD
PA THE BOULEVARD, LANGFORD LANE, KIDLINGTON, OXFORD OX5 1GB, ENGLAND
SN 0306-4549
J9 ANN NUCL ENERGY
JI Ann. Nucl. Energy
PD JUN
PY 2014
VL 68
BP 43
EP 52
DI 10.1016/j.anucene.2013.12.012
PG 10
WC Nuclear Science & Technology
SC Nuclear Science & Technology
GA AE2GH
UT WOS:000333790800006
OA Green Published
DA 2021-04-21
ER
PT J
AU Deutsch, JM
AF Deutsch, J. M.
TI Biophysics software for interdisciplinary education and research
SO AMERICAN JOURNAL OF PHYSICS
LA English
DT Article
ID DNA; MOLECULES; PYTHON
AB Biophysics encompasses many disciplines, and so transcends the knowledge and skills of the individual student; its instruction therefore provides formidable challenges. This paper describes educational materials that were developed by the author and have been used successfully in an interdisciplinary course on biophysics, taken by undergraduates from a variety of disciplines. Projects were devised on topics that ranged from x-ray diffraction to the Hodgkin-Huxley equations. They are team-based and strongly encourage collaboration. Extensive use is made of software, written in Python/SciPy, which was modified by students to explore a large range of phenomena. This software can also be used in lectures, in the teaching of more traditional biophysics courses, and in research. (C) 2014 American Association of Physics Teachers.
C1 Univ Calif Santa Cruz, Dept Phys, Santa Cruz, CA 95064 USA.
RP Deutsch, JM (corresponding author), Univ Calif Santa Cruz, Dept Phys, Santa Cruz, CA 95064 USA.
EM josh@ucsc.edu
FU National Science FoundationNational Science Foundation (NSF) [CCLI
DUE-0942207]; Direct For Education and Human ResourcesNational Science
Foundation (NSF)NSF- Directorate for Education & Human Resources (EHR)
[0942207] Funding Source: National Science Foundation; Division Of
Undergraduate EducationNational Science Foundation (NSF)NSF- Directorate
for Education & Human Resources (EHR) [0942207] Funding Source: National
Science Foundation
FX The author would like to thank Dr. Barbara Goza and Hee-Sun Lee for
useful discussions, and Onuttom Narayan for helpful suggestions. The
author is also grateful to Michelle V. Mai for her critical insights in
making the material more relevant to life-science majors, and Diana
Deutsch for a very thorough and critical reading of the manuscript. This
material is based on work supported by the National Science Foundation
under Grant CCLI DUE-0942207.
CR AXELROD D, 1976, BIOPHYS J, V16, P1055, DOI 10.1016/S0006-3495(76)85755-4
Deutsch J. M., 2011, MECH MICROSCOPIC MIX
DEUTSCH JM, 1988, SCIENCE, V240, P922, DOI 10.1126/science.3363374
Dunn AR, 2007, NAT STRUCT MOL BIOL, V14, P246, DOI 10.1038/nsmb1206
Grier DG, 2003, NATURE, V424, P810, DOI 10.1038/nature01935
HODGKIN AL, 1952, J PHYSIOL-LONDON, V117, P500, DOI 10.1113/jphysiol.1952.sp004764
Holm C., 2008, SOFT MATTER CHARACTE, V2, P295
Khatib F, 2011, NAT STRUCT MOL BIOL, V18, P1175, DOI 10.1038/nsmb.2119
Kozlovsky Y, 1999, PHYS REV E, V59, P7025, DOI 10.1103/PhysRevE.59.7025
LAU KF, 1990, P NATL ACAD SCI USA, V87, P638, DOI 10.1073/pnas.87.2.638
MATSUYAMA T, 1993, CRIT REV MICROBIOL, V19, P117, DOI 10.3109/10408419309113526
Murray, 1993, MATH BIOL
Myers CR, 2007, COMPUT SCI ENG, V9, P75, DOI 10.1109/MCSE.2007.56
Oliphant TE, 2007, COMPUT SCI ENG, V9, P10, DOI 10.1109/MCSE.2007.58
Reif F., 1965, FUNDAMENTALS STAT TH
Serbus LR, 2005, DEVELOPMENT, V132, P3743, DOI 10.1242/dev.01956
Tinker RF, 2008, COMPUT SCI ENG, V10, P24, DOI 10.1109/MCSE.2008.108
Turing M. A., 1952, PHIL T R SOC B, V237, P37
Weiss T. F., 1992, J SCI EDUC TECHNOL, V1, P259
Wheeler DA, 2008, NATURE, V452, P872, DOI 10.1038/nature06884
Winters-Hilt S, 2003, BIOPHYS J, V84, P967, DOI 10.1016/S0006-3495(03)74913-3
WITTEN TA, 1981, PHYS REV LETT, V47, P1400, DOI 10.1103/PhysRevLett.47.1400
Woodside MT, 2008, CURR OPIN CHEM BIOL, V12, P640, DOI 10.1016/j.cbpa.2008.08.011
NR 23
TC 4
Z9 4
U1 1
U2 15
PU AMER ASSOC PHYSICS TEACHERS AMER INST PHYSICS
PI MELVILLE
PA STE 1 NO 1, 2 HUNTINGTON QUADRANGLE, MELVILLE, NY 11747-4502 USA
SN 0002-9505
EI 1943-2909
J9 AM J PHYS
JI Am. J. Phys.
PD MAY
PY 2014
VL 82
IS 5
DI 10.1119/1.4869198
PG 9
WC Education, Scientific Disciplines; Physics, Multidisciplinary
SC Education & Educational Research; Physics
GA AG2BA
UT WOS:000335218800011
DA 2021-04-21
ER
PT J
AU Mushtaq, A
Olaussen, K
AF Mushtaq, Asif
Olaussen, Kare
TI Automatic code generator for higher order integrators
SO COMPUTER PHYSICS COMMUNICATIONS
LA English
DT Article
DE Splitting methods; Modified integrators; Higher order methods; Automatic
code generation
AB Some explicit algorithms for higher order symplectic integration of a large class of Hamilton's equations have recently been discussed by Mushtaq et al. Here we present a Python program for automatic numerical implementation of these algorithms for a given Hamiltonian, both for double precision and multi-precision computations. We provide examples of how to use this program, and illustrate behavior of both the code generator and the generated solver module(s).
Program summary
Program title: HOMsPy: Higher Order (Symplectic) Methods in Python
Catalogue identifier: AESD_v1_0
Program summary URL: http://cpc.cs.qub.ac.uk/summaries/AESD_v1_0.html
Program obtainable from: CPC Program Library, Queen's University, Belfast, N. Ireland
Licensing provisions: Standard CPC licence, http://cpc.cs.qub.ac.uk/licence/licence.html
No. of lines in distributed program, including test data, etc.: 19423
No. of bytes in distributed program, including test data, etc.: 1970283
Distribution format: tar.gz
Programming language: Python 2.7.
Computer: PCs or higher performance computers.
Operating system: Linux, MacOS, MS Windows.
RAM: Kilobytes to a several gigabytes (problem dependent).
Classification: 4.3, 5.
External routines: SymPy library [1] for generating the code. NumPy library [2], and optionally mpmath [3] library for running the generated code. The matplotlib [4] library for plotting results.
Nature of problem:
We have developed algorithms [5] for numerical solution of Hamilton's equations.
(q) over dot(a) = partial derivative H (q, p)/partial derivative p(a)a, (p) over dot(a) = -partial derivative H (q, p)/partial derivative q(a), a = 1 ... N (1)
for Hamiltonians of the form
H (q, p) = T (p) + V (q) = (1 /2)p(T)Mp + V (q), (2)
with M a symmetric positive definite matrix. The algorithms preserve the symplectic property of the time evolution exactly, and are of orders tau(N) (for 2 <= N <= 8) in the timestep r. Although explicit, the algorithms are time-consuming and error-prone to implement numerically by hand, in particular for larger N.
Solution method:
We use computer algebra to perform all analytic calculations required for a specific model, and to generate the Python code for numerical solution of this model, including example programs using that code.
Restrictions:
In our implementation the mass matrix is assumed to be equal to the unit matrix, and V (q) must be sufficiently differentiable.
Running time:
Subseconds to eons (problem dependent). See discussion in the main article.
References:
[1] SymPy Development Team, http://sympy.org/.
[2] NumPy Developers, http://numpy.org/.
[3] F. Johansson etal., Python library for arbitrary-precision floating-point arithmetic, http://code.google. code/p/mpmath/ (2010).
[4] J.D. Hunter, Matplotlib: A 2D graphics environment, Computing in Science and Engineering 9, 90-95 (2007).
[5] A. Mushtaq, A. KvRrno, K. Olaussen, Higher order Geometric Integrators for a class of Hamiltonian systems, International Journal of Geometric Methods in Modern Physics, vol. 11, no. 1 (2014), 1450009-1-1450009-20. DOI: http://dx.doi.org/10.1142/S0219887814500091. arXiv.org:1301.7736. (C) 2014 Elsevier B.V. All rights reserved.
C1 [Mushtaq, Asif] NTNU, Inst Matemat Fag, N-7491 Trondheim, Norway.
[Olaussen, Kare] NTNU, Inst Fysikk, N-7491 Trondheim, Norway.
RP Olaussen, K (corresponding author), NTNU, Inst Fysikk, N-7491 Trondheim, Norway.
EM Asif.Mushtaq@math.ntnu.no; Kare.Olaussen@ntnu.no
FU Statoil via Roger Sollie, through a professor II grant in Applied
mathematical physics
FX We thank professor Anne Kvaerno for useful discussions, helpful
feedbacks, and careful proofreading. We also acknowledge support
provided by Statoil via Roger Sollie, through a professor II grant in
Applied mathematical physics.
CR Abramowitz M., 1968, HDB MATHEMATICAL FUN
Feynman R.P., 1992, PENGUIN SCI SERIES, P43
Goldstein H., 2001, CLASSICAL MECH, P589
McLachlan RI, 2002, ACT NUMERIC, V11, P341, DOI 10.1017/S0962492902000053
Mushtaq Asif, 2012, Proceedings of the World Congress on Engineering (WCE 2012), P247
Mushtaq A., 2012, NUMDIFF 13 MART LUTH
Mushtaq A., 2014, ENSEMBLE OCCUP UNPUB
Mushtaq A, 2014, INT J GEOM METHODS M, V11, DOI 10.1142/S0219887814500091
Newton I., 1687, PHILOS NATURALIS PRI
Sanz-Serna J.M., 1994, NUMERICAL HAMILTONIA
Stormer C., 1921, C R C INT STASSBOURG, V1920, P243
van der Walt S, 2011, COMPUT SCI ENG, V13, P22, DOI 10.1109/MCSE.2011.37
VERLET L, 1967, PHYS REV, V159, P98, DOI 10.1103/PhysRev.159.98
Wanner G., 2006, GEOMETRIC NUMERICAL
YOSHIDA H, 1990, PHYS LETT A, V150, P262, DOI 10.1016/0375-9601(90)90092-3
NR 15
TC 4
Z9 5
U1 0
U2 11
PU ELSEVIER SCIENCE BV
PI AMSTERDAM
PA PO BOX 211, 1000 AE AMSTERDAM, NETHERLANDS
SN 0010-4655
EI 1879-2944
J9 COMPUT PHYS COMMUN
JI Comput. Phys. Commun.
PD MAY
PY 2014
VL 185
IS 5
BP 1461
EP 1472
DI 10.1016/j.cpc.2014.01.012
PG 12
WC Computer Science, Interdisciplinary Applications; Physics, Mathematical
SC Computer Science; Physics
GA AE6FI
UT WOS:000334085600014
DA 2021-04-21
ER
PT J
AU Cottaar, S
Heister, T
Rose, I
Unterborn, C
AF Cottaar, Sanne
Heister, Timo
Rose, Ian
Unterborn, Cayman
TI BurnMan: A lower mantle mineral physics toolkit
SO GEOCHEMISTRY GEOPHYSICS GEOSYSTEMS
LA English
DT Article
DE open-source; elasticity
ID EARTHS LOWER MANTLE; SEISMIC VELOCITIES; PHASE-EQUILIBRIA; TRANSITION
ZONE; CHEMICAL HETEROGENEITY; CLUSTER-ANALYSIS; SOUND-VELOCITY;
SPIN-CROSSOVER; HIGH-PRESSURE; TRAVEL-TIME
AB We present BurnMan, an open-source mineral physics toolbox to determine elastic properties for specified compositions in the lower mantle by solving an Equation of State (EoS). The toolbox, written in Python, can be used to evaluate seismic velocities of new mineral physics data or geodynamic models, and as the forward model in inversions for mantle composition. The user can define the composition from a list of minerals provided for the lower mantle or easily include their own. BurnMan provides choices in methodology, both for the EoS and for the multiphase averaging scheme. The results can be visually or quantitatively compared to observed seismic models. Example user scripts show how to go through these steps. This paper includes several examples realized with BurnMan: First, we benchmark the computations to check for correctness. Second, we exemplify two pitfalls in EoS modeling: using a different EoS than the one used to derive the mineral physical parameters or using an incorrect averaging scheme. Both pitfalls have led to incorrect conclusions on lower mantle composition and temperature in the literature. We further illustrate that fitting elastic velocities separately or jointly leads to different Mg/Si ratios for the lower mantle. However, we find that, within mineral physical uncertainties, a pyrolitic composition can match PREM very well. Finally, we find that uncertainties on specific input parameters result in a considerable amount of variation in both magnitude and gradient of the seismic velocities.
C1 [Cottaar, Sanne; Rose, Ian] Univ Calif Berkeley, Dept Earth & Planetary Sci, Berkeley, CA 94720 USA.
[Cottaar, Sanne] Univ Cambridge Pembroke Coll, Dept Earth Sci, Cambridge CB2 1RF, England.
[Cottaar, Sanne] Univ Cambridge, Cambridge, England.
[Heister, Timo] Clemson Univ, Clemson, SC USA.
[Unterborn, Cayman] Ohio State Univ, Sch Earth Sci, Columbus, OH 43210 USA.
RP Rose, I (corresponding author), Univ Calif Berkeley, Dept Earth & Planetary Sci, Berkeley, CA 94720 USA.
EM ian.rose@berkeley.edu
OI Unterborn, Cayman/0000-0001-8991-3110; Heister,
Timo/0000-0002-8137-3903; Cottaar, Sanne/0000-0003-0493-6570
FU NSF FESD [1135452]; CIDER; NSF/CSEDINational Science Foundation (NSF)NSF
- Directorate for Geosciences (GEO) [1067513]; Draper's Company Research
Fellowship from Pembroke College, Cambridge; Computational
Infrastructure in Geodynamics initiative (CIG), through the NSF
[EAR-0949446]; University of California-DavisUniversity of California
System; King Abdullah University of Science and Technology (KAUST)King
Abdullah University of Science & Technology [KUS-C1-016-04]; NSFNational
Science Foundation (NSF) [EAR-1246670]; NSF CAREER grantNational Science
Foundation (NSF)NSF - Office of the Director (OD) [EAR-60023026];
Directorate For GeosciencesNational Science Foundation (NSF)NSF -
Directorate for Geosciences (GEO) [1135452, 1067513] Funding Source:
National Science Foundation; Division Of Earth SciencesNational Science
Foundation (NSF)NSF - Directorate for Geosciences (GEO) [1135452]
Funding Source: National Science Foundation
FX The authors are ordered alphabetically to represent their roughly equal
contributions to the code and this manuscript. SC, TH, IR, and CU are
grateful for the possibility to participate in CIDER 2012, where this
work was initiated. CIDER 2012 is funded through NSF FESD grant 1135452.
CIDER also funded a follow-up meeting for this project. We thank all the
fellow member of the Cider Mg/Si team for their input: Valentina Magni,
Yu Huang, JiaChao Liu, Marc Hirschmann, and Barbara Romanowicz. We thank
Lars Stixrude for providing benchmarking calculations and Motohiko
Murakami for providing various parameters. We also welcomed helpful
discussions with Zack Geballe, Bill McDonough, Quentin Williams, Wendy
Panero, and Wolfgang Bangerth. SC is supported through NSF/CSEDI grant
1067513 and Draper's Company Research Fellowship from Pembroke College,
Cambridge. TH is supported in part through the Computational
Infrastructure in Geodynamics initiative (CIG), through the NSF
EAR-0949446 and The University of California-Davis and by Award
KUS-C1-016-04, made by King Abdullah University of Science and
Technology (KAUST). IR is supported through NSF grant EAR-1246670. CU is
supported by NSF CAREER grant EAR-60023026 to Wendy R. Panero.
CR ANDERSON OL, 1982, PHILOS T R SOC A, V306, P21, DOI 10.1098/rsta.1982.0063
Antonangeli D, 2011, SCIENCE, V331, P64, DOI 10.1126/science.1198429
BROWN JM, 1981, GEOPHYS J ROY ASTR S, V66, P579, DOI 10.1111/j.1365-246X.1981.tb04891.x
Cammarano F, 2005, EARTH PLANET SC LETT, V232, P227, DOI 10.1016/j.epsl.2005.01.031
Cobden L, 2009, J GEOPHYS RES-SOL EA, V114, DOI 10.1029/2008JB006262
Connolly JAD, 2005, EARTH PLANET SC LETT, V236, P524, DOI 10.1016/j.epsl.2005.04.033
Davies DR, 2012, EARTH PLANET SC LETT, V353, P253, DOI 10.1016/j.epsl.2012.08.016
Deschamps F, 2003, PHYS EARTH PLANET IN, V140, P277, DOI 10.1016/j.pepi.2003.09.004
Deschamps F, 2012, EARTH PLANET SC LETT, V349, P198, DOI 10.1016/j.epsl.2012.07.012
DZIEWONSKI AM, 1981, PHYS EARTH PLANET IN, V25, P297, DOI 10.1016/0031-9201(81)90046-7
He YM, 2012, J GEOPHYS RES-SOL EA, V117, DOI 10.1029/2012JB009436
Hernandez ER, 2013, EARTH PLANET SC LETT, V364, P37, DOI 10.1016/j.epsl.2013.01.005
Holland TJ, 2013, J PETROL, V54, P1901, DOI 10.1093/petrology/egt035
Houser C, 2008, GEOPHYS J INT, V174, P195, DOI 10.1111/J.1365-246X.2008.03763.X
Inoue T, 2010, PHYS EARTH PLANET IN, V183, P245, DOI 10.1016/j.pepi.2010.08.003
ITA J, 1992, J GEOPHYS RES-SOL EA, V97, P6849, DOI 10.1029/92JB00068
Jackson MG, 2010, NATURE, V466, P853, DOI 10.1038/nature09287
KARATO S, 1990, REV GEOPHYS, V28, P399, DOI 10.1029/RG028i004p00399
KENNETT BLN, 1995, GEOPHYS J INT, V122, P108, DOI 10.1111/j.1365-246X.1995.tb03540.x
Kudo Y, 2012, EARTH PLANET SC LETT, V349, P1, DOI 10.1016/j.epsl.2012.06.040
Kustowski B, 2008, J GEOPHYS RES-SOL EA, V113, DOI 10.1029/2007JB005169
Lekic V, 2012, EARTH PLANET SC LETT, V357, P68, DOI 10.1016/j.epsl.2012.09.014
Li C, 2008, GEOCHEM GEOPHY GEOSY, V9, DOI 10.1029/2007GC001806
Lin JF, 2007, SCIENCE, V317, P1740, DOI 10.1126/science.1144997
Lin JF, 2013, REV GEOPHYS, V51, P244, DOI 10.1002/rog.20010
Mao Z, 2011, EARTH PLANET SC LETT, V309, P179, DOI 10.1016/j.epsl.2011.06.030
Matas J, 2007, GEOPHYS J INT, V170, P764, DOI 10.1111/j.1365-246X.2007.03454.x
Matas J, 2007, EARTH PLANET SC LETT, V259, P51, DOI 10.1016/j.epsl.2007.04.028
Mattern E, 2005, GEOPHYS J INT, V160, P973, DOI 10.1111/j.1365-246X.2004.02549.x
MCDONOUGH WF, 1995, CHEM GEOL, V120, P223, DOI 10.1016/0009-2541(94)00140-4
Megnin C, 2000, GEOPHYS J INT, V143, P709, DOI 10.1046/j.1365-246X.2000.00298.x
MINSTER JB, 1981, PHILOS T R SOC A, V299, P319, DOI 10.1098/rsta.1981.0025
Mosca I, 2012, J GEOPHYS RES-SOL EA, V117, DOI 10.1029/2011JB008851
Murakami M., 2013, PHYS CHEM DEEP EARTH, P183
Murakami M, 2007, EARTH PLANET SC LETT, V256, P47, DOI 10.1016/j.epsl.2007.01.011
Murakami M, 2012, NATURE, V485, P90, DOI 10.1038/nature11004
Murakami M, 2009, EARTH PLANET SC LETT, V277, P123, DOI 10.1016/j.epsl.2008.10.010
Nakagawa T, 2012, GEOCHEM GEOPHY GEOSY, V13, DOI 10.1029/2012GC004325
Nakajima Y, 2012, J GEOPHYS RES-SOL EA, V117, DOI 10.1029/2012JB009151
Noguchi M, 2013, PHYS CHEM MINER, V40, P81, DOI 10.1007/s00269-012-0549-1
Nomura R, 2011, NATURE, V473, P199, DOI 10.1038/nature09940
Poirier J., 1991, INTRO PHYS EARTH
Ritsema J, 2011, GEOPHYS J INT, V184, P1223, DOI 10.1111/j.1365-246X.2010.04884.x
Schuberth BSA, 2012, GEOPHYS J INT, V188, P1393, DOI 10.1111/j.1365-246X.2011.05333.x
Simmons NA, 2012, J GEOPHYS RES-SOL EA, V117, DOI 10.1029/2012JB009525
Simmons NA, 2010, J GEOPHYS RES-SOL EA, V115, DOI 10.1029/2010JB007631
Stixrude L, 2005, GEOPHYS J INT, V162, P610, DOI 10.1111/j.1365-246X.2005.02642.x
Stixrude L, 2012, ANNU REV EARTH PL SC, V40, P569, DOI 10.1146/annurev.earth.36.031207.124244
Stixrude L, 2011, GEOPHYS J INT, V184, P1180, DOI 10.1111/j.1365-246X.2010.04890.x
Styles E, 2011, GEOPHYS J INT, V184, P1371, DOI 10.1111/j.1365-246X.2010.04914.x
Tackley PJ, 2000, SCIENCE, V288, P2002, DOI 10.1126/science.288.5473.2002
To A., 2005, EARTH PLANET SC LETT, V233, P1447
Trampert J, 2001, PHYS EARTH PLANET IN, V124, P255, DOI 10.1016/S0031-9201(01)00201-1
Trampert J, 2004, SCIENCE, V306, P853, DOI 10.1126/science.1101996
WATT JP, 1976, REV GEOPHYS, V14, P541, DOI 10.1029/RG014i004p00541
Wu ZQ, 2013, PHYS REV LETT, V110, DOI 10.1103/PhysRevLett.110.228501
Zhang ZG, 2013, EARTH PLANET SC LETT, V379, P1, DOI 10.1016/j.epsl.2013.07.034
NR 57
TC 47
Z9 48
U1 1
U2 32
PU AMER GEOPHYSICAL UNION
PI WASHINGTON
PA 2000 FLORIDA AVE NW, WASHINGTON, DC 20009 USA
SN 1525-2027
J9 GEOCHEM GEOPHY GEOSY
JI Geochem. Geophys. Geosyst.
PD APR
PY 2014
VL 15
IS 4
BP 1164
EP 1179
DI 10.1002/2013GC005122
PG 16
WC Geochemistry & Geophysics
SC Geochemistry & Geophysics
GA AH9VR
UT WOS:000336493400021
OA Bronze
DA 2021-04-21
ER
PT J
AU Lyonnet, F
Schienbein, I
Stau, F
Wingerter, A
AF Lyonnet, F.
Schienbein, I.
Stau, F.
Wingerter, A.
TI PyR@TE Renormalization group equations for general gauge theories
SO COMPUTER PHYSICS COMMUNICATIONS
LA English
DT Article
DE Renormalization group equations; Quantum field theory; Running coupling
constants; Model building; Physics beyond the standard model
ID QUANTUM-FIELD THEORY; MODEL; SUPERSYMMETRY; PROGRAM; SPHENO
AB Although the two-loop renormalization group equations for a general gauge field theory have been known for quite some time, deriving them for specific models has often been difficult in practice. This is mainly due to the fact that, albeit straightforward, the involved-calculations are quite long, tedious and prone to error. The present work is an attempt to facilitate the practical use of the renormalization group equations in model building. To that end, we have developed two completely independent sets of programs written in Python and Mathematica, respectively. The Mathematica scripts will be part of an upcoming release of SARAH 4. The present article describes the collection of Python routines that we dubbed PyR@TE which is an acronym for "Python Renormalization group equations At Two-loop for Everyone". In PyR@TE, once the user specifies the gauge group and the particle content of the model, the routines automatically generate the full two-loop renormalization group equations for all (dimensionless and dimensionful) parameters. The results can optionally be exported to LATEX and Mathematica, or stored in a Python data structure for further processing by other programs. For ease of use, we have implemented an interactive mode for PyR@TE in form of an IPython Notebook. As a first application, we have generated with PyR@TE the renormalization group equations for several non-supersymmetric extensions of the Standard Model and found some discrepancies with the existing literature. Program summary Program title: PyR@TE Catalogue identifier: AERV_v1_0 Program summary URL: http://cpc.cs.qub.ac.uk/summaries/AERV_v1_0.html Program obtainable from: CPC Program Library, Queen's University, Belfast, N. Ireland Licensing provisions: Standard CPC licence, http://cpc.cs.qub.ac.uk/licence/licence.html No. of lines in distributed program, including test data, etc.: 924959 No. of bytes in distributed program, including test data, etc.: 495197 Distribution format: tar.gz Programming language: Python. Computer: Personal computer. Operating system: Tested on Fedora 15, MacOS 10 and 11, Ubuntu 12. Classification: 11.1. External routines: SymPy, PyYAML, NumPy, IPython, SciPy
Nature of problem:
Deriving the renormalization group equations for a general quantum field theory.
Solution method:
Group theory, tensor algebra
Running time:
Tens of seconds per model (one-loop), tens of minutes (two-loop) (C) 2014 Elsevier B.V. All rights reserved.
C1 [Lyonnet, F.; Schienbein, I.; Wingerter, A.] UJF Grenoble 1, CNRS IN2P3, INPG, Lab Phys Subatom & Cosmol, F-38026 Grenoble, France.
[Stau, F.] Univ Bonn, Bathe Ctr Theoret Phys, D-53115 Bonn, Germany.
[Stau, F.] Univ Bonn, Inst Phys, D-53115 Bonn, Germany.
RP Lyonnet, F (corresponding author), UJF Grenoble 1, CNRS IN2P3, INPG, Lab Phys Subatom & Cosmol, 53 Ave Martyrs, F-38026 Grenoble, France.
EM florian.lyonnet@lpsc.in2p3.fr
OI Staub, Florian/0000-0001-5911-5804
FU Ph.D. fellowship of the French Ministry for Education and Research;
Theory-LHC- France initiative [CNRS/IN2P3]; DAAD project PROCOPE
[54366701]
FX We would like to thank Lorenzo Basso, Renato Fonseca, D.R.T. Jones,
Werner Porod, and Stuart Raby for useful discussions. We are also
indebted to Tomas Jazo and Josselin Proudom for carefully proof-reading
the draft of this article. F.L. would like to thank Tomas Jezo for
introducing him to Python and for always patiently answering his
questions, and Matthew Rocklin for clarifying questions regarding the
backward compatibility of SymPy. We are indebted to HepForge for hosting
our project on their web pages. This work has been supported by a Ph.D.
fellowship of the French Ministry for Education and Research and by the
Theory-LHC- France initiative of the CNRS/IN2P3. The work of F.S. has
been supported by the DAAD project PROCOPE Nr. 54366701.
CR Aad G, 2012, PHYS LETT B, V716, P1, DOI 10.1016/j.physletb.2012.08.020
Allanach BC, 2010, COMPUT PHYS COMMUN, V181, P232, DOI 10.1016/j.cpc.2009.09.015
Allanach BC, 2002, COMPUT PHYS COMMUN, V143, P305, DOI 10.1016/S0010-4655(01)00460-X
Arina C, 2012, NUCL PHYS B, V865, P430, DOI 10.1016/j.nuclphysb.2012.07.029
Basso L, 2010, PHYS REV D, V82, DOI 10.1103/PhysRevD.82.055018
Branco GC, 2012, PHYS REP, V516, P1, DOI 10.1016/j.physrep.2012.02.002
Chatrchyan S, 2012, PHYS LETT B, V716, P30, DOI 10.1016/j.physletb.2012.08.021
Cheung C, 2012, J HIGH ENERGY PHYS, DOI 10.1007/JHEP07(2012)105
Degrassi G., 2012, J HIGH ENERGY PHYS, V1208
Dubois P. F., 1996, COMPUTERS PHYS, V10
Ellwanger U, 2007, COMPUT PHYS COMMUN, V177, P399, DOI 10.1016/j.cpc.2007.05.001
Evans C., 2001, YAML AINT MARKUP LAN
Fonseca R.M., RENORMALIZATION GROU
Fonseca RM, 2012, COMPUT PHYS COMMUN, V183, P2298, DOI 10.1016/j.cpc.2012.05.017
Giudice GF, 2004, NUCL PHYS B, V699, P65, DOI 10.1016/j.nuclphysb.2004.08.001
HABER HE, 1985, PHYS REP, V117, P75, DOI 10.1016/0370-1573(85)90051-1
HOLDOM B, 1986, PHYS LETT B, V166, P196, DOI 10.1016/0370-2693(86)91377-8
Holthausen M., 2012, J HIGH ENERGY PHYS, V1202, P037
Ishiwata K, 2011, PHYS REV D, V84, DOI 10.1103/PhysRevD.84.055025
JACK I, 1982, NUCL PHYS B, V207, P474, DOI 10.1016/0550-3213(82)90212-7
JACK I, 1983, J PHYS A-MATH GEN, V16, P1101, DOI 10.1088/0305-4470/16/5/026
JACK I, 1985, NUCL PHYS B, V249, P472, DOI 10.1016/0550-3213(85)90088-4
Jones E., SCIPY OPEN SOURCE SC
Luo MX, 2003, PHYS REV LETT, V90, DOI 10.1103/PhysRevLett.90.011601
Lyonnet F., 2013, PYR TE WEB PAGE
MACHACEK ME, 1984, NUCL PHYS B, V236, P221, DOI 10.1016/0550-3213(84)90533-9
MACHACEK ME, 1983, NUCL PHYS B, V222, P83, DOI 10.1016/0550-3213(83)90610-7
MACHACEK ME, 1985, NUCL PHYS B, V249, P70, DOI 10.1016/0550-3213(85)90040-9
Martin S.P., HEPPH9709356
NILLES HP, 1984, PHYS REP, V110, P1, DOI 10.1016/0370-1573(84)90008-5
Perez F, 2007, COMPUT SCI ENG, V9, P21, DOI 10.1109/MCSE.2007.53
Pirogov YF, 1999, EUR PHYS J C, V10, P629, DOI 10.1007/s100529900035
Porod W, 2003, COMPUT PHYS COMMUN, V153, P275, DOI 10.1016/S0010-4655(03)00222-4
Porod W, 2012, COMPUT PHYS COMMUN, V183, P2458, DOI 10.1016/j.cpc.2012.05.021
SHER M, 1989, PHYS REP, V179, P273, DOI 10.1016/0370-1573(89)90061-6
Sperling M, 2013, J HIGH ENERGY PHYS, DOI 10.1007/JHEP07(2013)132
Staub F., 2013, ARXIV13097223HEPPH
Staub F, 2013, COMPUT PHYS COMMUN, V184, P1792, DOI 10.1016/j.cpc.2013.02.019
Staub F, 2011, COMPUT PHYS COMMUN, V182, P808, DOI 10.1016/j.cpc.2010.11.030
Staub F, 2010, COMPUT PHYS COMMUN, V181, P1077, DOI 10.1016/j.cpc.2010.01.011
SymPy Development Team, SYMP PYTH LIB SYMB M
Van Rossum G., 1991, CWI Q, V4, P283
Wingerter A, 2011, PHYS REV D, V84, DOI 10.1103/PhysRevD.84.095012
NR 43
TC 47
Z9 47
U1 0
U2 7
PU ELSEVIER SCIENCE BV
PI AMSTERDAM
PA PO BOX 211, 1000 AE AMSTERDAM, NETHERLANDS
SN 0010-4655
EI 1879-2944
J9 COMPUT PHYS COMMUN
JI Comput. Phys. Commun.
PD MAR
PY 2014
VL 185
IS 3
BP 1130
EP 1152
DI 10.1016/j.cpc.2013.12.002
PG 23
WC Computer Science, Interdisciplinary Applications; Physics, Mathematical
SC Computer Science; Physics
GA AB6SF
UT WOS:000331919100044
DA 2021-04-21
ER
PT J
AU Leinonen, J
AF Leinonen, Jussi
TI High-level interface to T-matrix scattering calculations: architecture,
capabilities and limitations
SO OPTICS EXPRESS
LA English
DT Article
ID LIGHT-SCATTERING; PARTICLES
AB The PyTMatrix package was designed with the objective of providing a simple, extensible interface to T-Matrix electromagnetic scattering calculations performed using an extensively validated numerical core. The interface, implemented in the Python programming language, facilitates automation of the calculations and further analysis of the results through direct integration of both the inputs and the outputs of the calculations to numerical analysis software. This article describes the architecture and design of the package, illustrating how the concepts in the physics of electromagnetic scattering are mapped into data and code models in the computer software. The resulting capabilities and their consequences for the usability and performance of the package are explored. (C) 2014 Optical Society of America
C1 Finnish Meteorol Inst, FI-00101 Helsinki, Finland.
RP Leinonen, J (corresponding author), Finnish Meteorol Inst, POB 503, FI-00101 Helsinki, Finland.
EM jussi.leinonen@fmi.fi
OI Leinonen, Jussi/0000-0002-6560-6316
FU Academy of FinlandAcademy of FinlandEuropean Commission [255718];
Finnish Funding Agency for Technology and Innovation (Tekes)Finnish
Funding Agency for Technology & Innovation (TEKES) [3155/31/2009]
FX The research leading to this article was supported by the Academy of
Finland (grant 255718) and the Finnish Funding Agency for Technology and
Innovation (Tekes; grant 3155/31/2009). The author would like to thank
Dr. M. I. Mishchenko for the permission to include his T-matrix code as
a part of PyTMatrix under the MIT license. Thanks are also in order to
the early users of PyTMatrix who submitted feedback and bug reports, in
particular Dr. D. Moisseev, Dr. J. Richard, Dr. J. Hardin and K.
Muhlbauer. The NumPy and SciPy communities are gratefully acknowledged
for making these tools freely available.
CR AYDIN K, 2000, LIGHT SCATTERING NON, pCH16
Bringi V. N., 2001, POLARIMETRIC DOPPLER
Fernandes AD, 2006, EVOL BIOINFORM, V2, P251
Fung J, 2012, J QUANT SPECTROSC RA, V113, P212, DOI 10.1016/j.jqsrt.2012.06.007
GAUTSCHI W, 1994, ACM T MATH SOFTWARE, V20, P21, DOI 10.1145/174603.174605
Gogoi A, 2011, J QUANT SPECTROSC RA, V112, P2713, DOI 10.1016/j.jqsrt.2011.07.010
Hellmers J, 2012, J QUANT SPECTROSC RA, V113, P2243, DOI 10.1016/j.jqsrt.2012.07.006
Jones E., 2001, SCIPY OPEN SOURCE SC
Leinonen J., PYTMATRIX KDP EXAMPL
Leinonen J., PYTHON CODE T MATRIX
Leinonen J, 2012, J APPL METEOROL CLIM, V51, P392, DOI 10.1175/JAMC-D-11-056.1
Mishchenko MI, 1998, J QUANT SPECTROSC RA, V60, P309, DOI 10.1016/S0022-4073(98)00008-9
Mishchenko MI, 1996, J QUANT SPECTROSC RA, V55, P535, DOI 10.1016/0022-4073(96)00002-7
MISHCHENKO MI, 2000, LIGHT SCATTERING NON, pCH6
Oliphant TE, 2007, COMPUT SCI ENG, V9, P10, DOI 10.1109/MCSE.2007.58
Testud J, 2001, J APPL METEOROL, V40, P1118, DOI 10.1175/1520-0450(2001)040<1118:TCONDT>2.0.CO;2
van de Hulst H. C., 1981, LIGHT SCATTERING SMA
WATERMAN PC, 1965, PR INST ELECTR ELECT, V53, P805, DOI 10.1109/PROC.1965.4058
NR 18
TC 46
Z9 48
U1 0
U2 4
PU OPTICAL SOC AMER
PI WASHINGTON
PA 2010 MASSACHUSETTS AVE NW, WASHINGTON, DC 20036 USA
SN 1094-4087
J9 OPT EXPRESS
JI Opt. Express
PD JAN 27
PY 2014
VL 22
IS 2
BP 1655
EP 1660
DI 10.1364/OE.22.001655
PG 6
WC Optics
SC Optics
GA 302EQ
UT WOS:000330585100046
PM 24515171
OA DOAJ Gold
DA 2021-04-21
ER
PT B
AU Masciola, M
Jonkman, J
Robertson, A
AF Masciola, Marco
Jonkman, Jason
Robertson, Amy
GP ASME
TI EXTENDING THE CAPABILITIES OF THE MOORING ANALYSIS PROGRAM: A SURVEY OF
DYNAMIC MOORING LINE THEORIES FOR INTEGRATION INTO FAST
SO 33RD INTERNATIONAL CONFERENCE ON OCEAN, OFFSHORE AND ARCTIC ENGINEERING,
2014, VOL 9A: OCEAN RENEWABLE ENERGY
LA English
DT Proceedings Paper
CT 33rd ASME International Conference on Ocean, Offshore and Arctic
Engineering
CY JUN 08-13, 2014
CL San Francisco, CA
SP ASME, Ocean, Offshore & Arct Engn Div
ID STEADY-STATE ANALYSIS; FINITE-ELEMENT; NUMERICAL-SIMULATION; CABLE
MODEL; SYSTEM; FORMULATION
AB Techniques to model dynamic mooring lines take various forms. The most widely used models include a heuristic representation of the physics (such as a lumped-mass system), a finite-element analysis discretization of the lines (discretized in space), or a finite-difference model (which is discretized in both space and time). In this paper, the authors explore the features of the various models, weigh the advantages of each, and propose a plan for implementing one dynamic mooring line model into the open-source Mooring Analysis Program (MAP). MAP is currently used as a module for the FAST offshore wind turbine computer-aided engineering (CAE) tool to model mooring systems quasi-statically, although dynamic mooring capabilities are desired. Based on the exploration in this paper, the lumped-mass representation is selected for implementation in MAP based on its simplicity, low computational cost, and ability to provide physics similar to those captured by higher-order models.
To begin, the underlying theories defining the three classes of dynamic mooring line models are identified and explored. This leads to insight into the capabilities of each representation. These capabilities are weighed against the current needs of the FAST wind turbine CAE tool, to which MAP will be coupled. Based on the assessment, a plan for integrating the dynamic mooring line theory into the current MAP structure is developed. Common problems arising from the determination of the model static equilibrium and known issues with numerical stability are addressed. Because MAP is a module that FAST can call, a plan consistent with the FAST modularization framework principles is described. Adding dynamic mooring line capabilities extends the features in MAP and also allows uncoupled analysis to be performed through MAP's native Python bindings.
C1 [Masciola, Marco; Jonkman, Jason; Robertson, Amy] Natl Renewable Energy Lab, Golden, CO 80401 USA.
RP Masciola, M (corresponding author), Natl Renewable Energy Lab, Golden, CO 80401 USA.
CR ABLOW CM, 1983, OCEAN ENG, V10, P443, DOI 10.1016/0029-8018(83)90046-X
Adrezin R, 1996, J AEROSPACE ENG, V9, P114, DOI 10.1061/(ASCE)0893-1321(1996)9:4(114)
Asher U.M., 1998, COMPUTER METHODS ORD
Buckham B, 2004, J APPL MECH-T ASME, V71, P476, DOI 10.1115/1.1755691
Buckham B, 2003, OCEAN ENG, V30, P453, DOI 10.1016/S0029-8018(02)00029-X
Buckham B. J., 2003, THESIS U VICTORIA BR
Chai YT, 2002, OCEAN ENG, V29, P627, DOI 10.1016/S0029-8018(01)00038-5
Chiou R., 1989, THESIS OREGON STATE
CHUNG J, 1993, J APPL MECH-T ASME, V60, P371, DOI 10.1115/1.2900803
DALANE JI, 1997, MARINE STRUCTURES, V10, P611
DEZOYSA APK, 1978, OCEAN ENG, V5, P209, DOI 10.1016/0029-8018(78)90038-0
Driscoll FR, 2000, APPL OCEAN RES, V22, P169, DOI 10.1016/S0141-1187(00)00002-X
Flannery B. P., 1992, NUMERICAL RECIPES C
FRISWELL MI, 1995, J WATERW PORT C-ASCE, V121, P98, DOI 10.1061/(ASCE)0733-950X(1995)121:2(98)
Garrett DL, 2005, OCEAN ENG, V32, P802, DOI 10.1016/j.oceaneng.2004.10.010
GARRETT DL, 1982, J ENERG RESOUR-ASME, V104, P302, DOI 10.1115/1.3230419
Gatti-Bono C., 2004, J INT SOC OFFSHORE P, V14
Gobat JI, 2006, OCEAN ENG, V33, P1373, DOI 10.1016/j.oceaneng.2005.07.012
GOBAT JI, 2000, THESIS MIT WOODS HOL
Golub GH., 1996, MATRIX COMPUTATIONS
Griffin O. M., 1989, J OFFSHORE MECH ARCT, V11, P298
Hall JF, 2006, EARTHQ ENG STRUCT D, V35, P525, DOI 10.1002/eqe.541
HUANG S, 1994, OCEAN ENG, V21, P587, DOI 10.1016/0029-8018(94)90008-6
Hughes T. J. R., 1977, Computer Methods in Applied Mechanics and Engineering, V10, P135, DOI 10.1016/0045-7825(77)90001-9
Inman D.J., 2007, ENG VIBRATIONS
Irvine M., 1993, CABLE STRUCTURES
Jonkman J., 2013, 51 AIAA AER M DALL T
Jonkman J.M, 2005, FAST USERS GUIDE UPD
Jonkman J. M., 2013, 51 AIAA AER SCI M DA
Kaczmarczyk S, 2003, J SOUND VIB, V262, P219, DOI 10.1016/S0022-460X(02)01137-9
Kennedy Robert M., 1981, OCEANS 81, P966, DOI 10.1109/OCEANS.1981.1151553
Ketchman Jeffrey, 1975, OCEAN 75 Conference, P98, DOI 10.1109/OCEANS.1975.1154031
Lambert C, 2007, IEEE-ASME T MECH, V12, P32, DOI 10.1109/TMECH.2006.886251
LEONARD JW, 1981, ENG STRUCT, V3, P153, DOI 10.1016/0141-0296(81)90024-9
Liu Y, 2002, L2 STABILITY ANAL DI
LO A, 1982, J ENG MECH DIV-ASCE, V108, P605
Masciola M, 2013, 23 INT OFFSH POL ENG
Masciola M, 2013, J OFFSHORE MECH ARCT, V135, DOI 10.1115/1.4023795
Masciola MD, 2012, J WATERW PORT COAST, V138, P164, DOI 10.1061/(ASCE)WW.1943-5460.0000117
Mehrabi AB, 1998, J STRUCT ENG-ASCE, V124, P1313, DOI 10.1061/(ASCE)0733-9445(1998)124:11(1313)
Mekha BB, 1996, J STRUCT ENG-ASCE, V122, P142, DOI 10.1061/(ASCE)0733-9445(1996)122:2(142)
Merchant H. C., 1973, P MTS IEEE OCEANS, V1, P390
Nahon M, 1999, AIAA MODELING AND SIMULATION TECHNOLOGIES CONFERENCE, P214
Nicoll R. A., 2004, THESIS U VICTORIA BR
NORDGREN RP, 1974, J APPL MECH-T ASME, V41, P777, DOI 10.1115/1.3423387
O'Neill B., 1966, ELEMENTARY DIFFERENT
PEYROT AH, 1979, COMPUT STRUCT, V10, P805
POWELL G, 1981, INT J NUMER METH ENG, V17, P1455, DOI 10.1002/nme.1620171003
RAN ZH, 2000, THESIS TEXAS A M U C
Rupe R. C., 1975, T ASME, V74-WA, P1046
Sachin G, 2007, PHYSICS0702224 ARXIV
Song H., 2013, 23 INT OFFSH POL ENG
Sprague M. A., 2014, AIAA SCI TECHN FOR S
Starossek U., 1994, STRUCT ENG INT, V4, P171, DOI DOI 10.2749/101686694780601908
TURNER MJ, 1956, J AERONAUT SCI, V23, P805, DOI 10.2514/8.3664
Walton T.S., 1960, Mathematics of Computation, V14, P27, DOI 10.2307/2002982
Wang PH, 1998, J SOUND VIB, V209, P223, DOI 10.1006/jsvi.1997.1227
WEBSTER RL, 1980, COMPUT STRUCT, V11, P137, DOI 10.1016/0045-7949(80)90153-4
Williams P, 2007, J GUID CONTROL DYNAM, V30, P753, DOI 10.2514/1.20433
Wu S., 1995, MAR STRUCT, V8, P585
Zienkiewicz OC., 1977, FINITE ELEMENT METHO, V3
Zueck R., 1995, INT S CABL DYN LIEG
NR 62
TC 4
Z9 4
U1 0
U2 0
PU AMER SOC MECHANICAL ENGINEERS
PI NEW YORK
PA THREE PARK AVENUE, NEW YORK, NY 10016-5990 USA
BN 978-0-7918-4553-0
PY 2014
AR UNSP V09AT09A032
PG 13
WC Energy & Fuels; Engineering, Ocean; Engineering, Mechanical
SC Energy & Fuels; Engineering
GA BD7SC
UT WOS:000363499000032
DA 2021-04-21
ER
PT B
AU Alioli, M
Morandini, M
Masarati, P
AF Alioli, Mattia
Morandini, Marco
Masarati, Pierangelo
GP ASME
TI COUPLED MULTIBODY-FLUID DYNAMICS SIMULATION OF FLAPPING WINGS
SO PROCEEDINGS OF THE ASME INTERNATIONAL DESIGN ENGINEERING TECHNICAL
CONFERENCES AND COMPUTERS AND INFORMATION IN ENGINEERING CONFERENCE,
2013, VOL 7B
LA English
DT Proceedings Paper
CT ASME International Design Engineering Technical Conferences / Computers
and Information in Engineering Conference (IDETC/CIE)
CY AUG 04-07, 2013
CL Portland, OR
SP ASME, Design Engn Div, ASME, Comp & Informat Engn Div
ID PROPULSION; AIRFOIL
AB This paper deals with the coupled structural and fluid-dynamics analysis of flexible flapping wings using multibody dynamics. A general-purpose multidisciplinary multibody solver is coupled with a computational fluid dynamics code by means of a general-purpose, meshless boundary interfacing approach based on Moving Least Squares with Radial Basis Functions. The general-purpose, free software multibody solver MBDyn is used. A nonlinear 4-node shell element has been used for the structural model. The fluid dynamics code is based on a stabilized finite element approximation of the unsteady Navier-Stokes equations. The method (often referred to in the literature as G2 method) has been implemented within the programming environment provided by the free software project FEniCS, a collection of libraries specifically designed for the automated and efficient solution of differential equations. FEniCS provides extensive scripting capabilities, with a domain-specific language for the specification of variational formulations of Partial Differential Equations that is embedded within the programming language Python. This approach makes it possible to easily and quickly build complex simulation codes that are, at the same time, extremely efficient and easily adapted to run in parallel. The coupling of the multibody and Navier-Stokes codes is strictly enforced at each time step. The fluid dynamics discretization is automatically refined to keep the error on the overall lift and drag coefficients below a user-defined tolerance. The method is first tested by computing the drag force of a non-oscillating NACA 0012 airfoil traveling in air Subsequently, the drag and lift forces on a rigid and flexible oscillating NACA 0012 wing are compared with experimental data. Encouraging results obtained from the modeling and analysis of the dynamics and aeroelasticity of flexible oscillating wing models confirm the ability of the structural and fluid dynamics models to capture the physics of the problem.
C1 [Alioli, Mattia; Morandini, Marco; Masarati, Pierangelo] Politecn Milan, Dipartimento Sci & Tecnol Aerospaziali, I-20156 Milan, Italy.
RP Masarati, P (corresponding author), Politecn Milan, Dipartimento Sci & Tecnol Aerospaziali, Via Masa 34, I-20156 Milan, Italy.
EM mattia.alioli@mail.polimi.it; marco.morandini@polimi.it;
pierangelo.masarati@.polimi.it
RI Masarati, Pierangelo/I-3898-2012; Morandini, Marco/E-5117-2012
OI Masarati, Pierangelo/0000-0002-9347-7654; Morandini,
Marco/0000-0002-1992-9077
CR Guermond JL, 1997, J COMPUT PHYS, V132, P12, DOI 10.1006/jcph.1996.5587
Heathcote S, 2008, J FLUID STRUCT, V24, P183, DOI 10.1016/j.jfluidstructs.2007.08.003
Heathcote S, 2004, AIAA J, V42, P2196, DOI 10.2514/1.5299
Hoffman J., 2007, APPL MATH BODY SOUL, DOI [10.1007/978-3-540-46533-1, DOI 10.1007/978-3-540-46533-1]
John V, 2002, J COMPUT APPL MATH, V147, P287, DOI 10.1016/S0377-0427(02)00437-5
Jones KD, 1998, AIAA J, V36, P1240, DOI 10.2514/2.505
Malhan R., 2013, AM HEL SOC 5 INT SPE
MALHAN R., 2012, 53 AIAA ASME ASCE AH
Masarati P., 2011, MULTIBODY DYNAMICS
Masarati P., 2011, 29 AIAA APPL AER C
Quaranta G., 2005, COUPLED PROBLEMS 200
Rognes M. E., 2012, SIAM J SCI COMPUTING
Young J, 2004, AIAA J, V42, P2042, DOI 10.2514/1.5070
NR 13
TC 0
Z9 0
U1 0
U2 0
PU AMER SOC MECHANICAL ENGINEERS
PI NEW YORK
PA THREE PARK AVENUE, NEW YORK, NY 10016-5990 USA
BN 978-0-7918-5597-3
PY 2014
AR UNSP V07BT10A014-1
PG 11
WC Automation & Control Systems; Engineering, Mechanical; Operations
Research & Management Science
SC Automation & Control Systems; Engineering; Operations Research &
Management Science
GA BD6ZU
UT WOS:000362796000014
DA 2021-04-21
ER
PT B
AU Kos, L
AF Kos, Leon
CA ITM-TF Contributors
BE Biljanovic, P
Butkovic, Z
Skala, K
Golubic, S
CicinSain, M
Sruk, V
Ribaric, S
Gros, S
Vrdoljak, B
Mauher, M
Cetusic, G
TI Visualization Support for Code Development in EUROfusion Integrated
Modelling
SO 2014 37TH INTERNATIONAL CONVENTION ON INFORMATION AND COMMUNICATION
TECHNOLOGY, ELECTRONICS AND MICROELECTRONICS (MIPRO)
LA English
DT Proceedings Paper
CT 37th International Convention on Information and Communication
Technology, Electronics and Microelectronics (MIPRO)
CY MAY 26-30, 2014
CL Opatija, CROATIA
SP MIPRO Croatian Soc, IEEE Reg 8, Ericsson Nikola Tesla Zagreb, T Croatian Telecom Zagreb, Koncar Elect Ind Zagreb, InfoDom Zagreb, HEP Croatian Elect Co Zagreb, VIPNet Zagreb, Storm Comp Zagreb, Transmitters & Commun Co Zagreb, King ICT Zagreb, IN2 Zagreb, Altpro Zagreb, Microsoft Croatia, Hewlett Packard Croatia, Micro Link Zagreb, Mjerne Tehnologije Zagreb, Selmet Zagreb, Ib ProCADD Ljubljana, Nomen Rijeka, Croatian Post & Elect Commun Agcy, Univ Zagreb, Univ Rijeka, IEEE Croatia Sect, Rudjer Boskov Inst Zagreb, Univ Rijeka Fac Engn & Maritime Studies, Univ Zagreb, Fac Elect Engn & Comp Zagreb, Univ Zagreb, Fac Org & Informat Varazdin, Minist Sci, Educ & Sports Republ Croatia, Minist Maritime Affairs, Transport & Infrastructure Republ Croatia, Minist Econ Republ Croatia, Croatian Chamber Econ
ID UNIVERSAL ACCESS LAYER
AB The need for diverse visualization tools within integrated modelling is based on the requirement of physics codes to be coupled under the tailored database model from which these visualization tools read data. We discuss these visualization requirements for fusion integrated modeling, and the existing visualization tools developed within the scope of the European Integrated Modelling (EU IM) framework. The datastructure model provides fundamental description for data exchange between codes that are developed in a variety of programming languages. From this description in XML schema definition one can generate required language interfaces and database access layer routines. The persistent storage database has to efficiently support codes running on HPC with memory caching mechanisms. Visualization tools can be treated as one of the codes running in-situ or as post-process. The complexity of the fusion datastructure and several visualization tools requires that scientists describe standard visualizations in XML schema directly in order to instruct visualization tools what are meaningful visualizations available in the very database. Besides standard visualizations there are specific visualizations that cannot be easily described (as mapping or axes linking) without algorithm. For the custom visualizations we provide Python snippets collected in user-shared library. Interfaces to scientific workflow engine Kepler for visualization with VisIt visualization tool and Matplotlib are presented.
C1 [Kos, Leon; ITM-TF Contributors] Univ Ljubljana, LECAD, Ljubljana, Slovenia.
RP Kos, L (corresponding author), Univ Ljubljana, LECAD, Ljubljana, Slovenia.
EM leon.kos@lecad.fs.uni-lj.si
RI Artaud, Jean-Francois J/J-2068-2012; Schneider, Mireille/B-7821-2010
OI Kos, Leon/0000-0002-1790-7093
CR Altintas I, 2004, 16TH INTERNATIONAL CONFERENCE ON SCIENTIFIC AND STATISTICAL DATABASE MANAGEMENT, PROCEEDINGS, P423
Altintas I, 2006, LECT NOTES COMPUT SC, V4145, P118
Coster DP, 2010, IEEE T PLASMA SCI, V38, P2085, DOI 10.1109/TPS.2010.2056707
Cummings J, 2010, EUROMICRO WORKSHOP P, P428, DOI 10.1109/PDP.2010.97
Docan C., 2010, HPDC, P25, DOI DOI 10.1145/1851476.1851481
Docan C, 2010, CONCURR COMP-PRACT E, V22, P1181, DOI 10.1002/cpe.1567
Galonska A, 2013, COMPUT PHYS COMMUN, V184, P638, DOI 10.1016/j.cpc.2012.10.024
Haefele M, 2010, EUROMICRO WORKSHOP P, P498, DOI 10.1109/PDP.2010.76
Herman I., 2006, OVERVIEW XSLT XPATH
Hoenen O., 2013, EPS 2013 EUR C 37D 4
Imbeaux F, 2010, COMPUT PHYS COMMUN, V181, P987, DOI 10.1016/j.cpc.2010.02.001
LLNL, VISIT DES OV OV DES
Manduchi G, 2008, FUSION ENG DES, V83, P462, DOI 10.1016/j.fusengdes.2007.08.021
Pinches S., 2013, 55 ANN M APS DIV PLA, V58
Whitlock B., 2011, P 11 EUR C PAR GRAPH, P101, DOI DOI 10.2312/EGPGV/EGPGV11/101-109
NR 15
TC 0
Z9 0
U1 0
U2 2
PU IEEE
PI NEW YORK
PA 345 E 47TH ST, NEW YORK, NY 10017 USA
BN 978-953-233-081-6
PY 2014
BP 392
EP 397
PG 6
WC Engineering, Electrical & Electronic
SC Engineering
GA BB8DQ
UT WOS:000346438700077
DA 2021-04-21
ER
PT J
AU Sanchez-Martinez, M
Crehuet, R
AF Sanchez-Martinez, M.
Crehuet, R.
TI Application of the maximum entropy principle to determine ensembles of
intrinsically disordered proteins from residual dipolar couplings
SO PHYSICAL CHEMISTRY CHEMICAL PHYSICS
LA English
DT Article
ID STRUCTURAL ENSEMBLES; STATE ENSEMBLES; NMR; RECOGNITION; SIMULATIONS;
REFINEMENT; LANDSCAPE; FRAMEWORK; EFFICIENT; MODELS
AB We present a method based on the maximum entropy principle that can re-weight an ensemble of protein structures based on data from residual dipolar couplings (RDCs). The RDCs of intrinsically disordered proteins (IDPs) provide information on the secondary structure elements present in an ensemble; however even two sets of RDCs are not enough to fully determine the distribution of conformations, and the force field used to generate the structures has a pervasive influence on the refined ensemble. Two physics-based coarse-grained force fields, Profasi and Campari, are able to predict the secondary structure elements present in an IDP, but even after including the RDC data, the re-weighted ensembles differ between both force fields. Thus the spread of IDP ensembles highlights the need for better force fields. We distribute our algorithm in an open-source Python code.
C1 [Sanchez-Martinez, M.; Crehuet, R.] CSIC, Inst Adv Chem Catalunya IQAC, Madrid, Spain.
RP Crehuet, R (corresponding author), CSIC, Inst Adv Chem Catalunya IQAC, Madrid, Spain.
EM ramon.crehuet@iqac.csic.es
RI Sanchez-Martinez, Melchor/A-3392-2014; Crehuet, Ramon/G-4140-2011
OI Sanchez-Martinez, Melchor/0000-0002-3674-8577; Crehuet,
Ramon/0000-0002-6687-382X
FU Ministerio de Economia y CompetitividadSpanish Government
[CTQ2012-33324]; Generalitat de CatalunyaGeneralitat de Catalunya
[2009SGR01472]; Ministerio de Economia y CompetitividadSpanish
Government
FX We would like to thank X. Salvatella and P. Bernado for critically
reading the manuscript. We acknowledge financial support from the
Ministerio de Economia y Competitividad (CTQ2012-33324) and the
Generalitat de Catalunya (2009SGR01472). MS-M thanks the Ministerio de
Economia y Competitividad for a predoctoral fellowship. We thank the
CCUC and the RES (BCV-2013-3-0015) for computational resources.
CR Angyan AF, 2013, MOLECULES, V18, P10548, DOI 10.3390/molecules180910548
Babu MM, 2011, CURR OPIN STRUC BIOL, V21, P432, DOI 10.1016/j.sbi.2011.03.011
Beauchamp KA, 2014, BIOPHYS J, V106, P1381, DOI 10.1016/j.bpj.2014.02.009
Berlin K, 2013, J AM CHEM SOC, V135, P16595, DOI 10.1021/ja4083717
Bernado P, 2005, P NATL ACAD SCI USA, V102, P17002, DOI 10.1073/pnas.0506202102
Boomsma W, 2014, PLOS COMPUT BIOL, V10, DOI 10.1371/journal.pcbi.1003406
Fenwick RB, 2011, EUR BIOPHYS J BIOPHY, V40, P1339, DOI 10.1007/s00249-011-0754-8
Burgi R, 2001, J BIOMOL NMR, V19, P305, DOI 10.1023/A:1011295422203
Cavalli A, 2013, J CHEM PHYS, V138, DOI 10.1063/1.4793625
Chen K, 2012, TOP CURR CHEM, V326, P47, DOI 10.1007/128_2011_215
Choy WY, 2001, J MOL BIOL, V308, P1011, DOI 10.1006/jmbi.2001.4750
Cong X, 2013, J CHEM THEORY COMPUT, V9, P5158, DOI 10.1021/ct400534k
Daughdrill GW, 2012, MOL BIOSYST, V8, P308, DOI 10.1039/c1mb05243h
Esteban-Martin S, 2010, J AM CHEM SOC, V132, P4626, DOI 10.1021/ja906995x
Feldman HJ, 2000, PROTEINS, V39, P112, DOI 10.1002/(SICI)1097-0134(20000501)39:2<112::AID-PROT2>3.0.CO;2-B
Fisher CK, 2012, BIOCOMPUT-PAC SYM, P82
Fisher CK, 2011, CURR OPIN STRUC BIOL, V21, P426, DOI 10.1016/j.sbi.2011.04.001
Fisher CK, 2010, J AM CHEM SOC, V132, P14919, DOI 10.1021/ja105832g
Fuxreiter M, 2012, MOL BIOSYST, V8, P168, DOI 10.1039/c1mb05234a
Hsu WL, 2013, PROTEIN SCI, V22, P258, DOI 10.1002/pro.2207
Iesmantavicius V, 2014, ANGEW CHEM INT EDIT, V53, P1548, DOI 10.1002/anie.201307712
Iglesias Jelisa, 2013, Intrinsically Disord Proteins, V1, pe25323, DOI 10.4161/idp.25323
Irback A, 2005, BIOPHYS J, V88, P1560, DOI 10.1529/biophysj.104.050427
Irback A, 2006, J COMPUT CHEM, V27, P1548, DOI 10.1002/jcc.20452
Irback Anders, 2009, PMC Biophys, V2, P2, DOI 10.1186/1757-5036-2-2
JAYNES ET, 1957, PHYS REV, V106, P620, DOI 10.1103/PhysRev.106.620
Jensen MR, 2008, J AM CHEM SOC, V130, P8055, DOI 10.1021/ja801332d
Jensen MR, 2014, P NATL ACAD SCI USA, V111, pE1557, DOI 10.1073/pnas.1323876111
Jensen MR, 2009, STRUCTURE, V17, P1169, DOI 10.1016/j.str.2009.08.001
Jones E., 2001, SCIPY OPEN SOURCE SC
Jonsson SA, 2012, PROTEINS, V80, P2169, DOI 10.1002/prot.24107
Mao AH, 2010, P NATL ACAD SCI USA, V107, P8183, DOI 10.1073/pnas.0911107107
Marsh J. A., 2007, J MOL BIOL, V367, P1494
Marsh JA, 2008, J AM CHEM SOC, V130, P7804, DOI 10.1021/ja802220c
Marsh JA, 2012, PROTEINS, V80, P556, DOI 10.1002/prot.23220
Meier S, 2007, J AM CHEM SOC, V129, P9799, DOI 10.1021/ja0724339
Mohan A, 2006, J MOL BIOL, V362, P1043, DOI 10.1016/j.jmb.2006.07.087
Mohan Sabitha, 2013, 2013 Conference on Lasers & Electro-Optics. Europe & International Quantum Electronics Conference (CLEO EUROPE/IQEC), DOI 10.1109/CLEOE-IQEC.2013.6800917
Nodet G, 2009, J AM CHEM SOC, V131, P17908, DOI 10.1021/ja9069024
Obolensky OI, 2007, J BIOMOL NMR, V39, P1, DOI 10.1007/s10858-007-9169-3
Olsson S, 2014, J CHEM THEORY COMPUT, V10, P3484, DOI 10.1021/ct5001236
Olsson S, 2013, PLOS ONE, V8, DOI 10.1371/journal.pone.0079439
Olsson S, 2011, J MAGN RESON, V213, P182, DOI 10.1016/j.jmr.2011.08.039
Ozenne V, 2012, BIOINFORMATICS, V28, P1463, DOI 10.1093/bioinformatics/bts172
Patil A, 2010, J STAT SOFTW, V35, P1
Pitera JW, 2012, J CHEM THEORY COMPUT, V8, P3445, DOI 10.1021/ct300112v
Presse S, 2013, REV MOD PHYS, V85, P1115, DOI 10.1103/RevModPhys.85.1115
Richter B, 2007, J BIOMOL NMR, V37, P117, DOI 10.1007/s10858-006-9117-7
Rieping W, 2005, SCIENCE, V309, P303, DOI 10.1126/science.1110428
Roux B, 2013, J CHEM PHYS, V138, DOI 10.1063/1.4792208
Rozycki B, 2011, STRUCTURE, V19, P109, DOI 10.1016/j.str.2010.10.006
Salmon Loic, 2012, Methods Mol Biol, V895, P115, DOI 10.1007/978-1-61779-927-3_9
Schneider R, 2012, MOL BIOSYST, V8, P58, DOI 10.1039/c1mb05291h
Uversky VN, 2008, ANNU REV BIOPHYS, V37, P215, DOI 10.1146/annurev.biophys.37.032807.125924
Varadi M, 2014, NUCLEIC ACIDS RES, V42, pD326, DOI 10.1093/nar/gkt960
Vitalis A, 2009, J COMPUT CHEM, V30, P673, DOI 10.1002/jcc.21005
Wang JH, 2011, INT J MOL SCI, V12, P3205, DOI 10.3390/ijms12053205
Weinstock DS, 2007, J AM CHEM SOC, V129, P4858, DOI 10.1021/ja0677517
White AD, 2014, J CHEM THEORY COMPUT, V10, P3023, DOI 10.1021/ct500320c
Zweckstetter M, 2008, NAT PROTOC, V3, P679, DOI 10.1038/nprot.2008.36
NR 60
TC 12
Z9 12
U1 0
U2 14
PU ROYAL SOC CHEMISTRY
PI CAMBRIDGE
PA THOMAS GRAHAM HOUSE, SCIENCE PARK, MILTON RD, CAMBRIDGE CB4 0WF, CAMBS,
ENGLAND
SN 1463-9076
EI 1463-9084
J9 PHYS CHEM CHEM PHYS
JI Phys. Chem. Chem. Phys.
PY 2014
VL 16
IS 47
BP 26030
EP 26039
DI 10.1039/c4cp03114h
PG 10
WC Chemistry, Physical; Physics, Atomic, Molecular & Chemical
SC Chemistry; Physics
GA AT8UW
UT WOS:000345208200041
PM 25358803
OA Other Gold, Green Published
DA 2021-04-21
ER
PT S
AU Bhimji, W
Bristow, T
Washbrook, A
AF Bhimji, W.
Bristow, T.
Washbrook, A.
GP IOP
TI HEPDOOP: High-Energy Physics Analysis using Hadoop
SO 20TH INTERNATIONAL CONFERENCE ON COMPUTING IN HIGH ENERGY AND NUCLEAR
PHYSICS (CHEP2013), PARTS 1-6
SE Journal of Physics Conference Series
LA English
DT Proceedings Paper
CT 20th International Conference on Computing in High Energy and Nuclear
Physics (CHEP)
CY OCT 14-18, 2013
CL Amsterdam, NETHERLANDS
AB We perform a LHC data analysis workflow using tools and data formats that are commonly used in the "Big Data" community outside High Energy Physics (HEP). These include Apache Avro for serialisation to binary files, Pig and Hadoop for mass data processing and Python Scikit-Learn for multi-variate analysis. Comparison is made with the same analysis performed with current HEP tools in ROOT.
C1 [Bhimji, W.; Bristow, T.; Washbrook, A.] Univ Edinburgh, SUPA Sch Phys & Astron, Edinburgh, Midlothian, Scotland.
RP Bhimji, W (corresponding author), Univ Edinburgh, SUPA Sch Phys & Astron, Edinburgh, Midlothian, Scotland.
EM wbhimji@staffmail.ed.ac.uk
CR Brun R, 1997, NUCL INSTRUM METH A, V389, P81, DOI 10.1016/S0168-9002(97)00048-X
Dean J, 2004, USENIX ASSOCIATION PROCEEDINGS OF THE SIXTH SYMPOSIUM ON OPERATING SYSTEMS DESIGN AND IMPLEMENTATION (OSDE '04), P137
Hoecker Andreas, 2007, ARXIVPHYSICS0703039
Melnik S, 2010, PROC VLDB ENDOW, V3, P330
Olston C, 2008, P 2008 ACM SIGMOD IN, P1099, DOI DOI 10.1145/1376616.1376726
Perez F, 2007, COMPUT SCI ENG, V9, P21, DOI 10.1109/MCSE.2007.53
Shvachko K., 2010, SYMPOSIUM, P1, DOI DOI 10.1109/MSST.2010.5496972
NR 7
TC 2
Z9 2
U1 0
U2 1
PU IOP PUBLISHING LTD
PI BRISTOL
PA DIRAC HOUSE, TEMPLE BACK, BRISTOL BS1 6BE, ENGLAND
SN 1742-6588
EI 1742-6596
J9 J PHYS CONF SER
PY 2014
VL 513
AR 022004
DI 10.1088/1742-6596/513/2/022004
PG 5
WC Physics, Nuclear; Physics, Particles & Fields
SC Physics
GA BB2TA
UT WOS:000342287200046
OA Bronze
DA 2021-04-21
ER
PT S
AU Alloul, A
Christensen, ND
Degrande, C
Duhr, C
Fuks, B
AF Alloul, Adam
Christensen, Neil D.
Degrande, Celine
Duhr, Claude
Fuks, Benjamin
GP IOP
TI New developments in FEYNRULES.
SO 15TH INTERNATIONAL WORKSHOP ON ADVANCED COMPUTING AND ANALYSIS
TECHNIQUES IN PHYSICS RESEARCH (ACAT2013)
SE Journal of Physics Conference Series
LA English
DT Proceedings Paper
CT 15th International Workshop on Advanced Computing and Analysis
Techniques in Physics Research (ACAT)
CY MAY 16-21, 2013
CL Chinese Acad Sci, Inst High Energy Phys, Beijing, PEOPLES R CHINA
SP Chinese Acad Sci, Natl Nat Sci Fdn China, Brookhaven Natl Lab, Peking Univ, Chinese Acad Sci, Theoret Phys Ctr Sci Facilities, Sugon
HO Chinese Acad Sci, Inst High Energy Phys
ID GENERATION; AMPLITUDES; MASS
AB The program FEYNRULES is a MATHEMATICA package developed to facilitate the implementation of new physics theories into high-energy physics tools. Starting from a minimal set of information such as the model gauge symmetries, its particle content, parameters and Lagrangian, FEYNRULES provides all necessary routines to extract automatically from the Lagrangian (that can also be computed semi-automatically for supersymmetric theories) the associated Feynman rules. These can be further exported to several Monte Carlo event generators through dedicated interfaces, as well as translated into a PYTHON library, under the so-called UFO model format, agnostic of the model complexity, especially in terms of Lorentz and/or color structures appearing in the vertices or of number of external legs. In this work, we briefly report on the most recent new features that have been added to FEYNRULES, including full support for spin-3/2 fermions, a new module allowing for the automated diagonalization of the particle spectrum and a new set of routines dedicated to decay width calculations.
C1 [Alloul, Adam; Fuks, Benjamin] Univ Strasbourg, Inst Pluridisciplinaire Hubert Curien, Dept Rech Subatom, CNRS IN2P3, 23 Rue Loess, F-67037 Strasbourg, France.
[Alloul, Adam] Univ Haute Alsace, Grp Rech Phys Hautes Energies GRPHE, IUT Colmar, 34 rue Grillenbreit BP 50568, F-68008 Colmar 68008, France.
[Christensen, Neil D.] Univ Pittsburgh, PITTsburgh Particle Phys Astrophys & Cosmol Ctr, Pittsburgh, PA 15260 USA.
[Degrande, Celine] Univ Illinois, Dept Phys, 1110 W green St, Urbana, IL 61801 USA.
[Duhr, Claude] Swiss Fed Inst Technol, Inst Theoret Phys, CH-8093 Zurich, Switzerland.
[Duhr, Claude] Univ Durham, Inst Particle Phys Phenomenol, Durham DH1 3LE, England.
[Fuks, Benjamin] CERN, Div Theory, Dept Phys, CH-1211 Geneva 23, Switzerland.
RP Alloul, A (corresponding author), Univ Strasbourg, Inst Pluridisciplinaire Hubert Curien, Dept Rech Subatom, CNRS IN2P3, 23 Rue Loess, F-67037 Strasbourg, France.
EM adam.alloul@iphc.cnrs.fr; neilc@pitt.edu; cdegrand@illinois.edu;
duhrc@itp.phys.ethz.ch; benjamin.fuks@iphc.cnrs.fr
OI Fuks, Benjamin/0000-0002-0041-0566; Duhr, Claude/0000-0001-5820-3570
FU Theory-LHC France [CNRS/IN2P3]; French [ANR 12 JS05 002 01]; French
Ministry for Education and Research; U. S. Department of EnergyUnited
States Department of Energy (DOE) [DE-FG02-13ER42001]; NSFNational
Science Foundation (NSF) [PHY0757889]; LHC-TI under U. S. National
Science FoundationNational Science Foundation (NSF) [NSF-PHY-0705682];
ERCEuropean Research Council (ERC)European Commission ["IterQCD"];
[DE-FG02-95ER40896]
FX We are grateful to the organizers of the ACAT2013 conference for
arranging such a nice event. This work has been partially supported by
the Theory-LHC France initiative of the CNRS/IN2P3 and the French ANR 12
JS05 002 01 BATS@ LHC. A. A. has been supported by a Ph. D. fellowship
of the French Ministry for Education and Research. Ce. D. was supported
in part by the U. S. Department of Energy under Contract No.
DE-FG02-13ER42001 and by the NSF grant PHY0757889; N. D. C. was
supported in part by the LHC-TI under U. S. National Science Foundation,
grant NSF-PHY-0705682, by PITT PACC, and by the U. S. Department of
Energy under grant No. DE-FG02-95ER40896 and C. D. was supported by the
ERC grant "IterQCD".
CR Aad G, 2012, PHYS LETT B, V716, P1, DOI 10.1016/j.physletb.2012.08.020
Alloul A, 2013, EUR PHYS J C, V73, DOI 10.1140/epjc/s10052-013-2325-x
Alwall J, 2008, AIP CONF PROC, V1078, P84, DOI 10.1063/1.3052056
ALWALL J, UNPUB
Alwall J, 2007, J HIGH ENERGY PHYS, DOI 10.1088/1126-6708/2007/09/028
Alwall J, 2011, J HIGH ENERGY PHYS, DOI 10.1007/JHEP06(2011)128
Belyaev A, 2012, 12076082
Chatrchyan S, 2012, PHYS LETT B, V716, P30, DOI 10.1016/j.physletb.2012.08.021
Christensen N D, 2013, 13081668
Christensen N, 2011, EUR PHYS J C, V71, DOI 10.1140/epjc/s10052-011-1541-5
Christensen ND, 2012, EUR PHYS J C, V72, DOI 10.1140/epjc/s10052-012-1990-5
Christensen ND, 2009, COMPUT PHYS COMMUN, V180, P1614, DOI 10.1016/j.cpc.2009.02.018
de Aquino P, 2012, COMPUT PHYS COMMUN, V183, P2254, DOI 10.1016/j.cpc.2012.05.004
Degrande C, 2012, COMPUT PHYS COMMUN, V183, P1201, DOI 10.1016/j.cpc.2012.01.022
Duhr C, 2011, COMPUT PHYS COMMUN, V182, P2404, DOI 10.1016/j.cpc.2011.06.009
Fuks B, 2012, INT J MOD PHYS A, V27, DOI 10.1142/S0217751X12300074
Gleisberg T, 2004, J HIGH ENERGY PHYS
Gleisberg T, 2009, J HIGH ENERGY PHYS, DOI 10.1088/1126-6708/2009/02/007
Hahn T, 2001, COMPUT PHYS COMMUN, V140, P418, DOI 10.1016/S0010-4655(01)00290-9
Kilian W, 2011, EUR PHYS J C, V71, DOI 10.1140/epjc/s10052-011-1742-y
Maltoni F, 2003, J HIGH ENERGY PHYS, DOI 10.1088/1126-6708/2003/02/027
Moretti M., 2001, HEPPH0102195
PUKHOV A, 2004, HEPPH0412191
STELZER T, 1994, COMPUT PHYS COMMUN, V81, P357, DOI 10.1016/0010-4655(94)90084-1
NR 24
TC 8
Z9 8
U1 0
U2 1
PU IOP PUBLISHING LTD
PI BRISTOL
PA DIRAC HOUSE, TEMPLE BACK, BRISTOL BS1 6BE, ENGLAND
SN 1742-6588
EI 1742-6596
J9 J PHYS CONF SER
PY 2014
VL 523
AR 012044
DI 10.1088/1742-6596/523/1/012044
PG 6
WC Computer Science, Theory & Methods; Engineering, Electrical &
Electronic; Physics, Applied
SC Computer Science; Engineering; Physics
GA BA9NZ
UT WOS:000339627300044
OA Bronze
DA 2021-04-21
ER
PT S
AU Conte, E
Fuks, B
AF Conte, Eric
Fuks, Benjamin
GP IOP
TI MADANALYSIS 5: status and new developments
SO 15TH INTERNATIONAL WORKSHOP ON ADVANCED COMPUTING AND ANALYSIS
TECHNIQUES IN PHYSICS RESEARCH (ACAT2013)
SE Journal of Physics Conference Series
LA English
DT Proceedings Paper
CT 15th International Workshop on Advanced Computing and Analysis
Techniques in Physics Research (ACAT)
CY MAY 16-21, 2013
CL Chinese Acad Sci, Inst High Energy Phys, Beijing, PEOPLES R CHINA
SP Chinese Acad Sci, Natl Nat Sci Fdn China, Brookhaven Natl Lab, Peking Univ, Chinese Acad Sci, Theoret Phys Ctr Sci Facilities, Sugon
HO Chinese Acad Sci, Inst High Energy Phys
AB MADANALYSIS 5 is a new PYTHON/C++ package facilitating phenomenological analyses that can be performed in the framework of Monte Carlo simulations of collisions to be produced in high-energy physics experiments. It allows one, by means of a user-friendly interpreter, to perform professional physics analyses in a very simple way. Starting from event samples as generated by any Monte Carlo event generator, large classes of selections can be implemented through intuitive commands, many standard kinematical distributions can be automatically represented by histograms and all results are eventually gathered into detailed HTML and LATEX reports. In this work, we briefly report on the latest developments of the code, focusing on the interface to the FASTJET program dedicated to jet reconstruction.
C1 [Conte, Eric] Univ Haute Alsace, GRPHE, IUT Colmar, 34 Rue Grillenbreit BP 50568, F-68008 Colmar, France.
[Fuks, Benjamin] CERN, Dept Phys, Div Theory, CH-1211 Geneva, Switzerland.
[Fuks, Benjamin] Univ Strasbourg, CNRS IN2P3, Inst Pluridisciplinaire Hubert Curien, Dept Rech Subatomiques, F-67037 Strasbourg, France.
RP Conte, E (corresponding author), Univ Haute Alsace, GRPHE, IUT Colmar, 34 Rue Grillenbreit BP 50568, F-68008 Colmar, France.
EM eric.conte@iphc.cnrs.fr; benjamin.fuks@iphc.cnrs.fr
OI Fuks, Benjamin/0000-0002-0041-0566
FU French ANRFrench National Research Agency (ANR) [12 JS05 002 01];
CNRS/IN2P3Centre National de la Recherche Scientifique (CNRS)
FX The authors are grateful to the ACAT organizers for the very nice
conference. They also thank A.Alloul for his numerous checks of the code
and for his help during the development and the validation of the later
releases. This work has been supported by the French ANR 12 JS05 002 01
BATS@ LHC and by the Theory-LHC France initiative of the CNRS/IN2P3.
CR ABE F, 1992, PHYS REV D, V45, P1448, DOI 10.1103/PhysRevD.45.1448
Alwall J, 2007, COMPUT PHYS COMMUN, V176, P300, DOI 10.1016/j.cpc.2006.11.010
ALWALL J, IN PRESS
Alwall J, 2011, J HIGH ENERGY PHYS, DOI 10.1007/JHEP06(2011)128
Blazey G C, 2000, HEPEX0005012, P47
Brun R, 1997, NUCL INSTRUM METH A, V389, P81, DOI 10.1016/S0168-9002(97)00048-X
Cacciari M, 2008, J HIGH ENERGY PHYS, DOI 10.1088/1126-6708/2008/04/063
Cacciari M, 2012, EUR PHYS J C, V72, DOI 10.1140/epjc/s10052-012-1896-2
CATANI S, 1993, NUCL PHYS B, V406, P187, DOI 10.1016/0550-3213(93)90166-M
Conte E, 2013, COMPUT PHYS COMMUN, V184, P222, DOI 10.1016/j.cpc.2012.09.009
de Favereau J, 2013, 13076346
Degrande C, 2012, COMPUT PHYS COMMUN, V183, P1201, DOI 10.1016/j.cpc.2012.01.022
Dobbs M, 2001, COMPUT PHYS COMMUN, V134, P41, DOI 10.1016/S0010-4655(00)00189-2
Dokshitzer YL, 1997, JHEP, V08, DOI DOI 10.1088/1126-6708/1997/08/001
ELLIS SD, 1993, PHYS REV D, V48, P3160, DOI 10.1103/PhysRevD.48.3160
Salam GP, 2007, J HIGH ENERGY PHYS, DOI 10.1088/1126-6708/2007/05/086
Wobisch M, 1998, HEPPH9907280
NR 17
TC 3
Z9 3
U1 0
U2 0
PU IOP PUBLISHING LTD
PI BRISTOL
PA DIRAC HOUSE, TEMPLE BACK, BRISTOL BS1 6BE, ENGLAND
SN 1742-6588
EI 1742-6596
J9 J PHYS CONF SER
PY 2014
VL 523
AR 012032
DI 10.1088/1742-6596/523/1/012032
PG 8
WC Computer Science, Theory & Methods; Engineering, Electrical &
Electronic; Physics, Applied
SC Computer Science; Engineering; Physics
GA BA9NZ
UT WOS:000339627300032
OA Bronze
DA 2021-04-21
ER
PT S
AU Xing, AT
Arumugam, S
Holloway, L
Goozee, G
AF Xing, Aitang
Arumugam, Sankar
Holloway, Lois
Goozee, Gary
GP IOP
TI PyCMSXiO: an external interface to script treatment plans for the Elekta
(R) CMS XiO treatment planning system
SO XVII INTERNATIONAL CONFERENCE ON THE USE OF COMPUTERS IN RADIATION
THERAPY (ICCR 2013)
SE Journal of Physics Conference Series
LA English
DT Proceedings Paper
CT 17th International Conference on the Use of Computers in Radiation
Therapy (ICCR)
CY MAY 06-09, 2013
CL Australasian Coll Phys Scientists & Engineers Med, Melbourne, AUSTRALIA
SP Australian Inst Radiography, Royal Australian & New Zealand Coll Radiologists, Varian Med Syst, Brainlab, Elekta, Australian Govt Initiat, Australian Clin Dosimetry Serv
HO Australasian Coll Phys Scientists & Engineers Med
AB Scripting in radiotherapy treatment planning systems not only simplifies routine planning tasks but can also be used for clinical research. Treatment planning scripting can only be utilized in a system that has a built-in scripting interface. Among the commercially available treatment planning systems, Pinnacle (Philips) and Raystation (Raysearch Lab.) have inherent scripting functionality. CMS XiO (Elekta) is a widely used treatment planning system in radiotherapy centres around the world, but it does not have an interface that allows the user to script radiotherapy plans. In this study an external scripting interface, PyCMSXiO, was developed for XiO using the Python programming language. The interface was implemented as a python package/library using a modern object-oriented programming methodology. The package was organized as a hierarchy of different classes (objects). Each class (object) corresponds to a plan object such as the beam of a clinical radiotherapy plan. The interface of classes was implemented as object functions. Scripting in XiO using PyCMSXiO is comparable with Pinnacle scripting. This scripting package has been used in several research projects including commissioning of a beam model, independent three-dimensional dose verification for IMRT plans and a setup-uncertainty study. Ease of use and high-level functions provided in the package achieve a useful research tool. It was released as an open-source tool that may benefit the medical physics community.
C1 [Xing, Aitang; Arumugam, Sankar; Holloway, Lois; Goozee, Gary] Liverpool Hosp, Liverpool Ctr, Sydney, NSW, Australia.
[Xing, Aitang; Arumugam, Sankar; Holloway, Lois; Goozee, Gary] Liverpool Hosp, Macarthur Canc Therapy Ctr, Sydney, NSW, Australia.
[Xing, Aitang; Arumugam, Sankar; Holloway, Lois; Goozee, Gary] Liverpool Hosp, Ingham Inst, Sydney, NSW, Australia.
[Holloway, Lois] Univ Sydney, Inst Med Phys, Sch Phys, Sydney, NSW, Australia.
[Holloway, Lois] Univ Wollongong, Ctr Med Radiat Phys, Wollongong, NSW, Australia.
[Goozee, Gary] Univ New S Wales, South Western Sydney Clin Sch, Sydney, NSW, Australia.
RP Xing, AT (corresponding author), Liverpool Hosp, Liverpool Ctr, Sydney, NSW, Australia.
EM aitang.xing@sswahs.nsw.gov.au
RI Arumugam, S./AAT-7570-2020
OI Goozee, Gary/0000-0002-7989-1787; Holloway, Lois/0000-0003-4337-2165
CR Arumugam S, 2013, MED DOSIM, V13
Geoghegan S, 2007, SCRIPTING PINNALCE 3
Janssen T, 2013, INT J RADIAT ONCOL, V85, P873, DOI 10.1016/j.ijrobp.2012.05.045
Mason D., 2013, PYDICOM USER GUIDE
Panchal A, 2010, MED PHYS, V37, DOI 10.1118/1.3468652
Taylor R., 2013, QATRACK TOOL MANAGIN
Xing A, 2011, 14 WORLD C LUNG CANC
Xing A, J PHYS C SER, V444
Yang DS, 2012, MED PHYS, V39, P1542, DOI 10.1118/1.3683646
NR 9
TC 0
Z9 0
U1 0
U2 1
PU IOP PUBLISHING LTD
PI BRISTOL
PA DIRAC HOUSE, TEMPLE BACK, BRISTOL BS1 6BE, ENGLAND
SN 1742-6588
EI 1742-6596
J9 J PHYS CONF SER
PY 2014
VL 489
AR 012063
DI 10.1088/1742-6596/489/1/012063
PG 5
WC Physics, Applied; Radiology, Nuclear Medicine & Medical Imaging
SC Physics; Radiology, Nuclear Medicine & Medical Imaging
GA BA4LN
UT WOS:000335976400063
OA Bronze
DA 2021-04-21
ER
PT S
AU Erdmann, M
Fischer, R
Glaser, C
Klingebiel, D
Komm, M
Muller, G
Rieger, M
Steggemann, J
Urban, M
Winchen, T
AF Erdmann, M.
Fischer, R.
Glaser, C.
Klingebiel, D.
Komm, M.
Mueller, G.
Rieger, M.
Steggemann, J.
Urban, M.
Winchen, T.
GP IOP
TI A Web-Based Development Environment for Collaborative Data Analysis
SO 15TH INTERNATIONAL WORKSHOP ON ADVANCED COMPUTING AND ANALYSIS
TECHNIQUES IN PHYSICS RESEARCH (ACAT2013)
SE Journal of Physics Conference Series
LA English
DT Proceedings Paper
CT 15th International Workshop on Advanced Computing and Analysis
Techniques in Physics Research (ACAT)
CY MAY 16-21, 2013
CL Chinese Acad Sci, Inst High Energy Phys, Beijing, PEOPLES R CHINA
SP Chinese Acad Sci, Natl Nat Sci Fdn China, Brookhaven Natl Lab, Peking Univ, Chinese Acad Sci, Theoret Phys Ctr Sci Facilities, Sugon
HO Chinese Acad Sci, Inst High Energy Phys
AB Visual Physics Analysis (VISPA) is a web-based development environment addressing high energy and astroparticle physics. It covers the entire analysis spectrum from the design and validation phase to the execution of analyses and the visualization of results. VISPA provides a graphical steering of the analysis flow, which consists of self-written, re-usable Python and C++ modules for more demanding tasks. All common operating systems are supported since a standard internet browser is the only software requirement for users. Even access via mobile and touch-compatible devices is possible. In this contribution, we present the most recent developments of our web application concerning technical, state-of-the-art approaches as well as practical experiences. One of the key features is the use of workspaces, i.e. user-configurable connections to remote machines supplying resources and local file access. Thereby, workspaces enable the management of data, computing resources (e.g. remote clusters or computing grids), and additional software either centralized or individually. We further report on the results of an application with more than 100 third-year students using VISPA for their regular particle physics exercises during the winter term 2012/13. Besides the ambition to support and simplify the development cycle of physics analyses, new use cases such as fast, location-independent status queries, the validation of results, and the ability to share analyses within worldwide collaborations with a single click become conceivable.
C1 [Erdmann, M.; Fischer, R.; Glaser, C.; Klingebiel, D.; Mueller, G.; Rieger, M.; Urban, M.; Winchen, T.] Rhein Westfal TH Aachen, Phys Inst A 3, Aachen, Germany.
[Komm, M.] Catholic Univ Louvain, Particle Phys & Phenomen, Ctr Cosmol, Louvain, Belgium.
[Steggemann, J.] CERN, European Org Nucl Res, Geneva, Switzerland.
RP Rieger, M (corresponding author), Rhein Westfal TH Aachen, Phys Inst A 3, Aachen, Germany.
EM vispa@lists.rwth-aachen.de; rieger@physik.rwth-aachen.de
RI Steggemann, Jan/AAL-5700-2020
OI Steggemann, Jan/0000-0003-4420-5510; Erdmann, Martin/0000-0002-1653-1303
FU Ministerium fur Wissenschaft und Forschung; Nordrhein- Westfalen;
Bundesministerium fur Bildung und Forschung BMBFFederal Ministry of
Education & Research (BMBF); Helmholz Alliance Physics; Deutsche
Forschungsgemeinschaft DFGGerman Research Foundation (DFG)
FX We wish to thank the organizers of the ACAT2013 conference for their
kind support. This work is supported by the Ministerium fur Wissenschaft
und Forschung, Nordrhein- Westfalen, the Bundesministerium fur Bildung
und Forschung ( BMBF), and the Helmholz Alliance Physics at the
Terascale. M Rieger and C Glaser gratefully thank for support by the
Deutsche Forschungsgemeinschaft ( DFG) and T Winchen gratefully
acknowledges funding by the Friedrich- Ebert- Stiftung.
CR Bretz HP, 2012, J INSTRUM, V7, DOI 10.1088/1748-0221/7/08/T08005
Brun R, 1997, NUCL INSTRUM METH A, V389, P81, DOI 10.1016/S0168-9002(97)00048-X
Erdmann M, 2012, PHYS EXTENSION LIB P
Holdener III AT, 2008, AJAX DEFINITIVE GUID
NR 4
TC 3
Z9 3
U1 0
U2 6
PU IOP PUBLISHING LTD
PI BRISTOL
PA DIRAC HOUSE, TEMPLE BACK, BRISTOL BS1 6BE, ENGLAND
SN 1742-6588
EI 1742-6596
J9 J PHYS CONF SER
PY 2014
VL 523
AR 012021
DI 10.1088/1742-6596/523/1/012021
PG 5
WC Computer Science, Theory & Methods; Engineering, Electrical &
Electronic; Physics, Applied
SC Computer Science; Engineering; Physics
GA BA9NZ
UT WOS:000339627300021
OA Bronze
DA 2021-04-21
ER
PT B
AU Wyszynski, O
AF Wyszynski, Oskar
CA NA61 Collaboration
GP IEEE
TI Trigger System of the NA61/SHINE Experiment at the CERN SPS
SO 2014 19TH IEEE-NPSS REAL TIME CONFERENCE (RT)
LA English
DT Proceedings Paper
CT 19th IEEE-NPSS Real Time Conference (RT)
CY MAY 26-30, 2014
CL Nara, JAPAN
SP Osaka Univ, Res Nucl Phys, IEEE Nucl & Plasma Sci Soc
AB This paper describes technical aspects and details of the trigger system of NA61/SHINE experiment. The SPS Heavy Ion and Neutrino Experiment (SHINE) is a large acceptance hadron spectrometer designed for comprehensive studies of hadron production in hadron-proton, hadron-nucleus and nucleus-nucleus collisions at the CERN Super Proton Synchrotron. The NA61/SHINE physics programme requires measurements of a large number of reactions recorded using different trigger conditions. This motivated a flexible solution for the NA61/SHINE trigger system. The trigger uses signals from scintillation, Cherenkov and calorimeter detectors. In total up to 16 signals have to be processed for a trigger decision. The core of the system is based on a single field-programmable gate array (FPGA), running with a 120 MHz clock which offers a resolution of 8.3 ns. Moreover, it is capable of parallel selecting events which satisfy up to four trigger definitions. Events corresponding to these trigger definitions are recorded with relative frequencies which can be selected using 12 bit prescalers. The working parameters of the trigger, such as coincidences and delays, are set up remotely via a Java application, designed to be handled by users. The performance parameters are monitored by Python based monitoring software, which is capable of displaying values of beam counters and beam time structure. Furthermore, the monitoring software calculates summary information, such as trigger probabilities, which are crucial from a user's point of view. It also detects pre-failure states of the trigger system, such as false scaler counts, and gives the opportunity to prepare the necessary repair procedure before reaching an irrecoverable state.
C1 [Wyszynski, Oskar] Jagiellonian Univ, Krakow, Poland.
RP Wyszynski, O (corresponding author), Jagiellonian Univ, Krakow, Poland.
EM oskar.wyszynski@cern.ch
CR [Anonymous], 1972, 4100E CAMAC EUR COMM
CAEN C111C, ETH CAMAC CRAT CONTR
CMCAMAC, CMC206 UN LOG MOD
NA61/SHINE Collaboration, NIM A IN PRESS
Xilinx, SPART 3 XC3S1500
NR 5
TC 0
Z9 0
U1 0
U2 0
PU IEEE
PI NEW YORK
PA 345 E 47TH ST, NEW YORK, NY 10017 USA
BN 978-1-4799-3659-5
PY 2014
PG 4
WC Computer Science, Hardware & Architecture; Engineering, Electrical &
Electronic
SC Computer Science; Engineering
GA BF3WU
UT WOS:000380588000015
DA 2021-04-21
ER
PT S
AU Erdmann, M
Fischer, R
Glaser, C
Klingebiel, D
Komm, M
Muller, G
Rieger, M
Steggemann, J
Urban, M
Winchen, T
AF Erdmann, M.
Fischer, R.
Glaser, C.
Klingebiel, D.
Komm, M.
Mueller, G.
Rieger, M.
Steggemann, J.
Urban, M.
Winchen, T.
GP IOP
TI A Browser-Based Multi-User Working Environment for Physicists
SO 20TH INTERNATIONAL CONFERENCE ON COMPUTING IN HIGH ENERGY AND NUCLEAR
PHYSICS (CHEP2013), PARTS 1-6
SE Journal of Physics Conference Series
LA English
DT Proceedings Paper
CT 20th International Conference on Computing in High Energy and Nuclear
Physics (CHEP)
CY OCT 14-18, 2013
CL Amsterdam, NETHERLANDS
AB Many programs in experimental particle physics do not yet have a graphical interface, or demand strong platform and software requirements. With the most recent development of the VISTA project, we provide graphical interfaces to existing software programs and access to multiple computing clusters through standard web browsers. The scalable client-server system allows analyses to be performed in sizable teams, and disburdens the individual physicist from installing and maintaining a software environment. The VISPA graphical interfaces are implemented in HTML, Java Script and extensions to the Python webserver. The webserver uses SSH and RPC to access user data, code and processes on remote sites. As example applications we present graphical interfaces for steering the reconstruction framework OFFLINE of the Pierre-Auger experiment, and the analysis development toolkit PXL. The browser based VISTA system was field-tested in biweekly homework of a third year physics course by more than 100 students. We discuss the system deployment and the evaluation by the students.
C1 [Erdmann, M.; Fischer, R.; Glaser, C.; Klingebiel, D.; Komm, M.; Mueller, G.; Rieger, M.; Steggemann, J.; Urban, M.; Winchen, T.] Rhein Westfal TH Aachen, Phys Inst 3A, Aachen, Germany.
RP Erdmann, M (corresponding author), Rhein Westfal TH Aachen, Phys Inst 3A, Aachen, Germany.
EM gero.mueller@physik.rwth-aachen.de
RI Steggemann, Jan/AAL-5700-2020
OI Steggemann, Jan/0000-0003-4420-5510; Erdmann, Martin/0000-0002-1653-1303
CR Argiro S, 2007, NUCL INSTRUM METH A, V580, P1485, DOI 10.1016/j.nima.2007.07.010
Bretz HP, 2012, J INSTRUM, V7, DOI 10.1088/1748-0221/7/08/T08005
Marco C, 2010, J PHYS CONF SER, V219, DOI 10.1088/1742-6596/219/6/062039
Thain D, 2005, CONCURR COMP-PRACT E, V17, P323, DOI 10.1002/cpe.938
NR 4
TC 1
Z9 1
U1 0
U2 5
PU IOP PUBLISHING LTD
PI BRISTOL
PA DIRAC HOUSE, TEMPLE BACK, BRISTOL BS1 6BE, ENGLAND
SN 1742-6588
EI 1742-6596
J9 J PHYS CONF SER
PY 2014
VL 513
AR 062034
DI 10.1088/1742-6596/513/6/062034
PG 5
WC Physics, Nuclear; Physics, Particles & Fields
SC Physics
GA BB2TA
UT WOS:000342287200313
OA Bronze
DA 2021-04-21
ER
PT S
AU Cieszewski, R
Pozniak, K
Romaniuk, R
AF Cieszewski, Radoslaw
Pozniak, Krzysztof
Romaniuk, Ryszard
BE Romaniuk, RS
TI Python based High-Level Synthesis compiler
SO PHOTONICS APPLICATIONS IN ASTRONOMY, COMMUNICATIONS, INDUSTRY, AND
HIGH-ENERGY PHYSICS EXPERIMENTS 2014
SE Proceedings of SPIE
LA English
DT Proceedings Paper
CT Conference on Photonics Applications in Astronomy, Communications,
Industry, and High-Energy Physics Experiments
CY MAY 26-JUN 01, 2014
CL Wilga, POLAND
SP Warsaw Univ Technol, Fac Elect & Informat Technologies, Inst Elect Syst, Photonics Soc Poland, SPIE, Polish Acad Sci, Comm Elect & Telecommunicat, Enhanced European Coordinat Accelerator R & D, IEEE Poland Sect, SEP, Polish Comm Optoelectron, EuroFus Collaborat, EuroFus Poland
DE FPGA; Algorithmic Synthesis; High-Level Synthesis; Behavioral Synthesis;
Hot Plasma Physics Experiment; Python; Compiler
AB This paper presents a python based High-Level synthesis (HLS) compiler. The compiler interprets an algorithmic description of a desired behavior written in Python and map it to VHDL. FPGA combines many benefits of both software and ASIC implementations. Like software, the mapped circuit is flexible, and can be reconfigured over the lifetime of the system. FPGAs therefore have the potential to achieve far greater performance than software as a result of bypassing the fetch-decode-execute operations of traditional processors, and possibly exploiting a greater level of parallelism. Creating parallel programs implemented in FPGAs is not trivial. This article describes design, implementation and first results of created Python based compiler.
C1 [Cieszewski, Radoslaw; Pozniak, Krzysztof; Romaniuk, Ryszard] Warsaw Univ Technol, Inst Elect Syst, Nowowiejska 15-19, PL-00665 Warsaw, Poland.
RP Cieszewski, R (corresponding author), Warsaw Univ Technol, Inst Elect Syst, Nowowiejska 15-19, PL-00665 Warsaw, Poland.
EM R.Cieszewski@stud.elka.pw.edu.pl
RI Pozniak, Krzysztof/AAO-7377-2020; Romaniuk, Ryszard S/B-9140-2011
OI Pozniak, Krzysztof/0000-0001-5426-1423; Romaniuk, Ryszard
S/0000-0002-5710-4041
CR Asanovic K, 2009, COMMUN ACM, V52, P56, DOI 10.1145/1562764.1562783
Babb J., 1999, Seventh Annual IEEE Symposium on Field-Programmable Custom Computing Machines (Cat. No.PR00375), P70, DOI 10.1109/FPGA.1999.803669
Berdychowski PP, 2010, PHOTONICS APPL ASTRO
Bowyer B., 2005, EETIMES
Bujnowski K., 2007, PHOTONICS APPL ASTRO
Bujnowski K., 2007, PHOTONICS APPL ASTRO
Cieszewski R, 2013, PROC SPIE, V8903, DOI 10.1117/12.2035385
Cong J, 2011, IEEE T COMPUT AID D, V30, P473, DOI 10.1109/TCAD.2011.2110592
Coussy P., 2008, HIGH LEVEL SYNTHESIS
GAJSKI DD, 1994, IEEE DES TEST COMPUT, V11, P44, DOI 10.1109/54.329454
Gajski DD, 1992, HIGH LEVEL SYNTHESIS, V34
Gokhale M, 1997, 5TH ANNUAL IEEE SYMPOSIUM ON FIELD-PROGRAMMABLE CUSTOM COMPUTING MACHINES, P165, DOI 10.1109/FPGA.1997.624616
Kolasinski P., 2007, PHOTONICS APPL ASTRO
Liang Y, 2012, J ELECTR COMPUT ENG, V2012, DOI 10.1155/2012/649057
Meredith M., 2004, EETIMES, P04
Philippe C., 2008, EURASIP J EMBEDDED S, V2008
Pozniak K., 2013, PHOTONICS APPL ASTRO, V8903
Zabolotny W. M., 2011, PHOTONICS APPL ASTRO
Zabolotny WM, 2003, P SOC PHOTO-OPT INS, V5125, P223, DOI 10.1117/12.531581
Zabolotny WM, 2010, PHOTONICS APPL ASTRO
Zabolotny WM, 2011, PROC SPIE, V8008, DOI 10.1117/12.905281
Zabolotny WM, 2006, PROC SPIE, V6347, DOI 10.1117/12.714532
NR 22
TC 2
Z9 2
U1 0
U2 1
PU SPIE-INT SOC OPTICAL ENGINEERING
PI BELLINGHAM
PA 1000 20TH ST, PO BOX 10, BELLINGHAM, WA 98227-0010 USA
SN 0277-786X
EI 1996-756X
BN 978-1-62841-369-4
J9 PROC SPIE
PY 2014
VL 9290
AR 92903A
DI 10.1117/12.2075988
PG 8
WC Engineering, Electrical & Electronic; Optics; Physics, Applied
SC Engineering; Optics; Physics
GA BC6NK
UT WOS:000354182200118
DA 2021-04-21
ER
PT J
AU Mulani, SB
Duggirala, V
Kapania, RK
AF Mulani, Sameer B.
Duggirala, Vedavyas
Kapania, Rakesh K.
TI Curvilinearly T-Stiffened Panel-Optimization Framework Under Multiple
Load Cases Using Parallel Processing
SO JOURNAL OF AIRCRAFT
LA English
DT Article; Proceedings Paper
CT 53rd AIAA/ASME/ASCE/AHS/ASC Structures, Structural Dynamics and
Materials Conference (SDM)
CY APR 23-27, 2012
CL Honolulu, HI
SP AIAA, ASME, ASCE, AHS, ASC
ID PRELIMINARY DESIGN; ALGORITHM
AB Future aerospace vehicles like hybrid wing/body, truss-braced wing, and "double bubble" would have pressurized noncircular fuselage structures and complex wing geometry. Traditional aircraft designs have led to the confidence and experience of designing such structures using the knowledge base built over the years and the resulting rules of thumb. However, there is a lack of experience of load calculations and design of complex, multifunctional, aircraft structural concepts for future aerospace vehicles. Designing such structures will require a physics-based optimization framework. Therefore, a new optimization framework, EBF3PanelOpt, is being developed. Commercial software MD-PATRAN (geometry modeling and mesh generation) and MD-NASTRAN (finite-element analysis) are integrated in EBF3PanelOpt framework using the Python programming environment to design stiffened panels with curvilinear stiffeners. Currently, EBF3PanelOpt optimizes the stiffened panel with curvilinear blade stiffeners, where the loads are applied only through the plate. During the optimization, the mass is minimized with the constraints on buckling, von Mises stress, and crippling or local failure of the stiffeners. EBF3PanelOpt is enhanced to have curvilinear T stiffeners with or without axial loads in addition to loads through plate. The panel/stiffener geometry is defined in a parametric fashion based on design variables that include variables for orientation and shape of the stiffeners, the thicknesses and heights of the webs and flanges of the stiffeners, and the plate-pocket thicknesses. This framework is supported with coarse-grained parallelism using Python to analyze multiple designs on the cluster. Using this framework, a vertical stabilizer skin panel of transport aircraft panel having two extreme load cases is optimized using with or without stiffener loads. When the equivalent uniform loads are applied only through the plate, the plate buckling becomes critical, but combined buckling of plate and stiffeners becomes critical when the loads are applied through both the plate and the stiffeners. When the uniform in-plane compressive loads are applied through both the plate and the stiffeners, the panel with straight T stiffeners is more optimal than the panel with curvilinear T stiffeners.
C1 [Mulani, Sameer B.; Kapania, Rakesh K.] Virginia Polytech Inst & State Univ, Dept Aerosp & Ocean Engn, Blacksburg, VA 24061 USA.
[Duggirala, Vedavyas] Virginia Polytech Inst & State Univ, Dept Comp Sci & Engn, Blacksburg, VA 24061 USA.
RP Mulani, SB (corresponding author), Virginia Polytech Inst & State Univ, Dept Aerosp & Ocean Engn, Blacksburg, VA 24061 USA.
CR Amdahl G. M., 1967, P APR 18 20 1967 SPR, P483, DOI [10.1145/1465482, DOI 10.1145/1465482.1465560]
ANDERSON MS, 1979, AIAA J, V17, P892, DOI 10.2514/3.61242
[Anonymous], 2013, INT TER RES CHIP
[Anonymous], 2009, VISUALDOC 6 2 DES CO
ATIQULLAH MM, 1995, AIAA J, V33, P2386, DOI 10.2514/3.12996
Bedair OK, 1997, INT J MECH SCI, V39, P33, DOI 10.1016/0020-7403(96)00017-3
BUSHNELL D, 1987, COMPUT STRUCT, V27, P541, DOI 10.1016/0045-7949(87)90280-X
Buswell RA, 2007, AUTOMAT CONSTR, V16, P224, DOI 10.1016/j.autcon.2006.05.002
BUTLER R, 1994, COMPUT STRUCT, V52, P1107, DOI 10.1016/0045-7949(94)90177-5
Collier C., 2002, 43 AIAA ASME ASCE AH
Colson B, 2010, OPTIM ENG, V11, P583, DOI 10.1007/s11081-008-9077-8
Cooper K., 2004, 42 AIAA AER SCI M EX
Dalcin L., 2011, MPI PYTHON
Dean J, 2004, USENIX ASSOCIATION PROCEEDINGS OF THE SIXTH SYMPOSIUM ON OPERATING SYSTEMS DESIGN AND IMPLEMENTATION (OSDE '04), P137
Deb K, 2002, IEEE T EVOLUT COMPUT, V6, P182, DOI 10.1109/4235.996017
Eldred M. S., 2000, 8 AIAA USAF NASA ISS
Grail B., 1992, CR190608 NASA
GURDAL Z, 1993, COMPOS ENG, V3, P1131, DOI 10.1016/0961-9526(93)90070-Z
Gurdal Z., 1992, 33 AIAA ASME ASCE AH
Kapania R.K., 2005, AIAA 5 AV TECHN INT
Kreisselmeier G., 1980, P IFAC S SERIES, P113, DOI [DOI 10.1016/S1474-6670(17)65584-8, 10.1016/B978-0-08-024488-4.50022-X]
Lamberti L, 2003, INT J NUMER METH ENG, V57, P1351, DOI 10.1002/nme.781
Mulani S. B., 2000, P AEROMAT 2010 C EXP
Mulani S. B., 2010, P 13 AIAA ISSMO MULT
Mulani SB, 2013, THIN WALL STRUCT, V63, P13, DOI 10.1016/j.tws.2012.09.008
Mulani SB, 2010, J AIRCRAFT, V47, P1898, DOI 10.2514/1.47411
Nicholas E. D., 1998, P 6 INT C AL ALL TOY
Niu M. C. Y., 2005, AIRFRAME STRESS ANAL, P444
Plassmann P. E., 2004, J AEROSPACE COMPUTIN, V1, P116
Renton J, 2004, J AIRCRAFT, V41, P986, DOI 10.2514/1.4039
STROUD WJ, 1984, TP2215 NASA
Taminger K, 2002, P 13 SOL FREEF FABR, P482
Taminger K., 2003, P 3 ANN AUT COMP C S
Taminger K. M. B., 2002, 2002 INT C MET POWD, P51
van Bloemen Waanders B. G., 2001, 42 AIAA ASMBASCE AHS
Venter G., 2006, J AEROSPACE COMPUTIN, V3, P123, DOI DOI 10.2514/1.17873
Venter G, 2009, J AEROS COMP INF COM, V6, P635, DOI 10.2514/1.43224
Wang XC, 1997, INT J NUMER METH ENG, V40, P75
York CB, 1998, COMPUT STRUCT, V68, P665, DOI 10.1016/S0045-7949(98)00050-9
NR 39
TC 6
Z9 7
U1 1
U2 17
PU AMER INST AERONAUTICS ASTRONAUTICS
PI RESTON
PA 1801 ALEXANDER BELL DRIVE, STE 500, RESTON, VA 22091-4344 USA
SN 0021-8669
EI 1533-3868
J9 J AIRCRAFT
JI J. Aircr.
PD SEP-OCT
PY 2013
VL 50
IS 5
BP 1540
EP 1554
DI 10.2514/1.C032064
PG 15
WC Engineering, Aerospace
SC Engineering
GA AA8VK
UT WOS:000331372900019
DA 2021-04-21
ER
PT J
AU Fiers, M
Lambert, E
Pathak, S
Maes, B
Bienstman, P
Bogaerts, W
Dumon, P
AF Fiers, Martin
Lambert, Emmanuel
Pathak, Shibnath
Maes, Bjorn
Bienstman, Peter
Bogaerts, Wim
Dumon, Pieter
TI Improving the design cycle for nanophotonic components
SO JOURNAL OF COMPUTATIONAL SCIENCE
LA English
DT Article
DE Nanophotonics; Designing and modeling optical components; Optical
circuit design; Parametrized cell; Python
ID FABRICATION
AB We present IPKISS, a software framework that greatly simplifies the design of nanophotonic components. In this approach, all steps in the workflow are based on a single high-level definition of the component, in a Python script. Because there is only one description, the design flow becomes less error prone due to incorrect definitions, and the overall reproducibility is greatly improved.
Furthermore it enables easy closed-loop modeling of components and circuits. Also, previous work can easily be built upon because lower level blocks can seamlessly be replaced by new blocks. While we illustrate the application in photonics, this software and the used design patterns can be extended to other domains such as RF design and to multidomain physics such as opto-electronics. (C) 2013 Elsevier B.V. All rights reserved.
C1 [Fiers, Martin; Lambert, Emmanuel; Pathak, Shibnath; Maes, Bjorn; Bienstman, Peter; Bogaerts, Wim; Dumon, Pieter] Univ Ghent, IMEC, Photon Res Grp INTEC, B-9000 Ghent, Belgium.
RP Fiers, M (corresponding author), Univ Ghent, IMEC, Photon Res Grp INTEC, Sint Pietersnieuwstr 41, B-9000 Ghent, Belgium.
EM martin.fiers@intec.ugent.be; emmanuel.lambert@intec.ugent.be;
shibnath.pathak@intec.ugent.be; bjorn.maes@umons.ac.be;
peter.bienstman@intec.ugent.be; wim.bogaerts@intec.ugent.be;
pieter.dumon@intec.ugent.be
RI Pathak, Shibnath/F-7579-2014; Bogaerts, Wim/E-7285-2013
OI Bogaerts, Wim/0000-0003-1112-8950; Pathak, Shibnath/0000-0001-6667-2609;
Maes, Bjorn/0000-0003-3935-7990
FU Interuniversity Attraction Pole (IAP) Photonics@be of the Belgian
Science Policy Office; ERC NaResCo Starting grant; Special Research Fund
of Ghent UniversityGhent University
FX This work is supported by the Interuniversity Attraction Pole (IAP)
Photonics@be of the Belgian Science Policy Office and the ERC NaResCo
Starting grant. M. Fiers acknowledges the Special Research Fund of Ghent
University. We acknowledge Y. De Koninck for his useful comments.
CR Bienstman P, 2001, OPT QUANT ELECTRON, V33, P327, DOI 10.1023/A:1010882531238
Bogaerts W, 2008, OPT QUANT ELECTRON, V40, P801, DOI 10.1007/s11082-008-9265-y
Dumon P, 2009, ELECTRON LETT, V45, P581, DOI 10.1049/el.2009.1353
Esterbrook C., 2001, LINUX J
Fiers M., 2012, J OPTICAL SOC AM B
Lambert E, 2011, COMPUT SCI ENG, V13, P53, DOI 10.1109/MCSE.2010.98
Oskooi AF, 2010, COMPUT PHYS COMMUN, V181, P687, DOI 10.1016/j.cpc.2009.11.008
Pathak S., 2011, 2011 IEEE 8th International Conference on Group IV Photonics (GFP), P45, DOI 10.1109/GROUP4.2011.6053710
Selvaraja SK, 2009, J LIGHTWAVE TECHNOL, V27, P4076, DOI 10.1109/JLT.2009.2022282
NR 9
TC 14
Z9 14
U1 0
U2 5
PU ELSEVIER
PI AMSTERDAM
PA RADARWEG 29, 1043 NX AMSTERDAM, NETHERLANDS
SN 1877-7503
J9 J COMPUT SCI-NETH
JI J. Comput. Sci.
PD SEP
PY 2013
VL 4
IS 5
SI SI
BP 313
EP 324
DI 10.1016/j.jocs.2013.05.008
PG 12
WC Computer Science, Interdisciplinary Applications; Computer Science,
Theory & Methods
SC Computer Science
GA 270CS
UT WOS:000328297100002
OA Green Published
DA 2021-04-21
ER
PT J
AU Bianchi, RM
Bruneliere, R
AF Bianchi, Riccardo Maria
Bruneliere, Renaud
TI WATCHMAN project-A Python CASE framework for High Energy Physics data
analysis in the LHC era
SO JOURNAL OF COMPUTATIONAL SCIENCE
LA English
DT Article; Proceedings Paper
CT 3rd European Meeting on Python in Science (EuroSciPy)
CY JUL 08-11, 2010
CL Paris, FRANCE
DE Large Hadron Collider; LHC; High Energy Physics; Data analysis; Computer
aided software engineering; Python; Physics framework
AB The world's largest particle collider LHC is taking data at CERN, in Geneva, providing a huge amount of data to be looked at, of the order of several Petabytes per year. Nowadays, Data Analysis in High Energy Physics (HEP) means handling billions of experimental data in custom software frameworks. Physicists have to access and select data interacting with the experiment using dedicated tools; they also have to apply filter functions and analysis algorithms to test hypotheses about the physics underlain. Modern HEP experiments rely on complex software frameworks, hence writing the analysis code is not always an easy task, and the learning curve is usually quite steep. Moreover each hypothesis requires a dedicated analysis, in order to have a better control on it and to be able to validate the results among different groups of researchers. And the writing of so many analyses can be error prone and time consuming.
In order to ease the writing of such data analysis code, we built a software-generator: the idea is that the user inserts the settings of the physics analyses, and the final analysis code is automatically and dynamically generated, ready to be run on data. Python helped us to build such a package. Its high-level and dynamic nature, together with its flexibility and prototyping speed are the key features which made our choice.
So we conceived and developed WATCHMAN, a Python CASE (Computer-Aided Software Engineering) framework to automatically generate reliable, easy to maintain and easy to validate HEP data analysis code. (C) 2012 Elsevier B.V. All rights reserved.
C1 [Bianchi, Riccardo Maria] CERN, European Org Nucl Res, CH-1211 Geneva, Switzerland.
[Bruneliere, Renaud] Univ Freiburg, Inst Phys, D-79106 Freiburg, Germany.
RP Bianchi, RM (corresponding author), CERN, European Org Nucl Res, CH-1211 Geneva, Switzerland.
EM rbianchi@cern.ch
OI Bianchi, Riccardo Maria/0000-0001-7345-7798
CR ATLAS-collaboration, 2009, ATLPHYSPUB2009084 CE
ATLAS-collaboration, 2010, ATLAS PLOTS ECM DEP
Bianchi R.M., WATCHMAN HIGHLY AUTO
Lavrijsen W., PYROOT PYTHON ROOT B
NR 4
TC 1
Z9 1
U1 0
U2 1
PU ELSEVIER SCIENCE BV
PI AMSTERDAM
PA PO BOX 211, 1000 AE AMSTERDAM, NETHERLANDS
SN 1877-7503
J9 J COMPUT SCI-NETH
JI J. Comput. Sci.
PD SEP
PY 2013
VL 4
IS 5
SI SI
BP 325
EP 333
DI 10.1016/j.jocs.2012.04.005
PG 9
WC Computer Science, Interdisciplinary Applications; Computer Science,
Theory & Methods
SC Computer Science
GA 270CS
UT WOS:000328297100003
DA 2021-04-21
ER
PT J
AU Audren, B
Blas, D
Lesgourgues, J
Sibiryakov, S
AF Audren, B.
Blas, D.
Lesgourgues, J.
Sibiryakov, S.
TI Cosmological constraints on Lorentz violating dark energy
SO JOURNAL OF COSMOLOGY AND ASTROPARTICLE PHYSICS
LA English
DT Article
DE dark energy theory; modified gravity; dark energy experiments;
cosmological parameters from CMBR
ID VECTOR-FIELDS; UNIVERSE
AB The role of Lorentz invariance as a fundamental symmetry of nature has been lately reconsidered in different approaches to quantum gravity. It is thus natural to study whether other puzzles of physics may be solved within these proposals. This may be the case for the cosmological constant problem. Indeed, it has been shown that breaking Lorentz invariance provides Lagrangians that can drive the current acceleration of the universe without experiencing large corrections from ultraviolet physics. In this work, we focus on the simplest model of this type, called Theta CDM, and study its cosmological implications in detail. At the background level, this model cannot be distinguished from Lambda CDM. The differences appear at the level of perturbations. We show that in Theta CDM, the spectrum of CMB anisotropies and matter fluctuations may be affected by a rescaling of the gravitational constant in the Poisson equation, by the presence of extra contributions to the anisotropic stress, and finally by the existence of extra clustering degrees of freedom. To explore these modifications accurately, we modify the Boltzmann code class. We then use the parameter inference code MONTE PYTHON to confront Theta CDM with data from WMAP-7, SPT and WiggleZ. We obtain strong bounds on the parameters accounting for deviations from Lambda CDM. In particular, we find that the discrepancy between the gravitational constants appearing in the Poisson and Friedmann equations is constrained at the level of 1.8%.
C1 [Audren, B.; Lesgourgues, J.] Ecole Polytech Fed Lausanne, LPPC, ITP, FSB, CH-1015 Lausanne, Switzerland.
[Blas, D.; Lesgourgues, J.] CERN, Dept Phys, Theory Grp, CH-1211 Geneva 23, Switzerland.
[Lesgourgues, J.] Univ Savoie, CNRS, LAPTh, F-74941 Annecy Le Vieux, France.
[Sibiryakov, S.] Russian Acad Sci, Inst Nucl Res, Moscow 117312, Russia.
[Sibiryakov, S.] Moscow MV Lomonosov State Univ, Fac Phys, Moscow 119991, Russia.
RP Audren, B (corresponding author), Ecole Polytech Fed Lausanne, LPPC, ITP, FSB, CH-1015 Lausanne, Switzerland.
EM Benjamin.Audren@epfl.ch; Diego.Blas@cern.ch; Julien.Lesgourgues@cern.ch;
Sergey.Sibiryakov@cern.ch
RI Blas, Diego/AAL-8192-2021
OI Blas, Diego/0000-0003-2646-0112; Sibiryakov, Sergey/0000-0001-9972-8875
FU Russian FederationRussian Federation [NS-5590.2012.2]; Russian Ministry
of Science and EducationMinistry of Education and Science, Russian
Federation [8412]; RFBRRussian Foundation for Basic Research (RFBR)
[11-02-01528, 12-02-01203]; Dynasty Foundation; Swiss National Science
FoundationSwiss National Science Foundation (SNSF)European Commission
FX We are grateful to Mikhail Ivanov, Gregory Gabadadze, Roman Scoccimarro
and Takahiro Tanaka for useful discussions. D.B. and S.S. thank the
organizers and participants of the Kavli IPMU Focus Week on Gravity and
Lorentz Violations for the encouraging interest and valuable comments.
S.S. thanks the Center for Cosmology and Particle Physics of NYU for
hospitality during the completion of this work. This work was supported
in part by the Grant of the President of Russian Federation
NS-5590.2012.2 (S.S.), the Russian Ministry of Science and Education
under the contract 8412 (S.S.), the RFBR grants 11-02-01528 (S.S.),
12-02-01203 (S.S.) and by the Dynasty Foundation (S.S.). J.L. and B. A.
acknowledge support from the Swiss National Science Foundation.
CR Ade PAR, 2014, ASTRON ASTROPHYS, V571, DOI 10.1051/0004-6361/201321569
Amendola L., ARXIV12061225 EUCL T
Amendola L., 2010, THEORY OBSERVATIONS, P506
Armendariz-Picon C., 2010, JCAP, V07, P010
Audren B., 2013, JCAP, V02
Barausse E, 2011, PHYS REV D, V83, DOI 10.1103/PhysRevD.83.124043
Bean R, 2010, PHYS REV D, V81, DOI 10.1103/PhysRevD.81.083534
Bednik G., ARXIV13050011
Bernardeau F, 2002, PHYS REP, V367, P1, DOI 10.1016/S0370-1573(02)00135-7
Blas D, 2011, J HIGH ENERGY PHYS, DOI 10.1007/JHEP04(2011)018
Blas D, 2010, PHYS REV LETT, V104, DOI 10.1103/PhysRevLett.104.181302
Blas D., 2011, JCAP, V07, P026
Blas D., 2012, JCAP, V10, P057
Blas D, 2011, PHYS REV D, V84, DOI 10.1103/PhysRevD.84.064004
Carroll SM, 2004, PHYS REV D, V70, DOI 10.1103/PhysRevD.70.123525
Copeland EJ, 2006, INT J MOD PHYS D, V15, P1753, DOI 10.1142/S021827180600942X
D'Amico G, 2013, PHYS REV D, V87, DOI 10.1103/PhysRevD.87.064037
Donnelly W, 2010, PHYS REV D, V82, DOI 10.1103/PhysRevD.82.064032
Dutta S, 2010, PHYS REV D, V82, DOI 10.1103/PhysRevD.82.083501
Foster BZ, 2006, PHYS REV D, V73, DOI 10.1103/PhysRevD.73.104012
Foster BZ, 2005, PHYS REV D, V72, DOI 10.1103/PhysRevD.72.044017
Groot Nibbelink S., 2005, PHYS REV LETT, V94
Horava P, 2009, PHYS REV D, V79, DOI 10.1103/PhysRevD.79.084008
Jacobson T, 2001, PHYS REV D, V64, DOI 10.1103/PhysRevD.64.024028
Jacobson T., POS QG PH
Jacobson T, 2010, PHYS REV D, V81, DOI 10.1103/PhysRevD.81.101502
Keisler R, 2011, ASTROPHYS J, V743, DOI 10.1088/0004-637X/743/1/28
Kobayashi T., 2010, JCAP, V04, P025
Komatsu E, 2011, ASTROPHYS J SUPPL S, V192, DOI 10.1088/0067-0049/192/2/18
Kostelecky VA, 2011, REV MOD PHYS, V83, DOI 10.1103/RevModPhys.83.11
Lesgourgues J., 2013, NEUTRINO COSMOLOGY
Li BJ, 2008, PHYS REV D, V77, DOI 10.1103/PhysRevD.77.024032
Lim EA, 2005, PHYS REV D, V71, DOI 10.1103/PhysRevD.71.063504
Lue A, 2004, PHYS REV D, V69, DOI 10.1103/PhysRevD.69.044005
MA CP, 1995, ASTROPHYS J, V455, P7, DOI 10.1086/176550
Mattingly D, 2005, LIVING REV RELATIV, V8, DOI 10.12942/lrr-2005-5
Nakashima M, 2011, PHYS REV D, V84, DOI 10.1103/PhysRevD.84.084051
Parkinson D, 2012, PHYS REV D, V86, DOI 10.1103/PhysRevD.86.103518
Pujolas O., 2012, JHEP, V01, P062
Robbers G, 2008, PHYS REV LETT, V100, DOI 10.1103/PhysRevLett.100.111101
Zlosnik TG, 2008, PHYS REV D, V77, DOI 10.1103/PhysRevD.77.084010
Zuntz JA, 2008, PHYS REV LETT, V101, DOI 10.1103/PhysRevLett.101.261102
NR 42
TC 30
Z9 30
U1 0
U2 0
PU IOP PUBLISHING LTD
PI BRISTOL
PA TEMPLE CIRCUS, TEMPLE WAY, BRISTOL BS1 6BE, ENGLAND
SN 1475-7516
J9 J COSMOL ASTROPART P
JI J. Cosmol. Astropart. Phys.
PD AUG
PY 2013
IS 8
AR 039
DI 10.1088/1475-7516/2013/08/039
PG 28
WC Astronomy & Astrophysics; Physics, Particles & Fields
SC Astronomy & Astrophysics; Physics
GA 213CZ
UT WOS:000324032800042
DA 2021-04-21
ER
PT J
AU Patzak, B
Rypl, D
Kruis, J
AF Patzak, B.
Rypl, D.
Kruis, J.
TI MuPIF - A distributed multi-physics integration tool
SO ADVANCES IN ENGINEERING SOFTWARE
LA English
DT Article
DE Multi-physics simulations; Software integration; System integration;
Distributed computing; Interactive computing; Object-oriented design
ID SOFTWARE; FRAMEWORK
AB This paper presents the design of a multi-physics integration tool with an object-oriented architecture that facilitates the implementation of multi-physics and multi-level simulations assembled from independently developed applications (components). The tool provides high-level support for mutual data exchange between codes, including support for different discretization techniques and specific field transfer operators, being aware of the underlying physical phenomena. Parallel and distributed applications and aspects of the applications are also addressed. Each application is required to implement application and data interfaces, which allow abstract access to solution domains and fields, and provide services for steering individual applications. The Python scripting language is extended by modules representing interfaces to existing codes. The high-level language serves as a glue to tie the modules or components together and to create a specialized application. The capabilities of the tool are demonstrated on two examples that illustrate staggered thermo-mechanical analysis and distributed field mapping. (C) 2012 Civil-Comp Ltd and Elsevier Ltd. All rights reserved.
C1 [Patzak, B.; Rypl, D.; Kruis, J.] Czech Tech Univ, Fac Civil Engn, Prague 16629, Czech Republic.
RP Patzak, B (corresponding author), Czech Tech Univ, Fac Civil Engn, Thakurova 7, Prague 16629, Czech Republic.
EM borek.patzak@fsv.cvut.cz
RI Patzak, Borek/E-2472-2013; Kruis, Jaroslav/D-1423-2016
OI Patzak, Borek/0000-0002-3373-9333; Kruis, Jaroslav/0000-0001-9932-829X
FU Grant Agency of the Czech RepublicGrant Agency of the Czech Republic
[P105/10/1402]
FX This work has been supported by the Grant Agency of the Czech Republic,
under Project No. P105/10/1402.
CR Boost, C WRAPP GEN
Brown D. L., 1997, Scientific Computing in Object-Oriented Parallel Environments. First International Conference, ISCOPE 97. Proceedings, P177
Buis S, 2006, CONCURR COMP-PRACT E, V18, P231, DOI 10.1002/cpe.914
Coveney PV, 2007, COMPUT PHYS COMMUN, V176, P406, DOI 10.1016/j.cpc.2006.11.011
de Sturler E., 2001, ARCHITECTURE SCI SOF
Fowler M, 2003, UML DISTILLED BRIEF
Geist GA, 1997, INT J SUPERCOMPUT AP, V11, P224, DOI 10.1177/109434209701100305
Gunney BTN, 2006, J PARALLEL DISTR COM, V66, P1419, DOI 10.1016/j.jpdc.2006.03.011
Karmesin S., POOMA PARALLEL OBJEC
Karypis G, 1998, SIAM J SCI COMPUT, V20, P359, DOI 10.1137/S1064827595287997
LUTZ M, 2007, LEARNING PYTHON
Montarnal P., 2005, INT C COMP METH COUP
MuPIF, 2011, MULT INT FRAM
Oldham JD, 2002, POOMA A C TOOLKIT HI
Parker SG, 1997, IEEE COMPUT SCI ENG, V4, P50, DOI 10.1109/99.641609
RealityGrid, 2011, DISTR COMP ENV
Redler R, 2010, GEOSCI MODEL DEV, V3, P87, DOI 10.5194/gmd-3-87-2010
Robinson A., 2008, P 46 AIAA AER SCI M
Rossum G, 2006, INTRO PYTHON
Sanner MF, 1999, J MOL GRAPH MODEL, V17, P57
Schroeder W, 2006, VISUALIZATION TOOLKI
SCIRun, 2011, SCIRUN SCI COMP PROB
Stewart JR, 2004, FINITE ELEM ANAL DES, V40, P1599, DOI 10.1016/j.finel.2003.10.006
*SWIG, SIMPL WRAPP INT GEN
van der Velde P, 2007, ADV ENG SOFTW, V38, P182, DOI 10.1016/j.advengsoft.2006.05.007
van Liere R., 1997, COMPUTATIONAL STEERI
NR 26
TC 15
Z9 16
U1 0
U2 6
PU ELSEVIER SCI LTD
PI OXFORD
PA THE BOULEVARD, LANGFORD LANE, KIDLINGTON, OXFORD OX5 1GB, OXON, ENGLAND
SN 0965-9978
EI 1873-5339
J9 ADV ENG SOFTW
JI Adv. Eng. Softw.
PD JUN-JUL
PY 2013
VL 60-61
BP 89
EP 97
DI 10.1016/j.advengsoft.2012.09.005
PG 9
WC Computer Science, Interdisciplinary Applications; Computer Science,
Software Engineering; Engineering, Multidisciplinary
SC Computer Science; Engineering
GA 168YN
UT WOS:000320744000011
DA 2021-04-21
ER
PT J
AU Stewart, B
Hylton, DJ
Ravi, N
AF Stewart, Brianna
Hylton, Derrick J.
Ravi, Natarajan
TI A Systematic Approach for Understanding Slater-Gaussian Functions in
Computational Chemistry
SO JOURNAL OF CHEMICAL EDUCATION
LA English
DT Article
DE Upper-Division Undergraduate; Graduate Education/Research; Physical
Chemistry; Computer-Based Learning; Computational Chemistry;
Mathematics/Symbolic Mathematics; Quantum Chemistry
ID MOLECULAR-ORBITAL METHODS
AB A systematic way to understand the intricacies of quantum mechanical computations done by a software package known as "Gaussian" is undertaken via an undergraduate research project. These computations involve the evaluation of key parameters in a fitting procedure to express a Slater-type orbital (STO) function in terms of the linear combination of Gaussian-type orbital (GTO) functions. A procedure for the optimization process based on the Newton-Raphson method is developed and is applied to STO-2G and STO-3G basis sets. Satisfactory results obtained by this procedure are used to illustrate the importance of ab initio computations for inclusion in the chemistry or physics undergraduate curriculum. Programming languages such as Python and Maple Were employed to obtain the results.
C1 [Stewart, Brianna; Hylton, Derrick J.; Ravi, Natarajan] Spelman Coll, Dept Phys, Atlanta, GA 30314 USA.
RP Ravi, N (corresponding author), Spelman Coll, Dept Phys, Atlanta, GA 30314 USA.
EM nravi@spelman.edu
FU National Science FoundationNational Science Foundation (NSF) [PREM
DMR-0934142]; Department of EnergyUnited States Department of Energy
(DOE) [DE-FG52-09NA29318]
FX N.R. acknowledges the support by the National Science Foundation Grant
PREM DMR-0934142 and the Department of Energy Grant DE-FG52-09NA29318.
The authors would like to thank Beatriz Cardelino for useful discussions
and helpful suggestions.
CR BECKE AD, 1988, PHYS REV A, V38, P3098, DOI 10.1103/PhysRevA.38.3098
Boyd D. B., 2007, REV COMPUTATIONAL CH, V3
BOYS SF, 1950, PROC R SOC LON SER-A, V200, P542, DOI 10.1098/rspa.1950.0036
CHAN TF, 1985, SIAM J NUMER ANAL, V22, P904, DOI 10.1137/0722054
DITCHFIELD R, 1971, J CHEM PHYS, V54, P724, DOI 10.1063/1.1674902
Hargrove J, 2012, NANOSCALE, V4, P4443, DOI 10.1039/c2nr30823a
HEHRE WJ, 1969, J CHEM PHYS, V51, P2657, DOI 10.1063/1.1672392
HEHRE WJ, 1972, J CHEM PHYS, V56, P2257, DOI 10.1063/1.1677527
HEHRE WJ, 1970, J CHEM PHYS, V52, P2769, DOI 10.1063/1.1673374
Kudin K.N., 2013, GAUSSIAN 09 REVISION
LEE CT, 1988, PHYS REV B, V37, P785, DOI 10.1103/PhysRevB.37.785
Lewars E., 2003, COMPUTATIONAL CHEM I, P188
Pye CC, 2012, J CHEM EDUC, V89, P1405, DOI 10.1021/ed300032f
Ravi N., 2011, 57FE MOSSBAUER SPECT, P1
Romeijn H. E., 2002, HDB GLOBAL OPTIMIZAT, V2
Schleyer P. v. R., 1986, AB INITIO MOL ORBITA, P68
STEWART RF, 1969, J CHEM PHYS, V50, P2485, DOI 10.1063/1.1671406
NR 17
TC 3
Z9 3
U1 1
U2 22
PU AMER CHEMICAL SOC
PI WASHINGTON
PA 1155 16TH ST, NW, WASHINGTON, DC 20036 USA
SN 0021-9584
EI 1938-1328
J9 J CHEM EDUC
JI J. Chem. Educ.
PD MAY
PY 2013
VL 90
IS 5
BP 609
EP 612
DI 10.1021/ed300807y
PG 4
WC Chemistry, Multidisciplinary; Education, Scientific Disciplines
SC Chemistry; Education & Educational Research
GA 154XI
UT WOS:000319709600015
DA 2021-04-21
ER
PT J
AU Braun, M
AF Braun, Moritz
TI Different approaches to the numerical solution of the 3D Poisson
equation implemented in Python
SO COMPUTING
LA English
DT Article
DE Poisson Equation; Finite element method; Python; Factorization approach
AB The numerical solution of the three-dimensional Poisson equation with Dirichlet boundary conditions, which is of importance for a wide field of applications in Computational Physics and Theoretical Chemistry is considered using the method of finite elements for a model problem. The direct, the iterative and the factorized direct methods for solving the corresponding linear system of equations are discussed and implemented in the scripting language Python http://www.python.org making use of the numpy http://www.numpy.org and pysparse http://pysparse.sourceforge. net extensions. The relative performance of the different approaches is compared and it is shown, that the factorized direct method is vastly superior for larger problem sizes. A formalism for implementing the Dirichlet boundary conditions in the factorization approach is derived and presented in some detail, since it is to the best of our knowledge new.
C1 Univ S Africa, Dept Phys, ZA-0003 Pretoria, South Africa.
RP Braun, M (corresponding author), Univ S Africa, Dept Phys, POB 392, ZA-0003 Pretoria, South Africa.
EM moritz.braun@gmail.com
RI Braun, Moritz/R-1744-2016
OI Braun, Moritz/0000-0001-8710-7561
FU University of South Africa (UNISA)
FX Financial support by the University of South Africa (UNISA) is
acknowledged.
CR Berger RJF, 2005, ADV QUANTUM CHEM, V50, P235, DOI 10.1016/S0065-3276(05)50011-X
Fogolari F, 2002, J MOL RECOGNIT, V15, P377, DOI 10.1002/jmr.577
Froese-Fischer C., 1977, HARTREE FOCK METHOD
Parr R.G., 1989, DENSITY FUNCTIONAL T
Schellingerhout N.W., 1995, THESIS U GRONINGEN N
Solin P, 2006, PUR APPL MATH, P1
NR 6
TC 1
Z9 1
U1 0
U2 4
PU SPRINGER WIEN
PI WIEN
PA SACHSENPLATZ 4-6, PO BOX 89, A-1201 WIEN, AUSTRIA
SN 0010-485X
EI 1436-5057
J9 COMPUTING
JI Computing
PD MAY
PY 2013
VL 95
IS 1
SU S
SI SI
BP S49
EP S60
DI 10.1007/s00607-013-0300-x
PG 12
WC Computer Science, Theory & Methods
SC Computer Science
GA AK7TH
UT WOS:000338630100005
DA 2021-04-21
ER
PT J
AU Neufeld, E
Szczerba, D
Chavannes, N
Kuster, N
AF Neufeld, Esra
Szczerba, Dominik
Chavannes, Nicolas
Kuster, Niels
TI A novel medical image data-based multi-physics simulation platform for
computational life sciences
SO INTERFACE FOCUS
LA English
DT Article
DE computational life sciences; multi-physics; modelling; anatomical
models; image based; high-performance computing
ID HYPERTHERMIA; APPLICATOR; PATIENT; HEAD
AB Simulating and modelling complex biological systems in computational life sciences requires specialized software tools that can perform medical image data-based modelling, jointly visualize the data and computational results, and handle large, complex, realistic and often noisy anatomical models. The required novel solvers must provide the power to model the physics, biology and physiology of living tissue within the full complexity of the human anatomy (e. g. neuronal activity, perfusion and ultrasound propagation). A multi-physics simulation platform satisfying these requirements has been developed for applications including device development and optimization, safety assessment, basic research, and treatment planning. This simulation platform consists of detailed, parametrized anatomical models, a segmentation and meshing tool, a wide range of solvers and optimizers, a framework for the rapid development of specialized and parallelized finite element method solvers, a visualization toolkit-based visualization engine, a PYTHON scripting interface for customized applications, a coupling framework, and more. Core components are cross-platform compatible and use open formats. Several examples of applications are presented: hyperthermia cancer treatment planning, tumour growth modelling, evaluating the magneto-haemodynamic effect as a biomarker and physics-based morphing of anatomical models.
C1 [Neufeld, Esra; Szczerba, Dominik; Chavannes, Nicolas; Kuster, Niels] Fdn Res Informat Technol Soc ITIS, CH-8004 Zurich, Switzerland.
[Kuster, Niels] Swiss Fed Inst Technol, CH-8092 Zurich, Switzerland.
RP Neufeld, E (corresponding author), Fdn Res Informat Technol Soc ITIS, Zeughausstr 43, CH-8004 Zurich, Switzerland.
EM neufeld@itis.ethz.ch
OI Kuster, Niels/0000-0002-5827-3728
FU CTI; SNSF CO-ME; SciEx; ISJRP
FX Various components of this work have been supported by different
organizations: CTI, SNSF CO-ME, SciEx, ISJRP. Various partners have
contributed to the development: ETHZ, Erasmus MC, FDA, MIT, EPFL,
UniBas, University Hospital Zurich, CABMM, CSCS, UZH, SPEAG, ZMT.
CR Balay S., 2010, PETSC PORTABLE EXTEN
Balay S, 2011, XDMF EXTENSIBLE DATA
Christ A, 2010, PHYS MED BIOL, V55, pN23, DOI 10.1088/0031-9155/55/2/N01
Hines ML, 1997, NEURAL COMPUT, V9, P1179, DOI 10.1162/neco.1997.9.6.1179
Hirsch S, 2011, INT J MULTISCALE COM, V9, P231, DOI 10.1615/IntJMultCompEng.v9.i2.70
Kyriakou A, 2012, PHYSIOL MEAS, V33, P117, DOI 10.1088/0967-3334/33/2/117
Lloyd BA, 2008, PHILOS T R SOC A, V366, P3301, DOI 10.1098/rsta.2008.0092
Neufeld E, 2009, P EUR SOC HYP ONC ES, P14
Neufeld E, 2008, HIGH RESOLUTION HYPE
Paulides MM, 2007, INT J HYPERTHER, V23, P567, DOI 10.1080/02656730701670478
Paulides MM, 2007, INT J HYPERTHER, V23, P59, DOI 10.1080/02656730601150522
SAPARETO SA, 1984, INT J RADIAT ONCOL, V10, P787, DOI 10.1016/0360-3016(84)90379-1
Schroeder W., 2003, VISUALIZATION TOOLKI
van der Zee J, 2002, ANN ONCOL, V13, P1173, DOI 10.1093/annonc/mdf280
van Rossum G, 2003, PYTHON LANGUAGE REFE
NR 15
TC 10
Z9 10
U1 0
U2 20
PU ROYAL SOC
PI LONDON
PA 6-9 CARLTON HOUSE TERRACE, LONDON SW1Y 5AG, ENGLAND
SN 2042-8898
EI 2042-8901
J9 INTERFACE FOCUS
JI Interface Focus
PD APR 6
PY 2013
VL 3
IS 2
SI SI
AR 20120058
DI 10.1098/rsfs.2012.0058
PG 6
WC Biology
SC Life Sciences & Biomedicine - Other Topics
GA 094SS
UT WOS:000315284700003
PM 24427518
OA Bronze, Green Published
DA 2021-04-21
ER
PT J
AU Hafermann, H
Werner, P
Gull, E
AF Hafermann, Hartmut
Werner, Philipp
Gull, Emanuel
TI Efficient implementation of the continuous-time hybridization expansion
quantum impurity solver
SO COMPUTER PHYSICS COMMUNICATIONS
LA English
DT Article
DE CT-QMC; CT-HYB; DMFT; Dynamical mean-field theory
ID ELECTRONIC-STRUCTURE CALCULATIONS; OPEN-SOURCE SOFTWARE; MONTE-CARLO;
FIELD; FERMIONS; SYSTEMS; LATTICE
AB Strongly correlated quantum impurity problems appear in a wide variety of contexts ranging from nanoscience and surface physics to material science and the theory of strongly correlated lattice models, where they appear as auxiliary systems within dynamical mean-field theory. Accurate and unbiased solutions must usually be obtained numerically, and continuous-time quantum Monte Carlo algorithms, a family of algorithms based on the stochastic sampling of partition function expansions, perform well for such systems. With the present paper we provide an efficient and generic implementation of the hybridization expansion quantum impurity solver, based on the segment representation. We provide a complete implementation featuring most of the recently developed extensions and optimizations. Our implementation allows one to treat retarded interactions and provides generalized measurement routines based on improved estimators for the self-energy and for vertex functions. The solver is embedded in the ALPS-DMFT application package.
Program summary
Program title: ct-hyb
Catalogue identifier: AEOL_v1_0
Program summary URL: http://cpc.cs.qub.ac.uk/summaries/AEOL_v1_0.html
Program obtainable from: CPC Program Library, Queen's University, Belfast, N. Ireland
Licensing provisions: Use of the hybridization expansion impurity solvers requires citation of this paper. Use of any ALPS program requires citation of the ALPS [1] paper.
No. of lines in distributed program, including test data, etc.: 650044
No. of bytes in distributed program, including test data, etc.: 20553265
Distribution format: tar.gz
Programming language: C++/Python.
Computer: Desktop PC, high-performance computers.
Operating system: Unix, Linux, OSX, Windows.
Has the code been vectorized or parallelized?: Yes, MPI parallelized.
RAM: 1 GB
Classification: 7.3.
External routines: ALPS [1, 2, 3], BLAS [4, 5], LAPACK [6], HDF5 [7]
Nature of problem:
Quantum impurity models were originally introduced to describe a magnetic transition metal ion in a non-magnetic host metal. They are widely used today. In nanoscience they serve as representations of quantum dots and molecular conductors. In condensed matter physics, they are playing an increasingly important role in the description of strongly correlated electron materials, where the complicated many-body problem is mapped onto an auxiliary quantum impurity model in the context of dynamical mean-field theory, and its cluster and diagrammatic extensions. They still constitutes a non-trivial many-body problem, which takes into account the (possibly retarded) interaction between electrons occupying the impurity site. Electrons are allowed to dynamically hop on and off the impurity site, which is described by a time-dependent hybridization function.
Solution method:
The quantum impurity model is solved using a continuous-time quantum Monte Carlo algorithm which is based on a perturbation expansion of the partition function in the impurity-bath hybridization. Monte Carlo configurations are represented as segments on the imaginary time interval and individual terms correspond to Feynman diagrams which are stochastically sampled to all orders using a Metropolis algorithm. For a detailed review on the method, we refer the reader to [8].
Running time:
1-8 h.
References:
[1] B. Bauer, L D. Carr, H. G. Evertz, A. Feiguin, J. Freire, S. Fuchs, L Gamper, J. Gukelberger, E. Gull, S. Guertler, A. Hehn, R. Igarashi, S. V. Isakov, D. Koop, P. N. Ma, P. Mates, H. Matsuo, O. Parcollet, G. Pawlowski, J. D. Picon, L. Pollet, E. Santos, V. W. Scarola, U. Schollwock, C. Silva, B. Surer, S. Todo, S. Trebst, M. Troyer, M. L. Wall, P. Werner and S. Wessel, Journal of Statistical Mechanics: Theory and Experiment 2011, P05001(2011).
[2] F. Alet, P. Dayal, A. Grzesik, A. Honecker, M..Korner, A. Lauchli, S. R. Manmana, I. P. McCulloch, F. Michel, R. M. Noack, G. Schmid, U. Schollwock, F. Stockli, S. Todo, S. Trebst, M. Troyer, P. Werner, S. Wessel, J. Phys. Soc. Japan 74S (2005) 30.
[3] A. Albuquerque, F. Alet, P. Corboz, P. Dayal, A. Feiguin, S. Fuchs, L Gamper, E. Gull, S. Gurtler, A. Honecker, R. Igarashi, M. Korner, A. Kozhevnikov, A. Lauchli, S. Manmana, M. Matsumoto, I. McCulloch, F. Michel, R. Noack, G. Pawlowski, L. Pollet, T. Pruschke, U. Schollwock, S. Todo, S. Trebst, M. Troyer, P. Werner and S. Wessel, J. Magn. Magn. Mater. 310, 1187 (2007), proceedings of the 17th International Conference on Magnetism The International Conference on Magnetism.
[4] C. L. Lawson, R J. Hanson, D. R. Kincaid, and F. T. Krogh, ACM Transactions on Mathematical Software 5, 324 (1979).
[5] L. S. Blackford, J. Demmel, I. Du, G. Henry, M. Heroux, L. Kaufman, A. Lumsdaine, A. Petitet, and R. C. Whaley, ACM Trans. Math. Softw. 28, 135 (2002).
[6] E. Anderson, Z. Bai, C. Bischof, S. Blackford, J. Demmel, J. Dongarra; J. Du Croz, A. Greenbaum, S. Hammarling, A. McKenney, and D. Sorensen, LAPACK Users' Guide, 3rd ed. (Society for Industrial and Applied Mathematics, Philadelphia, PA, 1999).
[7] The HDF Group, Hierarchical data format version 5, http://www.hdfgroup.org/HDF5 (2000-2010).
[8] E. Gull, A. J. Millis, A. I. Lichtenstein, A. N. Rubtsov, M. Troyer and P. Werner, Rev. Mod. Phys. 83, 349 (2011). (C) 2012 Elsevier B.V. All rights reserved.
C1 [Hafermann, Hartmut] Ecole Polytech, CNRS, F-91128 Palaiseau, France.
[Werner, Philipp] Univ Fribourg, Dept Phys, CH-1700 Fribourg, Switzerland.
[Gull, Emanuel] Univ Michigan, Ann Arbor, MI 48109 USA.
[Gull, Emanuel] Max Planck Inst Phys Komplexer Syst, D-01187 Dresden, Germany.
RP Gull, E (corresponding author), Univ Michigan, Ann Arbor, MI 48109 USA.
EM hartmut.hafermann@cpht.polytechnique.fr; philipp.werner@unifr.ch;
gull@phys.columbia.edu
RI Werner, Philipp/C-7247-2009; Gull, Emanuel C/A-2362-2010
OI Gull, Emanuel C/0000-0002-6082-1260
FU DFGGerman Research Foundation (DFG)European Commission [FOR 1346]; SNF
Grant [200021_140648]; wider ALPS community [20-22]
FX P. W. acknowledges support by DFG FOR 1346 and SNF Grant 200021_140648.
We gratefully acknowledge support by the wider ALPS [20-22] community.
CR Albuquerque AF, 2007, J MAGN MAGN MATER, V310, P1187, DOI 10.1016/j.jmmm.2006.10.304
Alet F, 2005, J PHYS SOC JPN, V74, P30, DOI 10.1143/JPSJS.74S.30
ANDERSON PW, 1961, PHYS REV, V124, P41, DOI 10.1103/PhysRev.124.41
[Anonymous], 2000, HDF GROUP HIER DAT F
Bauer B, 2011, J STAT MECH-THEORY E, DOI 10.1088/1742-5468/2011/05/P05001
Boehnke L, 2011, PHYS REV B, V84, DOI 10.1103/PhysRevB.84.075145
BRAKO R, 1981, J PHYS C SOLID STATE, V14, P3065, DOI 10.1088/0022-3719/14/21/023
Bulla R, 1998, J PHYS-CONDENS MAT, V10, P8365, DOI 10.1088/0953-8984/10/37/021
Chitra R, 2001, PHYS REV B, V63, DOI 10.1103/PhysRevB.63.115110
Dai X., 2012, ARXIV E PRINTS
Georges A, 2004, AIP CONF PROC, V715, P3
GEORGES A, 1992, PHYS REV LETT, V69, P1240, DOI 10.1103/PhysRevLett.69.1240
Georges A, 1996, REV MOD PHYS, V68, P13, DOI 10.1103/RevModPhys.68.13
Gull E, 2008, EPL-EUROPHYS LETT, V82, DOI 10.1209/0295-5075/82/57003
Gull E, 2011, REV MOD PHYS, V83, P349, DOI 10.1103/RevModPhys.83.349
Gull E, 2011, PHYS REV B, V83, DOI 10.1103/PhysRevB.83.075122
Hafermann H, 2009, PHYS REV LETT, V102, DOI 10.1103/PhysRevLett.102.206401
Hafermann H, 2012, PHYS REV B, V85, DOI 10.1103/PhysRevB.85.205106
Hanson R, 2007, REV MOD PHYS, V79, P1217, DOI 10.1103/RevModPhys.79.1217
Haule K, 2007, PHYS REV B, V75, DOI 10.1103/PhysRevB.75.155113
Held K, 2007, ADV PHYS, V56, P829, DOI 10.1080/00018730701619647
Held K, 2006, PHYS STATUS SOLIDI B, V243, P2599, DOI 10.1002/pssb.200642053
HOLSTEIN T, 1959, ANN PHYS-NEW YORK, V8, P325, DOI 10.1016/0003-4916(59)90002-8
Kotliar G, 2006, REV MOD PHYS, V78, P865, DOI 10.1103/RevModPhys.78.865
Lauchli AM, 2009, PHYS REV B, V80, DOI 10.1103/PhysRevB.80.235117
Maier T, 2005, REV MOD PHYS, V77, P1027, DOI 10.1103/RevModPhys.77.1027
METZNER W, 1989, PHYS REV LETT, V62, P324, DOI 10.1103/PhysRevLett.62.324
Parragh N, 2012, PHYS REV B, V86, DOI 10.1103/PhysRevB.86.155158
Rubtsov AN, 2012, ANN PHYS-NEW YORK, V327, P1320, DOI 10.1016/j.aop.2012.01.002
Rubtsov AN, 2008, PHYS REV B, V77, DOI 10.1103/PhysRevB.77.033101
Rubtsov AN, 2005, PHYS REV B, V72, DOI 10.1103/PhysRevB.72.035122
Rubtsov AN, 2004, JETP LETT+, V80, P61, DOI 10.1134/1.1800216
Smith JL, 2000, PHYS REV B, V61, P5184, DOI 10.1103/PhysRevB.61.5184
Toschi A, 2007, PHYS REV B, V75, DOI 10.1103/PhysRevB.75.045118
Werner P, 2006, PHYS REV B, V74, DOI 10.1103/PhysRevB.74.155107
Werner P, 2006, PHYS REV LETT, V97, DOI 10.1103/PhysRevLett.97.076405
Werner P, 2010, PHYS REV LETT, V104, DOI 10.1103/PhysRevLett.104.146401
Werner P, 2007, PHYS REV LETT, V99, DOI 10.1103/PhysRevLett.99.146404
NR 38
TC 47
Z9 47
U1 2
U2 54
PU ELSEVIER
PI AMSTERDAM
PA RADARWEG 29, 1043 NX AMSTERDAM, NETHERLANDS
SN 0010-4655
EI 1879-2944
J9 COMPUT PHYS COMMUN
JI Comput. Phys. Commun.
PD APR
PY 2013
VL 184
IS 4
BP 1280
EP 1286
DI 10.1016/j.cpc.2012.12.013
PG 7
WC Computer Science, Interdisciplinary Applications; Physics, Mathematical
SC Computer Science; Physics
GA 104EI
UT WOS:000315974100021
DA 2021-04-21
ER
PT J
AU Horvath, A
AF Horvath, Arpad
TI The Cxnet Complex Network Analyser Software
SO ACTA POLYTECHNICA HUNGARICA
LA English
DT Article
DE complex network; graph theory; education; software
AB The study of complex networks has become important in several fields of science such as biology, sociology and physics. The collection of network data and the storage, analysis and visualisation of these data have become important contributors to the knowledge of programmers working in these fields. Our cxnet software connects several software packages of the Python language to make these tasks easier. One of the main goals of this development is to provide a comfortable application programming interface for students to develop their own programs. The cxnet software package is able to create the software package network of the Ubuntu Linux distribution. This network is a directed network with several types of vertices and connections. It changes quite fast and can be created easily. These properties make it an ideal object of investigation. The present paper describes some useful measures of the properties of the complex networks, the usage of the cxnet package with some examples, and our experiences in the education.
C1 Obuda Univ, Alba Regia Univ Ctr, Szekesfehervar, Hungary.
RP Horvath, A (corresponding author), Obuda Univ, Alba Regia Univ Ctr, Szekesfehervar, Hungary.
EM horvath.arpad@arek.uni-obuda.hu
CR Albert R, 2002, REV MOD PHYS, V74, P47, DOI 10.1103/RevModPhys.74.47
BATAGELJ V, 2002, PAJEK ANAL VISUALIZA
Borner K, 2010, SCIENTOMETRICS, V83, P863, DOI 10.1007/s11192-009-0149-0
Colizza V, 2007, BMC MED, V5, DOI 10.1186/1741-7015-5-34
Csardi G, 2006, INTERJOURNAL COMPLEX
Dorogovtsev SN, 2008, REV MOD PHYS, V80, P1275, DOI 10.1103/RevModPhys.80.1275
Hagberg A, 2008, 7 PYTH SCI C SCIPY20, V7, P11, DOI DOI 10.1016/J.JELECTROCARD.2010.09.003
Hertzog R., 2012, DEBIAN ADM HDB
Horvath A., 2010, ACTA PHYS DEBRECINA, VXLIV, P37
Kitsak M, 2007, PHYS REV E, V75, DOI 10.1103/PhysRevE.75.056115
Liu YY, 2011, NATURE, V473, P167, DOI 10.1038/nature10011
Maillart T, 2008, PHYS REV LETT, V101, DOI 10.1103/PhysRevLett.101.218701
Myers CR, 2003, PHYS REV E, V68, DOI 10.1103/PhysRevE.68.046116
Newman MEJ, 2004, PHYS REV E, V69, DOI [10.1103/PhysRevE.69.026113, 10.1103/PhysRevE.69.066133]
Newman MEJ, 2003, SIAM REV, V45, P167, DOI 10.1137/S003614450342480
Ravasz E, 2003, PHYS REV E, V67, DOI 10.1103/PhysRevE.67.026112
Siek Jeremy G., 2001, BOOST GRAPH LIB USER
Sousa O.F., 2009, J COMPUTATIONAL INTE, V1, P127, DOI 10.6062/jcis.2009.01.02.0015
Summerfield M, 2010, PROGRAMMING PYTHON
Tosi S., 2009, MATPLOTLIB PYTHON DE
Vazquez A, 2002, PHYS REV E, V65, DOI 10.1103/PhysRevE.65.066130
Zlatic V, 2006, PHYS REV E, V74, DOI 10.1103/PhysRevE.74.016115
NR 22
TC 4
Z9 4
U1 0
U2 4
PU BUDAPEST TECH
PI BUDAPEST
PA BECSI UT 96-B, BUDAPEST, H-1034, HUNGARY
SN 1785-8860
J9 ACTA POLYTECH HUNG
JI Acta Polytech. Hung.
PY 2013
VL 10
IS 6
BP 43
EP 58
PG 16
WC Engineering, Multidisciplinary
SC Engineering
GA 252HD
UT WOS:000326994800003
DA 2021-04-21
ER
PT S
AU del Rio, MS
AF del Rio, M. Sanchez
BE Susini, J
Dumas, P
TI New challenges in ray tracing simulations of X-ray optics
SO 11TH INTERNATIONAL CONFERENCE ON SYNCHROTRON RADIATION INSTRUMENTATION
(SRI 2012)
SE Journal of Physics Conference Series
LA English
DT Proceedings Paper
CT 11th International Conference on Synchrotron Radiation Instrumentation
(SRI)
CY JUL 09-13, 2012
CL Lyon, FRANCE
SP ESRF, SOLEIL
AB The construction of new synchrotron sources and the refurbishment and upgrade of existing ones has boosted in the last years the interest in X-ray optics simulations for beamline design and optimization. In the last years we conducted a full renewal of the well established SHADOW ray tracing code, ending with a modular version SHADOW3 interfaced to multiple programming languages (C, C++, IDL, Python). Some of the new features of SHADOW3 are presented. From the physics point of view, SHADOW3 has been upgraded for dealing with lens systems. X-ray partial coherence applications demand an extension of traditional ray tracing methods into a hybrid ray-tracing wave-optics approach. The software development is essential for fulfilling the requests of the ESRF Upgrade Programme, and some examples of calculations are also presented.
C1 European Synchrotron Radiat Facil, F-38043 Grenoble, France.
RP del Rio, MS (corresponding author), European Synchrotron Radiat Facil, BP 220, F-38043 Grenoble, France.
EM srio@esrf.eu
CR Bergback Knudsen E, J PHYS C SERIES
Canestrari N, J PHYS C SERIES
Canestrari N, 2011, PROC SPIE, V8141, DOI 10.1117/12.893433
Chubar O., 1998, P 6 EUR PART ACC C E, P1177
del Rio MS, 2012, J SYNCHROTRON RADIAT, V19, P366, DOI 10.1107/S0909049512003020
del Rio MS, 2011, PROC SPIE, V8141
del Rio MS, 2011, J SYNCHROTRON RADIAT, V18, P708, DOI 10.1107/S0909049511026306
Evans-Lutterodt K, 2007, PHYS REV LETT, V99, DOI 10.1103/PhysRevLett.99.134801
Leitenberger W, 2003, PHYSICA B, V336, P63, DOI 10.1016/S0921-4526(03)00270-9
Osterhoff M, 2011, NEW J PHYS, V13, DOI 10.1088/1367-2630/13/10/103026
Sanchez del Rio M, J PHYS C SERIES
Sanchez del Rio M, 2012, SPIE P, V8141
Schroer CG, 2005, APPL PHYS LETT, V87, DOI 10.1063/1.2053350
Zhang L, J PHYS C SERIES
NR 14
TC 2
Z9 2
U1 0
U2 9
PU IOP PUBLISHING LTD
PI BRISTOL
PA DIRAC HOUSE, TEMPLE BACK, BRISTOL BS1 6BE, ENGLAND
SN 1742-6588
EI 1742-6596
J9 J PHYS CONF SER
PY 2013
VL 425
AR 162003
DI 10.1088/1742-6596/425/16/162003
PG 6
WC Instruments & Instrumentation; Physics, Applied; Physics,
Multidisciplinary
SC Instruments & Instrumentation; Physics
GA BFL51
UT WOS:000320403700225
OA Bronze
DA 2021-04-21
ER
PT S
AU Khwaldeh, A
Tahat, A
Marti, J
Tahat, M
AF Khwaldeh, Ali
Tahat, Amani
Marti, Jordi
Tahat, Mofleh
BE Giannakopoulos, G
Sakas, DP
Vlachos, DS
KyriakiManessi, D
TI Atomic data mining numerical methods, source code SQlite with Python
SO PROCEEDINGS OF THE 2ND INTERNATIONAL CONFERENCE ON INTEGRATED
INFORMATION (IC-ININFO 2012)
SE Procedia Social and Behavioral Sciences
LA English
DT Proceedings Paper
CT 2nd International Conference on Integrated Information (IC-ININFO)
CY AUG 30-SEP 03, 2012
CL Budapest, HUNGARY
DE Python; atomic data; database; data mining algorithms; data model;
collaborative intelligence; machine learning
AB This paper introduces a recently published Python data mining book (chapters, topics, samples of Python source code written by its authors) to be used in data mining via world wide web and any specific database in several disciplines (economic, physics, education, marketing. etc). The book started with an introduction to data mining by explaining some of the data mining tasks involved classification, dependence modelling, clustering and discovery of association rules. The book addressed that using Python in data mining has been gaining some interest from data miner community due to its open source, general purpose programming and web scripting language; furthermore, it is a cross platform and it can be run on a wide variety of operating systens such as Linux, Windows, FreeBSD, Macintosh, Solaris, OS/2, Amiga, AROS, AS/400, BeOS, OS/390, z/OS, Palm OS, QNX, VMS, Psion, Acorn RISC OS, VxWorks, PlayStation, Sharp Zaurus, Windows CE and even PocketPC. Finally this book can be considered as a teaching textbook for data mining in which several methods such as machine learning and statistics are used to extract high-level knowledge from real-world datasets. (C) 2013 The Authors. Published by Elsevier Ltd. Selection and/or peer-review under responsibility of The 2nd International Conference on Integrated
C1 [Khwaldeh, Ali] Philadelphia Univ, Fac Engn, Dept Comp Engn, Amman 19392, Jordan.
EM amani.tahat@upc.edu
RI Marti, Jordi/H-5414-2015
OI Marti, Jordi/0000-0002-3721-9634
CR Downey A., 2012, THINK PYTHON
Han J., 2006, DATA MINING CONCEPTS
Khwaldeh Ali, 2011, ATOMIC DATA MINING N
Ling M., 2011, PYTHON PAPER, V6, P04
McKinney W., 2012, PYTHON DATA ANAL
Russell Matthew A., 2011, MINING SOCIAL WEB
Tahat A, 2011, INT J DATA DATABASE, V3, P1
NR 7
TC 1
Z9 1
U1 0
U2 5
PU ELSEVIER SCIENCE BV
PI AMSTERDAM
PA SARA BURGERHARTSTRAAT 25, PO BOX 211, 1000 AE AMSTERDAM, NETHERLANDS
SN 1877-0428
J9 PROCD SOC BEHV
PY 2013
VL 73
BP 232
EP 239
DI 10.1016/j.sbspro.2013.02.046
PG 8
WC Information Science & Library Science
SC Information Science & Library Science
GA BFR79
UT WOS:000321102300033
OA Bronze
DA 2021-04-21
ER
PT S
AU Brun, F
Accardo, A
Kourousias, G
Dreossi, D
Pugliese, R
AF Brun, Francesco
Accardo, Agostino
Kourousias, George
Dreossi, Diego
Pugliese, Roberto
GP IEEE
TI Effective implementation of ring artifacts removal filters for
synchrotron radiation microtomographic images
SO 2013 8TH INTERNATIONAL SYMPOSIUM ON IMAGE AND SIGNAL PROCESSING AND
ANALYSIS (ISPA)
SE International Symposium on Image and Signal Processing and Analysis
LA English
DT Proceedings Paper
CT 8th International Symposium on Image and Signal Processing and Analysis
(ISPA)
CY SEP 04-06, 2013
CL Trieste, ITALY
SP Univ Trieste, Dept Engn & Arch, Univ Zagreb, Fac Elect Engn &d Comp, EURASIP, IEEE Signal Proc Soc, Italy Chapter, DAVe, FONDAZIONE CRTRIESTE, CHIRON, FIMI, ESTECO
AB The presence of ring artifacts in X-ray computed microtomography affects the qualitative and quantitative analyses of the reconstructed images. Although digital image processing approaches to ring artifacts removal via direct filtering of the reconstructed images exist, in synchrotron radiation microtomography this issue is usually faced during the actual reconstruction process by de-striping the sinogram image. In this work different de-striping algorithms are discussed and preliminary compared using a suitably created test image as well as actual imaged data. Because of the increasing need for fast reconstruction workflows, details of the implementation and the related computational aspects are also considered. Moreover, the hardware and software solution developed and deployed for the SYRMEP (SYnchrotron Radiation for MEdical Physics) microtomographic beamline of the Italian synchrotron radiation facility (Elettra - Sincrotrone Trieste S.C.p.A) is presented. This solution is based on a high-performance computing (HPC) cluster with the Oracle Grid Engine distributed resource management (DRM) system and Python code that takes advantage of the NumPy and SciPy libraries.
C1 [Brun, Francesco; Accardo, Agostino] Univ Trieste, Dept Engn & Architecture, Via A Valerio 10, I-34137 Trieste, Italy.
[Kourousias, George; Dreossi, Diego; Pugliese, Roberto] Eletra Sincrotrone Trieste S CpA, I-34149 Trieste, Italy.
RP Brun, F (corresponding author), Univ Trieste, Dept Engn & Architecture, Via A Valerio 10, I-34137 Trieste, Italy.
EM fbrun@units.it
RI Brun, Francesco/ABD-3891-2020
OI Brun, Francesco/0000-0003-0155-5326
CR Abu Anas EM, 2011, BIOMED ENG ONLINE, V10, DOI 10.1186/1475-925X-10-72
Anas E M A, 2010, 2010 6th International Conference on Electrical & Computer Engineering (ICECE 2010), P638, DOI 10.1109/ICELCE.2010.5700774
Axelsson M, 2006, LECT NOTES COMPUT SC, V4174, P61
Barrett JF, 2004, RADIOGRAPHICS, V24, P1679, DOI 10.1148/rg.246045065
Boin M, 2006, OPT EXPRESS, V14, P12071, DOI 10.1364/OE.14.012071
Brun F, 2011, IEEE IMAGE PROC, P405, DOI 10.1109/ICIP.2011.6116535
Brun F, 2009, 11th International Congress of the IUPESM. World Congress on Medical Physics and Biomedical Engineering. Image Processing, Biosignal Processing, Modelling and Simulation, Biomechanics, P926
Brun F, 2010, NUCL INSTRUM METH A, V615, P326, DOI 10.1016/j.nima.2010.02.063
Eskicioglu AM, 1995, IEEE T COMMUN, V43, P2959, DOI 10.1109/26.477498
Munch B, 2009, OPT EXPRESS, V17, P8567, DOI 10.1364/OE.17.008567
Raven C, 1998, REV SCI INSTRUM, V69, P2978, DOI 10.1063/1.1149043
Rivers M, 1998, TUTORIAL INTRO XRAY
Sadi F, 2010, COMPUT BIOL MED, V40, P109, DOI 10.1016/j.compbiomed.2009.11.007
Sijbers J, 2004, PHYS MED BIOL, V49, pN247, DOI 10.1088/0031-9155/49/14/N06
NR 14
TC 17
Z9 17
U1 0
U2 4
PU IEEE
PI NEW YORK
PA 345 E 47TH ST, NEW YORK, NY 10017 USA
SN 1845-5921
BN 978-953-184-194-8; 978-953-184-187-0
J9 INT SYMP IMAGE SIG
PY 2013
BP 672
EP +
PG 2
WC Engineering, Electrical & Electronic; Imaging Science & Photographic
Technology
SC Engineering; Imaging Science & Photographic Technology
GA BC0XH
UT WOS:000349789200119
DA 2021-04-21
ER
PT J
AU Conte, E
Fuks, B
Serret, G
AF Conte, Eric
Fuks, Benjamin
Serret, Guillaume
TI MADANALYSIS 5, a user-friendly framework for collider phenomenology
SO COMPUTER PHYSICS COMMUNICATIONS
LA English
DT Article
DE Particle physics phenomenology; Monte Carlo event generators; Hadron
colliders
ID AUTOMATIC-GENERATION; FEYNRULES
AB We present MADANALYSIS 5, a new framework for phenomenological investigations at particle colliders. Based on a C++ kernel, this program allows us to efficiently perform, in a straightforward and user-friendly fashion, sophisticated physics analyses of event files such as those generated by a large class of Monte Carlo event generators. MADANALYSIS 5 comes with two modes of running. The first one, easier to handle, uses the strengths of a powerful PYTHON interface in order to implement physics analyses by means of a set of intuitive commands. The second one requires one to implement the analyses in the C++ programming language, directly within the core of the analysis framework. This opens unlimited possibilities concerning the level of complexity which can be reached, being only limited by the programming skills and the originality of the user.
Program summary
Program title: MadAnalysis 5
Catalogue identifier: AENO_v1_0
Program summary URL: http://cpc.cs.qub.ac.uk/summaries/AENO_v1_0.html
Program obtainable from: CPC Program Library, Queen's University, Belfast, N. Ireland
Licensing provisions: Permission to use, copy, modify and distribute this program is granted under the terms of the GNU General Public License.
No. of lines in distributed program, including test data, etc.: 31087
No. of bytes in distributed program, including test data, etc.: 399105
Distribution format: tar.gz
Programming language: PYTHON, C++.
Computer: All platforms on which Python version 2.7, Root version 5.27 and the g++ compiler are available. Compatibility with newer versions of these programs is also ensured. However, the Python version must be below version 3.0.
Operating system: Unix, Linux and Mac OS operating systems on which the above-mentioned versions of Python and Root, as well as g++, are available.
Classification: 11.1.
External routines: ROOT (http://root.cern.ch/drupal/)
Nature of problem: Implementing sophisticated phenomenological analyses in high-energy physics through a flexible, efficient and straightforward fashion, starting from event files such as those produced by Monte Carlo event generators. The event files can have been matched or not to parton-showering and can have been processed or not by a (fast) simulation of a detector. According to the sophistication level of the event files (parton-level, hadron-level, reconstructed-level), one must note that several input formats are possible.
Solution method: We implement an interface allowing the production of predefined as well as user-defined histograms for a large class of kinematical distributions after applying a set of event selection cuts specified by the user. This therefore allows us to devise robust and novel search strategies for collider experiments, such as those currently running at the Large Hadron Collider at CERN, in a very efficient way.
Restrictions: Unsupported event file format.
Unusual features: The code is fully based on object representations for events, particles, reconstructed objects and cuts, which facilitates the implementation of an analysis.
Running time: It depends on the purposes of the user and on the number of events to process. It varies from a few seconds to the order of the minute for several millions of events. (C) 2012 Elsevier B.V. All rights reserved.
C1 [Fuks, Benjamin; Serret, Guillaume] Univ Strasbourg, CNRS, IN2P3, Inst Pluridisciplinaire Hubert Curien,Dept Rech S, F-67037 Strasbourg, France.
[Conte, Eric] Univ Haute Alsace, GRPHE, IUT Colmar, F-68008 Colmar, France.
RP Fuks, B (corresponding author), Univ Strasbourg, CNRS, IN2P3, Inst Pluridisciplinaire Hubert Curien,Dept Rech S, 23 Rue Loess, F-67037 Strasbourg, France.
EM eric.conte@iphc.cnrs.fr; benjamin.fuks@ires.in2p3.fr;
guillaume.serret@iphc.cnrs.fr
OI Fuks, Benjamin/0000-0002-0041-0566
FU Theory-LHC France-initiative of the CNRS/IN2P3; French Ministry for
Education and Research
FX The authors are extremely grateful to J. Andrea for being the first user
and beta-tester of this program. We also thank the MADGRAPH 5
development team (J. Alwall, F. Maltoni, O. Mattelaer and T. Stelzer)
and R. Frederix for commenting and supporting the development of
MADANALYSIS 5 as well as our colleagues from Strasbourg for their help
in testing and debugging the code, J.L. Agram, A. Alloul, A. Aubin, E.
Chabert, C. Collard, A. Gallo, P. Lansonneur and S. Marrazzo. Finally,
we acknowledge V. Boucher, J. de Favereau and P. Demin for their help in
administrating our web and SVN server. This work has been supported by
the Theory-LHC France-initiative of the CNRS/IN2P3 and a Ph.D.
fellowship of the French Ministry for Education and Research.
CR Alwall J, 2007, COMPUT PHYS COMMUN, V176, P300, DOI 10.1016/j.cpc.2006.11.010
Alwall J, 2008, AIP CONF PROC, V1078, P84, DOI 10.1063/1.3052056
Alwall J, 2007, J HIGH ENERGY PHYS, DOI 10.1088/1126-6708/2007/09/028
Alwall J, 2011, J HIGH ENERGY PHYS, DOI 10.1007/JHEP06(2011)128
Bahr M, 2008, EUR PHYS J C, V58, P639, DOI 10.1140/epjc/s10052-008-0798-9
Berger CF, 2011, PHYS REV LETT, V106, DOI 10.1103/PhysRevLett.106.092001
Berger CF, 2010, PHYS REV D, V82, DOI 10.1103/PhysRevD.82.074002
Berger CF, 2009, PHYS REV LETT, V102, DOI 10.1103/PhysRevLett.102.222001
Boos E, 2004, NUCL INSTRUM METH A, V534, P250, DOI 10.1016/j.nima.2004.07.096
Boos E., ARXIVHEPPH0109068
Brun R, 1997, NUCL INSTRUM METH A, V389, P81, DOI 10.1016/S0168-9002(97)00048-X
Cacciari M, 2006, PHYS LETT B, V641, P57, DOI 10.1016/j.physletb.2006.08.037
Cafarella A, 2009, COMPUT PHYS COMMUN, V180, P1941, DOI 10.1016/j.cpc.2009.04.023
Cascioli F, 2012, PHYS REV LETT, V108, DOI 10.1103/PhysRevLett.108.111601
Catani S, 2001, J HIGH ENERGY PHYS
Christensen N, 2011, EUR PHYS J C, V71, DOI 10.1140/epjc/s10052-011-1541-5
Christensen ND, 2012, EUR PHYS J C, V72, DOI 10.1140/epjc/s10052-012-1990-5
Christensen ND, 2009, COMPUT PHYS COMMUN, V180, P1614, DOI 10.1016/j.cpc.2009.02.018
Corcella G, 2001, J HIGH ENERGY PHYS, DOI 10.1088/1126-6708/2001/01/010
Cullen G, 2012, EUR PHYS J C, V72, DOI 10.1140/epjc/s10052-012-1889-1
Czakon M, 2009, J HIGH ENERGY PHYS, DOI 10.1088/1126-6708/2009/08/085
de Aquino P., ARXIV11082041
Degrande C, 2012, COMPUT PHYS COMMUN, V183, P1201, DOI 10.1016/j.cpc.2012.01.022
Demin P., 2005, EXROOTANALY IN PRESS
Dobbs M, 2001, COMPUT PHYS COMMUN, V134, P41, DOI 10.1016/S0010-4655(00)00189-2
Duhr C, 2011, COMPUT PHYS COMMUN, V182, P2404, DOI 10.1016/j.cpc.2011.06.009
Ellis R.K., 2009, J HIGH ENERGY PHYS, V04, P077
Frederix R, 2008, J HIGH ENERGY PHYS, DOI 10.1088/1126-6708/2008/09/122
Frederix R, 2009, J HIGH ENERGY PHYS, DOI 10.1088/1126-6708/2009/10/003
Fuks B, 2012, INT J MOD PHYS A, V27, DOI 10.1142/S0217751X12300074
Giammanco A, 2006, SPRINGER PROC PHYS, V108, P311
GIELE W, ARXIVHEPPH0204316
Gleisberg T, 2008, EUR PHYS J C, V53, P501, DOI [10.1140/epjc/s10052-007-0495-0, 10.1140/epjc/sl0052-007-0495-0]
Gleisberg T, 2004, J HIGH ENERGY PHYS
Gleisberg T, 2009, J HIGH ENERGY PHYS, DOI 10.1088/1126-6708/2009/02/007
GLEISBERG T, 2008, J HIGH ENERGY PHYS
Hasegawa K, 2008, NUCL PHYS B-PROC SUP, V183, P268, DOI 10.1016/j.nuclphysbps.2008.09.115
Hirschi V, 2011, J HIGH ENERGY PHYS, DOI 10.1007/JHEP05(2011)044
Kilian W, 2011, EUR PHYS J C, V71, DOI 10.1140/epjc/s10052-011-1742-y
Krauss F, 2002, J HIGH ENERGY PHYS
Maltoni F, 2003, J HIGH ENERGY PHYS, DOI 10.1088/1126-6708/2003/02/027
Mangano ML, 2007, J HIGH ENERGY PHYS, DOI 10.1088/1126-6708/2007/01/013
Mangano ML, 2003, J HIGH ENERGY PHYS, DOI 10.1088/1126-6708/2003/07/001
Moretti M., ARXIVHEPPH0102195
Mrenna S, 2004, J HIGH ENERGY PHYS, DOI 10.1088/1126-6708/2004/05/040
Nakamura K, 2010, J PHYS G NUCL PARTIC, V37, P1, DOI 10.1088/0954-3899/37/7A/075021
Ossola G, 2008, J HIGH ENERGY PHYS, DOI 10.1088/1126-6708/2008/03/042
Ovyn S., ARXIV09032225
PLOTHOWBESCH H, 1993, COMPUT PHYS COMMUN, V75, P396, DOI 10.1016/0010-4655(93)90051-D
Pukhov A., ARXIVHEPPH9908288
Pukhov A., ARXIVHEPPH0412191
Pumplin J, 2002, J HIGH ENERGY PHYS, DOI 10.1088/1126-6708/2002/07/012
Semenov A, 1998, COMPUT PHYS COMMUN, V115, P124, DOI 10.1016/S0010-4655(98)00143-X
SEMENOV A, ARXIV08050555, P34903
SEYMOUR MH, ARXIV08032231
Sjostrand T, 2008, COMPUT PHYS COMMUN, V178, P852, DOI 10.1016/j.cpc.2008.01.036
Sjostrand T, 2006, J HIGH ENERGY PHYS, DOI 10.1088/1126-6708/2006/05/026
STELZER T, 1994, COMPUT PHYS COMMUN, V81, P357, DOI 10.1016/0010-4655(94)90084-1
van Hameren A, 2009, J HIGH ENERGY PHYS, DOI 10.1088/1126-6708/2009/09/106
Zanderighi G., ARXIV08103524
NR 60
TC 289
Z9 292
U1 0
U2 5
PU ELSEVIER
PI AMSTERDAM
PA RADARWEG 29, 1043 NX AMSTERDAM, NETHERLANDS
SN 0010-4655
EI 1879-2944
J9 COMPUT PHYS COMMUN
JI Comput. Phys. Commun.
PD JAN
PY 2013
VL 184
IS 1
BP 222
EP 256
DI 10.1016/j.cpc.2012.09.009
PG 35
WC Computer Science, Interdisciplinary Applications; Physics, Mathematical
SC Computer Science; Physics
GA 037VK
UT WOS:000311134400027
OA Green Accepted
DA 2021-04-21
ER
PT J
AU Gramfort, A
Luessi, M
Larson, E
Engemann, DA
Strohmeier, D
Brodbeck, C
Goj, R
Jas, M
Brooks, T
Parkkonen, L
Hamalainen, M
AF Gramfort, Alexandre
Luessi, Martin
Larson, Eric
Engemann, Denis A.
Strohmeier, Daniel
Brodbeck, Christian
Goj, Roman
Jas, Mainak
Brooks, Teon
Parkkonen, Lauri
Haemaelaeinen, Matti
TI MEG and EEG data analysis with MNE-Python
SO FRONTIERS IN NEUROSCIENCE
LA English
DT Article
DE electroencephalography (EEG); magnetoencephalography (MEG);
neuroimaging; software; python; open-source
ID SURFACE-BASED ANALYSIS; BRAIN; FMRI; DIPOLE
AB Magnetoencephalography and electroencephalography (M/EEG) measure the weak electromagnetic signals generated by neuronal activity in the brain. Using these signals to characterize and locate neural activation in the brain is a challenge that requires expertise in physics, signal processing, statistics, and numerical methods. As part of the MNE software suite, MNE-Python is an open-source software package that addresses this challenge by providing state-of-the-art algorithms implemented in Python that cover multiple methods of data preprocessing, source localization, statistical analysis, and estimation of functional connectivity between distributed brain regions. All algorithms and utility functions are implemented in a consistent manner with well-documented interfaces, enabling users to create M/EEG data analysis pipelines by writing Python scripts. Moreover, MNE-Python is tightly integrated with the core Python libraries for scientific comptutation (NumPy, SciPy) and visualization (matplotlib and Mayavi), as well as the greater neuroimaging ecosystem in Python via the Nibabel package. The code is provided under the new BSD license allowing code reuse, even in commercial products. Although MNE-Python has only been under heavy development for a couple of years, it has rapidly evolved with expanded analysis capabilities and pedagogical tutorials because multiple labs have collaborated during code development to help share best practices. MNE-Python also gives easy access to preprocessed datasets, helping users to get started quickly and facilitating reproducibility of methods by other researchers. Full documentation, including dozens of examples, is available at http://martinos.org/mne.
C1 [Gramfort, Alexandre] Telecom ParisTech, CNRS LTCI, Inst Mines Telecom, F-75014 Paris, France.
[Gramfort, Alexandre; Luessi, Martin; Haemaelaeinen, Matti] Massachusetts Gen Hosp, Athinoula A Martinos Ctr Biomed Imaging, Charlestown, MA USA.
[Gramfort, Alexandre; Luessi, Martin; Haemaelaeinen, Matti] Harvard Univ, Sch Med, Charlestown, MA USA.
[Gramfort, Alexandre] CEA Saclay, NeuroSpin, F-91191 Gif Sur Yvette, France.
[Larson, Eric] Univ Washington, Inst Learning & Brain Sci, Seattle, WA 98195 USA.
[Engemann, Denis A.] Forschungszentrum Juelich, Inst Neurosci & Med Cognit Neurosci INM 3, Julich, Germany.
[Engemann, Denis A.] Univ Hosp, Dept Psychiat, Brain Imaging Lab, Cologne, Germany.
[Strohmeier, Daniel] Ilmenau Univ Technol, Inst Biomed Engn & Informat, Ilmenau, Germany.
[Brodbeck, Christian; Brooks, Teon] NYU, Dept Psychol, New York, NY 10003 USA.
[Goj, Roman] Univ Stirling, Sch Nat Sci, Psychol Imaging Lab, Stirling FK9 4LA, Scotland.
[Jas, Mainak; Parkkonen, Lauri] Aalto Univ, Sch Sci, Dept Biomed Engn & Computat Sci, Espoo, Finland.
[Jas, Mainak; Parkkonen, Lauri; Haemaelaeinen, Matti] Aalto Univ, Sch Sci, Brain Res Unit, OV Lounasmaa Lab, Espoo, Finland.
RP Gramfort, A (corresponding author), Telecom ParisTech, CNRS LTCI, Inst Mines Telecom, 37-39 Rue Dareau, F-75014 Paris, France.
EM alexandre.gramfort@telecom-paristech.fr
RI Parkkonen, Lauri/G-6755-2012; Brodbeck, Christian/R-2207-2019; Jas,
Mainak/AAW-4508-2020; Hamalainen, Matti S/C-8507-2013; Engemann,
Denis-Alexander/AAC-2846-2021
OI Parkkonen, Lauri/0000-0002-0130-0801; Brodbeck,
Christian/0000-0001-8380-639X; Jas, Mainak/0000-0002-3199-9027;
Engemann, Denis-Alexander/0000-0002-7223-1014; Brooks, Teon
L/0000-0001-7344-3230; Gramfort, Alexandre/0000-0001-9791-4404
FU National Institute of Biomedical Imaging and BioengineeringUnited States
Department of Health & Human ServicesNational Institutes of Health (NIH)
- USANIH National Institute of Biomedical Imaging & Bioengineering
(NIBIB) [5R01EB009048, P41RR014075]; National Institute on Deafness and
Other Communication Disorders fellowship [F32DC012456]; NSFNational
Science Foundation (NSF) [0958669, 1042134]; Swiss National Science
Foundation Early Postdoc; Mobility fellowship [148485]; NYUAD Institute
[G1001]; National Science Foundation Graduate Research
FellowshipNational Science Foundation (NSF) [DGE-1342536]; "aivoAALTO"
program; German Research FoundationGerman Research Foundation (DFG) [Ha
2899/8-2]; [ERC-YStG-263584]; NATIONAL CENTER FOR RESEARCH
RESOURCESUnited States Department of Health & Human ServicesNational
Institutes of Health (NIH) - USANIH National Center for Research
Resources (NCRR) [S10RR031599] Funding Source: NIH RePORTER; NATIONAL
INSTITUTE OF BIOMEDICAL IMAGING AND BIOENGINEERINGUnited States
Department of Health & Human ServicesNational Institutes of Health (NIH)
- USANIH National Institute of Biomedical Imaging & Bioengineering
(NIBIB) [R01EB009048, R01EB009048, R01EB009048, R01EB009048,
R01EB009048, R01EB009048, R01EB009048, R01EB009048] Funding Source: NIH
RePORTER; Direct For Social, Behav & Economic ScieNational Science
Foundation (NSF)NSF - Directorate for Social, Behavioral & Economic
Sciences (SBE) [0958669] Funding Source: National Science Foundation;
Division Of Behavioral and Cognitive SciNational Science Foundation
(NSF)NSF - Directorate for Social, Behavioral & Economic Sciences (SBE)
[0958669] Funding Source: National Science Foundation
FX This work was supported by National Institute of Biomedical Imaging and
Bioengineering grants 5R01EB009048 and P41RR014075, National Institute
on Deafness and Other Communication Disorders fellowship F32DC012456,
and NSF awards 0958669 and 1042134. The work of Alexandre Gramfort was
partially supported by ERC-YStG-263584. Martin Luessi was partially
supported by the Swiss National Science Foundation Early Postdoc.
Mobility fellowship 148485. Christian Brodbeck was supported by grant
G1001 from the NYUAD Institute. Teon Brooks was supported by the
National Science Foundation Graduate Research Fellowship under Grant No.
DGE-1342536. Lauri Parkkonen was supported by the "aivoAALTO" program.
Daniel Strohmeier was supported by grant Ha 2899/8-2 from the German
Research Foundation.
CR Aine CJ, 2012, NEUROINFORMATICS, V10, P141, DOI 10.1007/s12021-011-9132-z
Becker R, 1996, TECHNICAL REPORT
BENJAMINI Y, 1995, J R STAT SOC B, V57, P289, DOI 10.1111/j.2517-6161.1995.tb02031.x
Buitinck L., 2013, EUR C MACH LEARN PRI
Carp J, 2012, FRONT NEUROSCI-SWITZ, V6, DOI 10.3389/fnins.2012.00149
Carp J, 2012, NEUROIMAGE, V63, P289, DOI 10.1016/j.neuroimage.2012.07.004
Dalal SS, 2011, COMPUT INTEL NEUROSC, V2011, DOI 10.1155/2011/758973
Dale AM, 2000, NEURON, V26, P55, DOI 10.1016/S0896-6273(00)81138-1
Dale AM, 1999, NEUROIMAGE, V9, P179, DOI 10.1006/nimg.1998.0395
Delorme A, 2004, J NEUROSCI METH, V134, P9, DOI 10.1016/j.jneumeth.2003.10.009
Delorme A, 2011, COMPUT INTEL NEUROSC, V2011, DOI 10.1155/2011/130714
Desikan RS, 2006, NEUROIMAGE, V31, P968, DOI 10.1016/j.neuroimage.2006.01.021
Destrieux C, 2010, NEUROIMAGE, V53, P1, DOI 10.1016/j.neuroimage.2010.06.010
Dubois PF, 2005, COMPUT SCI ENG, V7, P80, DOI 10.1109/MCSE.2005.54
Fischl B, 2004, CEREB CORTEX, V14, P11, DOI 10.1093/cercor/bhg087
Fischl B, 1999, NEUROIMAGE, V9, P195, DOI 10.1006/nimg.1998.0396
Fries P, 2009, ANNU REV NEUROSCI, V32, P209, DOI 10.1146/annurev.neuro.051508.135603
Gorgolewski Krzysztof, 2011, Front Neuroinform, V5, P13, DOI 10.3389/fninf.2011.00013
Gramfort A, 2013, NEUROIMAGE, V70, P410, DOI 10.1016/j.neuroimage.2012.12.051
Gramfort A., 2013, NEUROIMAGE, DOI [10.1016/j.neuroimage.2013.10.027, DOI 10.1016/J.NEUR0IMAGE.2013.10.027.(IN]
Gramfort A, 2012, PHYS MED BIOL, V57, P1937, DOI 10.1088/0031-9155/57/7/1937
Gramfort A, 2011, LECT NOTES COMPUT SC, V6801, P600, DOI 10.1007/978-3-642-22092-0_49
Gramfort A, 2010, BIOMED ENG ONLINE, V9, DOI 10.1186/1475-925X-9-45
Gramfort A, 2010, IEEE T BIO-MED ENG, V57, P1051, DOI 10.1109/TBME.2009.2037139
Granger CWJ, 1964, SPECTRAL ANAL EC TIM
Gross J, 2001, P NATL ACAD SCI USA, V98, P694, DOI 10.1073/pnas.98.2.694
Gross J, 2013, NEUROIMAGE, V65, P349, DOI 10.1016/j.neuroimage.2012.10.001
HAMALAINEN M, 1993, REV MOD PHYS, V65, P413, DOI 10.1103/RevModPhys.65.413
HAMALAINEN MS, 1994, MED BIOL ENG COMPUT, V32, P35, DOI 10.1007/BF02512476
Hunter JD, 2007, COMPUT SCI ENG, V9, P90, DOI 10.1109/MCSE.2007.55
Hyvarinen A, 2000, NEURAL NETWORKS, V13, P411, DOI 10.1016/S0893-6080(00)00026-5
Klockner A, 2012, PARALLEL COMPUT, V38, P157, DOI 10.1016/j.parco.2011.09.001
Lachaux JP, 1999, HUM BRAIN MAPP, V8, P194, DOI 10.1002/(SICI)1097-0193(1999)8:4<194::AID-HBM4>3.0.CO;2-C
Larson E, 2013, NEUROIMAGE, V64, P365, DOI 10.1016/j.neuroimage.2012.09.006
Litvak V, 2011, COMPUT INTEL NEUROSC, V2011, DOI 10.1155/2011/852961
Maris E, 2007, J NEUROSCI METH, V164, P177, DOI 10.1016/j.jneumeth.2007.03.024
Nichols TE, 2002, HUM BRAIN MAPP, V15, P1, DOI 10.1002/hbm.1058
Nolte G, 2004, CLIN NEUROPHYSIOL, V115, P2292, DOI 10.1016/j.clinph.2004.04.029
Oostenveld R, 2011, COMPUT INTEL NEUROSC, V2011, DOI 10.1155/2011/156869
Pantazis D, 2005, NEUROIMAGE, V25, P383, DOI 10.1016/j.neuroimage.2004.09.040
Pascual-Marqui RD, 2002, METHOD FIND EXP CLIN, V24, P5
Pedregosa F, 2011, J MACH LEARN RES, V12, P2825
Ramachandran P, 2011, COMPUT SCI ENG, V13, P40, DOI 10.1109/MCSE.2011.35
Ridgway GR, 2012, NEUROIMAGE, V59, P2131, DOI 10.1016/j.neuroimage.2011.10.027
Schelter B., 2006, HDB TIME SERIES ANAL
SCHERG M, 1985, ELECTROEN CLIN NEURO, V62, P32, DOI 10.1016/0168-5597(85)90033-4
Schoffelen JM, 2009, HUM BRAIN MAPP, V30, P1857, DOI 10.1002/hbm.20745
Tadel F, 2011, COMPUT INTEL NEUROSC, V2011, DOI 10.1155/2011/879716
TallonBaudry C, 1997, NEUROREPORT, V8, P1103, DOI 10.1097/00001756-199703240-00008
Uusitalo MA, 1997, MED BIOL ENG COMPUT, V35, P135, DOI 10.1007/BF02534144
van der Walt S, 2011, COMPUT SCI ENG, V13, P22, DOI 10.1109/MCSE.2011.37
Van Essen DC, 2012, NEUROIMAGE, V62, P2222, DOI 10.1016/j.neuroimage.2012.02.018
VanVeen BD, 1997, IEEE T BIO-MED ENG, V44, P867, DOI 10.1109/10.623056
WANG JZ, 1992, IEEE T BIO-MED ENG, V39, P665, DOI 10.1109/10.142641
Wipf D, 2009, NEUROIMAGE, V44, P947, DOI 10.1016/j.neuroimage.2008.02.059
Wolters CH, 2007, SIAM J SCI COMPUT, V30, P24, DOI 10.1137/060659053
NR 56
TC 402
Z9 403
U1 4
U2 26
PU FRONTIERS MEDIA SA
PI LAUSANNE
PA AVENUE DU TRIBUNAL FEDERAL 34, LAUSANNE, CH-1015, SWITZERLAND
EI 1662-453X
J9 FRONT NEUROSCI-SWITZ
JI Front. Neurosci.
PY 2013
VL 7
AR 267
DI 10.3389/fnins.2013.00267
PG 13
WC Neurosciences
SC Neurosciences & Neurology
GA AW9HF
UT WOS:000346567300262
PM 24431986
OA DOAJ Gold, Green Published
DA 2021-04-21
ER
PT J
AU Quackenbush, S
Gavin, R
Li, Y
Petriello, F
AF Quackenbush, Seth
Gavin, Ryan
Li, Ye
Petriello, Frank
TI W physics at the LHC with FEWZ 2.1
SO COMPUTER PHYSICS COMMUNICATIONS
LA English
DT Article
DE W; NNLO
AB We present an updated version of the FEWZ (Fully Exclusive Wand Z production) code for the calculation of W-+/- and y* /Z production at next-to-next-to-leading order in the strong coupling. Several new features and observables are introduced, and an order-of-magnitude speed improvement over the performance of FEWZ 2.0 is demonstrated. New phenomenological results for W-+/- production and comparisons with LHC data are presented, and used to illustrate the range of physics studies possible with the features of FEWZ 2.1. We demonstrate with an example the importance of directly comparing fiducial-region measurements with theoretical predictions, rather than first extrapolating them to the full phase space.
Program summary
Program title: FEWZ 2.1
Catalogue identifier: AEJP_v1_1
Program summary URL: http://cpc.cs.qub.ac.uk/summaries/AEJP_v1_1.html
Program obtainable from: CPC Program Library, Queen's University, Belfast, N. Ireland
Licensing provisions: Standard CPC licence, http://cpc.cs.qub.ac.uk/licence/licence.html
No. of lines in distributed program, including test data, etc.: 12003230
No. of bytes in distributed program, including test data, etc.: 769
Distribution format: tar.gz
Programming language: Fortran 77, C++, Python 2.4.
Computer: x86/x86-64.
Operating system: Unix/Linux, Mac OSX.
RAM: 200 Mbytes
Classification: 11.1.
External routines: CUBA (included), LHAPDF (optional)
Catalogue identifier of previous version: AEJP_v1_0
Journal reference of previous version: Comput. Phys. Comm. 182 (2011) 2388
Does the new version supersede the previous version?: Yes
Nature of problem: Calculation of hadroproduction of W bosons, with differential distributions, at next-to-next-to-leading order in the strong coupling.
Solution method: Integral reduction, sector decomposition, numerical integration
Reasons for new version: Reintroduction of W boson to FEWZ 2
Summary of revisions: Addition of W boson production, now can run in W or Z/gamma mode. LHAPDF interface added. Large speed improvements achieved through caching repeat function calls. New observables and histograms. Improved histogram binning.
Additional comments: Running with all histograms on requires approx. 1 GB of disk space to store intermediate files.
!!!!! The distribution file for this program is over 290 Mbytes and therefore is not delivered directly when download or e-mail is requested. Instead an html file giving details of how the program can be obtained is sent !!!!!
Running time: 2 hours to achieve NNLO precision in cross section on multicore machines, up to a few days for quality kinematic distributions. Cluster running is considerably faster. (C) 2012 Elsevier B.V. All rights reserved.
C1 [Quackenbush, Seth; Petriello, Frank] Argonne Natl Lab, Div High Energy Phys, Argonne, IL 60439 USA.
[Gavin, Ryan] Paul Scherrer Inst, CH-5232 Villigen, Switzerland.
[Li, Ye; Petriello, Frank] Northwestern Univ, Dept Phys & Astron, Evanston, IL 60208 USA.
RP Quackenbush, S (corresponding author), Argonne Natl Lab, Div High Energy Phys, Argonne, IL 60439 USA.
EM squackenbush@hep.anl.gov
FU US DOEUnited States Department of Energy (DOE) [DE-AC02-06CH11357];
Swiss National Science FoundationSwiss National Science Foundation
(SNSF)European Commission; [DE-FG02-91ER40684]
FX We thank U. Klein for considerable input and testing during the initial
stages of integrating the W code, and K. Mueller, W. Sakumoto, S.
Stoynev, and H. Yoo for substantial helpful feedback. This research is
supported by the US DOE under contract DE-AC02-06CH11357 and the grant
DE-FG02-91ER40684, and by the Swiss National Science Foundation.
CR Aad G., ARXIV11095141HEPEX A
Alekhin S., ARXIV09083128HEPPH
Anastasiou C, 2004, PHYS REV D, V69, DOI 10.1103/PhysRevD.69.094008
Anastasiou C, 2003, PHYS REV LETT, V91, DOI 10.1103/PhysRevLett.91.182002
Anastasiou C, 2004, PHYS REV D, V69, DOI 10.1103/PhysRevD.69.076010
Anastasiou C, 2005, NUCL PHYS B, V724, P197, DOI 10.1016/j.nuclphysb.2005.06.036
Anastasiou C., 2011, JHEP, V1103, P038
Ball RD, 2012, NUCL PHYS B, V855, P153, DOI 10.1016/j.nuclphysb.2011.09.024
Balossini G, 2010, J HIGH ENERGY PHYS, DOI 10.1007/JHEP01(2010)013
Baur U, 1999, PHYS REV D, V59, DOI 10.1103/PhysRevD.59.013002
Berger C.F., 2011, JHEP, V1104, P092
Bernaciak C, ARXIV12014804HEPPH
Boughezal R., ARXIV11117041HEPPH
Calame CMC, 2006, J HIGH ENERGY PHYS
Cao QH, 2004, PHYS REV LETT, V93, DOI 10.1103/PhysRevLett.93.042001
Catani S., 2010, JHEP, V1005, P006
Catani S, 2009, PHYS REV LETT, V103, DOI 10.1103/PhysRevLett.103.082001
Chatrchyan S., 2011, JHEP, V1110, P132
Cooper-Sarkar A.M., ARXIV11122107HEPPH Z
Dittmaier S, 2002, PHYS REV D, V65, DOI 10.1103/PhysRevD.65.073007
Gavin R, 2011, COMPUT PHYS COMMUN, V182, P2388, DOI 10.1016/j.cpc.2011.06.008
Hamberg R, 2002, NUCL PHYS B, V644, P403, DOI 10.1016/S0550-3213(02)00814-3
HAMBERG R, 1991, NUCL PHYS B, V359, P343, DOI 10.1016/0550-3213(91)90064-5
Jimenez-Delgado P, 2009, PHYS REV D, V80, DOI 10.1103/PhysRevD.80.114011
Kilgore W.B., ARXIV11074798HEPPH
Li Y, 2009, PHYS REV D, V80, DOI 10.1103/PhysRevD.80.014024
Martin AD, 2009, EUR PHYS J C, V63, P189, DOI 10.1140/epjc/s10052-009-1072-5
Melnikov K, 2006, PHYS REV D, V74, DOI 10.1103/PhysRevD.74.114017
Melnikov K, 2006, PHYS REV LETT, V96, DOI 10.1103/PhysRevLett.96.231803
Petriello F, 2008, PHYS REV D, V77, DOI 10.1103/PhysRevD.77.115004
Stewart IW, 2011, PHYS REV LETT, V106, DOI 10.1103/PhysRevLett.106.032001
Stewart IW, 2010, PHYS REV D, V81, DOI 10.1103/PhysRevD.81.094035
Watt G, 2011, J HIGH ENERGY PHYS, DOI 10.1007/JHEP09(2011)069
WHALLEY MR, ARXIVHEPPH0508110, P14013
NR 34
TC 157
Z9 157
U1 0
U2 3
PU ELSEVIER
PI AMSTERDAM
PA RADARWEG 29, 1043 NX AMSTERDAM, NETHERLANDS
SN 0010-4655
EI 1879-2944
J9 COMPUT PHYS COMMUN
JI Comput. Phys. Commun.
PD JAN
PY 2013
VL 184
IS 1
BP 209
EP 214
DI 10.1016/j.cpc.2012.09.005
PG 6
WC Computer Science, Interdisciplinary Applications; Physics, Mathematical
SC Computer Science; Physics
GA 037VK
UT WOS:000311134400025
DA 2021-04-21
ER
PT J
AU de Sa, WP
AF de Sa, W. P.
TI Tokamak TCABR: Acquisition system, data analysis, and remote
participation using MDSplus
SO FUSION ENGINEERING AND DESIGN
LA English
DT Article; Proceedings Paper
CT 8th IAEA Technical Meeting on Control, Data Acquisition, and Remote
Participation for Fusion Research
CY JUN 20-24, 2011
CL San Francisco, CA
DE MDSplus; Remote participation; Data acquisition; Control; Tokamak; TCABR
AB Each plasma physics laboratory has a proprietary scheme to control and data acquisition system. Usually, it is different from one laboratory to another. It means that each laboratory has its own way to control the experiment and retrieving data from the database. Fusion research relies to a great extent on international collaboration and this private system makes it difficult to follow the work remotely. The TCABR data analysis and acquisition system has been upgraded to support a joint research programme using remote participation technologies. The choice of MDSplus (Model Driven System plus) is proved by the fact that it is widely utilized, and the scientists from different institutions may use the same system in different experiments in different tokamaks without the need to know how each system treats its acquisition system and data analysis. Another important point is the fact that the MDSplus has a library system that allows communication between different types of language (JAVA, Fortran, C, C++, Python) and programs such as MATLAB, IDL, OCTAVE. In the case of tokamak TCABR interfaces (object of this paper) between the system already in use and MDSplus were developed, instead of using the MDSplus at all stages, from the control, and data acquisition to the data analysis. This was done in the way to preserve a complex system already in operation and otherwise it would take a long time to migrate. This implementation also allows add new components using the MDSplus fully at all stages. (c) 2012 Elsevier B.V. All rights reserved.
C1 Univ Sao Paulo, Inst Fis, BR-05508090 Sao Paulo, Brazil.
RP de Sa, WP (corresponding author), Univ Sao Paulo, Inst Fis, Rua Matao Travessa R 187,Cidade Univ, BR-05508090 Sao Paulo, Brazil.
EM pires@if.usp.br
RI de Sa, Wanderley/D-8611-2012
OI de Sa, Wanderley/0000-0002-8821-8412
CR de Sa WP, 2010, FUSION ENG DES, V85, P618, DOI 10.1016/j.fusengdes.2010.04.067
Fagundes AN, 2002, BRAZ J PHYS, V32, P50
Fagundes AN, 2000, FUSION ENG DES, V48, P213, DOI 10.1016/S0920-3796(00)00129-0
Fredian TW, 2002, FUSION ENG DES, V60, P229, DOI 10.1016/S0920-3796(02)00013-3
Galvao RMO, 2001, PLASMA PHYS CONTR F, V43, P1181, DOI 10.1088/0741-3335/43/9/302
Neto A, 2007, FUSION ENG DES, V82, P1315, DOI 10.1016/j.fusengdes.2007.05.069
NR 6
TC 3
Z9 3
U1 0
U2 7
PU ELSEVIER SCIENCE SA
PI LAUSANNE
PA PO BOX 564, 1001 LAUSANNE, SWITZERLAND
SN 0920-3796
J9 FUSION ENG DES
JI Fusion Eng. Des.
PD DEC
PY 2012
VL 87
IS 12
SI SI
BP 2199
EP 2202
DI 10.1016/j.fusengdes.2012.04.022
PG 4
WC Nuclear Science & Technology
SC Nuclear Science & Technology
GA 078ZF
UT WOS:000314138900065
DA 2021-04-21
ER
PT J
AU Varilly, P
Angioletti-Uberti, S
Mognetti, BM
Frenkel, D
AF Varilly, Patrick
Angioletti-Uberti, Stefano
Mognetti, Bortolo M.
Frenkel, Daan
TI A general theory of DNA-mediated and other valence-limited colloidal
interactions
SO JOURNAL OF CHEMICAL PHYSICS
LA English
DT Article
ID THERMODYNAMICS
AB We present a general theory for predicting the interaction potentials between DNA-coated colloids, and more broadly, any particles that interact via valence-limited ligand-receptor binding. Our theory correctly incorporates the configurational and combinatorial entropic factors that play a key role in valence-limited interactions. By rigorously enforcing self-consistency, it achieves near-quantitative accuracy with respect to detailed Monte Carlo calculations. With suitable approximations and in particular geometries, our theory reduces to previous successful treatments, which are now united in a common and extensible framework. We expect our tools to be useful to other researchers investigating ligand-mediated interactions. A complete and well-documented Python implementation is freely available at http://github.com/patvarilly/DNACC. (C) 2012 American Institute of Physics. [http://dx.doi.org/10.1063/1.4748100]
C1 [Varilly, Patrick; Angioletti-Uberti, Stefano; Mognetti, Bortolo M.; Frenkel, Daan] Univ Cambridge, Dept Chem, Cambridge CB2 1EW, England.
RP Varilly, P (corresponding author), Univ Cambridge, Dept Chem, Lensfield Rd, Cambridge CB2 1EW, England.
RI Mognetti, Bortolo Matteo/C-6255-2008; Angioletti-Uberti,
Stefano/U-9083-2019; Frenkel, Daan/G-2580-2014; Varilly,
Patrick/C-8118-2013
OI Mognetti, Bortolo Matteo/0000-0002-7960-8224; Angioletti-Uberti,
Stefano/0000-0003-2917-2415; Frenkel, Daan/0000-0002-6362-2021; Varilly,
Patrick/0000-0003-4619-8174
FU Engineering and Physical Sciences Research CouncilUK Research &
Innovation (UKRI)Engineering & Physical Sciences Research Council
(EPSRC) [EP/I000844/1, EP/I001352/1] Funding Source: researchfish
CR Alivisatos AP, 1996, NATURE, V382, P609, DOI 10.1038/382609a0
Angioletti-Uberti S, 2012, NAT MATER, V11, P518, DOI [10.1038/nmat3314, 10.1038/NMAT3314]
Badjic JD, 2005, ACCOUNTS CHEM RES, V38, P723, DOI 10.1021/ar040223k
BELL GI, 1984, BIOPHYS J, V45, P1051, DOI 10.1016/S0006-3495(84)84252-6
Biancaniello PL, 2005, PHYS REV LETT, V94, DOI 10.1103/PhysRevLett.94.058302
Chen Q, 2011, NATURE, V469, P381, DOI 10.1038/nature09713
Dreyfus R, 2010, PHYS REV E, V81, DOI 10.1103/PhysRevE.81.041404
Dreyfus R, 2009, PHYS REV LETT, V102, DOI 10.1103/PhysRevLett.102.048301
Estro L. A., 2006, FRAGMENT BASED APPRO, P11, DOI DOI 10.1002/3527608761.CH2
Frenkel D., 2001, UNDERSTANDING MOL SI
Geerts N, 2010, SOFT MATTER, V6, P4647, DOI 10.1039/c001603a
Hunter RJ, 2000, FDN COLLOID SCI
Kern N, 2003, J CHEM PHYS, V118, P9882, DOI 10.1063/1.1569473
Leunissen ME, 2011, J CHEM PHYS, V134, DOI 10.1063/1.3557794
Leunissen ME, 2010, J AM CHEM SOC, V132, P1903, DOI 10.1021/ja907919j
Licata NA, 2006, PHYS REV E, V74, DOI 10.1103/PhysRevE.74.041408
LIU JS, 2002, MONTE CARLO STRATEGI
Macfarlane RJ, 2011, SCIENCE, V334, P204, DOI 10.1126/science.1210493
Mammen M, 1998, ANGEW CHEM INT EDIT, V37, P2755
Markham NR, 2005, NUCLEIC ACIDS RES, V33, pW577, DOI 10.1093/nar/gki591
Martinez-Veracoechea FJ, 2011, P NATL ACAD SCI USA, V108, P10963, DOI 10.1073/pnas.1105351108
Maye MM, 2010, NAT NANOTECHNOL, V5, P116, DOI [10.1038/nnano.2009.378, 10.1038/NNANO.2009.378]
Mirkin CA, 1996, NATURE, V382, P607, DOI 10.1038/382607a0
Mognetti BM, 2012, SOFT MATTER, V8, P2213, DOI 10.1039/c2sm06635a
Mognetti BM, 2012, P NATL ACAD SCI USA, V109, pE378, DOI 10.1073/pnas.1119991109
Nykypanchuk D, 2008, NATURE, V451, P549, DOI 10.1038/nature06560
Nykypanchuk D, 2007, LANGMUIR, V23, P6305, DOI 10.1021/la0637566
Ouldridge TE, 2011, J CHEM PHYS, V134, DOI 10.1063/1.3552946
Park SY, 2008, NATURE, V451, P553, DOI 10.1038/nature06508
Rogers WB, 2012, P NATL ACAD SCI USA, V109, pE380, DOI 10.1073/pnas.1121102109
Rogers WB, 2011, P NATL ACAD SCI USA, V108, P15687, DOI 10.1073/pnas.1109853108
Rosi NL, 2005, CHEM REV, V105, P1547, DOI 10.1021/cr030067f
Rothemund PWK, 2006, NATURE, V440, P297, DOI 10.1038/nature04586
SantaLucia J, 2004, ANNU REV BIOPH BIOM, V33, P415, DOI 10.1146/annurev.biophys.32.110601.141800
Tan ZQ, 2012, J CHEM PHYS, V136, DOI 10.1063/1.3701175
Taussky O., 1949, AM MATH MON, V56, P672, DOI DOI 10.1080/00029890.1949.11990209
Torquato S, 2009, SOFT MATTER, V5, P1157, DOI 10.1039/b814211b
TRELOAR LRG, 1946, T FARADAY SOC, V42, P77, DOI 10.1039/tf9464200077
Valignat MP, 2005, P NATL ACAD SCI USA, V102, P4225, DOI 10.1073/pnas.0500507102
Yamakawa H., 1971, MODERN THEORY POLYM
Zaccarelli E, 2006, J CHEM PHYS, V124, DOI 10.1063/1.2177241
Zhu FQ, 2012, J COMPUT CHEM, V33, P453, DOI 10.1002/jcc.21989
NR 42
TC 73
Z9 74
U1 3
U2 59
PU AMER INST PHYSICS
PI MELVILLE
PA 1305 WALT WHITMAN RD, STE 300, MELVILLE, NY 11747-4501 USA
SN 0021-9606
EI 1089-7690
J9 J CHEM PHYS
JI J. Chem. Phys.
PD SEP 7
PY 2012
VL 137
IS 9
AR 094108
DI 10.1063/1.4748100
PG 15
WC Chemistry, Physical; Physics, Atomic, Molecular & Chemical
SC Chemistry; Physics
GA 010JZ
UT WOS:000309092000016
PM 22957556
DA 2021-04-21
ER
PT J
AU Hanwell, MD
Curtis, DE
Lonie, DC
Vandermeersch, T
Zurek, E
Hutchison, GR
AF Hanwell, Marcus D.
Curtis, Donald E.
Lonie, David C.
Vandermeersch, Tim
Zurek, Eva
Hutchison, Geoffrey R.
TI Avogadro: an advanced semantic chemical editor, visualization, and
analysis platform
SO JOURNAL OF CHEMINFORMATICS
LA English
DT Article
ID BLUE-OBELISK; CHEMISTRY; PROGRAM; SIMULATIONS; EFFICIENT; LANGUAGE;
CRYSTAL
AB Background: The Avogadro project has developed an advanced molecule editor and visualizer designed for cross-platform use in computational chemistry, molecular modeling, bioinformatics, materials science, and related areas. It offers flexible, high quality rendering, and a powerful plugin architecture. Typical uses include building molecular structures, formatting input files, and analyzing output of a wide variety of computational chemistry packages. By using the CML file format as its native document type, Avogadro seeks to enhance the semantic accessibility of chemical data types.
Results: The work presented here details the Avogadro library, which is a framework providing a code library and application programming interface (API) with three-dimensional visualization capabilities; and has direct applications to research and education in the fields of chemistry, physics, materials science, and biology. The Avogadro application provides a rich graphical interface using dynamically loaded plugins through the library itself. The application and library can each be extended by implementing a plugin module in C++ or Python to explore different visualization techniques, build/manipulate molecular structures, and interact with other programs. We describe some example extensions, one which uses a genetic algorithm to find stable crystal structures, and one which interfaces with the PackMol program to create packed, solvated structures for molecular dynamics simulations. The 1.0 release series of Avogadro is the main focus of the results discussed here.
Conclusions: Avogadro offers a semantic chemical builder and platform for visualization and analysis. For users, it offers an easy-to-use builder, integrated support for downloading from common databases such as PubChem and the Protein Data Bank, extracting chemical data from a wide variety of formats, including computational chemistry output, and native, semantic support for the CML file format. For developers, it can be easily extended via a powerful plugin mechanism to support new features in organic chemistry, inorganic complexes, drug design, materials, biomolecules, and simulations. Avogadro is freely available under an open-source license from http://avogadro.openmolecules.net.
C1 [Hanwell, Marcus D.; Hutchison, Geoffrey R.] Univ Pittsburgh, Dept Chem, Pittsburgh, PA 15260 USA.
[Hanwell, Marcus D.] Kitware Inc, Dept Comp Sci, Clifton Pk, NY 12065 USA.
[Curtis, Donald E.] Coe Coll, Dept Comp Sci, Cedar Rapids, IA 52402 USA.
[Lonie, David C.; Zurek, Eva] SUNY Buffalo, Dept Chem, Buffalo, NY 14260 USA.
RP Hanwell, MD (corresponding author), Univ Pittsburgh, Dept Chem, 219 Parkman Ave, Pittsburgh, PA 15260 USA.
EM marcus.hanwell@kitware.com
RI Hutchison, Geoffrey/B-3109-2009; Zurek, Eva/J-4387-2012
OI Hutchison, Geoffrey/0000-0002-1757-1980; Zurek, Eva/0000-0003-0738-867X;
Hanwell, Marcus/0000-0002-5851-5272
FU University of PittsburghUniversity of Pittsburgh; Engineering Research
Development Center [W912HZ-11-P-0019]; NSFNational Science Foundation
(NSF) [DMR-1005413]
FX We wish to thank the many contributors to the Avogadro project,
including developers, testers, translators, and users. We thank
SourceForge for providing resources for issue tracking and managing
releases, Launchpad for hosting language translations, and Kitware for
additional dashboard resources. MDH and GRH thank the University of
Pittsburgh for support. DEC would like to thank Jan Halborg Jensen for
designing the GAMESS-US interface and supporting Avogadro in its
infancy; believing Avogadro could be better than what was available. MDH
acknowledges the Engineering Research Development Center
(W912HZ-11-P-0019) for financial support. EZ and DL acknowledge the NSF
(DMR-1005413) for financial support.
CR Adams S, 2011, J CHEMINFORMATICS, V3, DOI 10.1186/1758-2946-3-38
[Anonymous], 2012, CHEMBIO3D
[Anonymous], 2012, QT FRAMEWORK
[Anonymous], 2012, HYPERCHEM
[Anonymous], 2012, AVOGADRO DOWNLOADS
[Anonymous], 2012, PYTHON SCRIPTING AVO
[Anonymous], 2012, GNU GEN PUBL LIC V2
[Anonymous], 2012, OPENQUBE SOURCE
[Anonymous], 2012, COMPILING AVOGADRO L
[Anonymous], 2012, PYQT SCRIPTING EXAMP
[Anonymous], 2012, AVOGADRO TRANSLATION
[Anonymous], 2012, AVOGADRO CITATIONS G
[Anonymous], 2012, GAUSSVIEW 5, V5
[Anonymous], 2012, SCIGRESS
[Anonymous], 2012, SPARTAN
[Anonymous], 2012, PYTHON EXTENSIONS AV
[Anonymous], 2012, COMPILING AVOGADRO W
Bernstein N, 2010, J PHYS CHEM A, V114, P11948, DOI 10.1021/jp103447w
Bingol B, 2013, J PHYS CHEM B, V117, P4177, DOI 10.1021/jp3010053
Bode BM, 1998, J MOL GRAPH MODEL, V16, P133, DOI 10.1016/S1093-3263(99)00002-9
Burkhardt SE, 2011, J MATER CHEM, V21, P9553, DOI 10.1039/c1jm10664c
Burkhardt SE, 2010, J PHYS CHEM C, V114, P16776, DOI 10.1021/jp106082f
Closser KD, 2010, J PHYS CHEM A, V114, P8023, DOI 10.1021/jp103532q
DeLano W. L., 2002, PYMOL MOL GRAPHICS S
Fleisher AJ, 2011, CHEMPHYSCHEM, V12, P1808, DOI 10.1002/cphc.201100038
Forster S, 2012, J PHYS ORG CHEM
Gilbert ATB, 2008, QUI THE Q CHEM USER
Guennebaud G., 2010, EIGEN V2
Guha R, 2006, J CHEM INF MODEL, V46, P991, DOI 10.1021/ci050400b
Hanson RM, 2012, JMOL OPEN SOURCE JAV
Hanwell MD, 2012, OPENQUBE
Hassinen T, 2012, GHEMICAL
Hlawacek G, 2011, NANO LETT, V11, P333, DOI 10.1021/nl103739n
Hu WH, 2011, J PHYS CHEM LETT, V2, P1925, DOI 10.1021/jz200729a
Humphrey W, 1996, J MOL GRAPH MODEL, V14, P33, DOI 10.1016/0263-7855(96)00018-5
Ide T, 2011, J ORG CHEM, V76, P9504, DOI 10.1021/jo201650t
James CA, 2012, OPENSMILES
Kapla J, 2012, J PHYS CHEM B, V116, P244, DOI 10.1021/jp209268p
Kokalj A, 2003, COMP MATER SCI, V28, P155, DOI 10.1016/S0927-0256(03)00104-6
Kokalj A, 1999, J MOL GRAPH MODEL, V17, P176, DOI 10.1016/S1093-3263(99)00028-5
Kudin K.N., 2013, GAUSSIAN 09 REVISION
Lonie D, 2011, XTALOPT
Lonie DC, 2011, COMPUT PHYS COMMUN, V182, P372, DOI 10.1016/j.cpc.2010.07.048
Madison TA, 2011, J PHYS CHEM C, V115, P17558, DOI 10.1021/jp2047085
Mandal D, 2012, J PHYS CHEM A, V116, P2536, DOI 10.1021/jp2100057
Martinez JM, 2003, J COMPUT CHEM, V24, P819, DOI 10.1002/jcc.10216
Martinez L, 2009, J COMPUT CHEM, V30, P2157, DOI 10.1002/jcc.21224
Mayorkas N, 2011, PHYS CHEM CHEM PHYS, V13, P6808, DOI 10.1039/c0cp02334e
Mehlhorn K, 2012, BALLVIEW
Menegazzo N, 2012, NEW J CHEM, V36, P963, DOI 10.1039/c2nj20930f
Mera-Adasme R, 2011, J PHYS CHEM A, V115, P4397, DOI 10.1021/jp107498h
Momma K, 2011, J APPL CRYSTALLOGR, V44, P1272, DOI 10.1107/S0021889811038970
Murray-Rust P, 2011, J CHEMINFORMATICS, V3, DOI 10.1186/1758-2946-3-43
Murray-Rust P, 2011, J CHEMINFORMATICS, V3, DOI 10.1186/1758-2946-3-44
O'Boyle NM, 2011, J CHEMINFORMATICS, V3, DOI 10.1186/1758-2946-3-37
O'Boyle NM, 2011, J CHEMINFORMATICS, V3, DOI 10.1186/1758-2946-3-33
Patel DG, 2011, J POLYM SCI POL PHYS, V49, P557, DOI 10.1002/polb.22224
Popov AV, 2011, PHYS CHEM CHEM PHYS, V13, P14914, DOI 10.1039/c1cp20952c
SAYLE RA, 1995, TRENDS BIOCHEM SCI, V20, P374, DOI 10.1016/S0968-0004(00)89080-5
Schaftenaar G, 2000, J COMPUT AID MOL DES, V14, P123, DOI 10.1023/A:1008193805436
SCHMIDT MW, 1993, J COMPUT CHEM, V14, P1347, DOI 10.1002/jcc.540141112
Schutz M., 2010, MOLPRO VERSION 2010
Shao Y, 2006, PHYS CHEM CHEM PHYS, V8, P3172, DOI 10.1039/b517914a
Sitzmann M, 2008, SAR QSAR ENVIRON RES, V19, P1, DOI 10.1080/10629360701843540
Stewart J. J. P, 2008, MOPAC2009
Tarini M, 2006, IEEE T VIS COMPUT GR, V12, P1237, DOI 10.1109/TVCG.2006.115
Thomas J, 2009, CCP1 GUI PROJECT
Tian HN, 2011, J MATER CHEM, V21, P12462, DOI 10.1039/c1jm12071a
Valiev M, 2010, COMPUT PHYS COMMUN, V181, P1477, DOI 10.1016/j.cpc.2010.04.018
Vandermeersch T, 2012, AVOGADRO PYTHON TERM
Vandermeersch T, 2009, PYTHON EXAMPLE
WEININGER D, 1988, J CHEM INF COMP SCI, V28, P31, DOI 10.1021/ci00057a005
Werner HJ, 2012, WIRES COMPUT MOL SCI, V2, P242, DOI 10.1002/wcms.82
Yao CJ, 2010, INORG CHEM, V49, P8347, DOI 10.1021/ic100857y
2001, MAT STUDIO
NR 75
TC 2893
Z9 2916
U1 17
U2 371
PU BMC
PI LONDON
PA CAMPUS, 4 CRINAN ST, LONDON N1 9XW, ENGLAND
SN 1758-2946
J9 J CHEMINFORMATICS
JI J. Cheminformatics
PD AUG 13
PY 2012
VL 4
AR 17
DI 10.1186/1758-2946-4-17
PG 17
WC Chemistry, Multidisciplinary; Computer Science, Information Systems;
Computer Science, Interdisciplinary Applications
SC Chemistry; Computer Science
GA 077JO
UT WOS:000314024000001
PM 22889332
OA DOAJ Gold, Green Published
DA 2021-04-21
ER
PT J
AU Wimmer, M
AF Wimmer, M.
TI Algorithm 923: Efficient Numerical Computation of the Pfaffian for Dense
and Banded Skew-Symmetric Matrices
SO ACM TRANSACTIONS ON MATHEMATICAL SOFTWARE
LA English
DT Article
DE Algorithms; Performance; Pfaffian; skew-symmetric matrix; canonical
form; unitary congruence; topological charge
AB Computing the Pfaffian of a skew-symmetric matrix is a problem that arises in various fields of physics. Both computing the Pfaffian and a related problem, computing the canonical form of a skew-symmetric matrix under unitary congruence, can be solved easily once the skew-symmetric matrix has been reduced to skew-symmetric tridiagonal form. We develop efficient numerical methods for computing this tridiagonal form based on Gaussian elimination, using a skew-symmetric, blocked form of the Parlett-Reid algorithm, or based on unitary transformations, using block Householder transformations and Givens rotations, that are applicable to dense and banded matrices, respectively. We also give a complete and fully optimized implementation of these algorithms in Fortran (including a C interface), and also provide Python, Matlab and Mathematica implementations for convenience. Finally, we apply these methods to compute the topological charge of a class D nanowire, and show numerically the equivalence of definitions based on the Hamiltonian and the scattering matrix.
C1 Leiden Univ, Inst Lorentz, NL-2300 RA Leiden, Netherlands.
RP Wimmer, M (corresponding author), Leiden Univ, Inst Lorentz, POB 9506, NL-2300 RA Leiden, Netherlands.
EM wimmer@lorentz.leidenuniv.nl
RI Wimmer, Michael/B-1052-2011
OI Wimmer, Michael/0000-0001-6654-2310
FU German academic exchange service DAADDeutscher Akademischer Austausch
Dienst (DAAD)
FX The author acknowledges support from the German academic exchange
service DAAD.
CR Aasen J. O., 1971, BIT (Nordisk Tidskrift for Informationsbehandling), V11, P233, DOI 10.1007/BF01931804
Akhmerov AR, 2011, PHYS REV LETT, V106, DOI 10.1103/PhysRevLett.106.057001
Anderson E., 1999, LAPACK USERS GUIDE
Bajdich M, 2008, PHYS REV B, V77, DOI 10.1103/PhysRevB.77.115112
Bajdich M, 2009, ACTA PHYS SLOVACA, V59, P81, DOI 10.2478/v10155-010-0095-7
Bardarson JH, 2008, J PHYS A-MATH THEOR, V41, DOI 10.1088/1751-8113/41/40/405203
Beri B, 2009, PHYS REV B, V79, DOI 10.1103/PhysRevB.79.245315
BUNCH JR, 1977, MATH COMPUT, V31, P163, DOI 10.2307/2005787
BUNCH JR, 1982, MATH COMPUT, V38, P475, DOI 10.2307/2007283
Evers F, 2008, REV MOD PHYS, V80, P1355, DOI 10.1103/RevModPhys.80.1355
Fu L, 2007, PHYS REV B, V76, DOI 10.1103/PhysRevB.76.045302
Fulga IC, 2011, PHYS REV B, V83, DOI 10.1103/PhysRevB.83.155429
GALBIATI G, 1994, DISCRETE APPL MATH, V51, P269, DOI 10.1016/0166-218X(92)00034-J
GIBBS NE, 1976, SIAM J NUMER ANAL, V13, P236, DOI 10.1137/0713023
Golub GH., 1996, MATRIX COMPUTATIONS
Gonzalez-Ballestero C, 2011, COMPUT PHYS COMMUN, V182, P2213, DOI 10.1016/j.cpc.2011.04.025
HAAKE F, 2004, QUANTUM SIGNATURES C
Hastings MB, 2011, ANN PHYS-NEW YORK, V326, P1699, DOI 10.1016/j.aop.2010.12.013
Higham N. J., 2002, ACCURACY STABILITY N
Hua LK, 1944, AM J MATH, V66, P470, DOI 10.2307/2371910
Irony D, 2006, SIAM J MATRIX ANAL A, V28, P398, DOI 10.1137/040610106
KAUFMAN L, 1984, ACM T MATH SOFTWARE, V10, P73, DOI 10.1145/356068.356074
Kaufman L, 2000, ACM T MATH SOFTWARE, V26, P551, DOI 10.1145/365723.365733
Kaufman L, 2007, NUMER LINEAR ALGEBR, V14, P237, DOI 10.1002/nla.529
KITAEV A. Y., 2001, PHYS USPEKHI, V44, P131
Krauth W, 2006, STAT MECH ALGORITHMS
LEHOUCQ R., 1995, 72 LAPACK U TENN
Lutchyn RM, 2010, PHYS REV LETT, V105, DOI 10.1103/PhysRevLett.105.077001
Montvay I, 1996, NUCL PHYS B, V466, P259, DOI 10.1016/0550-3213(96)00086-7
MOORE G, 1991, NUCL PHYS B, V360, P362, DOI 10.1016/0550-3213(91)90407-O
Oreg Y, 2010, PHYS REV LETT, V105, DOI 10.1103/PhysRevLett.105.177002
Parlett BN, 1970, BIT, V10, P386, DOI DOI 10.1007/BF01934207
Potter AC, 2010, PHYS REV LETT, V105, DOI 10.1103/PhysRevLett.105.227003
Rote G, 2001, LECT NOTES COMPUT SC, V2122, P119
Rozloznik M, 2011, ACM T MATH SOFTWARE, V37, DOI 10.1145/1916461.1916462
Rubow J, 2011, COMPUT PHYS COMMUN, V182, P2530, DOI 10.1016/j.cpc.2011.07.010
SCHWARZ HR, 1968, NUMER MATH, V12, P231, DOI 10.1007/BF02162505
Stander J W, 1960, CAN J MATH, V12, P438, DOI 10.4153/cjm-1960-038-2
Thomas CK, 2009, PHYS REV E, V80, DOI 10.1103/PhysRevE.80.046708
Ward R. C., 1978, ACM Transactions on Mathematical Software, V4, P278, DOI 10.1145/355791.355798
Ward R. C., 1978, ACM Transactions on Mathematical Software, V4, P286, DOI 10.1145/355791.355799
Wimmer M, 2010, PHYS REV LETT, V105, DOI 10.1103/PhysRevLett.105.046803
Wimmer M, 2009, J COMPUT PHYS, V228, P8548, DOI 10.1016/j.jcp.2009.08.001
NR 43
TC 68
Z9 68
U1 1
U2 5
PU ASSOC COMPUTING MACHINERY
PI NEW YORK
PA 2 PENN PLAZA, STE 701, NEW YORK, NY 10121-0701 USA
SN 0098-3500
EI 1557-7295
J9 ACM T MATH SOFTWARE
JI ACM Trans. Math. Softw.
PD AUG
PY 2012
VL 38
IS 4
AR 30
DI 10.1145/2331130.2331138
PG 17
WC Computer Science, Software Engineering; Mathematics, Applied
SC Computer Science; Mathematics
GA 999RE
UT WOS:000308331100008
DA 2021-04-21
ER
PT J
AU Johansson, JR
Nation, PD
Nori, F
AF Johansson, J. R.
Nation, P. D.
Nori, Franco
TI QuTiP: An open-source Python framework for the dynamics of open quantum
systems
SO COMPUTER PHYSICS COMMUNICATIONS
LA English
DT Article
DE Open quantum systems; Lindblad master equation; Quantum Monte Carlo;
Python
ID SUPERCONDUCTING CIRCUITS; GROUND-STATE; OPTICS; PHYSICS; AMPLIFICATION;
PHOTON; CAVITY; JUMPS; IONS
AB We present an object-oriented open-source framework for solving the dynamics of open quantum systems written in Python. Arbitrary Hamiltonians, including time-dependent systems, may be built up from operators and states defined by a quantum object class, and then passed on to a choice of master equation or Monte Carlo solvers. We give an overview of the basic structure for the framework before detailing the numerical simulation of open system dynamics. Several examples are given to illustrate the build up to a complete calculation. Finally, we measure the performance of our library against that of current implementations. The framework described here is particularly well suited to the fields of quantum optics, superconducting circuit devices, nanomechanics, and trapped ions, while also being ideal for use in classroom instruction.
Program summary
Program title: QuTiP: The Quantum Toolbox in Python
Catalogue identifier: AEMB_v1_0
Program summary URL: http://cpc.cs.qub.ac.uk/summaries/AEMB_v1_0.html
Program obtainable from: CPC Program Library, Queen's University, Belfast, N. Ireland
Licensing provisions: GNU General Public License, version 3
No. of lines in distributed program, including test data, etc.: 16482
No. of bytes in distributed program, including test data, etc.: 213 438
Distribution format: tar.gz
Programming language: Python
Computer: i386, x86-64
Operating system: Linux, Mac OSX, Windows
RAM: 2+ Gigabytes
Classification: 7
External routines: NumPy (http://numpy.scipy.org/), SciPy (http://www.scipy.org/), Matplotlib (http://matplotlib.sourceforge.net/)
Nature of problem: Dynamics of open quantum systems.
Solution method: Numerical solutions to Lindblad master equation or Monte Carlo wave function method.
Restrictions: Problems must meet the criteria for using the master equation in Lindblad form.
Running time: A few seconds up to several tens of minutes, depending on size of underlying Hilbert space. (C) 2012 Elsevier B.V. All rights reserved.
C1 [Johansson, J. R.; Nation, P. D.; Nori, Franco] RIKEN, Adv Sci Inst, Wako, Saitama 3510198, Japan.
[Nation, P. D.; Nori, Franco] Univ Michigan, Dept Phys, Ann Arbor, MI 48109 USA.
RP Johansson, JR (corresponding author), RIKEN, Adv Sci Inst, 2-1 Hirosawa, Wako, Saitama 3510198, Japan.
EM robert@riken.jp; pnation@riken.jp
RI Johansson, Robert/C-6224-2008; Nation, Paul/E-9119-2010; Nori,
Franco/B-1222-2009
OI Johansson, Robert/0000-0002-4500-5775; Nation, Paul/0000-0002-0045-6118;
Nori, Franco/0000-0003-3682-7432
FU Japanese Society for the Promotion of Science (JSPS)Ministry of
Education, Culture, Sports, Science and Technology, Japan (MEXT)Japan
Society for the Promotion of Science [P11501, P11202]; National Science
Foundation (NSF)National Science Foundation (NSF) [0726909];
MEXTMinistry of Education, Culture, Sports, Science and Technology,
Japan (MEXT); JSPS-FIRST; [2301202]; Grants-in-Aid for Scientific
ResearchMinistry of Education, Culture, Sports, Science and Technology,
Japan (MEXT)Japan Society for the Promotion of ScienceGrants-in-Aid for
Scientific Research (KAKENHI) [21102002, 22224007, 11F01501] Funding
Source: KAKEN
FX J.R.J. and P.D.N. were supported by Japanese Society for the Promotion
of Science (JSPS) Foreign Postdoctoral Fellowship No. P11501 and P11202,
respectively. P.D.N. also acknowledges support from Kakenhi grant No.
2301202, and F.N. acknowledges partial support from the LPS, NSA, ARO,
DARPA, AFOSR, National Science Foundation (NSF) grant No. 0726909,
Grant-in-Aid for Scientific Research (S), MEXT Kakenhi on Quantum
Cybernetics, and the JSPS-FIRST program.
CR Armour AD, 2008, NEW J PHYS, V10, DOI 10.1088/1367-2630/10/9/095004
Ashhab S, 2010, PHYS REV A, V81, DOI 10.1103/PhysRevA.81.042311
Behnel S, 2011, COMPUT SCI ENG, V13, P31, DOI 10.1109/MCSE.2010.118
Blatt R, 2008, NATURE, V453, P1008, DOI 10.1038/nature07125
Blencowe MP, 2008, NEW J PHYS, V10, DOI 10.1088/1367-2630/10/9/095005
Breuer HP., 2002, THEORY OPEN QUANTUM
Buluta I, 2011, REP PROG PHYS, V74, DOI 10.1088/0034-4885/74/10/104401
Buluta I, 2009, SCIENCE, V326, P108, DOI 10.1126/science.1177838
Cao X, 2011, NEW J PHYS, V13, DOI 10.1088/1367-2630/13/7/073002
Casanova J, 2010, PHYS REV LETT, V105, DOI 10.1103/PhysRevLett.105.263603
DALIBARD J, 1992, PHYS REV LETT, V68, P580, DOI 10.1103/PhysRevLett.68.580
DUM R, 1992, PHYS REV A, V45, P4879, DOI 10.1103/PhysRevA.45.4879
FEYNMAN RP, 1982, INT J THEOR PHYS, V21, P467, DOI 10.1007/BF02650179
Forn-Diaz P, 2010, PHYS REV LETT, V105, DOI 10.1103/PhysRevLett.105.237001
Gardiner C. W., 2004, QUANTUM NOISE
Gleyzes S, 2007, NATURE, V446, P297, DOI 10.1038/nature05589
Guerlin C, 2007, NATURE, V448, P889, DOI 10.1038/nature06057
Haroche S., 2006, EXPLORING QUANTUM AT
Horvath GZK, 1997, CONTEMP PHYS, V38, P25
Hunter JD, 2007, COMPUT SCI ENG, V9, P90, DOI 10.1109/MCSE.2007.55
Jones E, 2011, SCIPY OPEN SOURCE SC
Klockner Andreas, 2011, PYOPENCL
LINDBLAD G, 1976, COMMUN MATH PHYS, V48, P119, DOI 10.1007/BF01608499
Matlab, 2011, MATL
MOLLOW BR, 1967, PHYS REV, V160, P1076, DOI 10.1103/PhysRev.160.1076
MOLMER K, 1993, J OPT SOC AM B, V10, P524, DOI 10.1364/JOSAB.10.000524
Nataf P, 2010, PHYS REV LETT, V104, DOI 10.1103/PhysRevLett.104.023601
Nation P.D., 2011, QUTIP QUANTUM TOOLBO
Nation PD, 2010, NEW J PHYS, V12, DOI 10.1088/1367-2630/12/9/095013
Nielsen M. A., 2000, QUANTUM COMPUTATION
O'Brien JL, 2009, NAT PHOTONICS, V3, P687, DOI 10.1038/nphoton.2009.229
O'Connell AD, 2010, NATURE, V464, P697, DOI 10.1038/nature08967
Oliphant TE, 2007, COMPUT SCI ENG, V9, P10, DOI 10.1109/MCSE.2007.58
Plenio MB, 1998, REV MOD PHYS, V70, P101, DOI 10.1103/RevModPhys.70.101
Schack R, 1997, COMPUT PHYS COMMUN, V102, P210, DOI 10.1016/S0010-4655(97)00019-2
Schoelkopf RJ, 2008, NATURE, V451, P664, DOI 10.1038/451664a
Schuch N, 2003, PHYS REV A, V67, DOI 10.1103/PhysRevA.67.032301
Shevchenko SN, 2010, PHYS REP, V492, P1, DOI 10.1016/j.physrep.2010.03.002
Steffen M, 2006, SCIENCE, V313, P1423, DOI 10.1126/science.1130886
Tan SM, 1999, J OPT B-QUANTUM S O, V1, P424, DOI 10.1088/1464-4266/1/4/312
TAVIS M, 1968, PHYS REV, V170, P379, DOI 10.1103/PhysRev.170.379
Teufel JD, 2011, NATURE, V475, P359, DOI 10.1038/nature10261
Vamivakas AN, 2010, NATURE, V467, P297, DOI 10.1038/nature09359
van Rossum G, 2011, PYTHON PROGRAMMING L
Vukics A, 2007, EUR PHYS J D, V44, P585, DOI 10.1140/epjd/e2007-00210-x
Walls D.F., 2008, QUANTUM OPTICS
WALLS DF, 1970, PHYS REV A, V1, P446, DOI 10.1103/PhysRevA.1.446
You JQ, 2011, NATURE, V474, P589, DOI 10.1038/nature10122
You JQ, 2005, PHYS TODAY, V58, P42, DOI 10.1063/1.2155757
NR 49
TC 437
Z9 443
U1 5
U2 45
PU ELSEVIER SCIENCE BV
PI AMSTERDAM
PA PO BOX 211, 1000 AE AMSTERDAM, NETHERLANDS
SN 0010-4655
J9 COMPUT PHYS COMMUN
JI Comput. Phys. Commun.
PD AUG
PY 2012
VL 183
IS 8
BP 1760
EP 1772
DI 10.1016/j.cpc.2012.02.021
PG 13
WC Computer Science, Interdisciplinary Applications; Physics, Mathematical
SC Computer Science; Physics
GA 946VB
UT WOS:000304384500024
DA 2021-04-21
ER
PT J
AU Nunio, F
Manil, P
AF Nunio, F.
Manil, P.
TI SALOME as a Platform for Magneto-Mechanical Simulation
SO IEEE TRANSACTIONS ON APPLIED SUPERCONDUCTIVITY
LA English
DT Article; Proceedings Paper
CT 22nd International Conference on Magnet Technology (MT)
CY SEP 12-16, 2011
CL ITER Org, Marseille, FRANCE
SP PACA Reg, CEA, IEEE CSC, Iberdrola Ingenieria & Construcc, SAU, Oxford Superconducting Technol, R KIND, Super Power Inc, Western Superconducting Technol Co Ltd
HO ITER Org
DE Computer simulation; open source software; superconducting magnets
AB The design of superconducting magnets for accelerator and detector components dedicated to the future of high energy physics requires detailed analysis of their mechanical behavior, in particular under magnetic and/or thermal solicitations. Numerical simulation is more and more used to optimize these components and to improve their performances in constrained environments.
In this context, the numerical modeling process may require to operate several computing codes, which involves the implementation of spatial discretization (meshes) that are not always compatible together. This situation raises the problem of data transfer and of possible dispersion of simulation parameters. Moreover, the numerical modeling process is not always formalized, and it can be difficult to iterate in the case of parametric studies.
This paper describes the implementation of magneto-mechanical numerical processes into the SALOME open source platform. SALOME is a numerical framework which offers interoperability between Computer-Aided Design (CAD) modeling and Computer-Aided Engineering (CAE) simulation software. It makes the implementation of coupling between computing codes (computation schemes) accessible. This simulation platform also provides a generic and efficient user interface, and is as well fully scriptable in Python language.
On the basis of the design of the SMC dipole magnet, the authors will describe the various modules of the platform (geometry, mesh, supervision and visualization), and present the status of the developments in progress. It will be pointed out what can be gained for magnet designers in terms of process formalization and transfer of know-how, and what is the level of complexity for the development of a dedicated software tool.
C1 [Nunio, F.; Manil, P.] CEA, Irfu, Ctr Saclay, F-91191 Gif Sur Yvette, France.
RP Nunio, F (corresponding author), CEA, Irfu, Ctr Saclay, F-91191 Gif Sur Yvette, France.
EM francois.nunio@cea.fr
OI Nunio, Francois/0000-0002-2485-2422
CR Bergeaud V., 2010, SNA MC 2010 C HIT ME, P21
Lyly Mikko, ELMER FINITE ELEMENT, P156
Manil P, 2010, IEEE T APPL SUPERCON, V20, P184, DOI 10.1109/TASC.2009.2039343
Verpaux P., 1988, CALCUL STRUCTURES IN, P261
NR 4
TC 2
Z9 3
U1 0
U2 6
PU IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
PI PISCATAWAY
PA 445 HOES LANE, PISCATAWAY, NJ 08855-4141 USA
SN 1051-8223
J9 IEEE T APPL SUPERCON
JI IEEE Trans. Appl. Supercond.
PD JUN
PY 2012
VL 22
IS 3
AR 4904904
DI 10.1109/TASC.2011.2180297
PG 4
WC Engineering, Electrical & Electronic; Physics, Applied
SC Engineering; Physics
GA 986SK
UT WOS:000307364700332
DA 2021-04-21
ER
PT J
AU Deslippe, J
Samsonidze, G
Strubbe, DA
Jain, M
Cohen, ML
Louie, SG
AF Deslippe, Jack
Samsonidze, Georgy
Strubbe, David A.
Jain, Manish
Cohen, Marvin L.
Louie, Steven G.
TI BerkeleyGW: A massively parallel computer package for the calculation of
the quasiparticle and optical properties of materials and nanostructures
SO COMPUTER PHYSICS COMMUNICATIONS
LA English
DT Article
DE Many-body physics; GW; Bethe-Salpeter equation; Quasiparticle; Optics;
Exciton
ID ELECTRON-HOLE INTERACTION; AB-INITIO CALCULATION; PSEUDOPOTENTIAL
METHOD; SEMICONDUCTORS; SPECTRA; TOOL; EXCITATIONS; INSULATORS;
ABSORPTION; ENERGIES
AB BerkeleyGW is a massively parallel computational package for electron excited-state properties that is based on the many-body perturbation theory employing the ab initio GW and GW plus Bethe-Salpeter equation methodology. It can be used in conjunction with many density-functional theory codes for ground-state properties, including PARATEC, PARSEC, Quantum ESPRESSO, SIESTA, and Octopus. The package can be used to compute the electronic and optical properties of a wide variety of material systems from bulk semiconductors and metals to nanostructured materials and molecules. The package scales to 10000s of CPUs and can be used to study systems containing up to 100s of atoms.
Program summary
Program title: BerkeleyGW
Catalogue identifier: AELG_v1_0
Program summary URL: http://cpc.cs.qub.ac.uk/summaries/AELG_v1_0.html
Program obtainable from: CPC Program Library, Queen's University, Belfast, N. Ireland
Licensing provisions: Open source BSD License. See code for licensing details. No. of lines in distributed program. including test data, etc.: 576540 No. of bytes in distributed program, including test data, etc.: 110608 809
Distribution format: tar.gz
Programming language: Fortran 90, C. C++, Python, Peri, BASH
Computer: Linux/UNIX workstations or clusters
Operating system: Tested on a variety of Linux distributions in parallel and serial as well as AIX and Mac OSX
RAM: (50-2000) MB per CPU (Highly dependent on system size)
Classification: 7.2, 7.3, 16.2, 18
External routines: BLAS, LAPACK, FFTW, ScaLAPACK (optional), MPI (optional). All available under open-source licenses.
Nature of problem: The excited state properties of materials involve the addition or subtraction of electrons as well as the optical excitations of electron-hole pairs. The excited particles interact strongly with other electrons in a material system. This interaction affects the electronic energies, wavefunctions and lifetimes. It is well known that ground-state theories, such as standard methods based on density-functional theory, fail to correctly capture this physics.
Solution method: We construct and solve the Dyson's equation for the quasiparticle energies and wavefunctions within the GW approximation for the electron self-energy. We additionally construct and solve the Bethe-Salpeter equation for the correlated electron-hole (exciton) wavefunctions and excitation energies.
Restrictions: The material size is limited in practice by the computational resources available. Materials with up to 500 atoms per periodic cell can be studied on large HPCs.
Additional comments: The distribution file for this program is approximately 110 Mbytes and therefore is not delivered directly when download or E-mail is requested. Instead a html file giving details of how the program can be obtained is sent.
Running time: 1-1000 minutes (depending greatly on system size and processor number). (C) 2011 Published by Elsevier B.V.
C1 [Deslippe, Jack; Samsonidze, Georgy; Strubbe, David A.; Jain, Manish; Cohen, Marvin L.; Louie, Steven G.] Univ Calif Berkeley, Lawrence Berkeley Natl Lab, Div Mat Sci, Berkeley, CA 94720 USA.
[Deslippe, Jack; Samsonidze, Georgy; Strubbe, David A.; Jain, Manish; Cohen, Marvin L.; Louie, Steven G.] Univ Calif Berkeley, Dept Phys, Berkeley, CA 94720 USA.
RP Deslippe, J (corresponding author), Univ Calif Berkeley, Lawrence Berkeley Natl Lab, Div Mat Sci, 1 Cyclotron Rd,Mail Stop 943-256, Berkeley, CA 94720 USA.
EM jdeslip@gmail.com
RI Jain, Manish/A-8303-2010; Samsonidze, Georgy/G-3613-2016
OI Jain, Manish/0000-0001-9329-6434; Samsonidze, Georgy/0000-0002-3759-1794
FU Office of Science, Office of Basic Energy Sciences, Materials Sciences
and Engineering Division, U.S. Department of EnergyUnited States
Department of Energy (DOE) [DE-AC02-05C1111231]; National Science
Foundation (NSF)National Science Foundation (NSF) [DMR10-1006184]
FX J.D. and M.J. acknowledge support from the Director, Office of Science,
Office of Basic Energy Sciences, Materials Sciences and Engineering
Division, U.S. Department of Energy under Contract No.
DE-AC02-05C1111231. G.S. acknowledges support under National Science
Foundation Grant No. DMR10-1006184. D.A.S. acknowledges support from the
NSF Graduate Fellowship Program. Computational resources have been
provided by NSF through TeraGrid resources at NICS and by DOE at
Lawrence Berkeley National Laboratory's NERSC facility.
CR Albrecht S, 1998, PHYS REV LETT, V80, P4510, DOI 10.1103/PhysRevLett.80.4510
Alemany MMG, 2004, PHYS REV B, V69, DOI 10.1103/PhysRevB.69.075101
Anderson E., 1999, LAPACK USERS GUIDE
Aryasetiawan F, 1998, REP PROG PHYS, V61, P237, DOI 10.1088/0034-4885/61/3/002
BALDERESCHI A, 1978, PHYS REV B, V17, P4710, DOI 10.1103/PhysRevB.17.4710
Benedict LX, 1998, PHYS REV LETT, V80, P4514, DOI 10.1103/PhysRevLett.80.4514
Benedict LX, 2002, PHYS REV B, V66, DOI 10.1103/PhysRevB.66.085116
Benedict LX, 1999, PHYS REV B, V59, P5441, DOI 10.1103/PhysRevB.59.5441
Blackford LS., 1997, SCALAPACK USERES GUI
Bruneval F, 2006, PHYS REV B, V74, DOI 10.1103/PhysRevB.74.045102
Castro A, 2006, PHYS STATUS SOLIDI B, V243, P2465, DOI 10.1002/pssb.200642067
CHELIKOWSKY JR, 1994, PHYS REV LETT, V72, P1240, DOI 10.1103/PhysRevLett.72.1240
COHEN ML, 1975, PHYS REV B, V12, P5575, DOI 10.1103/PhysRevB.12.5575
Deslippe J, 2007, NANO LETT, V7, P1626, DOI 10.1021/nl070577f
Fetter A.L., 1971, QUANTUM THEORY MANY
FLESZAR A, 1985, THESIS U TRIESTE
Frigo M, 2005, P IEEE, V93, P216, DOI 10.1109/JPROC.2004.840301
Giannozzi P, 2009, J PHYS-CONDENS MAT, V21, DOI 10.1088/0953-8984/21/39/395502
HANKE W, 1978, ADV PHYS, V27, P287, DOI 10.1080/00018737800101384
HAYDOCK R, 1980, COMPUT PHYS COMMUN, V20, P11, DOI 10.1016/0010-4655(80)90101-0
HEDIN L, 1965, PHYS REV, V139, pA796, DOI 10.1103/PhysRev.139.A796
Hedin L., 1970, SOLID STATE PHYS, V23, P1
Holm B, 1998, PHYS REV B, V57, P2108, DOI 10.1103/PhysRevB.57.2108
HYBERTSEN MS, 1987, PHYS REV B, V35, P5585, DOI 10.1103/PhysRevB.35.5585
HYBERTSEN MS, 1986, PHYS REV B, V34, P5390, DOI 10.1103/PhysRevB.34.5390
Ismail-Beigi S, 2001, PHYS REV LETT, V87, DOI 10.1103/PhysRevLett.87.087402
Ismail-Beigi S, 2006, PHYS REV B, V73, DOI 10.1103/PhysRevB.73.233103
Jain M., GOWO DIAGONALI UNPUB
JELLISON GE, 1993, APPL PHYS LETT, V62, P3348, DOI 10.1063/1.109067
KOHN W, 1965, PHYS REV, V140, P1133, DOI 10.1103/PhysRev.140.A1133
Kokalj A, 2003, COMP MATER SCI, V28, P155, DOI 10.1016/S0927-0256(03)00104-6
Louie SG, 2006, CONT CONCEPT CONDENS, P1
Marques MAL, 2003, COMPUT PHYS COMMUN, V151, P60, DOI 10.1016/S0010-4655(02)00686-0
Martin RM., 2004, ELECT STRUCTURE BASI
Marzari N, 1997, PHYS REV B, V56, P12847, DOI 10.1103/PhysRevB.56.12847
Mirali Z., 2010, NATURE, V467, P775
Mostofi AA, 2008, COMPUT PHYS COMMUN, V178, P685, DOI 10.1016/j.cpc.2007.11.016
Neaton JB, 2006, PHYS REV LETT, V97, DOI 10.1103/PhysRevLett.97.216405
Perdew JP, 1996, PHYS REV LETT, V77, P3865, DOI 10.1103/PhysRevLett.77.3865
Quek S. Y., FIRST PRINCIPL UNPUB
Rinke P, 2005, NEW J PHYS, V7, DOI 10.1088/1367-2630/7/1/126
Rohlfing M, 2000, PHYS REV B, V62, P4927, DOI 10.1103/PhysRevB.62.4927
Shih BC, 2010, PHYS REV LETT, V105, DOI 10.1103/PhysRevLett.105.146401
Soler JM, 2002, J PHYS-CONDENS MAT, V14, P2745, DOI 10.1088/0953-8984/14/11/302
Souza I, 2002, PHYS REV B, V65, DOI 10.1103/PhysRevB.65.035109
Spataru C.-D., 2004, THESIS U CALIFORNIA
Spataru CD, 2004, PHYS REV LETT, V92, DOI 10.1103/PhysRevLett.92.077402
Spataru CD, 2004, APPL PHYS A-MATER, V78, P1129, DOI 10.1007/s00339-003-2464-2
STRINATI G, 1988, RIV NUOVO CIMENTO, V11, P1, DOI 10.1007/BF02725962
Tao C, 2009, NANO LETT, V9, P3963, DOI 10.1021/nl901860n
van Schilfgaarde M, 2006, PHYS REV LETT, V96, DOI 10.1103/PhysRevLett.96.226402
Yang L, 2009, PHYS REV LETT, V103, DOI 10.1103/PhysRevLett.103.186802
Zhang GP, 2011, PHYS REV A, V84, DOI 10.1103/PhysRevA.84.023837
ZHANG SB, 1989, PHYS REV B, V40, P3162, DOI 10.1103/PhysRevB.40.3162
NR 54
TC 426
Z9 428
U1 8
U2 118
PU ELSEVIER
PI AMSTERDAM
PA RADARWEG 29, 1043 NX AMSTERDAM, NETHERLANDS
SN 0010-4655
EI 1879-2944
J9 COMPUT PHYS COMMUN
JI Comput. Phys. Commun.
PD JUN
PY 2012
VL 183
IS 6
BP 1269
EP 1289
DI 10.1016/j.cpc.2011.12.006
PG 21
WC Computer Science, Interdisciplinary Applications; Physics, Mathematical
SC Computer Science; Physics
GA 914ZI
UT WOS:000301992100013
DA 2021-04-21
ER
PT J
AU Ozturk, MK
AF Ozturk, M. Kaan
TI Trajectories of charged particles trapped in Earth's magnetic field
SO AMERICAN JOURNAL OF PHYSICS
LA English
DT Article
ID MOTION; MAGNETOTAIL; COMPUTER
AB This article presents the theory of relativistic charged-particle motion in Earth's magnetosphere, at a level suitable for undergraduate courses. I discuss particle and guiding center motion and derive the three adiabatic invariants associated with the three periodic motions in a dipolar field. I provide 12 computational exercises that can be used as classroom assignments or for self-study. Two of the exercises, drift-shell bifurcation and Speiser orbits, are adapted from active magnetospheric research. The PYTHON code provided in the supplement can be used to replicate the trajectories and can be easily extended for different field geometries. (C) 2012 American Association of Physics Teachers. [DOI:10.1119/1.3684537]
C1 Yeditepe Univ Informat Syst & Technol, TR-34755 Istanbul, Turkey.
RP Ozturk, MK (corresponding author), Yeditepe Univ Informat Syst & Technol, TR-34755 Istanbul, Turkey.
EM kaan.ozturk@yeditepe.edu.tr
CR BUCHNER J, 1989, J GEOPHYS RES, V94, P11821, DOI 10.1029/JA094iA09p11821
CHEN J, 1992, J GEOPHYS RES-SPACE, V97, P15011, DOI 10.1029/92JA00955
Garcia A.L., 2000, NUMERICAL METHODS PH
Griffiths DJ, 1999, INTRO ELECTRODYNAMIC, V3rd
Gurnett D. A., 2005, INTRO PLASMA PHYS SP
Hand L. N., 1998, ANAL MECH, P230
HUGGINS ER, 1979, AM J PHYS, V47, P992, DOI 10.1119/1.11604
Hughes W. J., 1995, INTRO SPACE PHYS, P227
Kivelson M. G., 1995, INTRO SPACE PHYS, P27
Lopez RE, 2008, ADV SPACE RES, V42, P1859, DOI 10.1016/j.asr.2007.11.010
McGuire GC, 2003, AM J PHYS, V71, P809, DOI 10.1119/1.1579496
Moldwin M., 2008, INTRO SPACE WEATHER, P79
Northrop T. G., 1963, ADIABATIC MOTION CHA
Ozturk MK, 2007, J GEOPHYS RES-SPACE, V112, DOI 10.1029/2006JA012102
Press W. H., 2007, NUMERICAL RECIPED AR
Roederer J. G., 1970, DYNAMICS GEOMAGNETIC
Schulz M., 1974, PARTICLE DIFFUSION R
Sharma A. S., 2006, INT C SUBSTORMS, V8, P279
Sturrock PA, 1994, PLASMA PHYS INTRO TH
Townsend L W, 2001, J Radiol Prot, V21, P5, DOI 10.1088/0952-4746/21/1/003
Walt M., 1994, INTRO GEOMAGNETICALL
Wolf R. A., 1995, INTRO SPACE PHYS, P288
NR 22
TC 13
Z9 14
U1 0
U2 10
PU AMER ASSOC PHYSICS TEACHERS AMER INST PHYSICS
PI MELVILLE
PA STE 1 NO 1, 2 HUNTINGTON QUADRANGLE, MELVILLE, NY 11747-4502 USA
SN 0002-9505
J9 AM J PHYS
JI Am. J. Phys.
PD MAY
PY 2012
VL 80
IS 5
BP 420
EP 428
DI 10.1119/1.3684537
PG 9
WC Education, Scientific Disciplines; Physics, Multidisciplinary
SC Education & Educational Research; Physics
GA 927YY
UT WOS:000302947800011
DA 2021-04-21
ER
PT J
AU Sainio, J
AF Sainio, J.
TI PyCOOL - A Cosmological Object-Oriented Lattice code written in Python
SO JOURNAL OF COSMOLOGY AND ASTROPARTICLE PHYSICS
LA English
DT Article
DE physics of the early universe; cosmological phase transitions; inflation
ID Q-BALLS; SIMULATIONS; BREAKING; UNIVERSE; CURVATON; PROGRAM; GPU
AB There are a number of different phenomena in the early universe that have to be studied numerically with lattice simulations. This paper presents a graphics processing unit (GPU) accelerated Python program called PyCOOL that solves the evolution of scalar fields in a lattice with very precise symplectic integrators. The program has been written with the intention to hit a sweet spot of speed, accuracy and user friendliness. This has been achieved by using the Python language with the PyCUDA interface to make a program that is easy to adapt to different scalar field models. In this paper we derive the symplectic dynamics that govern the evolution of the system and then present the implementation of the program in Python and PyCUDA. The functionality of the program is tested in a chaotic inflation preheating model, a single field oscillon case and in a supersymmetric curvaton model which leads to Q-ball production. We have also compared the performance of a consumer graphics card to a professional Tesla compute card in these simulations. We find that the program is not only accurate but also very fast. To further increase the usefulness of the program we have equipped it with numerous post-processing functions that provide useful information about the cosmological model. These include various spectra and statistics of the fields. The program can be additionally used to calculate the generated curvature perturbation. The program is publicly available under GNU General Public License at https://github.com/jtksai/PyCOOL. Some additional information can be found from http://www.physics.utu.fi/tiedostot/theory/particlecosmology/pycool/.
C1 [Sainio, J.] Univ Turku, Turku Sch Econ, FI-20500 Turku, Finland.
[Sainio, J.] Univ Turku, Dept Phys & Astron, FI-20014 Turku, Finland.
RP Sainio, J (corresponding author), Univ Turku, Turku Sch Econ, Rehtorinpellonkatu 3, FI-20500 Turku, Finland.
EM jani.sainio@utu.fi
FU OP Bank Group Research Foundation
FX This paper has been funded by OP Bank Group Research Foundation and the
author would like to personally thank Prof. Luis Alvarez for allowing
the author to pursue this research at Turku School of Economics.
CR AFFLECK I, 1985, NUCL PHYS B, V249, P361, DOI 10.1016/0550-3213(85)90021-5
Amin MA, 2010, J COSMOL ASTROPART P, DOI 10.1088/1475-7516/2010/12/001
[Anonymous], 2011, CUDA PROGR GUID VERS
[Anonymous], 2011, OPENCL OVERVIEW
Anselmi V., 2008, POS LATTICE 2008, P024
Banerjee S., ARXIV09103954
Belleman RG, 2008, NEW ASTRON, V13, P103, DOI 10.1016/j.newast.2007.07.004
Bertschinger E, 1998, ANNU REV ASTRON ASTR, V36, P599, DOI 10.1146/annurev.astro.36.1.599
Bond JR, 2009, PHYS REV LETT, V103, DOI 10.1103/PhysRevLett.103.071301
Brunner R.J., ARXIV07113414
Capuzzo-Dolcetta R., ARXIV09090879
Chambers A, 2008, PHYS REV LETT, V100, DOI 10.1103/PhysRevLett.100.041302
Chambers A, 2008, J COSMOL ASTROPART P, DOI 10.1088/1475-7516/2008/08/002
Chambers A, 2010, J COSMOL ASTROPART P, DOI 10.1088/1475-7516/2010/01/012
Chambers A, 2008, PHYS REV LETT, V101, DOI 10.1103/PhysRevLett.101.149903
Chung SK, 2010, CLASSICAL QUANT GRAV, V27, DOI 10.1088/0264-9381/27/13/135009
COPELAND EJ, 1995, PHYS REV D, V52, P1920, DOI 10.1103/PhysRevD.52.1920
Demchik V., ARXIV09033053
Easther R, 2010, J COSMOL ASTROPART P, DOI 10.1088/1475-7516/2010/10/025
Enqvist K, 2005, PHYS REV LETT, V94, DOI 10.1103/PhysRevLett.94.161301
Enqvist K, 2003, PHYS REV D, V68, DOI 10.1103/PhysRevD.68.103507
Enqvist K, 2003, PHYS REP, V380, P99, DOI 10.1016/S0370-1573(03)00119-4
Felder G.N., LATTICEEASY DOCUMENT
Felder G, 2008, COMPUT PHYS COMMUN, V178, P929, DOI 10.1016/j.cpc.2008.02.009
Ford EB, 2009, NEW ASTRON, V14, P406, DOI 10.1016/j.newast.2008.12.001
Frolov A. M., COMMUNICATION
Frolov AV, 2008, J COSMOL ASTROPART P, DOI 10.1088/1475-7516/2008/11/009
Gaburov E., ARXIV09024463
GLEISER M, 1994, PHYS REV D, V49, P2978, DOI 10.1103/PhysRevD.49.2978
Gleiser M, 2011, PHYS REV D, V83, DOI 10.1103/PhysRevD.83.096010
Groen D., ARXIV09074036
Hagiwara K, 2010, EUR PHYS J C, V70, P513, DOI 10.1140/epjc/s10052-010-1465-5
Hamaguchi K, 2004, PHYS REV D, V69, DOI 10.1103/PhysRevD.69.063504
Hiramatsu T, 2010, J COSMOL ASTROPART P, DOI 10.1088/1475-7516/2010/06/008
Huang ZQ, 2011, PHYS REV D, V83, DOI 10.1103/PhysRevD.83.123509
Ishikawa K.-I., 2008, POS LATTICE 2008, P013
Januszewski M, 2010, COMPUT PHYS COMMUN, V181, P183, DOI 10.1016/j.cpc.2009.09.009
Jonsson P, 2010, NEW ASTRON, V15, P509, DOI 10.1016/j.newast.2009.12.008
Kasuya S, 2000, PHYS REV LETT, V85, P2677, DOI 10.1103/PhysRevLett.85.2677
Kasuya S, 2004, PHYS LETT B, V578, P259, DOI 10.1016/j.physletb.2003.10.079
Kasuya S, 2000, PHYS REV D, V62, DOI 10.1103/PhysRevD.62.023512
Khanna G., ARXIV09094039
Klockner A., ARXIV09113456
Kusenko A, 1998, PHYS LETT B, V418, P46, DOI 10.1016/S0370-2693(97)01375-0
Mazumdara A, 2011, PHYS REP, V497, P85, DOI 10.1016/j.physrep.2010.08.001
McLachlan RI, 2002, ACT NUMERIC, V11, P341, DOI 10.1017/S0962492902000053
MCLACHLAN RI, 1995, SIAM J SCI COMPUT, V16, P151, DOI 10.1137/0916010
Micikevicius P, 2009, P 2 WORKSH GEN PURP
Multamaki T, 2002, PHYS LETT B, V535, P170, DOI 10.1016/S0370-2693(02)01730-6
Nakasato N., ARXIV09090541
Ord S., ARXIV09020915
Patra M, 2006, NUMER METH PART D E, V22, P936, DOI 10.1002/num.20129
Podolsky D, 2006, PHYS REV D, V73, DOI 10.1103/PhysRevD.73.023501
Portegies Zwart S, 2009, NEW ASTRON, V14, P369, DOI 10.1016/j.newast.2008.10.006
Sainio J, 2010, COMPUT PHYS COMMUN, V181, P906, DOI 10.1016/j.cpc.2010.01.002
Schive HY, 2010, ASTROPHYS J SUPPL S, V186, P457, DOI 10.1088/0067-0049/186/2/457
Szalay T., ARXIV08112055
Von Hoerner S., 1960, Zeitschrift fur Astrophysik, V50, P184
Von Hoerner S., 1963, Zeitschrift fur Astrophysik, V57, P47
Wang P, 2010, NEW ASTRON, V15, P581, DOI 10.1016/j.newast.2009.10.002
YOSHIDA H, 1990, PHYS LETT A, V150, P262, DOI 10.1016/0375-9601(90)90092-3
NR 61
TC 12
Z9 12
U1 0
U2 9
PU IOP PUBLISHING LTD
PI BRISTOL
PA TEMPLE CIRCUS, TEMPLE WAY, BRISTOL BS1 6BE, ENGLAND
SN 1475-7516
J9 J COSMOL ASTROPART P
JI J. Cosmol. Astropart. Phys.
PD APR
PY 2012
IS 4
AR 038
DI 10.1088/1475-7516/2012/04/038
PG 24
WC Astronomy & Astrophysics; Physics, Particles & Fields
SC Astronomy & Astrophysics; Physics
GA 937NK
UT WOS:000303665000038
DA 2021-04-21
ER
PT J
AU Rothkegel, A
Lehnertz, K
AF Rothkegel, Alexander
Lehnertz, Klaus
TI Conedy: A scientific tool to investigate complex network dynamics
SO CHAOS
LA English
DT Article
ID SPIKING NEURONS; SYNCHRONIZATION; SIMULATION; IMPLEMENTATION;
CONNECTIVITY
AB We present Conedy, a performant scientific tool to numerically investigate dynamics on complex networks. Conedy allows to create networks and provides automatic code generation and compilation to ensure performant treatment of arbitrary node dynamics. Conedy can be interfaced via an internal script interpreter or via a Python module. (C) 2012 American Institute of Physics. [doi:10.1063/1.3685527]
C1 [Rothkegel, Alexander; Lehnertz, Klaus] Univ Bonn, Dept Epileptol, D-53105 Bonn, Germany.
[Rothkegel, Alexander; Lehnertz, Klaus] Univ Bonn, Helmholtz Inst Radiat & Nucl Phys, D-53115 Bonn, Germany.
[Rothkegel, Alexander; Lehnertz, Klaus] Univ Bonn, Interdisciplinary Ctr Complex Syst, D-53175 Bonn, Germany.
RP Rothkegel, A (corresponding author), Univ Bonn, Dept Epileptol, Sigmund Freud Str 25, D-53105 Bonn, Germany.
EM alexander@rothkegel.de; klaus.lehnertz@ukb.uni-bonn.de
OI Lehnertz, Klaus/0000-0002-5529-8559
FU Deutsche ForschungsgemeinschaftGerman Research Foundation (DFG) [LE
660/4-2]
FX We are grateful to Gerrit Ansmann, Stephan Bialonski, Henning Dickten,
Justus Schwabedal, Ferdinand Stolz, and Tobias Wagner for their
contributions to the source code and documentation of Conedy and for
valuable comments on earlier versions of the manuscript. This work was
supported by the Deutsche Forschungsgemeinschaft (LE 660/4-2).
CR ABARBANEL HDI, 1993, REV MOD PHYS, V65, P1331, DOI 10.1103/RevModPhys.65.1331
Albert R, 2002, REV MOD PHYS, V74, P47, DOI 10.1103/RevModPhys.74.47
Alligood K.T., 1996, CHAOS INTRO DYNAMICA
Almendral JA, 2011, CHAOS, V21, DOI 10.1063/1.3570920
Arenas A, 2008, PHYS REP, V469, P93, DOI 10.1016/j.physrep.2008.09.002
Barabasi AL, 1999, SCIENCE, V286, P509, DOI 10.1126/science.286.5439.509
BARKLEY D, 1991, PHYSICA D, V49, P61, DOI 10.1016/0167-2789(91)90194-E
Barrat Alain, 2008, DYNAMICAL PROCESSES, V1st
Boccaletti S, 2006, PHYS REP, V424, P175, DOI 10.1016/j.physrep.2005.10.009
Bower J. M., 1998, BOOK GENESIS EXPLORI
Brette R, 2007, J COMPUT NEUROSCI, V23, P349, DOI 10.1007/s10827-007-0038-6
BROWN R, 1988, COMMUN ACM, V31, P1220, DOI 10.1145/63039.63045
Bullmore ET, 2009, NAT REV NEUROSCI, V10, P186, DOI 10.1038/nrn2575
Caldarelli G, 2002, PHYS REV LETT, V89, DOI 10.1103/PhysRevLett.89.258702
Carnevale NT, 2006, NEURON BOOK
Catanzaro M, 2005, PHYS REV E, V71, DOI 10.1103/PhysRevE.71.027103
Clewley R. H., PYDSTOOL SOFTWARE EN
Costa LD, 2007, ADV PHYS, V56, P167, DOI 10.1080/00018730601170527
Csardi G., 2006, INTERJOURNAL, V1695, DOI DOI 10.3724/SP.J.1087.2009.02191
Dhooge A, 2003, ACM T MATH SOFTWARE, V29, P141, DOI 10.1145/779359.779362
Donner RV, 2010, NEW J PHYS, V12, DOI 10.1088/1367-2630/12/3/033025
DRISCOLL JR, 1988, COMMUN ACM, V31, P1343, DOI 10.1145/50087.50096
Dyhrfjeld-Johnsen J, 2007, J NEUROPHYSIOL, V97, P1566, DOI 10.1152/jn.00950.2006
Ermentrout G, 2002, SIMULATING ANAL ANIM
Feldt S, 2007, PHYS REV E, V76, DOI 10.1103/PhysRevE.76.021920
Galassi M., 2009, GNU SCI LIB REFERENC
Gewaltig MO, 2007, SCHOLARPEDIA, V2, P1430, DOI DOI 10.4249/SCH0LARPEDIA.1430
Goodman Dan, 2008, Front Neuroinform, V2, P5, DOI 10.3389/neuro.11.005.2008
Hagberg A, 2008, 7 PYTH SCI C SCIPY20, V7, P11, DOI DOI 10.1016/J.JELECTROCARD.2010.09.003
Hegger R, 1999, CHAOS, V9, P413, DOI 10.1063/1.166424
Hines Michael L, 2009, Front Neuroinform, V3, P1, DOI 10.3389/neuro.11.001.2009
Hlavackova-Schindler K, 2007, PHYS REP, V441, P1, DOI 10.1016/j.physrep.2006.12.004
Izhikevich EM., 2007, DYNAMICAL SYSTEMS NE
Jahnke S, 2009, FRONT COMPUT NEUROSC, V3, DOI 10.3389/neuro.10.013.2009
Kantz H., 2003, NONLINEAR TIME SERIE, V2nd
Kloeden P.E., 1999, NUMERICAL SOLUTION S
Kuramoto Y., 1984, SPRINGER SERIES SYNE, V19
Kwon O, 2005, PHYS REV E, V72, DOI 10.1103/PhysRevE.72.066121
Lehnertz K, 2009, J NEUROSCI METH, V183, P42, DOI 10.1016/j.jneumeth.2009.05.015
MIROLLO RE, 1990, SIAM J APPL MATH, V50, P1645, DOI 10.1137/0150098
Morgan RJ, 2008, P NATL ACAD SCI USA, V105, P6179, DOI 10.1073/pnas.0801372105
Nawrath J, 2010, PHYS REV LETT, V104, DOI 10.1103/PhysRevLett.104.038701
Netoff TI, 2004, J NEUROSCI, V24, P8075, DOI 10.1523/JNEUROSCI.1509-04.2004
Newman MEJ, 2003, SIAM REV, V45, P167, DOI 10.1137/S003614450342480
Newman MEJ, 2001, PHYS REV E, V64, DOI [10.1103/PhysRevE.64.025102, 10.1103/PhysRevE.64.026118, 10.1103/PhysRevE.64.016132]
Nishikawa T, 2011, CHAOS, V21, DOI 10.1063/1.3605467
Oliphant TE, 2007, COMPUT SCI ENG, V9, P10, DOI 10.1109/MCSE.2007.58
Olmi S, 2010, PHYS REV E, V81, DOI 10.1103/PhysRevE.81.046119
Osipov GV, 2003, PHYS REV LETT, V91, DOI 10.1103/PhysRevLett.91.024101
Pastor-Satorras R, 2001, PHYS REV LETT, V86, P3200, DOI 10.1103/PhysRevLett.86.3200
Pecevski Dejan, 2009, Front Neuroinform, V3, P11
Perc M, 2005, NEW J PHYS, V7, DOI 10.1088/1367-2630/7/1/252
Percha B, 2005, PHYS REV E, V72, DOI 10.1103/PhysRevE.72.031909
Pereda E, 2005, PROG NEUROBIOL, V77, P1, DOI 10.1016/j.pneurobio.2005.10.003
Pikovsky A., 2001, SYNCHRONIZATION UNIV, DOI 10.1017/CBO9780511755743
Press W. H., 2007, NUMERICAL RECIPES AR
Ren J, 2010, PHYS REV LETT, V104, DOI 10.1103/PhysRevLett.104.058701
Rothkegel A, 2011, EPL-EUROPHYS LETT, V95, DOI 10.1209/0295-5075/95/38001
Rothkegel A, 2009, CHAOS, V19, DOI 10.1063/1.3087432
Stone TE, 2011, EPL-EUROPHYS LETT, V95, DOI 10.1209/0295-5075/95/38003
Stratton P, 2010, NEUROIMAGE, V52, P1070, DOI 10.1016/j.neuroimage.2010.01.027
Suykens JAK, 2008, CHAOS, V18, DOI 10.1063/1.2985139
Wang MS, 2006, CHINESE PHYS, V15, P2553, DOI 10.1088/1009-1963/15/11/016
Watts DJ, 1998, NATURE, V393, P440, DOI 10.1038/30918
Wu C. W., 2007, SYNCHRONIZATION COMP
Yorke, 1998, DYNAMICS NUMERICAL E
Zhou T, 2006, PROG NAT SCI-MATER, V16, P452
Zumdieck A, 2004, PHYS REV LETT, V93, DOI 10.1103/PhysRevLett.93.244103
NR 68
TC 16
Z9 16
U1 0
U2 11
PU AMER INST PHYSICS
PI MELVILLE
PA 1305 WALT WHITMAN RD, STE 300, MELVILLE, NY 11747-4501 USA
SN 1054-1500
EI 1089-7682
J9 CHAOS
JI Chaos
PD MAR
PY 2012
VL 22
IS 1
AR 013125
DI 10.1063/1.3685527
PG 8
WC Mathematics, Applied; Physics, Mathematical
SC Mathematics; Physics
GA 922VP
UT WOS:000302576900025
PM 22463001
DA 2021-04-21
ER
PT J
AU Curtis, JE
Raghunandan, S
Nanda, H
Krueger, S
AF Curtis, Joseph E.
Raghunandan, Sindhu
Nanda, Hirsh
Krueger, Susan
TI SASSIE: A program to study intrinsically disordered biological molecules
and macromolecular ensembles using experimental scattering restraints
SO COMPUTER PHYSICS COMMUNICATIONS
LA English
DT Article
DE Small-angle scattering; X-ray scattering; Neutron scattering;
Intrinsically disordered proteins; Protein structure; Computer modeling
ID SMALL-ANGLE NEUTRON; X-RAY-SCATTERING; RELATE 2 SETS; PROTEIN;
CONFORMATION; DYNAMICS; ROTATION; CHARMM
AB A program to construct ensembles of biomolecular structures that are consistent with experimental scattering data are described. Specifically, we generate an ensemble of biomolecular structures by varying sets of backbone dihedral angles that are then filtered using experimentally determined restraints to rapidly determine structures that have scattering profiles that are consistent with scattering data. We discuss an application of these tools to predict a set of structures for the HIV-1 Gag protein, an intrinsically disordered protein, that are consistent with small-angle neutron scattering experimental data. We have assembled these algorithms into a program called SASSIE for structure generation, visualization, and analysis of intrinsically disordered proteins and other macromolecular ensembles using neutron and X-ray scattering restraints.
Program summary
Program title: SASSIE
Catalogue identifier: AEKL_v1_0
Program summary URL: http://cpc.cs.qub.ac.uk/summaries/AEKL_v1_0.html
Program obtainable from: CPC Program Library, Queen's University, Belfast, N. Ireland
Licensing provisions: GNU General Public License v3
No. of lines in distributed program, including test data, etc.: 3 991 624
No. of bytes in distributed program, including test data, etc.: 826
Distribution format: tar.gz
Programming language: Python, C/C++, Fortran
Computer: PC/Mac
Operating system: 32- and 64-bit Linux (Ubuntu 10.04, Centos 5.6) and Mac OS X (10.6.6)
RAM: 1 GB
Classification: 3
External routines: Python 2.6.5, numpy 1.4.0, swig 1.3.40, scipy 0.8.0, Gnuplot-py-1.8, Tcl 8.5, Tk 8.5, Mac installation requires aquaterm 1.0 (or X window system) and Xcode 3 development tools.
Nature of problem: Open source software to generate structures of disordered biological molecules that subsequently allow for the comparison of computational and experimental results is limiting the use of scattering resources.
Solution method: Starting with an all atom model of a protein, for example, users can input regions to vary dihedral angles, ensembles of structures can be generated. Additionally, simple two-body rigid-body rotations are supported with and without disordered regions. Generated structures can then be used to calculate small-angle scattering profiles which can then be filtered against experimentally determined data. Filtered structures can be visualized individually or as an ensemble using density plots. In the modular and expandable program framework the user can easily access our subroutines and structural coordinates can be easily obtained for study using other computational physics methods.
Additional comments: The distribution file for this program is over 159 Mbytes and therefore is not delivered directly when download or Email is requested. Instead an html file giving details of how the program can be obtained is sent.
Running time: Varies depending on application. Typically 10 minutes to 24 hours depending on the number of generated structures.
C1 [Curtis, Joseph E.; Raghunandan, Sindhu; Nanda, Hirsh; Krueger, Susan] NIST, NIST Ctr Neutron Res, Gaithersburg, MD 20899 USA.
RP Curtis, JE (corresponding author), NIST, NIST Ctr Neutron Res, 100 Bur Dr,Mail Stop 6102, Gaithersburg, MD 20899 USA.
EM joseph.curtis@nist.gov
FU National Institute of Standards and Technology, US Department of
CommerceNational Institute of Standards & Technology (NIST) - USA
FX We acknowledge the support of the National Institute of Standards and
Technology, US Department of Commerce, for providing the neutron
facilities used in this work. Brand names are stated for clarity only
and their use does not imply an endorsement by NIST. We also thank Alan
Rein and Siddhartha A.K. Datta of the National Cancer
Institute-Frederick for valuable discussions and for help in obtaining
the experimental data reported in this paper.
CR Bernado P, 2007, J AM CHEM SOC, V129, P5656, DOI 10.1021/ja069124n
Branden C-I, 1991, INTRO PROTEIN STRUCT
BROOKS BR, 1983, J COMPUT CHEM, V4, P187, DOI 10.1002/jcc.540040211
Coffin J.M., 1997, RETROVIRUSES
Datta S.A.K., 2010, J MOL BIOL
Datta SAK, 2007, J MOL BIOL, V365, P812, DOI 10.1016/j.jmb.2006.10.073
Datta SAK, 2007, J MOL BIOL, V365, P799, DOI 10.1016/j.jmb.2006.10.072
Drake FL, 2001, PYTHON REFERENCE MAN
Dunker AK, 2001, J MOL GRAPH MODEL, V19, P26, DOI 10.1016/S1093-3263(00)00138-8
Frenkel D., 1996, UNDERSTANDING MOL SI
Golub GH., 1996, MATRIX COMPUTATIONS
Grishaev A, 2005, J AM CHEM SOC, V127, P16621, DOI 10.1021/ja054342m
Hardy D. J., 2007, NAMD LITE
HEIDORN DB, 1988, BIOCHEMISTRY-US, V27, P909, DOI 10.1021/bi00403a011
KABSCH W, 1976, ACTA CRYSTALLOGR A, V32, P922, DOI 10.1107/S0567739476001873
KABSCH W, 1978, ACTA CRYSTALLOGR A, V34, P827, DOI 10.1107/S0567739478001680
Kent MS, 2010, BIOPHYS J, V99, P1940, DOI 10.1016/j.bpj.2010.07.016
Kernighan Brian W., 1988, C PROGRAMMING LANGUA
Krueger S, 1998, J BIOL CHEM, V273, P20001, DOI 10.1074/jbc.273.32.20001
MacKerell AD, 1998, J PHYS CHEM B, V102, P3586, DOI 10.1021/jp973084f
McQuarrie D. A., 1976, STAT MECH
Nanda H, 2010, BIOPHYS J, V99, P2516, DOI 10.1016/j.bpj.2010.07.062
Neylon C, 2008, EUR BIOPHYS J BIOPHY, V37, P531, DOI 10.1007/s00249-008-0259-2
Ousterhout J.K., 1994, TCL TK TOOLKIT
Pelikan M, 2009, GEN PHYSIOL BIOPHYS, V28, P174, DOI 10.4149/gpb_2009_02_174
Perkins SJ, 2008, METHOD CELL BIOL, V84, P375, DOI 10.1016/S0091-679X(07)84013-1
ROTERMAN IK, 1989, J BIOMOL STRUCT DYN, V7, P421, DOI 10.1080/07391102.1989.10508503
SCHLICK T, 2000, MOL MODELING SIMULAT
Svergun D, 1995, J APPL CRYSTALLOGR, V28, P768, DOI 10.1107/S0021889895007047
Svergun DI, 1998, P NATL ACAD SCI USA, V95, P2267, DOI 10.1073/pnas.95.5.2267
Uversky VN, 2002, PROTEIN SCI, V11, P739, DOI 10.1110/ps.4210102
van Rossum G, 1995, PYTHON TUTORIAL TECH
Wright PE, 1999, J MOL BIOL, V293, P321, DOI 10.1006/jmbi.1999.3110
NR 33
TC 77
Z9 77
U1 0
U2 20
PU ELSEVIER
PI AMSTERDAM
PA RADARWEG 29, 1043 NX AMSTERDAM, NETHERLANDS
SN 0010-4655
EI 1879-2944
J9 COMPUT PHYS COMMUN
JI Comput. Phys. Commun.
PD FEB
PY 2012
VL 183
IS 2
BP 382
EP 389
DI 10.1016/j.cpc.2011.09.010
PG 8
WC Computer Science, Interdisciplinary Applications; Physics, Mathematical
SC Computer Science; Physics
GA 868OA
UT WOS:000298531400019
DA 2021-04-21
ER
PT S
AU Price, RL
Ramirez, J
Rovito, TV
Mendoza-Schrock, O
AF Price, Rebecca L.
Ramirez, Juan, Jr.
Rovito, Todd V.
Mendoza-Schrock, Olga
GP IEEE
TI Electro-Optical Synthetic Civilian Vehicle Data Domes
SO PROCEEDINGS OF THE 2012 IEEE NATIONAL AEROSPACE AND ELECTRONICS
CONFERENCE (NAECON)
SE IEEE National Aerospace and Electronics Conference
LA English
DT Proceedings Paper
CT IEEE National Aerospace and Electronics Conference (NAECON)
CY JUL 25-27, 2012
CL Dayton, OH
SP IEEE, IEEE Aerosp & Elect Syst Soc (AESS), IEEE Dayton Sect
DE synthetic data; civilian vehicle models; data domesBlender; LuxRender;
Python; pattern recogntion; open-source
AB This paper will look at using open source tools (Blender, LuxRender, and Python) to generate a large data set to be used to train an object recognition system. The model produces camera position, camera attitude, and synthetic camera data that can be used for exploitation purposes. We focus on electro-optical (EO) visible sensors to simplify the rendering but this work could be extended to use other rendering tools that support different modalities. The key idea of this paper is to provide an architecture to produce synthetic training data which is modular in design and constructed on open-source off-the-shelf software yielding a physics accurate virtual model of the object we want to recognize. For this paper the objects we are focused on are civilian vehicles. This architecture shows how leveraging existing open-source software allows for practical training of Electro-Optical object recognition algorithms
C1 [Price, Rebecca L.; Ramirez, Juan, Jr.; Rovito, Todd V.; Mendoza-Schrock, Olga] Air Force Res Lab, Wright Patterson AFB, OH 45433 USA.
RP Price, RL (corresponding author), Air Force Res Lab, Wright Patterson AFB, OH 45433 USA.
CR Amazon ECU (Elastic Compute Unit, 2010, AM ECU EL COMP UN
Dungan K., 2010, P SPIE, V7699
Ramirez Jr J, NAECON 2012
NR 3
TC 3
Z9 3
U1 0
U2 1
PU IEEE
PI NEW YORK
PA 345 E 47TH ST, NEW YORK, NY 10017 USA
SN 0547-3578
BN 978-1-4673-2792-3
J9 PROC NAECON IEEE NAT
PY 2012
BP 140
EP 143
PG 4
WC Engineering, Aerospace; Engineering, Electrical & Electronic
SC Engineering
GA BGY50
UT WOS:000324589100031
DA 2021-04-21
ER
PT B
AU Kolos, S
Boutsioukis, G
Hauser, R
AF Kolos, S.
Boutsioukis, G.
Hauser, R.
GP IEEE
TI High-Performance Scalable Information Service for the ATLAS Experiment
SO 2012 18TH IEEE-NPSS REAL TIME CONFERENCE (RT)
LA English
DT Proceedings Paper
CT 18th IEEE-NPSS Real Time Conference (RT)
CY JUN 09-15, 2012
CL Univ Calif, U S Dept Energy, Lawrence Berkeley Natl Lab (Berkelly Lab),
Berkeley, CA
SP IEEE, Nucl & Plasma Sci Soc, Univ Calif
HO Univ Calif, U S Dept Energy, Lawrence Berkeley Natl Lab (Berkelly Lab)
AB The ATLAS experiment is being operated by highly distributed computing system which is constantly producing a lot of status information which is used to monitor the experiment operational conditions as well as to assess the quality of the physics data being taken. For example the ATLAS High Level Trigger(HLT) algorithms are executed on the online computing farm consisting from about 1500 nodes. Each HLT algorithm is producing few thousands histograms, which have to be integrated over the whole farm and carefully analyzed in order to properly tune the event rejection. In order to handle such non-physics data the Information Service (IS) facility has been developed in the scope of the ATLAS Trigger and Data Acquisition (TDAQ) project. The IS provides high-performance scalable solution for information exchange in distributed environment. In the course of an ATLAS data taking session the IS handles about hundred gigabytes of information which is being constantly updated with the update interval varying from a second to few tens of seconds. IS provides access to any information item on request as well as distributing notification to all the information subscribers. In latter case IS subscribers receive information within few milliseconds after it was updated. IS can handle arbitrary types of information including histograms produced by the HLT applications and provides C++, Java and Python API. The Information Service is a primarily and in most cases a unique source of information for the majority of the online monitoring analysis and GUI applications, used to control and monitor the ATLAS experiment. Information Service provides streaming functionality allowing efficient replication of all or part of the managed information. This functionality is used to duplicate the subset of the ATLAS monitoring data to the CERN public network with the latency of few milliseconds, allowing efficient real-time monitoring of the data taking from outside the protected ATLAS network. Each information item in IS has an associated URL which can be used to access that item online via HTTP protocol. This functionality is being used by many online monitoring applications which can run in a WEB browser, providing real-time monitoring information about ATLAS experiment over the globe. This paper will describe design and implementation of the IS and present performance results which have been taken in the ATLAS operational environment.
C1 [Kolos, S.] Univ Calif Irvine, Irvine, CA 92717 USA.
[Boutsioukis, G.] Aristotle Univ Thessaloniki, Thessaloniki, Greece.
[Hauser, R.] Michigan State Univ, E Lansing, MI USA.
RP Kolos, S (corresponding author), Univ Calif Irvine, Irvine, CA 92717 USA.
EM ser-guei.kolos@cern.ch; georgios.boutsioukis@cern.ch;
reiner.hauser@cern.ch
CR Kolos S, 2007, 15 IEEE REAL TIM C I
Scholtes I, 2008, IEEE T NUCL SCI, V55, P1610, DOI 10.1109/TNS.2008.924057
NR 2
TC 0
Z9 0
U1 0
U2 0
PU IEEE
PI NEW YORK
PA 345 E 47TH ST, NEW YORK, NY 10017 USA
BN 978-1-4673-1082-6; 978-1-4673-1083-3
PY 2012
PG 5
WC Computer Science, Hardware & Architecture; Engineering, Electrical &
Electronic
SC Computer Science; Engineering
GA BEH30
UT WOS:000316572100002
DA 2021-04-21
ER
PT S
AU Benkevitch, LV
Oberoi, D
Benjamin, MD
Sokolov, IV
AF Benkevitch, L. V.
Oberoi, D.
Benjamin, M. D.
Sokolov, I. V.
BE Ballester, P
Egret, D
Lorente, NPF
TI HART: An Efficient Modeling Framework for Simulated Solar Imaging
SO ASTRONOMICAL DATA ANALYSIS SOFTWARE AND SYSTEMS XXI
SE Astronomical Society of the Pacific Conference Series
LA English
DT Proceedings Paper
CT 21st Annual Conference on Astronomical Data Analysis Software and
Systems
CY NOV 06-10, 2011
CL Observ Paris, Paris, FRANCE
SP European So Observ
HO Observ Paris
ID SUN
AB The Haystack & AOSS Ray Tracer (HART) is a software tool for modeling propagation of electromagnetic radiation through a realistic description of the magnetized solar corona, along with the associated radiative transfer effects. Its primary outputs are solar brightness temperature (or flux density) images corresponding to a user-specified coronal description and radio frequency. HART is based on native high-efficiency algorithms coded in the C language, and provides convenient command-line (Python) and graphical user interfaces. HART is a necessary tool for enabling the extraction of solar physics from the images that will be produced by the new generation of low radio frequency arrays like the Murchison Widefield Array (MWA), Low Frequency Array (LOFAR) and Long Wavelength Array (LWA).
C1 [Benkevitch, L. V.; Oberoi, D.] MIT, Haystack Observ, Off Route 40, Westford, MA 01886 USA.
[Benjamin, M. D.] Princeton Univ, REU student, Haystack, Princeton, NJ 08544 USA.
[Sokolov, I. V.] Univ Michigan, AOSS, 2455 Hayward St, Ann Arbor, MI 48109 USA.
RP Benkevitch, LV (corresponding author), MIT, Haystack Observ, Off Route 40, Westford, MA 01886 USA.
RI Sokolov, Igor V/H-9860-2013
OI Sokolov, Igor V/0000-0002-6118-0469
FU National Science FoundationNational Science Foundation (NSF); Division
of Astronomical Sciences and Atmospheric and Geospace Sciences to the
MIT Haystack Observatory; ADASS XXI organizers
FX This work was supported partly under grants from the National Science
Foundation, Division of Astronomical Sciences and Atmospheric and
Geospace Sciences to the MIT Haystack Observatory. LVB acknowledges the
financial aid from ADASS XXI organizers and the travel grant from AAS
which enabled him to attend this conference.
CR Benkevitch L., 2010, 10065635 ARXIV
Benkevitch L., 2011, 11102516 ARXIV
de Vos M., 2009, IEEE P, V97, P1431, DOI DOI 10.1109/JPROC.2009.2020509
Ellingson SW, 2009, P IEEE, V97, P1421, DOI 10.1109/JPROC.2009.2015683
Lonsdale CJ, 2009, P IEEE, V97, P1497, DOI 10.1109/JPROC.2009.2017564
Oberoi D, 2011, ASTROPHYS J LETT, V728, DOI 10.1088/2041-8205/728/2/L27
Saito K., 1970, Annals of the Tokyo Astronomical Observatory, V12, P53
Thejappa G, 2008, ASTROPHYS J, V676, P1338, DOI 10.1086/528835
NR 8
TC 3
Z9 3
U1 0
U2 1
PU ASTRONOMICAL SOC PACIFIC
PI SAN FRANCISCO
PA 390 ASHTON AVE, SAN FRANCISCO, CA 94112 USA
SN 1050-3390
BN 978-1-58381-804-6
J9 ASTR SOC P
PY 2012
VL 461
BP 475
EP +
PG 2
WC Astronomy & Astrophysics; Computer Science, Interdisciplinary
Applications
SC Astronomy & Astrophysics; Computer Science
GA BDT65
UT WOS:000314795700103
DA 2021-04-21
ER
PT S
AU Kolos, S
Boutsioukis, G
Hauser, R
AF Kolos, S.
Boutsioukis, G.
Hauser, R.
GP IOP
TI High-Performance Scalable Information Service for the ATLAS Experiment
SO INTERNATIONAL CONFERENCE ON COMPUTING IN HIGH ENERGY AND NUCLEAR PHYSICS
2012 (CHEP2012), PTS 1-6
SE Journal of Physics Conference Series
LA English
DT Proceedings Paper
CT International Conference on Computing in High Energy and Nuclear Physics
(CHEP)
CY MAY 21-25, 2012
CL New York Univ, New York, NY
SP Brookhaven Natl Lab, ACEOLE, Data Direct Networks, Dell, European Middleware Initiat, Nexsan
HO New York Univ
AB The ATLAS[1] experiment is operated by a highly distributed computing system which is constantly producing a lot of status information which is used to monitor the experiment operational conditions as well as to assess the quality of the physics data being taken. For example the ATLAS High Level Trigger(HLT) algorithms are executed on the online computing farm consisting from about 1500 nodes. Each HLT algorithm is producing few thousands histograms, which have to be integrated over the whole farm and carefully analyzed in order to properly tune the event rejection. In order to handle such non-physics data the Information Service (IS) facility has been developed in the scope of the ATLAS Trigger and Data Acquisition (TDAQ)[2] project. The IS provides a high-performance scalable solution for information exchange in distributed environment. In the course of an ATLAS data taking session the IS handles about a hundred gigabytes of information which is being constantly updated with the update interval varying from a second to a few tens of seconds. IS provides access to any information item on request as well as distributing notification to all the information subscribers. In the latter case IS subscribers receive information within a few milliseconds after it was updated. IS can handle arbitrary types of information, including histograms produced by the HLT applications, and provides C++, Java and Python API. The Information Service is a unique source of information for the majority of the online monitoring analysis and GUI applications used to control and monitor the ATLAS experiment. Information Service provides streaming functionality allowing efficient replication of all or part of the managed information. This functionality is used to duplicate the subset of the ATLAS monitoring data to the CERN public network with a latency of a few milliseconds, allowing efficient real-time monitoring of the data taking from outside the protected ATLAS network. Each information item in IS has an associated URL which can be used to access that item online via HTTP protocol. This functionality is being used by many online monitoring applications which can run in a WEB browser, providing real-time monitoring information about the ATLAS experiment over the globe. This paper describes the design and implementation of the IS and presents performance results which have been taken in the ATLAS operational environment.
C1 [Kolos, S.] Univ Calif Irvine, Irvine, CA 92717 USA.
[Boutsioukis, G.] Aristotle Univ Thessaloniki, Thessaloniki, Greece.
[Hauser, R.] Michigan State Univ, E Lansing, MI 48824 USA.
RP Kolos, S (corresponding author), Univ Calif Irvine, Irvine, CA 92717 USA.
EM Serguei.Kolos@cern.ch
CR Aad G, 2008, J INSTRUM, V3, DOI 10.1088/1748-0221/3/08/S08003
[Anonymous], 2003, ATLASTDR016 CERN
Dotti A, 2010, J PHYS CONF SER, V219, DOI 10.1088/1742-6596/219/3/032037
Ilchenko Y, 2010, J PHYS CONF SER, V219, DOI 10.1088/1742-6596/219/2/022035
Kolos S, 2007, IEEE T NUCL SCI, V55, P1
Scholtes I, 2008, IEEE T NUCL SCI, V55, P1610, DOI 10.1109/TNS.2008.924057
NR 6
TC 5
Z9 5
U1 0
U2 0
PU IOP PUBLISHING LTD
PI BRISTOL
PA DIRAC HOUSE, TEMPLE BACK, BRISTOL BS1 6BE, ENGLAND
SN 1742-6588
J9 J PHYS CONF SER
PY 2012
VL 396
AR 012026
DI 10.1088/1742-6596/396/1/012026
PG 11
WC Physics, Multidisciplinary; Physics, Nuclear; Physics, Particles &
Fields
SC Physics
GA BDT23
UT WOS:000314749800026
DA 2021-04-21
ER
PT S
AU Rybkin, G
AF Rybkin, Grigory
CA ATLAS Collaboration
GP IOP
TI ATLAS software packaging
SO INTERNATIONAL CONFERENCE ON COMPUTING IN HIGH ENERGY AND NUCLEAR PHYSICS
2012 (CHEP2012), PTS 1-6
SE Journal of Physics Conference Series
LA English
DT Proceedings Paper
CT International Conference on Computing in High Energy and Nuclear Physics
(CHEP)
CY MAY 21-25, 2012
CL New York Univ, New York, NY
SP Brookhaven Natl Lab, ACEOLE, Data Direct Networks, Dell, European Middleware Initiat, Nexsan
HO New York Univ
AB Software packaging is indispensable part of build and prerequisite for deployment processes. Full ATLAS software stack consists of TDAQ, HLT, and Offline software. These software groups depend on some 80 external software packages. We present tools, package PackDist, developed and used to package all this software except for TDAQ project. PackDist is based on and driven by CMT, ATLAS software configuration and build tool, and consists of shell and Python scripts. The packaging unit used is CMT project. Each CMT project is packaged as several packages-platform dependent (one per platform available), source code excluding header files, other platform independent files, documentation, and debug information packages (the last two being built optionally). Packaging can be done recursively to package all the dependencies. The whole set of packages for one software release, distribution kit, also includes configuration packages and contains some 120 packages for one platform. Also packaged are physics analysis projects (currently 6) used by particular physics groups on top of the full release. The tools provide an installation test for the full distribution kit. Packaging is done in two formats for use with the Pacman and RPM package managers. The tools are functional on the platforms supported by ATLAS-GNU/Linux and Mac OS X. The packaged software is used for software deployment on all ATLAS computing resources from the detector and trigger computing farms, collaboration laboratories computing centres, grid sites, to physicist laptops, and CERN VMFS and covers the use cases of running all applications as well as of software development.
C1 [Rybkin, Grigory; ATLAS Collaboration] Univ Paris 11, CNRS, IN2P3, Lab Accelerateur Lineaire, F-91405 Orsay, France.
RP Rybkin, G (corresponding author), Univ Paris 11, CNRS, IN2P3, Lab Accelerateur Lineaire, F-91405 Orsay, France.
EM Grigori.Rybkine@cern.ch
CR Albrand S, 2010, J PHYS CONF SER, V219, DOI 10.1088/1742-6596/219/4/042012
Arnault C, 2004, P INT C COMP HIGH EN
Arnault C, 2001, P INT C COMP HIGH EN
De Salvo A, 2012, J PHYS CONF SER, V396, DOI 10.1088/1742-6596/396/3/032030
Obreshkov E, 2008, NUCL INSTRUM METH A, V584, P244, DOI 10.1016/j.nima.2007.10.002
NR 5
TC 2
Z9 2
U1 0
U2 1
PU IOP PUBLISHING LTD
PI BRISTOL
PA DIRAC HOUSE, TEMPLE BACK, BRISTOL BS1 6BE, ENGLAND
SN 1742-6588
EI 1742-6596
J9 J PHYS CONF SER
PY 2012
VL 396
AR 052063
DI 10.1088/1742-6596/396/5/052063
PG 4
WC Physics, Multidisciplinary; Physics, Nuclear; Physics, Particles &
Fields
SC Physics
GA BDT23
UT WOS:000314749803054
DA 2021-04-21
ER
PT S
AU Watts, G
AF Watts, G.
GP IOP
TI ROOT.NET: Using ROOT from .NET languages like C# and F#
SO INTERNATIONAL CONFERENCE ON COMPUTING IN HIGH ENERGY AND NUCLEAR PHYSICS
2012 (CHEP2012), PTS 1-6
SE Journal of Physics Conference Series
LA English
DT Proceedings Paper
CT International Conference on Computing in High Energy and Nuclear Physics
(CHEP)
CY MAY 21-25, 2012
CL New York Univ, New York, NY
SP Brookhaven Natl Lab (BNL), ACEOLE, Data Direct Networks, Dell, European Middleware Initiat, Nexsan
HO New York Univ
AB ROOT. NET provides an interface between Microsoft's Common Language Runtime (CLR) and .NET technology and the ubiquitous particle physics analysis tool, ROOT. ROOT.NET automatically generates a series of efficient wrappers around the ROOT API. Unlike pyROOT, these wrappers are statically typed and so are highly efficient as compared to the Python wrappers. The connection to .NET means that one gains access to the full series of languages developed for the CLR including functional languages like F# (based on OCaml). Many features that make ROOT objects work well in the .NET world are added (properties, IEnumerable interface, LINQ compatibility, etc.). Dynamic languages based on the CLR can be used as well, of course (Python, for example). Additionally it is now possible to access ROOT objects that are unknown to the translation tool. This poster will describe the techniques used to effect this translation, along with performance comparisons, and examples. All described source code is posted on the open source site CodePlex.
C1 Univ Washington, Dept Phys, Seattle, WA 98195 USA.
RP Watts, G (corresponding author), Univ Washington, Dept Phys, Box 351560, Seattle, WA 98195 USA.
EM gwatts@uw.edu
CR Brun R, 1997, NUCL INSTRUM METH A, V389, P81, DOI 10.1016/S0168-9002(97)00048-X
Syme D., 2010, EXPERT F 2 0
Watts G, 2012, J PHYS C SE IN PRESS
NR 3
TC 0
Z9 0
U1 0
U2 2
PU IOP PUBLISHING LTD
PI BRISTOL
PA DIRAC HOUSE, TEMPLE BACK, BRISTOL BS1 6BE, ENGLAND
SN 1742-6588
J9 J PHYS CONF SER
PY 2012
VL 396
AR 052074
DI 10.1088/1742-6596/396/5/052074
PG 8
WC Physics, Multidisciplinary; Physics, Nuclear; Physics, Particles &
Fields
SC Physics
GA BDT23
UT WOS:000314749803065
DA 2021-04-21
ER
PT S
AU Stagni, F
Charpentier, P
AF Stagni, F.
Charpentier, P.
CA LHCb Collaboration
BE Teodorescu, L
TI The LHCb DIRAC-based production and data management operations systems
SO 14TH INTERNATIONAL WORKSHOP ON ADVANCED COMPUTING AND ANALYSIS
TECHNIQUES IN PHYSICS RESEARCH (ACAT 2011)
SE Journal of Physics Conference Series
LA English
DT Proceedings Paper
CT 14th International Workshop on Advanced Computing and Analysis
Techniques in Physics Research (ACAT)
CY SEP 05-09, 2011
CL Brunel Univ, Uxbridge, ENGLAND
SP Sci & Technol, Facilities Council (STFC), Durham Univ, Inst Particle Phys Phenomenol (IPPP), Brookhaven Natl Lab, Dell
HO Brunel Univ
AB The LHCb computing model was designed in order to support the LHCb physics program, taking into account LHCb specificities (event sizes, processing times etc ... ). Within this model several key activities are defined, the most important of which are real data processing (reconstruction, stripping and streaming, group and user analysis), Monte-Carlo simulation and data replication. In this contribution we detail how these activities are managed by the LHCbDIRAC Data Transformation System. The LHCbDIRAC Data Transformation System leverages the workload and data management capabilities provided by DIRAC, a generic community grid solution, to support data-driven workflows (or DAGs). The ability to combine workload and data tasks within a single DAG allows to create highly sophisticated workflows with the individual steps linked by the availability of data. This approach also provides the advantage of a single point at which all activities can be monitored and controlled. While several interfaces are currently supported (including python API and CLI), we will present the ability to create LHCb workflows through a secure web interface, control their state in addition to creating and submitting jobs. To highlight the versatility of the system we present in more detail experience with real data of the 2010 and 2011 LHC run.
C1 [Stagni, F.; Charpentier, P.] CERN, PH Dept, CH-1211 Geneva 23, Switzerland.
RP Stagni, F (corresponding author), CERN, PH Dept, CH-1211 Geneva 23, Switzerland.
EM federico.stagni@cern.ch; philippe.charpentier@cern.ch
CR Bonifazi Federico, 2008, Journal of Physics: Conference Series, DOI 10.1088/1742-6596/119/4/042005
Diaz RG, 2011, J GRID COMPUT, V9, P65, DOI 10.1007/s10723-010-9175-7
Tsaregorodtsev A, 2008, Journal of Physics: Conference Series, DOI 10.1088/1742-6596/119/6/062048
NR 3
TC 9
Z9 9
U1 0
U2 0
PU IOP PUBLISHING LTD
PI BRISTOL
PA DIRAC HOUSE, TEMPLE BACK, BRISTOL BS1 6BE, ENGLAND
SN 1742-6588
J9 J PHYS CONF SER
PY 2012
VL 368
AR 012010
DI 10.1088/1742-6596/368/1/012010
PG 8
WC Computer Science, Interdisciplinary Applications; Physics, Applied;
Physics, Mathematical
SC Computer Science; Physics
GA BBN48
UT WOS:000307497200010
DA 2021-04-21
ER
PT S
AU Bretz, HP
Erdmann, M
Fischer, R
Hinzmann, A
Klingebiel, D
Komm, M
Lingemann, J
Rieger, M
Muller, G
Steggemann, J
Winchen, T
AF Bretz, H-P
Erdmann, M.
Fischer, R.
Hinzmann, A.
Klingebiel, D.
Komm, M.
Lingemann, J.
Rieger, M.
Mueller, G.
Steggemann, J.
Winchen, T.
BE Teodorescu, L
TI Visual physics analysis - from desktop to physics analysis at your
fingertips
SO 14TH INTERNATIONAL WORKSHOP ON ADVANCED COMPUTING AND ANALYSIS
TECHNIQUES IN PHYSICS RESEARCH (ACAT 2011)
SE Journal of Physics Conference Series
LA English
DT Proceedings Paper
CT 14th International Workshop on Advanced Computing and Analysis
Techniques in Physics Research (ACAT)
CY SEP 05-09, 2011
CL Brunel Univ, Uxbridge, ENGLAND
SP Sci & Technol, Facilities Council (STFC), Durham Univ, Inst Particle Phys Phenomenol (IPPP), Brookhaven Natl Lab, Dell
HO Brunel Univ
AB Visual Physics Analysis (VISPA) is an analysis environment with applications in high energy and astroparticle physics. Based on a data-flow-driven paradigm, it allows users to combine graphical steering with self-written C++ and Python modules. This contribution presents new concepts integrated in VISPA: layers, convenient analysis execution, and web-based physics analysis. While the convenient execution offers full flexibility to vary settings for the execution phase of an analysis, layers allow to create different views of the analysis already during its design phase. Thus, one application of layers is to define different stages of an analysis (e.g. event selection and statistical analysis). However, there are other use cases such as to independently optimize settings for different types of input data in order to guide all data through the same analysis flow. The new execution feature makes job submission to local clusters as well as the LHC Computing Grid possible directly from VISPA. Web-based physics analysis is realized in the VISPA@Web project, which represents a whole new way to design and execute analyses via a standard web browser.
C1 [Bretz, H-P; Erdmann, M.; Fischer, R.; Hinzmann, A.; Klingebiel, D.; Komm, M.; Lingemann, J.; Rieger, M.; Mueller, G.; Steggemann, J.; Winchen, T.] Rhein Westfal TH Aachen, Phys Inst A 3, D-52062 Aachen, Germany.
RP Bretz, HP (corresponding author), Rhein Westfal TH Aachen, Phys Inst A 3, D-52062 Aachen, Germany.
EM rfischer@physik.rwth-aachen.de
RI Steggemann, Jan/AAL-5700-2020
OI Steggemann, Jan/0000-0003-4420-5510; Erdmann, Martin/0000-0002-1653-1303
CR Bos B, TEXT CSS MEDIA TYPE
Brodski M, 2010, P ACAT2010 JAIP IND, V064
Crockford D., APPL JSON MEDIA TYPE
Donno F, 2008, CERNITNOTE2008002
Erdmann M, 2010, ASTROPART PHYS, V33, P201, DOI 10.1016/j.astropartphys.2010.01.011
Garrett J.J., 2005, AJAX NEW APPROACH WE
Hickson I., 2011, HTML5 VOCABULARY ASS
Sencha Inc, 2011, JAVASCRIPT FRAM RICH
Thain D, 2005, CONCURR COMP-PRACT E, V17, P323, DOI 10.1002/cpe.938
2008, QT CROSS PLATFORM AP
NR 10
TC 0
Z9 0
U1 0
U2 1
PU IOP PUBLISHING LTD
PI BRISTOL
PA DIRAC HOUSE, TEMPLE BACK, BRISTOL BS1 6BE, ENGLAND
SN 1742-6588
J9 J PHYS CONF SER
PY 2012
VL 368
AR 012039
DI 10.1088/1742-6596/368/1/012039
PG 6
WC Computer Science, Interdisciplinary Applications; Physics, Applied;
Physics, Mathematical
SC Computer Science; Physics
GA BBN48
UT WOS:000307497200039
DA 2021-04-21
ER
PT J
AU Gavin, R
Li, Y
Petriello, F
Quackenbush, S
AF Gavin, Ryan
Li, Ye
Petriello, Frank
Quackenbush, Seth
TI FEWZ 2.0: A code for hadronic Z production at next-to-next-to-leading
order
SO COMPUTER PHYSICS COMMUNICATIONS
LA English
DT Article
DE Z; Drell-Yan; NNLO
ID PARTON DISTRIBUTIONS
AB We introduce an improved version of the simulation code FEWZ (Fully Exclusive W and Z Production) for hadron collider production of lepton pairs through the Drell-Yan process at next-to-next-to-leading order (NNLO) in the strong coupling constant. The program is fully differential in the phase space of leptons and additional hadronic radiation. The new version offers users significantly more options for customization. FEWZ now bins multiple, user-selectable histograms during a single run, and produces parton distribution function (PDF) errors automatically. It also features a significantly improved integration routine, and can take advantage of multiple processor cores locally or on the Condor distributed computing system. We illustrate the new features of FEWZ by presenting numerous phenomenological results for LHC physics. We compare NNLO QCD with initial ATLAS and CMS results, and discuss in detail the effects of detector acceptance on the measurement of angular quantities associated with Z-boson production. We address the issue of technical precision in the presence of severe phase-space cuts.
Program summary
Program title: FEWZ
Catalogue identifier: AEJP_v1_0
Program summary URL: http://cpc.cs.qub.ac.uk/summaries/AEJP_v1_0.html
Program obtainable from: CPC Program Library. Queen's University, Belfast, N. Ireland
Licensing provisions: Standard CPC licence, http://cpc.cs.qub.ac.uk/licence/licence.html
No. of lines in distributed program, including test data, etc.: 6 280 771
No. of bytes in distributed program, including test data, etc.: 1 73 027 645
Distribution format: tar.gz
Programming language: Fortran 77, C++, Python
Computer: Mac, PC
Operating system: Mac OSX. Unix/Linux
Has the code been vectorized or parallelized?: Yes. User-selectable. 1 to 219
RAM: 200 Mbytes for common parton distribution functions
Classification: 11.1
External routines: CUBA numerical integration library, numerous parton distribution sets (see text): these are provided with the code.
Nature of problem: Determination of the Drell-Yan Z/photon production cross section and decay into leptons, with kinematic distributions of leptons and jets including full spin correlations, at next-to-next-to-leading order in the strong coupling constant.
Solution method: Virtual loop integrals are decomposed into master integrals using automated techniques. Singularities are extracted from real radiation terms via sector decomposition, which separates singularities and maps onto suitable phase space variables. Result is convoluted with parton distribution functions. Each piece is numerically integrated over phase space, which allows arbitrary cuts on the observed particles. Each sample point may be binned during numerical integration, providing histograms, and reweighted by parton distribution function error eigenvectors, which provides PDF errors.
Restrictions: Output does not correspond to unweighted events, and cannot be interfaced with a shower Monte Carlo.
Additional comments: !!!!! The distribution file for this program is over 170 Mbytes and therefore is not delivered directly when download or E-mail is requested. Instead a html file giving details of how the program can be obtained is sent.
Running time: One day for total cross sections with 0.1% integration errors assuming typical cuts. up to 1 week for smooth kinematic distributions with sub-percent integration errors for each bin. (C) 2011 Elsevier B.V. All rights reserved.
C1 [Petriello, Frank; Quackenbush, Seth] Argonne Natl Lab, Div High Energy Phys, Argonne, IL 60439 USA.
[Gavin, Ryan; Li, Ye] Univ Wisconsin, Dept Phys, Madison, WI 53706 USA.
[Petriello, Frank] Northwestern Univ, Dept Phys & Astron, Evanston, IL 60208 USA.
RP Quackenbush, S (corresponding author), Argonne Natl Lab, Div High Energy Phys, Argonne, IL 60439 USA.
EM squackenbush@hep.anl.gov
FU US Department of Energy, Division of High Energy PhysicsUnited States
Department of Energy (DOE) [DE-AC02-06CH11357]
FX We are grateful to F. Stoeckli for inspiring and advising us on the
inclusion of the histogramming feature in FEWZ, and on the restructuring
of the numerical integration. We thank S. Yost and J. Qian for valuable
feedback on the original version of FEWZ, and M. Schmitt for useful
discussions on experimental capabilities and desires. We also thank W.
Sakumoto for alerting us to parity-violating moments for inclusion in
our code, T. Hahn for feedback regarding CUBA, K. Melnikov for helpful
comments, and N. Chiapolini for script suggestions. This work was
supported in part by the US Department of Energy, Division of High
Energy Physics, under Contract DE-AC02-06CH11357.
CR Aad G, 2010, J HIGH ENERGY PHYS, DOI 10.1007/JHEP12(2010)060
Adam NE, 2008, J HIGH ENERGY PHYS, DOI 10.1088/1126-6708/2008/09/133
ADAM NE, 2008, J HIGH ENERGY PHYS
Alekhin S, 2010, PHYS REV D, V81, DOI 10.1103/PhysRevD.81.014032
ALEKHIN S, ARXIV10073657HEPPH
ALTARELLI G, 1979, NUCL PHYS B, V157, P461, DOI 10.1016/0550-3213(79)90116-0
Anastasiou C, 2004, PHYS REV LETT, V93, DOI [10.1103/PhysRevLett.93.032002, 10.1103/PhysRevLett.93.262002]
Anastasiou C, 2004, PHYS REV D, V69, DOI 10.1103/PhysRevD.69.094008
Anastasiou C, 2003, PHYS REV LETT, V91, DOI 10.1103/PhysRevLett.91.182002
Anastasiou C, 2004, J HIGH ENERGY PHYS
Anastasiou C, 2004, PHYS REV D, V69, DOI 10.1103/PhysRevD.69.076010
Anastasiou C, 2005, NUCL PHYS B, V724, P197, DOI 10.1016/j.nuclphysb.2005.06.036
Anastasiou C., 2007, JHEP, V0709, P018
Ball RD, 2010, NUCL PHYS B, V838, P136, DOI 10.1016/j.nuclphysb.2010.05.008
Binoth T, 2000, NUCL PHYS B, V585, P741, DOI 10.1016/S0550-3213(00)00429-6
Campbell JM, 1999, PHYS REV D, V60, DOI 10.1103/PhysRevD.60.113006
Catani S, 2010, J HIGH ENERGY PHYS, DOI 10.1007/JHEP05(2010)006
Catani S, 2009, PHYS REV LETT, V103, DOI 10.1103/PhysRevLett.103.082001
COLLINS JC, 1977, PHYS REV D, V16, P2219, DOI 10.1103/PhysRevD.16.2219
Dissertori G., 2006, HP2 WORKSH ZUR SWITZ
Dittmaier S, 2002, PHYS REV D, V65, DOI 10.1103/PhysRevD.65.073007
Dittmar M, 1997, PHYS REV D, V56, P7284, DOI 10.1103/PhysRevD.56.7284
GIELE WT, ARXIVHEPPH0104053, P75006
Gluk M, 2008, PHYS LETT B, V664, P133, DOI 10.1016/j.physletb.2008.04.063
Hahn T, 2005, COMPUT PHYS COMMUN, V168, P78, DOI 10.1016/j.cpc.2005.01.010
Hamberg R, 2002, NUCL PHYS B, V644, P403, DOI 10.1016/S0550-3213(02)00814-3
HAMBERG R, 1991, NUCL PHYS B, V359, P343, DOI 10.1016/0550-3213(91)90064-5
HAYWOOD S, ARXIVHEPPH0003275
Jimenez-Delgado P, 2009, PHYS REV D, V80, DOI 10.1103/PhysRevD.80.114011
Khachatryan V, 2011, J HIGH ENERGY PHYS, DOI 10.1007/JHEP01(2011)080
Khoze VA, 2001, EUR PHYS J C, V19, P313, DOI 10.1007/s100520100616
Lai H. L., ARXIV10072241HEPPH
Martin AD, 2007, PHYS LETT B, V652, P292, DOI 10.1016/j.physletb.2007.07.040
Martin AD, 2009, EUR PHYS J C, V63, P189, DOI 10.1140/epjc/s10052-009-1072-5
Melnikov K, 2006, PHYS REV D, V74, DOI 10.1103/PhysRevD.74.114017
Melnikov K, 2006, PHYS REV LETT, V96, DOI 10.1103/PhysRevLett.96.231803
MIRKES E, 1995, PHYS REV D, V51, P4891, DOI 10.1103/PhysRevD.51.4891
MIRKES E, 1992, NUCL PHYS B, V387, P3, DOI 10.1016/0550-3213(92)90046-E
Nadolsky PM, 2008, PHYS REV D, V78, DOI 10.1103/PhysRevD.78.013004
Pumplin J, 2002, J HIGH ENERGY PHYS, DOI 10.1088/1126-6708/2002/07/012
Thain D, 2005, CONCURR COMP-PRACT E, V17, P323, DOI 10.1002/cpe.938
Thorne R. S., ARXIV10062753HEPPH
Tung WK, 2007, J HIGH ENERGY PHYS, DOI 10.1088/1126-6708/2007/02/053
NR 43
TC 356
Z9 355
U1 1
U2 21
PU ELSEVIER
PI AMSTERDAM
PA RADARWEG 29, 1043 NX AMSTERDAM, NETHERLANDS
SN 0010-4655
EI 1879-2944
J9 COMPUT PHYS COMMUN
JI Comput. Phys. Commun.
PD NOV
PY 2011
VL 182
IS 11
BP 2388
EP 2403
DI 10.1016/j.cpc.2011.06.008
PG 16
WC Computer Science, Interdisciplinary Applications; Physics, Mathematical
SC Computer Science; Physics
GA 815KZ
UT WOS:000294525800008
DA 2021-04-21
ER
PT J
AU Gonzalez-Ballestero, C
Robledo, LM
Bertsch, GF
AF Gonzalez-Ballestero, C.
Robledo, L. M.
Bertsch, G. F.
TI Numeric and symbolic evaluation of the pfaffian of general
skew-symmetric matrices
SO COMPUTER PHYSICS COMMUNICATIONS
LA English
DT Article
DE Skew symmetric matrices; Pfaffian
ID OPERATORS; STATES
AB Evaluation of pfaffians arises in a number of physics applications, and for some of them a direct method is preferable to using the deteminantal formula. We discuss two methods for the numerical evaluation of pfaffians. The first is tridiagonalization based on Householder transformations. The main advantage of this method is its numerical stability that makes unnecessary the implementation of a pivoting strategy. The second method considered is based on Aitken's block diagonalization formula. It yields to a kind of LU (similar to Cholesky's factorization) decomposition (under congruence) of arbitrary skew-symmetric matrices that is well suited both for the numeric and symbolic evaluations of the pfaffian. Fortran subroutines (FORTRAN 77 and 90) implementing both methods are given. We also provide simple implementations in Python and Mathematica for purpose of testing, or for exploratory studies of methods that make use of pfaffians.
Program summary
Program title: PFAFFIAN
Catalogue identifier: AEJD_v1_0
Program summary URL: http://cpc.cs.qub.ac.uk/summaries/AEJD_v1_0.html
Program obtainable from: CPC Program Library, Queen's University, Belfast, N. Ireland
Licensing provisions: Standard CPC licence, http://cpc.cs.qub.ac.uk/licence/licence.html
No. of lines in distributed program, including test data, etc.: 2281
No. of bytes in distributed program, including test data, etc.: 13 226
Distribution format: tar.gz
Programming language: Fortran 77 and 90
Computer: Any supporting a FORTRAN compiler
Operating system: Any supporting a FORTRAN compiler
RAM: a few MB
Classification: 4.8
Nature of problem: Evaluation of the pfaffian of a skew symmetric matrix. Evaluation of pfaffians arises in a number of physics applications involving fermionic mean field wave functions and their overlaps.
Solution method: Householder tridiagonalization. Aitken's block diagonalization formula.
Additional comments: Python and Mathematica implementations are provided in the main body of the paper.
Running time: Depends on the size of the matrices. For matrices with 100 rows and columns a few milliseconds are required. (C) 2011 Elsevier B.V. All rights reserved.
C1 [Gonzalez-Ballestero, C.; Robledo, L. M.] Univ Autonoma Madrid, Dept Fis Teor, E-28049 Madrid, Spain.
[Bertsch, G. F.] Univ Washington, Dept Phys, Seattle, WA 98195 USA.
[Bertsch, G. F.] Univ Washington, Inst Nucl Theory, Seattle, WA 98195 USA.
RP Robledo, LM (corresponding author), Univ Autonoma Madrid, Dept Fis Teor, E-28049 Madrid, Spain.
EM luis.robledo@uam.es
RI Gonzalez-Ballestero, Carlos/H-6608-2018; Robledo, Luis
Miguel/L-2557-2013
OI Gonzalez-Ballestero, Carlos/0000-0002-7639-0856; Robledo, Luis
Miguel/0000-0002-6061-1319
FU MICINN (Spain)Spanish Government [FPA2009-08958, FIS2009-07277];
Consolider-IngenioSpanish Government [CPAN CSD2007-00042, MULTIDARK
CSD2009-00064]
FX We acknowledge K. Roche for a careful reading of the manuscript and
several suggestions. This work was supported by MICINN (Spain) under
research grants FPA2009-08958, and FIS2009-07277, as well as by
Consolider-Ingenio 2010 Programs CPAN CSD2007-00042 and MULTIDARK
CSD2009-00064.
CR Aasen J. O., 1971, BIT (Nordisk Tidskrift for Informationsbehandling), V11, P233, DOI 10.1007/BF01931804
Bajdich M, 2008, PHYS REV B, V77, DOI 10.1103/PhysRevB.77.115112
BAUER FL, 1959, J SOC IND APPL MATH, V7, P107, DOI 10.1137/0107008
Berezin F A, 1966, METHOD 2 QUANTIZATIO
BUNCH JR, 1982, MATH COMPUT, V38, P475, DOI 10.2307/2007283
CAIANIELLO ER, 1973, COMBINATORICS RENORM
Campos I, 1999, EUR PHYS J C, V11, P507, DOI 10.1007/s100520050651
DONGARRA JJ, 1988, ACM T MATH SOFTWARE, V14, P1, DOI 10.1145/42288.42291
Golub GH., 1996, MATRIX COMPUTATIONS
HOUSEHOLDER AS, 1958, J ACM, V5, P339, DOI 10.1145/320941.320947
Klauder J.R., 1985, COHERENT STATES APPL
LANG GH, 1993, PHYS REV C, V48, P1518, DOI 10.1103/PhysRevC.48.1518
NEERGARD K, 1983, NUCL PHYS A, V402, P311, DOI 10.1016/0375-9474(83)90501-8
OHNUKI Y, 1978, PROG THEOR PHYS, V60, P548, DOI 10.1143/PTP.60.548
Robledo LM, 2009, PHYS REV C, V79, DOI 10.1103/PhysRevC.79.021302
RUBOW J, ARXIV11023576
Stephan JM, 2009, PHYS REV B, V80, DOI 10.1103/PhysRevB.80.184421
Thomas CK, 2009, PHYS REV E, V80, DOI 10.1103/PhysRevE.80.046708
Zhang F., 2005, SCHUR COMPLEMENT ITS
NR 19
TC 18
Z9 18
U1 1
U2 7
PU ELSEVIER SCIENCE BV
PI AMSTERDAM
PA PO BOX 211, 1000 AE AMSTERDAM, NETHERLANDS
SN 0010-4655
J9 COMPUT PHYS COMMUN
JI Comput. Phys. Commun.
PD OCT
PY 2011
VL 182
IS 10
BP 2213
EP 2218
DI 10.1016/j.cpc.2011.04.025
PG 6
WC Computer Science, Interdisciplinary Applications; Physics, Mathematical
SC Computer Science; Physics
GA 802CD
UT WOS:000293486000015
DA 2021-04-21
ER
PT J
AU Alwall, J
Herquet, M
Maltoni, F
Mattelaer, O
Stelzer, T
AF Alwall, Johan
Herquet, Michel
Maltoni, Fabio
Mattelaer, Olivier
Stelzer, Tim
TI MadGraph 5: going beyond
SO JOURNAL OF HIGH ENERGY PHYSICS
LA English
DT Article
DE QCD Phenomenology
ID AMPLITUDES; TREE; HELAS
AB MADGRAPH 5 is the new version of the MADGRAPH matrix element generator, written in the Python programming language. It implements a number of new, efficient algorithms that provide improved performance and functionality in all aspects of the program. It features a new user interface, several new output formats including C++ process libraries for PYTHIA 8, and full compatibility with FEYNRULES for new physics models implementation, allowing for event generation for any model that can be written in the form of a Lagrangian. MADGRAPH 5 builds on the same philosophy as the previous versions, and its design allows it to be used as a collaborative platform where theoretical, phenomenological and simulation projects can be developed and then distributed to the high-energy community. We describe the ideas and the most important developments of the code and illustrate its capabilities through a few simple phenomenological examples.
C1 [Alwall, Johan] Fermilab Natl Accelerator Lab, Dept Theoret Phys, Batavia, IL 60510 USA.
[Herquet, Michel] Nikhef Theory Grp, NL-1098 SJ Amsterdam, Netherlands.
[Maltoni, Fabio; Mattelaer, Olivier] Catholic Univ Louvain, Ctr Cosmol Particle Phys & Phenomenol CP3, B-1348 Louvain, Belgium.
[Stelzer, Tim] Univ Illinois, Dept Phys, Urbana, IL 61801 USA.
RP Alwall, J (corresponding author), Fermilab Natl Accelerator Lab, Dept Theoret Phys, POB 500, Batavia, IL 60510 USA.
OI Maltoni, Fabio/0000-0003-4890-0676
FU Belgian Federal Office for Scientific, Technical and Cultural Affairs
[P6/11-P]; IISN "MadGraph" conventionFonds de la Recherche Scientifique
- FNRS [4.4511.10]
FX It is a great pleasure for us to thank all the people who, directly or
indirectly, help and support our efforts and services to the high-energy
community and all our users for their continuous and patient feedback.
In particular, for the help in the extensive testing of the new version,
we thank Alexis Kalogeropoulos; for the validation of new physics models
(and much more) we thank the FEYNRULES core authors (Neil Christensen,
Claude Duhr, Benjamin Fuks) and associates (Priscila de Aquino, Celine
Degrande); for our cluster management we are grateful to Vincent
Boucher, Jerome de Favereau, Pavel Demin, and Larry Nelson; for the
great physics work and the fun, we in particular thank our colleagues
and collaborators: Pierre Artoisenet, Simon de Visscher, Rikkert
Frederix, Stefano Frixione, Nicolas Greiner, Kaoru Hagiwara, Junichi
Kanzaki, Valentin Hirschi, Qiang Li, Kentarou Mawatari, Roberto Pittau,
Tilman Plehn, Marco Zaro. This work is partially supported by the
Belgian Federal Office for Scientific, Technical and Cultural Affairs
through the 'Interuniversity Attraction Pole Program - Belgium Science
Policy' P6/11-P and by the IISN "MadGraph" convention 4.4511.10.
CR Aguilar-Saavedra JA, 2011, NUCL PHYS B, V843, P638, DOI 10.1016/j.nuclphysb.2010.10.015
Alioli S, 2010, J HIGH ENERGY PHYS, DOI 10.1007/JHEP06(2010)043
Alwall J, 2008, EUR PHYS J C, V53, P473, DOI 10.1140/epjc/s10052-007-0490-5
Alwall J, 2007, COMPUT PHYS COMMUN, V176, P300, DOI 10.1016/j.cpc.2006.11.010
Alwall J., ALOHA AUTOMATIC HELA
ALWALL J, ARXIV07123311
Alwall J, 2007, J HIGH ENERGY PHYS, DOI 10.1088/1126-6708/2007/09/028
Alwall J, 2009, J HIGH ENERGY PHYS, DOI 10.1088/1126-6708/2009/02/017
BERENDS FA, 1988, NUCL PHYS B, V306, P759, DOI 10.1016/0550-3213(88)90442-7
Berger CF, 2011, PHYS REV LETT, V106, DOI 10.1103/PhysRevLett.106.092001
Berger CF, 2010, PHYS REV D, V82, DOI 10.1103/PhysRevD.82.074002
Berger CF, 2009, PHYS REV LETT, V102, DOI 10.1103/PhysRevLett.102.222001
Boos E, 2004, NUCL INSTRUM METH A, V534, P250, DOI 10.1016/j.nima.2004.07.096
Britto R, 2005, NUCL PHYS B, V715, P499, DOI 10.1016/j.nuclphysb.2005.02.030
CARAVAGLIOS F, 1995, PHYS LETT B, V358, P332, DOI 10.1016/0370-2693(95)00971-M
Catani S, 2001, J HIGH ENERGY PHYS
Cho GC, 2006, PHYS REV D, V73, DOI 10.1103/PhysRevD.73.054002
Christensen N. D., AUTORNATED VALIDATIO
Christensen N, 2011, EUR PHYS J C, V71, DOI 10.1140/epjc/s10052-011-1541-5
Christensen ND, 2009, COMPUT PHYS COMMUN, V180, P1614, DOI 10.1016/j.cpc.2009.02.018
Conway J., PRETTY GOOD SIMULATO
Corcella G, 2001, J HIGH ENERGY PHYS, DOI 10.1088/1126-6708/2001/01/010
Czakon M, 2009, J HIGH ENERGY PHYS, DOI 10.1088/1126-6708/2009/08/085
Degrande C., UFO UNIVERSAL FEYNRU
DEGRANDE C, ARXIV11041798
Degrande C, 2011, J HIGH ENERGY PHYS, DOI 10.1007/JHEP03(2011)125
Del Duca V, 2000, NUCL PHYS B, V571, P51, DOI 10.1016/S0550-3213(99)00809-3
DENNER A, 1992, NUCL PHYS B, V387, P467, DOI 10.1016/0550-3213(92)90169-C
Draggiotis P, 1998, PHYS LETT B, V439, P157, DOI 10.1016/S0370-2693(98)01015-6
Draggiotis P, 2009, J HIGH ENERGY PHYS, DOI 10.1088/1126-6708/2009/04/072
Duhr C., ARXIV11024191
Duhr C, 2006, J HIGH ENERGY PHYS
Ellis RK, 2009, J HIGH ENERGY PHYS, DOI 10.1088/1126-6708/2009/04/077
Englert C, 2011, PHYS REV D, V83, DOI 10.1103/PhysRevD.83.095009
Frederix R, 2008, J HIGH ENERGY PHYS, DOI 10.1088/1126-6708/2008/09/122
Frederix R, 2009, J HIGH ENERGY PHYS, DOI 10.1088/1126-6708/2009/10/003
Frixione S, 2003, J HIGH ENERGY PHYS, DOI 10.1088/1126-6708/2003/08/007
Frixione S, 2002, J HIGH ENERGY PHYS
Frixione S., ARXIV11060155
Gleisberg T, 2008, EUR PHYS J C, V53, P501, DOI [10.1140/epjc/s10052-007-0495-0, 10.1140/epjc/sl0052-007-0495-0]
Gleisberg T, 2004, J HIGH ENERGY PHYS
Gleisberg T, 2009, J HIGH ENERGY PHYS, DOI 10.1088/1126-6708/2009/02/007
Gleisberg T, 2008, J HIGH ENERGY PHYS, DOI 10.1088/1126-6708/2008/12/039
Hagiwara K, 2008, EUR PHYS J C, V56, P435, DOI 10.1140/epjc/s10052-008-0663-x
Hagiwara K, 2011, EUR PHYS J C, V71, DOI 10.1140/epjc/s10052-010-1529-6
Han T, 2010, J HIGH ENERGY PHYS, DOI 10.1007/JHEP01(2010)123
Hasegawa K, 2008, NUCL PHYS B-PROC SUP, V183, P268, DOI 10.1016/j.nuclphysbps.2008.09.115
Hirschi V, 2011, J HIGH ENERGY PHYS, DOI 10.1007/JHEP05(2011)044
Hoeche S., HEPPH0602031
Hoche S, 2009, J HIGH ENERGY PHYS, DOI 10.1088/1126-6708/2009/05/053
KILIAN W, LCTOOL2001039
KILIAN W, ARXIV07084233
Krauss F, 2004, PHYS REV D, V70, DOI 10.1103/PhysRevD.70.114009
Krauss F, 2002, J HIGH ENERGY PHYS
Lavesson N, 2005, J HIGH ENERGY PHYS
Lonnblad L, 2002, J HIGH ENERGY PHYS
Maltoni F, 2003, PHYS REV D, V67, DOI 10.1103/PhysRevD.67.014026
Maltoni F, 2003, J HIGH ENERGY PHYS, DOI 10.1088/1126-6708/2003/02/027
Mangano ML, 2007, J HIGH ENERGY PHYS, DOI 10.1088/1126-6708/2007/01/013
Mangano ML, 2003, J HIGH ENERGY PHYS, DOI 10.1088/1126-6708/2003/07/001
MANGANO ML, 1991, PHYS REP, V200, P301, DOI 10.1016/0370-1573(91)90091-Y
Moretti M., HEPPH0102195
Mrenna S, 2004, J HIGH ENERGY PHYS, DOI 10.1088/1126-6708/2004/05/040
Murayama H., KEK9111
Nason P, 2004, J HIGH ENERGY PHYS, DOI 10.1088/1126-6708/2004/11/040
Ossola G, 2008, J HIGH ENERGY PHYS, DOI 10.1088/1126-6708/2008/03/042
Ovyn S., ARXIV09032225
PAPADOPOULOS CG, HEPPH0606320
Pukhov A., HEPPH9908288
Pukhov A., HEPPH0412191
Randall L, 1999, PHYS REV LETT, V83, P3370, DOI 10.1103/PhysRevLett.83.3370
SEYMOUR MH, ARXIV08032231
Sjostrand T, 2008, COMPUT PHYS COMMUN, V178, P852, DOI 10.1016/j.cpc.2008.01.036
Sjostrand T, 2006, J HIGH ENERGY PHYS, DOI 10.1088/1126-6708/2006/05/026
STELZER T, 1994, COMPUT PHYS COMMUN, V81, P357, DOI 10.1016/0010-4655(94)90084-1
van Hameren A, 2009, J HIGH ENERGY PHYS, DOI 10.1088/1126-6708/2009/09/106
Zanderighi G., ARXIV08103524
Zhang C, 2011, PHYS REV D, V83, DOI 10.1103/PhysRevD.83.034006
NR 78
TC 1876
Z9 1878
U1 3
U2 46
PU SPRINGER
PI NEW YORK
PA 233 SPRING ST, NEW YORK, NY 10013 USA
SN 1029-8479
J9 J HIGH ENERGY PHYS
JI J. High Energy Phys.
PD JUN
PY 2011
IS 6
AR 128
DI 10.1007/JHEP06(2011)128
PG 40
WC Physics, Particles & Fields
SC Physics
GA 797OG
UT WOS:000293136600055
OA Other Gold
DA 2021-04-21
ER
PT J
AU Bauer, B
Carr, LD
Evertz, HG
Feiguin, A
Freire, J
Fuchs, S
Gamper, L
Gukelberger, J
Gull, E
Guertler, S
Hehn, A
Igarashi, R
Isakov, SV
Koop, D
Ma, PN
Mates, P
Matsuo, H
Parcollet, O
Pawlowski, G
Picon, JD
Pollet, L
Santos, E
Scarola, VW
Schollwock, U
Silva, C
Surer, B
Todo, S
Trebst, S
Troyer, M
Wall, ML
Werner, P
Wessel, S
AF Bauer, B.
Carr, L. D.
Evertz, H. G.
Feiguin, A.
Freire, J.
Fuchs, S.
Gamper, L.
Gukelberger, J.
Gull, E.
Guertler, S.
Hehn, A.
Igarashi, R.
Isakov, S. V.
Koop, D.
Ma, P. N.
Mates, P.
Matsuo, H.
Parcollet, O.
Pawlowski, G.
Picon, J. D.
Pollet, L.
Santos, E.
Scarola, V. W.
Schollwoeck, U.
Silva, C.
Surer, B.
Todo, S.
Trebst, S.
Troyer, M.
Wall, M. L.
Werner, P.
Wessel, S.
TI The ALPS project release 2.0: open source software for strongly
correlated systems
SO JOURNAL OF STATISTICAL MECHANICS-THEORY AND EXPERIMENT
LA English
DT Article
DE density matrix renormalization group calculations; classical Monte Carlo
simulations; quantum Monte Carlo simulations; quantum phase transitions
(theory)
ID MONTE-CARLO METHOD; VISUALIZATIONS; PROVENANCE; ALGORITHM
AB We present release 2.0 of the ALPS (Algorithms and Libraries for Physics Simulations) project, an open source software project to develop libraries and application programs for the simulation of strongly correlated quantum lattice models such as quantum magnets, lattice bosons, and strongly correlated fermion systems. The code development is centered on common XML and HDF5 data formats, libraries to simplify and speed up code development, common evaluation and plotting tools, and simulation programs. The programs enable non-experts to start carrying out serial or parallel numerical simulations by providing basic implementations of the important algorithms for quantum lattice models: classical and quantum Monte Carlo (QMC) using non-local updates, extended ensemble simulations, exact and full diagonalization (ED), the density matrix renormalization group (DMRG) both in a static version and a dynamic time-evolving block decimation (TEBD) code, and quantum Monte Carlo solvers for dynamical mean field theory (DMFT). The ALPS libraries provide a powerful framework for programmers to develop their own applications, which, for instance, greatly simplify the steps of porting a serial code onto a parallel, distributed memory machine. Major changes in release 2.0 include the use of HDF5 for binary data, evaluation tools in Python, support for the Windows operating system, the use of CMake as build system and binary installation packages for Mac OS X and Windows, and integration with the VisTrails workflow provenance tool. The software is available from our web server at http://alps.comp-phys.org/.
C1 [Bauer, B.; Gamper, L.; Gukelberger, J.; Hehn, A.; Isakov, S. V.; Ma, P. N.; Mates, P.; Pollet, L.; Surer, B.; Troyer, M.; Werner, P.] ETH, CH-8093 Zurich, Switzerland.
[Carr, L. D.; Wall, M. L.] Colorado Sch Mines, Dept Phys, Golden, CO 80401 USA.
[Evertz, H. G.] Graz Univ Technol, Inst Theoret Phys, A-8010 Graz, Austria.
[Feiguin, A.] Univ Wyoming, Dept Phys & Astron, Laramie, WY 82071 USA.
[Freire, J.; Koop, D.; Mates, P.; Santos, E.; Silva, C.] Univ Utah, Sci Comp & Imaging Inst, Salt Lake City, UT 84112 USA.
[Fuchs, S.] Univ Gottingen, Inst Theoret Phys, D-3400 Gottingen, Germany.
[Gull, E.] Columbia Univ, New York, NY 10027 USA.
[Guertler, S.] Univ Bonn, Bethe Ctr Theoret Phys, D-53115 Bonn, Germany.
[Igarashi, R.] Japan Atom Energy Agcy, Ctr Computat Sci & E Syst, Tokyo 1100015, Japan.
[Todo, S.] Japan Sci & Technol Agcy, Core Res Evolut Sci & Technol, Kawaguchi, Saitama 3320012, Japan.
[Matsuo, H.; Todo, S.] Univ Tokyo, Dept Appl Phys, Tokyo 1138656, Japan.
[Parcollet, O.] CEA Saclay, Inst Phys Theor, CEA DSM IPhT CNRS URA 2306, F-91191 Gif Sur Yvette, France.
[Pawlowski, G.] Adam Mickiewicz Univ, Fac Phys, PL-61614 Poznan, Poland.
[Picon, J. D.] Ecole Polytech Fed Lausanne, Inst Theoret Phys, CH-1015 Lausanne, Switzerland.
[Pollet, L.] Harvard Univ, Dept Phys, Cambridge, MA 02138 USA.
[Scarola, V. W.] Virginia Tech, Dept Phys, Blacksburg, VA 24061 USA.
[Schollwoeck, U.] Univ Munich, Dept Phys, Arnold Sommerfeld Ctr Theoret Phys, D-80333 Munich, Germany.
[Schollwoeck, U.] Univ Munich, Ctr NanoSci, D-80333 Munich, Germany.
[Trebst, S.] Univ Calif Santa Barbara, Stn Q, Microsoft Res, Santa Barbara, CA 93106 USA.
[Wessel, S.] Rhein Westfal TH Aachen, Inst Solid State Theory, D-52056 Aachen, Germany.
[Wessel, S.] Univ Stuttgart, Inst Theoret Phys 3, D-70550 Stuttgart, Germany.
RP Troyer, M (corresponding author), ETH, CH-8093 Zurich, Switzerland.
EM troyer@comp-phys.org
RI Bauer, Bela/C-1984-2009; Scarola, Vito/AAG-7503-2019; Troyer,
Matthias/B-7826-2008; Parcollet, Olivier/C-2340-2008; Gull, Emanuel
C/A-2362-2010; Freire, Juliana/AAQ-4484-2020; Parcollet,
Olivier/AAE-2863-2021; Pollet, Lode C/F-6845-2010; Scarola,
Vito/G-5412-2012; Werner, Philipp/C-7247-2009; Igarashi,
Ryo/B-5024-2010; Schollwock, Ulrich JM/L-1220-2016; Santos, Emanuele
M/J-1980-2017; Trebst, Simon/C-5390-2008; Todo, Synge/G-5023-2014; Carr,
Lincoln/E-3819-2016; Evertz, Hans G/O-5975-2015; Pawlowski,
Grzegorz/B-4880-2019
OI Bauer, Bela/0000-0001-9796-2115; Scarola, Vito/0000-0002-8653-2723;
Troyer, Matthias/0000-0002-1469-9444; Parcollet,
Olivier/0000-0002-0389-2660; Gull, Emanuel C/0000-0002-6082-1260;
Freire, Juliana/0000-0003-3915-7075; Pollet, Lode C/0000-0002-7274-2842;
Scarola, Vito/0000-0002-8653-2723; Igarashi, Ryo/0000-0002-2894-7226;
Santos, Emanuele M/0000-0002-2806-4589; Trebst,
Simon/0000-0002-1479-9736; Todo, Synge/0000-0001-9338-0548; Carr,
Lincoln/0000-0002-4848-7941; Evertz, Hans G/0000-0002-2037-0725; Wall,
Michael/0000-0002-6223-0800; Pawlowski, Grzegorz/0000-0001-5019-1317
FU Pauli Center at ETH Zurich; NSFNational Science Foundation (NSF)
[PHY-0551164, DMR-0705847]; Aspen Center for Physics; Swiss National
Science FoundationSwiss National Science Foundation (SNSF)European
Commission; Jeffress Memorial Trust [J-992]; National Science
FoundationNational Science Foundation (NSF) [PHY-0903457, DMR-0955707];
Golden Energy Computing Organization at the Colorado School of Mines;
National Science FoundationNational Science Foundation (NSF); National
Renewable Energy Laboratories; Deutsche ForschungsgemeinschaftGerman
Research Foundation (DFG) [SFB 602]; Japan Society for the Promotion of
ScienceMinistry of Education, Culture, Sports, Science and Technology,
Japan (MEXT)Japan Society for the Promotion of Science [20540364]; MEXT
JapanMinistry of Education, Culture, Sports, Science and Technology,
Japan (MEXT); Army Research Office; DARPAUnited States Department of
DefenseDefense Advanced Research Projects Agency (DARPA); US Department
of EnergyUnited States Department of Energy (DOE); US National Science
FoundationNational Science Foundation (NSF); IBM; Grants-in-Aid for
Scientific ResearchMinistry of Education, Culture, Sports, Science and
Technology, Japan (MEXT)Japan Society for the Promotion of
ScienceGrants-in-Aid for Scientific Research (KAKENHI) [23540438,
20540364, 18540369] Funding Source: KAKEN; Division Of PhysicsNational
Science Foundation (NSF)NSF - Directorate for Mathematical & Physical
Sciences (MPS) [0903457] Funding Source: National Science Foundation
FX The development of ALPS has profited from support of the Pauli Center at
ETH Zurich, the Swiss HP2C initiative, the Kavli Institute
for Theoretical Physics in Santa Barbara through NSF grants PHY-0551164
and DMR-0705847, the Aspen Center for Physics, the Swiss National
Science Foundation, the Jeffress Memorial Trust, Grant No. J-992, the
National Science Foundation under grants PHY-0903457 and DMR-0955707,
the Golden Energy Computing Organization at the Colorado School of Mines
for the use of resources acquired with financial assistance from the
National Science Foundation and the National Renewable Energy
Laboratories, the Deutsche Forschungsgemeinschaft through the
collaborative research center SFB 602, Japan Society for the Promotion
of Science through KAKENHI No. 20540364, Next-Generation Supercomputer
Project from MEXT Japan, and a grant from the Army Research Office with
funding from the DARPA OLE program.; Work on VisTrails is primarily
supported by grants and contracts from the US National Science
Foundation, the US Department of Energy, and IBM.
CR Albuquerque AF, 2007, J MAGN MAGN MATER, V310, P1187, DOI 10.1016/j.jmmm.2006.10.304
Alet F, 2005, J PHYS SOC JPN, V74, P30, DOI 10.1143/JPSJS.74S.30
Alet F, 2005, PHYS REV E, V71, DOI 10.1103/PhysRevE.71.036706
Ambegaokar V, 2010, AM J PHYS, V78, P150, DOI 10.1119/1.3247985
Bavoil L, 2005, IEEE VISUALIZATION 2005, PROCEEDINGS, P135, DOI 10.1109/visual.2005.1532788
CALLAHAN S, 2006, 2006 SCIFLOW IEEE WO
CLIFFORD B, 2007, CONCURR COMP-PRACT E, V20, P565
Czarnecki K., 2000, GENERATIVE PROGRAMMI
Daley AJ, 2004, J STAT MECH-THEORY E, DOI 10.1088/1742-5468/2004/04/P04005
Evertz HG, 2003, ADV PHYS, V52, P1, DOI 10.1080/0001873021000049195
EVERTZ HG, 1993, PHYS REV LETT, V70, P875, DOI 10.1103/PhysRevLett.70.875
Freire J, 2008, COMPUT SCI ENG, V10, P11, DOI 10.1109/MCSE.2008.79
Freire J, 2006, LECT NOTES COMPUT SC, V4145, P10
Georges A, 1996, REV MOD PHYS, V68, P13, DOI 10.1103/RevModPhys.68.13
Gull E, 2008, EPL-EUROPHYS LETT, V82, DOI 10.1209/0295-5075/82/57003
Gull E, 2011, COMPUT PHYS COMMUN, V182, P1078, DOI 10.1016/j.cpc.2010.12.050
HIRSCH JE, 1986, PHYS REV LETT, V56, P2521, DOI 10.1103/PhysRevLett.56.2521
Katzgraber HG, 2006, J STAT MECH-THEORY E, DOI 10.1088/1742-5468/2006/03/P03018
Koop D, 2010, LECT NOTES COMPUT SC, V6187, P397, DOI 10.1007/978-3-642-13818-8_28
Kotliar G, 2006, REV MOD PHYS, V78, P865, DOI 10.1103/RevModPhys.78.865
LANCZOS C, 1950, J RES NAT BUR STAND, V45, P255, DOI 10.6028/jres.045.026
LEE EA, 1995, P IEEE, V83, P773, DOI 10.1109/5.381846
Lutz M., 2009, LEARNING PYTHON
Lutz M, 2006, PROGRAMMING PYTHON
Maier T, 2005, REV MOD PHYS, V77, P1027, DOI 10.1103/RevModPhys.77.1027
Murg V, 2007, PHYS REV A, V75, DOI 10.1103/PhysRevA.75.033605
Prokof'ev NV, 1998, J EXP THEOR PHYS+, V87, P310, DOI 10.1134/1.558661
Rubtsov AN, 2005, PHYS REV B, V72, DOI 10.1103/PhysRevB.72.035122
Rubtsov AN, 2004, JETP LETT+, V80, P61, DOI 10.1134/1.1800216
Sandvik AW, 1999, PHYS REV B, V59, P14157, DOI 10.1103/PhysRevB.59.R14157
SANDVIK AW, 1991, PHYS REV B, V43, P5950, DOI 10.1103/PhysRevB.43.5950
Santos E, 2009, IEEE T VIS COMPUT GR, V15, P1539, DOI 10.1109/TVCG.2009.195
Scheidegger CE, 2007, IEEE T VIS COMPUT GR, V13, P1560, DOI 10.1109/TVCG.2007.70584
Schollwock U, 2005, REV MOD PHYS, V77, P259, DOI 10.1103/RevModPhys.77.259
Siek Jeremy G., 2001, BOOST GRAPH LIB USER
Silva CT, 2007, COMPUT SCI ENG, V9, P82, DOI 10.1109/MCSE.2007.106
SWENDSEN RH, 1987, PHYS REV LETT, V58, P86, DOI 10.1103/PhysRevLett.58.86
Syljuasen OF, 2002, PHYS REV E, V66, DOI 10.1103/PhysRevE.66.046701
Todo S, 2001, PHYS REV LETT, V87, DOI 10.1103/PhysRevLett.87.047203
Trebst S, 2004, PHYS REV E, V70, DOI 10.1103/PhysRevE.70.046701
Troyer M, 2003, PHYS REV LETT, V90, DOI 10.1103/PhysRevLett.90.120201
Troyer M, 1998, LECT NOTES COMPUT SC, V1505, P191
Verstraete F., 2004, ARXIVCONDMAT0407066
Vidal G, 2007, PHYS REV LETT, V99, DOI 10.1103/PhysRevLett.99.220405
Vidal G, 2004, PHYS REV LETT, V93, DOI 10.1103/PhysRevLett.93.040502
Vidal G, 2003, PHYS REV LETT, V91, DOI 10.1103/PhysRevLett.91.147902
Wang FG, 2001, PHYS REV E, V64, DOI 10.1103/PhysRevE.64.056101
Wang FG, 2001, PHYS REV LETT, V86, P2050, DOI 10.1103/PhysRevLett.86.2050
Werner P, 2006, PHYS REV B, V74, DOI 10.1103/PhysRevB.74.155107
Werner P, 2006, PHYS REV LETT, V97, DOI 10.1103/PhysRevLett.97.076405
Wessel S, 2004, PHYS REV A, V70, DOI 10.1103/PhysRevA.70.053615
Wessel S, 2007, J STAT MECH-THEORY E, DOI 10.1088/1742-5468/2007/12/P12005
WHITE SR, 1992, PHYS REV LETT, V69, P2863, DOI 10.1103/PhysRevLett.69.2863
White SR, 2004, PHYS REV LETT, V93, DOI 10.1103/PhysRevLett.93.076401
WOLFF U, 1989, PHYS REV LETT, V62, P361, DOI 10.1103/PhysRevLett.62.361
NR 55
TC 502
Z9 505
U1 8
U2 90
PU IOP PUBLISHING LTD
PI BRISTOL
PA TEMPLE CIRCUS, TEMPLE WAY, BRISTOL BS1 6BE, ENGLAND
SN 1742-5468
J9 J STAT MECH-THEORY E
JI J. Stat. Mech.-Theory Exp.
PD MAY
PY 2011
AR P05001
DI 10.1088/1742-5468/2011/05/P05001
PG 34
WC Mechanics; Physics, Mathematical
SC Mechanics; Physics
GA 784WW
UT WOS:000292190200002
DA 2021-04-21
ER
PT S
AU Binet, S
AF Binet, Sebastien
GP IOP
TI ng: What next-generation languages can teach us about HENP frameworks in
the manycore era
SO INTERNATIONAL CONFERENCE ON COMPUTING IN HIGH ENERGY AND NUCLEAR PHYSICS
(CHEP 2010)
SE Journal of Physics Conference Series
LA English
DT Proceedings Paper
CT International Conference on Computing in High Energy and Nuclear Physics
(CHEP)
CY OCT 18-22, 2010
CL Taipei, TAIWAN
AB Current High Energy and Nuclear Physics (HENP) frameworks were written before multicore systems became widely deployed. A 'single-thread' execution model naturally emerged from that environment, however, this no longer fits into the processing model on the dawn of the manycore era. Although previous work focused on minimizing the changes to be applied to the LHC frameworks (because of the data taking phase) while still trying to reap the benefits of the parallel-enhanced CPU architectures, this paper explores what new languages could bring to the design of the next-generation frameworks.
Parallel programming is still in an intensive phase of R&D and no silver bullet exists despite the 30+ years of literature on the subject. Yet, several parallel programming styles have emerged: actors, message passing, communicating sequential processes, task-based programming, data flow programming, ... to name a few.
We present the work of the prototyping of a next-generation framework in new and expressive languages (python and Go) to investigate how code clarity and robustness are affected and what are the downsides of using languages younger than FoRTRAN/C/C++.
C1 Univ Paris 11, Lab Accelerateur Lineaire, F-91898 Orsay, France.
RP Binet, S (corresponding author), Univ Paris 11, Lab Accelerateur Lineaire, F-91898 Orsay, France.
EM binet@cern.ch
OI Binet, Sebastien/0000-0003-4913-6104
CR Amdahl G. M, 1967, AFIPS C P, P483, DOI DOI 10.1145/1465482.1465560
[Anonymous], C PROGRAMMING LANGUA
[Anonymous], ROOT FRAM
Binet S, 2009, CHEP
*HASK, HASK PROGR LANG
Mato P, 1998, LHCB98064
Moore E, 1965, ELECT MAGAZINE
ng-go-gaudi mercurial repository, NG GO GAUDI MERCURIA
*PYTH, PYTH PROGR LANG
Singh S, 2007, ICFP
Vala, VALA
NR 11
TC 0
Z9 0
U1 0
U2 0
PU IOP PUBLISHING LTD
PI BRISTOL
PA DIRAC HOUSE, TEMPLE BACK, BRISTOL BS1 6BE, ENGLAND
SN 1742-6588
J9 J PHYS CONF SER
PY 2011
VL 331
AR 042002
DI 10.1088/1742-6596/331/4/042002
PG 6
WC Physics, Nuclear; Physics, Particles & Fields
SC Physics
GA BZF21
UT WOS:000301337200002
DA 2021-04-21
ER
PT S
AU Fernandez, PF
Clemencic, M
Cousin, N
AF Fernandez, Paloma Fuente
Clemencic, Marco
Cousin, Nicolas
CA LHCb Collaboration
GP IOP
TI LHCb Tag Collector
SO INTERNATIONAL CONFERENCE ON COMPUTING IN HIGH ENERGY AND NUCLEAR PHYSICS
(CHEP 2010)
SE Journal of Physics Conference Series
LA English
DT Proceedings Paper
CT International Conference on Computing in High Energy and Nuclear Physics
(CHEP)
CY OCT 18-22, 2010
CL Taipei, TAIWAN
AB The LHCb physics software consists of hundreds of packages, each of which is developed by one or more physicists. When the developers have some code changes that they would like released, they commit them to the version control system, and enter the revision number into a database. These changes have to be integrated into a new release of each of the physics analysis applications. Tests are then performed by a nightly build system, which rebuilds various configurations of the whole software stack and executes a suite of run-time functionality tests. A Tag Collector system has been developed using solid standard technologies to cover both the use cases of developers and integration managers. A simple Web interface, based on an AJAX-like technology, is available. Integration with SVN and Nightly Build System, is possible via a Python API. Data are stored in a relational database with the help of an ORM (Object-Relational Mapping) library.
C1 [Fernandez, Paloma Fuente; Clemencic, Marco; Cousin, Nicolas; LHCb Collaboration] CERN, CH-1211 Geneva, Switzerland.
RP Fernandez, PF (corresponding author), CERN, Route Meyrin, CH-1211 Geneva, Switzerland.
EM pfuentef@cern.ch; marco.clemencic@cern.ch; nicolas.cousin@cern.ch
CR [Anonymous], LHCB TAG COLLECTOR
[Anonymous], LHCB NIGHTLY BUILDS
Arnault C, 2000, INT C COMP HIGH EN P
Clemencic M, 2010, INT C COMP HIGH EN P
Kruzelecki K, 2010, J PHYS CONF SER, V219, DOI 10.1088/1742-6596/219/4/042042
Ormancey E, 2008, J PHYS C SER, V119
*PYTH, PYTH PROGR LANG
NR 7
TC 0
Z9 0
U1 0
U2 0
PU IOP PUBLISHING LTD
PI BRISTOL
PA DIRAC HOUSE, TEMPLE BACK, BRISTOL BS1 6BE, ENGLAND
SN 1742-6588
J9 J PHYS CONF SER
PY 2011
VL 331
AR 042009
DI 10.1088/1742-6596/331/4/042009
PG 5
WC Physics, Nuclear; Physics, Particles & Fields
SC Physics
GA BZF21
UT WOS:000301337200009
OA Bronze
DA 2021-04-21
ER
PT S
AU de Cosa, A
AF de Cosa, A.
GP IOP
TI CMS Configuration Editor: GUI based application for user analysis job
SO INTERNATIONAL CONFERENCE ON COMPUTING IN HIGH ENERGY AND NUCLEAR PHYSICS
(CHEP 2010)
SE Journal of Physics Conference Series
LA English
DT Proceedings Paper
CT International Conference on Computing in High Energy and Nuclear Physics
(CHEP)
CY OCT 18-22, 2010
CL Taipei, TAIWAN
AB We present the user interface and the software architecture of the Configuration Editor for the CMS experiment. The analysis workflow is organized in a modular way integrated within the CMS framework that organizes in a flexible way user analysis code. The Python scripting language is adopted to define the job configuration that drives the analysis workflow. It could be a challenging task for users, especially for newcomers, to develop analysis jobs managing the configuration of many required modules. For this reason a graphical tool has been conceived in order to edit and inspect configuration files. A set of common analysis tools defined in the CMS Physics Analysis Toolkit (PAT) can be steered and configured using the Config Editor. A user-defined analysis workflow can be produced starting from a standard configuration file, applying and configuring PAT tools according to the specific user requirements. CMS users can adopt this tool, the Config Editor, to create their analysis visualizing in real time which are the effects of their actions. They can visualize the structure of their configuration, look at the modules included in the workflow, inspect the dependences existing among the modules and check the data flow. They can visualize at which values parameters are set and change them according to what is required by their analysis task. The integration of common tools in the GUI needed to adopt an object-oriented structure in the Python definition of the PAT tools and the definition of a layer of abstraction from which all PAT tools inherit.
C1 Ist Nazl Fis Nucl, Sez Napoli, I-80126 Naples, Italy.
RP de Cosa, A (corresponding author), Ist Nazl Fis Nucl, Sez Napoli, Complesso Monte St Angelo,Ed 6,Via Cintia, I-80126 Naples, Italy.
EM annapaola.de.cosa@cern.ch
CR Adam W, 2010, J PHYS CONF SER, V219, DOI 10.1088/1742-6596/219/3/032017
Fabozzi F, 2008, IEEE T NUCL SCI, V55, P3539, DOI 10.1109/TNS.2008.2006979
Hinzmann A., P CHEP 2010 C TAIP T
JONES CD, 2006, P CHEP 2006 MUMB IND
Lassila-Perini K., P CHEP 2010 C TAIP T
NR 5
TC 0
Z9 0
U1 0
U2 0
PU IOP PUBLISHING LTD
PI BRISTOL
PA DIRAC HOUSE, TEMPLE BACK, BRISTOL BS1 6BE, ENGLAND
SN 1742-6588
J9 J PHYS CONF SER
PY 2011
VL 331
AR 072046
DI 10.1088/1742-6596/331/7/072046
PG 6
WC Physics, Applied; Physics, Multidisciplinary; Physics, Nuclear
SC Physics
GA BZF07
UT WOS:000301299200046
DA 2021-04-21
ER
PT S
AU Echeverria, G
Lassabe, N
Degroote, A
Lemaignan, S
AF Echeverria, Gilberto
Lassabe, Nicolas
Degroote, Arnaud
Lemaignan, Severin
GP IEEE
TI Modular Open Robots Simulation Engine: MORSE
SO 2011 IEEE INTERNATIONAL CONFERENCE ON ROBOTICS AND AUTOMATION (ICRA)
SE IEEE International Conference on Robotics and Automation ICRA
LA English
DT Proceedings Paper
CT IEEE International Conference on Robotics and Automation (ICRA)
CY MAY 09-13, 2011
CL Shanghai, PEOPLES R CHINA
SP IEEE, Robot & Automat Soc, Minist Educ China, Minist Sci & Technol China, Natl Nat Sci Fdn China, Sci & Technol Commiss Shanghai Municipal, Shanghai Jiao Tong Univ, State Key Lab Mech Syst & Vibrat, Huazhong Univ Sci & Technol, State Key Lab Digital Mfg Equipment & Technol, Harbin Inst Technol, State Key Lab Robot & Syst, Zhejiang Univ, Inst Cyber-Syst & Control, Chinese Acad Sci, Shenyang Inst Automat, Beihang Univ, Robotics Inst, Beijing Res Inst Automat Machinery Ind, Tianjin Univ, Sch Mech Engn, ABB, YASKAWA Elect, KUKA, Willow Garage, Googol Tech., Adept Mobile Robots, Harbin Boshi Automat, Natl Instruments, Beijing Universal Pioneering Technol, Real-Time Control & Instrumentat Lab, GE Global Res, ALDEBARAN Robot, Int Federat Robot (IFR), Shanghai Jiao Tong Univ
AB This paper presents MORSE, a new open-source robotics simulator. MORSE provides several features of interest to robotics projects: it relies on a component-based architecture to simulate sensors, actuators and robots; it is flexible, able to specify simulations at variable levels of abstraction according to the systems being tested; it is capable of representing a large variety of heterogeneous robots and full 3D environments (aerial, ground, maritime); and it is designed to allow simulations of multiple robots systems. MORSE uses a "Software-in-the-Loop" philosophy, i.e. it gives the possibility to evaluate the algorithms embedded in the software architecture of the robot within which they are to be integrated. Still, MORSE is independent of any robot architecture or communication framework (middleware).
MORSE is built on top of Blender, using its powerful features and extending its functionality through Python scripts. Simulations are executed on Blender's Game Engine mode, which provides a realistic graphical display of the simulated environments and allows exploiting the reputed Bullet physics engine. This paper presents the conception principles of the simulator and some use-case illustrations.
C1 [Echeverria, Gilberto] RTRA STAE, 23 Ave Edouard Belin, F-31400 Toulouse, France.
[Lassabe, Nicolas] ONERA Ctr Toulouse DCSD, F-31055 Toulouse, France.
[Degroote, Arnaud; Lemaignan, Severin] CNRS, LAAS, F-31077 Toulouse, France.
[Degroote, Arnaud; Lemaignan, Severin] Univ Toulouse, UPS, INSA, INP,ISAE,LAAS, F-31077 Toulouse, France.
RP Echeverria, G (corresponding author), RTRA STAE, 23 Ave Edouard Belin, F-31400 Toulouse, France.
EM gechever@laas.fr; nicolas.lassabe@onera.fr; adegroot@laas.fr;
slemaign@laas.fr
OI Lemaignan, Severin/0000-0002-3391-8876
CR [Anonymous], BLEND 3D
[Anonymous], COGM ROB SIM
[Anonymous], BLEND ROB INT GROUP
[Anonymous], 2008, INT C SIM MOD PROGR
[Anonymous], 2008, WORKSH ROB SIM AV SO
Gerkey BP, 2003, PROCEEDINGS OF THE 11TH INTERNATIONAL CONFERENCE ON ADVANCED ROBOTICS 2003, VOL 1-3, P317
Goktogan A., 2007, 4 INT S MECH ITS APP
Joyeux S., 2005, WORKSH PRINC PRACT S
Kanehiro F, 2004, INT J ROBOT RES, V23, P155, DOI 10.1177/0278364904041324
Kanehiro F, 2002, 2002 IEEE INTERNATIONAL CONFERENCE ON ROBOTICS AND AUTOMATION, VOLS I-IV, PROCEEDINGS, P24, DOI 10.1109/ROBOT.2002.1013334
Koenig N., 2004, 2004 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS) (IEEE Cat. No.04CH37566), P2149
Kramer J, 2007, AUTON ROBOT, V22, P101, DOI 10.1007/s10514-006-9013-8
Lewis M, 2007, J COGN ENG DECIS MAK, V1, P98, DOI 10.1177/155534340700100105
Mallet A., 2010, P IEEE ICRA
Metta G., 2006, International Journal of Advanced Robotic Systems, V3, P43
Michel O., 2004, International Journal of Advanced Robotic Systems, V1, P39
Microsoft Robotics Developer Studio, MICR ROB DEV STUD
Mohamed N., 2008, INT C ROB AUT MECH R, P736
Petters S, 2008, LECT NOTES ARTIF INT, V5325, P183
Shakhimardanov A., 2007, IEEE RSJ INT C INT R
Simeon T, 2001, PROCEEDINGS OF THE 2001 IEEE INTERNATIONAL SYMPOSIUM ON ASSEMBLY AND TASK PLANNING (ISATP2001), P25, DOI 10.1109/ISATP.2001.928961
Smits Ruben, 2008, 2008 IEEE International Conference on Multisensor Fusion and Integration for Intelligent Systems (MFI 2008), P426, DOI 10.1109/MFI.2008.4648032
Watanabe Y., 2010, AM HEL SOC 66 ANN FO
Zykov V., 2008, INT C INT ROB SYST I
NR 24
TC 17
Z9 17
U1 0
U2 0
PU IEEE
PI NEW YORK
PA 345 E 47TH ST, NEW YORK, NY 10017 USA
SN 1050-4729
EI 2577-087X
BN 978-1-61284-385-8
J9 IEEE INT CONF ROBOT
PY 2011
PG 6
WC Automation & Control Systems; Engineering, Electrical & Electronic;
Robotics
SC Automation & Control Systems; Engineering; Robotics
GA BWZ18
UT WOS:000295396600008
DA 2021-04-21
ER
PT S
AU Brodski, M
Erdmann, M
Fischer, R
Hinzmann, A
Klimkovich, T
Klingebiel, D
Komm, M
Muller, G
Steggemann, J
Winchen, T
AF Brodski, M.
Erdmann, M.
Fischer, R.
Hinzmann, A.
Klimkovich, T.
Klingebiel, D.
Komm, M.
Mueller, G.
Steggemann, J.
Winchen, T.
GP IOP
TI Visual Physics Data Analysis in the Web Browser
SO INTERNATIONAL CONFERENCE ON COMPUTING IN HIGH ENERGY AND NUCLEAR PHYSICS
(CHEP 2010)
SE Journal of Physics Conference Series
LA English
DT Proceedings Paper
CT International Conference on Computing in High Energy and Nuclear Physics
(CHEP)
CY OCT 18-22, 2010
CL Taipei, TAIWAN
AB The project VISPA@WEB provides a novel graphical development environment for physics analyses which only requires a standard web browser on the client machine. It resembles the existing analysis environment available from the project Visual Physics Analysis VISPA, including the connection and configuration of modules for different tasks. High level logic can be programmed using the Python language, while performance-critical tasks can be implemented in C++ modules. The use cases range from simple teaching examples to highly complex scientific analyses.
C1 [Brodski, M.; Erdmann, M.; Fischer, R.; Hinzmann, A.; Klimkovich, T.; Klingebiel, D.; Komm, M.; Mueller, G.; Steggemann, J.; Winchen, T.] Rhein Westfal TH Aachen, Phys Inst 3A, D-52062 Aachen, Germany.
RP Brodski, M (corresponding author), Rhein Westfal TH Aachen, Phys Inst 3A, D-52062 Aachen, Germany.
EM erdmann@physik.rwth-aachen.de
RI Steggemann, Jan/AAL-5700-2020
OI Steggemann, Jan/0000-0003-4420-5510; Erdmann, Martin/0000-0002-1653-1303
CR Actis O., P 34 INT C HIGH EN P
Brodsky M., 2010, P 13 INT WORKSH ADV
Brun R, 1997, NUCL INSTRUM METH A, V389, P81, DOI 10.1016/S0168-9002(97)00048-X
Crockford D., 4627 RFC
ECMAScript language, ECMA262
Garrett J.J., 2005, AJAX NEW APPROACH WE
Kappler S, 2006, IEEE T NUCL SCI, V53, P506, DOI 10.1109/TNS.2006.870179
Komm M., 2010, THESIS RWTH AACHEN U
NR 8
TC 1
Z9 1
U1 0
U2 0
PU IOP PUBLISHING LTD
PI BRISTOL
PA DIRAC HOUSE, TEMPLE BACK, BRISTOL BS1 6BE, ENGLAND
SN 1742-6588
J9 J PHYS CONF SER
PY 2011
VL 331
AR 072056
DI 10.1088/1742-6596/331/7/072056
PG 5
WC Physics, Applied; Physics, Multidisciplinary; Physics, Nuclear
SC Physics
GA BZF07
UT WOS:000301299200056
DA 2021-04-21
ER
PT S
AU Belyaev, I
Savrina, D
AF Belyaev, Ivan
Savrina, Daria
GP IOP
TI Kali: The framework for fine calibration of the LHCb Electromagnetic
Calorimeter
SO INTERNATIONAL CONFERENCE ON COMPUTING IN HIGH ENERGY AND NUCLEAR PHYSICS
(CHEP 2010): EVENT PROCESSING
SE Journal of Physics Conference Series
LA English
DT Proceedings Paper
CT International Conference on Computing in High Energy and Nuclear Physics
(CHEP)
CY OCT 18-22, 2010
CL Taipei, TAIWAN
AB The precise calibration (at a level of below 1%) of the electromagnetic calorimeter (ECAL) of the LHCb experiment is an essential task for the fulfilment of the LHCb physics program. The final step of this task is performed with two calibration methods using the real data from the experimental setup. It is a very CPU-consuming procedure as both methods require processing of O(10(8)) events which must be selected, reconstructed and analyzed.
In this document we present the Kali framework developed within the LHCb software framework, which implements these two final calibration methods. It is integrated with Grid middleware and makes use of parallelism tools, such as python parallel processing modules, to provide an efficient way, both time and disk wise, for the final ECAL calibration.
The results of the fine calibration with the very first data collected by the LHCb experiment will also be presented. With the use of the Kali framework it took only two days of processing and allowed to achieve a calibration accuracy of 2-2.5% for the different ECAL areas.
C1 [Belyaev, Ivan; Savrina, Daria] Inst Theoret & Expt Phys, Moscow, Russia.
RP Belyaev, I (corresponding author), Inst Theoret & Expt Phys, Moscow, Russia.
EM Daria.Savrina@cern.ch
RI Diaz, Ricardo Graciani/I-5152-2016; Diaz, Ricardo
Graciani/AAH-1683-2020; Puig, Albert/L-5196-2017
OI Diaz, Ricardo Graciani/0000-0001-7166-5198; Diaz, Ricardo
Graciani/0000-0001-7166-5198; Puig, Albert/0000-0001-8868-2947; Belyaev,
Ivan/0000-0002-7458-7030
CR Alves AA, 2008, J INSTRUM, V3, DOI 10.1088/1748-0221/3/08/S08005
Brun Rene, ROOT USERS GUIDE 5 2
Korolko I., LHCB2000051
LHCb Coll, CERNLHCC2000036 LHCB
*LHCB COLL, CERNLHCC2003030 LHCB
*LHCB COLL, CERNLHCC984 LHCB COL
Voronchev K., CERNLHCB2006051
NR 7
TC 3
Z9 3
U1 0
U2 1
PU IOP PUBLISHING LTD
PI BRISTOL
PA DIRAC HOUSE, TEMPLE BACK, BRISTOL BS1 6BE, ENGLAND
SN 1742-6588
EI 1742-6596
J9 J PHYS CONF SER
PY 2011
VL 331
AR 032050
DI 10.1088/1742-6596/331/3/032050
PG 6
WC Instruments & Instrumentation; Physics, Nuclear; Physics, Particles &
Fields
SC Instruments & Instrumentation; Physics
GA BZE75
UT WOS:000301279900050
DA 2021-04-21
ER
PT S
AU Clemencic, M
Corti, G
Easo, S
Jones, CR
Miglioranzi, S
Pappagallo, M
Robbe, P
AF Clemencic, M.
Corti, G.
Easo, S.
Jones, C. R.
Miglioranzi, S.
Pappagallo, M.
Robbe, P.
CA LHCb Collaboration
GP IOP
TI The LHCb Simulation Application, Gauss: Design, Evolution and Experience
SO INTERNATIONAL CONFERENCE ON COMPUTING IN HIGH ENERGY AND NUCLEAR PHYSICS
(CHEP 2010): EVENT PROCESSING
SE Journal of Physics Conference Series
LA English
DT Proceedings Paper
CT International Conference on Computing in High Energy and Nuclear Physics
(CHEP)
CY OCT 18-22, 2010
CL Taipei, TAIWAN
AB The LHCb simulation application, Gauss, is based on the Gaudi framework and on experiment basic components such as the Event Model and Detector Description. Gauss also depends on external libraries for the generation of the primary events (PYTHIA 6, EvtGen, etc.) and on GEANT4 for particle transport in the experimental setup. The application supports the production of different types of events from minimum bias to B physics signals and particle guns. It is used for purely generator-level studies as well as full simulations. Gauss is used both directly by users and in massive central productions on the grid. The design and implementation of the application and its evolution due to evolving requirements will be described as in the case of the recently adopted Python-based configuration or the possibility of taking into account detectors conditions via a Simulation Conditions database. The challenge of supporting at the same time the flexibililty needed for the different tasks for which it is used, from evaluation of physics reach to background modeling, together with the stability and reliabilty of the code will also be described.
C1 [Clemencic, M.; Corti, G.; Miglioranzi, S.; Robbe, P.] European Org Nucl Res CERN, Geneva, Switzerland.
[Easo, S.] STFC Rutherford Appleton Lab, Didcot, Oxon, England.
[Jones, C. R.] Univ Cambridge, Cavendish Lab, Cambridge, England.
[Pappagallo, M.] Sez INFN Bari, Bari, Italy.
[Pappagallo, M.] Univ Bari, Bari, Italy.
[Robbe, P.] Univ Paris Sud, LAL, IN2P3, CNRS, Orsay, France.
RP Clemencic, M (corresponding author), European Org Nucl Res CERN, Geneva, Switzerland.
EM Silvia.Miglioranzi@cern.ch
RI Pappagallo, Marco/R-3305-2016
OI Pappagallo, Marco/0000-0001-7601-5602; Jones,
Roderic/0000-0002-6761-3966
CR Agostinelli S, 2003, NUCL INSTRUM METH A, V506, P250, DOI 10.1016/S0168-9002(03)01368-8
Allison J, 2006, IEEE T NUCL SCI, V53, P270, DOI 10.1109/TNS.2006.869826
Alves AA, 2008, LHC JINST, V3
Antunes Nobrega R, 2005, CERNLHCC2005019 LHCB
Bahr M, 2008, EUR PHYS J C, V58, P639, DOI 10.1140/epjc/s10052-008-0798-9
Barrand G, 2001, COMPUT PHYS COMMUN, V140, P45, DOI 10.1016/S0010-4655(01)00254-5
Belyaev I, 2001, P 2001 C COMPU HIGH
Brun R, 1996, NUCL INSTRUM METH A, V398, P81
Clemencic M, 2010, J PHYS CONF SER, V219, DOI 10.1088/1742-6596/219/4/042006
Dobbs M, 2001, COMPUT PHYS COMMUN, V134, P41, DOI 10.1016/S0010-4655(00)00189-2
Duellmann D, 2003, ARXIVPHYSICS0306084
G Graziani, HISTOGRAM DB ONLINE
Gleisberg T, 2009, J HIGH ENERGY PHYS, DOI 10.1088/1126-6708/2009/02/007
Kruzelecki K, 2010, J PHYS CONF SER, V219, DOI 10.1088/1742-6596/219/4/042042
Lange DJ, 2001, NUCL INSTRUM METH A, V462, P152, DOI 10.1016/S0168-9002(01)00089-4
Sjostrand T, 2008, COMPUT PHYS COMMUN, V178, P852, DOI 10.1016/j.cpc.2008.01.036
Sjostrand T, 2006, J HIGH ENERGY PHYS, DOI 10.1088/1126-6708/2006/05/026
NR 17
TC 409
Z9 411
U1 0
U2 6
PU IOP PUBLISHING LTD
PI BRISTOL
PA DIRAC HOUSE, TEMPLE BACK, BRISTOL BS1 6BE, ENGLAND
SN 1742-6588
EI 1742-6596
J9 J PHYS CONF SER
PY 2011
VL 331
AR 032023
DI 10.1088/1742-6596/331/3/032023
PG 6
WC Instruments & Instrumentation; Physics, Nuclear; Physics, Particles &
Fields
SC Instruments & Instrumentation; Physics
GA BZE75
UT WOS:000301279900023
DA 2021-04-21
ER
PT S
AU Gyurjyan, V
Abbott, D
Carbonneau, J
Gilfoyle, G
Heddle, D
Heyes, G
Paul, S
Timmer, C
Weygand, D
Wolin, E
AF Gyurjyan, V.
Abbott, D.
Carbonneau, J.
Gilfoyle, G.
Heddle, D.
Heyes, G.
Paul, S.
Timmer, C.
Weygand, D.
Wolin, E.
GP IOP
TI CLARA: A Contemporary Approach to Physics Data Processing
SO INTERNATIONAL CONFERENCE ON COMPUTING IN HIGH ENERGY AND NUCLEAR PHYSICS
(CHEP 2010): EVENT PROCESSING
SE Journal of Physics Conference Series
LA English
DT Proceedings Paper
CT International Conference on Computing in High Energy and Nuclear Physics
(CHEP)
CY OCT 18-22, 2010
CL Taipei, TAIWAN
AB CLARA (CLAS12 Reconstruction and Analysis framework) is CLAS12 physics data processing (PDP) application development framework based on a service oriented architecture (SOA). This framework allows users to design and deploy data processing services as well as dynamically compose PDP applications using available services. Services can be written in Java, C++, and Python languages. The PDP service bus provides a layer on top of a distributed pub-sub middleware implementation. This allows complex service composition and integration without writing a code. We believe that by deviating from the traditional self contained, monolithic PDP application models we can improve maintenance, scalability and quality of physics data analysis. The SOA approach also helps us to separate a specific service programmer from a PDP application designer. Examples of service creation and deployment, along with the CLAS12 track reconstruction application design are presented.
EM gurjyan@jlab.org
OI Heyes, William Graham/0000-0001-9902-8190; Paul,
Sebouh/0000-0003-0977-3491
CR Erl T., 2007, PRINCIPLES SERVICE D
Timmer C, 2007, P INT C COMP HIGH EN
Wolin E, P IEE NSS HAW US 200
NR 3
TC 3
Z9 3
U1 0
U2 3
PU IOP PUBLISHING LTD
PI BRISTOL
PA DIRAC HOUSE, TEMPLE BACK, BRISTOL BS1 6BE, ENGLAND
SN 1742-6588
J9 J PHYS CONF SER
PY 2011
VL 331
AR 032013
DI 10.1088/1742-6596/331/3/032013
PG 7
WC Instruments & Instrumentation; Physics, Nuclear; Physics, Particles &
Fields
SC Instruments & Instrumentation; Physics
GA BZE75
UT WOS:000301279900013
DA 2021-04-21
ER
PT B
AU Stark, AA
AF Stark, Antony A.
BE Berkman, PA
Lang, MA
Walton, DWH
Young, OR
TI Cosmology from Antarctica
SO SCIENCE DIPLOMACY: ANTARCTICA, SCIENCE, AND THE GOVERNANCE OF
INTERNATIONAL SPACES
LA English
DT Proceedings Paper
CT Antarctic Treaty Summit: Science-Policy Interactions in International
Governance
CY NOV 30-DEC 03, 2009
CL Smithsonian Inst, Washington, DC
SP Amer Geophys Union, Fdn Good Governance Int Spaces, Greenpeace, Japan Polar Res Assoc, KBR, Korean Polar Res Inst, Korea Supporters Assoc Polar Res, Lindblad Expedit, Pew Charitable Trusts, Stanford Univ, Dept Earth Sci, United Nations Environm Programme, Univ Sains Malaysia, Ctr Global Sustainabil Studies, World Wildlife Fund Australia
HO Smithsonian Inst
ID 1ST SEASON OBSERVATIONS; SMALL-SCALE ANISOTROPY; CMB POLARIZATION;
MICROWAVE SKY; SUBMILLIMETER-TELESCOPE; ATMOSPHERIC OPACITY;
WATER-VAPOR; 90 GHZ; DESIGN; RECEIVER
AB We are in a golden age of observational cosmology, where measurements of the universe have progressed from crude estimates to precise knowledge. Many of these observations are made from the Antarctic, where conditions are particularly favorable. When we use telescopes to look out at the distant universe, we are also looking back in time because the speed of light is finite. Looking out 13.7 billion years, the cosmic microwave background (CMB) comes from a time shortly after the big bang. The first attempt at CMB observations from the Antarctic plateau was an expedition to the South Pole in December 1986 by the Radio Physics Research group at Bell Laboratories. The measured sky noise and opacity were highly encouraging. In the austral summer of 1988-1989, three CMB groups participated in the "Cucumber" campaign, where a temporary summer-only site dedicated to CMB anisotropy measurements was set up 2 km from South Pole Station. Winter observations became possible with the establishment in 1990 of the Center for Astrophysical Research in Antarctica (CARA), a U.S. National Science Foundation Science and Technology Center, which developed year-round observing facilities in the "Dark Sector," a section of Amundsen-Scott South Pole Station dedicated to astronomical observations. Scientists at CARA fielded several astronomical instruments: Antarctic Sub-millimeter Telescope and Remote Observatory (AST/RO), South Pole Infrared Explorer (SPIREX), White Dish, Python, Viper, Arcminute Cosmology Bolometer Array Receiver (ACBAR), and Degree-Angular Scale Interferometer (DASI). By 2001, data from CARA, together with Balloon Observations of Millimetric Extragalactic Radiation and Geophysics (BOOMERANG), a CMB experiment on a long-duration balloon launched from Mc-Murdo Station on the coast of Antarctica, showed clear evidence that the overall geometry of the universe is flat, as opposed to being open or closed. This indicates that the total energy content of the universe is near zero, so that the energy needed to originate the material of the universe is balanced by negative gravitational energy. In 2002, the DASI group reported the detection of polarization in the CMB. These observations strongly support a "concordance model" of cosmology, where the dynamics of a flat universe are dominated by forces exerted by the mysterious dark energy and dark matter. The CMB observations continue on the Antarctic plateau. The South Pole Telescope (SPT) is a 10-m-diameter offset telescope that is beginning to measure anisotropies on scales much smaller than 1 degrees, as well as discovering new protogalaxies and clusters of galaxies. Plans are in progress to measure CMB polarization in detail, observations that will yield insights to phenomena in the first second of time.
C1 [Stark, Antony A.] Smithsonian Astrophys Observ, Cambridge, MA 02138 USA.
EM aas@cfa.harvard.edu
OI Stark, Antony/0000-0002-2718-9996
CR ALVAREZ DL, 1995, THESIS PRINCETON U P
Bussmann RS, 2005, ASTROPHYS J, V622, P1343, DOI 10.1086/427935
Carlstrom J. E., PUBLICATION IN PRESS
Chamberlin R. A., 2001, ASP C P, P172
Chamberlin RA, 1997, ASTROPHYS J, V476, P428, DOI 10.1086/303621
CHAMBERLIN RA, 1995, INT J INFRARED MILLI, V16, P907, DOI 10.1007/BF02066665
Church S, 2003, NEW ASTRON REV, V47, P1083, DOI 10.1016/j.newar.2003.09.033
Coble K, 1999, ASTROPHYS J, V519, pL5, DOI 10.1086/312093
de Bernardis P, 2000, NATURE, V404, P955, DOI 10.1038/35010035
DRAGOVAN M, 1989, AIP CONF PROC, V198, P97
DRAGOVAN M, 1994, ASTROPHYS J, V427, pL67, DOI 10.1086/187366
DRAGOVAN M, 1990, APPL OPTICS, V29, P463, DOI 10.1364/AO.29.000463
ENGARGIOLA G, 1994, REV SCI INSTRUM, V65, P1833, DOI 10.1063/1.1144831
Fixsen DJ, 1996, ASTROPHYS J, V473, P576, DOI 10.1086/178173
GAIER T, 1989, AIP CONF PROC, V198, P84
Ganga K, 1997, ASTROPHYS J, V484, P7, DOI 10.1086/304296
Gerecht E, 1999, IEEE T MICROW THEORY, V47, P2519, DOI 10.1109/22.809001
Goff J.A., 1946, T AM SOC HEAT VENT E, V52, P95
Groppi C, 2000, ASTR SOC P, V217, P48
Halverson NW, 2002, ASTROPHYS J, V568, P38, DOI 10.1086/338879
HARRISON ER, 1970, PHYS REV D, V1, P2726, DOI 10.1103/PhysRevD.1.2726
Holdaway M. A., 1995, 139 NAT RAD ASTR OB, V139
Honingh CE, 1997, IEEE T APPL SUPERCON, V7, P2582, DOI 10.1109/77.621767
Hu W, 1997, NEW ASTRON, V2, P323, DOI 10.1016/S1384-1076(97)00022-5
Indermuehle BT, 2005, PUBL ASTRON SOC AUST, V22, P73, DOI 10.1071/AS04037
Keating BG, 2003, PROC SPIE, V4843, P284, DOI 10.1117/12.459274
KOOI JW, 1992, IEEE T MICROW THEORY, V40, P812, DOI 10.1109/22.137383
KOOI JW, 1995, INT J INFRARED MILLI, V16, P2049, DOI 10.1007/BF02073409
Kovac JM, 2002, NATURE, V420, P772, DOI 10.1038/nature01269
Landau L. D., 1962, CLASSICAL THEORY FIE
Lane AP, 1997, ASTR SOC P, V141, P289
Lasenby AN, 1999, PHILOS T R SOC A, V357, P35, DOI 10.1098/rsta.1999.0313
Lay OP, 2000, ASTROPHYS J, V543, P787, DOI 10.1086/317115
Leitch EM, 2002, AIP CONF PROC, V616, P65
Leitch EM, 2002, NATURE, V420, P763, DOI 10.1038/nature01271
Leitch EM, 2002, ASTROPHYS J, V568, P28, DOI 10.1086/338878
Lueker M, 2010, ASTROPHYS J, V719, P1045, DOI 10.1088/0004-637X/719/2/1045
Lynch J. T., 1998, Astronomical Society of the Pacific Conference Series, V141, P54
Martin CL, 2004, ASTROPHYS J SUPPL S, V150, P239, DOI 10.1086/379661
Masi S, 2007, NEW ASTRON REV, V51, P236, DOI 10.1016/j.newar.2006.11.063
Masi S, 2006, ASTRON ASTROPHYS, V458, P687, DOI 10.1051/0004-6361:20053891
MEINHOLD PR, 1989, AIP CONF PROC, V198, P88
Nguyen H. T., 2008, P SOC PHOTO-OPT INS, V7020, P36
Nguyen HT, 1996, PUBL ASTRON SOC PAC, V108, P718, DOI 10.1086/133791
Novak G, 2000, ASTROPHYS J, V529, P241, DOI 10.1086/308231
Novak G., 1998, ASP C SERIES
OSTRIKER JP, 1995, NATURE, V377, P600, DOI 10.1038/377600a0
PAJOT F, 1986, ASTRON ASTROPHYS, V154, P55
PAJOT F, 1989, ASTRON ASTROPHYS, V223, P107
PEEBLES PJE, 1970, ASTROPHYS J, V162, P815, DOI 10.1086/150713
PENZIAS AA, 1965, ASTROPHYS J, V142, P419, DOI 10.1086/148307
PETERSON JB, 1989, AIP CONF PROC, V198, P116
Peterson JB, 2000, ASTROPHYS J, V532, pL83, DOI 10.1086/312576
Piacentini F, 2007, NEW ASTRON REV, V51, P244, DOI 10.1016/j.newar.2006.11.058
Platt SR, 1997, ASTROPHYS J, V475, pL1, DOI 10.1086/310453
Pryke C, 2002, ASTROPHYS J, V568, P46, DOI 10.1086/338880
Radford SJE, 1996, PUBL ASTRON SOC PAC, V108, P441, DOI 10.1086/133745
Reichardt CL, 2009, ASTROPHYS J, V694, P1200, DOI 10.1088/0004-637X/694/2/1200
RUHL JE, 1995, ASTROPHYS J, V453, pL1, DOI 10.1086/309739
Ruhl JE, 2004, PROC SPIE, V5498, P11, DOI 10.1117/12.552473
Runyan MC, 2003, NEW ASTRON REV, V47, P915, DOI 10.1016/j.newar.2003.09.001
Schieder R., 1989, Experimental Astronomy, V1, P101, DOI 10.1007/BF00457985
Schwerdtfeger W., 1984, WEATHER CLIMATE ANTA
SMYTHE WD, 1977, APPL OPTICS, V16, P2041, DOI 10.1364/AO.16.002041
Spergel DN, 2003, ASTROPHYS J SUPPL S, V148, P175, DOI 10.1086/377226
Staniszewski Z, 2009, ASTROPHYS J, V701, P32, DOI 10.1088/0004-637X/701/1/32
Stark AA, 1997, REV SCI INSTRUM, V68, P2200, DOI 10.1063/1.1148071
Stark AA, 2004, ASTROPHYS J, V614, pL41, DOI 10.1086/425304
Stark AA, 2001, PUBL ASTRON SOC PAC, V113, P567, DOI 10.1086/320281
Stark AA, 2000, PROC SPIE, V4015, P434, DOI 10.1117/12.390436
Swain MR, 1998, P SOC PHOTO-OPT INS, V3354, P480, DOI 10.1117/12.317274
Tauber J. A., 2005, IAU S, V201, P86
TUCKER GS, 1993, ASTROPHYS J, V419, pL45, DOI 10.1086/187133
UCHIDA Y, 1985, NATURE, V317, P699, DOI 10.1038/317699a0
Vieira JD, 2010, ASTROPHYS J, V719, P763, DOI 10.1088/0004-637X/719/1/763
Walker C., 2001, 12 INT S SPAC THZ TE
WALKER CK, 1992, INT J INFRARED MILLI, V13, P785, DOI 10.1007/BF01011595
Warren S. G., 1996, ENCY CLIMATE WEATHER, P32
Yngvesson K. S., 2001, 12 INT S SPAC THZ TE
Yoon KW, 2006, PROC SPIE, V6275, DOI 10.1117/12.672652
ZMUIDZINAS J, 1992, IEEE T MICROW THEORY, V40, P1797, DOI 10.1109/22.156607
NR 81
TC 1
Z9 1
U1 0
U2 0
PU SMITHSONIAN INST SCHOLARLY PRESS
PI WASHINGTON
PA PO BOX 37012, MRC 957, WASHINGTON, DC 20013-7012 USA
BN 978-1-935623-06-9
PY 2011
BP 197
EP 208
PG 12
WC Environmental Studies; International Relations
SC Environmental Sciences & Ecology; International Relations
GA BZC55
UT WOS:000301091500021
DA 2021-04-21
ER
PT S
AU Rees, N
AF Rees, N.
CA Diamond Controls Grp
Diamond Data Acquisition Grp
BE Garrett, R
Gentle, I
Nugent, K
Wilkins, S
TI The Diamond Beamline Controls and Data Acquisition Software Architecture
SO SRI 2009: THE 10TH INTERNATIONAL CONFERENCE ON SYNCHROTRON RADIATION
INSTRUMENTATION
SE AIP Conference Proceedings
LA English
DT Proceedings Paper
CT 10th International Conference on Synchrotron Radiation Instrumentation
CY SEP 27-OCT 02, 2009
CL Australian Synchrotron, Melbourne, AUSTRALIA
SP Australian Synchrotron, State Govt Victoria, Australian Govt, Dept Innovat, Ind Sci & Res, Elsevier, Int Atom Energy Agcy, Australian Res Council, Mol & Mat Struct Network, JJ X ray A s, Lightsources
HO Australian Synchrotron
DE Control systems; Data acquisition software
AB The software for the Diamond Light Source beamlines[1] is based on two complementary software frameworks: low level control is provided by the Experimental Physics and Industrial Control System (EPICS) framework[2][3] and the high level user interface is provided by the Java based Generic Data Acquisition or GDA[4][5]. EPICS provides a widely used, robust, generic interface across a wide range of hardware where the user interfaces are focused on serving the needs of engineers and beamline scientists to obtain detailed low level views of all aspects of the beamline control systems. The GDA system provides a high-level system that combines an understanding of scientific concepts, such as reciprocal lattice coordinates, a flexible python syntax scripting interface for the scientific user to control their data acquisition, and graphical user interfaces where necessary. This paper describes the beamline software architecture in more detail, highlighting how these complementary frameworks provide a flexible system that can accommodate a wide range of requirements.
C1 [Rees, N.; Diamond Controls Grp; Diamond Data Acquisition Grp] Diamond Light Source Ltd, Didcot OX11 0DE, Oxon, England.
RP Rees, N (corresponding author), Diamond Light Source Ltd, Diamond House,Harwell Sci & Innovat Campus, Didcot OX11 0DE, Oxon, England.
CR DALESIO L, 1993, ICALEPCS 1993 BERL G, P23029
ENDERBY MJ, 2004, NOBUGS 2004 VILL SWI, P23029
GIBBONS P, 2008, NOBUGS 2008 SYDN AUS, P23029
MOONEY T, EPICS MOTOR RECORD D, P23029
REES NP, 2005, ICALEPCS 2005 GEN SW, P23029
RIVERS M, TRAJECTORY SCANNING, P23029
HUDSON EXTENSIBLE CO, P23029
EXPT PHYS IND CONTRO
NR 8
TC 2
Z9 2
U1 0
U2 0
PU AMER INST PHYSICS
PI MELVILLE
PA 2 HUNTINGTON QUADRANGLE, STE 1NO1, MELVILLE, NY 11747-4501 USA
SN 0094-243X
BN 978-0-7354-0782-4
J9 AIP CONF PROC
PY 2010
VL 1234
BP 736
EP 739
DI 10.1063/1.3463315
PG 4
WC Physics, Applied
SC Physics
GA BRU32
UT WOS:000283705500167
DA 2021-04-21
ER
PT J
AU Rojas, JF
Morales, MA
Rangel, A
Torres, I
AF Rojas, J. F.
Morales, M. A.
Rangel, A.
Torres, I.
TI Computational physics: an educational proposal
SO REVISTA MEXICANA DE FISICA E
LA Spanish
DT Article
DE Computational physics; python; education; undergraduate computational
workshop
AB Nowadays there exist programming languages whose characteristics make them a very good didactic tool for learning many topics of physics. There are, also, typical learning physical problems that can not be completely explained and even understood using the blackboard, because they present a kind of complex behaviors such as non linearties or many degrees of freedom. That is why they do not have any analytical solution. In any case Computational Physics method is an alternative teaching tool what in practice contains all of the topics of basic programming and numerical methods. In this paper we aboard some issues, enable us, to conform what we will call "algorithmic education". We present some traditional physics education problems, based on numerical and visual algorithms, for a better conceptual understanding and models build up by the students it self. Just by using some elementary programming modules, we propose a strategy to build up models starting from a pre-differential conceptual interpretation, which can be particularly useful in the first period of university. The contribution consists in by using a few mathematical elements and resources, students can make more and more complex simulation models. Specifically, for the implementation of the "algorithmic education" we have used python, a programming language what pen-nits the develop of themes covering from the free particle movement, and damped harmonic oscillators, as well as the ideal or hard spheres gases and even Brownian motion walks. In all of these cases the same elementary programming modules have been used.
C1 [Rojas, J. F.; Morales, M. A.; Torres, I.] Benemerita Univ Autonoma Puebla, Fac Ciencias Fis Matemat, Puebla 72570, Mexico.
[Rangel, A.] Benemerita Univ Autonoma Puebla, Fac Ciencias Computac, Puebla 72570, Mexico.
RP Rojas, JF (corresponding author), Benemerita Univ Autonoma Puebla, Fac Ciencias Fis Matemat, Edif 190 18 & Av San Claudio,CU Col San Manuel, Puebla 72570, Mexico.
EM frojas@fcfm.buap.mx
CR BAKER A, 2007, COMPUTATIONAL PHYS E, P30
Boccara N, 2004, MODELING COMPLEX SYS
Burden R.L, 1998, ANALISIS NUMERICO
Cook DM, 2008, AM J PHYS, V76, P321, DOI 10.1119/1.2834739
EINSTEIN A, 1956, INVESTIGATIONNS THEO
Flake GW, 2001, COMPUTATIONAL BEAUTY
FRISH S, 1977, CURSO FISICA GEN, V1
Landau RH, 2008, AM J PHYS, V76, P296, DOI 10.1119/1.2837814
LANGTANGEN HP, 2004, PYTHON SCRIPTING COM
Lemons D. S., 2002, INTRO STOCHASTIC PRO
MARION JB, INTRO CLASSICAL MECH
NEWMAN MEJ, 2004, ARXIVCONDMAT0412004
PEITGEN HO, 1992, NEW FRONTIERS SCI
Strogatz SH., 1994, NONLINEAR DYNAMICS C
Timberlake T, 2008, AM J PHYS, V76, P334, DOI 10.1119/1.2870575
NR 15
TC 4
Z9 6
U1 0
U2 3
PU SOC MEXICANA FISICA
PI COYOACAN
PA APARTADO POSTAL 70-348, COYOACAN 04511, MEXICO
SN 1870-3542
J9 REV MEX FIS E
JI Rev. Mex. Fis. E.
PD JUN
PY 2009
VL 55
IS 1
BP 97
EP 111
PG 15
WC History & Philosophy Of Science; Physics, Multidisciplinary
SC History & Philosophy of Science; Physics
GA 464HA
UT WOS:000267494800013
DA 2021-04-21
ER
PT B
AU Landau, RH
Bordeianu, CC
Paez, MJ
AF Landau, Rubin H.
Bordeianu, Cristian C.
Paez, Manuel J.
BE Vlada, M
Albeanu, G
Popovici, DM
TI Computational Physics with Python
SO ICVL 2009 - PROCEEDINGS OF THE 4TH INTERNATIONAL CONFERENCE ON VIRTUAL
LEARNING
LA English
DT Proceedings Paper
CT 4th International Conference on Virtual Learning
CY OCT 30-NOV 01, 2009
CL Jassy, ROMANIA
SP European Year of Creativ & Innovat 2009, Intuit Consortium Network Excellence Europe
AB A coherent set of material for upper-division university education in computational physics/science has been developed at Oregon State University, USA. It contains an introductory course in scientific computing, a course in Computational Physics, and a coordinated collection of multimedia interactive animations which enhance the book and the courses. Computational Physics programs using Python programming language are presented and displayed. It is proposed that presentation using Python is a more effective and efficient way to teach physics than the traditional one.
C1 [Landau, Rubin H.] Oregon State Univ, Dept Phys, Corvallis, OR 97331 USA.
Univ Bucharest, Fac Phys, Bucharest 077125, Romania.
Univ Antioquia, Medellin, Colombia.
RP Landau, RH (corresponding author), Oregon State Univ, Dept Phys, Corvallis, OR 97331 USA.
EM cristian.bordeianu@brahms.fizica.unibuc.ro
CR AIP, 1995, SKILLS US FREQ PHYS
[Anonymous], EPIC ENGAGING PEOPLE
Bordeianu CC, 2009, EUR J PHYS, V30, P1049, DOI 10.1088/0143-0807/30/5/013
DOWNEY A., 2008, THINK LIKE COMPUTER
Landau R., 2008, SURVEY COMPUTATIONAL
LANDAU R. H., 2004, COMPUTING SCI ENG, P6
LANDAU RH, 2005, 1 COURSE SCI COMPUTI
Landau RH, 2007, COMPUTATIONAL PHYS P
Landau RH, 2008, AM J PHYS, V76, P296, DOI 10.1119/1.2837814
VIDEO, 2008, VIDEO LECT INTRO COM
NR 10
TC 0
Z9 0
U1 0
U2 4
PU BUCHAREST UNIVERSITY PRESS
PI BUCHAREST
PA SOS PANDURI NR 90-92, BUCHAREST, 050663, ROMANIA
PY 2009
BP 112
EP +
PG 2
WC Computer Science, Information Systems; Education & Educational Research
SC Computer Science; Education & Educational Research
GA BZQ29
UT WOS:000302384000011
DA 2021-04-21
ER
PT B
AU Chevillon, N
Tang, MC
Pregaldiny, F
Lallement, C
Madec, M
AF Chevillon, Nicolas
Tang, Mingchun
Pregaldiny, Fabien
Lallement, Christophe
Madec, Morgan
BE Napieralski, A
TI FinFET Compact Modeling and Parameter Extraction
SO MIXDES 2009: PROCEEDINGS OF THE 16TH INTERNATIONAL CONFERENCE MIXED
DESIGN OF INTEGRATED CIRCUITS AND SYSTEMS
LA English
DT Proceedings Paper
CT 16th International Conference Mixed Design of Integrated Circuits and
Systems
CY JUN 25-27, 2009
CL Lodz, POLAND
SP Tech Univ Lodz, Dept Microelect & Comp Sci, Warsaw Univ Technol, Inst Microelect & Optoelect, Poland Sect IEEE, CAS & ED Chapters, PAS, Sect Microelect, Comm Electr & Telecommun, PAS, Sect Signals, Elect Circuits & Syst, Comm Electr & Telecommun
DE FinFET; compact model; parameter extraction; optimization; 3-D
simulations; Verilog-A; python script; short-channel effects; quantum
effects
ID MOSFET
AB In this paper, we present a FinFET compact model and its associated parameter extraction methodology. This explicit model accounts for all major small geometry effects and allows accurate simulations of both n- and p-type FinFETs. The model core is physics-based (long-channel model) and some semiempirical corrections are introduced in order to accurately simulate the behavior of ultrashort (L = 25 nm) and ultrathin (W-Si =3 nm) FinFETs. The parameter extraction relies on a software suite allowing an automatic parameter extraction. In this work, the development of our parameter extraction procedure is based on 3-D simulation results. The optimization of parameters related to quantum effects, short-channel effects and channel length modulation illustrates the methodology of parameter extraction. Finally, we compare the FinFET characteristics (drain current and small signal parameters) obtained by our explicit compact model with 3-D numerical simulations for different Fin widths and channel lengths.
C1 [Chevillon, Nicolas; Tang, Mingchun; Pregaldiny, Fabien; Lallement, Christophe; Madec, Morgan] Univ Strasbourg, InESS, Strasbourg, France.
RP Chevillon, N (corresponding author), Univ Strasbourg, InESS, Strasbourg, France.
EM fabien.pregaldiny@iness.c-strasbourg.fr
RI Madec, Morgan/AAA-5910-2020; Napieralski, Andrzej/M-1621-2016
OI Napieralski, Andrzej/0000-0002-3844-3435; LALLEMENT,
christophe/0000-0002-0708-7212; Madec, Morgan/0000-0001-9537-642X
CR Diagne B, 2008, SOLID STATE ELECTRON, V52, P99, DOI 10.1016/j.sse.2007.06.020
Dunga MV, 2006, IEEE T ELECTRON DEV, V53, P1971, DOI 10.1109/TED.2005.881001
Liang XP, 2004, IEEE T ELECTRON DEV, V51, P1385, DOI 10.1109/TED.2004.832707
Lime F, 2008, IEEE T ELECTRON DEV, V55, P1441, DOI 10.1109/TED.2008.921980
Pregaldiny F, 2006, INT J NUMER MODEL EL, V19, P239, DOI 10.1002/jnm.609
Sallese JM, 2005, SOLID STATE ELECTRON, V49, P485, DOI 10.1016/j.sse.2004.11.013
Smit G. D. J., 2007, P WORKSH COMP MOD NS, V3, P520
TANG M, 2009, IEEE T ELEC IN PRESS
PYTHON DOCUMENTATION
NR 9
TC 3
Z9 3
U1 0
U2 2
PU IEEE
PI NEW YORK
PA 345 E 47TH ST, NEW YORK, NY 10017 USA
BN 978-83-928756-0-4
PY 2009
BP 55
EP 60
PG 6
WC Engineering, Electrical & Electronic
SC Engineering
GA BNY72
UT WOS:000275900300007
DA 2021-04-21
ER
PT S
AU Davis, K
Striegnitz, J
AF Davis, Kei
Striegnitz, Joerg
BE Eugster, P
TI Parallel/High-Performance Object-Oriented Scientific Computing: Today's
Research, Tomorrow's Practice Report on the 7th POOSC Workshop, ECOOP
2008
SO OBJECT-ORIENTED TECHNOLOGY: ECOOP 2008 WORKSHOP READER
SE Lecture Notes in Computer Science
LA English
DT Proceedings Paper
CT 22nd European Conference on Object-Oriented Programming (ECOOP 2008)
CY JUL 07-11, 2008
CL Paphos, CYPRUS
SP Univ Cyprus, Dept Comp Sci, AITO, ACM SIGPLAN, ACM SIGSOFT, IBM, Microsoft Res, Google
DE Parallel computing; high-perfomance computing; scientific computing;
object-oriented computing
AB While object-oriented programming has been embraced in industry, particularly in the form of C++, Java, and Python, its acceptance by the parallel scientific programming community is for various reasons incomplete. Nonetheless, various factors practically dictate the rise of language features that provide higher level abstractions than do C or older FORTRAN standards. These include increasingly complex physics models, numerical algorithms, and hardware (e.g. deep memory hierarchies, ever-increasing numbers of processors, and the advent of multi- and manycore processors and heterogeneous architectures). Our emphases are oil identifying specific problems impeding greater acceptance and widespread use of object-oriented programming in scientific computing; proposed and implemented solutions to these problems; and new or novel frameworks, approaches, techniques, or idioms for parallel/high-performance object-oriented scientific computing.
C1 [Davis, Kei] Los Alamos Natl Lab, Performance & Architecture Lab Comp Sci High Perf, CCS 1,MS B287, Los Alamos, NM 87545 USA.
[Striegnitz, Joerg] Univ Appl Sci Regensburg, Regensburg 93053, Germany.
RP Davis, K (corresponding author), Los Alamos Natl Lab, Performance & Architecture Lab Comp Sci High Perf, CCS 1,MS B287, Los Alamos, NM 87545 USA.
EM kei.davis@lanl.gov; joerg.striegnitz@informtik.fh-regensburg.de
NR 0
TC 0
Z9 0
U1 0
U2 1
PU SPRINGER-VERLAG BERLIN
PI BERLIN
PA HEIDELBERGER PLATZ 3, D-14197 BERLIN, GERMANY
SN 0302-9743
BN 978-3-642-02046-9
J9 LECT NOTES COMPUT SC
PY 2009
VL 5475
BP 104
EP +
PG 4
WC Computer Science, Software Engineering; Computer Science, Theory &
Methods
SC Computer Science
GA BJV46
UT WOS:000267260400011
DA 2021-04-21
ER
PT B
AU Bird, EF
Korwin-Kochanowski, R
Lock, R
AF Bird, Edward F.
Korwin-Kochanowski, Robin
Lock, Ruth
BE Mehdi, Q
Elmaghraby, A
Anderson, D
Chng, E
TI BLENDER: EXPERIENCE OF USING RAD TOOLS FOR 3D GAMES DESIGN
SO PROCEEDINGS OF CGAMES 2009 USA - 14TH INTERNATIONAL CONFERENCE ON
COMPUTER GAMES: AI, ANIMATION, MOBILE, INTERACTIVE MULTIMEDIA,
EDUCATIONAL AND SERIOUS GAMES
LA English
DT Proceedings Paper
CT 14th International Conference on Computer Games
CY JUL 29-AUG 02, 2009
CL Louisville, KY
SP IEEE Comp Soc, TCSIM, IEEE Comp Soc, Louisville Chapter, Soc Modelling & Simulat, IEE, British Comp Soc, Digital Games Res Assoc, Coventry Univ, Serious Games Inst, Int Journal Intelligent Games & Simulat, Univ Wolverhampton, Sch Comp & Informat Technol
DE Game development; Blender; Prototyping; AI; Python
AB The paper discusses the use of Blender in order to build a prototype 3D game. The game consists of three rooms, various objects, an animated figure and two non-player characters. Blender was used to model the 3D environment, animate the player character, apply physics to objects and impart behaviours onto non-player characters. The rooms were built separately and then integrated into a single application. The player can control the main character using the keyboard. The player character is viewed from a third-person perspective and is able to walk around the rooms and interact with the environment.
C1 [Bird, Edward F.; Korwin-Kochanowski, Robin; Lock, Ruth] Wolverhampton Univ, Wolverhampton WV1 1LY, W Midlands, England.
RP Bird, EF (corresponding author), Wolverhampton Univ, Wulfruna St, Wolverhampton WV1 1LY, W Midlands, England.
EM E.F.Bird@wlv.ac.uk; r.j.k2@wlv.ac.uk; Ruth.Lock@wlv.ac.uk
CR BARTON C, 2008, BLENDER GAMEKIT, P159
Brito A., 2008, BLENDER 3D ARCHITECT
GUMSTER J, 2009, BLENDER DUMMIES
MCKAY R, 2008, BLENDER GAMEKIT, P129
Mullen T., 2008, BOUNCE TUMBLE SPLASH
NAKAMURA T, 2006, THESIS TEXAS A M U, P19
VELDHUIZEN B, 2009, BLENDER
NR 7
TC 0
Z9 0
U1 0
U2 0
PU UNIV WOLVERHAMPTON
PI WOLVERHAMPTON
PA SCH COMPUTING & INFO TECH, WULFRUNA ST, WOLVERHAMPTON, WV1 1SB, ENGLAND
BN 978-0-9549016-7-7
PY 2009
BP 55
EP 62
PG 8
WC Computer Science, Artificial Intelligence; Computer Science, Software
Engineering; Computer Science, Theory & Methods
SC Computer Science
GA BOB92
UT WOS:000276131400008
DA 2021-04-21
ER
PT S
AU Pinhao, NR
AF Pinhao, N. R.
BE Petrovic, ZL
Malovic, G
Maric, D
TI Recent developments on PLASMAKIN - a software package to model the
kinetics in gas discharges
SO SECOND INTERNATIONAL WORKSHOP ON NON-EQUILIBRIUM PROCESSES IN PLASMAS
AND ENVIRONMENTAL SCIENCE
SE Journal of Physics Conference Series
LA English
DT Proceedings Paper
CT 2nd International Workshop on Non-Equilibrium Processes in Plasmas and
Environmental Science
CY AUG 23-26, 2008
CL Belgrade, SERBIA
SP Serbian Acad Sci & Arts, Inst Phys, Minist Sci & Technol Serbia, Hiden Anal
ID PRESSURE MERCURY DISCHARGES; BOLTZMANN-EQUATION; TRANSPORT;
APPROXIMATION; COMPUTATION; PROGRAM; VALUES; ATOMS; LINES; TERM
AB PLASMAKIN is a user-friendly software package to handle physical and chemical data used in plasma physics modeling and to compute the production and destruction terms in fluid models equations. These terms account for the particle or energy production and loss rates due to gas-phase and gas-surface reactions. The package has been restructured and expanded to (a) allow the simulation of atomic emission spectra taking into account line broadening processes and radiation trapping; (b) include a library to compute the electron kinetics, (c) include a database of species properties and reactions and, (d) include a Python interface to allow access from scripts and integration with other scientific software tools.
C1 ITN, P-2685 Sacavem, Portugal.
RP Pinhao, NR (corresponding author), ITN, Estrada Nacl 10, P-2685 Sacavem, Portugal.
EM npinhao@itn.pt
RI Pinhao, N/H-6825-2019
OI Pinhao, N/0000-0002-4185-2619
CR Carver GD, 1997, COMPUT PHYS COMMUN, V105, P197, DOI 10.1016/S0010-4655(97)00056-8
DALE D, 2008, MATPLOTLIB, P45011
DAMELINCOURT JJ, 1983, J APPL PHYS, V54, P3087, DOI 10.1063/1.332515
Hagelaar GJM, 2005, PLASMA SOURCES SCI T, V14, P722, DOI 10.1088/0963-0252/14/4/011
HUMLICEK J, 1982, J QUANT SPECTROSC RA, V27, P437, DOI 10.1016/0022-4073(82)90078-4
Jones E., 2001, SCIPY OPEN SOURCE SC
KARABOURNIOTIS D, 1986, NATO ASI SER B-PHYS, V149, P171
KEE RJ, 1996, CHEMKIN 3, P45011
KUMAR K, 1980, AUST J PHYS, V33, P343, DOI 10.1071/PH800343b
Loffhagen D, 1996, J PHYS D APPL PHYS, V29, P618, DOI 10.1088/0022-3727/29/3/021
Luque J., 1999, 99009 MP SRI INT
MOLISCH AF, 1993, COMPUT PHYS COMMUN, V74, P81, DOI 10.1016/0010-4655(93)90108-O
MOLISCH AF, 1993, COMPUT PHYS COMMUN, V77, P255, DOI 10.1016/0010-4655(93)90009-2
Molisch AF, 1996, COMPUT PHYS COMMUN, V93, P127, DOI 10.1016/0010-4655(95)00093-3
MORGAN WL, 1990, COMPUT PHYS COMMUN, V58, P127, DOI 10.1016/0010-4655(90)90141-M
Pancheshnyi SV., 2008, COMPUTER CODE ZDPLAS
Pinhao NR, 2004, PLASMA SOURCES SCI T, V13, P719, DOI 10.1088/0963-0252/13/4/023
Pinhao NR, 2001, COMPUT PHYS COMMUN, V135, P105, DOI 10.1016/S0010-4655(00)00226-5
PINHAO NR, 2007, PYTHON PAPERS, V2, P35
RALCHENKO Y, 1979, NIST ATOMIC SPECTRA, P45011
SAHALBRE.S, 1974, ASTRON ASTROPHYS, V35, P319
SAKAI Y, 1977, J PHYS D APPL PHYS, V10, P1035, DOI 10.1088/0022-3727/10/7/010
SEGUR P, 1984, J PHYS D APPL PHYS, V17, P2199, DOI 10.1088/0022-3727/17/11/007
STORMBERG HP, 1980, J APPL PHYS, V51, P1963, DOI 10.1063/1.327911
NR 24
TC 2
Z9 2
U1 0
U2 4
PU IOP PUBLISHING LTD
PI BRISTOL
PA DIRAC HOUSE, TEMPLE BACK, BRISTOL BS1 6BE, ENGLAND
SN 1742-6588
J9 J PHYS CONF SER
PY 2009
VL 162
AR 012006
DI 10.1088/1742-6596/162/1/012006
PG 11
WC Environmental Sciences; Physics, Applied; Physics, Fluids & Plasmas
SC Environmental Sciences & Ecology; Physics
GA BME53
UT WOS:000272024000006
DA 2021-04-21
ER
PT B
AU Lim, CS
Jain, A
AF Lim, Christopher S.
Jain, Abhinandan
GP IEEE COMPUTER SOC
TI Dshell plus plus : A Component Based, Reusable Space System Simulation
Framework
SO SMC-IT 2009: THIRD IEEE INTERNATIONAL CONFERENCE ON SPACE MISSION
CHALLENGES FOR INFORMATION TECHNOLOGY, PROCEEDINGS
LA English
DT Proceedings Paper
CT 3rd IEEE International Conference on Space Mission Challenges for
Information Technology
CY JUL 19-23, 2009
CL Pasadena, CA
SP IEEE
DE Aerospace simulation software
AB This paper describes the multi-mission Dshell++ simulation framework for high fidelity, physics-based simulation of spacecraft, robotic manipulation and mobility systems. Dshell++ is a C++/Python library which uses modern script-driven object-oriented techniques to allow component reuse and a dynamic run-time interface for complex, high-fidelity simulation of spacecraft and robotic systems. The goal of the Dshell++ architecture is to manage the inherent complexity of physics-based simulations while supporting component model reuse across missions. The framework provides several features that support a large degree of simulation configurability and usability.
C1 [Lim, Christopher S.; Jain, Abhinandan] CALTECH, Jet Prop Lab, Pasadena, CA 91009 USA.
RP Lim, CS (corresponding author), CALTECH, Jet Prop Lab, 4800 Oak Grove Dr, Pasadena, CA 91009 USA.
EM Christopher.S.Lim@jpl.nasa.gov
CR Balaram J., 2002, IEEE 2002 AER C BIG
BIESIADECKI J, 1997, 6 DIG AV SYST C IRV
BONFIGLIO EP, 2008, AIAA AAS ASTR SPEC C
BRAUN GL, 1977, CR2770 NASA
European Space Agency, 2005, SMP 2 0 HDB
JAIN A, 2003, INT S ART INT ROB AU
JAIN A, 2004, IEEE 2004 AER C BIG
JAIN A, 1992, 5 ANN C AER COMP C J
NAYAR H, 2008, P INT C SIM MOD PROG
NEMETH S, 2008, SPACEOPS 2008 C HEID
POMERANTZ M, 2009, SMC IT 2009
RODRIGUEZ G, 1991, INT J ROBOT RES, V10, P371, DOI 10.1177/027836499101000406
Wilcox B., 2007, J FIELD ROBOTICS, V24
CHEETAH PYTHON POWER
GTK PROJECT
PYTHON PROGRAMMING L
SIMPLIFIED WRAPPER I
GRAPHVIZ GRAPH VISUA
PYGTK GTK PYTHON
NR 19
TC 2
Z9 2
U1 0
U2 0
PU IEEE COMPUTER SOC
PI LOS ALAMITOS
PA 10662 LOS VAQUEROS CIRCLE, PO BOX 3014, LOS ALAMITOS, CA 90720-1264 USA
BN 978-0-7695-3637-8
PY 2009
BP 229
EP 236
DI 10.1109/SMC-IT.2009.35
PG 8
WC Engineering, Aerospace
SC Engineering
GA BMO51
UT WOS:000273122700030
DA 2021-04-21
ER
PT B
AU Wilson, RW
Stark, AA
AF Wilson, Robert W.
Stark, Antony A.
BE Krupnik, I
Lang, MA
Miller, SE
TI Cosmology from Antarctica
SO SMITHSONIAN AT THE POLES: CONTRIBUTIONS TO INTERNATIONAL POLAR YEAR
SCIENCE: A SMITHSONIAN CONTRIBUTION TO KNOWLEDGE
LA English
DT Proceedings Paper
CT Smithsonian Poles Symposium 2007
CY MAY 03-04, 2007
CL Smithsonian Inst, Washington, DC
HO Smithsonian Inst
ID 1ST SEASON OBSERVATIONS; WATER-VAPOR COLUMN; CMB POLARIZATION;
SUBMILLIMETER-TELESCOPE; ATMOSPHERIC OPACITY; MICROWAVE SKY; ANISOTROPY;
DESIGN; FLUCTUATIONS; RECEIVER
AB Four hundred thousand years after the Big Bang, electrons and nuclei combined to form atoms for the first time, allowing a sea of photons to stream freely through a newly transparent universe. After billions of years, those photons, highly redshifted by the universal cosmic expansion, have become the cosmic microwave background (CMB) radiation we see coming from all directions today. Observation of the CMB is central to observational cosmology, and the Antarctic plateau is an exceptionally good site for this work. The first attempt at CMB observations from the plateau was an expedition to the South Pole in December 1986 by the Radio Physics Research group at Bell Laboratories. No CMB anisotropies were observed, but sky noise and opacity were measured. The results were sufficiently encouraging that in the austral summer of 1988-1989, three CMB groups participated in the "Cucumber" campaign, where a temporary site dedicated to CMB anisotropy measurements was set up 2 km from South Pole Station. These were summer-only campaigns. Wintertime observations became possible in 1990 with the establishment of the Center for Astrophysical Research in Antarctica (CARA), a National Science Foundation Science and Technology Center. The CARA developed year-round observing facilities in the "Dark Sector," a section of Amundsen-Scott South Pole Station dedicated to astronomical observations. The CARA scientists fielded several astronomical instruments: Antarctic Submillimeter Telescope and Remote Observatory. (ASTIRO), South Pole Infrared Explorer (SPIREX), White Dish, Python, Viper, Arcminute Cosmology Bolometer Array Receiver (ACBAR), and Degree-Angular Scale Interferometer (DASI). By 2001, data from CARA, together with that from Balloon Observations of Millimetric Extragalactic Radiation and Geophysics (BOOMERANG-a CMB experiment on a long-duration balloon launched from McMurdo Station on the coast of Antarctica) showed clear evidence that the overall geometry of the universe is flat, as opposed to being positively or negatively curved. In 2002, the DASI group reported the detection of polarization in the CMB. These observations strongly support a "concordance model" of cosmology, where the dynamics of a flat universe are dominated by forces exerted by the mysterious dark energy and dark matter. The CMB observations continue on the Antarctic plateau. The South Pole Telescope (SPT) is a newly operational 10-m-diameter offset telescope designed to rapidly measure anisotropies on scales much smaller than 1 degrees.
C1 [Wilson, Robert W.; Stark, Antony A.] Smithsonian Astrophys Observ, Cambridge, MA 02138 USA.
EM rwilson@cfa.harvard.edu
CR ALVAREZ DL, 1995, THESIS PRINCETON U P
Bussmann RS, 2005, ASTROPHYS J, V622, P1343, DOI 10.1086/427935
Chamberlin R, 2002, ASTR SOC P, V266, P172
Chamberlin RA, 1997, ASTROPHYS J, V476, P428, DOI 10.1086/303621
CHAMBERLIN RA, 1995, INT J INFRARED MILLI, V16, P907, DOI 10.1007/BF02066665
Church S, 2003, NEW ASTRON REV, V47, P1083, DOI 10.1016/j.newar.2003.09.033
COBLE K, 1999, ASTROPHYS J LETT, V519, P5
de Bernardis P, 2000, NATURE, V404, P955, DOI 10.1038/35010035
DRAGOVAN M, 1989, AIP CONF PROC, V198, P97
DRAGOVAN M, 1994, ASTROPHYS J, V427, pL67, DOI 10.1086/187366
DRAGOVAN M, 1990, APPL OPTICS, V29, P463, DOI 10.1364/AO.29.000463
ENGARGIOLA G, 1994, REV SCI INSTRUM, V65, P1833, DOI 10.1063/1.1144831
Fixsen DJ, 1996, ASTROPHYS J, V473, P576, DOI 10.1086/178173
GAIER T, 1989, AIP CONF PROC, V198, P84
Ganga K, 1997, ASTROPHYS J, V484, P7, DOI 10.1086/304296
Gerecht E, 1999, IEEE T MICROW THEORY, V47, P2519, DOI 10.1109/22.809001
Goff J.A., 1946, T AM SOC HEAT VENT E, V52, P95
Groppi C, 2000, ASTR SOC P, V217, P48
Halverson NW, 2002, ASTROPHYS J, V568, P38, DOI 10.1086/338879
HARRISON ER, 1970, PHYS REV D, V1, P2726, DOI 10.1103/PhysRevD.1.2726
HOLDAWAY MA, 1995, 139 NAT RAD ASTR OBS
Honingh CE, 1997, IEEE T APPL SUPERCON, V7, P2582, DOI 10.1109/77.621767
Hu W, 1997, NEW ASTRON, V2, P323, DOI 10.1016/S1384-1076(97)00022-5
Indermuehle BT, 2005, PUBL ASTRON SOC AUST, V22, P73, DOI 10.1071/AS04037
Keating BG, 2003, PROC SPIE, V4843, P284, DOI 10.1117/12.459274
KOOI JW, 1995, INT J INFRARED MILLI, V16, P2049, DOI 10.1007/BF02073409
KOOI JW, 2002, IEEE T MICROW THEORY, V40, P812
Kovac JM, 2002, NATURE, V420, P772, DOI 10.1038/nature01269
LANDSBERG RH, 1998, B AM ASTRONOMICAL SO, V30, P903
Lane AP, 1997, ASTR SOC P, V141, P289
LASENBY AN, 1998, P MOR WORKSH FUND PA
Lay OP, 2000, ASTROPHYS J, V543, P787, DOI 10.1086/317115
Leitch EM, 2002, AIP CONF PROC, V616, P65
Leitch EM, 2002, NATURE, V420, P763, DOI 10.1038/nature01271
Leitch EM, 2002, ASTROPHYS J, V568, P28, DOI 10.1086/338878
Lynch J. T., 1998, Astronomical Society of the Pacific Conference Series, V141, P54
Martin CL, 2004, ASTROPHYS J SUPPL S, V150, P239, DOI 10.1086/379661
Masi S, 2007, NEW ASTRON REV, V51, P236, DOI 10.1016/j.newar.2006.11.063
Masi S, 2006, ASTRON ASTROPHYS, V458, P687, DOI 10.1051/0004-6361:20053891
MEINHOLD PR, 1989, AIP CONF PROC, V198, P88
Nguyen HT, 1996, PUBL ASTRON SOC PAC, V108, P718, DOI 10.1086/133791
Novak G, 2000, ASTROPHYS J, V529, P241, DOI 10.1086/308231
OSTRIKER JP, 1995, NATURE, V377, P600, DOI 10.1038/377600a0
PAJOT F, 1986, ASTRON ASTROPHYS, V154, P55
PAJOT F, 1989, ASTRON ASTROPHYS, V223, P107
PEEBLES PJE, 1970, ASTROPHYS J, V162, P815, DOI 10.1086/150713
PENZIAS AA, 1965, ASTROPHYS J, V142, P419, DOI 10.1086/148307
PETERSON JB, 1989, AIP CONF PROC, V198, P116
PETERSON JB, 2000, ASTROPHYSICAL J LETT, V532, P83
Piacentini F, 2007, NEW ASTRON REV, V51, P244, DOI 10.1016/j.newar.2006.11.058
Platt SR, 1997, ASTROPHYS J, V475, pL1, DOI 10.1086/310453
Radford SJE, 1996, PUBL ASTRON SOC PAC, V108, P441, DOI 10.1086/133745
REICHARDT CL, 2008, ARXIVEPRINTS08011491
Ruhl JE, 2004, PROC SPIE, V5498, P11, DOI 10.1117/12.552473
RUHL JE, 2001, ASTROPHYSICAL J LETT, V453, pL1
Runyan MC, 2003, NEW ASTRON REV, V47, P915, DOI 10.1016/j.newar.2003.09.001
Schieder R., 1989, Experimental Astronomy, V1, P101, DOI 10.1007/BF00457985
Schwerdtfeger W., 1984, WEATHER CLIMATE ANTA
SMYTHE WD, 1977, APPL OPTICS, V16, P2041, DOI 10.1364/AO.16.002041
Spergel DN, 2003, ASTROPHYS J SUPPL S, V148, P175, DOI 10.1086/377226
Stark AA, 1997, REV SCI INSTRUM, V68, P2200, DOI 10.1063/1.1148071
Stark AA, 2004, ASTROPHYS J, V614, pL41, DOI 10.1086/425304
Stark AA, 2001, PUBL ASTRON SOC PAC, V113, P567, DOI 10.1086/320281
Stark AA, 2000, PROC SPIE, V4015, P434, DOI 10.1117/12.390436
Swain MR, 1998, P SOC PHOTO-OPT INS, V3354, P480, DOI 10.1117/12.317274
Tauber J. A., 2005, IAU S, V201, P86
TUCKER GS, 1993, ASTROPHYS J, V419, pL45, DOI 10.1086/187133
UCHIDA Y, 1985, NATURE, V317, P699, DOI 10.1038/317699a0
WALKER C, 2001, 12 INT S SPAC TER TE
WALKER CK, 1992, INT J INFRARED MILLI, V13, P785, DOI 10.1007/BF01011595
Warren S. G., 1996, ENCY CLIMATE WEATHER, P32
YNGVESSON KS, 2001, 12 INT S SPAC TER TE, P14
YOON KW, 2006, P SOC PHOTO-OPT INS, P6275
ZMUIDZINAS J, 1992, IEEE T MICROW THEORY, V40, P1797, DOI 10.1109/22.156607
NR 74
TC 0
Z9 0
U1 0
U2 0
PU SMITHSONIAN INST PRESS
PI WASHINGTON
PA 900 JEFFERSON DRIVE SW, WASHINGTON, DC 20560 USA
BN 978-0-9788460-1-5
PY 2009
BP 359
EP 367
DI 10.5479/si.097884601X.27
PG 9
WC Anthropology; Ecology; Geography; Geography, Physical; History &
Philosophy Of Science; Meteorology & Atmospheric Sciences
SC Anthropology; Environmental Sciences & Ecology; Geography; Physical
Geography; History & Philosophy of Science; Meteorology & Atmospheric
Sciences
GA BJC33
UT WOS:000264705700027
DA 2021-04-21
ER
PT J
AU van der Linden, GW
Wijnoltz, F
Scholten, J
Busch, PJ
Poelman, AJ
Smeets, PH
de Groot, B
Koppers, WR
AF van der Linden, G. W.
Wijnoltz, F.
Scholten, J.
Busch, P. J.
Poelman, A. J.
Smeets, P. H.
de Groot, B.
Koppers, W. R.
TI Design of the Magnum-PSI safety, control and data acquisition system
SO FUSION ENGINEERING AND DESIGN
LA English
DT Article; Proceedings Paper
CT 6th IAEA Technical Meeting on Control, Data Acquisition, and Remote
Participation for Fusion Research
CY JUN 04-08, 2007
CL Inuyama, JAPAN
SP IAEA, Natl Inst Fus Sci
DE ITER; plasma generator; experiment control; data acquisition;
distributed control; Magnum-PSI
ID PLASMA-SURFACE INTERACTION; ITER
AB The FOM-Institute for Plasma Physics Rijnhuizen has started the construction of Magnum-PSI, a magnetized (3 T), steady-state, large area (80cm(2)) high-flux (up to 10(24) H(+)ions m(-2) s(-1)) plasma generator. The aim of this linear plasma device is to provide a controlled, highly accessible laboratory experiment facility in which the interaction of a magnetized plasma with different surfaces can be studied in detail. Magnum-PSI consists of several subsystems including vacuum, cooling, plasma source, target station, and a number of diagnostic systems. The safety, Control, Data Acquisition and Communication (CODAC) system integrates these subsystems and provides an interface for the Magnum-PSI users. The CODAC system is designed in parallel with the Magnum-PSI hardware to maximize compatibility and usability. The CODAC system must handle the step-by-step construction and expansion of the Magnum-PSI system without compromising human and system safety in each step, combine manual and remote control, and provide a central data storage and implement automated experiment execution.
Key features of the CODAC system are a layered, modular and distributed design, the use of intelligent devices (e.g. commercial computer controlled cooling units), a centralized data storage built on HDF5, and a communication layer built on ZeroC (TM) Ice (TM). A significant part of the design is based on existing open source software components, integrated using C and Python code. (c) 2008 Elsevier B.V. All rights reserved.
C1 [van der Linden, G. W.; Wijnoltz, F.; Scholten, J.; Busch, P. J.; Poelman, A. J.; Smeets, P. H.; de Groot, B.; Koppers, W. R.] EURATOM, FOM, Inst Plasma Phys Rijnhuizen, NL-3439 MN Nieuwegein, Netherlands.
RP van der Linden, GW (corresponding author), EURATOM, FOM, Inst Plasma Phys Rijnhuizen, Edisonbaan 14, NL-3439 MN Nieuwegein, Netherlands.
EM G.W.vanderLinden@rijnhuizen.nl
CR de Groot B, 2005, FUSION ENG DES, V74, P155, DOI 10.1016/j.fusengdes.2005.06.054
Kleyn AW, 2006, VACUUM, V80, P1098, DOI 10.1016/j.vacuum.2006.02.019
VANROOIJ G, APPL PHYS LETT, V90
*ZEROC INC, DIFF IC CORBA
NR 4
TC 3
Z9 3
U1 0
U2 6
PU ELSEVIER SCIENCE SA
PI LAUSANNE
PA PO BOX 564, 1001 LAUSANNE, SWITZERLAND
SN 0920-3796
J9 FUSION ENG DES
JI Fusion Eng. Des.
PD APR
PY 2008
VL 83
IS 2-3
BP 273
EP 275
DI 10.1016/j.fusengdes.2008.01.001
PG 3
WC Nuclear Science & Technology
SC Nuclear Science & Technology
GA 313ZP
UT WOS:000256778800025
DA 2021-04-21
ER
PT B
AU Langtangen, HP
Cai, X
AF Langtangen, Hans Petter
Cai, Xing
BE Bock, HG
Kostina, E
Phu, HX
Rannacher, R
TI On the Efficiency of Python for High-Performance Computing: A Case Study
Involving Stencil Updates for Partial Differential Equations
SO MODELING, SIMULATION AND OPTIMIZATION OF COMPLEX PROCESSES
LA English
DT Proceedings Paper
CT 3rd International Conference on High Performance Scientific Computing
CY MAR 06-10, 2006
CL Hanoi, VIETNAM
SP Interdisciplinary Ctr Sci Comp, Int PhD Prog, Univ Heidelberg, Complex Proc, Modeling, Simulat & Optimizat, Gottlieb Daimler, Karl Benz Fdn, DFG, Res Ctr Matheon, Berlin Bradenburg Acad Sci & Humanities, Abdus Salam Int Ctr Theoret Phys, Vietnamese Acad Sci &Technol, Inst Math, Vietnam Natl Program Basic Sci, Hochiminh City Univ Technol
AB The purpose of this paper is to assess the loss of computational efficiency that may occur when scientific codes are written in the Python programming language instead of Fortran or C. Our test problems concern the application of a seven-point finite stencil for a three-dimensional, variable coefficient, Laplace operator. This type of computation appears in lots of codes solving partial differential equations, and the variable coefficient is a key ingredient to capture the arithmetic complexity of stencils arising in advanced multi-physics problems in heterogeneous media.
Different implementations of the stencil operation are described: pure Python loops over Python arrays, Psyco-acceleration of pure Python loops, vectorized loops (via shifted slice expressions), inline C++ code (via Weave), and migration of stencil loops to Fortran 77 (via F2py) and C. The performance of these implementations are compared against codes written entirely in Fortran 77 and C. We observe that decent performance is obtained with vectorization or migration of loops to compiled code. Vectorized loops run between two and five times slower than the pure Fortran and C codes. Mixed-language implementations, Python-Fortran and Python-C, where only the loops are implemented in Fortran or C, run at the same speed as the pure Fortran and C codes.
At present, there are three alternative (and to some extent competing) implementations of Numerical Python: numpy, numarray, and Numeric. Our tests uncover Significant performance differences between these three alternatives. Numeric is fastest on scalar operations with array indexing, while numpy is fastest on vectorized operations with array slices.
We also present parallel versions of the stencil operations, where the loops are migrated to C for efficiency, and where the message passing statements are written in Python, using the high-level pypar interface to MPI For the current test problems, there are hardly any efficiency loss by doing the message passing in Python. Moreover, adopting the Python interface of MPI gives a more elegant parallel implementation, both due to a simpler syntax of MPI calls and due to the efficient array slicing functionality that comes with Numerical Python.
C1 [Langtangen, Hans Petter; Cai, Xing] Simula Res Lab, N-1325 Lysaker, Norway.
RP Langtangen, HP (corresponding author), Simula Res Lab, POB 134, N-1325 Lysaker, Norway.
EM hpl@simula.no; xingca@simula.no
CR CAI X, 2005, SCI PROGRAMMING, V13, P31, DOI DOI 10.1155/2005/619804
CAI X, 2006, LECT NOTES COMPUTATI, V51, P295
LANGTANGEN HP, 2006, SCRIPTING UTILITIES
LANGTANGEN HP, 2008, PYTHON SCRIPTING COM
*NETL, NETL REP NUM SOFTW
RAMACHANDRAN P, PERFORMANCE VARIOUS
*SCIPY, SCIPY SOFTW PACK
*SCIPY, WEAV PART SCIPY PACK
VANROSSUM G, EXTENDING EMBEDDING
2004, PYMPI SOFTWARE PACKA
2004, MATLAB CODE VECTORIZ
SOFTWARE RUNNING COM
2004, PYPAR SOFTWARE PACKA
NR 13
TC 16
Z9 16
U1 0
U2 4
PU SPRINGER-VERLAG BERLIN
PI BERLIN
PA HEIDELBERGER PLATZ 3, D-14197 BERLIN, GERMANY
BN 978-3-540-79408-0
PY 2008
BP 337
EP 357
DI 10.1007/978-3-540-79409-7_23
PG 21
WC Mathematics, Applied
SC Mathematics
GA BII10
UT WOS:000259629900023
DA 2021-04-21
ER
PT J
AU Borcherds, PH
AF Borcherds, P. H.
TI Python: a language for computational physics
SO COMPUTER PHYSICS COMMUNICATIONS
LA English
DT Article; Proceedings Paper
CT Conference on Computational Physics (CCP 2006)
CY AUG 29-SEP 01, 2006
CL Gyeongju, SOUTH KOREA
DE education; graphics; computational physics; computation; python
AB Python is a relatively new computing language, created by Guido van Rossum [A.S. Tanenbaum, R. van Renesse, H. van Staveren, G.J. Sharp, S.J. Mullender, A.J. Jansen, G. van Rossum, Experiences with the Amoeba distributed operating system, Communications of the ACM 33 (1990) 46-63; also on-line at http://www.cs.vu.nl/pub/amoeba/. [6]], which is particularly suitable for teaching a course in computational physics. There are two questions to be considered: (i) For whom is the course intended? (ii) What are the criteria for a suitable language, and why choose Python? The criteria include the nature of the application. High performance computing requires a compiled language, e.g., FORTRAN. For some applications a computer algebra, e.g., Maple, is appropriate. For teaching, and for program development, an interpreted language has considerable advantages: Python appears particularly suitable. Python's attractions include (i) its system of modules which makes it easy to extend, (ii) its excellent graphics (VPython module), (iii) its excellent on line documentation, (iv) it is free and can be downloaded from the web. Python and VPython will be described briefly, and some programs demonstrated. (C) 2007 Elsevier B.V. All rights reserved.
C1 Univ Birmingham, Sch Phys & Astron, Birmingham B15 2TT, W Midlands, England.
RP Borcherds, PH (corresponding author), Univ Birmingham, Sch Phys & Astron, Birmingham B15 2TT, W Midlands, England.
EM p.h.borcherds@bham.ac.uk
CR Borcherds P. H., 1986, Physics Education, V21, P238, DOI 10.1088/0031-9120/21/4/008
DONALDSON T, PYTHON 1 PROGRAMMING
DOWNEY A, THINK LIKE COMPUTER
LUTZ M, 1999, LEARNING PYTHON
Scherer D, 2000, COMPUT SCI ENG, V2, P56, DOI 10.1109/5992.877397
TANENBAUM AS, 1990, COMMUN ACM, V33, P46, DOI 10.1145/96267.96281
NR 6
TC 7
Z9 7
U1 0
U2 11
PU ELSEVIER SCIENCE BV
PI AMSTERDAM
PA PO BOX 211, 1000 AE AMSTERDAM, NETHERLANDS
SN 0010-4655
J9 COMPUT PHYS COMMUN
JI Comput. Phys. Commun.
PD JUL
PY 2007
VL 177
IS 1-2
BP 199
EP 201
DI 10.1016/j.cpc.2007.02.019
PG 3
WC Computer Science, Interdisciplinary Applications; Physics, Mathematical
SC Computer Science; Physics
GA 191WC
UT WOS:000248161700080
DA 2021-04-21
ER
PT J
AU Backer, A
AF Baecker, Arnd
TI Computational physics education with Python
SO COMPUTING IN SCIENCE & ENGINEERING
LA English
DT Article
AB Educators at an institution in Germany have started using Python to teach computational physics. The author describes how graphical visualizations also play an important role, which he illustrates here with a few simple examples.
C1 Tech Univ Dresden, Inst Theoret Phys, D-8027 Dresden, Germany.
RP Backer, A (corresponding author), Tech Univ Dresden, Inst Theoret Phys, Mommsenstr 13, D-8027 Dresden, Germany.
EM baecker@physik.tu-dresden.de
OI Backer, Arnd/0000-0002-4321-8099
NR 0
TC 8
Z9 8
U1 0
U2 6
PU IEEE COMPUTER SOC
PI LOS ALAMITOS
PA 10662 LOS VAQUEROS CIRCLE, PO BOX 3014, LOS ALAMITOS, CA 90720-1314 USA
SN 1521-9615
J9 COMPUT SCI ENG
JI Comput. Sci. Eng.
PD MAY-JUN
PY 2007
VL 9
IS 3
BP 30
EP 33
DI 10.1109/MCSE.2007.48
PG 4
WC Computer Science, Interdisciplinary Applications
SC Computer Science
GA 156RY
UT WOS:000245668100006
DA 2021-04-21
ER
PT J
AU Torras, J
Deumens, E
Trickey, SB
AF Torras, J.
Deumens, E.
Trickey, S. B.
TI Software integration in multi-scale simulations: The PUPIL system
SO JOURNAL OF COMPUTER-AIDED MATERIALS DESIGN
LA English
DT Article
DE multi-scale simulations; software inter-operation; QM/MM software
AB The state of the art for computational tools in both computational chemistry and computational materials physics includes many algorithms and functionalities which are implemented again and again. Several projects aim to reduce, eliminate, or avoid this problem. Most such efforts seem to be focused within a particular specialty, either quantum chemistry or materials physics. Multi-scale simulations, by their very nature however, cannot respect that specialization. In simulation of fracture, for example, the energy gradients that drive the molecular dynamics (MD) come from a quantum mechanical treatment that most often derives from quantum chemistry. That "QM" region is linked to a surrounding "CM" region in which potentials yield the forces. The approach therefore requires the integration or at least inter-operation of quantum chemistry and materials physics algorithms. The same problem occurs in "QM/MM" simulations in computational biology. The challenge grows if pattern recognition or other analysis codes of some kind must be used as well. The most common mode of inter-operation is user intervention: codes are modified as needed and data files are managed "by hand" by the user (interactively and via shell scripts). User intervention is however inefficient by nature, difficult to transfer to the community, and prone to error. Some progress (e.g Sethna's work at Cornell [C.R. Myers et al., Mat. Res. Soc. Symp. Proc., 538(1999) 509, C.-S. Chen et al., Poster presented at the Material Research Society Meeting (2000)]) has been made on using Python scripts to achieve a more efficient level of interoperation. In this communication we present an alternative approach to merging current working packages without the necessity of major recoding and with only a relatively light wrapper interface. The scheme supports communication among the different components required for a given multi-scale calculation and access to the functionalities of those components for the potential user. A general main program allows the management of every package with a special communication protocol between their interfaces following the directives introduced by the user which are stored in an XML structured file. The initial prototype of the PUPIL (Program for User Packages Interfacing and Linking) system has been done using Java as a fast, easy prototyping object oriented (OO) language. In order to test it, we have applied this prototype to a previously studied problem, the fracture of a silica nanorod. We did so joining two different packages to do a QM/MD calculation. The results show the potential for this software system to do different kind of simulations and its simplicity of maintenance.
C1 Univ Florida, Dept Chem, Quantum Theory Project, Gainesville, FL 32611 USA.
Univ Florida, Dept Phys, Quantum Theory Project, Gainesville, FL 32611 USA.
RP Trickey, SB (corresponding author), Univ Florida, Dept Chem, Quantum Theory Project, Gainesville, FL 32611 USA.
EM trickey@qtp.ufl.edu
RI Torras, Juan/F-5622-2015
OI Torras, Juan/0000-0001-8737-7609
CR CHEN CS, 2000, MAT RES SOC M
Frantziskonis G, 2003, PHYS REV B, V68, DOI 10.1103/PhysRevB.68.024105
Myers CR, 1999, MATER RES SOC SYMP P, V538, P509
*OBJ MAN GROUP, OMG COMM OBJ REQ BRO
Smith W, 1996, J MOL GRAPHICS, V14, P136, DOI 10.1016/S0263-7855(96)00043-4
*SUN MICR INC, JAV NAT INT 5 0 SPEC
Taylor CE, 2003, COMP MATER SCI, V27, P204, DOI [10.1016/S0927-0256(03)00002-8, 10.1016/S0927-0256(02)00002-8]
Taylor DE, 2005, MOL PHYS, V103, P2019, DOI 10.1080/00268970500131199
THIEL W, PROGRAM MNDO97 VERSI
Zhu T, 2003, MOL SIMULAT, V29, P671, DOI 10.1080/0892702031000103220
NR 10
TC 20
Z9 20
U1 0
U2 4
PU SPRINGER
PI DORDRECHT
PA VAN GODEWIJCKSTRAAT 30, 3311 GZ DORDRECHT, NETHERLANDS
SN 0928-1045
J9 J COMPUT-AIDED MATER
JI J. Comput-Aided Mater. Des.
PD OCT
PY 2006
VL 13
IS 1-3
BP 201
EP 212
DI 10.1007/s10820-006-9011-3
PG 12
WC Computer Science, Interdisciplinary Applications; Materials Science,
Multidisciplinary
SC Computer Science; Materials Science
GA 088FV
UT WOS:000240798400014
DA 2021-04-21
ER
PT J
AU Graham, G
Afaq, A
Evans, D
Guglielmo, G
Wicklund, E
Love, P
AF Graham, Greg
Afaq, Anzar
Evans, David
Guglielmo, Gerald
Wicklund, Eric
Love, Peter
TI Contextual constraint modeling in Grid application workflows
SO CONCURRENCY AND COMPUTATION-PRACTICE & EXPERIENCE
LA English
DT Article; Proceedings Paper
CT GGF Workshop on Workflow in Grid Systems
CY MAR 09, 2004
CL Berlin, GERMANY
SP GGF
DE workflow; Python; inversion of control
AB This paper introduces a new mechanism for specifying constraints in distributed workflows. By introducing constraints in a contextual form, it is shown how different people and groups within collaborative communities can cooperatively constrain workflows. A comparison with existing state-of-the-art workflow systems is made. These ideas are explored in practice with an illustrative example from High-Energy Physics. Copyright (c) 2005 John Wiley & Sons, Ltd.
C1 Fermilab Natl Accelerator Lab, Batavia, IL 60510 USA.
Univ Lancaster, Dept Phys, Lancaster, England.
RP Graham, G (corresponding author), Fermilab Natl Accelerator Lab, POB 500, Batavia, IL 60510 USA.
EM ggraham@fnal.gov
CR [Anonymous], ENABLING GRIDS E SCI
Berman F., 2003, GRID COMPUTING MAKIN
Bhatia D, 1997, CONCURRENCY-PRACT EX, V9, P555, DOI 10.1002/(SICI)1096-9128(199706)9:6<555::AID-CPE308>3.0.CO;2-X
FOSTER I, 2002, P 14 INT C SCI STAT
Foster I, 2004, GRID BLUEPRINT NEW C
GRAHAM GE, 2003, P CHEP 2003 TUCT007
Gropp W, 1996, USING MPI PORTABLE P
KEAYS R, 2002, THESIS U QUEENSLAND
Pierce B., 1991, BASIC CATEGORY THEOR
RAMAN R, 1998, P 7 IEEE INT S HIGH
VONLASZEEWSI G, 2004, P 37 HAW INT C SYST
OPEN CI GRID
GRID2003
NR 13
TC 0
Z9 0
U1 0
U2 0
PU JOHN WILEY & SONS LTD
PI CHICHESTER
PA THE ATRIUM, SOUTHERN GATE, CHICHESTER PO19 8SQ, W SUSSEX, ENGLAND
SN 1532-0626
J9 CONCURR COMP-PRACT E
JI Concurr. Comput.-Pract. Exp.
PD AUG 25
PY 2006
VL 18
IS 10
BP 1277
EP 1292
DI 10.1002/cpe.989
PG 16
WC Computer Science, Software Engineering; Computer Science, Theory &
Methods
SC Computer Science
GA 074IC
UT WOS:000239804900016
DA 2021-04-21
ER
PT J
AU Rickett, CD
Choi, SE
Rasmussen, CE
Sottile, MJ
AF Rickett, Christopher D.
Choi, Sung-Eun
Rasmussen, Craig E.
Sottile, Matthew J.
TI Rapid prototyping frameworks for developing scientific applications: A
case study
SO JOURNAL OF SUPERCOMPUTING
LA English
DT Article; Proceedings Paper
CT 5th Symposium of the Los-Alamos-Computer-Science-Institute
CY OCT 12-14, 2004
CL Santa Fe, NM
SP Los Alamos Comp Sci Inst
DE components; CCA; python
AB In this paper, we describe a Python-based framework for the rapid prototyping of scientific applications. A case study was performed using a problem specification developed for Marmot, a project at the Los Alamos National Laboratory aimed at re-factoring standard physics codes into reusable and extensible components. Components were written in Python, ZPL, Fortran, and C++ following the Marmot component design. We evaluate our solution both qualitatively and quantitatively by comparing it to a single-language version written in C.
C1 S Dakota Sch Mines & Technol, Rapid City, SD 57701 USA.
Los Alamos Natl Lab Los Alamos, Los Alamos, NM 87545 USA.
RP Rickett, CD (corresponding author), S Dakota Sch Mines & Technol, Rapid City, SD 57701 USA.
EM Christopher.Rickett@gold.sdsmt.edu; sungeun@lanl.gov;
crasmussen@lanl.gov; matt@lanl.gov
RI Sottile, Matthew/J-1386-2019
OI Sottile, Matthew/0000-0001-7436-5246
CR Armstrong R., 1999, Proceedings. The Eighth International Symposium on High Performance Distributed Computing (Cat. No.99TH8469), P115, DOI 10.1109/HPDC.1999.805289
BEAZLEY DM, 2003, FUTURE GENERATION CO, V19
Chamberlain B.L., 2001, THESIS U WASHINGTON
DEMMEL JW, 2003, LBNL44289
DICKENSON RE, 2002, CAN WE ADV OUR WEATH, V83
GEUS R, 2003, PYCON DC 2003
Godunov S.K, 1959, MATTHEW, V47, P271
KOHN S, 2001, P 10 SIAM C PAR PROC
LOWRIE R, 2003, LAUR044031 LOS AL NA
LOWRIE RB, 2004, LAUR044032 LOS AL NA
LOWRIE RB, 2003, LAUR044035 LOS AL NA
Peterson P., F2PY FORTRAN PYTHON
PYRE A, PYTHON FRAMEWORK
RASMUSSEN C, 2001, P LOS AL COMP SCI S
*SCIRUN, 2002, SCI COMP PROBL SOLV
SNYDER L, 1999, PROGRAMMING GUIDE ZP
NR 16
TC 7
Z9 7
U1 0
U2 1
PU SPRINGER
PI DORDRECHT
PA VAN GODEWIJCKSTRAAT 30, 3311 GZ DORDRECHT, NETHERLANDS
SN 0920-8542
EI 1573-0484
J9 J SUPERCOMPUT
JI J. Supercomput.
PD MAY
PY 2006
VL 36
IS 2
BP 123
EP 134
DI 10.1007/s11227-006-7953-6
PG 12
WC Computer Science, Hardware & Architecture; Computer Science, Theory &
Methods; Engineering, Electrical & Electronic
SC Computer Science; Engineering
GA 042DA
UT WOS:000237506300003
DA 2021-04-21
ER
PT S
AU Murakami, K
Yoshida, H
AF Murakami, K.
Yoshida, H.
GP IEEE
TI A Geant4-Python Interface: Development and Its Applications
SO 2006 IEEE NUCLEAR SCIENCE SYMPOSIUM CONFERENCE RECORD, VOL 1-6
SE IEEE Nuclear Science Symposium and Medical Imaging Conference
LA English
DT Proceedings Paper
CT 15th International Workshop on Room-Temperature Semiconductor X- and
Gamma-Ray Detectors/ 2006 IEEE Nuclear Science Symposium
CY OCT 29-NOV 04, 2006
CL San Diego, CA
SP IEEE
DE Geant4; Python; Scripting; ROOT; GUI
AB We present a Geant4-Python interface called "Geant4Py", which provides a set of Python modules for using Geant4 on Python. Also we show various applications using Geant4Py, including compile-free scripts, on-line histogramming analysis with ROOT, web applications, GUI applications and tools for physics validation, educational uses and medical simulation, discussing runtime performance which can be tuned between execution speed and interactivity for each use-case. These applications show the flexibility and usefulness of dynamic configuration of user applications using Python.
C1 [Murakami, K.] High Energy Accelerator Res Org KEK, Tsukuba, Ibaraki, Japan.
[Yoshida, H.] Univ Educ, Naruto, Japan.
RP Murakami, K (corresponding author), High Energy Accelerator Res Org KEK, Tsukuba, Ibaraki, Japan.
EM Koichi.Murakami@kek.jp
OI Murakami, Koichi/0000-0001-5777-5796
FU CREST of Japan Science and Technology Agency (JST)Japan Science &
Technology Agency (JST)Core Research for Evolutional Science and
Technology (CREST)
FX Manuscript received November 27, 2006. This work was supported in part
by CREST of Japan Science and Technology Agency (JST).
CR Agostinelli S, 2003, NUCL INSTRUM METH A, V506, P250, DOI 10.1016/S0168-9002(03)01368-8
Allison J, 2006, IEEE T NUCL SCI, V53, P270, DOI 10.1109/TNS.2006.869826
NR 2
TC 2
Z9 2
U1 0
U2 2
PU IEEE
PI NEW YORK
PA 345 E 47TH ST, NEW YORK, NY 10017 USA
SN 1095-7863
BN 978-1-4244-0561-9
J9 IEEE NUCL SCI CONF R
PY 2006
BP 98
EP 100
DI 10.1109/NSSMIC.2006.356115
PG 3
WC Engineering, Electrical & Electronic; Physics, Applied
SC Engineering; Physics
GA BUC53
UT WOS:000288875600018
DA 2021-04-21
ER
PT J
AU Pfeiffer, A
Moneta, L
Innocente, V
Lee, HC
Ueng, WL
AF Pfeiffer, A
Moneta, L
Innocente, V
Lee, HC
Ueng, WL
TI The LCG PI project: Using interfaces for physics data analysis
SO IEEE TRANSACTIONS ON NUCLEAR SCIENCE
LA English
DT Article
AB In the context of the LHC computing grid (LCG) project, the applications area develops and maintains that part of the physics applications software and associated infrastructure that is shared among the LHC experiments. The "physicist interface" (PI) project of the LCG application area encompasses the interfaces and tools by which physicists will directly use the software, providing implementations based on agreed standards like the analysis systems subsystem (AIDA) interfaces for data analysis. In collaboration with users from the experiments, work has started with implementing the AIDA interfaces for (binned and unbinned) histogramming, fitting and minimization as well as manipulation of tuples. These implementations have been developed by re-using existing packages either directly or by using a (thin) layer of wrappers. In addition, bindings of these interfaces to the Python interpreted language have been done using the dictionary subsystem of the LCG applications area/SEAL project. The actual status and the future planning of the project will be presented.
C1 CERN, CH-1211 Geneva 23, Switzerland.
Acad Sinica, Taipei 115, Taiwan.
RP Pfeiffer, A (corresponding author), CERN, CH-1211 Geneva 23, Switzerland.
EM Andreas.Pfeiffer@cern.ch
CR BRUN R, 1996, P AIHEPN 96 LAUS SWI
CHYTRACEK R, 2003, P ACAT WORKSH TSUK J, V534, P115
Cirrone G. A. P., 2004, IEEE T NUCL SCI, V51
DONSZELMANN M, 2004, P CHEP 04 INT SWITZ, P445
Gamma E., 1995, DESIGN PATTERNS
PAPADOPOULOS I, 2004, P CHEP 04 INT SWITZ, P475
PFEIFFER A, 2003, P ACAT WORKSH TSUK J, V534, P106
VANROSSURN G, 2003, INTRO PYTHON
NR 8
TC 6
Z9 5
U1 0
U2 2
PU IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
PI PISCATAWAY
PA 445 HOES LANE, PISCATAWAY, NJ 08855 USA
SN 0018-9499
J9 IEEE T NUCL SCI
JI IEEE Trans. Nucl. Sci.
PD DEC
PY 2005
VL 52
IS 6
BP 2823
EP 2826
DI 10.1109/TNS.2005.860150
PN 2
PG 4
WC Engineering, Electrical & Electronic; Nuclear Science & Technology
SC Engineering; Nuclear Science & Technology
GA 012WP
UT WOS:000235371900016
OA Green Published
DA 2021-04-21
ER
PT B
AU Bunn, J
van Lingen, F
Newman, H
Steenberg, C
Thomas, M
Ali, A
Anjum, A
Azim, T
Khan, F
Rehman, WU
McClatchey, R
In, JU
AF Bunn, J
van Lingen, F
Newman, H
Steenberg, C
Thomas, M
Ali, A
Anjum, A
Azim, T
Khan, F
Rehman, WU
McClatchey, R
In, JU
GP IEEE Comp Soc
TI JClarens: A Java framework for developing and deploying web services for
grid computing
SO 2005 IEEE International Conference on Web Services, Vols 1 and 2,
Proceedings
LA English
DT Proceedings Paper
CT IEEE International Conference on Services Computing
CY JUL 11-15, 2005
CL Orlando, FL
SP IEEE Comp Soc Tech Comm Serv Comp, BEA Syst, IEEE, IBM Res, IEEE IT Profess Magazine
AB High Energy Physics (HEP) and other scientific communities have adopted Service Oriented Architectures (SOA) [1][2] as part of a larger Grid computing effort. This effort involves the integration of many legacy applications and programming libraries into a SOA framework. The Grid Analysis Environment (GAE) [3] is such a service oriented architecture based on the Clarens Grid Services Framework [4][5] and is being developed as part of the Compact Muon Solenoid (CMS) [61 experiment at the Large Hadron Collider (LHC) [7] at European Laboratory for Particle Physics (CERN) [8]. Clarens provides a set of authorization, access control, and discovery services, as well as XMLRPC and SOAP access to all deployed services. Two implementations of the Clarens Web Services Framework (Python and Java) offer integration possibilities for a wide range of programming languages. This paper describes the Java implementation of the Clarens Web Services Framework called 'JClarens.' and several web services of interest to the scientific and Grid community that have been deployed using JClarens.
C1 CALTECH, Pasadena, CA 91125 USA.
RP Bunn, J (corresponding author), CALTECH, Pasadena, CA 91125 USA.
RI McClatchey, Richard H/M-4183-2015
OI McClatchey, Richard H/0000-0002-0042-5960; Anjum,
Ashiq/0000-0002-3378-1152
CR AKARSU E, 1998, WEBFLOW HIGH LEVEL P
AKARSU E, 1999, 8 IEEE INT S HIGH PE
Ali A, 2004, IEEE INTERNATIONAL CONFERENCE ON WEB SERVICES, PROCEEDINGS, P716, DOI 10.1109/ICWS.2004.1314803
BALLINTIJN M, 2004, P CHEP
BUYYA R, GRIDBUS TOOLKIT ENAB
CASANOVA H, 1997, INT J SUPERCOMPUTING, V11
Foster I, 1997, INT J SUPERCOMPUT AP, V11, P115, DOI 10.1177/109434209701100205
FOSTER I, 2002, 14 INT C SCI STAT DA
GRIMSHAW A, 1997, COMMUNICATIONS ACM, V40
IN J, 2004, P COMP HIGH EN PHYS
IN JU, 2005, P 19 IEEE INT PAR DI
JOHNSTON W, 1999, 8 IEEE INT S HIGH PE
LEGRAND I, INT WORKSH ADV COMP
STEENBERG C, 2004, CLARENS GRID ENABLED
TEENBERG C, 2003, CLARENS WEB SERVICE
VANLINGEN F, 2004, GRID ENABLED ANAL AR
NR 16
TC 2
Z9 2
U1 0
U2 1
PU IEEE COMPUTER SOC
PI LOS ALAMITOS
PA 10662 LOS VAQUEROS CIRCLE, PO BOX 3014, LOS ALAMITOS, CA 90720-1264 USA
BN 0-7695-2409-5
PY 2005
BP 141
EP 148
PG 8
WC Computer Science, Information Systems
SC Computer Science
GA BCU72
UT WOS:000231306500016
OA Green Accepted
DA 2021-04-21
ER
PT S
AU Jourdren, H
AF Jourdren, H
BE Plewa, T
Linde, T
Weirs, VG
TI HERA: A hydrodynamic AMR platform for multi-physics Simulations
SO ADAPTIVE MESH REFINEMENT - THEORY AND APPLICATIONS
SE LECTURE NOTES IN COMPUTATIONAL SCIENCE AND ENGINEERING
LA English
DT Proceedings Paper
CT Workshop on Adaptive Mesh Refinement Methods
CY SEP 03-05, 2003
CL Univ Chicago, Chicago, IL
HO Univ Chicago
AB The development at CEA/DAM of a new AMR multi-physics hydrocode platform led to convincing results on a wide range of applications, from interface instabilities to charge computations in detonics.
In this paper, we focus on:
1. A selection of numerical results illustrating gains to be expected from AMR in such fields, including precise comparisons between AMR and uniform grids (up to 100 millions cells in 2D using CEA's teraflops machine TERA-1).
2. An introduction to the hyperbolic framework and resulting suite of consistent multimaterial compressible flow solvers (hydrodynamics, hypo-elasticity, nT hydro and nT MHD).
3. A presentation of an innovative hydrocode architecture, allowing three different parallel modes at runtime: (i) a MPI mode for uniform or well-balanced AMR grids, (ii) a multithread mode on SMPs and (iii) a hybrid MPI/multithread mode on clusters of SMPs. Multithreading is used there to diminish grain sizes, to control memory cache effects and dynamic load balancing.
4. Finally, an overview of the user-model API is given, in both C++ and Python vector modes, for platform extensions using Strang-type operator splitting.
C1 CEA, DAM Ile France, Dept Sci Simulat & Informat, F-91680 Bruyeres Le Chatel, France.
RP Jourdren, H (corresponding author), CEA, DAM Ile France, Dept Sci Simulat & Informat, BP 12, F-91680 Bruyeres Le Chatel, France.
CR Despres B, 2001, NUMER MATH, V89, P99, DOI 10.1007/s002110000242
Gentile NA, 2001, J COMPUT PHYS, V172, P543, DOI 10.1006/jcph.2001.6836
GERASIMOV BL, 1982, LECT NOTE PHYS, V170, P211
GITTINGS ML, 1992, DEF NUCL AG NUM METH
Godunov S.K, 1959, MATTHEW, V47, P271
LAGOUTIRE F, 2000, THESIS U PARIS 6
VONNEUMANN J, 1950, J APPL PHYS, V21, P232, DOI 10.1063/1.1699639
YOUNGS D, AWRE449235
NR 8
TC 19
Z9 19
U1 0
U2 2
PU SPRINGER-VERLAG BERLIN
PI BERLIN
PA HEIDELBERGER PLATZ 3, D-14197 BERLIN, GERMANY
SN 1439-7358
BN 3-540-21147-0
J9 LECT NOTES COMP SCI
PY 2005
VL 41
BP 283
EP 294
PG 12
WC Astronomy & Astrophysics; Mathematics, Applied
SC Astronomy & Astrophysics; Mathematics
GA BBP86
UT WOS:000226971600019
DA 2021-04-21
ER
PT B
AU Clarke, JA
Mark, ER
AF Clarke, JA
Mark, ER
GP IEEE Comp Soc
TI Extending post-processing and runtime capabilities of the CTH shock
physics code
SO Proceedings of the HPCMP, Users Group Conference 2005
LA English
DT Proceedings Paper
CT Annual Conference on High Performance Computing Modernization Program
CY JUN 27, 2005-JUN 30, 2006
CL Nashville, TN
SP DoD Sci & Technol Comm, User Advocacy Grp, HPCMPO Outreach Team, US Dept Defense, UGC
AB CTH is a multi-material, large deformation, strong shock wave, solid mechanics code that runs on most UNIX workstations and MPP supercomputers. CTH is one of the most heavily used computational structural mechanics codes on DoD High Performance Computing (HPC) platforms. While CTH includes some internal graphics capabilities, it is preferable to take advantage of widely used scientific visualization packages like EnSight and Para View to analyze the results of calculations. A new method has been devised that extends the capabilities of CTH to allow three dimensional polygonal models to be written directly from a running calculation in a format compatible to both EnSight and ParaView. Additionally, an interpreter for the scripting language Python has been embedded into CTH, and it's post-processor Spymaster. Embedded Python allows for almost limitless, parallel capabilities to be added that do not require a recompilation or relinking of the CTH executable. Examples of these capabilities include one and two way code coupling, and Behind Armor Debris (BAD) applications.
C1 USA, Res Lab, Aberdeen Proving Ground, MD 21010 USA.
RP Clarke, JA (corresponding author), USA, Res Lab, Aberdeen Proving Ground, MD 21010 USA.
CR BEAZLEY DM, 1997, P 6 INT PYTH C SAN J, P21
Clarke JA, 2002, CONCURR COMP-PRACT E, V14, P1161, DOI 10.1002/cpe.685
Clarke JA, 1997, IEEE COMPUT SCI ENG, V4, P55, DOI 10.1109/99.590858
Cummings J, 2002, J SUPERCOMPUT, V23, P39, DOI 10.1023/A:1015733102520
LITTLEFIELD DL, 2001, BRIEF DESCRIPTION NW
NR 5
TC 1
Z9 1
U1 0
U2 2
PU IEEE COMPUTER SOC
PI LOS ALAMITOS
PA 10662 LOS VAQUEROS CIRCLE, PO BOX 3014, LOS ALAMITOS, CA 90720-1264 USA
BN 0-7695-2496-6
PY 2005
BP 300
EP 303
PG 4
WC Computer Science, Interdisciplinary Applications; Multidisciplinary
Sciences
SC Computer Science; Science & Technology - Other Topics
GA BEA11
UT WOS:000236397600046
DA 2021-04-21
ER
PT S
AU Yeh, P
AF Yeh, P
BE Metzler, SD
TI Noodle: An environment for HENP data processing and analysis
SO 2003 IEEE NUCLEAR SCIENCE SYMPOSIUM, CONFERENCE RECORD, VOLS 1-5
SE IEEE Nuclear Science Symposium and Medical Imaging Conference
LA English
DT Proceedings Paper
CT IEEE Nuclear Science Symposium/Medical Imaging Conference
CY OCT 19-25, 2003
CL Portland, OR
SP IEEE, Nucl & Plasma Sci Soc
DE object-oriented; scripting environment; python; data processing
AB Noodle is an object-oriented environment for typical data processing in high energy and nuclear physics where data processing modules written in C++ can be dynamically loaded with ease. The primary design goal is to allow physicists to concentrate on data, logic and physics rather than tedious programming. It provides an intuitive object-oriented scripting interface in python language to control module parameters and data flow. Noodle also provides dependency resolution to encourage data versioning. The usecases and system architecture will be shown.
C1 Natl Taiwan Univ, Dept Phys, Taipei 106, Taiwan.
RP Yeh, P (corresponding author), Natl Taiwan Univ, Dept Phys, Taipei 106, Taiwan.
CR Brun R, 1997, NUCL INSTRUM METH A, V389, P81, DOI 10.1016/S0168-9002(97)00048-X
NR 1
TC 0
Z9 0
U1 0
U2 0
PU IEEE
PI NEW YORK
PA 345 E 47TH ST, NEW YORK, NY 10017 USA
SN 1095-7863
BN 0-7803-8257-9
J9 IEEE NUCL SCI CONF R
PY 2004
BP 821
EP 823
PG 3
WC Nuclear Science & Technology; Imaging Science & Photographic Technology;
Radiology, Nuclear Medicine & Medical Imaging
SC Nuclear Science & Technology; Imaging Science & Photographic Technology;
Radiology, Nuclear Medicine & Medical Imaging
GA BAS86
UT WOS:000223398000180
DA 2021-04-21
ER
PT S
AU Innocente, V
Lee, HC
Moneta, L
Pfeiffer, A
Ueng, WL
AF Innocente, V
Lee, HC
Moneta, L
Pfeiffer, A
Ueng, WL
BE Seibert, JA
TI The LCG PI project: Using interfaces for physics data analysis
SO 2004 IEEE NUCLEAR SCIENCE SYMPOSIUM CONFERENCE RECORD, VOLS 1-7
SE IEEE Nuclear Science Symposium and Medical Imaging Conference
LA English
DT Proceedings Paper
CT Nuclear Science Symposium/Medical Imaging Conference
CY OCT 16-22, 2004
CL Rome, ITALY
SP IEEE Commun Soc, Nucl & Plasma Sci Soc
AB In the context of the LHC Computing Grid (LCG) project, the Applications Area develops and maintains that part of the physics applications software and associated infrastructure that is shared among the LHC experiments. The "Physicist Interface" (PI) project of the LCG Application Area encompasses the interfaces and tools by which physicists will directly use the software, providing implementations based on agreed standards like the AIDA interfaces for data analyis. In collaboration with users from the experiments, work has started with implementing the AIDA interfaces for (binned and unbinned) histogramming, fitting and minimization as well as manipulation of tuples. These implementations have been developed by re-using existing packages either directly or by using a (thin) layer of wrappers. In addition, bindings of these interfaces to the Python interpreted language have been done using the dictionary subsystem of the LCG-AA/SEAL project. The actual status and the future planning of the project will be presented.
C1 CERN, CH-1211 Geneva, Switzerland.
RP Innocente, V (corresponding author), CERN, CH-1211 Geneva, Switzerland.
EM Andreas.Pfeiffer@cern.ch
OI Pfeiffer, Andreas/0000-0001-5328-448X
CR Cirrone GAP, 2004, IEEE T NUCL SCI, V51, P2056, DOI 10.1109/tns.2004.836124
Gamma E., 1995, DESIGN PATTERNS
MONETA L, 2003, P ACAT WORKSH DEC
SERBO V, 2004, P CHEP 2004 C SEP IN
NR 4
TC 0
Z9 0
U1 0
U2 0
PU IEEE
PI NEW YORK
PA 345 E 47TH ST, NEW YORK, NY 10017 USA
SN 1095-7863
BN 0-7803-8700-7
J9 IEEE NUCL SCI CONF R
PY 2004
BP 2082
EP 2085
PG 4
WC Nuclear Science & Technology; Physics, Nuclear; Radiology, Nuclear
Medicine & Medical Imaging
SC Nuclear Science & Technology; Physics; Radiology, Nuclear Medicine &
Medical Imaging
GA BCZ05
UT WOS:000232002103015
DA 2021-04-21
ER
PT B
AU Ali, A
Anjum, A
Azim, T
Thomas, M
Steenberg, C
Newman, H
Bunn, J
Haider, R
Rehman, WU
AF Ali, A
Anjum, A
Azim, T
Thomas, M
Steenberg, C
Newman, H
Bunn, J
Haider, R
Rehman, WU
GP IEEE Computer Society
TI JClarens: A Java based interactive physics analysis environment for data
intensive applications
SO IEEE INTERNATIONAL CONFERENCE ON WEB SERVICES, PROCEEDINGS
LA English
DT Proceedings Paper
CT IEEE International Conference on Web Services (ICWS 2004)
CY JUL 06-09, 2004
CL San Diego, CA
SP IEEE, TCSC
AB In this paper we describe JClarens; a Java based implementation of the Clarens remote data server. JClarens provides web services for an interactive analysis environment to dynamically access and analyze the tremendous amount of data scattered across various locations. Additionally this research is aimed to develop a service oriented Grid Enabled Portal (GEP) that provides interface and access to several Grid services to give a homogeneous and optimized view of the distributed and heterogeneous environment. Other than showing platform independent behavior provided by Java, the use of XML-RPC based Web Services enabled JClarens to be a language neutral server and demonstrated interoperability with its Python variant. Extreme care has been taken in the usage and manipulation of various Java libraries to cater the needs of high performance computing. The overall exercise has yielded in a prototype with strong emphasis on security and virtual organization management (VOM). This shall provide a common platform to support development of larger, more flexible framework with future aims to integrate it with a loosely coupled, decentralized, and autonomous framework for Grid enabled Analysis Environment (GAE).
C1 Natl Univ Sci & Technol, Rawalpindi, Pakistan.
RP Ali, A (corresponding author), Natl Univ Sci & Technol, Rawalpindi, Pakistan.
OI Anjum, Ashiq/0000-0002-3378-1152
CR ASHIQ A, 2003, GRID COOP C SHANGH
DULLMANN D, 2003, COMPUTING HIGH ENERG
Foster I, 2001, INT J HIGH PERFORM C, V15, P200, DOI 10.1177/109434200101500302
Foster I, 2002, PHYSL GRID OPEN GRID
STEENBERG CD, 2003, P CHEP 2003
NR 5
TC 1
Z9 1
U1 0
U2 0
PU IEEE COMPUTER SOC
PI LOS ALAMITOS
PA 10662 LOS VAQUEROS CIRCLE, PO BOX 3014, LOS ALAMITOS, CA 90720-1264 USA
BN 0-7695-2167-3
PY 2004
BP 716
EP 723
DI 10.1109/ICWS.2004.1314803
PG 8
WC Computer Science, Information Systems; Computer Science,
Interdisciplinary Applications
SC Computer Science
GA BAP71
UT WOS:000223166900084
OA Green Accepted
DA 2021-04-21
ER
PT J
AU Akasaka, N
Akiyama, A
Araki, S
Furukawa, K
Katoh, T
Kawamoto, T
Komada, I
Kudo, K
Naito, T
Nakamura, T
Odagiri, J
Ohnishi, Y
Sato, M
Suetake, M
Takeda, S
Takeuchi, Y
Yamamoto, N
Yoshioka, M
Kikutani, E
AF Akasaka, N
Akiyama, A
Araki, S
Furukawa, K
Katoh, T
Kawamoto, T
Komada, I
Kudo, K
Naito, T
Nakamura, T
Odagiri, J
Ohnishi, Y
Sato, M
Suetake, M
Takeda, S
Takeuchi, Y
Yamamoto, N
Yoshioka, M
Kikutani, E
TI KEKB accelerator control system
SO NUCLEAR INSTRUMENTS & METHODS IN PHYSICS RESEARCH SECTION A-ACCELERATORS
SPECTROMETERS DETECTORS AND ASSOCIATED EQUIPMENT
LA English
DT Article
DE accelerator control; control computer system; EPICS; safety control
system; timing system
AB The KEKB accelerator control system including a control computer system, a timing distribution system, and a safety control system are described. KEKB accelerators were installed in the same tunnel where the TRISTAN accelerator was. There were some constraints due to the reused equipment. The control system is based on Experimental Physics and Industrial Control System (EPICS). In order to reduce the cost and labor for constructing the KEKB control system, as many CAMAC modules as possible are used again. The guiding principles of the KEKB control computer system are as follows: use EPICS as the controls environment, provide a two-language system for developing application programs, use VMEbus as frontend computers as a consequence of EPICS, use standard buses, such as CAMAC, GPIB, VXIbus, ARCNET, RS-232 as field buses and use ergonomic equipment for operators and scientists. On the software side, interpretive Python and SAD languages are used for coding application programs. The purpose of the radiation safety system is to protect personnel from radiation hazards. It consists of an access control system and a beam interlock system. The access control system protects people from strong radiation inside the accelerator tunnel due to an intense beam, by controlling access to the beamline area. On the other hand, the beam interlock system prevents people from radiation exposure by interlocking the beam operation. For the convenience of accelerator operation and access control, the region covered by the safety system is divided into three major access control areas: the KEKB area, the PF-AR area, and the beam-transport (BT) area. The KEKB control system required a new timing system to match a low longitudinal acceptance due to a low-alpha machine. This timing system is based on a frequency divider/multiply technique and a digital delay technique. The RF frequency of the KEKB rings and that of the injector Linac are locked with a common divisor frequency. The common divisor frequency determines the injection timing. The RF bucket selection system is also described. (C) 2002 Elsevier Science B.V. All rights reserved.
C1 High Energy Accelerator Res Org, KEK, Tsukuba, Ibaraki 3050801, Japan.
RP Katoh, T (corresponding author), High Energy Accelerator Res Org, KEK, Oho 1-1, Tsukuba, Ibaraki 3050801, Japan.
RI Kikutani, Eiji/E-8263-2012; Kikutani, Eiji/AAC-6587-2019; Furukawa,
Kazuro/C-2639-2011
OI Kikutani, Eiji/0000-0003-1518-8045; Kikutani, Eiji/0000-0003-1518-8045;
Furukawa, Kazuro/0000-0003-4187-2836
CR Akiyama A., 1999, Proceedings of the 1999 Particle Accelerator Conference (Cat. No.99CH36366), P343, DOI 10.1109/PAC.1999.795700
AKIYAMA A, 1997, P ICALEPCS 97 BEIJ C, P243
CROWLEYMILLING MC, 1974, IICO742 CERN LAB
DALESIO L, 1995, P ICALEPCS 95 CHIC U
DALESIO LR, 1993, P INT C ACC LARG EXP, P179
KAJI M, 1998, P 1 AS PART ACC C AP, P501
KATOH T, 1995, CONTROL SYSTEM DESIG, P2205
KATOH T, 1997, HARIMA SCI GARDEN CI, P452
KATOH T, 1998, P 1 AS PART ACC C TS, P504
KATOH T, 1999, P 7 INT C ACC LARG E, P101
KATOH T, 1987, COMMUNICATION SYSTEM, P67
KATOH T, 1999, P 7 INT C ACC LARG E, P214
KATOH T, 1994, P CTDCA VECC CALC DE
KATOH T, 2001, P WORKSH ACC OP 2001, P5
KATOH T, 1997, P ICALEPCS97 BEIJ CH, P15
*KEKB DES GROUP, 1995, 957 KEKB DES GROUP
KIKUTANI E, 1999, P 7 INT C ACC LARG E, P278
KRAIMER M, 1999, ICALEPCS 99 TRIEST I
LUTZ M, 1996, PROGRAMMING PYTHON
NAITO T, 1997, P ICALEPCS 97 BEIJ N, P247
NAKAMURA TT, 1999, P 7 ITN C ACC LARG E, P406
NAKAMURA TT, 2000, P 7 EUR PART ACC C E, P1865
ODAGIRI J, 1999, P 7 INT C ACC LARG E, P367
Odagiri J., 1997, P ICALEPCS 97 BEIJ C, P240
ODAGIRI JI, 2001, P PAC2001 CHIC ILL U
OUSTERHOUT JK, HIGHER LEVEL PROGRAM
*PCL, PCL PROC CONTR LANG
TAKEDA S, 1987, 1987 PART ACC C MARC
VANROSSUM G, PHYTHON TUTORIAL
*VIST CONTR SYST I, VSYST SOFTW PROD
Wolfram S., 1991, MATH SYSTEM DOING MA, V2nd ed.
YAMAMOTO N, 1883, P EPAC 2000 VIENN AU
YAMAMOTO N, 1998, P 1 AS PART ACC C TS, P498
YAMAMOTO N, 1999, P 7 INT C ACC LARG E, P600
NR 34
TC 9
Z9 9
U1 1
U2 9
PU ELSEVIER SCIENCE BV
PI AMSTERDAM
PA PO BOX 211, 1000 AE AMSTERDAM, NETHERLANDS
SN 0168-9002
J9 NUCL INSTRUM METH A
JI Nucl. Instrum. Methods Phys. Res. Sect. A-Accel. Spectrom. Dect. Assoc.
Equip.
PD FEB 21
PY 2003
VL 499
IS 1
BP 138
EP +
AR PII S0168-9002(02)01786-2
DI 10.1016/S0168-9002(02)01786-2
PG 30
WC Instruments & Instrumentation; Nuclear Science & Technology; Physics,
Nuclear; Physics, Particles & Fields
SC Instruments & Instrumentation; Nuclear Science & Technology; Physics
GA 652EA
UT WOS:000181365800009
DA 2021-04-21
ER
PT S
AU Backer, A
AF Backer, A
BE DegliEsposti, M
Graffi, S
TI Numerical aspects of eigenvalue and eigenfunction computations for
chaotic quantum systems
SO MATHEMATICAL ASPECTS OF QUANTUM MAPS
SE Lecture Notes in Physics
LA English
DT Proceedings Paper
CT Summer School on the Mathematical Aspects of Quantum Maps
CY SEP 01-11, 2001
CL UNIV BOLOGNA, BOLOGNA, ITALY
HO UNIV BOLOGNA
ID STATISTICAL PROPERTIES; SPECTRAL STATISTICS; HELMHOLTZ EQUATION;
INTEGRAL-EQUATION; PERIODIC-ORBITS; SMOOTH BOUNDARY; 3D BILLIARD;
QUANTIZATION; MAPS; DIRICHLET
AB We give an introduction to some of the numerical aspects in quantum chaos. The classical dynamics of two-dimensional area-preserving maps on the torus is illustrated using the standard map and a perturbed cat map. The quantization of area-preserving maps given by their generating function is discussed and for the computation of the eigenvalues a computer program in Python is presented. We illustrate the eigenvalue distribution for two types of perturbed cat maps, one leading to COE and the other to CUE statistics. For the eigenfunctions of quantum maps we study the distribution of the eigenvectors and compare them with the corresponding random matrix distributions. The Husimi representation allows for a direct comparison of the localization of the eigenstates in phase space with the corresponding classical structures. Examples for a perturbed cat map and the standard map with different parameters are shown.
Billiard systems and the corresponding quantum billiards are another important class of systems (which are also relevant to applications, for example in mesoscopic physics). We provide a detailed exposition of the boundary integral method, which is one important method to determine the eigenvalues and eigenfunctions of the Helmholtz equation. We discuss several methods to determine the eigenvalues from the Fredholm equation and illustrate them for the stadium billiard. The occurrence of spurious solutions is discussed in detail and illustrated for the circular billiard, the stadium billiard, and the annular sector billiard.
We emphasize the role of the normal derivative function to compute the normalization of eigenfunctions, momentum representations or autocorrelation functions in a very efficient and direct way. Some examples for these quantities are given and discussed.
C1 Univ Ulm, Theoret Phys Abt, D-89081 Ulm, Germany.
RP Backer, A (corresponding author), Univ Ulm, Theoret Phys Abt, Albert Einstein Allee 11, D-89081 Ulm, Germany.
OI Backer, Arnd/0000-0002-4321-8099
CR Abramowitz M., 1984, POCKETBOOK MATH FUNC
Arnold V. I., 1968, ERGODIC PROBLEMS CLA
AURICH R, 1993, PHYSICA D, V64, P185, DOI 10.1016/0167-2789(93)90255-Y
Aurich R, 1996, PHYSICA D, V92, P101, DOI 10.1016/0167-2789(95)00278-2
AURICH R, COMMUNICATION
Backer A, 1997, J PHYS A-MATH GEN, V30, P1991, DOI 10.1088/0305-4470/30/6/023
Backer A, 2002, J PHYS A-MATH GEN, V35, P539, DOI 10.1088/0305-4470/35/3/307
Backer A, 1999, J PHYS A-MATH GEN, V32, P4795, DOI 10.1088/0305-4470/32/26/301
BACKER A, 1995, PHYS REV E, V52, P2463, DOI 10.1103/PhysRevE.52.2463
BACKER A, 2001, QUANTUM CHAOS QUANTU, P717
BALAZS NL, 1989, ANN PHYS-NEW YORK, V190, P1, DOI 10.1016/0003-4916(89)90259-5
Baltes H.P., 1976, SPECTRA FINITE SYSTE
BERRY MV, 1979, ANN PHYS-NEW YORK, V122, P26, DOI 10.1016/0003-4916(79)90296-3
BERRY MV, 1977, P ROY SOC LOND A MAT, V356, P375, DOI 10.1098/rspa.1977.0140
BERRY MV, 1984, PROC R SOC LON SER-A, V392, P15, DOI 10.1098/rspa.1984.0022
BISWAS D, 1990, PHYS REV A, V42, P3170, DOI 10.1103/PhysRevA.42.3170
BOASMAN PA, 1994, NONLINEARITY, V7, P485, DOI 10.1088/0951-7715/7/2/010
BOASMAN PA, 1995, P R SOC-MATH PHYS SC, V449, P629, DOI 10.1098/rspa.1995.0063
BOASMANN PA, 1992, THESIS H H WILLS PHY
BOGOMOLNY EB, 1992, NONLINEARITY, V5, P805, DOI 10.1088/0951-7715/5/4/001
BOHIGAS O, 1984, PHYS REV LETT, V52, P1, DOI 10.1103/PhysRevLett.52.1
Bouzouina A, 1996, COMMUN MATH PHYS, V178, P83, DOI 10.1007/BF02104909
BRODY TA, 1981, REV MOD PHYS, V53, P385, DOI 10.1103/RevModPhys.53.385
BURMEISTER B, 1995, THESIS U HAMBURG, V2
BURMEISTER B, 1995, UNPUB EXACT TRACE FO
BURTON AJ, 1971, PROC R SOC LON SER-A, V323, P201, DOI 10.1098/rspa.1971.0097
Ciskowski R.D., 1991, BOUNDARY ELEMENT MET
DeBievre S, 1996, COMMUN MATH PHYS, V176, P73, DOI 10.1007/BF02099363
DEBIEVRE S, 2001, CONT MATH, V289
DEBIEVRE S, 1996, PHYSIQUE THEORIQUE, V69, P1
DEGLIESPOSTI M, 1993, ANN I H POINCARE PHY, V58, P33
DEMATOS MB, 1995, ANN PHYS-NEW YORK, V237, P46, DOI 10.1006/aphy.1995.1003
Dietz B, 1993, CHAOS, V3, P581, DOI 10.1063/1.165962
Doron E, 1992, CHAOS, V2, P117, DOI 10.1063/1.165914
DUARTE P, 1994, ANN I H POINCARE-AN, V11, P359
ECKHARDT B, 1986, J PHYS A-MATH GEN, V19, P1823, DOI 10.1088/0305-4470/19/10/023
Eckmann JP, 1997, HELV PHYS ACTA, V70, P44
ESPOSTI MD, 1995, COMMUN MATH PHYS, V167, P471, DOI 10.1007/BF02101532
Giorgilli A, 2000, PHYS LETT A, V272, P359, DOI 10.1016/S0375-9601(00)00452-7
HAAG G, 1999, THESIS U ULM
Haake F., 2001, SPRINGER SERIES SYNE
Hannay J. H., 1980, Physica D, V1D, P267, DOI 10.1016/0167-2789(80)90026-3
HARAYAMA T, 1992, PHYS LETT A, V165, P417, DOI 10.1016/0375-9601(92)90341-I
HELLER EJ, 1984, PHYS REV LETT, V53, P1515, DOI 10.1103/PhysRevLett.53.1515
HELLER EJ, 1991, P 1989 HOUCHES SCH C
HESSE T, 1997, THESIS U ULM
HORNBERGER K, 1999, J PHYS A, V33, P2829
Keating JP, 2000, NONLINEARITY, V13, P747, DOI 10.1088/0951-7715/13/3/313
KEATING JP, 1991, NONLINEARITY, V4, P309, DOI 10.1088/0951-7715/4/2/006
KEATING JP, 1991, NONLINEARITY, V4, P277, DOI 10.1088/0951-7715/4/2/005
Ketzmerick R, 1999, PHYSICA D, V131, P247, DOI 10.1016/S0167-2789(98)00230-9
KLEINMAN RE, 1974, SIAM REV, V16, P214, DOI 10.1137/1016029
Kosztin I, 1997, INT J MOD PHYS C, V8, P293, DOI 10.1142/S0129183197000278
KURLBERG P, 2001, INTERNAT MATH RES NO, P995
KUTTLER JR, 1984, SIAM REV, V26, P163, DOI 10.1137/1026033
LAZUTKIN VF, 2000, RAMARK SOME REMARKS
Li BW, 1998, PHYS REV E, V57, P4095, DOI 10.1103/PhysRevE.57.4095
LI BW, 1994, J PHYS A-MATH GEN, V27, P5509, DOI 10.1088/0305-4470/27/16/017
MARTIN PA, 1982, WAVE MOTION, V4, P391, DOI 10.1016/0165-2125(82)90007-5
MEISS JD, 1992, REV MOD PHYS, V64, P795, DOI 10.1103/RevModPhys.64.795
MEZZADRI F, 1999, THESIS U BRISTOL
Pisani C, 1996, ANN PHYS-NEW YORK, V251, P208, DOI 10.1006/aphy.1996.0113
PORTER CE, 1956, PHYS REV, V104, P483, DOI 10.1103/PhysRev.104.483
PRIMACK H, 1995, PHYS REV LETT, V74, P4831, DOI 10.1103/PhysRevLett.74.4831
PROSEN T, 1994, J PHYS A-MATH GEN, V27, pL459, DOI 10.1088/0305-4470/27/13/001
Prosen T, 1997, PHYS LETT A, V233, P332, DOI 10.1016/S0375-9601(97)00492-1
Prosen T, 1997, PHYS LETT A, V233, P323, DOI 10.1016/S0375-9601(97)00499-4
PROSEN T, 1993, J PHYS A-MATH GEN, V26, P1105, DOI 10.1088/0305-4470/26/5/029
RIDDELL RJ, 1979, J COMPUT PHYS, V31, P42, DOI 10.1016/0021-9991(79)90061-5
RIDDELL RJ, 1979, J COMPUT PHYS, V31, P21, DOI 10.1016/0021-9991(79)90060-3
ROBNIK M, 1984, J PHYS A-MATH GEN, V17, P1049, DOI 10.1088/0305-4470/17/5/027
SARACENO M, 1990, ANN PHYS-NEW YORK, V199, P37, DOI 10.1016/0003-4916(90)90367-W
SCHANZ H, 1995, CHAOS SOLITON FRACT, V5, P1289, DOI 10.1016/0960-0779(94)E0066-X
Sieber M, 1997, PHYS REV E, V55, P2279, DOI 10.1103/PhysRevE.55.2279
SIEBER M, 1990, PHYS LETT A, V148, P415, DOI 10.1016/0375-9601(90)90492-7
SIEBER M, 1993, J PHYS A-MATH GEN, V26, P6217, DOI 10.1088/0305-4470/26/22/022
Sieber M, 1998, NONLINEARITY, V11, P1607, DOI 10.1088/0951-7715/11/6/010
Simonotti FP, 1997, PHYS REV E, V56, P3859, DOI 10.1103/PhysRevE.56.3859
SOMMERFELD A, 1984, VORLESUNGEN THEORETI, V6
STEIL G, 1999, EMERGING APPL NUMBER, P617
Strelcyn J.-M., 1991, COLLOQ MATH, V62, P331
Tasaki S, 1997, PHYS REV E, V56, pR13, DOI 10.1103/PhysRevE.56.R13
TUALLE JM, 1995, CHAOS SOLITON FRACT, V5, P1085, DOI 10.1016/0960-0779(94)E0056-U
VERGINI E, 1995, PHYS REV E, V52, P2204, DOI 10.1103/PhysRevE.52.2204
Zelditch S, 1997, ANN I FOURIER, V47, P305, DOI 10.5802/aif.1568
ZYCZKOWSKI K, 1992, ACTA PHYS POL B, V23, P245
NR 86
TC 32
Z9 32
U1 0
U2 3
PU SPRINGER-VERLAG BERLIN
PI BERLIN
PA HEIDELBERGER PLATZ 3, D-14197 BERLIN, GERMANY
SN 0075-8450
EI 1616-6361
BN 3-540-02623-1
J9 LECT NOTES PHYS
PY 2003
VL 618
BP 91
EP 144
PG 54
WC Mathematics; Physics, Mathematical
SC Mathematics; Physics
GA BW90P
UT WOS:000183559100004
DA 2021-04-21
ER
PT J
AU Cummings, J
Aivazis, M
Samtaney, R
Radovitzky, R
Mauch, S
Meiron, D
AF Cummings, J
Aivazis, M
Samtaney, R
Radovitzky, R
Mauch, S
Meiron, D
TI A virtual test facility for the simulation of dynamic response in
materials
SO JOURNAL OF SUPERCOMPUTING
LA English
DT Article; Proceedings Paper
CT 2nd Symposium of the Los Alamos-Computer-Science-Institute
CY OCT 15-18, 2001
CL SANTA FE, NEW MEXICO
SP Los Alamos Comp Sci Inst
DE parallel computing; shock physics simulation
AB The Center for Simulating Dynamic Response of Materials at the California Institute of Technology is constructing a virtual shock physics facility for studying the response of various target materials to very strong shocks. The Virtual Test Facility (VTF) is an end-to-end, fully three-dimensional simulation of the detonation of high explosives (HE), shock wave propagation, solid material response to pressure loading, and compressible turbulence. The VTF largely consists of a parallel fluid solver and a parallel solid mechanics package that are coupled together by the exchange of boundary data. The Eulerian fluid code and Lagrangian solid mechanics model interact via a novel approach based on level sets. The two main computational packages are integrated through the use of Pyre, a problem solving environment written in the Python scripting language. Pyre allows application developers to interchange various computational models and solver packages without recompiling code, and it provides standardized access to several data visualization engines and data input mechanisms. In this paper, we outline the main components of the VTF, discuss their integration via Pyre, and describe some recent accomplishments in large-scale simulation using the VTF.
C1 CALTECH, Pasadena, CA 91125 USA.
Princeton Plasma Phys Lab, Princeton, NJ 08543 USA.
MIT, Cambridge, MA 02139 USA.
RP Cummings, J (corresponding author), CALTECH, 1200 E Calif Blvd, Pasadena, CA 91125 USA.
RI Radovitzky, Raul A/A-5353-2009
OI Radovitzky, Raul A/0000-0001-6339-2708
CR COHEN RE, 2000, THERMAL EQUATION STA
GLAISTER P, 1988, J COMPUT PHYS, V74, P382, DOI 10.1016/0021-9991(88)90084-8
Godunov S.K, 1959, MATTHEW, V47, P271
GUITINO AM, 2001, UNPUB J COMPUTER AID
LEW A, 2001, UNPUB J COMPUTER AID
MORANO E, UNPUB LEVEL SET APPR
PARASHAR M, 1999, DAGH DATA MANAGEMENT
PULLIN DI, 1980, J COMPUT PHYS, V34, P231, DOI 10.1016/0021-9991(80)90107-2
Samtaney R, 1997, PHYS FLUIDS, V9, P1783, DOI 10.1063/1.869294
SAMTANEY R, 1994, J FLUID MECH, V269, P45, DOI 10.1017/S0022112094001485
VAN LEER B, 1977, J COMPUT PHYS, V23, P276, DOI 10.1016/0021-9991(77)90095-X
NR 11
TC 26
Z9 26
U1 0
U2 4
PU KLUWER ACADEMIC PUBL
PI DORDRECHT
PA VAN GODEWIJCKSTRAAT 30, 3311 GZ DORDRECHT, NETHERLANDS
SN 0920-8542
J9 J SUPERCOMPUT
JI J. Supercomput.
PD AUG
PY 2002
VL 23
IS 1
BP 39
EP 50
DI 10.1023/A:1015733102520
PG 12
WC Computer Science, Hardware & Architecture; Computer Science, Theory &
Methods; Engineering, Electrical & Electronic
SC Computer Science; Engineering
GA 558CF
UT WOS:000175947100004
DA 2021-04-21
ER
PT B
AU Campbell, A
Gerhards, R
Grab, C
Martyniak, J
Mkrtchyan, T
Levonian, S
Nowak, J
AF Campbell, A
Gerhards, R
Grab, C
Martyniak, J
Mkrtchyan, T
Levonian, S
Nowak, J
BE Chen, HS
TI A dataflow meta-computing framework for event processing in the H1
experiment
SO PROCEEDINGS OF CHEP 2001
LA English
DT Proceedings Paper
CT International Conference on Computing in High Energy and Nuclear Physics
(CHEP 01)
CY SEP 03-07, 2001
CL BEIJING, PEOPLES R CHINA
SP Inst High Energy Phys, Chinese Acad Sci, Natl Nat Sci Fdn China
DE dataflow; meta-computing; CORBA; C plus; python; Java; H1
ID HERA
AB Linux based networked PCs clusters are replacing both the VME non uniform direct memory access systems and SMP shared memory systems used previously for the online event filtering and reconstruction.
To allow an optimal use of the distributed resources of PC clusters an open software framework is presently being developed based on a dataflow paradigm for event processing. This framework allows for the distribution of the data of physics events and associated calibration data to multiple computers from multiple input sources for processing and the subsequent collection of the processed events at multiple outputs.
The basis of the system is the event repository, basically a first-in first-out event store which may be read and written in a manner similar to sequential file access. Events are stored in and transferred between repositories as suitably large sequences to enable high throughput. Multiple readers can read simultaneously from a single repository to receive event sequences and multiple writers can insert event sequences to a repository. Hence repositories are used for event distribution and collection.
To support synchronisation of the event flow the repository implements barriers. A barrier must be written by all the writers of a repository before any reader can read the barrier. A reader must read a barrier before it may receive data from behind it. Only after all readers have read the barrier is the barrier removed from the repository. A barrier may also have attached data. In this way calibration data can be distributed to all processing units.
The repositories are implemented as multi-threaded CORBA objects in C++ and CORBA is used for all data transfers. Job setup scripts are written in python and interactive status and histogram display is provided by a Java program. Jobs run under the PBS batch system providing shared use of resources for online triggering, offline mass reprocessing and user analysis jobs.
C1 DESY, D-2000 Hamburg, Germany.
RP Campbell, A (corresponding author), DESY, Notkestr 85, D-2000 Hamburg, Germany.
RI Levonian, Sergey/M-8693-2015
CR Abt I, 1997, NUCL INSTRUM METH A, V386, P310, DOI 10.1016/S0168-9002(96)00893-5
BERTHON U, NEW DATA ANAL ENV H1
BERTHON U, REPROCESSING H1 DATA
BLOBEL V, FPACK GEN STANDALONE
BLOBEL V, LOOK SYSTEM DATA ANA
CAMPBELL AJ, 1992, IEEE T NUCL SCI, V39, P255, DOI 10.1109/23.277493
JOHNSON AS, JAVA ANAL STUDIO
NR 7
TC 0
Z9 0
U1 0
U2 0
PU SCIENCE PRESS
PI MONMOUTH JUNCTION
PA 2031 US HIGHWAY 130 STE F, MONMOUTH JUNCTION, NJ 08852-3014 USA
BN 1-880132-77-X
PY 2001
BP 651
EP 655
PG 3
WC Computer Science, Hardware & Architecture; Computer Science, Information
Systems; Computer Science, Software Engineering
SC Computer Science
GA BU64P
UT WOS:000176592500173
DA 2021-04-21
ER
PT S
AU Pfeiffer, A
AF Pfeiffer, A
BE Vandoni, CE
TI Introduction to the Anaphe/LHC plus plus software suite
SO 2000 CERN SCHOOL OF COMPUTING
SE C E R N REPORTS
LA English
DT Proceedings Paper
CT 2000 CERN School of Computing Conference
CY SEP 17-30, 2000
CL MARATHON, GREECE
SP CERN
AB The Anaphe/LHC++ project is an ongoing effort to provide an Object-Oriented software environment for future HEP experiments. It is based on standards-conforming solutions, together with HEP-specific extensions and components. Data persistence is provided by the Objectivity/DB Object Database (ODBMS), while the visualisation is based on Qt (for 2-D presentation) and OpenInventor (for 3-D). To complement the standard package, a set of C++ class libraries for histogram management, Ntuple-like analysis (based on Objectivity/DB) and for presentation graphics (based on Open Inventor) have been developed. A new tool for physics data analysis, named Lizard, has been developed based on a set of abstract interfaces (as defined by the AIDA project). Its implementation is based on the python scripting language and the existing C++ class libraries of Anaphe/LHC++.
C1 CERN, Geneva, Switzerland.
RP Pfeiffer, A (corresponding author), CERN, Geneva, Switzerland.
CR *CLHEP, CLASS LIB HEP
*GEANT4, TOOLK SIM PASS PART
MOSCICKI JT, MINIMIZATION FITTING
*RD45, PERS OBJ MAN HEP
NR 4
TC 0
Z9 0
U1 0
U2 0
PU C E R N
PI GENEVA
PA MEYRIN, CH-1211 GENEVA, SWITZERLAND
SN 0007-8328
BN 92-9083-178-2
J9 CERN REPORT
PY 2000
VL 2000
IS 13
BP 43
EP 49
PG 7
WC Computer Science, Software Engineering; Computer Science, Theory &
Methods
SC Computer Science
GA BT50N
UT WOS:000173158800003
DA 2021-04-21
ER
EF