Gaia Data Release 3: Summary of the content and survey properties

Article number, page 1 of 23

Introduction

The European Space Agency’s (ESA) Gaia mission ([42]), launched in 2013, is now at its third data release (Gaia DR3). The aim of this paper is to present the Gaia DR3 data products, providing a brief overview of the new features introduced in the processing and discussing the quality of the data. Gaia EDR3 (Gaia Collaboration, Brown et al. [35]) was the first instalment of the full Gaia DR3 and included astrometry and broad band photometry for a total of 1.8 billion objects based on 34 months of satellite operations. Radial velocities for 7 million sources were copied over from the second data release, Gaia DR2, where a small number of spurious radial velocities were removed (Seabroke et al. [99]).

Gaia DR3 complements Gaia EDR3, introducing a vast array of new data products based on the same source catalogue and raw observations at the basis of Gaia EDR3. Indeed, the astrometry and broad band photometry in $G$, $G_{\mathrm{BP}}$, and $G_{\mathrm{RP}}$ from Gaia EDR3 are repeated in the new catalogue (but see § 3.1 for $G$ correction). New data products in Gaia DR3 include mean low-resolution blue and red photometer (BP/RP) spectra and high-resolution Radial Velocity Spectrometer (RVS) spectra; new estimates of mean radial velocities to a fainter limiting magnitude; line broadening and chemical composition information derived from RVS spectra; variable-star classification and characterisation; and photometric time series for over 20 classes of variables. Photometric time series for all sources, variable and non-variable, are available for a field centred on the Andromeda galaxy. A large sample of Solar System objects (SSOs) with orbital solutions and epoch observations is part of the data release, together with reflectance spectra for a subset of those. Gaia DR3 also includes results for non-single stars (NSS), quasars, and galaxies. For a large fraction of the objects, the catalogue lists astrophysical parameters (APs) determined from parallaxes, broad band photometry, and the mean RVS or mean BP/RP spectra.

To enhance the scientific exploitation of the data, the Gaia archive includes pre-computed cross-matches with selected external optical and near-infrared photometric and spectroscopic surveys. In addition to the catalogues described in Gaia Collaboration, Brown et al. [35], Gaia DR3 includes the sixth data release from the Radial Velocity Experiment (RAVE) survey (Steinmetz et al. [103]). The Gaia Universe Model Snapshot (GUMS, version 20, Robin et al. [93]) and the corresponding simulated Gaia catalogue (GOG) were also already included in Gaia EDR3. The details on these supplementary data can be found in the online documentation¹.

This paper is organised as follows. § 2 summarises the Gaia instruments and their wavelength ranges; § 3 summarises the properties of the astrometric and photometric data already included in Gaia EDR3; § 4 presents the BP/RP spectra; § 5 outlines the RVS data processing, listing the improvements implemented in Gaia DR3, and presents the new RVS data products, the line broadening velocity ($v_{\mathrm{broad}}$) and the $G_{\mathrm{RVS}}$ magnitude. § 6 deals with the variable source content of the catalogue including the Gaia Andromeda Photometric SurvEy (GAPS); § 7 discusses the NSS content of the release; § 8 comments on the SSOs; § 9 provides information about the APs; § 10 presents the extended object (EO) data processing and results. § 11 describes the extragalactic content of Gaia DR3, that is, information on galaxies and quasi-stellar objects (QSOs) or ‘quasars’ derived by several data processing modules. § 12 comments on the quality of the release and § 13 provides references to the software tools that are offered to the users to deal with Gaia DR3 data. Finally, § 14 presents some concluding remarks.

Each data product section briefly summarises the main limitations of the data, and makes reference to the relevant Gaia Collaboration and Gaia Data Processing and Analysis Consortium (DPAC) papers where more details can be found. For a number of technical details, we refer the reader to the online documentation.

All the papers accompanying Gaia DR3 are published in the Astronomy & Astrophysics special issue on Gaia DR3. Finally, we recall that all the Gaia data releases are made available through the archive hosted by ESA², as described in Gaia Collaboration, Brown et al. [35] and references therein. Partner and affiliated data centres in Europe, the United States, Japan, Australia, and South Africa provide access to the data through their own facilities.

Data processing

The Gaia satellite has three main instruments on board: the astrometric instrument collecting images in Gaia’s white-light $G$-band (330–1050 nm), the blue BP and red RP prism photometers for low-resolution spectra, and, finally, the RVS. BP/RP spectral data cover the wavelength ranges 330–680 nm and 640–1050 nm, respectively. The resolution is variable and ranges from 30 to 100 for BP and 70 to 100 for RP in $\lambda/\Delta\lambda$, depending on the position in the spectrum and on the CCD (see Carras­co et al. [13]). In the RVS spectra, the starlight is dispersed over about 1100 pixels in the Gaia telescopes scanning direction (along-scan, AL), sampling the wavelength range from 845 to 872 nm. The resolving power is $R \sim11\,500$ (with a resolution element of about 3 pixels). As the wings of the spectra are excluded from the processing, the effective wavelength range of the processed spectra is reduced to 846–870 nm (Sartoretti et al. [96]). Similarly to Gaia EDR3, Gaia DR3 is based on data collected over a 34-month time interval (for details see Gaia Collaboration, Brown et al. [35]). The Gaia data processing is the responsibility of DPAC and is presented in Gaia Collaboration, Prusti et al. [42].

The basic statistics on the source numbers for each of the data products in Gaia DR3 can be found in Table 1, with further details in Table 2. The categories listed in the tables are described in the text below. A visual impression of the release contents is given in Figure 1 which shows histograms of the distribution in $G$ of the main categories of data products in Gaia DR3.

Data product or source type	Number of sources	Comments
Total	1 811 709 771
5-parameter astrometry	585 416 709
6-parameter astrometry	882 328 109
2-parameter astrometry	343 964 953
Gaia-CRF3 sources	1 614 173
ICRF3 sources used for frame orientation	2007
Gaia-CRF3 sources used for frame spin	428 034
$G$-band	1 806 254 432
$G_{\mathrm{BP}}$-band	1 542 033 472
$G_{\mathrm{RP}}$-band	1 554 997 939
Photometric time series	11 754 237
Gaia Andromeda Photometric Survey	1 257 319	Photometric time series for all sources in $5.5^\circ$ radius field around M31
Radial velocity	33 812 183	$G_{\mathrm{RVS}} < 14$, $3100 < T_{\mathrm{eff}} < 14\,500$ K
$G_{\mathrm{RVS}}$-band	32 232 187
$v_{\mathrm{broad}}$	3 524 677	spectral line broadening parameter
Radial velocity time series	1898	Sample of Cepheids and RR Lyrae
BP/RP mean spectra	219 197 643	$G < 17.65$ with small number of exceptions
RVS mean spectra	999 645	AFGK spectral types with SNR $> 20$, and sample of lower SNR spectra
Variable sources	10 509 536	See Table 2
Object DSC classification	1 590 760 469
Self-organised map of poorly classified sources	1	30 × 30 map and prototype spectra
APs from mean BP/RP spectra	470 759 263	$G < 19$, see Table 2
APs from mean RVS spectra	5 591 594
Chemical abundances from mean RVS spectra	2 513 593	Up to 12 elements
DIBs from mean RVS spectra	472 584
Non-single stars	813 687	astrometric, spectroscopic, eclipsing, orbits, trends, see Table 2
QSO candidates	6 649 162	High completeness, low purity
QSO redshifts	6 375 063
QSO host galaxy detected	64 498
QSO host galaxy profile	15 867
Galaxy candidates	4 842 342	High completeness, low purity
Galaxy redshifts	1 367 153
Galaxy profiles	914 837
Solar System objects	158 152	Epoch astrometry and photometry
SSO reflectance spectra	60 518
Total galactic extinction maps	5	HEALPix levels 6–9, and optimum HEALPix level
Science alerts	2612	Triggered in the period 25-07-2014 to 28-05-2017

Data product or source type	Number of sources	Comments
Variable sources
Total	10 509 536
Classified with supervised machine learning	9 976 881	24 variability types or type groups
Active galactic nuclei	872 228
Cepheids	15 021
Compact companions	6 306
Eclipsing binaries	2 184 477
Long-period variables	1 720 588
Microlensing events	363
Planetary transits	214
RR Lyrae stars	271 779
Short-timescale variables	471 679
Solar-like rotational modulation variables	474 026
Upper-main-sequence oscillators	54 476
Astrophysical parameters from mean BP/RP spectra
Total	470 759 263	$G < 19$
Spectroscopic parameters	470 759 263
Interstellar extinction and distances	470 759 263
MCMC samples from the BP/RP AP estimation	449 297 716
APs assuming an unresolved binary	348 711 151
MCMC samples from BP/RP unresolved binary AP estimation	348 711 151
Evolutionary parameters	128 611 111	mass, age, evolutionary stage
Stars with emission-line classifications	57 511
Sources with spectral types	217 982 837
Hot stars with spectroscopic parameters	2 382 015
Ultra-cool stars	94 158
Cool stars with activity index	1 349 499
Sources with H$\alpha$ emission measurements	235 384 119
Non-single stars
Total	813 687
Acceleration solutions	338 215
Orbital astrometric solutions	169 227	including astroSpectroSB1 combined solutions
Orbital spectroscopic solutions (SB1/SB2)	220 372	including astroSpectroSB1 combined solutions
Trend spectroscopic solutions	56 808
Eclipsing binaries	87 073	including eclipsingSpectro combined solutions

Astrometry and broad band photometry

The astrometric and broad band photometry content of Gaia DR3 is the same as that for Gaia EDR3, except for the addition of the $G_{\mathrm{RVS}}$ photometry, but for convenience we summarise here the properties of these data products. Gaia EDR3 provided celestial positions and the apparent brightness in $G$ for 1.8 billion sources. For 1.5 billion of those sources, parallaxes, proper motions, and the $(G_{\mathrm{BP}} - G_{\mathrm{RP}})$ colour were also published. Gaia DR3 therefore contains some 585 million sources with five-parameter astrometry (two positions, the parallax, and two proper motion components), and about 882 million sources with six-parameter (6-p) astrometry, including an additional pseudo-colour parameter. We refer to Gaia Collaboration, Brown et al. [35] for details on Gaia EDR3, a summary of the astrometric

and photometric data processing, and a summary of the limitations of these data and guidance on their use. Detailed descriptions of the astrometry in Gaia EDR3 (and thus also Gaia DR3) are provided in Lindegren et al. [65], while the broad band photometry is described in detail in Riello et al. [89]. Details on the construction of the Gaia DR3 source list can be found in Torra et al. [109], while the basic inputs to the astrometric and $G$-band photometric processing (the $G$-band source image positions and fluxes) are described in Rowell et al. [95]. The validation of the astrometry and broad band photometry is summarised in Fabricius et al. [28]. The photometric pass-bands for $G$, $G_{\rm BP}$, and $G_{\rm RP}$ are provided in Riello et al. [89], and the one for $G_{\rm RVS}$ in Sartoretti et al. [97].

Finally, the Gaia Science Alerts project and content are described in Hodgkin et al. [51].

G-band photometry for 6-p and 2-p sources

Section 7.2 of Gaia Collaboration, Brown et al. [35] and Section 8.3 of Riello et al. [89] describe a correction to be applied to the $G$-band photometry for sources with 6-p and 2-p astrometric solutions. This correction was provided in the form of Python code and Astronomical Data Query Language (ADQL) recipes. We note here that these corrections are included in Gaia DR3 and therefore should not be applied when working with photometry extracted from the Gaia DR3 data tables in the Gaia archive.

As noted in Gaia Collaboration, Brown et al. [35] the $G$-band photometry for a small number of sources is not listed in Gaia EDR3. This issue has not been fixed for Gaia DR3. The magnitudes can be found in a separate table to be provided through the Gaia DR3 ‘known issues’ web pages (for details see section 8.2 in Riello et al. [89]).

Celestial reference frame

Given that the astrometry in Gaia DR3 is unchanged from Gaia EDR3, it follows that the source positions and proper motions are provided with respect to the Gaia-CRF3, the third realisation of the Gaia celestial reference frame. Gaia-CRF3 is aligned with the third realisation of the International Celestial Reference Frame in the radio (ICRF3) to about 0.01 mas root-mean-square (RMS) at epoch J2016.0 (barycentric coordinate time, TCB), and globally non-rotating with respect to quasars to within 0.005 mas yr$^{-1}$ RMS. The Gaia-CRF3 is defined by the positions and proper motions of 1 614 173 QSO-like sources that were selected using Gaia EDR3 astrometry. For the alignment and spin of the Gaia-CRF3, the special sets of 2007 and 428 034 sources, respectively, were used based on the preliminary astrometric solutions known as AGIS3.1 ([65]). The construction and properties of the Gaia-CRF3, the comparison to the ICRF3, and the procedure to fix the alignment and spin of the astrometric solution are described in Gaia Collaboration, Klioner et al. (2022).

Systematic errors

The systematic errors present in the astrometry and broad band photometry published in Gaia EDR3 carry over to Gaia DR3. The conclusions from investigations during the data processing for Gaia EDR3 astrometry were that the global parallax bias for Gaia DR3, as measured from quasars, is −17 µas. The RMS angular (i.e. source to source) covariances of the parallaxes and proper motions on small scales are ∼ 26 µas and ∼ 33 µas yr$^{-1}$, respectively (see Lindegren et al. [65], for details). The parallax bias (and the proper motion systematic errors) varies as a function of magnitude, colour, and celestial position. This is extensively investigated in Lindegren et al. [64] and a recipe for correcting the parallaxes is given. The systematic errors in the broad band photometry are described in Riello et al. [89].

Since the release of Gaia EDR3 several investigations of the systematic errors have been published. The parallax bias was investigated for specific sets of sources by Stassun & Torres [102], and Ren et al. [88] (eclipsing binaries); Huang et al. [54] (Red Clump stars); Zinn [119] (red giant branch stars with asteroseismic parallaxes); Wang et al. [112] (red giant stars), Flynn et al. [30] (stellar clusters); Groenewegen [48], Kovacs & Karamiqucham [59], Riess et al. [90], and Molnar et al. [74] (Cepheids and RR Lyrae stars); Maíz Apellániz [71], and Maíz Apellániz et al. [72] (Magellanic Clouds and globular clusters). The small-scale covariances are investigated in Zinn [119], Vasiliev & Baumgardt [111], and Maíz Apellániz et al. [72], while assessments of the parallax uncertainties can be found in El-Badry et al. [24], Vasiliev & Baumgardt [111], and Maíz Apellániz et al. [72].

The systematic errors in the proper motions and positions of stars in Gaia EDR3 were investigated by Cantat-Gaudin & Brandt [11] and Lunz et al. [66]. Cantat-Gaudin & Brandt [11] demonstrated that the proper motions of bright ($G \lesssim13$) stars show a residual spin with respect to the fainter stars by up to 80 µas yr$^{-1}$, and they provide a recipe to correct for this effect. Lunz et al. [66] found differences in the alignment to the ICRF3 between bright and faint sources of about 0.5 mas. Systematic errors in the broad band photometry were investigated by Niu et al. [77], Yang et al. [116], and Thanjavur et al. [108].

We stress here that including the above references does not imply an endorsement by the Gaia Collaboration of the results or any systematic error correction recipes provided in those papers.

Nevertheless, community efforts to investigate the quality of the Gaia data are highly appreciated and have in several cases led to an improvement in the validation procedures used during the Gaia data processing.

BP/RP spectra

BP/RP spectral observations are transmitted to the ground from the satellite in small windows surrounding the position of the source. The size of the BP/RP windows is 60 pixels AL by 12 pixels in the across scan (AC) direction, corresponding to an area in the sky of approximately 3.5″ by 2.1″. The size of the window affects the detection of sources in crowded regions, resulting in partially overlapping windows. These windows are excluded from the Gaia DR3 data processing. A special treatment will be implemented in future data releases.

Data processing

The BP/RP processing is described in detail in De Angeli et al. [19] and Montegriffo et al. [75]. The spectra are first calibrated to an internal reference instrument which is homogeneous across all devices, observing configurations, and time. This is achieved purely from BP/RP data for a sufficiently large subset of sources selected to cover all calibration units (windowing strategies, gates, magnitude ranges, time). The internal calibration removes a number of effects such as bias, background, geometry, differential dispersion, and variations in response and in the line spread function (LSF) across the focal plane ([13, 19]). The internal reference system is linked to the absolute system (both in terms of flux and wavelength) via the external calibration, which is based on a dedicated catalogue of spectro-photometric calibrators ([81]). More details concerning the calibration of the BP/RP spectral data to the absolute reference system are presented in Montegriffo et al. [75].

We release mean low-resolution BP/RP spectra for about 220 million sources. They are selected to have a reasonable number of observations (more than 15 CCD transits) and to be sufficiently bright to ensure good signal-to-noise ratio (S/N), that is, $G < 17.65$. To this list, we add a few samples of specific objects that could be as faint as $G \sim21.43$: about 500 sources used for the calibration of the BP/RP data, a catalogue of about 100 000 white dwarf candidates, 17 000 galaxies, about 100 000 QSOs, about 19 000 ultra-cool dwarfs, 900 objects that were considered to be representative for each of the 900 neurons of the self-organising maps (SOMs) used by the outlier analysis (OA) module (see § 9) and 19 solar analogues (De Angeli et al. [19]).

The S/N of the BP/RP spectra varies depending on the magnitude and colour of the source. In the range $9 < G < 12$, it can reach 1000 in the central part of the RP spectral range and it is of the order of 100 at $G \sim15$. These spectra have been extensively used as input for further data processing inside DPAC (see e.g. § 8, § 9, § 12), which provides a strong validation of their exceptional quality.

Data representation

The source mean spectra are provided in a continuous representation: they are described by an array of coefficients to be applied to a set of basis functions. We use a set of 55 basis functions for BP and 55 basis functions for RP (referred to as ‘bases’) defined as a linear combination of Hermite functions ([13, 19]. In low-S/N spectra, as for instance at faint magnitudes, it is possible that higher order bases are over-fitting the noise in the observed data. In particular, low-S/N spectra when sampled in pseudo-wavelength can exhibit unrealistic features (wiggles). To mitigate this problem, Carrasco et al. [13] suggest a statistical criterion to select the coefficients that can be dropped without losing information (truncation). Non-truncated and truncated spectra are in agreement within the noise. It should be noted that sharp features in the spectra, such as emission lines, can only be reproduced using higher order bases and therefore imply a larger number of significant coefficients. As the rejection criterion is statistical, it might happen that too few or too many coefficients are removed. This might affect faint objects with sharp spectral features, such as QSOs, or emission line stars. The effects of the truncation for various object classes are described in Sect. 3.4.3 of De Angeli et al. [19].

To allow the users to decide how many coefficients are relevant for their scientific case, all the 55 coefficients of the basis functions are released. The number of coefficients returned by the truncation criterion is given in the parameters $\mathit{bp\_n\_relevant\_bases}$ and $\mathit{rp\_np\_relevant\_bases}$ available in the $\mathit{xp\_summary3}$ table and in the mean continuous spectra available via Datalink³.

In addition to the continuous representation, sampled spectra representation (i.e. in the form of integrated flux vs. pixel) in both internal and absolute flux can be calculated using the Python package GaiaXPy made available with Gaia DR3 (see § 13). Sampling the spectra on a discrete grid in pseudo-wavelengths or absolute wavelengths results in a loss of information. In particular, the full covariance matrix is provided in the continuous representation, whereas it cannot be calculated in a spectrum sampled on a grid with more points than the number of basis function coefficients. Although sampled spectra are made available for a subset of sources in Gaia DR3, we strongly encourage users of the BP/RP spectra to make use of the continuous representation to maximise the scientific use of these data.

RVS data products

Gaia DR2 was the first release to include RVS radial velocities based on 22 months of data for stars at $G_{\mathrm{RVS}} \le12$ and with effective temperatures $3500 < T_{\mathrm{eff}} < 6900$ K.

Gaia DR3 contains newly determined radial velocities for about 33.8 million stars with $G_{\mathrm{RVS}} \le14$ and with $3100 \le T_{\mathrm{eff}} \le14500$ K. Additional data products are published for the first time: $v_{\mathrm{broad}}$ for about 3.5 million stars, $G_{\mathrm{RVS}}$ magnitudes for more than 32 million stars, mean spectra for slightly less than 1 million stars, and epoch radial velocities for about 1000 RR Lyrae and roughly 800 Cepheids of different types ([92]). All these data come with quality parameters. Users are advised to treat the Gaia DR3 radial velocity catalogue as completely independent of Gaia DR2.

The spectroscopic pipeline and the improvements since Gaia DR2 are described in Chapter 6 of the online documentation [96]⁴. All the products of the spectroscopic pipeline are available in the table $\mathit{gaia\_source}$⁴, except for the mean spectra (see § 5.2). The transit radial velocities for the Cepheids and RR Lyrae stars are published in the table $\mathit{vari\_epoch\_radial\_velocity}$⁵.

The products of the RVS pipeline, their properties, and their validation are described in more detail in dedicated papers: the radial velocity determination is described in Katz et al. [57], the specific treatments to measure the hot star radial velocities in Blomme et al. [6], and the radial velocity processing of the double-lined spectra is presented in Damerdji et al. [18]. The $v_{\mathrm{broad}}$ determination is discussed in Frémat et al. [32] and the $G_{\mathrm{RVS}}$ magnitudes and the RVS pass-band in Sartoretti et al. [97].

For each valid RVS spectrum entering the pipeline, the transit radial velocity is computed through a fit of the RVS spectrum relative to an appropriate synthetic template spectrum. In Gaia DR3, for stars cooler than 7000 K, the template input parameters are mostly taken from intermediate results of Apsis ([16]) based on Gaia DR2 BP/RP spectra (see § 9). For hotter stars, they were derived as explained in Blomme et al. [6].

Data processing improvements

A number of improvements were implemented in the Gaia DR3 RVS pipeline. Here we list the most significant. Bright stars are processed using the method already implemented in Gaia DR2, that is, the combined radial velocity for stars at $G_{\mathrm{RVS}} < 12$ is the median of the single-transit radial velocities. However, this method is not very efficient when the S/N is low. The radial velocities of faint stars in the range $12 < G_{\mathrm{RVS}} < 14$ are obtained from the averaged single-transit cross-correlation functions referring to the Solar System barycentre.

To improve performance at the faint end, we implemented an improved stray-light-correction procedure. The correction map is estimated every 30 hours from the faint-star spectra edges and from the virtual objects⁶, while in Gaia DR2 a single stray-light map was computed from the Ecliptic Pole scanning law⁷ data.

We introduce a deblending procedure when the transit spectra are contaminated by nearby sources falling inside the RVS window. These deblended spectra are then used to obtain radial velocities and mean spectra, while in Gaia DR2 they were simply removed from the pipeline. This allows us to process a larger number of epoch spectra per source, increasing the S/N. However, we point out that only the clean non-blended transits are used to derive $v_{\mathrm{RVS}}$, and $v_{\mathrm{broad}}$.

The LSF was calibrated in both the AL and AC directions. The LSF-AL calibration has reduced the systematic shifts between the two fields of view affecting the wavelength calibration zero point and the epoch radial velocities. The LSF-AC calibration was also used in the deblending procedure and in the estimation of the flux lost outside the window for estimation of $G_{\mathrm{RVS}}$.

$v_{\mathrm{broad}}$ is computed for each transit, excluding deblended spectra, and then averaged. In addition to the projected rotational velocity, $v \sin i$, $v_{\mathrm{broad}}$ can include other physical effects like macro-turbulence, residual instrumental effects (LSF model uncertainty), and template mismatches.

We consider that a spectrum can be contaminated by nearby sources with $G_{\mathrm{RVS}} < 15$ even if they are not located inside the RVS window. In that case, the target spectrum is removed when the contaminant differs from the target source by less than 3 mag. This effect was neglected in Gaia DR2.

Finally, $G_{\mathrm{RVS}}$ (grvs_mag in gaia_source) is calculated as the median of the single-transit $G_{\mathrm{RVS}}$ measurements. Values of $G_{\mathrm{RVS}}$ fainter than 14.1 were regarded as spurious and removed because they could have been caused by an inaccurate background estimate.

RVS spectra

The published mean RVS spectra are identified using the column has_rvs in the gaia_source table. Their spectra are available through the Datalink interface in the table rvs_mean_spectrum⁸. The mean spectra and their processing are described in detail in Seabroke et al. [98]. In summary, the transit RVS spectra are extracted, cleaned, deblended (if needed), wavelength calibrated and normalised either to their pseudo-continuum or by scaling with a constant (the latter for cool stars or noisy spectra). The spectra are then shifted to the rest frame using the epoch radial velocities for the bright stars for $G_{\mathrm{RVS}} \le12$ mag, or using the combined radial velocity for the faint stars; they are then interpolated into a common wavelength array spanning 846–870 nm with a step of 0.01 nm and averaged. S/N information is also provided in the table gaia_source.

The released RVS spectra are selected from among stars of spectral type AFGK with S/N > 20. In addition, a sample of low-S/N spectra spanning all spectral types is added (see Seabroke et al. [98]). A known issue is that the published spectra are not uniformly distributed over the sky.

Caveats

The contamination of the flux of an RVS target source by the flux of a nearby bright source can produce a spurious population of stars with high radial velocities. This issue was known from Gaia DR2 (Seabroke et al. [99]). In addition bright-star contamination produces biased radial velocities which do not necessarily stand out from the overall radial velocity distribution when two sources are separated by 1.8″ in the direction perpendicular to the scan. These stars were removed from the catalogue (Katz et al. [57]).

Radial velocity uncertainties are generally very small, of the order of a few km s$^{-1}$ or even less than 1 km s$^{-1}$ at the bright end. However, they are slightly underestimated for bright stars. A correction is proposed in Babusiaux et al. [5].

Variables

The approach to the variability analysis in successive data releases is iterative, including at each data release more variability types with lower signal to noise. In Gaia DR3 we publish a total of more than 10 million variable sources in about 24 variability types (and their time series), in addition to approximately 2.5 million galaxies. The two-dimensional structure of galaxies is observed by Gaia over a range of position angles. This can induce spurious (non-intrinsic) photometric variability. This effect is used to identify galaxies, but their time series are not released.

The Gaia DR3 variability content is a great leap in comparison with Gaia DR2 where we reached more than 550 000 stars, with six variability types.

In the variability pipeline (VARI) processing, the variability was first tested in the time domain, and was then characterised in the Fourier domain and classified by multiple classifiers. We refer to the documentation and to Eyer et al. [26] for more processing details. The input data are mostly the time series of field-of-view transits of the broad band photometry in the calibrated $G$, $G_{\mathrm{BP}}$, and $G_{\mathrm{RP}}$ bands. Additionally, for Cepheid and RR Lyrae stars we use radial velocity time series from the RVS instrument (see § 5.2) (Ripepi et al. [92]; ); Clementini et al. [15]); the long-period variable (LPV) analysis included RP spectral time series (which are not part of Gaia DR3, Lebzelter et al. [63]); and short-timescale variables are based on per-CCD G-band photometry. We refer to Distaso et al. (2022) for more information about solar-like variables; Marton et al. [73] for young stellar objects; Wyrzykowski et al. [115] concerning microlensing events; Gomel et al. [46] for ellipsoidal variables with possible compact object secondaries; and Carnerero et al. (2022) for information on active galactic nuclei (AGN) candidates. Candidate eclipsing binaries are described in Mowlavi et al. [76]. A subset of those also have parameters in the NSS tables (see § 7).

Variability products in Gaia DR3 can be accessed as follows. In the gaia_source table the field phot_variable_flag is set to ‘VARIABLE’ when a source appears in any of the vari_* tables, except for the vari_summary⁹ table which includes non-variable sources that appear in GAPS (see below). The vari_summary table lists statistical parameters for all variable sources and all sources in GAPS. The time series in $G$, $G_{\mathrm{BP}}$, and $G_{\mathrm{RP}}$ bands of all sources listed in the vari_summary table are available as light curve data through the Datalink interface.

The quality of this data base is impressive. For instance, concerning LPVs, the catalogue includes about 1.7 million sources with $G$ variability amplitudes greater than 0.1 mag (0.2 mag in Gaia DR2). The period is derived for about 392 000 of them. In Gaia DR2 we only identified about 151 000 LPV sources. Concerning Gaia DR3 data, in many cases it was possible to identify the spectral class from RP spectra, that is, cool giants of spectral types M (oxygen-rich) and C (carbon-rich). This classification was based on the presence in the RP spectra of numerous molecular absorption bands, mainly those due to TiO and related oxides for M-type and molecular bands mostly associated with CH and C$_2$ molecules for C-type stars (Lebzelter et al. [63]).

More than 270 000 confirmed RR Lyrae stars are released in Gaia DR3, almost doubling the Gaia DR2 RR Lyrae catalogue. In addition, we provide a better characterisation of the RR Lyrae pulsational and astrophysical parameters. This, along with the improved astrometry published with Gaia EDR3, make this sample the largest, most homogeneous all-sky catalogue of RR Lyrae stars published so far (Clementini et al. [15]). A small, but significant number of micro-lensing event candidates (363 in total, of which 90 are new) are identified [115]. While testing the exoplanet detection method, two Gaia-discovered transiting extra-solar planets were found from the epoch photometry [80]. This demonstrates the feasibility of the detection approach and Gaia’s potential for discovering exoplanet candidates. About 214 exoplanet candidates are released in Gaia DR3.

A large number of ellipsoidal variable candidates were detected. Their variability is due to the tidal interaction with a companion in a close binary system. About 6000 short-period ellipsoidal variables have relatively large-amplitude modulations in $G$, possibly indicating a massive, unseen secondary. Among those, 262 systems have a higher probability of having a compact secondary. Follow-up observations are needed to verify the true nature of these variables [46].

Gaia Andromeda Photometric Survey

The photometric time series for all Gaia sources located within a $5.5^\circ$ radius centred on the Andromeda galaxy are part of GAPS, which contains more than 1.2 million sources. Whether or not a source appears in the GAPS survey is indicated in the gaia_source table by setting the field in_andromeda_survey to ‘true’. The time-series statistics for GAPS sources are available in the vari_summary table. Evans et al. [25] give more details.

Caveats

As a caveat, a number of sources show more than one type of variability. In general this overlap can be scientifically explained. However, this is not always the case, for instance a few objects were classified as both long period and short timescale [63]. Holl et al. [53] discuss spurious periodic variations in the photometric data, due to instrumental effects.

Non-single stars

About 800 000 solutions for NSS including astrometric [49], spectroscopic (single lined SB1; and double lined SB2) [47], and eclipsing binaries [100] are published in Gaia DR3, with either orbital elements or trend parameters, or combinations of these.

NSS archive tables

The NSS tables are organised according to the type of solution: nss_two_body_orbit¹⁰ contains the orbital parameters for all the binary categories; nss_acceleration_astro¹¹ contains accelerations for sources with an astrometric motion better described using a quadratic or cubic proper motion; nss_non_linear_spectro¹² presents trend (long period) solutions of spectroscopic binaries; nss_vim_fl¹³ includes objects that exhibit photocentric displacements due to the photometric variability of one component, requiring the correction of the astrometric parameters.

This catalogue outnumbers existing surveys by large factors, spanning a large range of binary types, periods, and magnitudes. The potential of the Gaia DR3 binary star content is outlined in Gaia Collaboration, Arenou et al. (2022).

Caveats

Binaries can simultaneously belong to different classes; for example, astrometric binaries can also be spectroscopic binaries (identified as astroSpectroSB1 in the nss_solution_type in the table nss_two_body_orbit) or eclipsing binaries can also be spectroscopic binaries (identified as eclipsingSpectroSB1). In many cases of multiple solutions, combined solutions have been computed and included in the Gaia DR3 catalogue. However, combined solutions are not always provided, and sources can be found in several tables simultaneously. More information and advice on how to deal with these cases can be found in Gaia Collaboration, Arenou et al. (2022).

Acceleration solutions are not always in agreement with expectations from known orbits in external catalogues, and a fraction of them could have had an orbital solution. In general the parallaxes and proper motions derived by the NSS processing are more precise than those derived by the Gaia astrometric global iterative solution (see § 3) which assumes that all stars are single. This is not always the case for the acceleration solutions, which should be used with caution [5] (Gaia Collaboration, Arenou et al. 2022).

Spurious solutions around the satellite precession period (62.97 days) or for some short periods can be found for SB1. Formal uncertainties are not rescaled according to the goodness of fit for all the binary types, but only for the astrometric solutions. The NSS sample is far from complete. This is because of a number of selection effects due to data processing and additional filtering. Statistical studies on the data should take this into account.

Solar System objects

In Gaia DR3, about 160 000 SSOs were processed and analysed. As in previous data releases, known SSOs are searched for by matching the observed transits to computed transits based on the information on the satellite orbit, the scanning law, and a numerical integration of the SSO motion. Tanga et al. [105] gives a description of the selection and processing. After filtering the list for possible contaminants, the final input selection had 3 513 248 transits for 156 837 known asteroids. Planetary satellites were also added following a similar procedure. In total, 31 planetary satellites are included. In addition, Gaia DR3 includes the astrometry of unknown moving sources based on the AL motion of objects observed from December 2016 to June 2017. The final input list of unidentified SSOs for Gaia DR3 comprises 4522 transits, corresponding to 1531 groups of chained transits of objects that at the time of processing were considered unmatched. Later, Tanga et al. [105] identify 712 SSOs. These sources still appear as unmatched in the table sso_source¹⁴. It cannot be excluded that some of the still unmatched sources can be linked to known objects.

The astrometric accuracy of the orbits is impressive and remains at sub-mas level for $G < 17$, reaching an exceptional value of $\sim0.25$ mas for $12 < G < 15$ mag.

Gaia DR3 contains spectro-photometry for more than 60 000 asteroids the majority of which have been observed with $G$ between $\sim18$ and 20. The internally calibrated BP/RP epoch spectra are divided by the solar analogue spectrum to obtain epoch reflectance spectra that are subsequently averaged and sampled in 16 bands in wavelength. Only spectra derived using more than three epochs and with an average S/N of higher than 13 are published. No further rejection is applied. Poor spectra are flagged on a wavelength-by-wavelength basis introducing the sso_reflectance_spectrum_flag¹⁵, an array of 16 integers, one for each wavelength of the spectral bands (Gaia Collaboration, Galluccio et al. 2022). The main properties of the reflectance spectra are described in § 12.

Object characterisation

Gaia DR3 includes APs for stars, galaxies, and QSOs. About 1600 million objects have class probabilities ($G < 21$), about 470 million stars have stellar parameters ($G < 19$), and there are about 6 million QSOs and about 1.3 million galaxy candidate redshifts. More details can be found in Creevey et al. [16] concerning the AP content and an overview of the methods used in the software (Apsis) to produce these data.

Data processing

In total, 13 modules in the Apsis software provide 43 primary APs along with auxiliary parameters in 538 fields which appear in ten tables of the Gaia archive. A subset of APs is available in gaia_source. The astrophysical characterisation makes use of Gaia EDR3 broad band photometry and parallaxes, and Gaia DR3 mean RVS spectra and internally calibrated sampled mean BP/RP spectra. The stellar APs comprise atmospheric properties, evolutionary parameters, metallicity, individual chemical element abundances, and extinction parameters, along with other characterisation such as equivalent widths of the H$\alpha$ line and activity index for cool active stars.

The discrete source classifier (DSC) produces the object classification, that is, it assigns class probabilities to all sources for five main classes, using different classifiers; these classes are QSO, galaxy, star, white dwarf, and physical binary star. The result of this classification is then used by four modules to initiate the processing of galaxies, QSOs, outliers, and extinction. The unresolved galaxy classifier (UGC) and the QSO classifier (QSOC) modules (Delchambre et al. [21]) provide redshifts for candidate galaxies and QSOs. A more detailed discussion about the Gaia DR3 extragalactic content can be found in § 11.

The OA module performs an unsupervised classification for sources with lower probabilities from DSC, using SOMs (Kohonen [58]). OA groups similar objects into neurons on a 30 $\times$ 30 grid according to the similarity of their BP/RP spectra, as reported in oa_neuron_information¹⁶ (Creevey et al. [16], figure 11, for examples). Other multi-dimensional data comprise a total Galactic extinction map at four healpix levels as well an optimal-level map derived by the total Galactic extinction module (TGE). The above data products are detailed in Delchambre et al. [21].

For the stellar and interstellar medium characterisation, the General Stellar Parametriser for Photometry (GSP-Phot) derives astrophysical parameters ($T_{\mathrm{eff}}$, $\log g$, $A_G$, distance, ...) from BP/RP spectra down to $G = 19$ assuming they are all stars (Andrae et al. [3]). The multiple source classifier (MSC) derives similar stellar parameters under the hypothesis that all objects are unresolved binaries using the same input data as GSP-Phot. The General Stellar Parametriser for Spectroscopy (GSP-Spec) derives atmospheric parameters using the mean RVS spectra. It additionally derives 13 chemical species and diffuse interstellar band (DIB) equivalent widths (see Recio-Blanco et al. [87], for details). Other modules analyse only a selected class of objects. This is the case for the Extended Stellar Parametriser (ESPs) dealing with emission line stars (ESP-ELS), hot stars (7000 < $T_{\mathrm{eff}}$ < 50 000 K. ESP-HS), cool stars (ESP-CS), and ultra-cool dwarfs ($T_{\mathrm{eff}} < 2500$ K, ESP-UCD). These modules together produce $T_{\mathrm{eff}}$ and $\log g$ but also $\nu\sin i$, spectral type classifications, H$\alpha$ equivalent widths, and chromospheric activity index (using the calcium infrared triplet in the RVS domain (Lanzafame et al. [61])).

The Final Luminosity Age Mass Estimator (FLAME) derives evolutionary parameters from data that are processed by GSP-Phot and GSP-Spec. These comprise the stellar radius, luminosity, gravitational redshift, mass, age and evolutionary stage for stars. All of these data products appear in the astrophysical_parameters¹⁷ and astrophysical_parameters_supp¹⁸ tables in the Gaia archive. In addition to these data products, Markov Chain Monte Carlo (MCMC) samples are provided for two of the modules, GSP-Phot and MSC through the Datalink interface.

To date, this is the most extensive catalogue of astrophysical parameters homogeneously derived. It is based on Gaia-only data, and it will be superseded only by the fourth data release, Gaia DR4. Until then, it will remain the reference for upcoming ground- and space-based surveys. References to some applications and advice on the use of the APs can be found in the papers listed in § 12.

Caveats

The AP quality, although quite reliable on average, does vary depending on the quality of the input data (parallaxes, magnitudes, and spectra) but it can also vary based on the assumptions in the methods. The users should be aware of several issues. Variability is not taken into account in the processing, because only mean spectra and mean broad band magnitudes are used as input. Therefore, parameters of stars showing large variability might be inaccurate.

APs derived for objects in high-density regions are affected by crowding. For instance $[\mathrm{M/H}]$ in the core of a dense cluster can significantly differ from the value in the outskirts.

GSP-Phot distance estimates are underestimated for sources with large parallax errors because of an extinction prior which then dominates the distance inference. GSP-Phot metallicity is poorly derived for metal-poor stars ($[\mathrm{M/H}] < -1$), and a metallicity calibration tool is proposed to the users to calibrate these. The data show residual degeneracy among the parameters, for instance $T_{\mathrm{eff}}$ and the extinction in the $G$, $A_G$, from GSP-Phot.

MSC treats all stars as unresolved binaries in BP/RP spectra; DSC shows poor performance on the classification of physical binaries and white dwarfs.

Additional information about the limitations of the APs are detailed in Babusiaux et al. [5], while both the quality and validation of the stellar and non-stellar content are discussed more extensively in Fouesneau et al. [31] and Delchambre et al. [21], respectively. In general, users are advised to use quality flags and follow the recommendations detailed in the above papers.

Extended objects

The Gaia on-board system is designed to detect point-like sources (de Bruijne et al. [20]). However, EOs such as galaxies and galaxies hosting a QSO can be detected if their central region is sufficiently compact and bright. In this case, it is possible to reconstruct a two-dimensional light profile of the extended source. At each passage Gaia observes the nine one-dimensional Astro Field (AF) windows and the two-dimensional Sky Mapper (SM) windows. As the scan angle changes from one observation to the next, after a sufficient number of transits a large part of the source is covered by the observations. Dedicated software can combine the observed windows on ground to infer the two-dimensional light profile parameters. During the first 34 months of operations about 116 million transits in the AF and SM focal planes were collected and processed. The EO pipeline compares the observations with a large number of simulations of galaxy images deriving the best-fit surface brightness profile parameters.

The lists of objects that were processed are derived from external catalogues. The list of QSOs was set up by merging the major catalogues of QSOs or AGN candidates published before 2018. In addition, we make use of an unpublished selection of candidates based on Gaia DR2 QSOs, and classified on the basis of their photometric variability (Rimoldini et al. [91]). The list of galaxies was derived from a preliminary analysis of Gaia DR2 sources with a match in the allWISE catalogue (Cutri et al. [17]). An unsupervised heuristic method (Krone-Martins et al. [60]) selects sources for a subsequent analysis. More than 1 million previously identified QSOs are analysed, identifying the host galaxy around approximately 64 000 of them. The surface brightness profiles of the host is published for a subset of about 15 000 QSOs, with a robust solution. Their Sérsic indexes indicate that they are mostly disc-like galaxies. Concerning the galaxy sample, two profiles are published (a free Sérsic profile and a de Vaucouleurs one). About 940 000 galaxies were analysed and robust solutions were derived for about 914 000 of them. The distribution of the parameters indicates that Gaia mostly detects elliptical galaxies.

These data can be found in the Gaia DR3 tables qso_candidates¹⁹ and galaxy_candidates²⁰. Details on the data processing and input lists can be found in Ducourant et al. [23]. This impressive data base is the first all-sky catalogue of two-dimensional light-profile parameters of galaxies, and QSO host galaxies derived at a resolution of about 200 mas.

The use of input lists clearly favours purity over completeness, even if a residual contamination by stars is still present.

Gaia observations are limited to the central area of the galaxy. For this reason, the derived brightness profile parameters do not always agree with external catalogues ([5, 23]).

Gaia DR3 extragalactic content

Extragalactic objects are classified or analysed by several modules in the Gaia data-processing system using various input data and methods. An overview of the extragalactic processing and content in Gaia DR3 is given in Gaia Collaboration, Bailer-Jones et al. (2022).

A predefined list of objects is analysed to derive the surface brightness profiles by the EO module. Classification of extragalactic objects is performed independently by two modules: the VARI module uses photometric light curves, whereas DSC uses the BP/RP spectra and astrometry. The modules QSOC and UGC estimate the redshifts of QSO and galaxy candidates (respectively) identified by DSC. Finally, the OA module performs a clustering of low-probability classifications from DSC.

The fact that different methods, input data, and training data are used to classify or select extragalactic objects has an important consequence: there is no common definition of QSOs or galaxies across the various Gaia modules. DSC and OA focus on completeness rather than purity. UGC processes objects with a higher probability of being galaxies according to the DSC classification, whereas QSOC uses a very low threshold on the DSC QSO probability, in order to analyse as many sources as possible. As a result, the sample of QSO candidates with redshifts is complete rather than pure, whereas that of the galaxy candidates with redshifts is of higher purity, although some contamination remains (see below).

Extragalactic object tables

Each one of the above modules provides independent results that are available across several tables. The table astrophysical_parameters lists all the parameters produced by DSC. The oa_neuron_information contains the SOMs from the OA module. The tables vari_classifier_result²¹ and vari_agn²² present the parameters of the AGNs identified through the photometric light-curve analysis. From the above tables, we produce two integrated tables in Gaia DR3, qso_candidates and galaxy_candidates, where all good QSO and galaxy candidates are listed. The selection rules are detailed in Gaia Collaboration, Bailer-Jones et al. (2022). The gaia_source table includes the DSC classification probability as well as two flags, in_qso_candidates and in_galaxy_candidates, which indicate the presence of the source in the integrated table(s).

Already in Gaia EDR3, a list of 1.6 million compact extragalactic sources was used to define the Gaia-CRF3 (Gaia Collaboration, Klioner et al. (2022)). These sources were identified from Gaia EDR3 astrometry by means of positional cross-matching with 17 external catalogues of QSOs and AGNs, and were subsequently filtered using astrometric criteria to remove stellar contaminants. The sample spans a magnitude range 13.4 < $G$ < 21.4 with a median positional error of 0.45 mas. By construction this sample is of high purity.

Completeness and purity

The integrated tables of extragalactic objects favour completeness over purity. qso_candidates contains 6.6 million candidate QSOs and galaxy_candidates contains 4.8 million candidate galaxies, with global purities estimated to be 50% and 70% respectively. Contamination is mostly due to fact that there are many more stars in the Gaia survey, and arises in part from the choice of probability classification threshold for inclusion. Furthermore, some modules chose not to filter out regions such as the Magellanic Clouds and the Galactic plane, which are dense regions of stars and therefore also of contaminants. A very small fraction of sources are too bright to be genuine QSOs or galaxies. The same astrometric filtering as for Gaia-CRF3 applied to the content of the integrated QSO table qso_candidates gives a set of $\sim$ 1.9 million sources, which are identified by the flag astrometric_selection (Gaia Collaboration, Bailer-Jones et al. (2022)). Purer subsets of the two tables, containing $\sim$ 1.9 million probable QSOs and $\sim$ 2.9 million probable galaxies (both with around 95% purity) can be selected using the queries on EO, DSC, and VARI quality flag parameters listed in Gaia Collaboration, Bailer-Jones et al. (2022). Concerning QSOs, this new sample has about 1.8 million sources in common with the astrometric sample.

Finally, the DSC star class could be used to select a purer star sample, rejecting objects that are in the qso_candidates or galaxy_candidates, that is, those identified as candidate galaxies, QSOs, or extended objects. However, concerning extended objects, the DSC has not used morphological information in its classification (see for details Delchambre et al. [21]). Indeed there is no general morphological classification in Gaia DR3. Only a limited number of galaxy and QSO candidates (based on non-Gaia data) were analysed for evidence of a host galaxy using 2D morphology built up from the 1D scans (see § 10). As a consequence, residual contamination from extragalactic sources can still be present in a star sample selected following the above criteria.

The scientific performances of the catalogue

The scientific quality and the potential of the Gaia DR3 data are demonstrated in a number of accompanying papers providing basic science applications and additional validation of the catalogue. These illustrate the limitations of the data, providing advice and guidance to the users on specific scientific problems. The following topics are treated.

Gaia Collaboration, Creevey et al. (2022) present clean samples of very high-quality astrophysical parameters of stars derived from low-resolution BP/RP and RVS spectra all across the HR diagram, selected through severe quality cuts. These data have a long-term legacy value for future follow-up studies and missions and are released together with Gaia DR3 in separate tables. These samples include about 3 million OBA young disc stars, roughly 3 million FGKM stars, and about 21 000 ultra-cool dwarfs. Gaia Collaboration, Creevey et al. (2022) identify specific subsamples of particular interest to the community such as approximately 6 000 solar-analogues and 15 000 carbon stars, and provide homogeneous parameters for a subsample of the 111 Gaia spectro-photometric standard stars defined by Pancino et al. [82]. For the ultra-cool dwarfs Gaia and 2MASS data are combined to provide radius and luminosity.

Gaia Collaboration, Recio-Blanco et al. (2022) use Gaia astrometry, radial velocities, chemo-dynamical analysis of disc and halo populations, producing the largest all-sky chemical map to date and analysing the abundance of streams from accretion events. This map includes chemical information for 597 open clusters, which represents the largest compilation of abundances homogeneously derived for clusters.

Gaia Collaboration, Schultheis et al. (2022) discuss the distribution of the DIB at 862 nm in the RVS spectral range in connection with the interstellar extinction to within a few kiloparsecs of the Sun. The paper provides the most precise measurement to date of the rest frame wavelength of the DIB at 8620.86 $\pm$ 0.019 Å.

Gaia Collaboration, Drimmel et al. (2022) explore non-axisymmetric features in the disc of the Milky Way in both configuration and velocity space. These authors use Gaia DR3 APs and variability classifications to select various stellar populations in the disc of the Milky Way, tracing the spiral structure up to 4–5 kpc from the Sun. About 6 million red giant branch stars allow the authors to map the velocity field in the disc up to 8 kpc from the Sun, allowing the detection of signatures of the Galactic bar.

Gaia Collaboration, Arenou et al. (2022) present a clean catalogue of binary stars, discussing its completeness and some statistical features of the orbital elements in comparison with external catalogues. In addition, a catalogue of tens of thousands of stellar masses is provided. Several compact object candidates are identified. The catalogue includes sources found in rare evolutionary stages such as EL CVn, underlining the potential of analysing both photometric and orbital data. New binary UCD candidates are discovered and their masses estimated; two new exo-planets are found, and several dozen candidates are identified. The catalogue is made available together with Gaia DR3 tables.

Gaia Collaboration, Galluccio et al. (2022) present reflectance spectra, derived from BP/RP spectra, for SSOs, discussing their statistical properties. The ($z-i$) colours are derived from the spectra, where $z$ and $i$ represent the values of the reflectances estimated with spline interpolation at 748 and 893 nm, respectively. ($z-i$) allows the identification of asteroid families sharing similar properties and common origin. An interesting feature is that the spectral slope²³ of the mean reflectance spectra and the depth of the 1 $\mu$m absorption band seem to show an increase with time for the S-type family, which is possibly due to space weathering effects on the asteroid surfaces. The catalogue is included in the data release.

Gaia Collaboration, Montegrïffo et al. (2022) demonstrate the potential of synthetic photometry obtained from flux-calibrated BP/RP spectra for pass-bands fully enclosed in the Gaia wavelength range. This photometry has been used internally for validation purposes. Synthetic photometry in various photometric systems can be produced using the Python package GaiaXPy (see § 13). High-quality external photometry in large and medium passbands is reproduced at a few percent level in general and up to the milli-mag level when the synthetic photometry is standardised using an external reference catalogue. For a subset of 13 wide and medium bands, we release the Gaia Synthetic Photometry Catalogue (GSPC), an all-sky space-based catalogue of standardised photometry for the majority of the stars with released spectra and $G < 17.65$. A separate catalogue contains synthetic photometry in a selection of relevant bands for 100 000 white dwarfs selected from Gaia EDR3.

Lastly, Gaia Collaboration, De Ridder et al. (2022) investigate the properties of high-mass main sequence pulsators, showing that Gaia DR3 data are precise and accurate enough to identify nearby OBAF-type pulsators.

Software tools

A number of Python software tools²⁴ are made available to the scientific community. The following are some examples.

• The Python package GaiaXPy which offers the possibility to compute synthetic photometry from BP/RP spectra in various photometric systems (see Gaia Collaboration, Montegriffo et al. 2022, and Sect. 12), and to simulate BP/RP mean spectra from a custom input absolute spectral energy distribution. In addition sampled mean spectra can be derived from the continuous basis function representation. Detailed information can be found in De Angeli et al. [19].

• A tool to re-calibrate the GSP-Phot metallicity output (see Creevey et al. [16], Appendix E for detail) based on comparison with external surveys.

• A package to re-calibrate the GSP-Spec metallicity, $[\alpha/\mathrm{Fe}]$, and $\log g$ (Recio-Blanco et al. [87]).

• A script to derive bolometric corrections for the stars analysed by FLAME.

• A package to extract the extinction as a function of Galactic coordinates from the two-dimensional TGE Galactic maps.

• A tool to visualise the neurons in the OA SOMs.

• A tutorial on the programmatic download of large sets of Datalink items ($> 5000$ elements).

• Examples of cone search scripts.

We point out that the GSP-Phot and GSP-Spec re-calibration tools proposed here have their own limitations, as described in the related documentation. They provide tentative expressions to be used at the discretion of the user, depending on the science case.

Conclusions

Gaia DR3 expands Gaia EDR3 with a rich and large set of data products containing detailed astrophysical information for the same source catalogue. The quantity, quality and variety of astrophysical data constitute a major advance in the series of Gaia data releases. This release includes the largest collection of all-sky spectrophotometry and radial velocities and the largest collection of variable sources ever produced. The availability of APs from low- and high-resolution spectra greatly surpasses existing catalogues. This catalogue includes a spectrophotometric and dynamical survey of SSOs of the highest accuracy. The NSS content outnumbers all existing binary star catalogues, and Gaia DR3 also contains the first all-sky space-based survey of QSO host and galaxy two-dimensional light profiles.

In this paper we briefly summarise the main additions and improvements to the data processing and we include comments on the data quality. However, the Gaia DR3 data products are numerous and complex and not all the details could be presented here. A more complete overview, insights into known issues with the data, and advice on the use of the data can be found in the papers accompanying the release and in the online documentation.

The nominal Gaia mission ended on July 2019. The mission has been extended since then to the end of 2022, with an indicative approval to extend the mission to the end of 2025. In 2025, the propellant for the micro-propulsion system is expected to be exhausted, and the precision on the attitude and spin rate of the satellite required for the astrometry can then no longer be maintained. At this point, the Gaia mission end of life will be reached and over 10 years of data will have been collected. In this context, two further data releases are foreseen, Gaia DR4 and Gaia DR5 which will include the data from the extended mission. Gaia DR4 will be based on 66 months of data, including a six-month period when the satellite was operated with a reversed direction of the precession of the spin axis around the direction to the Sun. This will mitigate the degeneracy between the across scan motion of the sources and their parallaxes, reducing this specific source of systematic errors on the astrometry. A major new aspect of Gaia DR4 is that it will include all time series data, that is, epoch astrometry, broad band photometry, radial velocities, and epoch BP, RP, and RVS spectra for all sources. In addition, we plan to release full astrometric, photometric, and radial-velocity catalogues; all the available variable-star and NSS solutions; an extended sample of source classifications and multiple APs for stars, and extragalactic objects. Finally, an updated extra-solar planet list will be published. The publication of Gaia DR4 is expected not before the end of 2025. Gaia DR5 will be the final release from the Gaia mission, and will be based on data collected over the full nominal plus extended mission periods and including all the data products mentioned above, as well as the Gaia legacy archive. Gaia DR5 is expected not before the end of 2030.

Figure 2 summarises the astrometric uncertainties of the Gaia data releases so far and also shows the extrapolated uncertainties for Gaia DR4 and Gaia DR5. The latter are extrapolated from the Gaia DR3 performance according to the amount of data collected, 5.5 years and an expected 10 years, respectively.²⁵ With time the signal-to-noise ratio for all Gaia data products, including parallaxes, improves as $\sqrt{t}$, and therefore the precisions for Gaia DR4 and Gaia DR5 are expected to improve by factors of 1.4 and 1.9, respectively. For the proper motions, the improvement goes as $t^{1.5}$, which means improvements by factors of 2.7 and 6.6 with respect to Gaia DR3. For a more extensive discussion of the expected gains in future Gaia data releases, we refer to Brown [10].

There is therefore still much more to look forward to, but for now we invite the reader to explore the veritable supermarket of astronomical and astrophysical information that is Gaia DR3.

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. Acknowledgements are given in Appendix A.

9 Institute of Astronomy, University of Cambridge, Madingley Road, Cambridge CB3 0HA, United Kingdom

10 Department of Astronomy, University of Geneva, Chemin Pegasi 51, 1290 Versoix, Switzerland

11 European Space Agency (ESA), European Space Astronomy Centre (ESAC), Camino bajo del Castillo, s/n, Urbanizacion Villafranca del Castillo, Villanueva de la Cañada, 28692 Madrid, Spain

12 Aurora Technology for European Space Agency (ESA), Camino bajo del Castillo, s/n, Urbanizacion Villafranca del Castillo, Villanueva de la Cañada, 28692 Madrid, Spain

13 Institut de Ciències del Cosmos (ICCUB), Universitat de Barcelona (IEEC-UB), Martí i Franquès 1, 08028 Barcelona, Spain

14 Lohrmann Observatory, Technische Universität Dresden, Mommsenstraße 13, 01062 Dresden, Germany

15 Lund Observatory, Department of Astronomy and Theoretical Physics, Lund University, Box 43, 22100 Lund, Sweden

16 CNES Centre Spatial de Toulouse, 18 avenue Edouard Belin, 31401 Toulouse Cedex 9, France

17 Institut d’Astronomie et d’Astrophysique, Université Libre de Bruxelles CP 226, Boulevard du Triomphe, 1050 Brussels, Belgium

18 F.R.S.-FNRS, Rue d’Egmont 5, 1000 Brussels, Belgium

19 INAF - Osservatorio Astrofisico di Arcetri, Largo Enrico Fermi 5, 50125 Firenze, Italy

20 Max Planck Institute for Astronomy, Königstuhl 17, 69117 Heidelberg, Germany

21 INAF - Osservatorio Astrofisico di Torino, via Osservatorio 20, 10025 Pino Torinese (TO), Italy

22 European Space Agency (ESA, retired)

23 University of Turin, Department of Physics, Via Pietro Giuria 1, 10125 Torino, Italy

24 INAF - Osservatorio di Astrofisica e Scienza dello Spazio di Bologna, via Piero Gobetti 93/3, 40129 Bologna, Italy

25 DAPCOM for Institut de Ciències del Cosmos (ICCUB), Universitat de Barcelona (IEEC-UB), Martí i Franquès 1, 08028 Barcelona, Spain

26 Royal Observatory of Belgium, Ringlaan 3, 1180 Brussels, Belgium

27 Observational Astrophysics, Division of Astronomy and Space Physics, Department of Physics and Astronomy, Uppsala University, Box 516, 751 20 Uppsala, Sweden

28 ALTEC S.p.a, Corso Marche, 79,10146 Torino, Italy

29 Sàrl, Geneva, Switzerland

30 Department of Astronomy, University of Geneva, Chemin d’Ecogia 16, 1290 Versoix, Switzerland

31 Mullard Space Science Laboratory, University College London, Holmbury St Mary, Dorking, Surrey RH5 6NT, United Kingdom

32 Gaia DPAC Project Office, ESAC, Camino bajo del Castillo, s/n, Urbanizacion Villafranca del Castillo, Villanueva de la Cañada, 28692 Madrid, Spain

33 Telespazio UK S.L. for European Space Agency (ESA), Camino bajo del Castillo, s/n, Urbanizacion Villafranca del Castillo, Villanueva de la Cañada, 28692 Madrid, Spain

34 SYRTE, Observatoire de Paris, Université PSL, CNRS, Sorbonne Université, LNE, 61 avenue de l’Observatoire 75014 Paris, France

35 National Observatory of Athens, I. Metaxa and Vas. Pavlou, Palaia Penteli, 15236 Athens, Greece

36 IMCCE, Observatoire de Paris, Université PSL, CNRS, Sorbonne Université, Univ. Lille, 77 av. Denfert-Rochereau, 75014 Paris, France

37 Serco Gestión de Negocios for European Space Agency (ESA), Camino bajo del Castillo, s/n, Urbanizacion Villafranca del Castillo, Villanueva de la Cañada, 28692 Madrid, Spain

38 Institut d’Astrophysique et de Géophysique, Université de Liège, 19c, Allée du 6 Août, B-4000 Liège, Belgium

39 CRAAG - Centre de Recherche en Astronomie, Astrophysique et Géophysique, Route de l’Observatoire Bp 63 Bouzareah 16340 Algiers, Algeria

40 Institute for Astronomy, University of Edinburgh, Royal Observatory, Blackford Hill, Edinburgh EH9 3HJ, United Kingdom

41 RHEA for European Space Agency (ESA), Camino bajo del Castillo, s/n, Urbanizacion Villafranca del Castillo, Villanueva de la Cañada, 28692 Madrid, Spain

42 ATG Europe for European Space Agency (ESA), Camino bajo del Castillo, s/n, Urbanizacion Villafranca del Castillo, Villanueva de la Cañada, 28692 Madrid, Spain

43 CIGUS CITIC - Department of Computer Science and Information Technologies, University of A Coruña, Campus de Elviña s/n, A Coruña, 15071, Spain

44 Université de Strasbourg, CNRS, Observatoire astronomique de Strasbourg, UMR 7550, 11 rue de l’Université, 67000 Strasbourg, France

45 Kavli Institute for Cosmology Cambridge, Institute of Astronomy, Madingley Road, Cambridge, CB3 0HA

46 Leibniz Institute for Astrophysics Potsdam (AIP), An der Sternwarte 16, 14482 Potsdam, Germany

47 CENTRA, Faculdade de Ciências, Universidade de Lisboa, Edif. C8, Campo Grande, 1749-016 Lisboa, Portugal

48 Department of Informatics, Donald Bren School of Information and Computer Sciences, University of California, Irvine, 5226 Donald Bren Hall, 92697-3440 CA Irvine, United States

49 INAF - Osservatorio Astrofisico di Catania, via S. Sofia 78, 95123 Catania, Italy

50 Dipartimento di Fisica e Astronomia "Ettore Majorana", Università di Catania, Via S. Sofia 64, 95123 Catania, Italy

51 INAF - Osservatorio Astronomico di Roma, Via Frascati 33, 00078 Monte Porzio Catone (Roma), Italy

52 Space Science Data Center - ASI, Via del Politecnico SNC, 00133 Roma, Italy

53 Department of Physics, University of Helsinki, P.O. Box 64, 00014 Helsinki, Finland

54 Finnish Geospatial Research Institute FGI, Geodeetinrinne 2, 02430 Masala, Finland

55 Institut UTINAM CNRS UMR6213, Université Bourgogne Franche-Comté, OSU THETA Franche-Comté Bourgogne, Observatoire de Besançon, BP1615, 25010 Besançon Cedex, France

56 HE Space Operations BV for European Space Agency (ESA), Keplerlaan 1, 2201AZ, Noordwijk, The Netherlands

57 Dpto. de Inteligencia Artificial, UNED, c/ Juan del Rosal 16, 28040 Madrid, Spain

58 Konkoly Observatory, Research Centre for Astronomy and Earth Sciences, Eötvös Loránd Research Network (ELKH), MTA Centre of Excellence, Konkoly Thege Miklós út 15-17, 1121 Budapest, Hungary

59 ELTE Eötvös Loránd University, Institute of Physics, 1117, Pázmány Péter sétány 1A, Budapest, Hungary

60 Instituut voor Sterrenkunde, KU Leuven, Celestijnenlaan 200D, 3001 Leuven, Belgium

61 Department of Astrophysics/IMAPP, Radboud University, P.O.Box 9010, 6500 GL Nijmegen, The Netherlands

62 University of Vienna, Department of Astrophysics, Türkenschanzstraße 17, A1180 Vienna, Austria

63 Institute of Physics, Laboratory of Astrophysics, Ecole Polytechnique Fédérale de Lausanne (EPFL), Observatoire de Sauverny, 1290 Versoix, Switzerland

64 Kapteyn Astronomical Institute, University of Groningen, Landleven 12, 9747 AD Groningen, The Netherlands

65 School of Physics and Astronomy / Space Park Leicester, University of Leicester, University Road, Leicester LE1 7RH, United Kingdom

66 Thales Services for CNES Centre Spatial de Toulouse, 18 avenue Edouard Belin, 31401 Toulouse Cedex 9, France

67 Dpto. Estadística e Investigación Operativa. Universidad de Cádiz, Avda. República Saharaui s/n, 11510 Puerto Real, Cádiz, Spain

68 Center for Research and Exploration in Space Science and Technology, University of Maryland Baltimore County, 1000 Hilltop Circle, Baltimore MD, USA

69 GSFC - Goddard Space Flight Center, Code 698, 8800 Greenbelt Rd, 20771 MD Greenbelt, United States

70 EURIX S.r.l., Corso Vittorio Emanuele II 61, 10128, Torino, Italy

71 Porter School of the Environment and Earth Sciences, Tel Aviv University, Tel Aviv 6997801, Israel

72 Harvard-Smithsonian Center for Astrophysics, 60 Garden St., MS 15, Cambridge, MA 02138, USA

73 ATOS for CNES Centre Spatial de Toulouse, 18 avenue Edouard Belin, 31401 Toulouse Cedex 9, France

74 HE Space Operations BV for European Space Agency (ESA), Camino bajo del Castillo, s/n, Urbanizacion Villafranca del Castillo, Villanueva de la Cañada, 28692 Madrid, Spain

75 Instituto de Astrofísica e Ciências do Espaço, Universidade do Porto, CAUP, Rua das Estrelas, PT4150-762 Porto, Portugal

76 LFCA/DAS,Universidad de Chile,CNRS,Casilla 36-D, Santiago, Chile

77 SISSA - Scuola Internazionale Superiore di Studi Avanzati, via Bonomea 265, 34136 Trieste, Italy

78 Telespazio for CNES Centre Spatial de Toulouse, 18 avenue Edouard Belin, 31401 Toulouse Cedex 9, France

79 University of Turin, Department of Computer Sciences, Corso Svizzera 185, 10149 Torino, Italy

80 Dpto. de Matemática Aplicada y Ciencias de la Computación, Univ. de Cantabria, ETS Ingenieros de Caminos, Canales y Puertos, Avda. de los Castros s/n, 39005 Santander, Spain

81 Centro de Astronomía - CITEVA, Universidad de Antofagasta, Avenida Angamos 601, Antofagasta 1270300, Chile

82 DLR Gesellschaft für Raumfahrtanwendungen (GfR) mbH Münchener Straße 20, 82234 Weßling

83 Centre for Astrophysics Research, University of Hertfordshire, College Lane, AL10 9AB, Hatfield, United Kingdom

84 University of Turin, Mathematical Department ""G.Peano"", Via Carlo Alberto 10, 10123 Torino, Italy

85 University of Antwerp, Onderzoeksgroep Toegepaste Wiskunde, Middelheimlaan 1, 2020 Antwerp, Belgium

86 INAF - Osservatorio Astronomico d’Abruzzo, Via Mentore Maggini, 64100 Teramo, Italy

87 Instituto de Astronomia, Geofísica e Ciências Atmosféricas, Universidade de São Paulo, Rua do Matão, 1226, Cidade Universitaria, 05508-900 São Paulo, SP, Brazil

88 APAVE SUDEUROPE SAS for CNES Centre Spatial de Toulouse, 18 avenue Edouard Belin, 31401 Toulouse Cedex 9, France

89 Mésocentre de calcul de Franche-Comté, Université de Franche-Comté, 16 route de Gray, 25030 Besançon Cedex, France

90 Theoretical Astrophysics, Division of Astronomy and Space Physics, Department of Physics and Astronomy, Uppsala University, Box 516, 751 20 Uppsala, Sweden

91 School of Physics and Astronomy, Tel Aviv University, Tel Aviv 6997801, Israel

92 Astrophysics Research Centre, School of Mathematics and Physics, Queen’s University Belfast, Belfast BT7 1NN, UK

93 Centre de Données Astronomique de Strasbourg, Strasbourg, France

94 Université Côte d’Azur, Observatoire de la Côte d’Azur, CNRS, Laboratoire Géoazur, Bd de l’Observatoire, CS 34229, 06304 Nice Cedex 4, France

95 Institute for Computational Cosmology, Department of Physics, Durham University, Durham DH1 3LE, UK

96 European Southern Observatory, Karl-Schwarzschild-Str. 2, 85748 Garching, Germany

97 Max-Planck-Institut für Astrophysik, Karl-Schwarzschild-Straße 1, 85748 Garching, Germany

98 Data Science and Big Data Lab, Pablo de Olavide University, 41013, Seville, Spain

99 Barcelona Supercomputing Center (BSC), Plaça Eusebi Güell 1-3, 08034-Barcelona, Spain

100 ETSE Telecomunicación, Universidade de Vigo, Campus Lagoas-Marcosende, 36310 Vigo, Galicia, Spain

101 Asteroid Engineering Laboratory, Space Systems, Luleå University of Technology, Box 848, S-981 28 Kiruna, Sweden

102 Vera C Rubin Observatory, 950 N. Cherry Avenue, Tucson, AZ 85719, USA

103 Department of Astrophysics, Astronomy and Mechanics, National and Kapodistrian University of Athens, Panepistimiopolis, Zografos, 15783 Athens, Greece

104 TRUMPF Photonic Components GmbH, Lise-Meitner-Straße 13, 89081 Ulm, Germany

105 IAC - Instituto de Astrofísica de Canarias, Via Láctea s/n, 38200 La Laguna S.C., Tenerife, Spain

106 Department of Astrophysics, University of La Laguna, Via Láctea s/n, 38200 La Laguna S.C., Tenerife, Spain

107 Faculty of Aerospace Engineering, Delft University of Technology, Kluyverweg 1, 2629 HS Delft, The Netherlands

108 Radagast Solutions

109 Laboratoire Univers et Particules de Montpellier, CNRS Université Montpellier, Place Eugène Bataillon, CC72, 34095 Montpellier Cedex 05, France

110 Université de Caen Normandie, Côte de Nacre Boulevard Maréchal Juin, 14032 Caen, France

111 LESIA, Observatoire de Paris, Université PSL, CNRS, Sorbonne Université, Université de Paris, 5 Place Jules Janssen, 92190 Meudon, France

112 SRON Netherlands Institute for Space Research, Niels Bohrweg 4, 2333 CA Leiden, The Netherlands

113 Astronomical Observatory, University of Warsaw, Al. Ujazdowskie 4, 00-478 Warszawa, Poland

114 Scalian for CNES Centre Spatial de Toulouse, 18 avenue Edouard Belin, 31401 Toulouse Cedex 9, France

115 Université Rennes, CNRS, IPR (Institut de Physique de Rennes) - UMR 6251, 35000 Rennes, France

116 INAF - Osservatorio Astronomico di Capodimonte, Via Moiariello 16, 80131, Napoli, Italy

117 Shanghai Astronomical Observatory, Chinese Academy of Sciences, 80 Nandan Road, Shanghai 200030, People’s Republic of China

118 University of Chinese Academy of Sciences, No.19(A) Yuquan Road, Shijingshan District, Beijing 100049, People’s Republic of China

119 Niels Bohr Institute, University of Copenhagen, Juliane Maries Vej 30, 2100 Copenhagen Ø, Denmark

120 DXC Technology, Retortvej 8, 2500 Valby, Denmark

121 Las Cumbres Observatory, 6740 Cortona Drive Suite 102, Goleta, CA 93117, USA

122 CIGUS CITIC, Department of Nautical Sciences and Marine Engineering, University of A Coruña, Paseo de Ronda 51, 15071, A Coruña, Spain

123 Astrophysics Research Institute, Liverpool John Moores University, 146 Brownlow Hill, Liverpool L3 5RF, United Kingdom

124 IPAC, Mail Code 100-22, California Institute of Technology, 1200 E. California Blvd., Pasadena, CA 91125, USA

125 IRAP, Université de Toulouse, CNRS, UPS, CNES, 9 Av. colonel Roche, BP 44346, 31028 Toulouse Cedex 4, France

126 MTA CSFK Lendület Near-Field Cosmology Research Group, Konkoly Observatory, MTA Research Centre for Astronomy and Earth Sciences, Konkoly Thege Miklós út 15-17, 1121 Budapest, Hungary

127 Departamento de Física de la Tierra y Astrofísica, Universidad Complutense de Madrid, 28040 Madrid, Spain

128 Ruđer Bošković Institute, Bijenička cesta 54, 10000 Zagreb, Croatia

129 Villanova University, Department of Astrophysics and Planetary Science, 800 E Lancaster Avenue, Villanova PA 19085, USA

130 INAF - Osservatorio Astronomico di Brera, via E. Bianchi, 46, 23807 Merate (LC), Italy

131 STFC, Rutherford Appleton Laboratory, Harwell, Didcot, OX11 0QX, United Kingdom

132 Charles University, Faculty of Mathematics and Physics, Astronomical Institute of Charles University, V Holešovičkách 2, 18000 Prague, Czech Republic

133 Department of Particle Physics and Astrophysics, Weizmann Institute of Science, Rehovot 7610001, Israel ¹³⁴ Department of Astrophysical Sciences, 4 Ivy Lane, Princeton University, Princeton NJ 08544, USA

¹³⁵ Departamento de Astrofísica, Centro de Astrobiología (CSIC-INTA), ESA-ESAC. Camino Bajo del Castillo s/n. 28692 Villanueva de la Cañada, Madrid, Spain

¹³⁶ naXys, University of Namur, Rempart de la Vierge, 5000 Namur, Belgium

¹³⁷ CGI Deutschland B.V. & Co. KG, Mornewegstr. 30, 64293 Darmstadt, Germany

¹³⁸ Institute of Global Health, University of Geneva

¹³⁹ Astronomical Observatory Institute, Faculty of Physics, Adam Mickiewicz University, Poznań, Poland

¹⁴⁰ H H Wills Physics Laboratory, University of Bristol, Tyndall Avenue, Bristol BS8 1TL, United Kingdom

¹⁴¹ Department of Physics and Astronomy G. Galilei, University of Padova, Vicolo dell’Osservatorio 3, 35122, Padova, Italy

¹⁴² CERN, Geneva, Switzerland

¹⁴³ Applied Physics Department, Universidade de Vigo, 36310 Vigo, Spain

¹⁴⁴ Association of Universities for Research in Astronomy, 1331 Pennsylvania Ave. NW, Washington, DC 20004, USA

¹⁴⁵ European Southern Observatory, Alonso de Córdova 3107, Casilla 19, Santiago, Chile

¹⁴⁶ Sorbonne Université, CNRS, UMR7095, Institut d’Astrophysique de Paris, 98bis bd. Arago, 75014 Paris, France

¹⁴⁷ Faculty of Mathematics and Physics, University of Ljubljana, Jadranska ulica 19, 1000 Ljubljana, Slovenia

A

The Gaia mission and data processing have financially been supported by, in alphabetical order by country:

• the Algerian Centre de Recherche en Astronomie, Astrophysique et Géophysique of Bouzareah Observatory;

• the Austrian Fonds zur Förderung der wissenschaftlichen Forschung (FWF) Hertha Firnberg Programme through grants T359, P20046, and P23737;

• the BELgian federal Science Policy Office (BEL-SPO) through various PROgramme de Développement d’Expériences scientifiques (PRODEX) grants and the Polish Academy of Sciences - Fonds Wetenschappelijk Onderzoek through grant VS.091.16N, and the Fonds de la Recherche Scientifique (FNRS), and the Research Council of Katholieke Universiteit (KU) Leuven through grant C16/18/005 (Pushing AsteRoseismology to the next level with TESS, GaiA, and the Sloan DIgital Sky SurvEy – PARADISE);

• the Brazil-France exchange programmes Fundação de Amparo à Pesquisa do Estado de São Paulo (FAPESP) and Coordenação de Aperfeiçoamento de Pessoal de Nível Superior (CAPES) - Comité Français d’Evaluation de la Coopération Universitaire et Scientifique avec le Brésil (COFECUB);

• the Chilean Agencia Nacional de Investigación y Desarrollo (ANID) through Fondo Nacional de Desarrollo Científico y Tecnológico (FONDECYT) Regular Project 1210992 (L. Chemin);

• the National Natural Science Foundation of China (NSFC) through grants 11573054, 11703065, and 12173069, the China Scholarship Council through grant 201806040200, and the Natural Science Foundation of Shanghai through grant 21ZR1474100;

• the Tenure Track Pilot Programme of the Croatian Science Foundation and the École Polytechnique Fédérale de Lausanne and the project TTP-2018-07-1171 ‘Mining the Variable Sky’, with the funds of the Croatian-Swiss Research Programme;

• the Czech-Republic Ministry of Education, Youth, and Sports through grant LG 15010 and INTER-EXCELLENCE grant LTAUSA18093, and the Czech Space Office through ESA PECS contract 98058;

• the Danish Ministry of Science;

• the Estonian Ministry of Education and Research through grant IUT40-1;

• the European Commission’s Sixth Framework Programme through the European Leadership in Space Astrometry (ELSA) Marie Curie Research Training Network (MRTN-CT-2006-033481), through Marie Curie project PIOF-GA-2009-255267 (Space AsteroSeismology & RR Lyrae stars, SAS-RRL), and through a Marie Curie Transfer-of-Knowledge (ToK) fellowship (MTKD-CT-2004-014188); the European Commission’s Seventh Framework Programme through grant FP7-606740 (FP7-SPACE-2013-1) for the Gaia European Network for Improved data User Services (GENIUS) and through grant 264895 for the Gaia Research for European Astronomy Training (GREAT-ITN) network;

• the European Cooperation in Science and Technology (COST) through COST Action CA18104 ‘Revealing the Milky Way with Gaia (MW-Gaia)’;

• the European Research Council (ERC) through grants 320360, 647208, and 834148 and through the European Union’s Horizon 2020 research and innovation excellent science programmes through Marie Skłodowska-Curie grant 745617 (Our Galaxy at full HD – Gal-HD) and 895174 (The build-up and fate of self-gravitating systems in the Universe) as well as grants 687378 (Small Bodies: Near and Far), 682115 (Using the Magellanic Clouds to Understand the Interaction of Galaxies), 695099 (A sub-percent distance scale from binaries and Cepheids – CepBin), 716155 (Structured ACCREtion Disks – SACCRD), 951549 (Sub-percent calibration of the extragalactic distance scale in the era of big surveys – UniverScale), and 101004214 (Innovative Scientific Data Exploration and Exploitation Applications for Space Sciences – EXPLORE);

• the European Science Foundation (ESF), in the framework of the Gaia Research for European Astronomy Training Research Network Programme (GREAT-ESF);

• the European Space Agency (ESA) in the framework of the Gaia project, through the Plan for European Cooperating States (PECS) programme through contracts C98090 and 4000106398/12/NL/KML for Hungary, through contract 4000115263/15/NL/IB for Germany, and through PROgramme de Développement d’Expériences scientifiques (PRODEX) grant 4000127986 for Slovenia;

• the Academy of Finland through grants 299543, 307157, 325805, 328654, 336546, and 345115 and the Magnus Ehrnrooth Foundation;

• the French Centre National d’Études Spatiales (CNES), the Agence Nationale de la Recherche (ANR) through grant ANR-10-IDEX-0001-02 for the ‘Investissements d’avenir’ programme, through grant ANR-15-CE31-0007 for project ‘Modelling the Milky Way in the Gaia era’ (MOD4Gaia), through grant ANR-14-CE33-0014-01 for project ‘The Milky Way disc formation in the Gaia era’ (ARCHEOGAL), through grant ANR-15-CE31-0012-01 for project ‘Unlocking the potential of Cepheids as primary distance calibrators’ (UnlockCepheids), through grant ANR-19-CE31-0017 for project ‘Secular evolution of galaxies’ (SEGAL), and through grant ANR-18-CE31-0006 for project ‘Galactic Dark Matter’ (GaDaMa), the Centre National de la Recherche Scientifique (CNRS) and its SNO Gaia of the Institut des Sciences de l’Univers (INSU), its Programmes Nationaux: Cosmologie et Galaxies (PNCG), Gravitation Références Astronomie Métrologie (PNGRAM), Planétologie (PNP), Physique et Chimie du Milieu Interstellaire (PCMI), and Physique Stellaire (PNPS), the ‘Action Fédératrice Gaia’ of the Observatoire de Paris, the Région de Franche-Comté, the Institut Polytechnique (INP) and the Institut National de Physique nucléaire et de Physique des Particules (IN2P3) co-funded by CNES;

• the German Aerospace Agency (Deutsches Zentrum für Luft- und Raumfahrt e.V., DLR) through grants 50QG0501, 50QG0601, 50QG0602, 50QG0701, 50QG0901, 50QG1001, 50QG1101, 50QG1401, 50QG1402, 50QG1403, 50QG1404, 50QG1904, 50QG2101, 50QG2102, and 50QG2202, and the Centre for Information Services and High Performance Computing (ZIH) at the Technische Universität Dresden for generous allocations of computer time;

• the Hungarian Academy of Sciences through the Lendület Programme grants LP2014-17 and LP2018-7 and the Hungarian National Research, Development, and Innovation Office (NKFIH) through grant KKP-135273 (‘SeismoLab’);

• the Science Foundation Ireland (SFI) through a Royal Society - SFI University Research Fellowship (M. Fraser);

• the Israel Ministry of Science and Technology through grant 3-18143 and the Tel Aviv University Center for Artificial Intelligence and Data Science (TAD) through a grant;

• the Agenzia Spaziale Italiana (ASI) through contracts I/037/08/0, I/058/10/0, 2014-025-R.0, 2014-025-R.1.2015, and 2018-24-HH.0 to the Italian Istituto Nazionale di Astrofisica (INAF), contract 2014-049-R.0/1/2 to INAF for the Space Science Data Centre (SSDC, formerly known as the ASI Science Data Center, ASDC), contracts I/008/10/0, 2013/030/I.0, 2013-030-I.0.1-2015, and 2016-17-I.0 to the Aerospace Logistics Technology Engineering Company (ALTEC S.p.A.), INAF, and the Italian Ministry of Education, University, and Research (Ministero dell’Istruzione, dell’Università e della Ricerca) through the Premiale project ‘MIning The Cosmos Big Data and Innovative Italian Technology for Frontier Astrophysics and Cosmology’ (MITiC);

• the Netherlands Organisation for Scientific Research (NWO) through grant NWO-M-614.061.414, through a VICI grant (A. Helmi), and through a Spinoza prize (A. Helmi), and the Netherlands Research School for Astronomy (NOVA);

• the Polish National Science Centre through HARMONIA grant 2018/30/M/ST9/00311 and DAINA grant 2017/27/L/ST9/03221 and the Ministry of Science and Higher Education (MNiSW) through grant DIR/WK/2018/12;

• the Portuguese Fundação para a Ciência e a Tecnologia (FCT) through national funds, grants SFRH/BD/128840/2017 and PTDC/FIS-AST/30389/2017, and work contract DL 57/2016/CP1364/CT0006, the Fundo Europeu de Desenvolvimento Regional (FEDER) through grant POCI-01-0145-FEDER-030389 and its Programa Operacional Competitividade e Internacionalização (COMPETE2020) through grants UIDB/04434/2020 and UIDP/04434/2020, and the Strategic Programme UIDB/00099/2020 for the Centro de Astrofísica e Gravitação (CENTRA);

• the Slovenian Research Agency through grant P1-0188;

• the Spanish Ministry of Economy (MINECO/FEDER, UE), the Spanish Ministry of Science and Innovation (MICIN), the Spanish Ministry of Education, Culture, and Sports, and the Spanish Government through grants BES-2016-078499, BES-2017-083126, BES-C-2017-0085, ESP2016-80079-C2-1-R, ESP2016-80079-C2-2-R, FPU16/03827, PDC2021-121059-C22, RTI2018-095076-B-C22, and TIN2015-65316-P (‘Computación de Altas Prestaciones VII’), the Juan de la Cierva Incorporación Programme (FJCI-2015-2671 and IJC2019-04862-I for F. Anders), the Severo Ochoa Centre of Excellence Programme (SEV2015-0493), and MICIN/AEI/10.13039/501100011033 (and the European Union through European Regional Development Fund ‘A way of making Europe’) through grant RTI2018-095076-B-C21, the Institute of Cosmos Sciences University of Barcelona (ICCUB, Unidad de Excelencia ‘María de Maeztu’) through grant CEX2019-000918-M, the University of Barcelona’s official doctoral programme for the development of an R+D+i project through an Ajuts de Personal Investigador en Formació (APIF) grant, the Spanish Virtual Observatory through project AyA2017-84089, the Galician Regional Government, Xunta de Galicia, through grants ED431B-2021/36, ED481A-2019/155, and ED481A-2021/296, the Centro de Investigación en Tecnoloxías de la Información y las Comunicacións (CITIC), funded by the Xunta de Galicia and the European Union (European Regional Development Fund – Galicia 2014–2020 Programme), through grant ED431G-2019/01, the Red Española de Supercomputación (RES) computer resources at MareNostrum, the Barcelona Supercomputing Centre - Centro Nacional de Supercomputación (BSC-CNS) through activities AECT-2017-2-0002, AECT-2017-3-0006, AECT-2018-1-0017, AECT-2018-2-0013, AECT-2018-3-0011, AECT-2019-1-0010, AECT-2019-2-0014, AECT-2019-3-0003, AECT-2020-1-0004, and DATA2-2020-1-0010, the Departament d’Innovació, Universitats i Empresa de la Generalitat de Catalunya through grant 2014-SGR-1051 for project ‘Models de Programació i Entorns d’Execució Parallels’ (MPEXPAR), and Ramon y Cajal Fellowship RYC2018-025968-I funded by MICIN/AEI/10.13039/501100011033 and the European Science Foundation (‘Investing in your future’);

• the Swedish National Space Agency (SNSA/Rymdstyrelsen);

• the Swiss State Secretariat for Education, Research, and Innovation through the Swiss Activities Nationales Complémentaires and the Swiss National Science Foundation through an Eccellenza Professorial Fellowship (award PCEFP2_194638 for R. Anderson);

• the United Kingdom Particle Physics and Astronomy Research Council (PPARC), the United Kingdom Science and Technology Facilities Council (STFC), and the United Kingdom Space Agency (UKSA) through the following grants to the University of Bristol, the University of Cambridge, the University of Edinburgh, the University of Leicester, the Mullard Space Sciences Laboratory of University College London, and the United Kingdom Rutherford Appleton Laboratory (RAL): PP/D006511/1, PP/D006546/1, PP/D006570/1, ST/I000852/1, ST/J005045/1, ST/K00056X/1, ST/K000209/1, ST/K000756/1, ST/L000561/1, ST/N000595/1, ST/N000641/1, ST/N000978/1, ST/N001117/1, ST/S000089/1, ST/S0000976/1, ST/S000984/1, ST/S001123/1, ST/S001948/1, ST/S001980/1, ST/S002103/1, ST/V000969/1, ST/W002469/1, ST/W002493/1, ST/W002671/1, ST/W002809/1, and EP/V520342/1.

The Gaia project and data processing have made use of:

• the Set of Identifications, Measurements, and Bibliography for Astronomical Data (SIMBAD, [114]), the ‘Aladin’ sky atlas’ ([8, 7]), and the VizieR catalogue access tool ([78]), all operated at the Centre de Données astronomiques de Strasbourg (CDS);

• the National Aeronautics and Space Administration (NASA) Astrophysics Data System (ADS);

• the Space ENvironment Information System (SPENVIS), initiated by the Space Environment and Effects Section (TEC-EES) of ESA and developed by the Belgian Institute for Space Aeronomy (BIRA-IASB) under ESA contract through ESA’s General Support Technologies Programme (GSTP), administered by the BELgian federal Science Policy Office (BELSPO);

• the software products TOPCAT, STIL, and STILTS (Taylor 2005, 2006);

• Matplotlib ([56]);

• IPython ([83]);

• Astropy, a community-developed core Python package for Astronomy ([4]);

• R ([84]);

• Vaex ([9];

• the Hipparcos-2 catalogue ([110]). The Hipparcos and Tycho catalogues were constructed under the responsibility of large scientific teams collaborating with ESA. The Consortia Leaders were Lennart Lindegren (Lund, Sweden: NDAC) and Jean Kovalevsky (Grasse, France: FAST), together responsible for the Hipparcos Catalogue; Erik Høg (Copenhagen, Denmark: TDAC) responsible for the Tycho Catalogue; and Catherine Turon (Meudon, France: INCA) responsible for the Hipparcos Input Catalogue (HIC);

• the Tycho-2 catalogue ((Høg et al. 2000)), the construction of which was supported by the Velux Foundation of 1981 and the Danish Space Board;

• The Tycho double star catalogue (TDSC, [27]), based on observations made with the ESA Hipparcos astrometry satellite, as supported by the Danish Space Board and the United States Naval Observatory through their double-star programme;

• data products from the Two Micron All Sky Survey (2MASS, [101]), which is a joint project of the University of Massachusetts and the Infrared Processing and Analysis Center (IPAC) / California Institute of Technology, funded by the National Aeronautics and Space Administration (NASA) and the National Science Foundation (NSF) of the USA;

• the ninth data release of the AAVSO Photometric All-Sky Survey (APASS, [50]), funded by the Robert Martin Ayers Sciences Fund;

• the first data release of the Pan-STARRS survey ((Chambers et al. 2016; Magnier et al. 2020a; Waters et al. 2020; Magnier et al. 2020c,b; Flewelling et al. 2020)). The Pan-STARRS1 Surveys (PS1) and the PS1 public science archive have been made possible through contributions by the Institute for Astronomy, the University of Hawaii, the Pan-STARRS Project Office, the Max-Planck Society and its participating institutes, the Max Planck Institute for Astronomy, Heidelberg and the Max Planck Institute for Extraterrestrial Physics, Garching, The Johns Hopkins University, Durham University, the University of Edinburgh, the Queen’s University Belfast, the Harvard-Smithsonian Center for Astrophysics, the Las Cumbres Observatory Global Telescope Network Incorporated, the National Central University of Taiwan, the Space Telescope Science Institute, the National Aeronautics and Space Administration (NASA) through grant NNX08AR22G issued through the Planetary Science Division of the NASA Science Mission Directorate, the National Science Foundation through grant AST-1238877, the University of Maryland, Eotvos Lorand University (ELTE), the Los Alamos National Laboratory, and the Gordon and Betty Moore Foundation;

• the second release of the Guide Star Catalogue (GSC2.3, [62]). The Guide Star Catalogue II is a joint project of the Space Telescope Science Institute (STScI) and the Osservatorio Astrofisico di Torino (OATo). STScI is operated by the Association of Universities for Research in Astronomy (AURA), for the National Aeronautics and Space Administration (NASA) under contract NAS5-26555. OATo is operated by the Italian National Institute for Astrophysics (INAF). Additional support was provided by the European Southern Observatory (ESO), the Space Telescope European Coordinating Facility (STECF), the International GEMINI project, and the European Space Agency (ESA) Astrophysics Division (nowadays SCI-S);

• the eXtended, Large (XL) version of the catalogue of Positions and Proper Motions (PPM-XL, [94]);

• data products from the Wide-field Infrared Survey Explorer (WISE), which is a joint project of the University of California, Los Angeles, and the Jet Propulsion Laboratory/California Institute of Technology, and NEOWISE, which is a project of the Jet Propulsion Laboratory/California Institute of Technology. WISE and NEOWISE are funded by the National Aeronautics and Space Administration (NASA);

• the first data release of the United States Naval Observatory (USNO) Robotic Astrometric Telescope (URAT-1, [117]);

• the fourth data release of the United States Naval Observatory (USNO) CCD Astrograph Catalogue (UCAC-4, [118]);

• the sixth and final data release of the Radial Velocity Experiment (RAVE DR6, [103 b]). Funding for RAVE has been provided by the Leibniz Institute for Astrophysics Potsdam (AIP), the Australian Astronomical Observatory, the Australian National University, the Australian Research Council, the French National Research Agency, the German Research Foundation (SPP 1177 and SFB 881), the European Research Council (ERC-StG 240271 Galactica), the Istituto Nazionale di Astrofisica at Padova, the Johns Hopkins University, the National Science Foundation of the USA (AST-09098326), the W.M. Keck foundation, the Macquarie University, the Netherlands Research School for Astronomy, the Natural Sciences and Engineering Research Council of Canada, the Slovenian Research Agency, the Swiss National Science Foundation, the Science & Technology Facilities Council of the UK, Opticon, Strasbourg Observatory, and the Universities of Basel, Groningen, Heidelberg, and Sydney. The RAVE website is at https://www.rave-survey.org/;

• the first data release of the Large sky Area Multi-Object Fibre Spectroscopic Telescope (LAMOST DR1, [67]);

• the K2 Ecliptic Plane Input Catalogue (EPIC, [55]);

• the ninth data release of the Sloan Digital Sky Survey (SDSS DR9, [1]). Funding for SDSS-III has been provided by the Alfred P. Sloan Foundation, the Participating Institutions, the National Science Foundation, and the United States Department of Energy Office of Science. The SDSS-III website is http://www.sdss3.org/. SDSS-III is managed by the Astrophysical Research Consortium for the Participating Institutions of the SDSS-III Collaboration including the University of Arizona, the Brazilian Participation Group, Brookhaven National Laboratory, Carnegie Mellon University, University of Florida, the French Participation Group, the German Participation Group, Harvard University, the Instituto de Astrofisica de Canarias, the Michigan State/Notre Dame/JINA Participation Group, Johns Hopkins University, Lawrence Berkeley National Laboratory, Max Planck Institute for Astrophysics, Max Planck Institute for Extraterrestrial Physics, New Mexico State University, New York University, Ohio State University, Pennsylvania State University, University of Portsmouth, Princeton University, the Spanish Participation Group, University of Tokyo, University of Utah, Vanderbilt University, University of Virginia, University of Washington, and Yale University;

• the thirteenth release of the Sloan Digital Sky Survey (SDSS DR13, [2]). Funding for SDSS-IV has been provided by the Alfred P. Sloan Foundation, the United States Department of Energy Office of Science, and the Participating Institutions. SDSS-IV acknowledges support and

resources from the Center for High-Performance Computing at the University of Utah. The SDSS web site is https://www.sdss.org/. SDSS-IV is managed by the Astrophysical Research Consortium for the Participating Institutions of the SDSS Collaboration including the Brazilian Participation Group, the Carnegie Institution for Science, Carnegie Mellon University, the Chilean Participation Group, the French Participation Group, Harvard-Smithsonian Center for Astrophysics, Instituto de Astrofísica de Canarias, The Johns Hopkins University, Kavli Institute for the Physics and Mathematics of the Universe (IPMU) / University of Tokyo, the Korean Participation Group, Lawrence Berkeley National Laboratory, Leibniz Institut für Astrophysik Potsdam (AIP), Max-Planck-Institut für Astronomie (MPIA Heidelberg), Max-Planck-Institut für Astrophysik (MPA Garching), Max-Planck-Institut für Extraterrestrische Physik (MPE), National Astronomical Observatories of China, New Mexico State University, New York University, University of Notre Dame, Observatório Nacional / MCTI, The Ohio State University, Pennsylvania State University, Shanghai Astronomical Observatory, United Kingdom Participation Group, Universidad Nacional Autónoma de México, University of Arizona, University of Colorado Boulder, University of Oxford, University of Portsmouth, University of Utah, University of Virginia, University of Washington, University of Wisconsin, Vanderbilt University, and Yale University;

– the second release of the SkyMapper catalogue (SkyMapper DR2, Onken et al. [79], Digital Object Identifier 10.25914/5ce60d31ce759). The national facility capability for SkyMapper has been funded through grant LE130100104 from the Australian Research Council (ARC) Linkage Infrastructure, Equipment, and Facilities (LIEF) programme, awarded to the University of Sydney, the Australian National University, Swinburne University of Technology, the University of Queensland, the University of Western Australia, the University of Melbourne, Curtin University of Technology, Monash University, and the Australian Astronomical Observatory. SkyMapper is owned and operated by The Australian National University’s Research School of Astronomy and Astrophysics. The survey data were processed and provided by the SkyMapper Team at the the Australian National University. The SkyMapper node of the All-Sky Virtual Observatory (ASVO) is hosted at the National Computational Infrastructure (NCI). Development and support the SkyMapper node of the ASVO has been funded in part by Astronomy Australia Limited (AAL) and the Australian Government through the Commonwealth’s Education Investment Fund (EIF) and National Collaborative Research Infrastructure Strategy (NCRIS), particularly the National eResearch Collaboration Tools and Resources (NeCTAR) and the Australian National Data Service Projects (ANDS);

– the Gaia-ESO Public Spectroscopic Survey (GES, [45, 86]). The Gaia-ESO Survey is based on data products from observations made with ESO Telescopes at the La Silla Paranal Observatory under programme ID 188.B-3002. Public data releases are available through the ESO Science Portal. The project has received funding from the Leverhulme Trust (project RPG-2012-541), the European Research Council (project ERC-2012-AdG 320360-Gaia-ESO-MW), and the Instituto Nazionale di Astrofisica, INAF (2012: CRA 1.05.01.09.16; 2013: CRA 1.05.06.02.07).

The GBOT programme uses observations collected at (i) the European Organisation for Astronomical Research in the Southern Hemisphere (ESO) with the VLT Survey Telescope (VST), under ESO programmes 092.B-0165, 093.B-0236, 094.B-0181, 095.B-0046, 096.B-0162, 097.B-0304, 098.B-0030, 099.B-0034, 0100.B-0131, 0101.B-0156, 0102.B-0174, and 0103.B-0165; and (ii) the Liverpool Telescope, which is operated on the island of La Palma by Liverpool John Moores University in the Spanish Observatorio del Roque de los Muchachos of the Instituto de Astrofísica de Canarias with financial support from the United Kingdom Science and Technology Facilities Council, and (iii) telescopes of the Las Cumbres Observatory Global Telescope Network.