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 (‘Com putación de Altas Prestaciones VII’), the Juan de la Cierva Incor poración 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 Devel opment 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 Tecnologías de la Información y las Comunicaciones (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 DATA-2020-1-0010, the Departa ment 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’).

[Results] We produced six homogeneous samples of stars with high-quality astrophysical parameters across the HR diagram for the community to exploit. We first focus on three samples that span a large parameter space: young massive disc stars (OBA; about 3 Million), FGKM spectral type stars (about 3 Million), and UCDs (about 20 000). We provide these sources along with additional information (either a flag or complementary parameters) as tables that are made available in the Gaia archive. We also identify 15 740 bone fide carbon stars and 5863 solar analogues, and provide the first homogeneous set of stellar parameters of the SPSS sample. We demonstrate some applications of these samples in different astrophysical contexts. We use a subset of the OBA sample to illustrate its usefulness in analysing the Milky Way rotation curve. We then use the properties of the FGKM stars to analyse known exoplanet systems. We also analyse the ages of some unseen UCD-companions to the FGKM stars. We additionally predict the colours of the Sun in various passbands (Gaia, 2MASS, WISE) using the solar-analogue sample.

This work presents results from the European Space Agency (ESA) space mission Gaia. Gaia data are 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.

[Aims] We produce homogeneous samples of stars with high-quality astrophysical parameters by exploiting Gaia DR3, while focusing on many regimes across the Hertzsprung-Russell (HR) diagram; spectral types OBA, FGKM, and ultracool dwarfs (UCDs). We also focus on specific subsamples of particular interest to the community: solar analogues, carbon stars, and the Gaia spectrophotometric standard stars (SPSS).

[Context] Gaia Data Release 3 (DR3) provides a wealth of new data products for the astronomical community to exploit, including astrophysical parameters for half a billion stars. In this work, we demonstrate the high quality of these data products and illustrate their use in different astrophysical contexts.

[Methods] We query the astrophysical parameter tables along with other tables in Gaia DR3 to derive the samples of the stars of interest. We validate our results using the Gaia catalogue itself and by comparison with external data.

With funding from the Spanish government through the "Severo Ochoa Centre of Excellence" accreditation (CEX2019-000918-M).

[Conclusions] Gaia DR3 contains a wealth of new high-quality astrophysical parameters for the community to exploit.

Gaia Collaboration, et al.

Peer reviewed