11institutetext:  Univ. Grenoble Alpes, CNRS, IPAG, 38000 Grenoble, France
22institutetext: GEPI, Observatoire de Paris, Université PSL, CNRS, 5 Place
Jules Janssen, 92190 Meudon, France 33institutetext: Dept. FQA, Institut de
Ciències del Cosmos, Universitat de Barcelona (IEEC-UB), Martí i Franquès 1,
E-08028 Barcelona, Spain 44institutetext: Kapteyn Astronomical Institute,
University of Groningen, Landleven 12, 9747 AD Groningen, The Netherlands
55institutetext: INAF - Osservatorio di Astrofisica e Scienza dello Spazio di
Bologna, via Piero Gobetti 93/3, 40129 Bologna, Italy 66institutetext:
Institut UTINAM - UMR 6213 - CNRS - University of Bourgogne Franche Comté,
France, OSU THETA, 41 bis avenue de l’Observatoire, 25000, Besançon, France
77institutetext: Mésocentre de Franche-Comté, University of Franche-Comté, 16
route de Gray, 25030 Besançon Cedex, France 88institutetext: Harvard-
Smithsonian Center for Astrophysics, 60 Garden St., MS 15, Cambridge, MA
02138, USA 99institutetext: INAF, Osservatorio Astronomico di Padova, Vicolo
Osservatorio, Padova, I-35131, Italy 1010institutetext: CITIC, Department of
Computer Science and Information Technologies, University of A Coruña, Campus
de Elviña S/N, 15071 A Coruña, Spain 1111institutetext: SYRTE, Observatoire de
Paris, Université PSL, CNRS, Sorbonne Université, LNE, 61 avenue de
l’Observatoire, 75014 Paris, France 1212institutetext: Instituto de
Astrofísica e Ciências do Espaço, Universidade do Porto, CAUP, Rua das
Estrelas, PT4150-762 Porto, Portugal 1313institutetext: INAF - Osservatorio
Astrofisico di Torino, Via Osservatorio 20, 10025 Pino Torinese, Torino, Italy
1414institutetext: LESIA, Observatoire de Paris, Université PSL, CNRS,
Sorbonne Université, Université Paris Cité, 5 place Jules Janssen, 92195
Meudon, France 1515institutetext: Leiden Observatory, Leiden University, Niels
Bohrweg 2, 2333 CA Leiden, The Netherlands 1616institutetext: SRON Netherlands
Institute for Space Research, Niels Bohrweg 4, 2333 CA Leiden, The Netherlands
1717institutetext: CITIC, Department of Nautical Sciences and Marine
Engineering, University of A Coruña, Campus de Elviña S/N, 15071 A Coruña,
Spain 1818institutetext: Observatoire astronomique de Strasbourg, Université
de Strasbourg, CNRS, 11 rue de l’Université, 67000 Strasbourg, France
1919institutetext: Max-Planck-Institut für Astronomie, Königstuhl 17, D-69117
Heidelberg, Germany

#  Gaia Data Release 3
Catalogue Validation

C. Babusiaux Gaia Data Release 3 Catalogue ValidationGaia Data Release 3
Catalogue ValidationGaia Data Release 3 Catalogue ValidationGaia Data Release
3 Catalogue Validation C. Fabricius Gaia Data Release 3 Catalogue
ValidationGaia Data Release 3 Catalogue Validation S. Khanna Gaia Data Release
3 Catalogue ValidationGaia Data Release 3 Catalogue Validation T. Muraveva
Gaia Data Release 3 Catalogue ValidationGaia Data Release 3 Catalogue
Validation C. Reylé Gaia Data Release 3 Catalogue ValidationGaia Data Release
3 Catalogue Validation F. Spoto Gaia Data Release 3 Catalogue ValidationGaia
Data Release 3 Catalogue Validation A. Vallenari Gaia Data Release 3 Catalogue
ValidationGaia Data Release 3 Catalogue Validation X. Luri Gaia Data Release 3
Catalogue ValidationGaia Data Release 3 Catalogue Validation F. Arenou Gaia
Data Release 3 Catalogue ValidationGaia Data Release 3 Catalogue Validation
M.A. Álvarez Gaia Data Release 3 Catalogue ValidationGaia Data Release 3
Catalogue Validation F. Anders Gaia Data Release 3 Catalogue ValidationGaia
Data Release 3 Catalogue Validation T. Antoja Gaia Data Release 3 Catalogue
ValidationGaia Data Release 3 Catalogue Validation E. Balbinot Gaia Data
Release 3 Catalogue ValidationGaia Data Release 3 Catalogue Validation C.
Barache Gaia Data Release 3 Catalogue ValidationGaia Data Release 3 Catalogue
Validation N. Bauchet Gaia Data Release 3 Catalogue ValidationGaia Data
Release 3 Catalogue Validation D. Bossini Gaia Data Release 3 Catalogue
ValidationGaia Data Release 3 Catalogue Validation D. Busonero Gaia Data
Release 3 Catalogue ValidationGaia Data Release 3 Catalogue Validation T.
Cantat-Gaudin Gaia Data Release 3 Catalogue ValidationGaia Data Release 3
Catalogue ValidationGaia Data Release 3 Catalogue ValidationGaia Data Release
3 Catalogue Validation J. M. Carrasco Gaia Data Release 3 Catalogue
ValidationGaia Data Release 3 Catalogue Validation C. Dafonte Gaia Data
Release 3 Catalogue ValidationGaia Data Release 3 Catalogue Validation S.
Diakité Gaia Data Release 3 Catalogue ValidationGaia Data Release 3 Catalogue
Validation F. Figueras Gaia Data Release 3 Catalogue ValidationGaia Data
Release 3 Catalogue Validation A. Garcia-Gutierrez Gaia Data Release 3
Catalogue ValidationGaia Data Release 3 Catalogue Validation A. Garofalo Gaia
Data Release 3 Catalogue ValidationGaia Data Release 3 Catalogue Validation A.
Helmi Gaia Data Release 3 Catalogue ValidationGaia Data Release 3 Catalogue
Validation Ó. Jiménez-Arranz Gaia Data Release 3 Catalogue ValidationGaia Data
Release 3 Catalogue Validation C. Jordi Gaia Data Release 3 Catalogue
ValidationGaia Data Release 3 Catalogue Validation P. Kervella Gaia Data
Release 3 Catalogue ValidationGaia Data Release 3 Catalogue Validation Z.
Kostrzewa-Rutkowska Gaia Data Release 3 Catalogue ValidationGaia Data Release
3 Catalogue ValidationGaia Data Release 3 Catalogue ValidationGaia Data
Release 3 Catalogue Validation N. Leclerc Gaia Data Release 3 Catalogue
ValidationGaia Data Release 3 Catalogue Validation E. Licata Gaia Data Release
3 Catalogue ValidationGaia Data Release 3 Catalogue Validation M. Manteiga
Gaia Data Release 3 Catalogue ValidationGaia Data Release 3 Catalogue
Validation A. Masip Gaia Data Release 3 Catalogue ValidationGaia Data Release
3 Catalogue Validation M. Monguió Gaia Data Release 3 Catalogue ValidationGaia
Data Release 3 Catalogue Validation P. Ramos Gaia Data Release 3 Catalogue
ValidationGaia Data Release 3 Catalogue ValidationGaia Data Release 3
Catalogue ValidationGaia Data Release 3 Catalogue Validation N. Robichon Gaia
Data Release 3 Catalogue ValidationGaia Data Release 3 Catalogue Validation A.
C. Robin Gaia Data Release 3 Catalogue ValidationGaia Data Release 3 Catalogue
Validation M. Romero-Gómez Gaia Data Release 3 Catalogue ValidationGaia Data
Release 3 Catalogue Validation A. Sáez Gaia Data Release 3 Catalogue
ValidationGaia Data Release 3 Catalogue Validation R. Santoveña Gaia Data
Release 3 Catalogue ValidationGaia Data Release 3 Catalogue Validation L.
Spina Gaia Data Release 3 Catalogue ValidationGaia Data Release 3 Catalogue
Validation G. Torralba Elipe Gaia Data Release 3 Catalogue ValidationGaia Data
Release 3 Catalogue Validation M. Weiler Gaia Data Release 3 Catalogue
ValidationGaia Data Release 3 Catalogue Validation

###### Abstract

Context.  The third Gaia data release (DR3) provides a wealth of new data
products. The early part of the release, Gaia EDR3, already provided the
astrometric and photometric data for nearly two billion sources. The full
release now adds improved parameters compared to Gaia DR2 for radial
velocities, astrophysical parameters, variability information, light curves,
and orbits for Solar System objects. The improvements are in terms of the
number of sources, the variety of parameter information, precision, and
accuracy. For the first time, Gaia DR3 also provides a sample of
spectrophotometry and spectra obtained with the Radial Velocity Spectrometer,
binary star solutions, and a characterisation of extragalactic object
candidates.

Aims.  Before the publication of the catalogue, these data have undergone a
dedicated transversal validation process. The aim of this paper is to
highlight limitations of the data that were found during this process and to
provide recommendations for the usage of the catalogue.

Methods.  The validation was obtained through a statistical analysis of the
data, a confirmation of the internal consistency of different products, and a
comparison of the values to external data or models.

Results.  Gaia DR3 is a new major step forward in terms of the number,
diversity, precision, and accuracy of the Gaia products. As always in such a
large and complex catalogue, however, issues and limitations have also been
found. Detailed examples of the scientific quality of the Gaia DR3 release can
be found in the accompanying data-processing papers as well as in the
performance verification papers. Here we focus only on the caveats that the
user should be aware of to scientifically exploit the data.

###### Key Words.:

Surveys – Catalogs – methods: data analysis – methods: statistical –

##  1 Introduction

This paper describes the validation of the third Gaia data release, Gaia DR3
(Gaia Collaboration et al., 2016; Vallenari, 2022). The validation of the
astrometric and photometric content can be found in the Gaia EDR3 validation
paper (Fabricius et al., 2021). We focus here on the new products of Gaia DR3,
which are summarised in Vallenari (2022). The main new products of Gaia DR3
are the radial velocities, as well as line broadening and $G_{\rm RVS}$
magnitude, astrophysical parameters, variable stars, Solar System objects, and
for the first time, spectra (both from the spectrophotometric instrument and
from the Radial Velocity Spectrometer (RVS)), non-single stars, and quasar
(QSO) and galaxy candidates, and associated characterisation. The processing
papers111https://www.cosmos.esa.int/web/gaia/dr3-papers and the on-line
documentation222https://gea.esac.esa.int/archive/documentation/GDR3/index.html
describe the data and their internal validation in detail. The performance
verification papers1 highlight the overall quality of the catalogue. In this
paper, we focus on presenting the caveats that the Gaia DR3 users should be
aware of. Although the scientific validation process has confirmed the high
quality of the Gaia EDR3 data, certain issues remain, and there are caveats.
We focus this paper on highlighting them, and we provide advice to the users.

The approach followed by the validation presented in this paper is a
transverse analysis of the properties of the catalogue content. Tests are
either internal (including overall statistics, correlations, and clustering
analysis between catalogue entries) or use external data, a Galaxy model, or
clusters. The comparison with a Galaxy model was made using the Gaia object
generator (GOG20) as a reference model or the Gaia universe model snapshot
(GUMS20, which contains the intrinsic properties of the objects generated by
GOG). They are described in detail in the on-line
documentation333https://gea.esac.esa.int/archive/documentation/GEDR3/Data_processing/chap_simulated/
and were released with the Gaia EDR3 set of catalogues444GOG is published in
the Gaia archive in the table gaiaedr3.gaia_ source_ simulation and GUMS in
the table gaiaedr3.gaia_ universe_ model.

Although the validation tests were designed to be transverse, we organise the
paper by product for convenience. We therefore discuss in turn the Radial
Velocity Spectrometer products (Sect. 2: radial velocities, line broadening,
$G_{\rm RVS}$ magnitude, and RVS spectra), the low-resolution (Blue and Red
Photometers, BP and RP) spectra (Sect. 3), the astrophysical parameters (Sect.
4), QSO and galaxy candidates (Sect. 5), non-single stars (Sect. 6), variables
(Sect. 7), and Solar System objects (Sect. 8).

##  2 Radial Velocity Spectrometer

While only radial velocities were provided in Gaia DR2 , more products from
the Radial Velocity Spectrometer (RVS) are available in Gaia DR3 within the
gaia_ source table: the radial velocities (radial_ velocity) down to $G_{\rm
RVS}$$<$14, the spectral line broadening (vbroad), and the magnitude $G_{\rm
RVS}$ estimated using the RVS spectra flux (grvs_ mag). Moreover, a subset of
RVS spectra are available through the rvs_ mean_ spectrum
datalink555https://geapre.esac.esa.int/archive/documentation/GDR3/Gaia_archive/chap_archive/.

###  2.1 Radial velocity

The radial velocity ($RV$) data are presented in detail in Katz & et al.
(2022). The radial velocities of hot stars are specifically targeted in Blomme
& et al. (2022).

Figure 1: Differences in radial velocities of the members of close pairs of
sources as a function of the square of the angular separation in arcsec2 (top)
and after filtering (bottom). The red lines enclose one of the criteria that
were used to filter the problematic cases.

####  2.1.1 Radial velocity contaminants

During the process of the internal validation of a preliminary version of the
catalogue, we detected erroneous radial velocities due to nearby bright
contaminant sources, which was a well-known issue for Gaia DR2 (Boubert et al.
(2019); Seabroke et al. (2021)). This is illustrated in Fig. 1 (top), where we
took all pairs of sources whose components are closer than 10 arcsec and
plotted their difference in radial velocity (absolute value) versus their
angular separation (squared). In this plot, optical pairs should contribute a
constant density of points at a given ordinate, while physical binaries should
contribute pairs with small $RV$ differences. If in a given transit, the
dispersion of the spectra is oriented close to the line of separation of the
two sources, the lines from both sources will be present in both spectra. If
in addition, the lines from the neighbour source are confused with the lines
of the (fainter) target source, this will give the target an erroneous $RV$,
which differs from the $RV$ of its neighbour in proportion to the separation.
This will normally just result in an outlying observation, but if a particular
scan direction dominates, the final radial velocity difference will become 145
km s-1 for each arcsecond of separation. In the upper panel of Fig. 1, we see
a rounded front, a parabola, closely matching the predicted 145 km s-1
arcsec-1 dependence. This is a clear sign that source confusion is occurring.
We also note a second, weaker front below the first. It corresponds to a
similar effect, but the two sources are separated
1$\aas@@fstack{\prime\prime}$8 in the direction perpendicular to the scan,
which is the limit at which two observations have independent data
acquisition.

As a result, we have a population of false high radial velocities, but also
sources with biased radial velocities that are not necessarily very high. Most
of these problematic sources have been filtered out of the data released in
Gaia DR3 based on the separation and magnitude difference of the pair for
sources with $|RV|>200$ km s-1 (see Katz & et al. (2022)). This filtering
causes the fronts to almost disappear (Fig. 1 bottom), although some hints of
them still remain.

In the top plot panel of Fig. 1, a vertical band of sources with very large
radial velocity differences at very small separations is visible as well. From
the pairs with separations smaller than 1.6 arcsec (corresponding to 2.56
arcsec2 in these plots) with velocity differences above and below 500 and
$-500$ km s-1 (enclosed within the red lines), we filtered out the members
with a higher $RV$, which in general correspond to the faint member of the
pair. This is a total of 57 stars.

Figure 2: Radial velocity of the primary star against the secondary for stars
of binary pairs from the El-Badry et al. (2021) catalogue before (left) and
after (right) filtering.

We also used the binary catalogue by El-Badry et al. (2021) to test the
internal consistency of the radial velocities. More than 100 000 pairs with a
probability of 90% of being bound according to this catalogue have a radial
velocity for both members in Gaia DR3. The comparison of their radial
velocities before filtering (Fig. 2 left) indicates that most of the pairs
closely follow the one-to-one line (agreement of $RV$), but some sources have
suspiciously high radial velocities, especially the secondary members. This
sample also shows correlated velocity differences and separations in a plot
similar to Fig. 1. Some problematic high radial velocities still remain in the
catalogue after filtering (Fig. 2 right panel).

The —radial_ velocity—¿600 km s-1 for 770 sources. We expect most of them to
be real high-velocity stars, but due to the low signal-to-noise ratio of most
of the spectra, it is difficult to know which fraction of the measurements is
truly spurious. We can stack all of them, however, to improve the summed
signal. All the spectra were corrected for radial velocity. If the $RV$ value
that was used was the correct value, the stacked spectra should have strong
lines in the expected places such as the calcium triplet lines. This is shown
in Fig. 3. However, if the radial velocity that was used for the correction
was incorrect, the triplet lines are shifted and appear in the wrong place:
for $RV>600$ km s-1 , they are at least 1.7 nm to the left of the expected
position, while for $RV<-600$ km s-1 , they appear at least 1.7 nm to the
right (dashed lines). These secondary peaks are also seen in the figure. They
are less sharp because the incorrect velocity corrections range from 600 to
900 km s-1 (i.e. between 1.7 and 2.6 nm), so they do not all peak in the same
place. In any case, the figure clearly shows that most measurements are good,
that is, most of the sources are real high-velocity targets.

Figure 3: Stacked spectra for all the sources with a radial velocity ¿600 km
s-1 (421 sources, top) and ¡-600 km s-1 (349 sources, bottom). Solid vertical
lines indicate the position of the calcium triplet, and dashed lines show the
same lines shifted by 1.7 nm, indicating where the spectral line would be if
the radial velocity correction were incorrect by 600 km s-1.

Radial velocities are provided down to $G_{\rm RVS}=14$. A few sources are
still fainter than $G>16$ (see Fig. 4), however. A high $(G_{\rm RVS}-G)$
could indicate contamination from nearby bright stars (affecting the $RV$
estimation), but also (for the faintest $G_{\rm RVS}$) an under-subtraction of
the background. We recommend caution for radial velocities with $(G_{\rm
RVS}-G)<-3$.

Figure 4: Uncertainty in radial velocity as a function of $G$.

####  2.1.2 Radial velocity systematics

Comparison with external catalogues shows that the zero-point of the radial
velocities is lower than 0.1 km s-1 than in the radial velocity standard
catalogue of Soubiran et al. (2018), Carmenes (Lafarga et al., 2020), and SIM
(Makarov & Unwin, 2015), but it is about -0.2 km s-1 for GALAH DR3 (Zwitter et
al., 2021), APOGEE DR16 (Ahumada et al., 2020), and GES DR3 (Gilmore et al.,
2012). The number of 5$\sigma$ outliers is smaller than 3%. The radial
velocity zero-point shows a decrease with metallicity in all surveys
illustrated in Fig. 5. The global change in radial velocity with magnitude is
not consistent across the surveys. However, Katz & et al. (2022) used
subsamples on which they found a consistent trend between APOGEE and GALAH and
propose a magnitude-term correction. For stars with rv_template_teff$>$8500 K,
the correction derived in Blomme & et al. (2022) is to be used instead.

Figure 5: Variation in radial velocity difference with APOGEE DR16 as a
function of the APOGEE metallicity.

The comparison of the median radial velocity with the GOG model does not show
any systematic significant difference throughout the sky, at least none that
can be attributed to the data themselves. Figure 6 shows the median value of
the radial velocity throughout the whole sky and per magnitude bin for DR3,
EDR3, and GOG20. From $G=4$ to $G=15$, the DR3 values agree with GOG20 at the
level of 1.5 km s-1 or lower. We note that it was at the level of 1 km s-1 or
lower with EDR3. The EDR3 values are not reliable at $G>13$ because there are
too few stars. This limit is pushed to $G>15$ for DR3. The dependence on the
$G$ magnitude seen in the data is not predicted by the model and might
indicate some systematics in the data or in the model that are not yet
understood.

Figure 6: Radial velocities averaged over the whole sky as a function of $G$
magnitude for DR3 (blue), GOG20 (green), and EDR3 (pink).

####  2.1.3 Radial velocity uncertainties

The different methods that were used to compute the radial velocity (indicated
by rv_ method_ used, see Katz & et al. 2022) lead to different error
distributions as a function of magnitude (Fig. 4). In particular, the limit of
using one method or the other is $G_{\rm RVS}$= 12, which produces the plume
of large errors at $G\sim 12$.

We tested the uncertainties on radial velocity given by radial_ velocity_
error using 48 944 stars in 804 open clusters in which at least ten members
were brighter than $G_{\rm RVS}$$<$14 with radial velocities. Membership was
derived using parallaxes and proper motions. The sample used for this test
includes the open clusters catalogued by Cantat-Gaudin et al. (2020) and new
clusters in Castro-Ginard et al. (2021). These clusters are typically closer
and more populated than average: 70% are located within 2 kpc of the Sun, and
50% of them have more than 140 identified members. We computed the difference
$\Delta RV$ between the radial velocity of each star and the bulk cluster
radial velocity (defined as the median of RVS radial velocities), and we
compared this value with the nominal uncertainty of each star. The results are
shown in Fig.7. In the ideal case, $\Delta RV$/radial_ velocity_ error should
follow a normal distribution (centred on zero and with a dispersion of 1), but
Fig. 7 shows that bright stars (with high rv_ expected_ sig_ to_ noise and
small radial_ velocity_ error) tend to have a much broader dispersion than
faint stars. It should be noted that several effects can broaden the
distribution, such as the gravitational redshift and convective blueshift
which affect stars of different spectral types differently, and also affect
unrecognised binaries, non-members that are still present in the distribution,
and the intrinsic internal dispersion of the clusters in different ways. While
some of the effects are difficult to quantify, the internal velocity
dispersion is about 0.5-1 km s-1(see e.g. Torres et al., 2021). It is
difficult to produce diagnostics on a per-cluster basis because the effect is
revealed in a statistical way. However, the pattern seems to be identical for
all clusters, and this is in favour of an instrumental effect.

Figure 7: Radial velocity uncertainties tested with open clusters. Top panel:
Absolute value of the difference between the radial velocity of a star and its
cluster median $|\Delta RV|$ normalised by the radial_ velocity_ error. The
black line is the lowess (locally weighted scatterplot smoothing). The slope
of the lowess for lower values of radial_velocity_error indicates that the
errors can be underestimated at the bright end (but see the text for a
discussion). Bottom panel: Difference between the radial velocity of a star
and its cluster median $\Delta RV$ normalised by the radial velocity error in
different radial velocity error bins.

Comparison with external catalogues and the wide binary catalogue of El-Badry
et al. (2021) confirms that the errors are underestimated for $G_{\rm
RVS}$$<12,$ but also for $T_{\rm eff}$ $<4500$ and $T_{\rm eff}$ $>6000$ K
(Fig. 8). As the external catalogues provide different error underestimation
estimates due to their own error estimation uncertainties, we used the wide
binaries to estimate a correction. To limit the impact from the gravitational
redshift, we selected stars with similar colours and magnitudes, using a
difference of 0.1 mag in $G_{\rm BP}-G_{\rm RP}$ and $G$. To avoid the
additional dependence on the temperature, we selected only systems in which
both components lay within 4500$<$rv_ template_ teff$<$6000 K. We further
removed 5$\sigma$ radial velocity outliers. This led to a total of 2452
systems that could be used. According to the APOGEE and GALAH comparison, we
split the fit into bright and faint regimes at $G_{\rm RVS}$=12 mag (which
corresponds to the magnitude separation between the different methods that
were used to derive the radial velocity) and fitted a second-order polynomial
to the factor $f_{\sigma}$ to apply to the standard deviation,

$f_{\sigma}(G_{\mathrm{RVS}})=a+b\leavevmode\nobreak\
G_{\mathrm{RVS}}+c\leavevmode\nobreak\ G_{\mathrm{RVS}}^{2},$ (1)

by maximising the product of the likelihoods of

$\frac{RV_{1}-RV_{2}}{\sqrt{(f_{\sigma}(G_{\mathrm{RVS1}})\sigma_{\mathrm{RVS1}})^{2}+(f_{\sigma}(G_{\mathrm{RVS2}})\sigma_{\mathrm{RVS2}})^{2}}}$
(2)

to be normally distributed. The coefficients we obtained are illustrated in
Fig. 8 and are provided in Table 1. The bright side is not constrained, so
that it should not be extrapolated beyond $G_{\rm RVS}$$<8$ mag. Based on the
comparison with Soubiran et al. (2018), the value at $G_{\rm RVS}$=8 mag seems
to be a good estimate for $G_{\rm RVS}$$<8$ mag. The $T_{\rm eff}$ ranges
showing a strong departure in Fig. 8 correspond to systematic offsets between
rv_ template_ teff and the GALAH temperature. For cool stars, the deviation
with APOGEE is found only for $T_{\rm eff}<4000$ K. The effect of the random
temperature template mismatch is included in the correction provided in Table
1, but not the systematics as we used an internal comparison. On the range
4500$<$rv_ template_ teff$<$6000 K, the median absolute deviation between rv_
template_ teff and the GALAH $T_{\rm eff}$ is 250 K. When the correction of
Table 1 was applied to wide binaries with a $T_{\rm eff}$ template that was
hotter and cooler, no correlation of an additional factor with rv_ template_
teff was detected.

Table 1: Coefficients to derive the standard error factor $f_{\sigma}$ that should be applied to radial_velocity_error according to Eq. 1 for $G_{\mathrm{RVS}}>8$ mag. $G_{\mathrm{RVS}}<12$ | $G_{\mathrm{RVS}}>12$
---|---
a | b | c | a | b | c
0.318 | 0.3884 | -0.02778 | 16.554 | -2.4899 | 0.09933

Figure 8: Standard error factor $f_{\sigma}$ that should be applied to
radial_velocity_error as a function of magnitude (left) and temperature
(right), estimated from the comparison with GALAH. In green we over-plot
$f_{\sigma}$ estimated from the wide binaries (Eq. 1 and Table 1).

###  2.2 Vbroad

The estimation of the line-broadening parameter, vbroad, is detailed in Fremat
& et al. (2022). The comparison of the spectral line-broadening parameter
vbroad with external catalogues shows that values lower than $\sim 10$ km s-1
are systematically overestimated, while higher values tend to be
underestimated for FGK stars, as illustrated in Fig. 9, which shows a similar
behaviour as the comparison with GALAH (Zwitter et al., 2021). More details
about the validation of the spectral line-broadening parameter can be found in
Fremat & et al. (2022).

Figure 9: Comparison of the spectral line broadening parameter with the De
Medeiros et al. (2014) catalogue of FGK stars, colour-coded by the template
temperature.

###  2.3 Grvs magnitude

The estimation of the $G_{\rm RVS}$ magnitude, grvs_ mag, is detailed in
Sartoretti & et al. (2022). The comparisons of $G_{\rm RVS}$ with the
Hipparcos magnitude and Tycho2 colours indicate no saturation issues. The
comparison of $G_{\rm RVS}$ with Gaia $G$ magnitude and $G_{\rm BP}$-$G_{\rm
RP}$ colour shows a change in behaviour at $G_{\rm RVS}$$>12$. To illustrate
this, we used here a sample of solar metallicity dwarfs selected from APOGEE
DR16 (Ahumada et al., 2020) that had low extinction ($A_{0}<0.05$ mag
according to Lallement et al. (2019)). An empirical robust spline regression
was derived to model the global relation of $G$-$G_{\rm RVS}$ versus $G_{\rm
BP}$-$G_{\rm RP}$ . The residuals from this spline are plotted as a function
of magnitude in Fig. 10. The effect appears to be much larger than the
internal variations observed with the $G$, $G_{\rm BP}$ , and $G_{\rm RP}$
magnitudes (Fabricius et al., 2021), but are still only at the 10 mmag level.

Figure 10: Residuals from a global relation $G_{\rm RVS}-G=f(G_{\rm BP}-G_{\rm
RP})$ for a sample of APOGEE low-extinction solar metallicity dwarfs.

Figure 11 shows the relative difference between DR3 and GOG20 in the $G=12$ to
13 magnitude range. The agreement is very good, except in the bulge and
Galactic plane, where the excess of simulated stars is large. This is expected
as only sources with unblended spectra were used to estimate $G_{\rm RVS}$
(Sartoretti & et al., 2022). Exploring these maps at other magnitude bins
shows that the completeness for $G_{\rm RVS}$ measurements is still high at
$G$ = 14 outside of the Galactic plane, and at fainter magnitudes, the star
counts start to drop. In the Galactic plane, the data already start to be
incomplete at $G$ = 11.

Figure 11: Relative difference of the number of stars with a $G_{\rm RVS}$
value between DR3 and GOG20 (DR3-GOG20)/DR3 in the magnitude range $12<G<13$
in Galactic coordinates. -1 (+1) corresponds to a deficit (an excess) of 100%
in DR3 data with regard to the GOG20 model.

###  2.4 RVS spectra

The main properties of the RVS spectra, available through the rvs_ mean_
spectrum datalink table, are described in Seabroke & et al. (2022). The sky
distribution of the sources with spectra, presented in Fig. 12, is non-
uniform. There are patches with a higher density of sources, and some regions
are basically empty. More details about how this sample was selected are given
in Seabroke & et al. (2022).

Figure 12: Galactic distribution of the sources for which RVS spectra are
available in the HEALPix map of order 6. White patches are regions without
sources.

The continuum calibration of the spectra was performed using different
methods, which resulted in different continuum levels. For faint targets,
$G_{\rm RVS}$ $>12$ (or rv_method_used=2), the method set the median value of
the flux at 1, which forces the continuum to be slightly above this value (see
Fig. 13 top). For brighter targets (or rv_method_used=1), the continuum is
slightly below 1 (see Fig. 13 middle). When the target is red, the continuum
is not flattened, and a positive slope is visible (see Fig. 13 bottom).

Figure 13: Three example spectra with three different continuum levels.

##  3 Spectrophotometry

Gaia DR3 provides low-resolution spectral data for about 220 million sources
for the first time. These data consist of two sets of coefficients with the
corresponding uncertainties and correlation matrices, available in the
xp_continuous_mean_spectrum table through the datalink interface5. One set of
coefficients is for the BP instrument, and the other set is for the RP
instrument. The only exception is DR3 source_ id$=5405570973190252288$. This
very red and faint source only has an RP spectrum. The coefficients are the
development of a spectrum in basis functions for the internal spectrum in
units of electrons per second per pseudo-wavelength within the Gaia aperture
as a function of pseudo-wavelength (De Angeli et al., 2022). Externally
calibrated spectra can be obtained through the GaiaXPy tool666https://gaia-
dpci.github.io/GaiaXPy-website/ (see also the cosmos
pages777https://www.cosmos.esa.int/web/gaia/auxiliary-data for the
configuration files that allow producing these externally calibrated spectra).
For a subset of the spectra with $G<15$, these externally calibrated sampled
spectra are available directly in the xp_ sampled_ mean_ spectrum table
through the datalink interface. However, the direct usage of the coefficients
is strongly recommended (De Angeli et al., 2022). GaiaXPy can also be used to
transform an external spectrum into the Gaia XP (shortcut for BP and/or RP)
continuous representation. A detailed description of the data is provided by
De Angeli et al. (2022) for the internal spectra, and by Montegriffo et al.
(2022) for the external spectra. Tests were performed to ensure the validity
of the spectrophotometric data, both for internal and external spectra. The
tests are described below.

###  3.1 Ensemble properties of the spectrophotometric data

Figure 14: Density of Gaia DR3 sources in the sky with available
xp_continuous_mean_spectrum in Galactic coordinates.

Figure 14 shows the sky density distribution for all sources with
xp_continuous_mean_spectrum in Gaia DR3. In addition to the natural variation
in source density, several distinct regions with a lower source density are
seen. The natural variation includes high densities along the Galactic plane
and in particular towards the Galactic centre, and decreasing densities
towards the Galactic poles. These artificial patterns in the sky distribution
of the sources result from the selection process of spectra to be published in
Gaia DR3, in particular, the requirement of at least 15 observations.

Figure 15 shows the distribution of the sources with XP spectra in the colour
– apparent magnitude diagram. The natural increase in the number of sources at
fainter apparent magnitudes is clearly visible. In addition to this,
artificial structures are superimposed. For a G magnitude brighter than 17.65,
all available XP spectra are included in Gaia DR3, while for fainter sources,
only a subset is included, with a focus on red sources. This results in the
break in the distribution at G = 17.65, and in the generally smaller number of
sources at larger magnitudes, with a larger proportion of red sources. A
detailed description of the selection process is provided by De Angeli et al.
(2022).

Figure 15: Magnitude-colour diagram for sources for which
xp_continuous_mean_spectrum is available in Gaia DR3.

###  3.2 Tests of source coefficients

The first test we performed on the coefficients of the XP spectra determined
the stability of the representation of internal spectra. The internal BP and
RP spectra are represented by a linear combination of basis functions, and the
integrated flux of a source is thus a linear combination of the integrals of
the basis functions over the entire real axis. The absolute values of these
integrals are about one, therefore we might expect the absolute values of the
coefficients of the spectrum of any particular source to not be significantly
higher than the integrated flux of the source. Coefficients that are very
large compared to the integrated flux would indicate that the source spectrum
is a linear combination of basis functions that mostly cancel each other out,
and would thus be an indicator of an unstable representation of the internal
spectrum. We compared the absolute values of all coefficients of all sources
with the integrated flux, and they are all lower than 3.8 times the integrated
flux. In most cases, the values are significantly lower. We therefore see no
indications for excessively large contributions from different basis functions
to internal spectra that are cancelling each other out.

The basis functions used to represent BP and RP spectra are constructed such
that they are efficient in representing typical stellar spectra (Carrasco et
al., 2021; De Angeli et al., 2022). As a consequence, the broad structure of a
spectrum is represented by the low-order basis functions, and detailed
spectral patterns are represented by higher-order basis functions. The
absolute values of the XP source coefficients should therefore decrease in
general with the order of the coefficient. Figure 16 shows the distributions
of the BP and RP coefficients, normalised with respect to the $L_{2}$-norm,
for all sources in Gaia DR3. In both instruments, most coefficients of the
majority of XP spectra are close to zero. To study this further, we compared
the sum of absolute values for the first five coefficients with the sum of the
remaining higher-order coefficients. We computed the difference between the
first and the second sum, with the uncertainty on the difference, and
considered sources for which the difference was smaller than five times the
error. Of all the sources with XP spectra in Gaia DR3, 26037 sources have BP
and 5470 sources have RP spectra that fulfil this criterion. The majority of
these sources are concentrated in the Galactic plane and towards the direction
of the Galactic centre. These sources may therefore be affected by crowding,
resulting in a contamination of the spectra by flux from nearby sources and
thus unexpected spectral shapes that require an unusual combination of basis
functions. The larger number of sources as compared to RP may result from the
larger number of faint sources in BP.

Figure 16: Source mean spectrum coefficients for all sources in the
xp_continuous_mean_spectrum table for BP (top) and RP (bottom). The colour
index indicates the source density.

The xp_summary table contains two parameters specifying the number of relevant
coefficients for each source in BP and RP, respectively (bp_ n_ relevant_
bases and rp_ n_ relevant_ bases). All coefficients with indices larger than
the specified number are considered to be consistent with being zero. Figure
17 shows the histogram of the number of relevant bases for BP and RP.

Figure 17: Number of relevant bases in the xp_summary table for BP (blue) and
RP (red).

No source has a relevant number of coefficients zero and 54. In the first
case, this is caused by assigning 55 coefficients as relevant if no
coefficients are found to be relevant, and the spectrum therefore agrees with
consisting of random noise alone. The lack of 54 relevant coefficients arises
because only one last coefficient prevents the computation of the standard
deviation (De Angeli et al., 2022).

###  3.3 Tests of the spectral shape

Due to the lower instrumental response at its edges, the flux values of the
sampled internal spectra should be lower in the outer samples than in the
central samples for sources with significant flux. These regions with low
fluxes at either side are referred to as the wings of the spectra. To evaluate
the behaviour of the spectra at the wings at either side, we integrated the
fluxes over the pseudo-wavelength ranges $[-\infty,0]$ and $[0,5]$ and
compared them to the integrated flux over the interval $[5,10]$. Analogously,
on the other side of the spectra, the integrals over the pseudo-wavelength
intervals $[60,\infty]$ and $[55,60]$ were compared with the integral over the
interval $[50,55]$. For the comparison, the difference between the integrals
was computed and normalised with respect to its uncertainty. The four
normalised differences are smaller than five for 204 to 796 RP sources and for
88 to 7411 BP sources. As was already the case for the test of the decreasing
coefficients, a small part of these sources is homogeneously distributed in
the sky, while the majority is concentrated in the Galactic plane and in the
direction of the Galactic centre. This indicates crowding and the resulting
contamination of the XP spectra with flux from nearby stars as a reason for
the non-decreasing spectral wings. The larger number of BP spectra in this
test that do not meet the threshold as compared to RP spectra may be a result
of the larger number of faint spectra in BP. Figure 18 shows the distribution
of sources with normalised differences larger than five when all XP
coefficients are used and when the representation is truncated at xp_ n_
relevant_ bases. The truncation results in an increase in the number of
sources above the chosen threshold, in particular, for very red sources. A
possible reason might be that the truncation results in an underestimated
error.

Figure 18: Comparison of the number of sources failing the small wings test in
BP (blue/cyan) and RP (red/magenta) when all source coefficients are
considered (solid lines) or only truncated coefficients are taken into account
(dashed lines) as a function of colour.

Noise may cause parts of the spectrum to be negative, in particular, for faint
sources. In order to determine the number of negative values in the sampled XP
spectra, we defined the negativity of a spectrum as

$z=\frac{\int\limits_{-\infty}^{\infty}\left|f(u)\right|\,{\rm
d}u-\int\limits_{-\infty}^{\infty}f(u)\,{\rm
d}u}{2\,\int\limits_{-\infty}^{\infty}\left|f(u)\right|\,{\rm d}u.}$

This measure for negativity $z$ is zero if the sampled XP spectrum is positive
at all values of the pseudo-wavelength $u$, and one if it is negative at all
values of $u$. Figure 19 shows the distribution of sources as a function of
the $L^{1}$ norm of the spectrum and the value of $z$ for BP and RP. The
majority of sources follows a general trend of low negativity for large
$L^{1}$ norms and increasing negativity and a wider spread in the distribution
as the norm decreases. The latter case corresponds to faint sources with
increasing negativity due to noise. The more pronounced tail of sources with
small $L^{1}$ norms in BP results from the larger number of faint BP spectra
as compared to RP. Only a small fraction of sources clearly lies beyond the
general relation between the $L^{1}$ norm and $z$. These outliers in general
result from an over-subtraction of the background in the spectra, shifting its
overall flux level towards negative values.

Figure 19: Distribution of sources in the $z$–$L^{1}$ norm plane for BP (top
panel) and RP (bottom panel).

###  3.4 Wiggling patterns

We tested whether truncation efficiently removes unnecessary wiggling patterns
in the XP spectra. Here we considered 6377 main-sequence star members (Cantat-
Gaudin et al., 2020) of 17 open clusters 888IC 4651, Melotte 20, Melotte 22,
NGC 2099, NGC 2168, NGC 2287, NGC 2506, NGC 2516, NGC 2632, NGC 3114, NGC
3532, NGC 3766, NGC 457, NGC 6405, NGC 6475, Stock 2, and Trumpler 19.. By
considering only the members of these open clusters, we ensured that our
testing sample is composed of stars with metallicities similar to the solar
value. We employed XP spectra that were externally calibrated by GaiaXPy with
the default constant wavelength step used for the xp_sampled_mean_spectrum
table.

First, we defined a coefficient that measures the wiggling level in XP
spectra. For each ith wavelength sampled portion of the spectrum, we
calculated

$\delta^{n}_{i}=\frac{|f_{i}-\mathrm{mean}(f_{i-n},f_{i+n})|}{\texttt{\small{phot\\_
g\\_ mean\\_ flux}}},$ (3)

where fi is the flux associated with the ith wavelength sample. The wiggling
coefficient is thus defined as the average of the $\delta^{n}_{i}$ across the
entire spectrum or a portion of it,

$w_{n}=\sum_{i}\frac{\delta^{n}_{i}}{N},$ (4)

where N is the number of wavelengths over which $\delta^{n}_{i}$ is
determined.

The wiggling coefficient w3 was calculated for each star within the spectral
range [450,900] nm. This coefficient is higher when the spectra contain more
undesired wiggles, but also for later spectral types, whose spectra typically
contain more molecular bands. In order to ensure that we probed the wiggling
and not real spectral features, we therefore defined a differential
coefficient

$\Delta w_{3}=\log_{10}(w_{3})-\log_{10}(\overline{w_{3}}),$ (5)

where w3 is the coefficient defined in Eq. 4 and measured for the jth star,
while $\overline{w_{3}}$ is the coefficient measured on the average spectrum
calculated over a sample of 100 dwarf stars with $|$phot_ g_ mean_ mag \-
phot_ g_ mean_ mag${}_{j}|<0.01$ mag and $|$bp_ rp-bp_ rp${}_{j}|<0.005$. By
averaging spectrum stars with very similar phot_ g_ mean_ mag and bp_ rp , we
obtained a single spectrum that contained the typical absorption features that
can be found in spectra of stars similar to the jth star and cleaned from
wiggles. Therefore, $\Delta$w3 is truly representative of the actual wiggling
shown by the jth spectrum, without the contribution from molecular bands or
any other spectral feature.

Figure 20 shows the cumulative histograms of the differential wiggling
coefficients $\Delta$w3 derived for stars in different bins of phot_ g_ mean_
mag. The coefficients derived from non-truncated spectra are plotted in the
left panel, and those from truncated spectra are shown in the right panel.
While fainter stars tend to have larger $\Delta$w3 in their non-truncated
spectra due to the lower signal-to-noise ratio, this dependence is
significantly reduced by truncation.

Figure 20: Cumulative histogram of the differential wiggling coefficient
$\Delta$w3 measured for stars with 0.5$<$bp_ rp$<$0.7 and within different
bins of phot_ g_ mean_ mag. The left panel shows $\Delta$w3 values measured in
the non-truncated spectra, and the right panel shows these values for
truncated spectra.

Wiggling in XP spectra might be enhanced by strong spectral features. It is
especially important to test this possibility in spectra of young accretors,
which are typically characterised by strong H$\alpha$ emission lines.
Therefore, we used XP spectra from 197 members of the star-forming regions
Chamaeleon I, IC 348, Lupus, NGC 2024, NGC 2068, ONC, Ophiucus, and R Coronae
Australis.

We defined a coefficient that measures the height of the H$\alpha$ line,

$H_{H\alpha}=\frac{f_{H\alpha}}{\mathrm{mean}(f_{(626-636)\cup(676-686)})},$
(6)

where fHα is the flux measured at 656nm, corresponding to the centre of the
H$\alpha$ line, while the mean flux at the denominator is measured at the base
of the H$\alpha$ line, that is, at all wavelengths within 626-636nm and within
676-686nm.

The differential wiggling coefficient $\Delta$w10 (see Eq. 4 and 5) is shown
in Fig. 21 (left panel) as a function of HHα for non-truncated spectra. Each
error bar represents the standard deviation of the coefficient
$\overline{w_{10}}$ measured on the comparison sample of 100 dwarfs. The plot
shows that wiggling increases with the height of the H$\alpha$ line.
Therefore, we conclude that the presence of strong spectral features enhance
wiggling in XP spectra.

In order to test whether truncation is able to fix or alleviate the problem,
we repeated the experiment on truncated XP spectra. The results are shown in
the right panel of Fig. 21, which indicates that truncation does not
significantly remove the additional wigging produced by strong H$\alpha$
lines. Instead, we found that the height of the H$\alpha$ line is affected by
truncation. We observe that for 23$\%$ of stars with H${}_{H\alpha}^{non-
trunc}$$>$1.1, HHα is reduced by more than 5$\%$ by truncation.

Figure 21: Differential wiggling $\Delta$w10 as a function of HHα. Non-
truncated spectra are shown on the left, and truncated spectra are shown on
the right.

###  3.5 Tests of the integrated fluxes

The calibration of XP integrated photometry and the spectra follow different
calibration procedures that only have low-level processing steps in common.
Although some differences might occur between the integrated fluxes from the
XP spectra and the integrated photometry, mainly because of potential
differences in passband calibration and noise, we expect to have comparable
results among these two different processes in principle. In order to test
this, we computed the ratio of the photometric and spectrum flux.

The distribution of this ratio shows that most sources have a value close to
one (Fig. 22). For BP, however, there is a significant population of sources
with values higher than one. This might be a result of a threshold of one
electron/s that was applied in the selection of transits in the integrated
photometry. Transits with a flux below this threshold were excluded from the
computation of the mean flux, resulting in a biased mean flux for faint
sources (Riello et al., 2021). This threshold was not applied in the
computation of the mean spectra, thus avoiding the bias towards too high
fluxes and leading to a better behaviour at low BP fluxes for the integrated
flux from the spectra.

Figure 22: Histogram of the ratio $r$ of the photometric and spectrum flux in
BP (left) and RP (right).

We compared the flux error uncertainties derived from photometric fluxes and
those derived from the spectra. Although these flux uncertainties are similar,
those derived from the photometric calibration tend to be slightly larger than
those derived from the spectra. The ratio of the uncertainty in XP fluxes and
on the flux resulting from integrating the spectra is shown in Fig. 23 for BP
and RP as a function of BP and RP photometric magnitude. The shift towards
larger photometric errors is clear, together with a dependence on the source
magnitude. This behaviour might result from underestimated uncertainties, in
particular for low-order coefficients in the source representation (De Angeli
et al., 2022) to which the integration of the spectra is particularly
sensitive. The distribution of the uncertainty ratio has strong tails towards
extreme values. The photometric uncertainties of hundreds of sources are
100–1000 times larger than those derived from the spectra.

Figure 23: Distribution of sources in decadic logarithm of the ratio of
photometric and spectroscopic uncertainty and XP magnitude for BP (top panel)
and RP (bottom panel).

###  3.6 Uncertainties of the XP coefficients

The analysis that we performed on the coefficients of the XP spectra tested
whether their uncertainties were evaluated correctly. To do this, we compared
pairs of stars using a chi-square,

$\chi^{2}=(X_{1}-X_{2})^{T}(C_{1}+C_{2})^{-1}(X_{1}-X_{2}),$ (7)

where $X_{1}$ and $X_{2}$ are the coefficients of the two stars in either the
BP or RP channel, while $C_{1}$ and $C_{2}$ are the associated covariance
matrices. In order to ensure that we compared stars with the same metallicity
and reddening, we applied Eq. 7 only to pairs of stars belonging to the same
open cluster (i.e. membership probability$\geq$0.7 from Cantat-Gaudin et al.
2020). We also excluded all stars with ruwe$>$1.4 from the comparison (to
remove binaries) and stars belonging to open clusters younger than 100 Myr.
This latter selection was necessary to avoid contamination due to differential
extinction. Furthermore, the two stars must have $G$ magnitudes and $G_{\rm
BP}-G_{\rm RP}$ colours that were consistent within their uncertainties. By
applying all these selection criteria, we obtained a controlled sample of 1560
stellar pairs whose $\chi^{2}$ values we were able to derive.

The $\chi^{2}$ values were then used to calculate the associated p-values (the
null hypothesis being that $\chi^{2}$ follows the expected chi-square
distribution, the degree of freedom being the number of coefficients)
separately for the BP and RP channels. If the $\chi^{2}$ indeed followed a
chi-square distribution, the p-values should be distributed uniformly. The
cumulative histograms of the p-value distributions of the 1560 stellar pairs
are shown in Fig. 24 (blue lines). The p-values of 49$\%$ and 56$\%$ of the
pairs are below 0.01 in BP and RP, respectively. This indicates that the
uncertainties of the XP coefficients are underestimated.

As a second test, we applied a more stringent criterion in the selection of
the pairs that were to be tested. Specifically, we imposed that the $G$,
$G_{\rm BP}$ and $G_{\rm RP}$ magnitudes of the two stars must be consistent
within their uncertainties. This reduced our sample to 501 pairs. The relative
p-value distributions are plotted as orange lines in Fig. 24: 25$\%$ and
26$\%$ of these pairs fail our test in BP and RP, respectively.

When we applied the further condition that the magnitudes $G_{1}$ and $G_{2}$
must be fainter than 16 mag, we further reduced the sample to 437 pairs. The
difference between this new sample, which is plotted as green lines in Fig.
24, and the previous sample is too small to observe significant effects in the
p-value distribution. The fraction of pairs that fails our test now decreased
to 22$\%$ and 24$\%$ in BP and RP, respectively.

Finally, we studied the p-value distributions obtained from the pairs composed
of stars with the same number of bp_ n_ relevant_ bases and rp_ n_ relevant_
bases. In this way, we compared spectra with similar wiggling levels. Applying
these criteria for the two bands separately, we obtained a sample of 148 and
109 pairs for BP and RP, respectively. The cumulative distributions are
plotted in Fig. 24 as red lines. The fraction of stars that do not pass the
test decreases to 21$\%$ for BP and 15$\%$ for RP, but it is still
significant.

Figure 24: Zoom on the p-value distributions obtained for the two bands BP
(left) and RP (right). The pairs with a p-value below 0.01 failed the test.

In order to estimate how much the errors are underestimated, we multiplied the
covariance matrix by various factors and then repeated the experiment on the
437 pairs that are fainter than G=16 mag. The resulting p-value distributions
are shown in Fig. 25. The figure shows that the variances are underestimated
by a factor that is between 1.2 and 1.5.

However, a detailed study of the coefficient error underestimation is
presented in De Angeli et al. (2022) by dividing the data into two groups of
transits for the same source and comparing the obtained values. They show that
the error underestimation depends on the coefficients. The lower-order
coefficients lead to the highest underestimation.

Figure 25: Same as Fig. 24 for the 437 pairs that are fainter than G=16 mag,
but have a covariance matrix (Cov) of one to three times its original value
for the two bands BP (left) and RP (right).

###  3.7 Comparison with external spectra

Figure 26 shows the median flux difference, normalised by the errors, between
the XP sampled and the CALSPEC9992021 March update spectra (Bohlin et al.,
2014) for the sources in common. A dip at $\sim$600 nm is visible. Figure 27
presents the median normalised flux difference within $560<\lambda<620$ nm
with both CALSPEC and NGSL (Heap & Lindler, 2016) as a function of magnitude.
It shows that the strength of this dip is magnitude dependent and has a
saturation effect. Figure 26 seems to suggest a difference in flux level
between BP and RP, but it is not statistically significant in the CALSPEC or
the NGSL sample. However, when the MILES library is used (Falcón-Barroso et
al., 2011) and the MILES spectra are normalised to the absolute flux of the XP
spectra in the common wavelength range, this difference in flux level becomes
significant. The bluest wavelengths show a colour-dependent trend that is
illustrated in Fig. 28. See also Montegriffo et al. (2022) for a discussion of
these features.

Figure 26: Median flux difference normalised by the errors between the XP
sampled spectra and the CALSPEC spectra normalised by the errors as a function
of wavelength. Dotted lines correspond to the 1$\sigma$ confidence interval.
Figure 27: Median flux difference normalised by the errors within
$560<\lambda<620$ nm between the XP sampled spectra and the CALSPEC (black
dots) or NGSL (grey dots) spectra normalised by the errors as a function of
magnitude. Figure 28: Median flux difference within $\lambda<350$ nm between
the XP sampled spectra and the CALSPEC (black dots) or NGSL (grey dots)
spectra normalised by the errors as a function of magnitude.

##  4 Stellar astrophysical parameters

An overview of the Gaia DR3 astrophysical products produced by 13 different
modules101010DSC: Discrete source classifier
GSP-Phot: General stellar parametriser from photometry
GSP-Spec: General stellar parametriser from spectroscopy
FLAME: Final luminosity age mass estimator
ESP-CS: Extended stellar parametriser for cool stars
ESP-UCD: Extended stellar parametriser for ultra-cool dwarfs
ESP-HS: Extended stellar parametriser for hot stars
ESP-ELS: Extended stellar parametriser for emission line stars
MSC: Multiple star classifier
QSOC: QSO classifier
UGC: Unresolved galaxy classifier
OA: Outlier analysis
TGE: Total Galactic extinction is presented in Creevey (2022). The non-stellar
content part of the astrophysical parameters is discussed in Sect. 5. This
section focuses on the stellar content, which is presented in detail in
Fouesneau (2022).

The astrophysical parameters are available in two tables: astrophysical_
parameters , and astrophysical_ parameters_ supp. We present here only the
validation results of the main parameters. In particular, the specialised
modules (ESP Creevey, 2022) are almost not discussed here. The Outlier
Analysis tables (oa_neuron_information, oa_neuron_xp_spectra) are not
discussed here either. They were successfully checked for internal but not
external consistency. Tests on the ESP and OA modules can be found in
Fouesneau (2022). GSP-Phot parameters (Andrae, 2022) were derived using
several spectral libraries (MARCS, PHOENIX, A, and OB). The values obtained
with these different libraries are presented in astrophysical_ parameters_
supp , while astrophysical_ parameters contains the values that were obtained
with what was selected as the best library indicated in the field
libname_gspphot. We mainly discuss the best library results of GSP-Phot here.

In this section, we compare the Galaxy model using GUMS as a reference. In
contrast to GOG, GUMS contains most of the astrophysical parameters, but they
are error free.

###  4.1 DSC

The development of the discrete source classifier (DSC) was mainly driven by
extragalactic source completeness (see the on-line documentation and
Delchambre (2022)). The purity for QSOs and galaxies is discussed in Sec. 5.
We did not find correlations between the classprob_ dsc_ binarystar
probabilities with Multiple Source Classifier (MSC) results or with known
binaries. White dwarfs are also often confused with hot main-sequence stars.
We therefore advise against using the physical binary and white dwarf class
probabilities111111they are needed to be able to adapt the DSC probabilities
to a new prior, however; see Sect. 11.3.2.7 of the on-line documentation.

###  4.2 Extinction

The extinction is provided as the monochromatic extinction $A_{0}$ at 541.4 nm
by GSP-Phot (azero_gspphot), the hot star module ESP-HS (azero_esphs), and the
multiple source module MSC (azero_msc). GSP-Phot and ESP-HS also provide
$A_{G}$ and $E(G_{\mathrm{BP}}-G_{\mathrm{RP}})$.

Figure 29: Density plot of the comparison of the monochromatic extinctions of
GSP-Phot with those derived by Lallement et al. (2018). The green line
corresponds to the 1.02 relation that is expected given the slight wavelength
difference between the two $A_{0}$.

The well-known and expected temperature - extinction degeneracy is discussed
in Andrae (2022), as is the effect of imposing the extinction to be positive
on the mean of low-extinction regions. Andrae (2022) also showed that the GSP-
Phot extinction values azero_ gspphot are globally overestimated with a
saturation at 10 mag (by construction) in their comparison with Bayestar19
(Green et al., 2019). We find the same trend in our comparison with the
monochromatic extinction $A_{0}$ at 550 nm derived from APOGEE, Gaia, and
2MASS by Lallement et al. (2018) shown in Fig. 29. The Lallement et al. (2018)
$A_{0}$ are consistent with the $A_{V}$ values from StarHorse provided with
APOGEE DR16 (Queiroz et al., 2020) with only the expected deviation for large
extinctions between $A_{V}$ and $A_{0}$ (See Sect. 11.2.3.1.4 of the on-line
documentation). We further confirmed this overestimation of the GSP-Phot
extinction values with the bstep extinctions provided with GALAH DR3 (Buder et
al., 2021) and the Lallement et al. (2019) 3D extinction map. It is also
confirmed with clusters in Fouesneau (2022). For nearby stars, the extinction
stays low because of the ad hoc extinction prior (Andrae, 2022). However, at
high Galactic latitudes, 22% of the stars have azero_ gspphot_ lower$>$0.16,
the highest extinction value expected according to the map of Schlegel et al.
(1998). As illustrated in Fig. 30, high extinction values occur at the bottom
of the main sequence for red giants (which can be confused with extincted hot
stars), but also for some stars near
$G_{\mathrm{BP}}-G_{\mathrm{RP}}$$\sim$0.2 and $\sim$2, which also have an
impact on the temperature that is estimated for these stars.

Figure 30: Hertzsprung-Russel diagram of low-extinction stars ($A_{0}<0.05$
mag according to Lallement et al. (2019)) with a parallax relative precision
lower than 10%, colour-coded with the mean extinction azero_gspphot. The
colour is saturated as black for values higher than 1 mag. In this low
extinction sample, $M_{G}$ is simply $G+5+5\log(\varpi/1000)$.

The global overestimation of GSP-Phot extinction values naturally leads to an
overestimation of the total Galactic extinctions provided in the table total_
galactic_ extinction_ map_ opt. This is shown in the comparison with Planck
(Planck Collaboration et al., 2016) in Fig. 31. As a0_ uncertainty provides
the error on the mean and can become very small when the number of stars
becomes high, we recommend using a0_
uncertainty$\times\sqrt{\texttt{\small{num\\_tracers\\_used}}}$ instead. While
the overall appearance of the Galactic extinction map is as expected (large-
scale dust filaments are clearly visible in the approximate expected relative
intensity; see Delchambre 2022), the $A_{0}$ estimates are systematically
overestimated in a large region of around 20 deg around the Galactic centre.
At high Galactic latitudes, the uncertainties are large enough for the $A_{0}$
overestimation to be not visible in Fig. 31.

Figure 31: Comparison of the total galactic extinction map (a0) with Planck
$E(B-V)$ normalised by the error $R_{N}=(\texttt{\small{a0}}-E(B-V)\times
3.1)/(\texttt{\small{a0\\_uncertainty}}\times\sqrt{\texttt{\small{num\\_tracers\\_used}}})$.
The white area corresponds to locations with status$>$0.

The multiple source classifier (MSC) finds 37% of its lower extinction azero_
msc_ lower$>0.05$ for the nearby star sample ($\varpi>20$ mas) for which no
significant extinction is expected. When compared to Traven et al. (2020), the
MSC extinction is also globally overestimated. We advise that the GSP-Phot
extinctions be preferred even for binary stars.

For the hot-star module, extinction azero_ esphs can reach very high values
for white dwarfs that are treated as hot main-sequence stars. We recommend to
filter them out using a colour-absolute magnitude diagram.

Another estimate of the extinction is provided through the diffuse
interstellar band (DIB) at 862 nm that is present in the RVS spectra: dibew_
gspspec. The details of the measurement are presented in Recio-Blanco (2022),
and details of its performance are reported in Schultheis (2022). The DIB
equivalent width correlates well with the extinction (Schultheis, 2022). The
large number of outliers in wavelength seen with wide binaries (Appendix A) is
due to wavelength clusters around 862.5 and 861.8 nm, most of which are
removed when only sources are kept that have dibqf_ gspspec$<2$. This
selection criterion is globally recommended for the DIB parameters
(Schultheis, 2022).

###  4.3 Teff, logg, metallicity, and abundances

The comparison of the astrophysical parameters with the GUMS model is
satisfactory within the model uncertainties outside the Galactic plane. The
exception is the GSP-Phot metallicity, which follows extinction patterns, as
expected from the extinction-temperature degeneracy.

Stars with $3000<T_{\rm eff}<8000K$ are analysed by GSP-Phot using the MARCS
and PHOENIX spectral libraries. Comparing the values of $T_{\rm eff}$, we find
a median difference $T_{\rm eff}$ (MARCS-PHOENIX)=-63 K (median absolute
deviation, MAD=145 K) due to the different temperature scale. $T_{\rm eff}$
estimated using the best library compilation presents nonphysical clustering
of points on the Hertzsprung-Russel (HR) diagram due to edge effects at the
library borders. This is most evident for the OB library border at 15000 K.

Figure 32 shows the comparison of the atmospheric parameters for the sources
derived by both GSP-Phot (Andrae, 2022) and GSP-Spec (Recio-Blanco, 2022). The
agreement between the two methods for the temperature is reasonable, has an
offset on the surface gravity for small $\log g$, and a very large dispersion
for the global metallicity.

Figure 32: Density plot of the comparison of the temperature (left), surface
gravity (middle), and global metallicity (right) provided by GSP-Phot (y-axis)
and GSP-Spec (x-axis). GSP-Phot has been filtered with parallax_ over_
error$>$5 and teff_ gspphot$<$10000\. GSP-Spec parameters have been filtered
with flags_ gspspec[1,4,8,13]=0 for $T_{\rm eff}$, flags_ gspspec[2,5,8,13]=0
for $\log g$, and flags_ gspspec[3,6,8]=0 for [M/H]. The dashed green line
shows the one-to-one correspondence. The median absolute deviation is
indicated in each panel.

Figure 33 shows the comparison of the atmospheric parameters derived by GSP-
Phot and GSP-Spec with APOGEE DR16 (Ahumada et al., 2020). The plots look
similar to GALAH DR3. The large dispersion of the GSP-Phot metallicity
explains Fig. 32. The GSP-Spec $T_{\rm eff}$ is slightly overestimated for
Teff$>5500$ versus APOGEE and GALAH, while GSP-Phot is not. GSP-Phot $\log g$
for small $\log g$ is overestimated. GSP-Spec $\log g$ is globally
underestimated, and a correction is proposed in Recio-Blanco (2022). GSP-Spec
median offsets and dispersion versus external catalogues after proposed
corrections are quite good and are provided in Recio-Blanco (2022). We also
caution the user against using GSP-Spec $\log g$ values for AGB stars.

Figure 33: Density plot of the comparison of the temperature (top), surface
gravity (middle), and global metallicity (bottom) provided by GSP-Phot (left)
and GSP-Spec (right) with APOGEE DR16. GSP-Phot has been filtered with
parallax_ over_ error$>$10, and teff_ gspphot$<$10000\. GSP-Spec parameters
have been filtered with flags_ gspspec[1,4,8,13]=0 for $T_{\rm eff}$, flags_
gspspec[2,5,8,13]=0 for $\log g$, and flags_ gspspec[3,6,8]=0 for [M/H]. The
RVS spectrum signal-to-noise ratio was not filtered.

The shift in metallicity for GSP-Phot shown in Fig. 32 and Fig. 33 is larger
than the literature values for open clusters. In Fig. 34 we averaged (median)
the mh_ gspphot inside each cluster. We used only open clusters (i.e of about
solar metallicity). We find a trend of the difference with literature values
(Gaia-literature) versus [M/H] literature. A zero-point difference of $-0.55$
is found. GSP-Phot metallicities should be used with caution, and ideally,
with a calibration (see Andrae 2022).

Figure 34: Comparison of mh_gspphot with literature values from open clusters.
We plot the median value for each cluster. The error bars show the dispersion
around the median. The red line indicates the zero value.

All the GSP-Spec parameters are found to be correlated with magnitude and
metallicity. Figure 35 illustrates this correlation for teff_ gspspec using
APOGEE DR16. The plot is similar to that for GALAH DR3. This correlation with
magnitude and metallicity leads to other unexpected correlations with
extinction or sky position that are shown in the comparison with the GUMS
model.

Figure 35: Correlation between the GSP-Spec parameters and magnitude (left)
and metallicity (right) illustrated here with the temperature residuals
compared to APOGEE DR16.

We also tested the correlation of the abundances with magnitude, temperature,
$\log g$, and logchisq_gspspec with open clusters and present it for NGC 7789
in Fig. 36. The plots show clear positive trends between mh_gspspec and the
two stellar parameters teff_ gspspec and logg_ gspspec. This correlation is
similar to that obtained with magnitude, as expected for a cluster in which
temperature and gravity are correlated with magnitude. It is also consistent
with what is seen in the comparison with external catalogues (Fig. 35). For
alphafe_gspspec, we observe correlations of the opposite sign. The [M/H] and
[$\alpha$/Fe] calibrations proposed in Recio-Blanco (2022) alleviate these
trends, but they do not remove them completely.

Figure 36: Abundance trends for mh_gspspec (top) and alphafe_gspspec (bottom)
as a function of teff_ gspspec (left column), logg_ gspspec (middle column),
and phot_ g_ mean_ mag (right column) for the stellar members of NGC 7789. The
symbols are colour-coded as a function of logchisq_ gspspec. Circles indicate
the uncorrected alphafe_gspspec values, and crosses represent the calibrated
mh_gspspec and alphafe_ gspspec values. The parameters have been filtered with
flags_gspspec[1:7,9:13]=0 and flags_gspspec[8]$<=$2.

The GSP-Spec abundances do not correlate very well with external catalogue
values in general (Recio-Blanco (2022)). However, calibration formulas are
proposed in Recio-Blanco (2022), and Gaia Collaboration & Recio-Blanco (2022)
showed that they allow retrieving the expected chemo-kinematical correlations
in the disc.

GSP-Spec ANN metallicities (available in astrophysical_ parameters_ supp) have
underestimated uncertainties (Table 2) and are offset by $\sim$-0.2 with
respect to APOGEE DR16. See Recio-Blanco (2022) for a proposed calibration of
the GSP-Spec ANN parameters as well.

The MSC metallicity and gravity are overestimated compared to those obtained
with GALAH (Traven et al., 2020). Hot stars are found to be assigned a
temperature of about 7500 K due to the empirical calibration based on APOGEE.
The poor convergence of the MSC values can be flagged as low logposterior_msc
values. The test using wide binaries (Table 2) indicates a low number of
outliers for the metallicity, but the errors are so large that most of the
possible values are covered. We advise using the MSC parameters with caution
in general (see also Fouesneau (2022) and the on-line documentation).

Tests using wide binaries (Table 2) and open clusters shows a strong
underestimation of the errors for all GSP-Phot parameters with an associated
large number of outliers. For GSP-Spec, they show an underestimation of the
errors for mh_ gspspec, alphafe_ gspspec, cafe_ gspspec, and crfe_gspspec,
while for some other elements, the uncertainties are slightly overestimated.
GSP-Spec values were discretised at two decimals, except for dibew_gspspec ,
which was discretised at three decimals, and Teff, which was stored as an
integer. This might cause some parameters to have similar upper and lower
values, in which case the discretisation step should be used as an uncertainty
estimate.

The published GSP-Phot MCMC samples contain 2000 points for $G<12$ , but only
the last 100 points are made available for $G>12$, except for a random 1%
subset that was given the full 2000 points. When only 100 points are
available, the upper or lower values of the GSP-Phot parameters, which were
determined on the full 2000 steps, may not be fully consistent with the MCMC.
This inconsistency is an indication of convergence issues. However, failed
convergence usually does not relate to strong outliers, which are cases when
the MCMC has converged to a very different solution. We find that 18% of the
2000-point chains present some problems such as multiple solutions, local
maxima of the posterior probability, or edge effects. The MSC inflated their
errors in post-processing, therefore the MSC MCMCs are not consistent with the
provided upper or lower values.

###  4.4 Distance and absolute magnitude

Figure 37: Global distribution of distance_gspphot against parallax for
sources with $\varpi/\sigma_{\varpi}>5$. The solid black line represents the
1/parallax relation. The map ($l,b$) on the right shows the sky distribution
of the clustering between the two parameters.

The global distribution of GSP-Phot distances against parallax is shown in
Fig. 37, where we consider the sources with $\varpi/\sigma_{\varpi}>5$. While
a large fraction of sources follows the inverse parallax curve, 37% are
5$\sigma$ outliers (considering only the parallax error). We measured the
clustering in this space using the Kullback-Leibler divergence (KLD; Kullback
& Leibler 1951, Fabricius et al. 2021), which is higher away from the plane
and in particular around the large and small Magellanic clouds (LMC and SMC).

Strong outliers in the GSP-Phot distances are seen in the comparison of the
estimates of the two wide binary components (Appendix A), while the relative
precision of the parallax is better than 20% in this sample. The distances are
shown to be systematically underestimated at large distances, and the relative
parallax precision is poor when the known cluster distances of Fouesneau
(2022) are used. This is also confirmed with the APOGEE DR16 red clump sample.
This seems to be due to a too strong prior (Andrae, 2022). The MSC distances
distance_ msc are shown to present a higher dispersion than the GSP-Phot
distances even for known binaries by Fouesneau (2022).

The GSP-Phot absolute magnitude estimate mg_ gspphot is compared to the
absolute magnitude computed directly from the parallax for a sample of stars
with negligible extinction in Fig. 38. It shows the combination of distance
outliers (leading the strong outliers) and extinction overestimation for stars
with $M_{G}\gtrsim 7$ (see Fig. 30, leading to a bias). Moreover, mg_ gspphot
is not correctly estimated in stars farther away than 1-2 kpc as an effect of
the underestimated distance. This is clearly illustrated in Fig.39, where the
distance modulus (m-M) is derived from distance_ gspphot.

Figure 38: Density plot of the difference between mg_ gspphot and the absolute
magnitude computed directly with the parallax for a sample of stars with
negligible extinction ($A_{\rm 0}$$<0.05$ according to Lallement et al.
(2019)) and a $\texttt{\small{parallax\\_over\\_error}}>10$. Figure 39:
Colour-magnitude diagram of NGC 6791 (left panel), mg_gspphot vs $G_{\rm
BP}-G_{\rm RP}$(central panel), and G vs distance modulus (m-M) derived from
distance_gspphot (right panel). The blue line in the right panel shows the
literature value, and the green lines in the left and central panels show the
PARSEC isochrone, which has the same parameters as the cluster.

We recommend using the deviation between the GSP-Phot distances and the
parallax121212 (parallax - 1000/distance_gspphot)/parallax_error to filter
GSP-Phot outliers. For a number of usages, it may be preferable to use the
parallax to estimate the distance and absolute magnitude (see Luri et al.
(2018)) over the GSP-Phot estimates.

###  4.5 Stellar evolution parameters

The stellar evolution parameters radius, mass, age, evolution stage, and
gravitational redshift are provided by the FLAME module (Creevey, 2022). They
are derived either from GSP-Phot parameters (fields named _ flame in the table
astrophysical_ parameters) or from GSP-Spec parameters (fields named _ flame_
spec in astrophysical_ parameters_ supp). For the mass, age, and evolutionary
stage, they use solar metallicity evolution models. The estimates of these
parameters for non-solar metallicity stars should therefore be used with
caution.

Figure 40 shows the comparison between the FLAME radius and the radius from
the JSDC stellar diameter catalogue (Bourges et al., 2017, v2, selecting stars
with $\chi^{2}<2$) for stars with relative parallax uncertainties smaller than
10%. The parallax is used to transform the JSDC angular diameter into radius.
The radius derived by FLAME using GSP-Spec Teff, radius_ flame_ spec, is
overestimated for blue main-sequence stars and for red giants, but it is
underestimated for very red giants ($G_{\mathrm{BP}}-G_{\mathrm{RP}}$$>$2.2).
The radius derived by FLAME using GSP-Phot Teff, radius_ flame, has the same
properties, but fewer outliers than radius_ gspphot provided directly by GSP-
Phot because FLAME directly uses the parallax to derive the luminosity for
this sample with a good parallax signal-to-noise ratio (see flags_flame). We
therefore recommend using radius_ flame for a radius estimation.

Figure 40: Comparison between JSDC radius and the FLAME radii based on GSP-
Phot (left) or GSP-Spec (right), colour-coded with the $G_{\rm BP}-G_{\rm RP}$
colour.

Masses from FLAME compare well with asteroseismic estimates for dwarfs and
subgiants (using Serenelli et al., 2017; Godoy-Rivera et al., 2021), but
strong outliers are seen for giants (using Yu et al., 2018). Comparison with
the GUMS model confirms the presence of a high-mass tail in the FLAME data
that is not predicted by the model. This tail is present in all Galactic
directions, even at high latitudes. It is associated with an excess of young
stars. These young ($<2$Gyr) and massive ($>2$$\,{\cal M}_{\odot}$) stars are
on the giant branch. We therefore recommend using the FLAME masses with
flags_flame[_spec] first character 1 (giant flag) only within the 1-2$\,{\cal
M}_{\odot}$ range and with caution, and taking their large uncertainties into
account.

The overestimation of the GSP-Phot extinction for low-mass stars
($M_{G}\gtrsim 7$, Fig. 30) also has an impact on the FLAME parameters. The
impact on masses $\mathcal{M}\lesssim 0.7$ is illustrated in Appendix D of
Gaia Collaboration & Arenou (2022). It has an impact on the luminosity similar
to what is shown in Fig. 38.

Strong outliers in the luminosity of giants are visible in the APOGEE red
clump sample. A few mismatches of the evolutionary stage may occur also for
giants that are confused with dwarfs with high extinction. They can be spotted
in an HR diagram using an independent extinction estimate.

The gravitational redshift determined by FLAME is compared to the one used in
GALAH DR3 (Zwitter et al., 2021) in Fig. 41 for sources with a gravitational
redshift error from FLAME lower than 1 km s-1. The gravitational redshift
based on GSP-Spec (gravredshift_ flame_ spec) has fewer outliers than the
redshift based on GSP-Phot (gravredshift_ flame), but it has a small bias of
0.05 km s-1, corresponding to the bias in $\log g$ discussed above.

Figure 41: Density plot of the comparison between the GALAH gravitational
redshift and the FLAME redshifts based on GSP-Phot (left) or GSP-Spec (right).

##  5 QSO and galaxies

Gaia DR3 includes two tables of extragalactic candidate sources, one for
quasars and one for galaxies, called qso_candidates and galaxy_candidates (QSO
and galaxy tables, for simplicity). These tables contain two main types of
added value columns: on the one hand, we can use the different labels that are
provided to tune the purity-to-completeness ratio of the sample, and on the
other hand, each table also contains physical properties of the objects such
as redshift, size, or variability. The astrophysical parameters (Creevey,
2022) associated with these tables, that is, classification and redshifts, are
described in Delchambre (2022), the surface brightness profiles are described
in Ducourant (2022), the variability is presented in Rimoldini et al. (2022)
and in Carnerero et al. (2022) for AGNs. Moreover, a global analysis of these
tables is presented in Gaia Collaboration & Bailer-Jones (2022). It is worth
noting that these tables have been constructed with the aim of completeness,
and as we show below, this means that their default purity is rather low.
However, it is possible to obtain a high-purity subsample (Gaia Collaboration
& Bailer-Jones, 2022), as discussed below.

###  5.1 Purity

The different labels that are included within the QSO and galaxy tables can be
used to create a subsample with different properties (see section 8 of (Gaia
Collaboration & Bailer-Jones, 2022) for a selection leading to 94% and 95%
purity in the QSO and galaxy tables, respectively). The QSO and galaxy tables
contain three common labels: vari_best_class_name = ”AGN”/”GALAXY”
(classification according to the stellar variability patterns (Rimoldini et
al., 2022)), classlabel_dsc = ”quasar”/”galaxy” (from the discrete source
classifier (Creevey, 2022)), classlabel_dsc_joint = ”quasar”/”galaxy” (similar
to the previous, but more restrictive since it requires DSC-Specmod and DSC-
Allosmod to agree, both with a score higher than 50%) and
classlabel_oa131313OA distinguishes extragalactic sources by bins of redshift,
so we select all quasars/galaxies at all redshifts to create this label.
(assigned by the self-organising map (SOM) (Creevey, 2022)). It is worth
noting that the results of the SOM were not used to construct these tables. In
other words, this label was attached to the sources that were previously
selected as candidates by other means. In addition to these shared class
labels, we can also identify unique labels for each table. In the QSO table we
have access to the astrometric_selection_flag (ASF), which allows us to select
only sources with a high probability of being quasars based on their
astrometry. We can also use the source_selection_flags to isolate the QSOs in
the qso_catalogue_name table (bit 3 set to 1), hereafter called QuasarObject
list, which effectively corresponds to the sources that are found in well-
known QSO catalogues that had enough raw data to be processed successfully by
the QSO pipeline. Finally, in the galaxy table, we can select the sources
whose morphology was fit reliably. We refer to this subset as EO solution.

In Fig. 42 we show the astrometric properties, normalised by their formal
uncertainties, of the different subsamples described above. Because
extragalactic sources are so far away from the Sun, the astrometry of these
objects might be expected to be dominated by observational errors. This is
what we indeed observe for some of the subsamples, for example those in panels
a and c, and also in panel e in the QSOs and panel b in the galaxies. However,
the other subsamples show clear deviations from the expected standard normal
probability distribution. These deviations in the DSC and OA subsamples are
due mostly to the astrometric signal of the Magellanic clouds and the Galactic
disc (Fig. 43). We note, however, that while $\sim 94$% of the sources in the
QSO table have a 5p or 6p astrometric solution, the galaxy table is mostly
dominated by 2p sources ( $\sim 71$%). Therefore, the conclusions we draw
concern only a portion of the galaxy table. In either case, it is clear that
some subsamples, namely vari_best_class_name and classlabel_ dsc_ joint, are
purer than others (classlabel_ dsc and classlabel_ oa) that were built for
completeness.

Figure 42: Astrometric properties of the different subsamples contained within
the QSO (top) and galaxy (bottom) candidate tables. Each panel contains the
distribution of parallaxes and proper motions, normalised to their errors. The
grey line corresponds to a normal distribution.

The sky distribution of the sources in these subsamples is presented in Fig.
43. These plots are difficult to relate directly to the purity as some modules
remove the LMC and SMC and the disk plane by force or using criteria that
depend on the density, while some others, such as DSC, do not. Moreover, a
constant misclassification rate over the sky leads to a higher density of
misclassified objects where the objects density is higher. However, in the LMC
and SMC areas, more than 10% of the sources are in the qso_candidates table,
so here the classification does not work well.

Figure 43: Sky distribution of the different subsamples contained within the
QSO (left) and galaxy (right) candidate tables.

A good idea of the main stellar types of the stellar contaminants in the QSO
and galaxy tables can be obtained by positioning those with a relative
parallax uncertainty lower than 20% in an HR diagram in Fig. 44. It shows that
the QSO candidate stellar contaminants are mainly white dwarfs and stars with
$G_{\rm BP}-G_{\rm RP}$$\sim$0.4, while galaxy candidate stellar contaminants
are mainly stars with $G_{\rm BP}-G_{\rm RP}$$\sim$1.4 or 0.8. Fig. 44 also
shows that the criteria for the purer samples proposed in Gaia Collaboration &
Bailer-Jones (2022) are efficient, but still retain a few contaminants. We use
here host_galaxy_detected=’true’ instead of
$\texttt{\small{host\\_galaxy\\_flag}}<6$ as the latter leads to eight times
more contaminants in our sample. These are due to the EO input catalogue,
however, the host galaxy has not been detected for them. By construction, the
entire QSO sample of Fig. 44 has astrometric_selection_flag=’false’.

Figure 44: Gaia DR3 low-extinction HR diagram (grey scale). The position of
sources in the QSO (top) and galaxy (bottom) candidate tables with
$\texttt{\small{parallax\\_over\\_error}}>5$ is overplotted with a red scaling
with the square root of the number of sources. Colour points correspond to the
stricter selection of candidates proposed in Gaia Collaboration & Bailer-Jones
(2022).

###  5.2 Morphological parameters

We compared the extended object morphological parameters of the galaxy table
(Ducourant, 2022) with the GAMA (Kelvin et al., 2012) and Dark Energy Survey
(DES Tarsitano et al., 2018) Sérsic profiles and with the SDSS DR16 (Ahumada
et al., 2020) de Vaucouleurs profiles. Figure 45 illustrates the comparison
with the DES Sérsic profiles. Saturation of the effective radius radius_
sersic and radius_ de_ vaucouleurs at 8000 mas and of the Sérsic $n$-index at
8 is visible. It corresponds to the boundaries of the algorithm. Compared to
DES, the Gaia DR3 index seems to be spread out more or less uniformly, with
DES preferring $n=4$. Essentially, the comparison with external catalogues of
galaxy profiles shows an overestimation of the Sérsic index and an
underestimation of the ellipticity. Both are a consequence of the fact that
Gaia observes a smaller area around the galaxies than external catalogues,
which prefer central measurements and are biased towards bulges (Ducourant,
2022).

Figure 45: Comparison of Sérsic index (panel a), and ellipticity (panel b)
from DES with the Gaia measurements.

The morphological parameters are accompanied by their formal uncertainties.
Since these are estimated from the variance resulting from the search for a
minimum in the residuals between model and observations, the provided
uncertainties reflect the quality of the convergence rather than the precision
of the estimation. In consequence, a fraction of sources may appear to have
extremely small uncertainties while in reality, this is just the byproduct of
a correlation with the convergence velocity.

###  5.3 Redshifts

Figure 46: Redshift distribution of the QSO (blue) and galaxy (orange)
candidate sources. The dotted blue line corresponds to the quasars that were
selected with the recommended QSOC redshift flags (flags_qsoc=0 or
flags_qsoc=16). The two peaks marked by the red arrows are discussed in the
text.

The provided galaxy redshift upper and lower values do not correspond to
confidence intervals, but to prediction limits based on machine-learning.
Still, the comparison with external catalogues indicates that
$(\texttt{\small{redshift\\_ugc\\_upper}}-\texttt{\small{redshift\\_ugc\\_lower}})/2$
gives a good estimate of the uncertainty. A redshift peak at about 0.07 (red
arrow in the orange histogram in Fig. 46) is found, which corresponds either
to very bright galaxies or to stellar contaminants with convergence issues.
The redshift range 0.070-0.071 should therefore be ignored (Delchambre, 2022).
A global overestimation of the redshifts for bright sources ($G<19$) is also
observed.

The QSO redshifts are log-normally distributed. To compare them to the
literature, we therefore used $Z=\log(\texttt{\small{redshift\\_qsoc}}+1),$
which is normally distributed with a standard deviation of
$\sigma=(\log(\texttt{\small{redhift\\_qsoc\\_upper}}+1)-\log(\texttt{\small{redhift\\_qsoc\\_lower}}+1))/2$
(Delchambre, 2022). The comparison with LQAC5 (Souchay et al., 2019) presents
33% of outliers at 5$\sigma$, which reduces to 8% when the flag flags_qsoc=0
or flags_qsoc=16 is used. This is due to the degeneracies between spectral
lines and redshift in the XP spectra (see Delchambre, 2022; Gaia Collaboration
& Bailer-Jones, 2022). A peak, this time at about 0.08, is also visible in the
redshift distribution of the QSO (red arrow in the blue histogram in Fig. 46).
The reason for this peak is that the MgII emission line is misclassified as
H$\beta$, a characteristic emission line of this specific redshift range
(Delchambre, 2022). However, only a small number of sources contributes to
this peak, and most of them have a non-zero flags_ qsoc.

##  6 Non-single stars

Gaia DR3 provides four tables for non-single stars (NSSs). The table
nss_two_body_orbit contains orbital two-body models, covering astrometric
(Halbwachs, 2022; Holl et al., 2022b), spectroscopic (Gosset, 2022; Damerdji &
et al., 2022), and eclipsing (Siopis, 2022) binaries as well combinations of
these. The model that is used is indicated in the field nss_solution_type, and
the parameters that are solved for a given solution are described in the
bit_index field. The tables nss_acceleration_astro and nss_non_linear_spectro
contain astrometric (Halbwachs, 2022) and spectroscopic (Gosset, 2022)
acceleration solutions, and nss_vim_fl contains variability-induced mover
(VIM) solutions (Halbwachs, 2022). Gaia Collaboration & Arenou (2022) also
present the overall content of these non-single star tables.

Figure 47: Density plot of the signal-to-noise ratio of the semi-major axis of
the photocentre orbit ($a_{0}$) derived from a Monte Carlo method as a
function of the value provided in the field significance.

###  6.1 Astrometric orbital elements

The orbital solutions for the astrometric binaries are presented using what is
called Thiele-Innes coefficients. They express the orbital motion of the
photocentre on the sky with a linear formulation. These coefficients replace
the more usual Campbell elements $a_{0},i,\omega,\text{and }\Omega$, which are
semi-major axis, inclination, longitude of periastron, and position angle of
the ascending node, respectively. The relations between the two parameter sets
are described in Halbwachs (2022).

In the transformation from Thiele-Innes to Campbell coefficients, it may be
useful to use Monte Carlo simulations that take the correlation matrix into
account instead of using local linear approximation formulas. In 87% of the
NSS sample, Gaussian errors in Thiele-Innes coefficients are transformed into
asymmetric distributions for at least one of the Campbell elements. Mostly in
the case of very low eccentricities, however, a number of sources shows a
significance parameter that disagrees with the signal-to-noise ratio that can
be derived from Monte Carlo simulations (Fig. 47). This seems to be due to an
overestimation of the Thiele-Innes coefficient errors. Despite this, the local
linear approximation formulas work well in deriving the error on the $a_{0}$
parameter even with a very strong overestimation of the Thiele-Innes
coefficient errors. As Orbital solutions are filtered to have
significance$>5$, using a Gaussian error model for $a_{0}$ is reasonable while
OrbitalTargetedSearch solutions need to be filtered. Due to an issue with the
significance of AstroSpectroSB1 (see the on-line documentation), it needs to
be verified that the signal-to-noise ratio is higher than 5. The issue is also
present for the spectroscopic part of the AstroSpectroSB1 solutions, for which
local linear approximation errors on $a_{1}$ can be used as soon as the
resulting signal-to-noise ratio is confirmed to be higher than 5. The local
linear approximation formulas for the Thiele-Innes coefficients can be found
in the appendix of Halbwachs (2022). Overall, to handle the Thiele-Innes
coefficients, usual Monte Carlo techniques such as MCMC should not be used.
Codes using automatic differentiation such as ADMB (Fournier et al., 2012) and
TMB (Kristensen et al., 2016) have been tested to work fine for signal-to-
noise ratios higher than 5.

The covariance matrix for very low eccentricity solutions may be problematic.
In these cases, the eccentricity and periastron time should be set to zero.
For AstroSpectroSB1 with eccentricity and argument of periastron fixed to zero
(bit_ index=65435), c_ thiele_ innes is fixed to the non-circular value
instead of zero. The statistical properties of the distribution of the orbital
elements are discussed in the appendix of Gaia Collaboration & Arenou (2022).

###  6.2 External comparisons

The comparison with external catalogues141414Pourbaix (2000); Jancart et al.
(2005); Gallenne et al. (2019); Kiefer et al. (2018, 2016); Halbwachs et al.
(2020); Eker et al. (2014); SB9 (Pourbaix et al., 2004); APOGEE DR16 apjoker
orbits (Price-Whelan et al., 2017); APOGEE SB2 (Kounkel et al., 2021); Orb6
(http://www.astro.gsu.edu/wds/orb6.html) shows that the orbital parameters
agree well with literature values when the periods are consistent. It also
confirms that the center_of_mass_velocity agrees better with literature binary
values than with the gaia_source.radial_velocity. The strongest disagreements
with external catalogues on the radial velocity semi-amplitude of the primary
are for stars that are known to be SB2 (double-line spectroscopic binary), but
are treated as SB1 (single-line) by NSS (Gosset, 2022).

The comparison of the literature orbits with astrometric acceleration
solutions in the nss_ acceleration_ astro table indicates that a significant
fraction might have had an orbital solution and that some Acceleration7 could
have been Acceleration9. This is intrinsic to the decision chain explained in
Halbwachs (2022). The acceleration values disagree with the expectations from
the known orbits and the Gaia observation times. The acceleration values
should therefore be used with caution.

The NSS parallaxes show a median difference with the gaia_source parallaxes
that is smaller than a few $\mu$as. The HR diagram derived using NSS
parallaxes (orbital and acceleration) is slightly sharper than the diagram
derived with the gaia_source parallaxes, which indicates that the parallaxes
are slightly more precise. The (statistical) improvement of the solutions does
not guarantee that the accelerations are all physical, however. When they are
compared to the long-term proper motion provided in the Hipparcos-Gaia
catalogue of accelerations (Kervella et al., 2022; Gaia Collaboration &
Arenou, 2022), the NSS proper motions improve versus the gaia_source proper
motions for the orbital solutions (moving from 21% of 5$\sigma$ outliers to
9%), but not the acceleration solutions (which have a much higher median
signal-to-noise ratio of the proper motion anomaly than the orbital solutions)
for which the comparison is slightly worse (moving from 80% outliers to 85%).
This highlights that proper motion and accelerations may both have absorbed
the orbital motion.

The temperature ratio of eclipsing binaries corresponds well to the ratio
derived by Eker et al. (2014), except for sources with a low g_ luminosity_
ratio. The correspondence with the MSC temperature ratio is poor, but these
ratios are to be used with caution (see Section 4.3). The uncertainties on the
eclipsing binary inclinations are suspiciously small.

###  6.3 Spurious solutions and error rescaling

To achieve the required radial velocity precision, the precise position of the
spectra at the epoch on the focal plane needs to be known. For this purpose,
the expected astrometric position as given by the predicted standard
astrometric motion is used, rather than the measured astrometric position at
the epoch, which would not be precise enough. However, if the astrometric
motion is perturbed, or if the astrometric solution is not correct, then the
computed epoch $RV$ will absorb this astrometric perturbation. This means that
the epoch radial velocities could increase by up to $\approx
0.146\times$astrometric_ excess_ noise (km/s) in the case of binaries. In the
best case, this would add an unmodelled additional dispersion and possibly a
small trend in the worst case. This may lead to spurious short period and
large ruwe SB1 solutions as well as to spurious solutions around the
precession period (62.97 days), see Gaia Collaboration & Arenou (2022).

The absence of a gaia_source.radial_velocity value for an SB1 solution should
warn the user: the source might have been considered peculiar, potentially
SB2, too hot, too cool, with emission lines, or contaminated by a nearby star.
Radial velocity variations can also be due to stellar pulsations instead of an
orbital motion (Gaia Collaboration & Arenou, 2022), so that the variability
information should also be confirmed for suspicious solutions.

While the errors were rescaled according to the goodness of fit for the
astrometric solutions (Orbital, AstroSpectroSB1, VIM, and acceleration), this
is not the case for the others. Because the mean goodness-of-fit distribution
of SB2 and eclipsing solutions is quite large, we recommend rescaling the
formal uncertainties for these solutions. The goodness_of_fit provided for SB2
solutions can deviate by up to 1.6 from the one that can be recomputed using
obj_func.

##  7 Variability

Gaia DR3 provides variability information for about 11.8 million sources,
including 10.5 million variable sources of about 30 types of variability (Eyer
et al., 2022) and 1.3 million sources (variable or not) in the Gaia Andromeda
Photometric Survey (GAPS) (Evans et al., 2022). Time-series photometry is
released for all these 11.8 million sources in the epoch_photometry datalink
table as well as their statistical parameters, and links to their potential
other variability table are listed in the vari_summary table. The variability
associated with galaxies provided in the galaxy_candidates table are mostly
artefacts due to their extension (Holl et al., 2022a) and therefore are not in
the vari_summary or epoch_photometry tables. Here, we present a brief overview
of some issues we found during the scientific validation, while for further
details, we suggest the readers to consult the on-line documentation2 and
papers1.

A number of sources show more than one type of variability. While most
overlaps between different classes can be explained scientifically, some stars
have contradicting classifications. For example, 3159 sources are classified
as both long-period and short-timescale variables. Detailed analyses of the
final classification for these sources are provided in Lebzelter et al.
(2022).

Intensity-averaged magnitudes in the BP (int_ average_ bp) and RP (int_
average_ rp) bands for four and two RR Lyrae stars, respectively, have
unreliable negative values reaching $BP=-88\pm 22$ mag. These six sources are
faint RR Lyrae variables ($G\sim 18.5-19$ mag) for which the specific objects
study pipeline for Cepheids and RR Lyrae stars (SOS Cep&RRL; Clementini et al.
2022) failed to fit data points with the model line. The values that were
provided were accordingly unreliable. Instead, other parameters that were
calculated for these stars, such as intensity-averaged $G$ magnitudes and
pulsation periods, are correct. It was therefore decided to include these
sources in the DR3 sample of RR Lyrae stars despite the incorrect BP and RP
intensity-averaged magnitude estimates.

For 286 RR Lyrae stars, absorption in the $G$ passband (g_ absorption) reaches
unreliably high values from 10 to 3367 mag. This is likely caused by the
imprecise estimation of the $G_{\rm RP}$ magnitudes for the faint sources (see
Clementini et al. 2022 for further details).

##  8 Solar System objects

Gaia DR3 provides information for 158 152 Solar System objects (SSO) with more
than 20 million observations (epoch astrometry). A large data-set of ultra-
accurate observations like this is made available in a single day for the
first time.

The sample contains $156\,801$ known numbered minor planets, $1\,320$
unmatched moving objects and $31$ natural satellites of planets. The source
selection is described in Tanga (2022).

All the main categories of Solar System bodies are present among the known
numbered minor planets: $447$ Near-Earth asteroids (NEAs), $154\,771$ main-
belt asteroids (MBAs), and a total of $1\,551$ Jupiter Trojans, Centaurs, and
more distant objects. Figure 48 shows the different categories in the semi-
major axis and eccentricity plane.

Figure 48: Asteroid population in Gaia DR3 in the $(a,e)$ plane, where $a$ is
the semi-major axis in au and $e$ is the eccentricity of the minor planets.
The legend shows the different categories: blue squares for NEAs, red stars
for MBAs, and green dots for Jupiter Trojans. For sake of clarity, the plot
does not show Centaurs and more distant objects.

The table sso_ source in the Gaia archive contains the number of observations
for each source. We would like to point out that the count of the number of
observations is incorrect for four sources. The explanation for how to obtain
the correct number of observations is provided in the on-line documentation.

###  8.1 Unmatched sources and natural satellites of planets

A small subsample of the data consists of $1\,320$ objects that were
considered unknown at the time of processing. We refer to them as unmatched
sources. Tanga (2022) performed a search to identify how many unmatched
sources can now be identified (February 2022), and they found an
identification for $712$ sources. We cannot exclude either that some of the
still-unmatched sources will be identified or linked to known objects when the
observations are sent to the Minor Planet Center151515The Minor Planet Center
is the single worldwide location for receipt and distribution of positional
measurements of minor planets, comets, and outer irregular natural satellites
of major planets https://minorplanetcenter.net/. All the sources will still
appear as unmatched in the sso_ source and sso_ observations tables in the
Gaia archive.

The sample also contains natural satellites of planets for the first time. For
a complete description of the process of selection, we refer to Tanga (2022)
(Sect. 3.1).

###  8.2 Orbit determination process

We used an orbit determination process to assess the quality of the data. This
process is similar to the process that was carried out to validate Gaia DR2
(Gaia Collaboration et al., 2018; Arenou et al., 2018).

We selected Gaia observations only for every known numbered minor planet in
Gaia DR3. We used a modified version of the OrbFit
software161616http://adams.dm.unipi.it/orbfit/ to fit the orbits to Gaia
observations alone. It is important to note that this software is completely
independent from everything that runs in the Gaia data processing, and we
improved it to fully exploit the accuracy of Gaia observations.

The results of the orbital fit can be summarised as an orbit (if the fit
converges), post-fit residuals in the $(\alpha\cos(\delta),\delta)$ space and
in the $(AL,AC)$ space, and rejection of incorrect quality or mistakenly
linked observations.

The modified version of the OrbFit software makes use of a non-linear weighted
least-squares algorithm to fit the orbits. The weight matrix for Gaia is the
quadratic sum of the systematic and random matrices, available in the sso_
observation table.

We also corrected the observations for the light bending. This is a different
approach than was applied in the validation of Gaia DR2 .

####  8.2.1 Orbit determination results: Orbit failures

The orbit fit procedure worked for almost all the known sources. It only
failed for $198$ objects. The reasons for this vary: the time spanned by the
observations was too short, too few observations were available, or a
combination of the two, as shown in Fig. 49.

Figure 49: Time span by the observations in Gaia DR3 (in days) vs the number
of observations for each known source. The red stars represent the objects for
which the orbit determination process did not converge.

The quality of the observations is not affected by the non-convergence of the
orbit. They were therefore all accepted and are available in the sso_
observations table.

####  8.2.2 Orbit-determination results: Post-fit residuals

The orbit-determination process is based on finding and removing bad-quality
observations, so that they do not affect the goodness of fit. The orbit-
determination software we used to validate the data rejects observations with
$\chi^{2}>25$ ($5\sigma$). This can happen because the quality of the data is
not as expected, because the weights that are used are too low, or because the
observations do not belong to the object. The latter case is called mistaken
linkage or incorrect identification. We decided to remove from the data only
the observations for which the absolute value of the along-scan post-fit
residuals was higher than $250$ mas and for which the absolute value of the
across-scan post-fit residuals was higher than $2\,500$ mas. In this way, we
cleaned the database from possible contaminants. At the same time, we wished
to keep the largest possible number of observations so that the community
could search for interesting features (e.g. the presence of satellites). As a
consequence, the sample can still contain some contaminants. For example, we
may only have removed part of a transit, but we decided to adopt a unique
approach that is valid for all the observations. Some observations can also be
rejected during the orbit-determination process, but this does not affect the
overall quality of the data.

After removing the bad-quality observations, we analysed the post-fit
residuals in the along-scan and across-scan directions. Figure 50 shows the
histogram of the post-fit residuals along-scan ($\Delta AL$) and across-scan
($\Delta AC$). These residuals are obtained as a rotation of the residuals in
$\alpha\cos(\delta)$ and $\delta$, where the rotation angle is the position
angle as given in the sso_observations table. The whole procedure has been
described in Gaia Collaboration et al. (2018).

Figure 50: Histogram of post-fit residuals of the selected observations in the
left: along-scan, right: across-scan direction.

The mean of the post-fit along-scan residuals is $0.03$ mas, and the standard
deviation is slightly larger than $5$ mas. This is exactly what we expected as
a result of the orbit-determination fit (we recall that we discarded
observations at $5$ $\sigma$ level). Post-fit residuals in the across-scan
direction are expected to be far larger than the corresponding along-scan
residuals as a result of the geometry of the spacecraft observations (Gaia
Collaboration et al., 2018), as the histogram in fig. 50 shows. The mean in
this case is close to $13$ mas, which shows that the across-scan observations
still contain a small bias. The standard deviation is larger than $200$ mas.
This is close to what we expected.

Figure 51: Density plot of the post-fit residuals as a function of the $G$
magnitude left: along-scan, right: across-scan

We now examine the $(\Delta AL,\Delta AC)$ post-fit residuals as a function of
the $G$ magnitude (Fig. 51). For very bright sources ($G<13$ mag), a full two-
dimensional window is transmitted, which means that across-scan information is
available, corresponding to what we show in Fig 51, where across-scan
residuals are at the milliarcsecond level when $G<13$ mag. Figure 51 shows the
increase in along-scan residuals when the source is fainter ($G>19$ mag). They
almost reach the detectability limit, but usually remain very small (inside
the $[-10,10]$ mas interval) for all the other sources.

Additional information about residuals and a comparison with Gaia DR2 are
available in Tanga (2022), even though the authors used a different set of
residuals that they obtained as a result of the internal process of the
observations and not from the validation. It has been proved in the same paper
that these residuals and the corresponding orbit can be considered equivalent
to those that were computed during the validation process.

###  8.3 Orbit accuracy: Comparison with known catalogues

The post-fit accuracy of the semi-major axis $(\sigma_{a})$ is a good
estimator of the orbit quality. We compared the post-fit $\sigma_{a}$ obtained
using Gaia observations alone with that available from the JPL Small Body
Database171717https://ssd.jpl.nasa.gov/ , which makes use of all the available
observations; see Fig. 52. The black line in the figure is the bisector of the
first quadrant: the orbits of the objects below the line have a better
uncertainty using Gaia observation alone. It is clear that Gaia DR3 alone is
still not enough to reach the final accuracy expected for Gaia (Gaia
Collaboration et al., 2018, Fig. 32), but the number of orbits for which the
accuracy is now better using Gaia alone has largely increased from Gaia DR2 .

Figure 52: Quality of the orbit determination measured by the post-fit
uncertainty of the semi-major axis for the whole sample of objects contained
in Gaia DR3 with respect to the current measurements from the JPL Small Body
Database. The black line is the bisector of the first quadrant.

##  9 Conclusions

The third data release of Gaia, DR3, provides a very large amount of new data.
This complex and diverse dataset has a number of caveats that the users should
be aware of. In this paper we summarised the main issues we found during the
transversal validation, and we provided links to the relevant papers or
documentation and recommendations. In particular, we highlighted that flags
provided with the data products should be used whenever available (e.g.
flags_gspspec, flags_flame, dibqf_gspspec, and flags_qsoc). We warned about
the error underestimation of the XP coefficients, GSP-Phot parameters, GSP-
Spec ANN, spectroscopic and eclipsing binary solutions with a poor goodness-
of-fit, and we provided a correction formula for the radial velocity error
estimates. The DSC white dwarf and binary star classifications should not be
used. A number of parameters were highlighted as to be used with caution (MSC
parameters, mh_gspphot, distance_gspphot, mg_gspphot, FLAME mass and ages for
giants, radial velocities for $(G_{\rm RVS}-G)<-3$, and astrometric binary
acceleration values). The corrections proposed in Recio-Blanco (2022) should
be applied to the GSP-Spec parameters. Some systematics such as those
presented for vbroad, the XP spectra dip, or extinction overestimation are to
be taken into account according to the science case. Filters need to be
applied to the QSO and galaxy candidates to have purer samples (Gaia
Collaboration & Bailer-Jones, 2022). Monte Carlo techniques should not be used
with the Thiele-Innes astrometric binary orbital parameters.

This paper focused on limitations in the data released in Gaia DR3. We
encourage a study of the other papers that accompany this data release for an
overview of the high quality of the Gaia products and a glance at the
wonderful science outcomes that can be expected from this wealth of data. We
hope that this paper will help users to find their way through the data so
that they can make the best of it.

###### 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://archives.esac.esa.int/gaia. This work has
made an extensive use of Aladin and the SIMBAD, VizieR databases operated at
the Centre de Données Astronomiques (Strasbourg) in France and of the software
TOPCAT (Taylor, 2005). This work has been supported by the Agence Nationale de
la Recherche (ANR project SEGAL ANR-19-CE31-0017). It has also received
funding from the project ANR-18-CE31-0006 and from the European Research
Council (ERC grant agreement No. 834148). ZKR acknowledges funding from the
Netherlands Research School for Astronomy (NOVA). This work was partially
funded by the Spanish MICIN/AEI/10.13039/501100011033 and by ”ERDF A way of
making Europe” by the “European Union” through grant RTI2018-095076-B-C21, and
the Institute of Cosmos Sciences University of Barcelona (ICCUB, Unidad de
Excelencia ’María de Maeztu’) through grant CEX2019-000918-M.

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##  Appendix A Wide binaries

We used the El-Badry et al. (2021) catalogue of wide binaries, further limited
to a chance alignment probability $\mathrm{R\\_chance\\_align}<0.1$, to test
the radial velocity error underestimation in Sec. 2.1.3. Here we test the
astrophysical parameter compatibility of the two components. A summary of the
results is listed in Table 2. The percentage of systems consistent within
1$\sigma$ was computed after removing the 5$\sigma$ outliers. If this
percentage is significantly lower (higher) than about 68, the errors are
underestimated (overestimated).

Table 2: Comparison of the astrophysical parameters derived for the two components of a wide binary (El-Badry et al. 2021).  Parameters | % of 5$\sigma$ outliers | % within 1$\sigma$
---|---|---
age_flame | 3 | 65
age_flame_spec | 0 | 66
mh_gspspec | 7 | 47
alphafe_gspspec | 5 | 49
fem_gspspec | 0 | 90
sife_gspspec | 0 | 73
cafe_gspspec | 2 | 58
tife_gspspec | 0 | 83
mgfe_gspspec | 0 | 70
feiim_gspspec | 0 | 64
sfe_gspspec | 0 | 79
nfe_gspspec | 0 | 67
crfe_gspspec | 0 | 44
nife_gspspec | 0 | 73
mh_gspspec_ann | 20 | 32
alphafe_gspspec_ann | 8 | 36
mh_gspphot | 45 | 25
azero_gspphot | 52 | 26
distance_gspphot | 22 | 35
mh_gspphot_marcs | 41 | 26
azero_gspphot_marcs | 52 | 26
distance_gspphot_marcs | 23 | 35
mh_gspphot_phoenix | 46 | 29
azero_gspphot_phoenix | 49 | 28
distance_gspphot_phoenix | 25 | 34
mh_gspphot_ob | 40 | 75
azero_gspphot_ob | 72 | 25
distance_gspphot_ob | 51 | 34
mh_gspphot_a | 51 | 47
azero_gspphot_a | 86 | 25
distance_gspphot_a | 58 | 30
mh_msc | 3 | 77
azero_msc | 1 | 88
distance_msc | 4 | 68
azero_esphs | 18 | 36
dib_gspspec_lambda | 34 | 61
dibew_gspspec | 7 | 59
dibp2_gspspec | 7 | 65