Astronomy & Astrophysics manuscript no. aa39587 ©ESO 2020
December 4, 2020
Gaia Early Data Release 3: Photometric content and validation
M. Riello 1, , F. De Angeli 1, D. W. Evans 1, P. Montegriﬀo2, J. M. Carrasco 3, G. Busso 1, L. Palaversa4, 1,
P. W. Burgess1, C. Diener1, M. Davidson5, N. Rowell5, C. Fabricius 3, C. Jordi 3, M. Bellazzini 2, E. Pancino 6, 7,
D. L. Harrison 1, C. Cacciari 2, F. van Leeuwen1, N. C. Hambly 5, S. T. Hodgkin 1, P. J. Osborne1,
G. Altavilla 8, 7, M. A. Barstow 9, A. G. A. Brown 10, M. Castellani 8, S. Cowell1, F. De Luise 11,
G. Gilmore 1, G. Giuﬀrida8, S. Hidalgo 12, G. Holland1, S. Marinoni 8, 7, C. Pagani9, A. M. Piersimoni 11,
L. Pulone 8, S. Ragaini2, M. Rainer 6, P. J. Richards13, N. Sanna6, N. A. Walton 1, M. Weiler3, and A. Yoldas1
1
Institute of Astronomy, University of Cambridge, Madingley Road, Cambridge CB3 0HA, UK
2
INAF – Osservatorio di Astroﬁsica e Scienza dello Spazio di Bologna, via Gobetti 93/3, 40129 Bologna, Italy
3
Institut de Ciències del Cosmos (ICC), Universitat de Barcelona (IEEC-UB), c/ Martí i Franquès, 1, 08028 Barcelona, Spain
4
Ru ¯der Boškovi´c Institute, Bijeniˇcka cesta 54, Zagreb, Croatia
5
Institute for Astronomy, School of Physics and Astronomy, University of Edinburgh, Royal Observatory, Blackford Hill, Edinburgh,
EH9 3HJ, UK
6
INAF – Osservatorio Astroﬁsico di Arcetri, Largo E. Fermi, 5, 50125 Firenze, Italy
7
Space Science Data Center - ASI, Via del Politecnico SNC, 00133 Roma, Italy
8
INAF – Osservatorio Astronomico di Roma, via Frascati 33, 00078 Monte Porzio Catone (Roma), Italy
9
School of Physics & Astronomy, University of Leicester, Leicester LE9 1UP, UK
10
Leiden Observatory, Leiden University, Niels Bohrweg 2, 2333 CA Leiden, The Netherlands
11
INAF - Osservatorio Astronomico d’Abruzzo, Via Mentore Maggini, 64100 Teramo, Italy
12
IAC - Instituto de Astroﬁsica de Canarias, Via Láctea s/n, 38200 La Laguna S.C., Tenerife, Spain
13
STFC, Rutherford Appleton Laboratory, Harwell, Didcot, OX11 0QX, United Kingdom
Received date; Accepted date
ABSTRACT
Context. Gaia Early Data Release 3 (Gaia EDR3) contains astrometry and photometry results for about 1.8 billion sources based on
observations collected by the European Space Agency Gaia satellite during the ﬁrst 34 months of its operational phase.
Aims. In this paper, we focus on the photometric content, describing the input data, the algorithms, the processing, and the validation
of the results. Particular attention is given to the quality of the data and to a number of features that users may need to take into
account to make the best use of the Gaia EDR3 catalogue.
Methods. The processing broadly followed the same procedure as for Gaia DR2, but with signiﬁcant improvements in several aspects
of the blue and red photometer (BP and RP) preprocessing and in the photometric calibration process. In particular, the treatment of
the BP and RP background has been updated to include a better estimation of the local background, and the detection of crowding
eﬀects has been used to exclude aﬀected data from the calibrations. The photometric calibration models have also been updated to
account for ﬂux loss over the whole magnitude range. Signiﬁcant improvements in the modelling and calibration of the Gaia point
and line spread functions have also helped to reduce a number of instrumental eﬀects that were still present in DR2.
Results. Gaia EDR3 contains 1.806 billion sources with G–band photometry and 1.540 billion sources with GBP and GRP photometry.
The median uncertainty in the G–band photometry, as measured from the standard deviation of the internally calibrated mean
photometry for a given source, is 0.2 mmag at magnitude G = 10 to 14, 0.8 mmag at G ≈ 17, and 2.6 mmag at G ≈ 19. The signiﬁcant
magnitude term found in the Gaia DR2 photometry is no longer visible, and overall there are no trends larger than 1 mmag/mag.
Using one passband over the whole colour and magnitude range leaves no systematics above the 1% level in magnitude in any of the
bands, and a larger systematic is present for a very small sample of bright and blue sources. A detailed description of the residual
systematic eﬀects is provided. Overall the quality of the calibrated mean photometry in Gaia EDR3 is superior with respect to DR2
for all bands.
Key words. catalogs – surveys – instrumentation: photometers – techniques: photometric – Galaxy: general
1. Introduction
Gaia Early Data Release 3 (EDR3, Gaia Collaboration et al.
2020) is based on data collected during the ﬁrst 34 months of
the nominal mission (Gaia Collaboration et al. 2016) and provides
an astrometric and photometric catalogue for more than
Corresponding author: M. Riello
e-mail: mriello@ast.cam.ac.uk
1.5 billion sources. Gaia DR3, planned for the ﬁrst half of 20221
,
will be based on the Gaia EDR3 astrometry and photometry but
will provide a much more comprehensive set of data, including
mean BP and RP spectra, radial velocities, detailed information
on many diﬀerent classes of variable sources, complementary astrometric
information on extended and non-single sources, and
classiﬁcation and astrophysical parameters for diﬀerent subsets
of sources. Although the number of sources in the Gaia EDR3
1
See https://www.cosmos.esa.int/web/gaia/release for updates.
Article number, page 1 of 35
arXiv:2012.01916v1[astro-ph.IM]3Dec2020
A&A proofs: manuscript no. aa39587
catalogue is only slightly larger than that of Gaia DR2, the
cyclic nature of the Gaia Data Processing Analysis Consortium
(DPAC) processing means that the new release is based on a
complete reprocessing of the mission data, allowing it to beneﬁt
from substantial improvements in the various charge-coupled
device (CCD) calibrations, instrument models, and photometric
and astrometric calibrations. Additionally, the inclusion of one
additional year of mission data with respect to Gaia DR2 has allowed
for the further reduction of the uncertainties on the source
photometry and astrometry.
This paper provides an overview of the photometric processing
that contributed to the Gaia EDR3 catalogue, focusing on
the improvements that were introduced for this data release. A
comprehensive view of the photometric processing and its evolution
over Gaia data releases is given by, in addition to this
paper, the set of papers published for Gaia DR1 (Carrasco et al.
2016; Evans et al. 2017; van Leeuwen et al. 2017), Gaia DR2
(Riello et al. 2018; Evans et al. 2018), and the companion online
documentation of the Gaia archive2
. Since the main focus
is on the Gaia photometry, this paper provides only a summary
of the BP and RP spectra pre-processing; the principles of the
internal calibration of the BP and RP spectra will be provided
in Carrasco et al. (in preparation), the spectroscopic content of
Gaia DR3 will be presented in De Angeli et al. (in preparation),
and the external calibration process will be discussed in Montegriﬀo
et al. (in preparation). Finally, in this paper we discuss the
quality of the Gaia EDR3 photometric data, providing guidelines
for making the best use of the catalogue and describing
some known issues that the end users should be aware of to avoid
problems in their own analysis.
2. Data used
Gaia EDR3 is based on 34 months of observations that started
on 25 July 2014 (10:30 UTC) and ended on 28 May 2017 (8:45
UTC), which corresponds to 1037.9 days. In the paper, mission
events are reported in the on-board mission timeline (OBMT),
expressed in units of satellite revolutions (21600 s) from an arbitrary
origin. A formula to convert OBMT to barycentric coordinate
time (TCB) is provided by Eq. (3) in Gaia Collaboration
et al. (2016). The time covered includes the range used for
Gaia DR2 with an additional one year of observations, providing
a 54% increase in time coverage.
A detailed description of the Gaia instruments is provided in
Gaia Collaboration et al. (2016) and a summary of the main characteristics
relevant to the photometric processing can be found
in Riello et al. (2018), Evans et al. (2018), and Carrasco et al.
(2016). The main events in the time range covered by Gaia DR2
are listed in Riello et al. (2018). In the additional year of observations
included in Gaia EDR3, one more decontamination
campaign was carried out. At the end of this last decontamination
campaign, the satellite focus had not degraded and therefore
it was not necessary to refocus the instruments. A list of the time
ranges covered by the various events and a description of additional
gaps in the data are presented in Appendix D.
The key input used by the photometric and low-resolution
spectra processing system PhotPipe for the measurement of G–
band ﬂuxes are the results of the Image Parameter Determination
(IPD) process performed by the Intermediate Data Update
(IDU) system. This task estimates the observation time, acrossscan
position (for 2D windows) and instrumental ﬂux of the
2
https://archives.esac.esa.int/gaia
source in each SM and AF window, along with their associated
formal uncertainties. The modelling of the window contents
is a complex process involving many calibrations, from
the electronic bias through to the point-spread function (PSF,
for 2D windows) or line-spread function (LSF, for 1D windows).
Signiﬁcant improvements have been made to these calibrations
between Gaia DR2 and Gaia EDR3 and hence to the
ﬁtted G–band ﬂuxes. Foremost is the quality of the PSF/LSF;
in Gaia DR2 a single library with very limited parameterisation
was used, whereas all of the major dependencies are activated in
Gaia EDR3. In this release the variation of the PSF and LSF with
time due to changes in focus and contamination level is tracked.
The wave number from Gaia DR2 was used, when available,
to properly represent the colour dependence of the proﬁles, and
the smearing eﬀect of the across-scan motion is included, along
with the variation across each charge-coupled device (CCD). To
better model the Gaia PSF a shapelets-based scheme has superseded
the product of the along scan (AL) and across scan (AC)
LSFs used in Gaia DR2. A detailed description of the PSF and
LSF modelling is provided in Rowell et al. (2020). The other
calibrations used in IPD, such as the electronic bias and nonuniformity,
dark signal, charge injection and release (Hambly
et al. 2018) have all been redetermined in IDU to improve their
self-consistency and resilience to data gaps. Enhancements have
been made to the masking of saturated samples, and to remove
suspected secondary sources within a window. Finally, a local
background has been ﬁtted for the majority of windows, allowing
a much better tracking of the extreme straylight features.
For GBP and GRP, PhotPipe starts from the reconstructed
satellite telemetry, which collates all acquisition information for
the BP and RP instrument into a single record for each transit and
deals with the generation and application of the various calibrations
required to produce bias and background corrected epoch
spectra, which are then geometrically calibrated, removing the
optical distortions and CCD geometric eﬀects. The bias and nonuniformity
mitigation (Hambly et al. 2018) is based on a set of
calibrations produced by IDU. Two key improvements have been
introduced for Gaia EDR3: the determination of the local background
for each BP and RP observation, including both straylight
and astrophysical background contributions; and an assessment
of the crowding status of each observation based on the
predictions of observations on the focal plane for all objects in
the source catalogue covering the entire time range spanned by
the processing. More information on these aspects of the BP and
RP pre-processing is provided in Sect. 3.
As already described in Riello et al. (2018), one critical piece
of input information used by PhotPipe is the cross match produced
by IDU. The purpose of the cross match is to identify
transits belonging to the same astrophysical source and to exclude
spurious detections of artefacts around bright sources. A
detailed description of this key process is provided in Torra et al.
(2020). It is critical for the end user of the Gaia EDR3 catalogue
to realise that a Gaia source and all its properties are deﬁned
by the set of transits that have been associated with it by the
cross match process. Direct comparisons of individual sources
between the Gaia DR2 and Gaia EDR3 catalogues should take
into account that: the fact that the source identiﬁer is the same
in the two data releases does not imply that the corresponding
astrophysical source is the same; even when the astrometry is
consistent it is still possible that a signiﬁcant fraction of the transits
that were associated with that source in the Gaia DR2 catalogue
are not any longer in the Gaia EDR3 catalogue. We therefore
strongly discourage the end user from drawing conclusions
based on comparisons source by source between Gaia DR2 and
Article number, page 2 of 35
M. Riello et al.: Gaia Early Data Release 3: Photometric content and validation
Gaia EDR3. We instead suggest to perform statistical comparisons
of similarly selected datasets from both archives (e.g. comparing
colour–magnitude diagrams for particular sky regions).
In the paper we will often make use of a sample of nearby
sources with good photometry and astrometry3
. We will refer to
this selection as ‘nearby source dataset’; if additional selection
criteria were applied they will be explicitly mentioned.
3. BP and RP spectra processing
Several aspects of the BP and RP (pre-)processing have not
changed with respect to Gaia DR2. Here we focus on a few important
improvements and additions that were introduced in the
latest processing, in particular on aspects that are relevant for the
generation of the photometric catalogue.
3.1. Crowding evaluation
The crowding evaluation process is an assessment of the crowding
status of a transit based on the pre-computed ‘scene’. This
is deﬁned as the predicted observation time and AC coordinate
for all objects in the source catalogue computed projecting their
known astrometric coordinates onto the focal plane given the
satellite attitude and geometry. The scene covers the entire time
range covered by the data. These predictions are used to assess
whether a given transit happened to be aﬀected by crowding.
Here we distinguish two cases: transits can be either contaminated
by a nearby source (that may or may not have had a window
assigned in that speciﬁc satellite scan of that region of the
sky) or blended when some additional source was captured by
the same window. Transits were ﬂagged as blended also when
the non-target source was just outside the window (within 5 TDI
periods in the AL direction and 2 pixel in the AC direction). The
crowding assessment of course takes into account also accidental
contamination or blend from the other ﬁeld of view (FoV).
The result of the crowding assessment for a given CCD observation
is an indication of its status as contaminated, blended or not
aﬀected by crowding.
While for the assessment of blends the simple knowledge
of the relative positions of window and scene sources is suﬃcient,
for the contamination evaluation an estimate of the AL
and AC LSFs well beyond the boundary of the window is required.
For the processing leading to Gaia EDR3, the contamination
surrounding a bright object has been characterised using
black-listed transits (Torra et al. 2020). These are transits that
were not cross-matched to any existing source and did not trigger
the generation of a new source because they were considered to
be spurious detections caused by diﬀraction spikes around bright
objects. In the AL direction the contamination proﬁles were described
using splines. In the AC direction a simpler approach
was taken interpolating linearly in magnitude space between the
central value and the distance at which the brightness level was
below the typical background. This distance was estimated from
the analysis of the black-listed transits as a function of the magnitude
of the central source. Figure 1 shows a typical 2D reconstruction
of the contamination around an object of magnitude
six for BP in the top panel and RP in the bottom panel. This can
then be scaled according to the magnitude of the contaminating
source. This simpliﬁed approach can only reproduce diﬀraction
spikes aligned with the AC and AL directions. There are however
indications of diagonal features at a much smaller level. These
3
The Gaia archive query on the gaia_source table required
parallax> 3, parallax_error< 1 and phot_proc_mode= 0.
Fig. 1. Reconstructed contamination due to an object of magnitude six
placed at the coordinate origin. The 2D map is the result of evaluating
the AL and AC contamination proﬁles. A full 2D mapping will be done
in the next release. BP contamination is shown in the top panel, RP in
the bottom panel. For each location in the plot the colour corresponds to
the contaminating ﬂux expected in a window centred at that coordinate
converted to magnitude for ease of interpretation.
will only be accounted for when a full 2D mapping of the contamination
will be implemented for the next release. The map
shown in the plot corresponds to an area of 7 arcmin AL by 1 arcmin
AC. The second peaks in the AL proﬁle at about 3500 TDI
period in BP and 6000 TDI period in RP from the contaminating
source is probably due to inner/outer reﬂection on the side faces
of the BP and RP prisms.
The top panel in Fig. 2 shows a small stretch of computed
scene, the corresponding observations and crowding evaluation
results covering about 4.5 arcmin in the AL scan direction and
about 1 arcmin in the AC direction close to a source of magnitude
5.4 (located at the origin of the coordinates). In this time
range, one of the two FoVs was observing a high-density region
near the Galactic centre. The scene objects are shown with ﬁlled
circles with size and colour proportional to the source brightness
(the brightest source observed in this time range appears
in yellow at coordinate (0,0), while fainter objects are represented
with small dark symbols) while the transits are shown
with coloured rectangles of size proportional to the size of the
BP and RP windows. The transit symbols are colour coded according
to the residual background (i.e. the background level
evaluated from the edge samples of the BP spectrum after the
application of the background calibration, lighter colours correspond
to larger residual background values). Larger red and
blue rectangles mark transits that have been assessed as contaminated
and blended respectively. From this example it is clear
that blending aﬀects a large fraction of transits, while contamination
is mostly relevant for transits at the same AC coordinate
as the bright object. Figure 2 also shows that not all sources in
the catalogue can be assigned a window during all scans (visible
as ﬁlled circles with no ﬁlled rectangle around them) and
that in the case of bright sources, some of the light coming from
the target object is present in the edge samples thus aﬀecting our
measurement of the residual background (visible as larger and
brighter ﬁlled circles with light-coloured ﬁlled rectangles). This
is even more evident in the bottom panel, which shows a zoom-in
on a few transits around a couple of sources of magnitude close
to 13. The symbol sizes and colours have the same meaning as
in the top panel. The two brightest sources (visible as red circles
at coordinates 285-295 arcsec AL and 30-36 arcsec AC) have
the highest residual background (shown by the lightest-coloured
ﬁlled rectangles in the same plot).
Even though no attempt was made in the processing leading
to Gaia EDR3 to correct the spectra for the eﬀects of crowding,
the results of the crowding evaluation were fundamental in
cleaning the inputs used in all the following calibration proceArticle
number, page 3 of 35
A&A proofs: manuscript no. aa39587
Fig. 2. Example of the computed scene for a short time range. Top panel:
Scene and transits for a small stretch of data (≈ 12 seconds or ≈ 4.5 arcmin
AL and ≈ 1 arcmin AC). Bottom panel: Zoom-in of a small group
of scene objects and transits around two sources of magnitude close to
13. In both panels, the colour coding of the rectangular symbols representing
the actual observations is by residual background ﬂux measured
in e−
s−1
sample−1
. The sources in the scene are represented by ﬁlled circles
with size and colour proportional to their magnitude. The colour
bar in the plot refers to the colour coding used for the observations (see
the text for more details).
dures from aﬀected data: observations ﬂagged as contaminated
or blended were not used in the computation of all calibrations.
A dedicated procedure to remove the eﬀect of crowding
from the actual spectra is being developed and will be in place
in the processing leading to Gaia DR4. Crowded observations
were not ﬁltered when computing mean spectra or mean source
photometry as this would have caused much reduced completeness
in dense sky regions, however the Gaia EDR3 catalogue
contains for each source contamination and blends counters (in
the columns phot_bp/rp_n_contaminated_transits and
phot_bp/rp_n_blended_transits in the gaia_source table)
which can be used to detect problematic cases. See also
Sect. 9.3 for some additional considerations for the end user.
3.2. Background calibration
The two main components of the background in the BP and RP
spectra are the straylight caused by diﬀraction from loose ﬁbres
in the sunshield (Fabricius et al. 2016) and the astrophysical
background (e.g. non-resolved sources, diﬀuse light from nearby
objects, zodiacal light). In the processing for Gaia DR2 the background
calibration was optimised to remove the straylight component
by accumulating background measurements (from empty
windows known as Virtual Objects) over periods of approximately
8 satellite revolutions (Riello et al. 2018). This process
generated 2D maps of resolution 1 degree in the AL direction
and 100 pixels in the AC direction (corresponding to approximately
17.7 arcsec). While this was appropriate for the smooth
behaviour of the straylight in most devices, it was clearly not
suﬃcient to characterise the small-scale variations due to the astrophysical
background. The validation of the Gaia DR2 data
showed clear indications that signiﬁcant residual background
was aﬀecting the performances in crowded regions and in areas
in the sky where the level of diﬀuse light is expected to be
higher.
The resolution of the background calibration is constrained
by the amount of background measurements available. In the latest
processing, in order to increase the resolution of the 2D maps,
science windows assigned to sources fainter than G = 18.95
were used to provide additional background measurements from
Fig. 3. Sky distribution of the source median residual background as
measured from the BP spectra. The residual background measurement
is obtained from the edge samples of all calibrated epoch spectra for a
given source and is given in units of e−
s−1
sample−1
.
Table 1. Conversion between the residual ﬂux level given in
e−
s−1
sample−1
and a magnitude diﬀerence at three diﬀerent magnitudes
(15, 17, and 19). The ∆ mag values listed in the table for a given level
of residual background indicate the corresponding expected change in
magnitude. We note that since this is a magnitude diﬀerence, the values
provided are applicable to both GBP and GRP.
residual ﬂux ∆ mag
[e−
s−1
sample−1
] 15 17 19
-0.5 -0.002 -0.015 -0.097
0.5 0.002 0.015 0.099
1.0 0.005 0.030 0.209
the edge samples in the window. This enabled increasing the resolution
to ≈ 0.5 degree in the AL direction and 8 arcsec in
the AC direction. Finally, to be able to characterise the local
astrophysical background, a k-nearest neighbour approach was
applied to the map residuals. The median of the 30 closest background
measurements (with a maximum distance of 25.6 arcsec)
was taken as the estimate of the local background for each ob-
servation.
To show the performance of the background calibration, we
deﬁned a quantity called residual background which is computed
for each transit as the median of the ﬂux values in the edge samples
of a spectrum. In the following we present the results of
the analysis on BP data as representative of both BP and RP.
Figure 3 shows the distribution in the sky of the median residual
background in BP spectra for the nearby source dataset with
the additional magnitude cut G > 17. Signatures of the Galactic
Plane and other crowded regions are still visible, but the background
ﬂux residuals are limited to the range [−0.5, 1.0] in e−
s−1
sample−1
. This is equivalent to an eﬀect at the mmag level
for a source of magnitude 15, a hundredth of a magnitude for
a source of magnitude 17 and a tenth of a magnitude level for
a source of magnitude 19 (a more detailed estimate is provided
in Table 1. The table is provided to help readers understand the
signiﬁcance of the features shown in Fig. 3. No correction for
this eﬀect is suggested here. As indicated by the sky map, the
size of this eﬀect is not constant over the sky and a correction
would have to be applied at the epoch level to take into account
the satellite scanning law and the overlapping ﬁelds of view.
Figure 4 shows the variation in time of the median residual
background in BP spectra observed by Gaia in the time range
covered by Gaia EDR3 (abscissa) with intra–day resolution (orArticle
number, page 4 of 35
M. Riello et al.: Gaia Early Data Release 3: Photometric content and validation
Fig. 4. Temporal distribution of the residual background measurements
in BP spectra. Each column in the heatmap shows the measurements
within a given OBMT day for each OBMT day. The OBMT revolution
is shown on the top abscissa axis for ease of interpretation. The
high–residual features are the Galactic Plane crossing the two FoVs either
in the Galaxy inner or outer direction (see the text for more details).
The gaps related to major events such as decontamination and
refocussing are visible. Other small gaps are due to telemetry data that
could not be included in the processing for various reasons.
dinate). For a given abscissa position (i.e. one OBMT day), the
ordinate shows the residual background variation within the four
OBMT revolutions of that day thus allowing a much higher level
of detail to be visible compared to a standard histogram. The
16 daily Galactic Plane (GP) crossings are clearly visible: eight
in the inner and eight in the outer direction of the Galaxy, four
for each FoV. The GP features are seen becoming progressively
steeper in the plot as a result of the spacecraft spin axis becoming
perpendicular to the GP itself and leading to a Galactic Plane
Scan (GPS) when both Gaia FoVs are eﬀectively scanning the
GP continuously for several days (e.g. at ≈ 1945 rev and then
again at ≈ 2120 rev, etc.). The eight thin streaks visible before
1200 rev are due to the LMC crossing the two FoVs at each revolution
during the ecliptic poles scanning mode (see below). After
that the LMC is still visible as increased density spots at periodic
intervals. The larger gaps are related to decontamination and refocus
events. Other minor gaps are due to outages in the daily
processing pipelines or genuine spacecraft events.
It is important to remark that the measure of the residual
background from the edge samples of a spectrum will unavoidably
be aﬀected by the presence of other sources in the window
(i.e. blending) and by contamination from nearby bright sources.
We could have used the results of the crowding evaluation to ﬁlter
out these cases, but this would have prevented us from creating
a full sky map as particularly dense regions would have had
very little data left. By using the median values for validation we
have mitigated problems with occasional blend or contamination
coming from the other FoV, but we should remember that in
crowded regions our results will be biassed towards larger positive
residuals.
3.3. Flux and LSF calibration and mean spectra
Mean source spectra will be released for the ﬁrst time in
Gaia DR3 and a detailed description of the processing that lead
to the generation of calibrated spectra will be provided for that
release (Carrasco et al., in preparation , Montegriﬀo et al., in
preparation). In this subsection we will only give a very brief
overview of this process considering that the reference colour
information used in the photometric processing was extracted
from calibrated mean source spectra.
The general ﬂow of the ﬂux and LSF calibration of the BP
and RP spectra is very similar to the one in place for the photometry:
also in this case the calibration is divided into an internal
calibration using a large number of sources to constrain the
calibration of all diﬀerent instrument conﬁgurations to a single
homogeneous system, and an external calibration which relies
on a small set of sources with high accuracy external data to tie
the internal system to the absolute one. As for the photometry,
no external data is used in the internal calibration of the spectra
implying that the process needs to be iterated between a step
creating a reference catalogue of spectra for all calibrators and
a step updating the calibrations. Once the reference catalogue is
established, a single run over all observations will generate the
ﬁnal set of calibrations.
During the internal calibration, the spectra are ﬁrst converted
to an internal wavelength scale, called ‘pseudo-wavelength’ applying
the calibrated diﬀerential dispersion function. The calibration
model for each calibration unit is then deﬁned as a kernel
function describing the ﬂux contribution at each pseudowavelength
from a range in pseudo-wavelength thus characterising
changes in response and LSF between diﬀerent observing
conditions and across the wavelength range covered by the
BP and RP instruments. The calibration is deﬁned as a forward
model, meaning a model that when applied to the mean source
spectrum predicts an observed spectrum for a given time, CCD,
FoV, window class, and gate.
The process of generating the mean source spectrum collects
all epoch spectra for a given source and ﬁts a function that oﬀers
the best predictions in the least squares sense when the calibration
is applied to it. This function is deﬁned as a superposition
of Hermite polynomials and it is continuous over the pseudowavelength
range covered by the BP and RP instruments. Integrals
of this function over speciﬁc wavelength ranges provide
the colour information used in the photometric processing, see
Sect. 4.4.
4. Photometric processing
The principles of the photometric calibration have been outlined
in Carrasco et al. (2016) while Riello et al. (2018) and Evans
et al. (2018) provided additional information on how the calibration
process was implemented for Gaia DR2. This section
provides a summary of the changes that were introduced in the
photometric processing for Gaia EDR3. The main diﬀerences
with respect to Gaia DR2 are: 1) the OBMT time range; 2) the
set of sources used to establish the photometric system; 3) the
large-scale (LS) calibration model and the type of colour information
used. The small-scale (SS) calibration model is the same
as in Gaia DR2, a simple zero point (see Riello et al. 2018).
The following sections provide detailed information about these
aspects.
4.1. Time range
The period used to establish the photometric system (‘INIT period’
hereafter) is composed of two time ranges: ≈ 2574.7 to
≈ 2811.7 OBMT rev and ≈ 4121.4 to ≈ 5230.1 (i.e. the end
of the period covered by Gaia EDR3). The two periods were seArticle
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A&A proofs: manuscript no. aa39587
Fig. 5. Temporal evolution (expressed in units of satellite revolutions) of
the response loss in BP due to contamination during the period covered
by Gaia EDR3. The preceding FoV is shown in black, the following
FoV is shown in light blue. The very thin grey shaded areas (marked
with ) are the three decontamination campaigns; the two vertical purple
lines (marked with ) are the two refocussing events; the shaded
orange areas show the two time ranges that constitute the INIT period.
lected because they have both the lowest and most stable contamination
level (see Riello et al. 2018). Additionally, the two periods
together cover ≈ 1345 OBMT rev corresponding to ≈ 336
days and together they provide almost twice the full sky coverage.
Figure 5 shows the response loss due to contamination measured
by the FirstLook instrument health monitor system. The
orange shaded areas show the two time ranges that constitute the
INIT period. Both time ranges start after a decontamination campaign;
the duration of the ﬁrst time range was selected in order
to avoid large variations in the response: in the time range between
the last two decontamination campaigns it is clear that the
preceding FoV is still aﬀected by contamination which builds up
during the time range reaching a response loss of ≈ 0.2 mag before
the last decontamination campaign. The second time range
used for the INIT period is instead very well behaved with a
nearly constant response level.
4.2. Algorithm overview
The photometric system is established using a set of calibrators
that were selected as described in Sect. 4.3. The iterative calibration
process follows the same principles used for Gaia DR2:
an initial set of reference source ﬂuxes is produced by accumulating
the uncalibrated epoch photometry from the INIT period
and then used to derive a set of LS calibrations. The calibrations
are then applied to produce calibrated epochs that are accumulated
for each source to produce an updated set of reference
ﬂuxes (see Riello et al. 2018; Carrasco et al. 2016, for
more details). Having an explicit time dependency in the calibration
model is not very practical due to the irregular time
evolution which is both smooth, during most periods, and discontinuous,
during decontamination and refocussing campaigns.
Instead the LS calibrations are solved independently over short
periods of ≈ 4 rev for each time range and instrument conﬁguration
(see Riello et al. 2018, for more information). The shorter
gate conﬁgurations (Gate04 and Gate07) for the AF CCDs are
particularly diﬃcult to calibrate due to the very few observations
acquired with these conﬁgurations. To improve the statistics,
the LS calibrations for these conﬁguration were solved over
extended time ranges of ≈ 20 OBMT revolutions. The same approach
was taken for the shorter gate conﬁgurations (Gate05 and
Gate07) for the BP CCDs. For RP instead the Gate05 and Gate07
conﬁgurations were calibrated using the Gate09 calibrations because
the CCD response did not show any additional major feature
in the longer Gate09 conﬁguration. LS calibration solutions
could not be derived for non-nominal calibration units (i.e. gated
observations for window class 1 and window class 2) even when
further extending the time ranges to ≈ 60 OBMT rev because of
the lack of a suﬃcient number of observations. These instrument
conﬁgurations were therefore calibrated using the corresponding
ungated calibrations. A total of 20 iterations were performed (see
Sect. 4.6 for a discussion of the convergence criteria).
Using the reference ﬂuxes from the last iteration, the SS and
LS were then solved iteratively in the same way as for Gaia DR2
(Riello et al. 2018). The resulting LS and SS calibrated mean
photometry for the calibrators represents the ﬁnal set of reference
ﬂuxes used to then derive the LS and SS calibrations for
the full Gaia EDR3 time range.
4.3. Selection of calibrators
A set of calibrators were selected among all sources observed
in the INIT period. The main purpose of this selection is to
provide a more compact dataset to use for the iterative initialisation
of the photometric system. The selection was designed
to provide a wide colour range and a uniform sky coverage in
both magnitude and colour. The main reason to require uniform
sky coverage is to ensure that each one of the time ranges for
which the LS calibrations are solved would have an adequate
set of calibrators observed regardless of the satellite scan direction.
To be selected, sources were required to have Gaia DR2
photometry in G, GBP and GRP (so that they could be assigned
to a colour-magnitude bin) and to have at least 5 available BP
and RP observations in the INIT period. The Gaia DR2 photometry
was only required for the selection stage and was not
used in the Gaia EDR3 calibration process. The magnitude range
was restricted to 5.0 ≤ G ≤ 19.0 and the colour to −1.0 ≤
GBP − GRP ≤ 6.0. In the regime G ≤ 13.5 sources will normally
be assigned a 2D window and gating will be used on board to
minimise the eﬀects of saturation. In order to have enough calibrators
to solve for daily calibrations for these instrument conﬁgurations,
all sources brighter than G = 13.5 were automatically
included by the selection process. At fainter magnitudes instead,
for each level k = 6 HEALPix (Górski et al. 2005) pixel a grid of
70 colour bins and 140 magnitude bins (in the ranges speciﬁed
above) was created and the ﬁrst four sources to be assigned to
each bin were selected. The order of the sources in each bin was
randomised (in a reproducible way) before the selection started.
Three additional conditions were added to this selection process:
1) all spectro-photometric standard stars (SPSS, Pancino et al.
2012) and sources from the passband validation library (PVL)
were automatically included in the selection (see Appendix B
for detailed information on these datasets); 2) all sources that
had epochs acquired with a gate conﬁguration were automatically
included in the selection to ensure proper linking between
the various gated conﬁgurations (see Evans et al. 2018, for more
details); 3) all sources with G−GRP ≤ 0.5(GBP−G)+1.2 were excluded
because contamination from extragalactic objects is quite
high in that region of the G − GRP versus GBP − G colour–colour
diagram. This process produced a selection of ≈ 100 million
sources.
4.4. Calibration models
The large scale (LS) calibration model describes features that
vary smoothly across the focal plane and that might change
Article number, page 6 of 35
M. Riello et al.: Gaia Early Data Release 3: Photometric content and validation
Fig. 6. Raw unit weight residuals of the photometry vs. the centring error δµ. The left panel shows G (all AF CCDs), the central panel shows GBP
and the right panel shows GRP. The solid black line shows the median of the distribution, the dashed lines show the ±1σ of the distribution. The
centring error is deﬁned as the diﬀerence between the source predicted AC position at the observing time and the nominal window centre. The
residuals were produced only for the set of calibrators and only using data from the INIT period.
smoothly with time over timescales of several satellite revolutions.
The main changes to the LS model for Gaia EDR3 are:
1) the colour information is represented by spectral shape coefﬁcients
(SSC) computed from the internally calibrated source
mean spectra; 2) the SSCs are no longer normalised as in
Gaia DR2 (see Appendix A in Riello et al. 2018) but are used
to form ﬂux ratios (see below); 3) additional terms have been
included to model the ﬂux loss caused by the on-board window
acquisition process. It is important to notice that because
of point 1 above, the colour information is now ﬁxed all the way
through the calibration process: in Gaia DR2 instead the reference
colour information was updated at every iteration making
the overall process less stable. In Gaia DR2 it was not possible
to use SSCs derived from the mean spectra since the internal calibration
process of the spectra was not considered to be mature
enough for use in the photometric calibration. For all bands the
LS model is a polynomial with a zero point z0, quadratic dependence
from the across scan (AC) position of the observation µ
and a quadratic dependence from the centring error δµ, deﬁned
as the diﬀerence between the predicted AC position at the observing
time4
and the nominal window centre. The raw residuals
(meaning with respect to the identity model) in Fig. 6 show that
there are no observations with a centring error larger than ±2 pix
for the G–band and for BP as well. For this reason only observations
with a centring error in the range ±2 pix were used to
solve for the calibration; the centring error was then clamped to
±2 pix when applying the calibrations to avoid problems caused
by extrapolation. For RP the situation is more complex due to
the optical design of the instrument: a wider range of ±4 pix
was required for the calibration solution and for clamping when
applying the calibration (additional information is available in
Appendix G).
The colour dependencies are modelled in terms of the SSCs
ﬂuxes computed from the internally calibrated mean spectra
(Sect. 3.3). The SSCs ﬂuxes are used to form diﬀerent ratios to
provide pseudo-colours. For Gaia EDR3 we deﬁned four SSC
ﬂux ratios:
r1 =
s0
s1 + s2
r2 =
s3
s1 + s2
(1)
r3 =
s4
s5 + s6
r4 =
s7
s5 + s6
, (2)
where s0 to s3 are the four SSCs ﬂuxes computed from the BP
mean spectrum and s4 to s7 are the four SSCs ﬂuxes computed
4
The predicted position is computed from the astrometric source parameters,
the reconstructed satellite attitude and the geometric calibration
for G–band and BP and RP.
from the RP mean spectrum. The wavelength ranges deﬁning
each SSC are the same as used in Gaia DR2 (see Table 5 in
Carrasco et al. 2016). The G–band LS model includes a linear
dependence from all the ratios deﬁned by Eq. 1 and Eq. 2. The
LS model for BP includes only the two ratios deﬁned by Eq. 1
while the model for RP includes only the two ratios deﬁned by
Eq. 2. The small scale (SS) calibration models the column-level
CCD sensitivity. The same model (a simple zero point for each 4
pixel wide AC bin) was used as in Gaia DR2. The LS calibration
models described above are summarised by Eq. 3 with Eq. 4
providing the three diﬀerent colour dependencies for G–band,
BP and RP:
f
Is
= z0 +
2
i=1
aiµi
+
2
j=1
bjδj
µ + C(r) (3)
C(r) =



c1r1 + c2r2 + c3r3 + c4r4 for G–band,
c1r1 + c2r2 for BP,
c1r3 + c2r4 for RP,
(4)
where f is the uncalibrated ﬂux of a given CCD observation for
a source s, Is is the reference source ﬂux, rk are deﬁned by Eqs.
1 and 2 and z0, ai, bj and ck are the model coeﬃcients to be ﬁtted
in the calibration procedure. We deﬁne as ‘calibration factor’ the
right hand side of Eq. 3 evaluated for a given CCD observation
of a given source. The ratio of the raw epoch ﬂux f and the
calibration factor provides the calibrated epoch ﬂux.
The time link calibration (mitigating the eﬀect of contamination)
that was introduced in Gaia DR2 was not required for
Gaia EDR3 because the throughput in the INIT period was more
stable and less aﬀected by contamination than the one used for
Gaia DR2. For Gaia DR2 an additional calibration was introduced
to help with the mixing between the diﬀerent instrument
calibrations: this was not used for Gaia EDR3 since the use of a
more compact set of calibrators allowed to perform more iterations
for the initialisation of the photometric system leading to a
better mixing between the diﬀerent instrument conﬁgurations.
4.5. Validation of the LS and SS calibrations
Figure 7 shows the time dependency of the calibration factor for
the whole focal plane. This eﬀectively shows the response loss
mainly caused by the contamination which aﬀected the early
stages of the mission more severely (Gaia Collaboration et al.
2016). It is interesting to note that the rate of contamination
changed behaviour between the two FoVs following the three decontamination
events, possibly indicating that the deposition of
the contaminant (assumed to be water ice) ﬂipped from one FoV
mirror system to the other. This can be seen in the bottom right
Article number, page 7 of 35
A&A proofs: manuscript no. aa39587
Fig. 7. Time dependence of the calibration factor for the whole focal plane showing the AF, BP, and RP CCDs for each row. This plot covers the
Window Class 1 and 2, ungated conﬁgurations. The blue line shows the preceding FoV and the red line shows the following FoV. The shaded area
shows the INIT period. The vertical green lines show the decontamination events and the purple vertical lines the refocussing events.
Fig. 8. Response as measured by the SS calibrations for the BP row
2 CCD in the Following FoV. Two sets of calibrations are shown corresponding
to the 1D window conﬁguration (labelled WC1 and shown
in dark orange) and the 2D window conﬁguration (labelled WC0 and
shown in black).
part of these diagrams. It is also noticeable that the behaviour is
very diﬀerent at diﬀerent locations of the focal plane.
An example of the quality of the SS calibrations (equivalent
to a 1D ﬂat ﬁeld) is shown in Fig. 8. Here two sets of calibrations
are shown for a particular CCD corresponding to 1D
(shown in red) and 2D (shown in black) conﬁgurations which
cover diﬀerent magnitude ranges. The fact that the two calibrations
overlap almost perfectly, even though they are produced
using completely independent datasets, conﬁrms that even the
smallest features visible in the calibrations are indeed real and
not noise. We can therefore conclude that the SS calibration is
measuring the CCD response to better than the mmag level.
One of the improvements made in the IPD processing leading
to Gaia EDR3 is a better handling of hot columns. Before the
LSF/PSF ﬁt is carried out, samples corresponding to identiﬁed
hot columns are masked. In Gaia DR2, the eﬀect of hot columns
was partially accounted for by the SS calibrations. This is shown
in Fig. 9 where for Gaia DR2 (upper plot) the hot columns can
be seen as ﬁve narrow peaks. In Gaia EDR3, these peaks are absent
showing that the hot columns have been dealt with correctly.
The CCD shown has a particularly large number of defective
columns with anomalous response.
The SS calibrations have been calculated for Gaia EDR3 in
three separate time periods. This is to provide a crude form of
time functionality to the calibration model. Long time periods
are needed to ensure that enough data is present for the calibrations,
especially for the gated observations. Comparisons between
the calibrations obtained for diﬀerent time periods conﬁrm
that the instrument response at the small–scale level does
not vary signiﬁcantly. Figure 10 shows an example of one of
the largest variations between SS calibrations covering diﬀerent
time ranges. Even in this case the diﬀerences are smaller than
Article number, page 8 of 35
M. Riello et al.: Gaia Early Data Release 3: Photometric content and validation
Fig. 9. Responses as measured by the SS calibrations for the AF5 Row
1 CCD in the Preceding FoV for the 1D and 2D window conﬁgurations,
labelled WC1 and WC0 respectively. The top panel shows the
Gaia DR2 calibration and the bottom one the Gaia EDR3 ones. The
symbols in the top panel show the location of the hot columns which are
no longer visible in the Gaia EDR3 calibration due to improvements in
the IPD.
Fig. 10. Diﬀerence in the SS calibration response between two time
periods for the AF9 row 1 CCD for the preceding (black) and following
(blue) FoV.
1 mmag. The typical diﬀerence between these two time periods
for all CCDS is 0.12 mmag.
4.6. Convergence of the reference system
The main method used to assess the convergence of the photometric
system is the same one as used in Gaia DR1 and DR2
and is described in Evans et al. (2017). This uses the L1 Norm
metric to determine the typical change in photometry using the
calibration coeﬃcients. Figure 11 shows this metric for four major
conﬁguration groupings. While this is much better than seen
in Gaia DR2 (Evans et al. 2018), the metric does not converge to
zero. Using these plots it was decided to terminate the iterations
at the 20th iteration.
Another set of metrics to analyse as a function of iteration
number is the calibration coeﬃcients. In an ideal system, when
the photometry has converged, the coeﬃcients will remain the
same between iterations. For Gaia EDR3, while this is true for
the coeﬃcients involving the AC position and the centring error
and also the overall calibration factor and standard deviation of
the solution, it is not true for the colour coeﬃcients and these
values can change by up to 0.01 over 20 iterations. The reason
Fig. 11. The L1 Norm convergence metric as a function of iteration for
four major conﬁguration groupings: AF Window Classes 0 and 1, 1D
BP and RP observations.
for this is that there is a strong correlation between the colour
coeﬃcients and this causes a partial degeneracy in the calibration
model. However, the overall calibration factor is stable at the
sub-mmag level and the only implication of this is that these
coeﬃcients cannot be used in additional validation analyses for
example plotting them as a function of time.
A single overall photometric system will form when there is
good mixing between the diﬀerent conﬁgurations, meaning that
each conﬁguration is calibrated with many calibrators and each
calibrator is observed under many diﬀerent conﬁgurations. Problems
can arise when conﬁgurations are limited to certain magnitude
ranges and if systematic eﬀects are present in the data that
are not accounted for by the calibration model. For the diﬀerent
gate conﬁgurations this is not a problem as the magnitude ranges
of activation of each gate are small and the uncertainty of the onboard
magnitude determination is large in comparison. However,
for the window class conﬁgurations (with boundaries at G = 13
and 16) the on-board magnitude accuracy is good (about 1%)
which means that the number of sources that are observed in
more than one window class is small. The eﬀect of this is that the
ability of the iterations to create a consistent photometric system
across all conﬁgurations is limited. This can be seen in Fig. 12
which shows the diﬀerence in photometry between subsequent
iterations as a function of magnitude for a test calibration model
(top panel) and the ﬁnal one used (bottom panel). In the test calibration
model, it can be seen that there are discontinuities occurring
in correspondence to window class conﬁguration changes.
While there is a physical reason for the discontinuity occurring at
G = 13 due to diﬀerent ﬂux loss eﬀects in 2D and 1D windows,
the jump at G = 16 can only be due to a problem in the convergence
to a consistent system across diﬀerent conﬁgurations. The
convergence process occurs very slowly due to the poor mixing
between these magnitude ranges. For the ﬁnal model an oﬀset
was introduced between the window class conﬁgurations separated
at G = 13 to speed up the convergence and the two faintest
window class conﬁgurations were combined into a single one.
The improvement can be seen in the lower panel of Fig. 12. The
lack of discontinuities at G = 13 and G = 16 can also be seen in
Fig. 17. We note however, that while this strategy will ensure that
the ﬁnal photometric system does not show discontinuities due
to poor mixing between diﬀerent instrument conﬁgurations, it is
still possible for unmodelled systematics to cause small inhomoArticle
number, page 9 of 35
A&A proofs: manuscript no. aa39587
Fig. 12. The diﬀerence between the G–band photometry in magnitudes
between diﬀerent iterations as a function of magnitude for a test (upper
panel) and the ﬁnal (lower panel) calibration model.
geneities in the internal photometric system that might require
further treatment (see also Sect. 7).
5. Mean source photometry
The generation of the mean G–band, GBP, and GRP source photometry
follows the same process used for Gaia DR2 and described
in Riello et al. (2018): epochs are calibrated by applying
the appropriate LS and SS calibrations and the resulting calibrated
epoch ﬂux is accumulated, for each band, to produce a
mean source ﬂux as the weighted mean of the valid contributions
(with the weight deﬁned as the inverse of the variance on the calibrated
epoch). The uncertainty on the weighted mean ﬂux, accounting
for any intrinsic scatter that might exist within the data,
will be given by
σf =
f2
i wi − f
2
wi
n − 1
1
√
wi
. (5)
It can be shown that if the underlying distribution is Gaussian,
then an inverse variance weighted mean is the maximum likelihood
estimator for the mean of the distribution (Lupton 1993).
Furthermore, this type of weighting always guarantees the maximum
signal to noise in the answer.
An epoch contribution to this weighted mean ﬂux is considered
valid when both the LS and SS calibrations have been successfully
applied and the calibrated ﬂux is at least 1 e−
s−1
. This
minimum ﬂux threshold was introduced in Gaia DR2 to mitigate
the impact of extreme outliers (Riello et al. 2018): the impact of
this ﬂux threshold for Gaia EDR3 is discussed in Sect. 8.1. Calibrated
epochs could also be excluded a priori from contributing
to the mean photometry in a given band depending on quality
metrics based on acquisition and processing ﬂags. AF observations
were excluded from the mean photometry when any
of the following criteria were met: AC trimmed windows acquired
around 2230 OBMT revolutions as part of a set of tests
that were performed to assess the impact of reducing the AC size
of AF windows; windows aﬀected by a charge injection; windows
that had some of the samples removed because of interFoV
truncation; windows for AF2 ROW5 with a reference AC
position larger than 1203 pixels (the data is severely aﬀected by
a deep trap in the serial register); windows for which the IPD
was ﬂagged as not successful; windows for which the source predicted
AC position was not available; AF observations in the periods
immediately following a decontamination campaign have
also been excluded due to large variations in the system response
caused by the focal plane having not reached thermal equilibrium
yet (see Appendix D for more information). BP and RP observations
were excluded from the mean photometry when any
of the following criteria were met: truncated windows; windows
aﬀected by a charge injection; windows acquired with multiple
gates; windows for which the source predicted AC and AL positions
were not available (this information is required for the preprocessing
of the epoch spectrum from which the raw epoch ﬂux
is produced); windows aﬀected by bad columns. To apply the LS
calibration to the epoch observations of a given source, it is necessary
to use the source SSCs derived from the internally calibrated
mean spectra. Depending on the availability of the SSCs
(see Sect. 4.4), there are three diﬀerent calibration procedures:
‘gold’ – when all eight SSCs are available; ‘silver’ – when for
either BP or RP some or all SSCs are missing; ‘bronze’ – when
SSCs are missing or incomplete for both BP and RP or if the
silver processing failed (see below). Since the calibration model
involves ratios of SSC ﬂuxes (see Eq. 1 and Eq. 2) the set of BP
SSCs is considered complete when all four SSCs are present and
s1 + s2 > 0 and analogously for RP but with s5 + s6 > 0. It is important
to stress that the ‘grade’ of a source is determined solely
by the availability of the SSCs and has no implications about the
availability of mean photometry in the various bands. In particular,
it is possible for a gold source to be missing the photometry
in any of its bands or for a bronze source to have photometry
in any of the bands. This is a consequence of the independence
between the spectral processing (leading to the generation of the
mean spectrum and SSCs) and the calibration of the integrated
photometry.
In order to calibrate non-gold sources it is necessary to produce
an estimate of the missing SSCs. For bronze sources, a set
of default SSCs are used for every source: this is analogous to
how bronze sources were calibrated in Gaia DR2. For silver
sources, the missing SSCs are estimated from the G–band and
the available BP and RP band using empirical relationships derived
using a set of gold sources. For silver sources an iterative
process is used to generate the mean photometry: an initial estimate
of the source photometry is derived using the default SSCs;
this initial guess is then used to obtain an updated set of SSCs for
the missing band using the empirical relationships described in
Appendix E; the resulting set of estimated SSCs is then used to
produce the updated mean photometry. The iterative process is
considered successful when the mean G ﬂux between two consecutive
iterations has changed by less than 0.05% or if a maximum
of 20 iterations is reached. If the mean G ﬂux fails to be
produced then the iterations are stopped and the source is then
handled as bronze.
A total of 1,602,086,411 sources were calibrated using the
gold procedure, 204,074,348 sources were calibrated using the
silver procedure and 746,399,821 sources were calibrated using
the bronze procedure. The actual number of sources for each
grade in the Gaia EDR3 archive will be lower because of various
Article number, page 10 of 35
M. Riello et al.: Gaia Early Data Release 3: Photometric content and validation
Fig. 13. Number of sources with gold, silver, and bronze G–band, BP,
and RP photometry as a function of G, GBP, and GRP magnitude in
the top, mid, and bottom panel, respectively. Although the photometric
grade (gold, silver, bronze) is the same for all bands of a given
source, there are sources with incomplete photometry (regardless of
their grade), as discussed in the text.
data quality ﬁlters applied during the catalogue preparation. The
magnitude distributions of the gold, silver and bronze sources
in Fig. 13 show that silver and bronze sources are concentrated
at the faint end where BP and RP spectra have lower signalto-noise
and completeness can be aﬀected by a combination of
crowding and the limitations in the resources available to the
on-board video processing unit (VPU) which do not allow the
allocation of a BP and RP window for every single observed
transit.
Figure 14 shows the uncertainty on the weighted mean as
a function of magnitude for the gold photometry. Only sources
with approximately 200 G–band CCD observations (and analogously
20 in GBP and GRP) have been included to allow comparing
with the predicted uncertainties (Jordi et al. 2010). The
dotted line in each of the three panels shows the predicted uncertainty
for a nominal mission and 200 CCD observations. The
dashed line in each of the three panels shows the same predictions
but combined with a calibration error of 2.0, 3.1, and 1.8
mmag for G, GBP, and GRP, respectively. The ﬁgure includes
also the Gaia DR2 and DR1 uncertainties for comparison (for
the latter only the G–band uncertainties are shown since the BP
and RP photometry was not part of that release). In the G–band
a large improvement can be seen in the range 10.5 G 11.5
thanks to improvements in the handling of saturated samples in
the IPD process. The G–band uncertainty can be seen to increase
in the range 11.5 G 13 to then drop again following the dark
dashed line (see top panel of Fig. 14). This increase in the uncertainty
is due to the fact that the PSF modelling did not include the
dependency from the AL rate (see Rowell et al. 2020): the eﬀect
is expected to become more signiﬁcant for longer gates which
is indeed reﬂected by the behaviour observed for the errors. The
AL rate eﬀect on the PSF will be included in the modelling for
Gaia DR4 which is therefore expected to have improved errors
in this magnitude range. A signiﬁcant improvement is also noticeable
at the very bright end, G 6, which is mostly due to
improvements in the handling of saturated samples in the IPD
Fig. 14. Distribution of the uncertainty on the weighted mean G (top
panel), GBP (central panel), and GRP (bottom panel) as a function of
the G, GBP, and GRP magnitude, respectively. Only sources with ≈ 20
transits (corresponding to ≈ 200 CCD observations in G) have been
included in this analysis. The black dotted line shows the expected uncertainties
for sources with 200 G–band (20 GBP, GRP) contributions for
a nominal mission with no calibration error. The dashed dark line shows
the same expected uncertainties with an additional calibration error on
the single measurement of 2.0 mmag for G–band, 3.1 mmag for GBP
and 1.8 mmag for GRP added in quadrature. The Gaia DR1 and DR2
uncertainties are shown for comparison.
process. For GBP and, to a larger extent (see Appendix G), GRP
the improvements at the brighter end are also due to the modelling
of ﬂux-loss in the photometric calibrations. At the fainter
end, instead, the improvements are due to the improvements in
the background mitigation, which for Gaia EDR3 includes an
estimate of the local background (see Sect. 3.2).
By plotting various statistics as a function of sky position it is
possible to identify problems with the processing. In Gaia DR2,
the skewness of the ﬂux distribution of each source was used
to identify periods where the calibration had been problematic.
During these periods, for example after decontamination, the
calibration had not worked well and caused observations acquired
during such periods (about four days) to be poorly calibrated
and become outliers for these sources. These sources
would tend to have larger skewness values than normal and they
would form great circles in the sky distribution of the skewness.
Figure 15 shows the sky distribution of the source G ﬂux skewness
for Gaia EDR3. As can be seen, larger skewness values do
not distribute along great circles but in areas of very high source
density (Galactic centre and LMC) and in regions with higher
scan coverage. This second eﬀect is not fully understood yet but
it is of much lower signiﬁcance than the one related to the sky
density.
Looking at the sky distribution of the faintest sources can
also provide useful insights on the quality of the photometry.
In Gaia DR2, the distribution of sources fainter than G = 21.7
showed a number of features in the shape of great circles therefore
indicating problems with the processing (see for example
Boubert et al. 2020). Figure 16 shows the sky distribution of
Gaia EDR3 sources with G > 22: the only visible features are
linked to the scanning law and are explained by the fact that reArticle
number, page 11 of 35
A&A proofs: manuscript no. aa39587
Fig. 15. Sky distribution of the median skewness of the G ﬂux. The
map was produced by computing for each level k = 8 HEALPix pixel
the median G ﬂux skewness value of all gold sources.
Fig. 16. Sky distribution of sources with G > 22. The only visible features
are related to the Gaia scanning law.
gions with higher number of observations (primarily because of
more frequent scans) tend to reach a fainter magnitude limit. No
other features are visible, indicating the lack of processing problems
and the improved quality of Gaia EDR3.
Comparisons with external catalogues are usually quite difﬁcult
to carry out since they involve diﬀerent passbands. Additionally,
if the comparison shows a discrepancy, it can be difﬁcult
to establish whether it should be ascribed to the internal
catalogue or external one. In Gaia DR1 (Evans et al. 2017) and
Gaia DR2 (Evans et al. 2018), a discontinuity was present in
the comparisons with APASS at G = 13 (Henden et al. 2015)
and with SDSS DR15 (Aguado et al. 2019) at G = 16. Since
at these magnitudes there are two important changes in the Gaia
window conﬁguration, it was reasonable to conclude that the discontinuities
were a result of the Gaia processing or observation
process. The equivalent comparisons have been carried out also
for Gaia EDR3 (using the colour transformations given in Appendix
C) and are presented in Fig. 17 showing that the discontinuities
are not visible anymore.
6. BP and RP ﬂux excess
In Gaia DR2 the background treatment for BP and RP was limited
to the mitigation of the time and CCD-dependent straylight
contribution (Riello et al. 2018) and was based on maps derived
from ≈ 8 revolutions. For this reason the maps were very insensitive
to variations in the local background level, which therefore
was still aﬀecting the GBP and GRP integrated photometry, especially
at the faint end. Evans et al. (2018) introduced a quality
Fig. 17. Comparisons of Gaia EDR3 with APASS (upper plot) and
SDSS DR15 (lower plot) showing that no discontinuities are detected
at G = 13 or G = 16.
metric, the BP and RP ﬂux excess factor deﬁned as a simple ratio
between the total ﬂux in BP and RP, and the G–band ﬂux:
C = (IBP + IRP)/IG. The motivation for C as a quality metric was
simply that because of the instrument passbands and response
the C ratio should be only slightly larger than one. The actual
distribution of C versus GBP − GRP colour is more complex with
the excess becoming progressively larger towards redder colour
while ﬂattening out to a constant level towards the blue end of
the colour range. In Gaia DR2 Evans et al. (2018) concluded that
large values of the excess factor C were caused by problems in
the GBP and/or GRP photometry and therefore recommended to
ﬁlter sources with a large excess factor considering them problematic.
Because of the strong dependence on colour, using the
BP and RP ﬂux excess can often lead to results that are diﬃcult
to interpret. To overcome this limitation, we introduce the
corrected BP and RP ﬂux excess factor C∗
deﬁned as:
C∗
= C − f(GBP − GRP), (6)
where f(GBP − GRP) is a function providing the expected excess
at a given colour for sources with good quality photometry. By
deﬁnition C∗
is expected to be close to zero with positive values
indicating that the source has more ﬂux in BP and RP than in
the G–band and vice versa for negative values. In order to derive
the colour dependency f(GBP − GRP) we used a sample of about
200,000 isolated and well observed sources based on a selection
of the Stetson secondary standards (see Appendix B.2 for a description
of this dataset) and a selection of the Ivezi´c et al. (2007)
Article number, page 12 of 35
M. Riello et al.: Gaia Early Data Release 3: Photometric content and validation
Table 2. Coeﬃcients of the polynominals f(x) = ai xi
ﬁtting the BP
and RP ﬂux excess factor C dependence on the x = GBP − GRP colour
with their applicability range.
ai x < 0.5 0.5 ≤ x < 4.0 x ≥ 4.0
a0 1.154360 1.162004 1.057572
a1 0.033772 0.011464 0.140537
a2 0.032277 0.049255 N/A
a3 N/A -0.005879 N/A
Fig. 18. Determination of the BP and RP ﬂux excess dependence on
GBP −GRP colour and its application to deﬁne the corrected excess factor
C∗
. Top panel: BP and RP ﬂux excess vs. GBP − GRP colour for the
set of standard sources from Stetson (2000) secondary standards and
Ivezi´c et al. (2007). The red line represents the combined ﬁt based on
two diﬀerent polynomials for the bluer-end and the central region and
a linear ﬁt for the red-end. Bottom panel: corrected ﬂux excess factor
C∗
vs. GBP − GRP colour for a set of nearby sources selected from the
Gaia EDR3 archive.
standards. Only Gaia EDR3 photometry from gold sources was
used in the analysis. Using a single polynomial to ﬁt the data
tends to perform poorly at the blue and red ends of the distribution.
The blue end of the distribution is better described by a
quadratic polynomial; the central part of the distribution is well
ﬁtted by a cubic polynomial whereas the red end can be well represented
by a linear ﬁt. The coeﬃcients of the three polynomials
and their applicable colour range are provided in Table 2. The
resulting ﬁt, valid in the colour range −1.0 ≤ GBP − GRP ≤ 7.0,
is shown in the top panel of Fig. 18 and was used to compute
the corrected BP and RP ﬂux excess C∗
for a selection of ≈ 6.8
million nearby sources which are shown in the bottom panel of
Fig. 18: the C∗
has a ﬂat distribution in colour centred on zero.
We note here that, for DPAC-internal operational reasons, the
Gaia EDR3 archive provides only the uncorrected BP and RP
ﬂux excess C via the phot_bp_rp_excess_factor column:
the user can compute the corrected BP and RP excess factor C∗
as deﬁned by Eq. 6 using the polynomials provided in Table 2.
The corrected BP and RP excess factor C∗
can be used to idenFig.
19. Eﬀect of residual background ﬂux and blending probability
on the corrected BP and RP ﬂux excess. Top panel: Distribution of corrected
BP and RP ﬂux excess vs. GBP −GRP colour for a subset of nearby
sources fainter than 17 in G–band. The symbols are colour coded by
the source median residual background as measured from the BP spectra.
Bottom panel: Distribution of corrected BP and RP ﬂux excess vs.
GBP−GRP colour for the same sources shown in the top panel but colourcoded
by blend probability (see text for details). For these plots sources
have been further selected to retain sources where the blend probability
for BP and RP was in agreement to within 50%.
tify sources for which the G–band photometry and BP and RP
photometry is not consistent. We will now consider a number of
possible problems that might occur in the processing to try and
quantify the size of their eﬀect on C∗
. This should help understanding
the possible causes for (some of) the large C∗
values
seen in the Gaia EDR3 photometry.
In Gaia EDR3 the background treatment for BP and RP has
been considerably improved to deal with local variations for each
individual transit (see Sect. 3.2). As we have shown, some systematic
eﬀect related to crowded regions seems to be still present
in the data judging from the analysis of residual background.
We have also pointed out how diﬃcult it is to disentangle background
from crowding eﬀects when measuring the residual background
using the edge samples of BP and RP spectra. The two
panels of Fig. 19 show the distribution of the ﬂux excess factor
versus colour for the same selection of sources with diﬀerent
colour coding: in the top panel the colour of the dots indicates the
median residual background in BP, while in the bottom panel the
colour-coding is by an estimated ‘blend probability’. This latter
parameter is a combination of the fraction of blended transits (as
available in Gaia EDR3) and an additional indicator resulting
from a clustering analysis of all BP and RP epoch spectra for
a given source. The number of blended transits included in the
release is based on the available source catalogue. There will be
cases where the blending source was not in the catalogue, this
could be due to the secondary source being too close and/or too
Article number, page 13 of 35
A&A proofs: manuscript no. aa39587
Fig. 20. Corrected BP and RP ﬂux excess vs. GBP − GRP colour distribution
for a selection of nearby sources with magnitude G > 17 and
more than ﬁve calibrated epoch spectra in both BP and RP. In this plot
the corrected ﬂux excess C∗
bkg has been computed after removing the
median background residual ﬂux for both BP and RP for each source .
Sources are colour-coded by the median residual background as in the
top panel of Fig. 19. See Table 1 for an estimate of the impact of the
residual background ﬂux at diﬀerent magnitudes.
faint with respect to the primary and therefore never detected or
in very crowded regions, because the priority scheme on board
simply favoured brighter sources. In the blend cases, some of the
epoch spectra present clearly multiple peaks showing the presence
of more than one source in the window, however the position
and brightness of the peaks change with the scan angle and
due to the scanning law, often two groups of epoch spectra form
with quite distinct features. This is what the clustering analysis
is trying to detect. We have deﬁned the metric based on the
clustering analysis as the fraction of the spectrum in which the
analysis clearly detected a split in the epoch spectra measurements
(e.g. if only in ten out of 50 samples used for the analysis
the epoch sample measurements formed two distinct groups, the
metric will be equal to 0.2). In the bottom panel of Fig. 19 the
fraction of blended transits and the newly deﬁned metric have
been multiplied to form a single blend probability. Clearly there
are large correlations between the residual background and the
blend probability and sources with large ﬂux excess tend to have
large values for both parameters. From the top plot is also clear
how the low ﬂux excess values are very likely due to a slight
overestimate of the background. There is however a population
of sources that have high blend probability and not so large residual
background. It is also important to notice that the residual
background estimates obtained for these sources are often not
suﬃcient to justify their position in the ﬂux excess versus colour
diagram. Using the residual background estimates to correct the
integrated BP and RP ﬂuxes entering the computation of the ﬂux
excess still leaves a signiﬁcant fraction of sources with large ﬂux
excess. This can be seen in Fig. 20.
It is therefore interesting to analyse in more detail some of
the sources with very small and large corrected excess factor
to assess the origin of the discrepancy between the G and BP
and RP photometry. First we considered the small number of
sources in the dataset shown in the top panel of Fig. 18 with
very low C∗
(e.g. C∗
≤ −0.15): analysis of the epoch spectra
for these sources showed that in all cases the background had
been over corrected, leading to anomalously low ﬂux level in
BP and RP. Looking instead at the mean spectra of the ≈ 100
sources with highest excess the situation was less clear. Sometimes
there was a clear indication of variability, sometimes there
was clear indication of occasional multiple sources (e.g. blends)
Fig. 21. Corrected ﬂux excess factor vs. GBP −GRP colour for a selection
of variable and extended sources collated from catalogues available in
the literature and cross-matched with the Gaia EDR3 catalogue. The
following types are shown: GALAXY, QSO, RRLyrae (including all
subtypes), Ecl (Eclipsing variables), LP (Long Period variables) or SR
(Semi-Regular variables), and PNe (Planetary Nebulae).
Fig. 22. Sky distribution of the median corrected ﬂux excess C∗
factor.
The map was produced by computing for all gold sources the median
C∗
value for each level k = 8 HEALPix pixel.
and sometimes the spectra did not show any apparent anomaly.
In all cases the background appeared to have been corrected appropriately.
To explore this further, we used a catalogue of ≈ 8
million sources that was collated from the literature (and then
cross-matched with the Gaia EDR3 catalogue) including several
diﬀerent types of variable stars, galaxies, quasars and planetary
nebulae. Figure 21 shows the corrected ﬂux excess C∗
versus
GBP −GRP colour plot for this selection colour-encoded with the
source type (for a subset of those deemed to be of most interest).
One important feature revealed by this plot is that galaxies
tend to have a large discrepancy between the G and the GBP, GRP
ﬂuxes. This is not surprising since the IPD and LSF/PSF modelling
producing the integrated G ﬂuxes is optimised for point
sources. Additionally, for extended sources the diﬀerent satellite
scan angle under which each epoch observation is acquired will
lead to large ﬂuctuations in the integrated G ﬂux. For BP and RP
the window size is much larger and will therefore mitigate the
Article number, page 14 of 35
M. Riello et al.: Gaia Early Data Release 3: Photometric content and validation
eﬀect, more so for sources with smaller angular sizes (see Appendix
F for further details). Improvements in the LSF/PSF modelling
and the BP and RP background mitigation in Gaia EDR3
have also led to an improvement in the BP and RP ﬂux excess. In
particular when comparing the sky distribution of the corrected
BP and RP ﬂux excess in Fig. 22 with the distribution of C from
Gaia DR2 (see Fig. 18 from Evans et al. 2018) it is clear that
the ecliptic plane pattern caused by zodiacal light is no longer
visible.
From Fig. 21 it is also evident that sources with anomalous
SEDs, primarily those dominated by emission lines such as
QSOs and PNe, also show positive values of the corrected ﬂux
excess. In the case of point-like sources such as QSOs this is primarily
due to the presence of strong emission line in the wavelength
range where the RP passband has a larger transmissivity
with respect to the G passband. For PNe both mechanisms (the
presence of an extended structure and of strong emission lines)
may be at play. Similar deviations from zero of the corrected ﬂux
excess can be expected for other analogous cases such as sources
in HII regions.
The analysis presented in this section is based only on
sources with gold photometry. Looking at at C∗
for silver and
bronze sources is only possible for the subset of these two samples
with photometry available for all bands: 34% and 0.0007%
of the whole sample, respectively. Although the distribution of
C∗
versus colour is similar to that of the gold sources, some deviations
can be observed. This is not surprising because of the
limitations in the calibration process caused by the partial or
complete lack of colour information. For these sources the most
reliable photometric information is the G–band: GBP and GRP
have been provided when possible but are clearly not expected
to be very reliable. Since C∗
is a quantity derived from the G,
GBP, and GRP ﬂuxes it is expected that its reliability and usefulness
will diminish as the quality of its components is reduced.
For this reason we argue that C∗
is a metric that end users will
ﬁnd eﬀective mostly for sources with gold photometry.
The recommendation for Gaia EDR3 is to treat the BP and
RP ﬂux excess C, or better the corrected one C∗
, purely as an
indicator of consistency between the G photometry and the GBP
and GRP photometry and not as a data quality indicator. In particular
it is clear from the analysis presented so far that C∗
on
its own it is not suﬃcient to discriminate between data aﬀected
by processing problems and sources that could be variable, extended
or peculiar, either because of nearby sources or because
of anomalous SEDs. In this sense a one-size-ﬁts-all approach to
quality ﬁltering based on C∗
is neither possible nor desirable.
Some suggestions for ﬁltering based on C∗
are given in Sect. 9.4
but end users should evaluate their suitability depending on the
scientiﬁc goal they are trying to achieve.
7. External calibration
The goal of external calibration is to provide for each of the ﬁlters
G, GBP and GRP the shape of the passbands and the corresponding
zero points to allow for the transformation of internally
calibrated source ﬂuxes into meaningful magnitudes. The
strategy employed to achieve this is the same one adopted for
Gaia DR2: the passband is described by a parametric model
whose shape is adapted to minimise the diﬀerences between observed
and synthetic ﬂuxes computed on a set of calibrators with
known spectral energy distribution (SED). The mean source ﬂux
np is given in units of e−
s−1
and is related to the source photon
ﬂux distribution np(λ) by the relation:
np = P
∞
0
np(λ) S (λ) dλ, (7)
where P represents the telescope pupil area and S (λ) is the system
overall response function including the scaling factor to convert
from photons to e−
. This function represents the system
passband and is modelled as the product between a reference
response function R∗(λ) and a parametric function based on a
linear combination of Legendre polynomials Pi(λnorm):
S (λ) = R∗(λ) exp


nR
i=0
ri Pi(λnorm)

 , (8)
where λnorm is a normalised wavelength ranging in the interval
[−1, +1] deﬁned as:
λnorm = 2
λ − λmin
λmax − λmin
− 1. (9)
The reference response R∗(λ) for the G–band is equal to the nominal
pre-launch response (Jordi et al. 2010):
RG(λ) = T0(λ) ρatt(λ) Q(λ), (10)
where: T0(λ) is the reﬂectivity of the telescope (mirrors); ρatt(λ)
is the attenuation due to rugosity (small-scale variations in the
smoothness of the surface) and molecular contamination of the
mirrors; Q(λ) is the quantum eﬃciency (QE) of the CCD. For
GBP and GRP the reference response R∗(λ) is a cubic spline interpolation
on a 1 nm ﬁne grid lookup table derived from the
BP and RP instrument model (Montegriﬀo et al., in preparation).
Provided that the reference response function is non negative, the
exponential form of the parametric function in Eq. 8 guarantees
the non negativity of the passband S (λ).
The determination of Gaia DR2 passbands relied uniquely
upon the set of Spectro-Photometric Standard Sources (SPSS,
Pancino et al. 2012; Altavilla et al. 2015; Marinoni et al. 2016,
see also Appendix B) as calibrators; however that experience revealed
the low sensitivity of the calibration strategy to the actual
shape of the passbands, witnessed also by rather large number of
published curves: two diﬀerent sets in Evans et al. (2018), others
in Weiler (2018) and Maíz Apellániz & Weiler (2018), all providing
minimal changes in the SPSS residuals between observed
and synthetic photometry. The problem is that the most reliable
and stable ﬂux calibrators, such as the SPSS, constitute necessarily
a limited set of spectral distributions that can only constrain
a subset of model components leaving others completely
unconstrained (Weiler et al. 2018). To mitigate this limitation,
for Gaia EDR3 we decided to employ a much larger set of auxiliary
calibrators covering a wide range in stellar types: we selected
a large number of sources (N 100000) from the Stetson
(2000) secondary standards with 10 < G < 20 (see Appendix
B.2 for more details) and reconstructed the corresponding SEDs
from externally calibrated BP and RP spectra. These data will
be publicly available with the forthcoming Gaia DR3 release,
while the complete description of the spectral instrument model
calibration and methods will be provided in Montegriﬀo et al.
(in preparation). The interested reader can ﬁnd a summary of
the approach followed for the external calibration of BP and RP
spectra in Appendix A. It is however worth mentioning that no
external photometry from the Stetson sample has been used in
the calibration process: the calibration relies entirely on Gaia
Article number, page 15 of 35
A&A proofs: manuscript no. aa39587
data. Furthermore, the usage of a large set of BP and RP spectra
ensures that the internal photometric system is robustly tied
to the photometric system of BP and RP spectra: extended validation
of such photometric system will be provided in Montegriﬀo
et al. (in preparation). Finally, the external calibration of
BP and RP spectra is based on the SPSS calibrators and hence
the Gaia EDR3 ﬂux scale is tied to the Vega ﬂux scale to an
accuracy of 1%. In the external calibration of the BP and RP
instrument model, the issues related to the limited size of the
SPSS dataset have been mitigated by the addition of diﬀerent
kind of calibrators, mostly emission line sources such as QSO
or WR stars that were used to constrain speciﬁc components
(LSF and absolute wavelength scale). The comparison between
Gaia EDR3 magnitudes and the synthetic ones computed with
a preliminary set of passbands revealed two diﬀerent problems
aﬀecting the internally calibrated ﬂux scales: a colour term in
the G band was present for sources with G < 13 and colour
GBP − GRP < 1.1; a small discontinuity in the GBP residuals was
visible around G 10.8. Magnitude G = 13 corresponds to the
transition between window class 0 and 1 (Gaia Collaboration
et al. 2016), therefore the ﬁrst issue has been interpreted as an
interaction between a non-optimal convergence of the internal
calibration between the two instrument conﬁgurations and the
known colour-dependent issues with the PSF calibration (Rowell
et al. 2020), rather than being due to some unidentiﬁed issue
aﬀecting the calibration of BP and RP spectra, a hypothesis enforced
also by the lack of a similar eﬀect in the GBP and GRP
residuals. Regarding the second eﬀect, discontinuities in the behaviour
of the GBP photometry around this value of G magnitude
were noticed also in Gaia DR2 (ﬁrst reports were in Arenou, F.
et al., 2018, but see also Weiler et al., 2018, and Maíz Apellániz
& Weiler, 2018). While the Gaia EDR3 and DR2 photometric
systems should be considered independent due to the substantial
diﬀerences in the processing that lead from the raw data to the ﬁnal
photometric catalogue and to the addition of a large amount
of new data, the fact that a similar feature is still present indicates
that this might be connected to a change in the instrument
conﬁguration, for example a gate activation, occurring at approximately
this magnitude and that has not yet been fully calibrated
out. To minimise these eﬀects in the ﬁnal photometry a correction
has been applied to the (epoch and mean) IG and IBP ﬂuxes
available from the Gaia EDR3 archive. The IG ﬂuxes correction
has been derived as a polynomial function of the BP and RP ﬂux
ratio:
I∗
G = IG
3
i=0
ci
IBP
IRP
i
, (11)
with c = (0.9938297, 0.0118275, −0.0019720, 2.253619 10−4
).
This correction has been applied only to sources with G < 13
and −0.5 < GBP − GRP < 1.1: the correction was clamped below
GBP − GRP = −0.5 to avoid extrapolations below that limit. Similarly,
the GBP discontinuity has been removed by correcting the
corresponding ﬂuxes:
I∗
BP = IBP × 10−0.4 δBP
, (12)
with δBP = 0.003763096 for G < 10.8. To avoid the creation of
artefacts in the data, such as visible gaps in the colour-magnitude
diagrams around the limiting magnitudes, these two corrections
have been applied gradually (a linear onset ±10% in ﬂux around
G = 13 and G = 10.8). The ﬁnal passbands were then computed
using only sources in the range 13 < G < 16, 11 < G < 16.5,
and G < 16.5 for G, GBP, and GRP, respectively; in all cases three
Fig. 23. Residuals between Gaia EDR3 photometry and synthetic magnitudes
obtained with the ﬁnal passbands and computed for a sample of
∼ 100000 SEDs obtained from externally calibrated BP and RP spectra
for G (top), GBP (middle), GRP (bottom) as function of G magnitudes
(left) and GBP − GRP (right). The colours red, blue, dark, and light grey
indicate sources with G < 10.8, 10.8 < G < 13, 13 < G < 16, and
G > 16, respectively.
Legendre polynomials have been used in Eq. 8 to model the passbands.
Figure 23 shows the ﬁnal residuals between Gaia EDR3
photometry and synthetic magnitudes derived from the externally
calibrated BP and RP spectra for the whole set of calibrators.
Residuals do not show signiﬁcant trends with colour and
the rms ranges from ∼ 0.01 mag for G to less than 5 mmag for
the GRP case. The downturn at faint magnitudes visible in all
three passbands for sources fainter than G 16.5 is possibly
caused by some bias in the background either in the integrated
photometry or in the BP and RP spectra. Since it is not possible
to conﬁdently attribute the origin of this eﬀect solely to the integrated
photometry in each of these plots, we do not consider it
appropriate to either attempt or suggest any correction for ﬂuxes
at the faint end. The residuals in the top left plot show that the
signiﬁcant magnitude term that was aﬀecting Gaia DR2 photometry
(Casagrande & VandenBerg 2018; Weiler 2018) is not
present anymore although a small trend below a few mmag/mag
cannot be excluded (an exception to this general behaviour is
represented by extremely blue and bright sources as discussed in
Sect. 8.4). Moreover the G–band residuals as a function of colour
(top right plot) show that the eﬀects of the two photometric systems
have been mitigated by the ﬂux scale correction applied to
the blue and bright end, leaving some residual diﬀerential colour
terms below the 1% level for GBP −GRP < 0.5. Residuals of GRP
as function of magnitude (bottom left panel) seems to indicate a
small trend for magnitudes G < 13; a linear ﬁt reveals a trend below
0.8 mmag/mag for which no correction has been attempted.
We are aware that such inhomogeneities in the photometry may
lead to claims for the presence of multiple passbands (as was the
case in Gaia DR2 for BP): however, given that the overall accuracy
of the absolute calibration is around 1% we consider it
unnecessary introducing additional complexities to account for
eﬀects that are below this level and that only concerns a small
subset of the whole catalogue.
The passbands are shown in Fig. 24 together with nominal
pre-launch curves (represented in grey colour) for comparison
and are distributed in electronic tabular format as part of this paper
and are available via the VizieR service. We emphasise here
that each new release of Gaia data is based on a complete reprocessing
of raw data (see also the considerations made at the
end of Sect. 2 and in Sect. 9.5), hence the photometric system
Article number, page 16 of 35
M. Riello et al.: Gaia Early Data Release 3: Photometric content and validation
Fig. 24. G (green), GBP (blue) and GRP (red) passbands for the
Gaia EDR3 photometric system; grey curves represent nominal prelaunch
passbands.
deﬁned by the internal calibration has no relations with photometric
systems of previous releases. This photometric system indeed
refers to an abstract ‘average instrument’ whose properties
descend not only from the real instrument but also from all the
calibration models that are layered in the long chain of the data
reduction pipelines. For this reason it is meaningless to compare
the Gaia EDR3 passbands with the diﬀerent sets released for
Gaia DR2 and changes in the instrument passband between different/subsequent
releases are not be interpreted as some sort of
evolution of the instrument.
Once the passbands have been deﬁned, the corresponding
zero points can be evaluated in the VEGAMAG and in the AB
systems following a standard procedure composed of three main
steps. First the synthetic ﬂuxes are computed for each calibrator
by evaluating the mean energy per wavelength unit
< fλ > =
fλ(λ) S (λ) λ dλ
S (λ) λ dλ
, (13)
for VEGAMAG, and the mean energy ﬂux per frequency unit
< fν > =
fλ(λ) S (λ) λ dλ
S (λ) (c/λ) dλ
, (14)
for AB. Then, the synthetic ﬂuxes are converted to magnitudes
by applying the relative zero point. For VEGAMAG:
mVEG = −2.5 log < fλ > +2.5 log
fVg
λ (λ)S (λ)λ dλ
S (λ)λ dλ
, (15)
where fVg
λ (λ) is the Vega spectrum from the CALSPEC Calibration
Database5
rescaled to set the ﬂux equal to f550 =
3.62286 10−11
W m−2
nm−1
at the wavelength λ = 550.0 nm,
which is assumed as the ﬂux of an unreddened A0V star with
V = 0 (see the online documentation for a detailed description
of the reference f550 ﬂux deﬁnition). Instead, for AB:
mAB = −2.5 log < fν > −56.10, (16)
where the value of the zero point corresponds to ﬂuxes measured
in units of W m−2
Hz−1
. Finally, the passband zero point is
5
Provided by the alpha_lyr_mod_002.fits ﬁle.
Fig. 25. Residuals between Gaia EDR3 magnitudes and synthetic ones
computed on SEDs from ground and space based observations of SPSS
(red symbols) and PVL (blue symbols) sources for G (top), GBP (middle),
and GRP (bottom) as function of G magnitudes (left) and GBP −GRP
(right)
computed as the mean value of the ratios between synthetic and
uncalibrated magnitudes for the whole set of calibrators:
ZPX =
mX
−2.5 log(np)
, (17)
where X stands for either VEGAMAG or AB.
The values of the zero points for both systems are reported in
Table 3 along with some useful passband related quantities such
as the ﬁlter full width at half maximum (FWHM), the mean photon
wavelength λ0 and the pivot wavelength λp (a useful parameter
introduced by Koornneef et al. (1986) that allows for an exact
conversion between the broadband ﬂuxes < fλ > and < fν >). It
is important to note that these passband zero points are not suitable
for synthetic magnitude evaluations for which the correct
value must be computed according to Eq. 15: Gaia ﬂuxes are in
units of e−
s−1
(not normalised by the telescope area) while synthetic
ﬂuxes refer to mean energy densities in units of W m−2
nm−1
.
Finally, Fig. 25 shows the residuals between the Gaia EDR3
magnitudes of SPSS (red dots) and PVL (blue dots) sources (see
Appendix B for more details) and the synthetic magnitudes computed
from the corresponding SEDs obtained from independent
ground or space based observations. Residuals are plotted for G,
GBP and GRP as function of G and GBP − GRP colour. The horizontal
grey lines represent the weighted mean of residuals that in
the vast majority of cases amounts to a few mmag. As explained
in Sect. 5 at the bright end residual saturation eﬀects dominate
the uncertainties.
8. Known issues with the published photometry
8.1. Overestimated mean GBP ﬂux for faint red sources
When computing the weighted mean ﬂux for a source in a given
band, epochs with a calibrated ﬂux lower than 1 e−
s−1
were excluded.
This lower limit was introduced for Gaia DR2 (Riello
et al. 2018) to prevent problems caused by extreme outliers in
the G–band, however the threshold was applied also to the generation
of GBP and GRP. For the G–band the lower ﬂux limit
should not cause any bias because it corresponds to G ≈ 25.8,
which is well below the on-board limit used by the VPU to consider
a source eligible for observation (de Bruijne et al. 2015).
Article number, page 17 of 35
A&A proofs: manuscript no. aa39587
Table 3. Photometric zero points in the VEGAMAG and AB systems, the FWHM, the mean photon wavelength λ0, the pivot wavelength λp for
G, GBP and GRP.
G GBP GRP Units
ZPVEG 25.6874 ± 0.0028 25.3385 ± 0.0028 24.7479 ± 0.0028 mag
ZPAB 25.8010 ± 0.0028 25.3540 ± 0.0023 25.1040 ± 0.0016 mag
FWHM 454.82 265.90 292.75 nm
λ0 639.07 518.26 782.51 nm
λp 621.79 510.97 776.91 nm
The on-board detection limit has resulted in epoch observations
having GVPU < 20.7 and even allowing for a generous error
on the on-board estimated (and uncalibrated) magnitude GVPU,
ﬂuxes much lower than the threshold cannot be observed as part
of a normal distribution (therefore they can only occur due to
problems in the processing). However, this minimum ﬂux threshold
can cause an overestimated mean BP ﬂux for faint sources,
which tend to have a red colour, and therefore have a much lower
ﬂux in GBP than GRP.
To exemplify the issue, we selected from the Gaia EDR3
archive all gold nearby sources with an error on the parallax
smaller than 1 mas providing a sample of ≈ 3.4 million sources.
The left panel of Fig. 26 shows the G vs GBP − G colour–
magnitude diagram (CMD) for these sources. A striking feature
of this CMD is the tail at the faint end of the main sequence
bending towards bluer colours, which is clearly unexpected. This
feature is produced by the fact that the 1 e−
s−1
minimum ﬂux
threshold adopted for the G–band was also applied to GBP: when
progressively fainter red sources are observed, the distribution
of their epoch photometry will be progressively more clipped at
its faint end leading to an overestimated mean ﬂux. To conﬁrm
the nature of this feature we performed a simple experiment in
which we regenerated the mean GBP source photometry removing
the minimum ﬂux threshold. The result of the experiment is
shown in the right panel of Fig. 26, which presents the CMD for
the same set of sources but using the new G∗
BP computed without
the low ﬂux threshold: the tail feature is no longer visible and the
sources previously located there have been redistributed towards
redder colours.
The top panel of Fig. 27 shows the diﬀerence between the
recomputed G∗
BP magnitude and the GBP from the Gaia EDR3
archive versus GBP. The plot shows that the two values are in
good agreement until GBP ≈ 20.3 at which point the discrepancy
between the two magnitudes grows progressively larger reaching
a size of several magnitudes. The inset panel focuses on the
transition region and includes the 16th (dashed), 50th (solid), and
86th (dashed) percentile lines, which conﬁrms that the eﬀect of
the low ﬂux threshold is modest for GBP < 20.3.
The low ﬂux threshold has also the eﬀect of reducing the
measured scatter in the GBP mean source photometry. To estimate
the size of this eﬀect we selected sources with at least 50
epoch observations in G∗
BP in a set of 5 mmag slices at G∗
BP=20,
21, 22, 23, 24 and computed the median and MAD of the calibrated
epoch ﬂuxes for all the sources in each slice using all
epochs or just those with ﬂux larger than 1 e−
s−1
. The analysis is
done in ﬂux-space because the error distributions in magnitudespace
are not symmetric with the discrepancy becoming progressively
larger towards fainter ﬂuxes (see Appendix H). The
scatter measured at these faint magnitudes will have signiﬁcant
contributions from non-poissonian sources of uncertainties, such
as background, contamination and blends. The results are sumFig.
26. Colour–magnitude diagram for a sample of ≈ 3.4 million
nearby sources selected from the Gaia EDR3 archive. The left panel
shows the CMD produced using the G and GBP magnitudes from the
Gaia EDR3 archive, which presents a tail like feature bending progressively
towards bluer colours for fainter G magnitudes. The right panel
shows the CMD for the same sources but with G∗
BP recomputed without
the low ﬂux threshold.
marised in Table 4: the increase in scatter when using all transits
is rather modest for sources close to G∗
BP ≈ 20 and only increases
to 30 − 40% at fainter G∗
BP magnitudes. Although the increase in
scatter for the G∗
BP=24 slice is smaller than for the G∗
BP=23 slice,
it should be noted that these two fainter slices have ten times
fewer epochs available than the brighter slices and therefore the
discrepancy is probably due to small number statistics. Finally,
it should be noted that at G∗
BP ≈ 22 the scatter is already of the
same order of magnitude as the mean ﬂux and since Fig. 27 suggests
that the corrected G∗
BP magnitude could reach values even
fainter than 25, it is unlikely that the photometry of these sources
would be of much scientiﬁc value.
In principle the same problem described for GBP could also
aﬀect GRP. The main reasons are that the same 1 e−
s−1
ﬂux
threshold was also applied when generating the GRP source photometry
and that the RP spectra undergo the same process as
the BP ones and therefore have similar error budgets. We therefore
performed the same analysis described above for GRP as
well. The bottom panel of Fig. 27 shows the diﬀerence between
the recomputed G∗
RP magnitude and the GRP and the GRP from
the Gaia EDR3 archive versus GRP. As it can be seen, the effect
in GRP is considerably smaller than in GBP; in particular at
GRP = 20 it is ≈ 0.05 magnitudes. Considering the typical uncertainty
in the GRP photometry at this magnitude (see Fig. 14)
an additional ﬁlter on GRP does not seem to be required.
Article number, page 18 of 35
M. Riello et al.: Gaia Early Data Release 3: Photometric content and validation
Fig. 27. Change in the GBP (top panel) and GRP (bottom panel) magnitude
when removing the 1 e−
s−1
ﬂux threshold vs. GBP and GRP magnitude
spectively for the sample of ≈ 3.4 million nearby sources. The
insets show a zoom of the transition region where the discrepancy between
the two magnitudes becomes signiﬁcant. The solid red lines show
the median of the residual distribution, and the two red dashed lines
show the 84th and 16th percentiles.
Finally, we used the same dataset to asses the impact of this
issue on the BP and RP ﬂux excess. Although a systematic eﬀect
is present, it is also small and generally well within the typical
uncertainties on the BP and RP ﬂux excess (see Sect. 9.4). This is
understandable since faint red sources will have the vast majority
of their ﬂux in the RP band and therefore even a large change in
BP ﬂux will not signiﬁcantly alter the value of the C ratio. The
signiﬁcance of the systematic diﬀerences in C and C∗
can be
further reduced by applying the ﬁltering suggested in Sect. 9.2.
See also the online Gaia EDR3 documentation for more details.
Table 4. Statistics for the ﬁve magnitude slices used to characterise the
scatter. The ﬁrst column provides the G∗
BP magnitude of the 5 mmag
slice; the second column is the percentage of epochs that are used when
the 1 e−
s−1
ﬂux threshold is applied; columns three and four provide the
median ˜f and MAD ﬂux when all epochs are used; columns ﬁve and six
provide the median ˜f and MAD ﬂux when the low ﬂux threshold is
applied; column seven provide the percentage increase in scatter when
the the low ﬂux threshold is not used.
All Threshold
G∗
BP Nt/N ˜f MAD ˜f MAD ∆σ
20 95.5% 139.90 32.06 140.74 31.49 1.8%
21 89.1% 56.28 29.52 62.36 26.15 12.9%
22 70.9% 23.34 29.51 40.02 22.86 29.1%
23 58.2% 10.15 29.60 34.70 21.04 40.7%
24 55.5% 5.60 27.27 31.25 20.55 32.7%
8.2. Sources with poor SSCs
While validating the photometry for Gaia EDR3 it was realised
that the G magnitude distribution has a small tail of very faint
sources extending as faint as G ≈ 25.5. Every Gaia transit has
an associated uncalibrated magnitude estimated on-board by the
VPU. By design (de Bruijne et al. 2015) the VPU detection magnitude
does not reach values fainter than ≈ 21 mag since the detection
algorithm will not assign a window to fainter detections.
Even allowing for a generous error in the on-board estimated
magnitude, it is clearly not possible for Gaia to have observed
sources much fainter than G ≈ 21. The cause of these unrealistically
faint sources was found to be due to unreliable reference
SSCs estimated from the mean spectra used in the calibration
process. It is indeed possible that if the SSCs ﬂuxes forming the
ratios used in the LS calibration model (see Sect. 4.4) have extreme
values the resulting calibration factor could have a value
considerably smaller than 1 leading to a much fainter calibrated
ﬂux. It should be noted that the calibrations do not have any
problem, the issue is caused by unreliable colour information
(i.e. the SSCs) being used when applying the calibration to the
epoch photometry of some sources.
The unreliable SSCs values were caused mostly by two different
issues: 1) sources with mean spectra signiﬁcantly aﬀected
by blending with another source leading to signiﬁcantly higher
ﬂux levels in the boundary SSCs; 2) red sources with extremely
low ﬂux in GBP leading to very low signal-to-noise mean spectrum
and hence very unreliable BP SSC values. Sources aﬀected
by this issue were identiﬁed from having extreme values of the
SSC ratios used when applying the LS calibration and their photometry
has been removed from the main Gaia EDR3 catalogue
(see Fabricius et al. 2020, for more information). A separate table
with ad hoc photometry produced by calibrating the sources
as bronze (i.e. using default SSC values) is available from the
Gaia EDR3 known issues web page.
8.3. Systematics due to use of default colour in the IPD
In determining the G–band ﬂuxes, an appropriate PSF or LSF
must be chosen in order to carry out the IPD (Rowell et al. 2020).
One of the parameters used to select the LSF/PSF is the colour of
the source and this is done using the νeﬀ value determined from
the mean BP and RP spectrum. In some cases, this value is not
available, so a default one is used: this will lead to a systematic
eﬀect in both astrometry and G–band photometry. In the former
case, the handling of the chromaticity eﬀects in the astrometric
solution automatically dealt also with this systematic (Lindegren
et al. 2020). Unfortunately the importance of this eﬀect was not
recognised early enough to be included in the photometric calibration
model and therefore the only option for Gaia EDR3 is to
derive a correction to the internally calibrated mean source photometry.
We note that the photometric calibrations (LS and SS)
were derived only with data for which the IPD used the appropriate
νeﬀ and therefore are unaﬀected by this issue.
The correction has been determined using a short period of
data for which the IPD was generated twice using the appropriate
νeﬀ values and the default one. The period was chosen such
that the scan direction would cross the Small Magellanic Cloud
so that a signiﬁcant number of blue stars were available for the
calibration. The analysis of this dataset showed that the systematic
generated by the use of a default νeﬀ is a function of νeﬀ,
AC position, CCD, FoV and magnitude. In principle the correction
should be applied to the epoch photometry before generating
the mean source photometry, however this approach is impractiArticle
number, page 19 of 35
A&A proofs: manuscript no. aa39587
Fig. 28. Systematic in G ﬂux caused by using a default νeﬀ in the IPD
process as a function of colour. The analysis is divided into the magnitude
ranges G < 13 (red), 13 < G < 16 (green), and G > 16 (blue). The
magenta lines are cubic polynomial ﬁts to these lines.
cal since the epoch photometry is not available in Gaia EDR3.
Since there are many observations contributing to the mean G–
band photometry in the dataset used, an average correction can
be calculated that is only dependent on source properties available
in the Gaia EDR3 archive: colour and magnitude.
Investigation as a function of magnitude showed that the systematic
was mainly dependent on the window class conﬁguration,
thus the analysis was divided into the magnitude ranges
G < 13, 13 < G < 16, and G > 16. There is a further slight
magnitude dependence at the faintest magnitudes, but this is not
taken into account. Figure 28 shows that there is no signiﬁcant
correction needed for the brightest range that corresponds to
sources observed mainly with 2D observations. This is probably
due to the colour calibration of the PSF not being as good
as that of the LSF (Rowell et al. 2020) and therefore the difference
between the normal and default νeﬀ processing not being
as pronounced. A simple cubic relationship as a function
of colour f(GBP − GRP) was ﬁtted to the measured systematic
and the coeﬃcients are given in Table 5 for the two magnitude
ranges for which the correction is required. The corrected G ﬂux
can therefore be obtained as I∗
G = IG × f(GBP − GRP). The applicability
range in GBP − GRP of these corrections is 0.25 to
3.0. Outside of this range, the correction values at these limits
should be used. We note that no correction is needed for G < 13.
To validate this correction, main sequence stars were selected
Table 5. Coeﬃcients of the correction to the G–band ﬂuxes to be applied
to the stars where a default νeﬀ has been used in the IPD. The
correction is a simple cubic polynomial in GBP − GRP. The applicability
range in GBP − GRP of these corrections is 0.25 to 3.0. Outside of
this range, use the correction values at these limits. We note that no
correction is needed for G < 13. The old ﬂuxes are multiplied by this
correction to obtain the corrected ﬂuxes.
G range c0 c1 c2 c3
13 < G < 16 1.00876 -0.02540 0.01747 -0.00277
G > 16 1.00525 -0.02323 0.01740 -0.00253
from the HR diagram for stars with parallax greater than 3 mas.
The split that can be seen in the G − GRP versus GBP − G plot,
shown in the upper panel of Fig. 29, is due to whether a star has
been processed with an actual or default νeﬀ. The lower panel
Fig. 29. Eﬀect of correcting the photometry of sources for which the
IPD used a default colour. Top panel: This shows a colour-colour diagram
of the reddest main sequence stars in the local neighbourhood with
parallax greater than 3 mas. The points are colour coded using the νeﬀ
value used in the processing. If the default νeﬀ was used, then the points
are coloured grey. Bottom panel: The same sources but with corrected
G–band photometry.
shows the same plot but with corrected G–band photometry for
stars that have had a default νeﬀ used in the IPD. These can
mostly be identiﬁed in the Gaia EDR3 archive as those having
astrometric_params_solved=95. There is also the case of
astrometric_params_solved=3 for which the value of νeﬀ
used cannot be established from the archive. However, since
most of these have used a default νeﬀ, the correction should be
applied (see Lindegren et al. 2020). As can be seen, the correction
removes the split.
Additional checks were carried out for stars redder than
GBP − GRP =3.0. Similar to that seen in Fig. 29, this correction
removes the split from the data even for the reddest stars. Due
to the nature of the cyclic processing chain, the νeﬀ value used
in the IPD, comes from the previous processing cycle, which
contained less data. This means that there are many sources that
used a default νeﬀ in IPD, but have a mean GBP − GRP value in
Gaia EDR3 catalogue that can be used to correct for this eﬀect
using the procedure described in this section. Since this is a large
number, a separate table in the archive is provided with the corrected
G–band photometry for the convenience of the user.
Article number, page 20 of 35
M. Riello et al.: Gaia Early Data Release 3: Photometric content and validation
Fig. 30. Residuals between Gaia EDR3 magnitudes and synthetic ones
from BP and RP spectra computed on all-sky sources brighter than G <
13 and GBP − GRP < −0.1 for G as function of G magnitudes (left) and
GBP − GRP (right)
8.4. G–band magnitude term for blue and bright sources
An anomaly has been detected in the data while looking at the
residuals between Gaia EDR3 and synthetic magnitudes derived
from BP and RP spectra for an all-sky sample of extremely blue
and bright sources (G < 13 and GBP − GRP < −0.1): the residuals
plotted in Fig. 30, show a trend of about 5 mmag/mag for
the magnitude range 8 < G < 13. More precisely, a linear ﬁt to
data in this range results in the relation ∆G = 0.054 − 0.0046G.
Brighter than G = 8, residuals are dominated by saturation effects.
Such trends are not seen in the GBP and GRP cases. The origin
of this behaviour is probably due to the PSF/LSF calibration
where problems are known to exist with the colour-dependent
part of the PSF model (Rowell et al. 2020). This magnitude trend
is not noticeable in Fig. 23 and Fig. 25 because very few sources
fall in this magnitude and colour interval: the interested reader
can ﬁnd some additional details and plots in the online documen-
tation.
9. Considerations for the end user
A major point made by this paper is that there is no silver bullet
when it comes to identifying problematic data in a large catalogue
like Gaia EDR3. The best approach is inevitably dependent
on the speciﬁc scientiﬁc goal that the end user is pursuing.
However, it is recognised that it can also be valuable to have
a set of prescriptions that could be applied when a preliminary
exploration of the data is required before committing to a more
detailed analysis. In this spirit, this section provides a number
of suggestions for possible quality ﬁlters that users may want to
consider while also pointing out some caveats. This section deals
only with the photometric content of Gaia EDR3, the reader is
referred to Lindegren et al. (2020) for what concerns the astrometric
content of the catalogue.
While it is obviously worthwhile including in the archive
query some basic restrictions (e.g. magnitude and/or colour
range, minimum number of observations, sky position, basic astrometric
parameters, etc.) to beneﬁt from the database indices
and restrict the data volume to a manageable size, we suggest
applying more detailed ﬁltering as a post-processing operation.
This allows the use of quantities that are not available in the
Gaia EDR3 archive and to tweak the selection criteria to assess
their impact.
9.1. Photometry release criteria
In order for the photometry of a given source to be published in
Gaia EDR3 the following basic criteria had to be met. When the
photometry did not meet these criteria the corresponding values
and relevant derived quantities have been nulliﬁed. In particular:
phot_g_n_obs≥ 10 is required for the G photometry to be
Table 6. Eﬀect of ﬁltering on GBP for the nearby source dataset. GBP
shows the maximum value allowed for phot_bp_mean_mag, IBP shows
the corresponding minimum value allowed for phot_bp_mean_flux.
GBP IBP N Fraction
20.3 103.61 7,575,348 68%
20.4 94.49 7,812,148 71%
20.5 86.18 8,047,835 73%
20.6 78.60 8,284,960 75%
20.7 71.68 8,523,503 77%
20.8 65.37 8,769,848 79%
20.9 59.62 9,026,012 82%
- - 11,069,066 100%
published; phot_bp_n_obs≥ 2 is required for the GBP photometry
to be published; phot_rp_n_obs≥ 2 is required for the GRP
photometry to be published. See also Lindegren et al. (2020) for
a discussion of the criteria applied to the astrometric quantities.
9.2. Filter on GBP
Section 8.1 showed that GBP tends to be systematically brighter
towards the faint end: it would therefore make sense to include
a restriction on GBP in the archive query. From Fig. 27 the restriction
GBP < m could be in the range 20.3 ≤ m ≤ 20.9, which
corresponds to the range where 50% of the sources should have
a GBP ﬂux that is unaﬀected by the systematic to where 50% of
the sources are systematically brighter by 0.2 mag. The value
chosen for m will have an impact on completeness and on the
magnitude and colour range of the selection. Of course if GBP
(or derived quantities) is not required in the analysis, then there
is no use in applying the ﬁlter.
To illustrate the eﬀect of this ﬁlter we used the set of the
nearby sources for which the appropriate νeﬀ was used in the IPD
process6
. Table 6 shows the fraction of sources that were retained
as a function of the magnitude threshold in the suggested range.
The brightest threshold caused nearly one third of the sources
to be excluded from the selection, the faintest threshold instead
caused 18% of the sources to be excluded.
9.3. Crowding effects
Gaia EDR3 includes two quantities that were not available in
Gaia DR2 and provide the number of BP and RP epoch transits
included in the mean source photometry that are likely to be
blended7
with one or more other sources (see Sect. 3.1). A useful
metric that can be computed for a source is the blending fraction
β, which is deﬁned as the sum of the number of blended transits
in BP and RP divided by the sum of the number of observations
in BP and RP8
. To avoid systematic problems caused by crowding,
sources could be required to have a low blend fraction, for
example β ≤ 0.1 allows only 10% of the epochs to be aﬀected
by blending.
6
This required an additional constraint to the criteria deﬁned in
Sect. 2: astrometric_params_solved>=31
7
Columns phot_bp_n_blended_transits and
phot_rp_n_blended_transits in gaia_source table in the
Gaia EDR3 archive.
8
In terms of Gaia EDR3 archive columns: β =
(phot_bp_n_blended_transits+phot_rp_n_blended_transits)
*1.0 / (phot_bp_n_obs + phot_rp_n_obs) where the term 1.0 is
required to ensure the ﬂoating point division.
Article number, page 21 of 35
A&A proofs: manuscript no. aa39587
There are a number of caveats that should be considered
when applying this kind of selection. First, the fact that a source
has β = 0.0 does not necessarily imply that the source is not
aﬀected by crowding. The reason is that the crowding assessment
(see Sect. 3.1) is limited to sources that are present in
the Gaia catalogue, meaning sources that have been acquired,
at least once, by Gaia. Close pairs, namely sources that are close
enough on the sky to never be resolved by Gaia as a non-single
source, will not be excluded by a ﬁlter on β. The second caveat
is that β does not take into account the ﬂux ratio between the
target source and the blending source(s): for example, if β = 0.5
but the target source is GBP = 14.0 and the blending source is
GBP = 19.0, then the eﬀect of the blending source on the target
source is probably negligible. In principle, users can assess this
eﬀect, at least in the case where the blending is from a source
that is close to the target source on the sky (an occasional blend
could be due to a source coming from the other FoV). First all
sources aﬀected by blending can be detected using a ﬁlter on
β, then for each of these sources a cone search query could be
performed to ﬁnd other sources in the Gaia EDR3 archive that
are close enough to the target source to result in a blend. Since
the size of a BP and RP window is approximately 3.5 × 2.1 arcsec
(AL×AC), sources that are closer than ≈ 1.05 arcsec should
always be blended whereas sources that are at a distance larger
than ≈ 1.05 arcsec but closer than ≈ 1.75 arcsec will occasionally
be blended depending on the satellite scan direction. Once
the blending source(s) have been identiﬁed it will become possible
to make a more informed decision on whether the blend is
likely to have a signiﬁcant eﬀect on the photometry or not.
9.4. Filter on BP and RP corrected ﬂux excess
Section 6 introduced the corrected BP and RP ﬂux excess, C∗
,
which is obtained for a given source by subtracting from the BP
and RP ﬂux excess C (see Evans et al. 2018) the expected excess
at the source colour produced by the polynomial deﬁned in Table
2. We remind the user that C∗
is not available as a column in
the Gaia EDR3 archive and therefore has to be computed as described
in Sect. 6. C∗
provides a measure of consistency between
the G–band, BP and RP photometry and therefore can be used
to exclude sources showing inconsistencies. Section 6 analysed
in detail diﬀerent possible causes of the inconsistency, showing
that it could originate in any of the bands. This is the major limitation
with C∗
: it only indicates the presence of an inconsistency,
without an indication to where it originates. Filtering on C∗
can
also be problematic when completeness is important since it will
have the eﬀect of excluding variable and extended sources (see
Fig. 21).
To devise a selection criteria for C∗
we made use of the Stetson
and Ivezic standards (Stetson 2000; Ivezi´c et al. 2007) to determine
the C∗
scatter versus G magnitude using all the sources
in the sample with G > 9 (to avoid problems with low number
statistics at the bright end). The scatter was measured in bins of
0.01 mag and the resulting dataset was then ﬁtted with a simple
power law in G magnitude:
σC∗ (G) = c0 + c1Gm
, (18)
with c0 = 0.0059898, c1 = 8.817481 · 10−12
, and m = 7.618399.
This ﬁt is considered to represent the 1σ scatter for a sample
of well behaved isolated stellar sources with good quality Gaia
photometry.
The top panel of Fig. 31 shows the C∗
dependence on G
magnitude with the σ and 3σ lines represented by the ﬁt described
above. The bottom panel of Fig. 31 shows the C∗
depenFig.
31. Corrected BP and RP ﬂux excess vs. magnitude for the Stetson
and Ivezic dataset (top panel) including the ±σ (solid) and ±3σ
(dashed) scatter lines and for the nearby source dataset (bottom panel)
including the ±σ (solid), ±3σ (dashed) and ±5σ (dotted) scatter lines.
The scatter lines are deﬁned by Eq. 18 with the ﬁt coeﬃcients provided
in the text.
Fig. 32. Colour–magnitude diagram for the nearby source sample for
all sources with GBP < 20.75 (left top panel), the subset of sources with
|C∗
| smaller than 5σ (central top panel) and smaller than 3σ (right top
panel). The bottom panel shows the fraction of sources selected using
the two thresholds.
dence on G magnitude for a sample of nearby sources (limited to
GBP < 20.75) showing the ±σ, ±3σ and ±5σ lines. A possible
ﬁlter on the corrected BP and RP ﬂux excess can be deﬁned in
terms of the ﬁtted scatter line as |C∗
| < NσC∗ . The ﬁlter should
only be applied for G > 4 mag as for brighter magnitudes the
eﬀects of saturation are still too large (see Sect. C.1). To illustrate
the eﬀect of the C∗
ﬁlter, we use the set of nearby sources
Article number, page 22 of 35
M. Riello et al.: Gaia Early Data Release 3: Photometric content and validation
with GBP < 20.75 (see Sect. 9.2). Figure 32 shows the CMDs
for the full dataset and then for two selections that applied a 5σ
and a 3σ cut on C∗
. The bottom panel of Fig. 32 shows the fraction
of sources as a function of G magnitude for the two ﬁltered
datasets. The eﬀect of the GBP magnitude ﬁlter (see Sect. 9.2)
is also clearly visible as a progressive brighter faint-magnitude
limit towards red colours visible in the CMDs. This also explains
the fact that the fraction of selected sources has a minimum at
G ≈ 19.5 to then increase again for fainter magnitudes where
the sources in the sample have bluer colours and are less likely
to have a large C∗
(see Fig. 18). The C∗
ﬁlter seems to be mostly
reducing the population of sources between the white dwarf and
main sequence.
9.5. Caveat on comparisons with previous releases
There are many reasons why the data from Gaia DR2 should not
be used in conjunction with or compared to that in EDR3. Due
to the way that the photometric systems have been set up in the
two releases, the passbands are diﬀerent and cannot be compared
directly. In general there will be colour terms between them. Although
the diﬀerence between the Gaia EDR3 and DR2 passbands
are smaller than between DR2 and DR1 (G–band only),
it will still amount to a few percent. Speciﬁcally for the G–
band, the PSF and LSF calibrations (Rowell et al. 2020) have
improved greatly for data release and a number of systematic
eﬀects have been corrected because of this, for example the linearity
of the magnitude scale. Also, since the PSF and LSF ﬁts
are now much better for point sources, the diﬀerence in the photometry
between extended and point sources will be ampliﬁed.
In some cases this diﬀerence will amount to 0.5 mag or more.
This issue is further discussed in Appendix F.
The source IDs are in the majority unchanged between
Gaia DR2 and EDR3, but it is still a signiﬁcant number (see
Sect. 7 of Torra et al. 2020, for a detailed discussion). Moreover,
the list of transits associated with a given source ID may
have changed signiﬁcantly following improvements in the cross
match process. Finally, comparisons between data releases are
not recommended in general since they mainly show issues that
are present in the old data that are no longer relevant. Interpretation
of the diﬀerences are also diﬃcult to make for the reasons
given above.
10. Conclusions
In this paper we have presented the photometric content of
Gaia EDR3 and described the process of producing calibrated
photometry for G–band, BP and RP. A few issues that have been
discovered during the validation of the photometry in preparation
for the data release have been discussed and possible mitigation
strategies have been suggested. Although it has been
stressed that selecting good quality data from the Gaia EDR3
catalogue must be tailored to the speciﬁc scientiﬁc goal of the
end user, a number of quality metrics have been presented:
the recommendation is to use them only in the preliminary exploratory
analysis while a better ad hoc approach is being devised.
Finally, we conclude providing a summary of the major
improvements in the Gaia EDR3 photometry.
– Apart from saturation eﬀects for very bright sources (see
Sect. C.1), the signiﬁcant magnitude term found in the
Gaia DR2 photometry (Casagrande & VandenBerg 2018;
Weiler 2018) is not visible anymore Fig. 25: over all there is
no trend larger than 1 mmag/mag. However for blue sources
(GBP-GRP < −0.1) there is an indication of a diﬀerential
colour term between sources brighter and fainter than G=11
at a level of 1% in magnitude (see Sect. 8.4).
– Using only one passband for the entire magnitude and colour
range, does not leave systematics above the 1% level in magnitude
in all bands; this increases to ∼ 2% for the 4148
sources with G < 13 and GBP − GRP < −0.1.
– Better background estimation has been carried out in all three
passbands. For the GBP and GRP bands this new processing
is described in Sect. 3.2. The validation of this is shown in
Fig. 22 where no trace of zodiacal light is present unlike in
Gaia DR2.
– The consistency between G, GBP, and GRP has improved as
is shown in Sect. 6.
– Better saturation handling for the G–band has been carried
out for Gaia EDR3. This is seen in Fig. 14, where the bump
at G = 11.2 has been reduced with respect to Gaia DR1 and
DR2. This can also be seen for G < 6 in this plot and in the
analysis of Sect. C.1 where the correction is close to zero.
– In the processing that lead to Gaia EDR3, the handling
of bad data was considerably improved. This involved preﬁltering
data that had been identiﬁed as problematic and excluding
some periods where the photometry was of poorer
quality and could not be calibrated well enough. This is evidenced
in Figs. 15 and 16 where no great circles (sometimes
referred to as ‘cat scratches’) are visible.
– In comparison between Gaia DR1 and DR2, no discontinuities
are seen at G = 13 and 16 in the comparison with external
photometry (see Fig. 17). This reﬂects the better stability
that has been achieved in establishing a consistent photometric
system between the window class conﬁgurations.
– Since the number of observations available for a given source
is, on average, larger in Gaia EDR3 than in DR2 (thanks to
the longer time range covered) the uncertainties on the mean
source photometry are also smaller. However, an even larger
overall improvement was achieved with the addition of more
terms to the calibration models as described in Sect. 4.4. This
is clearly visible in Fig. 14 since the comparison is based on
sources with the same number of observations in all three
data releases and therefore the diﬀerences cannot be ascribed
to the diﬀerent time ranges covered by the releases.
All the improvements listed above are part of the ongoing
huge eﬀort to extract the most homogeneous, precise and accurate
photometry from Gaia data. This requires the calibration of
hundreds of instrumental characteristics and inhomogeneities in
space and time, in the attempt to standardise the overall system.
As extensively discussed by Clem & Landolt (2013), with current
technology, this goal may be achieved up to typical uncertainties
of <∼ 1%. This can be seen as the current state-of-the-art
limit in photometry (see also Ivezi´c et al. 2007; Magnier et al.
2020, for recent, large-scale examples). The current sub-1% precision
we achieved for an enormous number of stars, covering
the full range of astrophysical parameters, allows us to reveal
tiny eﬀects, that we progressively try to understand and correct.
Through this continuous process, the precision and accuracy of
the photometric data increases with the subsequent data releases,
allowing for more sophisticated treatment and understanding of
the system complexity.
Acknowledgements. This work presents results from the European Space
Agency (ESA) space mission Gaia. Gaia data are being processed by the Gaia
Data Processing and Analysis Consortium (DPAC). Funding for the DPAC
is provided by national institutions, in particular the institutions participating
in the Gaia MultiLateral Agreement (MLA). The Gaia mission website is
https://www.cosmos.esa.int/gaia. The Gaia Archive website is https:
Article number, page 23 of 35
A&A proofs: manuscript no. aa39587
//archives.esac.esa.int/gaia. The Gaia photometric data processing has
been ﬁnancially supported by (in alphabetical order by country): the Tenure
Track Pilot Programme of the Croatian Science Foundation and the Ecole Polytechnique
Fédérale de Lausanne and the Project TTP-2018-07-1171 Mining
the Variable Sky, with funds of the Croatian-Swiss Research Programme; the
Agenzia Spaziale Italiana (ASI) through contracts I/037/08/0, I/058/10/0, 2014-
025-R.0, 2014-025-R.1.2015 and 2018-24-HH.0 to the Italian Istituto Nazionale
di Astroﬁsica (INAF), and INAF; the Spanish Ministry of Science, Innovation
and University (MICIU/FEDER, UE) through grants RTI2018-095076-BC21,
ESP2016-80079-C2-1-R, and the Institute of Cosmos Sciences University
of Barcelona (ICCUB, Unidad de Excelencia ’María de Maeztu’) through
grants MDM-2014-0369 and CEX2019-000918-M; the United Kingdom Particle
Physics and Astronomy Research Council (PPARC), the United Kingdom
Science and Technology Facilities Council (STFC), and the United Kingdom
Space Agency (UKSA) through the following grants to the University of
Bristol, the University of Cambridge, the University of Edinburgh, the University
of Leicester, the Mullard Space Sciences Laboratory of University College
London, and the United Kingdom Rutherford Appleton Laboratory (RAL):
PP/D006511/1, PP/D006546/1, PP/D006570/1, ST/I000852/1, ST/J005045/1,
ST/K00056X/1, ST/K000209/1, ST/K000756/1, ST/L006561/1, ST/N000595/1,
ST/N000641/1, ST/N000978/1, ST/N001117/1, ST/S000089/1, ST/S000976/1,
ST/S001123/1, ST/S001948/1, ST/S002103/1, and ST/V000969/1. We would
like to thank J. Martin-Fleitas for providing the response loss data that was
used to produce Figure 5; C. Ducourant and L. Galluccio for providing SDSS
parameters for a selection of galaxies; L. Rimoldini for providing the catalogue
of variable and extragalactic sources that was used to produce Figure 21;
M. J. Irwin for useful discussions on calibrating broad band photometry and
statistical methods; and C. Bailer-Jones for kindly reviewing an earlier version
of this manuscript. We additionally made use of TOPCAT (Taylor 2005,
http://www.starlink.ac.uk/topcat/), Astropy, a community-developed
core Python package in Astronomy (Astropy Collaboration et al. 2018), IPython
(Pérez & Granger 2007), and Matplotlib (Hunter 2007). This research has made
use of the SIMBAD database, operated at CDS, Strasbourg, France.
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M. Riello et al.: Gaia Early Data Release 3: Photometric content and validation
Appendix A: External calibration of BP and RP
spectra
Since externally calibrated BP and RP spectra have been used
to reconstruct the passbands of the Gaia EDR3 photometric systems
(Sect. 7), we give here a brief description of the models
and the strategy implemented to perform the external calibration
of the BP and RP spectra, provided that a full description will
be presented in Montegriﬀo et al. (in preparation) The general
scheme for the calibration of BP and RP spectra is similar to
the one implemented for the photometric data, being split into
an internal calibration - aimed at bringing all observations onto
a common reference system (often referred to as the ‘mean instrument’)
- and an external calibration describing an instrument
model capable of reproducing the observational mean spectrum
of a source given its SED. Once the instrument model is deﬁned,
it can be used to reconstruct the SED of each source starting from
the corresponding BP and RP mean spectra.
The process deﬁning the instrument model follows a forward
modelling approach. Given a number of calibrators it is possible
to derive the optimal set of model parameters that minimises in a
least squares sense the diﬀerence between predicted and observable
spectra. If we specify with u the location of a sample in the
AL reference system, the relationship between the observational
mean BP and RP spectrum ne(u) and the corresponding spectral
photon distribution np(λ) is given by:
ne(u) =
∞
0
np(λ) L(u − D(λ), λ) R(λ) dλ, (A.1)
where the instrument model is a combination of the eﬀective
monochromatic LSF L(u, λ), the dispersion relation D(λ) and
the overall instrument response function R(λ), which includes
the contribution of mirrors and prisms transmissivities, CCDs
quantum eﬃciency etc. Two distinct models are built for the BP
and RP instruments. The traditional approach to derive a simple
response of the instrument as a function of wavelength by
computing the ratio between the observational spectrum and the
SED for a limited set of (possibly featureless) calibrators does
not work very well for Gaia because the width of the LSF is
rather large compared to the wavelength scale of the response
variations: as a consequence, the derived response changes with
the spectral type of the calibrator. The LSF cannot be ignored
and must be taken into account.
If the previous relation is discretised to a ﬁnite wavelength
grid, it can be conveniently expressed as a matrix multiplication:
−→ne = K · −→np, (A.2)
where the matrix K represents the instrument model sampled on
the same wavelength grid of −→np and on the same sample grid
of −→ne. This alternative formulation points out clearly the linear
nature of the instrument model. In principle, given the availability
of an arbitrarily large set of calibrators spanning the widest
variety of SEDs, it should be possible to derive the instrument
matrix of Eq. A.2 at once from a least squares ﬁt: unfortunately
the pool of astrophysical sources that satisfy the requirements
for a reliable ﬂux calibrator (isolated and point-like source with
stable ﬂux and high S/N ratio) shrinks to a set of stars spanning
a limited range of astrophysical parameters, and SPSS represent
such a selection of sources. Moreover it can be demonstrated
through principal component analysis that a set of calibrators
such as the SPSS can only constrain a limited number of
instrument components (roughly one ﬁfth of the number of required
components). We note that a similar problem holds also
for the passband determination as discussed in Sect. 7. To mitigate
these limitations we have followed two parallel strategies:
1) we developed the current model starting from a nominal instrument
model (built on our pre-launch knowledge of the instrument
hardware) so that only constrained deviations are allowed
between the ﬁtted model and the starting one; 2) we extended
the calibrator set (SPSS) with a wide variety of sources featuring
strong emission lines over the entire wavelength range (mostly
QSO and WR stars) and several sources taken by the PVL set
(especially the reddest sources). Since these additional calibrators
often exhibit ﬂux variability (see Sect. B), we implemented
an iterative scheme where each update of the instrument state
is followed by a grey ﬂux calibration of all non-SPSS sources
(the ﬂux level of each source is scaled to minimise the diﬀerence
between predicted and observed ﬂux distributions). The model
is bootstrapped on the nominal model and, since its formulation
(Eq. A.1) is not linear with respect its parameters, the optimal
model is then evaluated employing the Diﬀerential Evolution algorithm
(Storn & Price 1997).
Once the instrument model is deﬁned, one could use the integral
equation A.1 to solve for the unknown SED, expressed
in some convenient parametric form, starting from the observational
spectrum. However, this equation is known as a Fredholm
Integral Equation of the ﬁrst kind and it is generally diﬃcult to
solve because the problem is essentially ill-conditioned: being
the observed spectrum np(u) aﬀected by noise, there are many
solutions that satisfy exactly an integral solution slightly perturbed
from the original. To mitigate this problem we adopted a
diﬀerent strategy: since BP and RP mean spectra are represented
as a linear combination of a proper set of basis functions Bi(u)
(Carrasco et al., in preparation), for each basis function we set up
an integral equation as Eq. A.1, where np(u) is substituted with
the basis Bi(u). In this way we can solve these equations to build
up a new set of basis functions (called ‘inverse basis functions’)
whose images through the instrument model are the basis functions
that represent the observed mean spectra. In other words,
by solving the integral equation for analytic functions that are by
deﬁnition noise free we can achieve a numerically stable representation
of externally calibrated SEDs. BP and RP spectra are
reconstructed separately and then merged to form a unique SED.
Appendix B: External datasets
Appendix B.1: Spectro-photometric standard stars
The SPSS were selected to be good ﬂux calibrators for Gaia,
according to the criteria detailed by Pancino et al. (2012). The
original requirement was to provide a ﬁnal precision of 1% and
an accuracy of 1–3% of the Gaia ﬂux calibration with respect
to Vega (Bohlin et al. 2014). Additionally, because no existing
dataset satisﬁed simultaneously all the requirements, a dedicated
observing campaign was carried out, including: (i) a strict constancy
monitoring of all candidate SPSS within ±0.5% (Marinoni
et al. 2016); (ii) a photometric campaign to provide BVR
magnitudes for all the candidate SPSS (Altavilla et al., submitted
to MNRAS); and (iii) a spectro-photometric campaign to derive
accurate ﬂux tables (Altavilla et al. 2015, Pancino et al., in
preparation)9
.
The preliminary SPSS version used to calibrate EDR3 contains
the best 113 SPSS out of a total of about 200 surviving
9
The SPSS ﬂux tables and ancillary data can be obtained in advance
of publication by contacting the authors.
Article number, page 25 of 35
A&A proofs: manuscript no. aa39587
SPSS candidates after constancy assessment (of the ≈ 300 initial
candidates, about 100 were discarded because of variability).
Therefore, the current SPSS release contains about half of
the full SPSS set and only 1500 spectra on a total of 6500
(roughly 25%). As a result, the extreme blue and red portions
of the SPSS ﬂux tables are expected to signiﬁcantly improve in
the future. The SPSS reference system is tied to the CALSPEC
reference system10
(Bohlin et al. 2019) by means of the three
pure-hydrogen white dwarfs G191-B2B, GD 71, and GD 153.
More speciﬁcally, the SPSS ﬂux reference system is tied to the
2013 version of CALSPEC, which is about 0.6% brighter than
the current version (Bohlin et al. 2014). A comparison of the 36
stars in common with the latest version of CALSPEC conﬁrms
that the SPSS are indeed 1% brighter. On the other hand, the synthetic
Johnson-Cousins magnitudes obtained from the SPSS are
on average 1% fainter than the reference magnitudes by (Landolt
1992), for the 37 stars in common. The ∼1% ﬂux diﬀerence
between diﬀerent standard systems appears to be the ultimate accuracy
limit of current observing technology (see the discussion
by Clem & Landolt 2013).
Appendix B.2: Stetson secondary standard stars
The publicly available Stetson secondary standards11
are a set of
more than 200 000 stars in diﬀerent wide ﬁelds such as stellar
clusters, standard ﬁelds, supernova remnants, planetary nebulae,
deep galactic ﬁelds and the like. Their Johnson-Cousins UBVRI
magnitudes are accurately calibrated (within 1%) on the Landolt
(1992) system with the method described by Stetson (2000)
and updated as in Stetson et al. (2019). The database is constantly
evolving, with daily updates, but the reference system remains
stably anchored to the Landolt (1992) one, only the number
of stars and their individual uncertainties change with time.
The version of the database that we used here was downloaded
in 2019. From a sample of 177 915 UBVRI secondary standard
stars assembled by merging the catalogues of Stetson’s standard
ﬁelds, we selected a subsample of ∼100 000 (a) having an unambiguous
counterpart in Gaia DR3 and (b) being strictly clustered
along the locus of bona ﬁde stars in the G − GBP versus G − GRP
colour diagram. The latter criterion ensures that the selected stars
have precise and consistent photometry in the three Gaia bands.
We stress again that, in the present context, we use this catalogue
only as a convenient sample of bona ﬁde, well-measured stars, as
all the data that we used to reﬁne the external calibration come
entirely from Gaia, namely original or synthetic G, GBP and GRP
photometry. The photometry available in the Stetson archive was
not used.
Appendix B.3: Passband validation library
The PVL was created as a validation set, covering a larger variety
of spectral types with respect to the ﬂux calibrators, the SPSS.
In the O and B spectral type range there are luminous blue variables,
β Cephei and α Cygni stars, slowly pulsating B stars, hot
subdwarfs, and a large fraction of binaries (Gaia Collaboration
et al. 2019; De Marco & Izzard 2017). Similar considerations
hold for very red stars, which can vary because of chromospheric
and magnetic activity, rotation and spots (Iwanek et al. 2019), or
10
https://www.stsci.edu/hst/instrumentation/
reference-data-for-calibration-and-tools/
astronomical-catalogs/calspec
11
https://www.canfar.net/storage/list/STETSON/
Standards
in the case of brown dwarfs, cloud coverage variations (Artigau
2018). Therefore, without a careful constancy monitoring on a
large range of timescales, it is very diﬃcult to pre-select suitable
ﬂux standards of extreme spectral types. For this reason the
SPSS list does not include stars of types later than M2 and contains
very few bright O and B stars. The PVL was thus built using
space-based HST (Hubble Space Telescope) spectra, by including:
(i) 39 CALSPEC hot stars that were not already included in
the SPSS set for the above reasons; (ii) 17 stars from the HOTSTAR
set (Khan & Worthey 2018), excluding stars in planetary
nebulae; (iii) the red star Proxima Centauri (Ribas et al. 2017);
(iv) the two brown dwarfs in the CALSPEC set (2M0036+18
and 2M0559-14)12
and (v) the red CALSPEC stars GJ 555 and
VB 8. We did not consider stars that showed known variability
above ±5% and the typical variability in the PVL set is ±2% (for
comparison it is ±0.5% for the SPSS). For this reason, we have
preferred to rely on the SPSS set as ﬂux calibrators to ensure we
can achieve our goal of 1% accuracy. All PVL stars that were calibrated
on the latest CALSPEC reference system were re-aligned
with the SPSS system, namely the 2013 CALSPEC system, by
increasing their ﬂux by 0.6% (Bohlin et al. 2014). In this way,
the combined SPSS + PVL dataset is as homogeneous as possible
and can be used to perform a range of validation tests. The
PVL ﬂux tables will be published together with the SPSS ones
(Pancino et al., in preparation).
Appendix C: Colour–colour transformations
This section gives colour-colour transformations that relate the
Gaia EDR3 photometric systems to other systems. Relationships
for Hipparcos (ESA 1997), Tycho-2 (Høg et al. 2000),
SDSS12 (Alam et al. 2015a), Johnson-Cousins (Stetson 2000)
and 2MASS (Skrutskie et al. 2006) are provided here. For all ﬁts,
except Johnson-Cousins, only those sources with small magnitude
error and small BP and RP excess ﬂux were used. In the
case of Johnson-Cousins, all available sources where used due
to the high quality of these standards. Gaia EDR3 sources with
G < 13 mag, photometry in the three Gaia passbands and in
the external photometric systems were cross-matched to these
external catalogues. The magnitude limit was used in order to
limit the inﬂuence of photometric noise on the derived relationships.
However, this magnitude range is not appropriate for the
SDSS12 transformations since SDSS12 sources brighter than
14 mag are saturated. Thus, for the SDSS12 transformations
Gaia EDR3 sources with σG < 0.01 and SDSS12 magnitudes
fainter than 15 were used. In order to obtain good quality ﬁts, ﬁltering
on data quality was applied to the data (see the Gaia EDR3
online documentation for more details). The validity of these ﬁts
is only applicable in the colour ranges used for the ﬁts (see Table
C.1). The coeﬃcients of the polynomials representing the
transformations derived between Gaia and Hipparcos, Tycho-
2, SDSS12, Johnson-Cousins and 2MASS can be found in Table
C.2. A selection of these photometric relationships can be
seen in Fig. C.1. A complete set of ﬁgures can be found in the
Gaia EDR3 online documentation. The relationships shown here
were derived using an early internal version of the release. Thus,
some sources used in the ﬁt could have been ﬁltered out in the
ﬁnal publication. The purpose of these relationships is to provide
a general transformation valid for the widest possible set of
12
These two faint and extremely red stars have negligible ﬂux bluewards
of 600–800 nm, and thus they have no HST blue spectra. To
cover the entire Gaia wavelength range, the spectra were extended to
330 nm, by adjusting the appropriate theoretical spectra from the Allard
et al. (2012) library.
Article number, page 26 of 35
M. Riello et al.: Gaia Early Data Release 3: Photometric content and validation
stellar populations and types. This will provide a reasonable estimate
of their photometry when transforming from one system to
another. There are cases in which diﬀerent types, particularly M
giants and dwarfs, that show diﬀerent behaviour in the colourcolour
diagram. In such cases, a single ﬁt was carried out for
the most populous type (usually M giants), covering the widest
range of colours. Thus, for many of the relationships shown here
the red end is only valid for M giants and not M dwarfs.
Appendix C.1: Saturation correction
The eﬀect of saturation on the photometry of bright stars is
shown in Fig. C.2. The impact of saturation on the results of
the G–band photometry has decreased with respect to Gaia DR2
because of improvements in the handling of saturation in the
PSF ﬁtting (Rowell et al. 2020). The ﬁgure shows the residuals
when Hipparcos or Tycho-2 photometry is transformed into
the Gaia EDR3 system, using the transformations in Table C.2,
and compared with the Gaia EDR3 photometry. The Tycho-2
and Hipparcos data are combined to derive empirical corrections.
The corrected magnitudes from the mean magnitudes in
Gaia EDR3, Gcorr
XP can be obtained with the following equations:
Gcorr
− G = −0.09892 + 0.059G − 0.009775G2
+0.0004934G3
(C.1)
Gcorr
BP − GBP = −0.9921 − 0.02598G + 0.1833G2
−0.02862G3
(C.2)
Gcorr
RP − GRP = −14.94 + 14.41GRP − 4.657G2
RP
+0.503G3
RP (C.3)
We note that the relationship for the corrected GRP is in terms
of GRP rather than in G. This is because the analysis in GRP had
a much smaller dispersion than in G. The relationships should
only be used in the following ranges:
2.0 < G < 8 for Eq. C.1
2.0 < G < 3.94 for Eq. C.2
2.0 < GRP < 3.45 for Eq. C.3
Appendix D: Gaps
In the period covered by Gaia EDR3 there are a number of gaps
in the photometric coverage. There are several factors that could
cause a gap, the most common are: decontamination and refocussing
events, gaps in the reconstructed attitude (closely related
to gaps in the crossmatch, Torra et al. 2020), satellite outages,
gaps in the BP and RP calibration libraries, quality ﬁltering
applied during the processing and for the selection of the
Gaia EDR3 content (Fabricius et al. 2020). Some gaps aﬀect
only certain instruments. Table D.1 provides a list of the known
gaps in the Gaia EDR3 photometry.
Decontamination campaigns involved actively heating different
parts of the focal plane assembly to allow the waterbased
contamination to sublimate and being vented out. A decontamination
campaign terminates when the active heating is
disabled, however at that stage the satellite is not yet in thermal
equilibrium, which is slowly reached over the course of several
revolutions. Using the photometric calibrations from the period
after the decontamination campaigns it was possible to detect
time ranges during which the system photometric response was
changing signiﬁcantly with each OBMT revolution. The LS calibrations
for these time ranges are not capable of tracking the
fast time evolution of the system and therefore the calibrated
AF epochs have been excluded from contributing to the source
photometry. The PSF/LSF modelling for these periods was also
problematic with the running solution unable to keep up with the
very rapid changes in the instrument. This resulted also in lower
quality raw ﬂuxes produced by the IPD process (see Figure 10
in Rowell et al. 2020). The LS calibration for BP and RP did not
show the same problems and therefore the corresponding epochs
were not excluded. Table D.2 provides the time ranges for which
epoch observations were excluded from the source photometry.
Appendix E: Mean and predicted SSCs for silver
and bronze photometry
A fraction of Gaia sources has incomplete colour information:
a source may be missing either or both BP and RP spectrum
shape coeﬃcient (SSC) sets. A ‘silver’ source has incomplete
or missing either BP or RP SSCs. A source can be classiﬁed as
‘bronze’ if it has incomplete or missing both BP and RP SSCs
or if silver processing has failed. A ‘gold’ source has complete
SSC information.
In order to calibrate the silver and bronze sources a statistical
approach has been adopted to estimate the missing SSCs.
In both cases a subset of ≈ 3 million sources was used for the
calibrations. This subset was a selection that further ﬂattened the
distributions in colour, magnitude and sky position and as such
is not dominated by the central colours or a particular region in
the sky. In the case of the bronze sources, a set of default colours
(SSC values) is used. This has been derived from the median
SSC values of the subset above. In order to estimate the missing
SSC values of the silver sources, we assume that the colour-SSC
space distribution of carefully selected 2.9 × 106
gold sources
in the sample above and with more than ﬁve valid transits (G,
BP and RP) is representative of the overall distribution of the
sources observed by Gaia. A ﬁfth-degree polynomial is then ﬁtted
to the SSC/G versus XP/G ﬂux ratio distributions in order to
determine empirical relationships from which the missing values
are estimated. The distribution of the sources and the results of
the procedure are shown in Fig. E.1.
Appendix F: Photometry of extragalactic sources
Section 6 showed that galaxies tend to have large values of
the corrected BP and RP ﬂux excess C∗
(see the top-left panel
of Fig. 21), this has already been used to select galaxies in
Gaia DR2 (see e.g. Liu et al. 2020). This behaviour can be explained
considering the Gaia acquisition system and processing
for G–band and BP and RP data. Fainter sources are acquired in
the AF CCDs using the window conﬁguration that corresponds
to a viewing size of 0.35×2.1 (AL×AC) arcsec. BP and RP spectra
are instead acquired with a window conﬁguration providing a
AL×AC viewing size of 3.5×2.1 arcsec. Since the BP and RP uncalibrated
epoch ﬂux is derived by integrating the pre-processed
epoch spectra (see Sect. 3), this is equivalent to aperture photometry
with a rectangular aperture. The G–band photometry instead
is the result of an LSF ﬁt, where the LSF (Rowell et al. 2020) is
optimised for point sources and therefore it is likely to produce
an underestimated ﬂux as the observed sources become progressively
less stellar-like. The net result is that the BP and RP ﬂux
will tend to be signiﬁcantly larger than the G–band one with the
colour of the source unlikely to play a noticeable role since its
eﬀect will be much smaller (see Fig. 18, top panel).
This explanation was veriﬁed using a sample of 146,605
galaxies selected from the SDSS DR12 release (Alam et al.
Article number, page 27 of 35
A&A proofs: manuscript no. aa39587
Fig. C.1. A selection of photometric relationships between Gaia EDR3 and Hipparcos, (top) Tycho-2, SDSS12, Johnson-Cousins and 2MASS
(bottom).
2015b) and extracted from CDS (Wenger et al. 2000). Figure F.1
shows the BP and RP corrected ﬂux excess versus the apparent
size of the galaxies as measured by the De Vaucouleurs radius.
The top panel shows the distribution of the galaxies conﬁrming
that C∗
increases with the angular size of the galaxy up to the
point when the galaxy becomes larger than the size of the BP
and RP window becoming ﬂat for larger sizes. The bottom panel
of Fig. F.1 shows the same dependency but with the colour scale
showing the diﬀerence between the Gaia G and the SDSS g magnitude.
Although the two bands are not the same, this diﬀerence
Article number, page 28 of 35
M. Riello et al.: Gaia Early Data Release 3: Photometric content and validation
Fig. C.2. Saturation corrections for G (left), GBP (centre) and GRP (right) passbands.
Fig. E.1. Results of the estimation procedure. Top: Distribution of
sources in the SSC/G vs. XP/G space (colour-coded according to the
legend) and the results of the corresponding ﬁts (black lines). Bottom:
Residuals.
can be used as a ﬁrst order approximation of the discrepancy
between the Gaia photometry and the SDSS photometry: as expected,
the discrepancy is smaller for galaxies of small apparent
size and then increases signiﬁcantly for progressively larger ob-
jects.
Because of the very small size of the AF windows, it is likely
that the G–band photometry of extended sources will show an
excess of scatter mimicking variability. The more elongated the
galaxy the larger the excess scatter is expected to be since the
measured epoch ﬂux will be aﬀected by the scanning direction
of the satellite. This eﬀect can be seen in Fig. F.2 which shows
the variability proxy13
for the G–band versus the G magnitude
(see e.g. Mowlavi et al. 2020) with the colour scale showing the
ratio between the semi-minor and semi-major axes of the galaxy
as available from the SDSS archive. As expected, the more elongated
the galaxy, the larger the excess scatter of the G–band photometry.
Because of the much larger size of the windows, this
13
The variability proxy corresponds to the estimated fractional error
on a single AF CCD observation assuming all observations have equal
weight.
Fig. F.1. Dependence of the corrected BP and RP ﬂux excess factor C∗
with the apparent size of the galaxy provided in terms of its de Vaucouleurs
radius for a sample of ≈ 146 thousand galaxies selected from
SDSS DR12. The top panel shows the density, the bottom panel the
colour scale shows the diﬀerence between the Gaia G–band magnitude
and the SDSS g magnitude.
Fig. F.2. Dependence of the variability proxy Aproxy,G for the G–band
with the apparent size of the galaxy provided in terms of its de Vaucouleurs
radius for a sample of ≈ 146 thousand galaxies selected from
SDSS DR12. The colour scale shows the ratio between semi-minor and
semi-major axes of the galaxies.
pseudo-variability is not observed in the BP and RP photometry.
Variability studies using Gaia data should take this into account
to avoid polluting their sample with galaxies.
Finally, it should be noted that the PSF and LSF modelling
and IPD determination has been considerably improved (Rowell
et al. 2020) in Gaia EDR3 for point sources: signiﬁcant diﬀerArticle
number, page 29 of 35
A&A proofs: manuscript no. aa39587
ences with respect to the Gaia DR2 photometry are therefore to
be expected for extended sources. This is yet another example of
the limitations in comparing the Gaia EDR3 and DR2 photome-
try.
Appendix G: RP zooming
The precision reached in the photometric calibrations is such that
even small eﬀects can be analysed in detail. An example of this
is oﬀered by the signature of RP zooming in the SS calibrations
for the RP CCDs.
The RP prism has a very low convergence (about 1%), which
reduces the total telescope focal length but only in the acrossscan
direction. It leads to the loss of samples located at the extreme
AC edges of the CCD: up to 70 pixels may be lost in the
RP FoV. Of course, the most aﬀected CCDs are the extreme ones
(row 1 and 7 for RP), and only a minor eﬀect is expected on
the central CCD (row 4). To mitigate for this eﬀect additional
optical elements are used to eﬀectively magnify the RP optical
path to fully cover the RP CCDs. The VPU allocates the window
positions based on a set of on-board lookup tables that take
into account the AC motion of the source on the focal plane:
for a given AC position of the window, as assigned by the VPU,
there will be a distribution of how well centred the source will
be within the window. Although this is true also for BP, the RP
zooming has the eﬀect of widening the distribution of this centring
error. This means that to eﬀectively model ﬂux loss in RP,
it is necessary to adopt a wider range for the centring error. This
is shown in the top panel of Fig. G.1 which shows the CCD response
determined by the SS calibration for an initial test run
which was using the ±2 pixel clamped range for the centring
error. The saw-tooth pattern is caused by residual ﬂux-loss that
was not corrected due to the restriction in centring error. When
the correction range for the ﬂux loss is expanded to ±4 pixel,
as shown in the bottom panel of Fig. G.1, the pattern fully disappears
in the preceding FoV (red) and is considerably reduced
in the following FoV (blue), although a systematic eﬀect is still
present at the ≈ 2.5 mmag level. The probable reason for the remaining
error is that the ﬂux loss terms in the LS calibrations is
only a quadratic in centring error and that for RP more terms are
needed.
Appendix H: On the use of ﬂuxes and magnitudes
The error distribution of the ﬂuxes is reasonably symmetric
and close to being Gaussian for most magnitudes (but see also
Sect. 8.1). This is the reason why all the photometric calibrations
are carried out in ﬂux-space. The transformation from ﬂuxes to
magnitudes is non-linear and would cause the error distribution
to become asymmetric. If the calibrations were to be done in
magnitude-space a bias would be created. While this would be
small, at the 1% level, the aim of the Gaia project is to push the
photometric accuracy to the mmag level and beyond. The use
of ﬂuxes in the photometric processing, with ﬂux errors being
Gaussian-distributed, has the additional advantage of supporting
the use of a maximum likelihood estimator for the generation
of mean photometry. Furthermore, using inverse variance
weighting ensures maximum signal to noise for the mean (see
e.g. Lupton 1993). In general, the asymmetry caused by the ﬂuxmagnitude
transformation is small, but since the photometry is
being published close to the magnitude limit it is important to
consider. The error asymmetry caused by this transformation between
plus and minus magnitudes for the epoch G photometry is
Fig. G.1. CCD response as a function of AC position as derived from
the SS calibration. The top panel shows the response when the ﬂux loss
has been modelled and corrected only in the range ±2 pix; the bottom
panel shows the response when the ﬂux loss has been modelled and
corrected in the range ±4 pix. The blue dots show the preceding FoV;
the red dots show the following FoV.
5%, 10%, and 20% for G magnitudes of 19, 20, and 21, respectively.
For BP, the ﬂuxes can get lower and have larger asymmetries.
This is the reason why magnitude errors are not given in the
Gaia archive – a single magnitude error is not suﬃcient. If working
in magnitude space is required, then lower and upper bounds
of the magnitude error should be computed from the I − σI and
I +σI values converted to magnitudes using the zeropoints given
in Table 3.
It is recommended that users work in ﬂux space at the faint
end, that is to adopt a forward modelling approach and compare
the model and data in ﬂux space not in magnitude space.
Appendix I: Gaia-related acronyms
Article number, page 30 of 35
M. Riello et al.: Gaia Early Data Release 3: Photometric content and validation
Table C.1. Applicable range for the relationships between the
Gaia EDR3 system and the other photometric systems considered.
Hipparcos relationships
G − HP = f(B − V) −0.25 < B − V < 1.9a
G − HP = f(V − I) −0.25 < V − I < 5.0
G − HP = f(GBP − GRP) −0.5 < GBP − GRP < 4.0
GBP − HP = f(V − I) −0.2 < V − I < 3.0
GRP − HP = f(V − I) −0.4 < V − I < 3.5
GBP − GRP = f(V − I) −0.5 < V − I < 3.5
Tycho-2 relationships
G − VT = f(BT − VT ) −0.2 < BT − VT < 2.0b
G − VT = f(GBP − GRP) −0.35 < GBP − GRP < 4.0
G − BT = f(GBP − GRP) −0.3 < GBP − GRP < 3.0
GBP − VT = f(BT − VT ) −0.2 < BT − VT < 2.5
GRP − VT = f(BT − VT ) −0.3 < BT − VT < 2.0c
GBP − GRP = f(BT − VT ) −0.3 < BT − VT < 2.0d
SDSS12 relationships
G − g = f(g − i) −1.0 < g − i < 9.0
G − r = f(r − i) −0.5 < r − i < 2.0
G − i = f(r − i) −0.35 < r − i < 2.0
GBP − g = f(g − i) −0.6 < g − i < 3.5
GRP − r = f(r − i) −0.9 < g − i < 8.0
GBP − GRP = f(g − i) −0.5 < g − i < 3.5e
G − r = f(GBP − GRP) 0.0 < GBP − GRP < 3.0f
G − i = f(GBP − GRP) 0.5 < GBP − GRP < 2.0
G − g = f(GBP − GRP) 0.3 < GBP − GRP < 3.0g
Johnson-Cousins relationships
G − V = f(V − IC) −0.4 < V − IC < 5.0
G − V = f(V − R) −0.15 < V − R < 2.3h
G − V = f(B − V) −0.4 < B − V < 3.3i
GBP − V = f(V − IC) 0.0 < V − IC < 4.0
GRP − V = f(V − IC) −0.4 < V − IC < 5.0
GBP − GRP = f(V − IC) −0.4 < V − IC < 5.0
G − V = f(GBP − GRP) −0.5 < GBP − GRP < 5.0
G − R = f(GBP − GRP) 0.0 < GBP − GRP < 4.0j
G − IC = f(GBP − GRP) −0.5 < GBP − GRP < 4.5
2MASS relationships
G − KS = f(H − KS ) −0.1 < H − KS < 0.4
GBP − KS = f(H − KS ) −0.1 < H − KS < 0.4
GRP − KS = f(H − KS ) −0.1 < H − KS < 0.4
GBP − GRP = f(H − KS ) −0.1 < H − KS < 0.4
G − KS = f(GBP − GRP) −0.5 < GBP − GRP < 2.5
G − H = f(GBP − GRP) −0.5 < GBP − GRP < 2.5
G − J = f(GBP − GRP) −0.5 < GBP − GRP < 2.5
G − KS = f(J − KS ) −0.2 < H − KS < 1.1
GBP − KS = f(J − KS ) −0.2 < H − KS < 1.1
GRP − KS = f(J − KS ) −0.2 < H − KS < 1.1
GBP − GRP = f(J − KS ) −0.1 < H − KS < 1.1
a
For B − V > 1.4 this is only valid for M giants.
b
For BT − VT > 1.7 this is only valid for M giants.
c
For BT − VT > 1.7 this is only valid for M giants.
d
For BT − VT > 1.7 this is only valid for M giants.
e
For GBP − GRP > 2.25 this is only valid for M giants.
f
For GBP − GRP > 2.0 this is only valid for M giants.
g
For GBP − GRP > 2.0 this is only valid for M giants.
h
For V − R > 0.9 this is only valid for M giants.
i
For B − V > 1.3 this is only valid for M giants.
j
For GBP − GRP > 2.0 this is only valid for M giants.
Article number, page 31 of 35
A&A proofs: manuscript no. aa39587
Table C.2. Coeﬃcients of the transformation polynomials derived between the Hipparcos, Tycho-2, SDSS12, Johnson-Cousins and 2MASS
systems and that of Gaia EDR3.
Hipparcos relationships
B − V (B − V)2
(B − V)3
σ
G − Hp -0.02392 -0.4069 0.04569 -0.0452 0.02417
V − I (V − I)2
(V − I)3
σ
G − Hp 0.01546 -0.4308 -0.01872 0.08191
GBP − Hp -0.02696 0.1086 -0.009148 0.004715 0.06
GRP − Hp -0.006437 -1.194 0.09962 0.1024
GBP − GRP -0.01612 1.274 -0.08143 0.082
GBP − GRP (GBP − GRP)2
(GBP − GRP)3
σ
G − Hp -0.01008 -0.2309 -0.1300 0.01894 0.06066
Tycho-2 relationships
BT − VT (BT − VT)2
(BT − VT)3
σ
G − VT -0.01072 -0.2870 0.05807 -0.06791 0.06084
GBP − VT -0.01868 0.2682 -0.1366 0.01272 0.04127
GRP − VT -0.04424 -1.197 0.4948 -0.1757 0.09359
GBP − GRP 0.02621 1.458 -0.6176 0.1817 0.06834
GBP − GRP (GBP − GRP)2
(GBP − GRP)3
(GBP − GRP)4
(GBP − GRP)5
σ
G − VT -0.01077 -0.0682 -0.2387 0.02342 0.05350
G − BT -0.004288 -0.8547 0.1244 -0.9085 0.4843 -0.06814 0.07063
SDSS12 relationships
g − i (g − i)2
(g − i)3
σ
G − g -0.1064 -0.4964 -0.09339 0.004444 0.0872
GBP − g 0.06213 -0.2059 -0.06478 0.007264 0.02944
GRP − g -0.3306 -0.9847 -0.02874 0.002112 0.04958
GBP − GRP 0.3971 0.777 -0.04164 0.008237 0.03846
r − i (r − i)2
(r − i)3
σ
G − r -0.01664 0.2662 -0.649 0.08227 0.123
G − i -0.01066 1.298 -0.7595 0.1492 0.07112
GBP − GRP (GBP − GRP)2
(GBP − GRP)3
(GBP − GRP)4
σ
G − r -0.09837 0.08592 0.1907 -0.1701 0.02263 0.03776
G − i -0.293 0.6404 -0.09609 -0.002104 0.04092
G − g 0.2199 -0.6365 -0.1548 0.0064 0.0745
Johnson-Cousins relationships
V − IC (V − IC)2
(V − IC)3
(V − IC)4
σ
G − V -0.01597 -0.02809 -0.2483 0.03656 -0.002939 0.0272
GBP − V -0.0143 0.3564 -0.1332 0.01212 0.0371
GRP − V 0.01868 -0.9028 -0.005321 -0.004186 0.03784
GBP − GRP -0.03298 1.259 -0.1279 0.01631 0.04459
V − R (V − R)2
(V − R)3
σ
G − V -0.03088 -0.04653 -0.8794 0.1733 0.0352
B − V (B − V)2
(B − V)3
σ
G − V -0.04749 -0.0124 -0.2901 0.02008 0.04772
GBP − GRP (GBP − GRP)2
(GBP − GRP)3
(GBP − GRP)4
σ
G − V -0.02704 0.01424 -0.2156 0.01426 0.03017
G − R -0.02275 0.3961 -0.1243 -0.01396 0.003775 0.03167
G − IC 0.01753 0.76 -0.0991 0.03765
2MASS relationships
H − KS (H − KS)2
σ
G − KS 0.5594 11.09 3.040 0.3743
GBP − KS 0.5922 15.36 1.691 0.499
GRP − KS 0.1882 10.3 -3.976 0.2956
GBP − GRP 0.1836 8.456 -3.781 0.2361
GBP − GRP (GBP − GRP)2
σ
G − KS -0.0981 2.089 -0.1579 0.08553
G − H -0.1048 2.011 -0.1758 0.07805
G − J 0.01798 1.389 -0.09338 0.04762
J − KS (J − KS)2
(J − KS)3
(J − KS)4
σ
G − KS 0.1683 3.803 -1.45 0.7867 0.1309
GBP − KS 0.1777 5.28 -4.384 4.451 -1.273 0.174
GRP − KS 0.08089 2.655 -1.488 1.618 -0.5068 0.07997
GBP − GRP 0.09396 2.581 -2.782 2.788 -0.8027 0.09668Article number, page 32 of 35
M. Riello et al.: Gaia Early Data Release 3: Photometric content and validation
Table D.2. List of time ranges for which certain epochs were excluded
from the mean source photometry because the calibration could not
track the fast changes in system response. The ﬁrst and second columns
provide the start and stop OBMT revolution of the exclusion period; the
third column provides the set of excluded epoch CCD observations.
From To CCDs
1324.10135 1336.08621 All AFs, both FoV
1336.08621 1344.07611 AF1 only, both FoV
2328.61877 2330.61571 AF1 preceding FoV only
2338.96237 2350.94621 All AFs, both FoV
2350.94621 2358.93543 AF1 only, both FoV
4121.38599 4133.39359 All AFs, both FoV
4133.39359 4141.39866 AF1 only, both FoV
Table I.1. Gaia-related acronyms used in the paper. Each acronym is
also deﬁned at its ﬁrst occurrence in the paper.
Acronym Description See
AC ACross scan Sect. 2
AF Astrometric Field (in Astro) Sect. 2
AL ALong scan (direction) Sect. 2
BP Blue Photometer Sect. 2
CCD Charge-Coupled Device Sect. 2
DPAC Data Processing and Analysis Consortium Sect. 1
FWHM Full Width at Half-Maximum Sect. 7
FoV Field of View Sect. 3.1
GPS Galactic Plane Scan Sect. 3.2
HEALPix Hierarchical Equal-Area iso-Latitude Pixelisation Sect. 4.3
IDU Intermediate Data Update Sect. 2
IPD Image Parameter Determination Sect. 2
LS Large Scale Sect. 4.2
LSF Line Spread Function Sect. 2
OBM T On-Board Mission Timeline Sect. 2
OBMT-Rev On-Board Mission Timeline in units of satellite revolutions Sect. 2
PVL Passband Validation Library Sect. 4.3
PSF Point Spread Function Sect. 2
RP Red Photometer Sect. 2
SM Sky Mapper Sect. 2
SPSS Spectro Photometric Standard Stars Sect. 4.3
SS Small Scale Sect. 4.2
SSC Spectrum Shape Coeﬃcients Sect. 4.4
TDI Time-Delayed Integration (CCD) Sect. 3.1
VPU Video Processing Unit Sect. 5
Article number, page 33 of 35
A&A proofs: manuscript no. aa39587
List of known time gaps in the data contributing to the Gaia EDR3’ photometry.
Start End Duration Cause
1105.086491 1105.397602 0.311111 Attitude
1185.162879 1185.354545 0.191667 Attitude
1189.165656 1189.353156 0.187500 Attitude
1193.017045 1193.035101 0.018056 Attitude
1241.843433 1241.889267 0.045833 Attitude
1261.364266 1261.537878 0.173611 Attitude
1297.893433 1297.936488 0.043056 Attitude
1316.490655 1316.491631 0.000976 Attitude
1316.491631 1324.101353 7.609722 Decontamination
1324.101353 1326.797599 2.696246 Attitude
1336.678154 1336.786488 0.108333 Attitude
1380.717043 1380.897598 0.180556 Attitude
1401.753153 1401.951764 0.198611 Attitude
1436.318431 1436.330931 0.012500 Attitude
1443.949918 1443.974918 0.025000 Refocussing
1471.942041 1472.237875 0.295833 Attitude
1498.048985 1498.240652 0.191667 Attitude
1623.572595 1623.694817 0.122222 Attitude
1649.117039 1649.139261 0.022222 Attitude
1649.650372 1649.672594 0.022222 Attitude
1650.165650 1650.183705 0.018056 Attitude
1650.205927 1650.221205 0.015278 Attitude
1650.576761 1650.589261 0.012500 Attitude
1651.092039 1651.104539 0.012500 Attitude
1651.189261 1651.835094 0.645833 Attitude
1652.335094 1652.453150 0.118056 Attitude
1655.672594 1655.694816 0.022222 Attitude
1770.014259 1770.214259 0.200000 Attitude
1773.710092 1773.835092 0.125000 Attitude
1788.114259 1788.168425 0.054167 Attitude
1849.126758 1849.144813 0.018056 Attitude
1919.019812 1919.125368 0.105556 Attitude
1943.455923 1943.553145 0.097222 Attitude
1951.342034 1951.465645 0.123611 Attitude
1962.371201 1962.478145 0.106944 Attitude
2094.003143 2095.568421 1.565278 Attitude
2099.223977 2099.412865 0.188889 Attitude
2111.240643 2111.457310 0.216667 Attitude
2139.417031 2139.523976 0.106944 Attitude
2142.289254 2142.394809 0.105556 Attitude
2147.971198 2147.994809 0.023611 Attitude
2150.432309 2150.535087 0.102778 Attitude
2154.119809 2154.223976 0.104167 Attitude
2165.160087 2165.178142 0.018056 Attitude
2172.835087 2173.064253 0.229167 Attitude
2178.246198 2178.261475 0.015278 Attitude
2179.636475 2179.764253 0.127778 Attitude
2192.251753 2195.218420 2.966667 Attitude
2233.850363 2233.878141 0.027778 Attitude
2233.898975 2233.921197 0.022222 Attitude
2233.967030 2234.010086 0.043056 Attitude
2235.662863 2235.676752 0.013889 Attitude
2246.647586 2246.842030 0.194444 Attitude
2330.615640 2330.615706 0.000066 Attitude
2330.615706 2338.962373 8.346667 Decontamination
2354.546195 2355.447584 0.901389 Attitude
2386.614250 2386.643417 0.029167 Attitude
2405.967028 2408.643417 2.676389 Attitude
2408.935083 2409.968417 1.033333 Attitude
2499.493415 2499.680915 0.187500 Attitude
Article number, page 34 of 35
M. Riello et al.: Gaia Early Data Release 3: Photometric content and validation
Start End Duration Cause
2574.644410 2574.727743 0.083333 Refocussing
2574.727743 2574.828137 0.100394 Attitude
2651.929524 2651.962858 0.033333 Attitude
2751.340634 2751.530912 0.190278 Attitude
3045.130908 3048.183685 3.052778 Attitude
3205.136461 3205.172572 0.036111 Attitude
3254.087849 3254.286460 0.198611 Attitude
3269.454515 3269.469793 0.015278 Attitude
3271.503127 3271.532293 0.029167 Attitude
3314.864237 3314.882293 0.018056 Attitude
3317.535070 3317.562848 0.027778 Attitude
3542.114234 3542.311456 0.197222 Attitude
3603.251733 3605.226733 1.975000 Attitude
4009.661450 4009.855894 0.194444 Attitude
4074.210060 4076.062838 1.852778 Attitude
4112.768393 4112.769320 0.000927 Attitude
4112.769320 4121.385986 8.616666 Decontamination
4182.015614 4182.029503 0.013889 Attitude
4263.518390 4263.718390 0.200000 Attitude
4399.057277 4399.251722 0.194444 Attitude
4477.440610 4477.737832 0.297222 Attitude
4478.018387 4478.083665 0.065278 Attitude
4545.371164 4545.383664 0.012500 Attitude
4626.857274 4627.057274 0.200000 Attitude
4729.669773 4729.683662 0.013889 Attitude
4795.372550 4795.805883 0.433333 Attitude
4845.776715 4845.819771 0.043056 Attitude
4873.516993 4873.558660 0.041667 Attitude
4965.730880 4965.753103 0.022222 Attitude
5056.171157 5056.215601 0.044444 Attitude
5078.630879 5078.835046 0.204167 Attitude
5203.610044 5203.651711 0.041667 Attitude
Article number, page 35 of 35