11institutetext:  Université Côte d’Azur, Observatoire de la Côte d’Azur,
CNRS, Laboratoire Lagrange, Bd de l’Observatoire, CS 34229, 06304 Nice Cedex
4, France 22institutetext: INAF - Osservatorio astronomico di Padova, Vicolo
Osservatorio 5, 35122 Padova, Italy33institutetext: CNES Centre Spatial de
Toulouse, 18 avenue Edouard Belin, 31401 Toulouse Cedex 9,
France44institutetext: Royal Observatory of Belgium, Ringlaan 3, 1180
Brussels, Belgium55institutetext: Observational Astrophysics, Division of
Astronomy and Space Physics, Department of Physics and Astronomy, Uppsala
University, Box 516, 751 20 Uppsala, Sweden66institutetext: Max Planck
Institute for Astronomy, K ̈onigstuhl 17, 69117 Heidelberg,
Germany77institutetext: CIGUS CITIC - Department of Computer Science and
Information Technologies, University of A Coruña, Campus de Elviña s/n, A
Coruña, 15071, Spain88institutetext: National Observatory of Athens, I. Metaxa
and Vas. Pavlou, Palaia Penteli, 15236 Athens, Greece99institutetext: INAF -
Osservatorio Astrofisico di Catania, via S. Sofia 78, 95123 Catania,
Italy1010institutetext: Aurora Technology for European Space Agency (ESA),
Camino bajo del Castillo, s/n, Urbanizacion Villafranca del Castillo,
Villanueva de la Cañada, 28692 Madrid, Spain1111institutetext: Dpto. de
Inteligencia Artificial, UNED, c/ Juan del Rosal 16, 28040 Madrid,
Spain1212institutetext: Institut d’Astrophysique et de Géophysique, Université
de Liège, 19c, Allée du 6 Août, B-4000 Liège, Belgium1313institutetext: INAF -
Osservatorio Astrofisico di Torino, via Osservatorio 20, 10025 Pino Torinese
(TO), Italy1414institutetext: Institute of Astronomy, University of Cambridge,
Madingley Road, Cambridge CB3 0HA, United Kingdom1515institutetext: INAF -
Osservatorio di Astrofisica e Scienza dello Spazio di Bologna, via Piero
Gobetti 93/3, 40129 Bologna, Italy1616institutetext: ATG Europe for European
Space Agency (ESA), Camino bajo del Castillo, s/n, Urbanizacion Villafranca
del Castillo, Villanueva de la Cañada, 28692 Madrid, Spain1717institutetext:
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, Spain1818institutetext: Laboratoire d’astrophysique
de Bordeaux, Univ. Bordeaux, CNRS, B18N, allée Geoffroy Saint-Hilaire, 33615
Pessac, France1919institutetext: Telespazio for CNES Centre Spatial de
Toulouse, 18 avenue Edouard Belin, 31401 Toulouse Cedex 9,
France2020institutetext: 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, Spain2121institutetext: GEPI,
Observatoire de Paris, Université PSL, CNRS, 5 Place Jules Janssen, 92190
Meudon, France2222institutetext: Centre for Astrophysics Research, University
of Hertfordshire, College Lane, AL10 9AB, Hatfield, United
Kingdom2323institutetext: APAVE SUDEUROPE SAS for CNES Centre Spatial de
Toulouse, 18 avenue Edouard Belin, 31401 Toulouse Cedex 9,
France2424institutetext: Theoretical Astrophysics, Division of Astronomy and
Space Physics, Department of Physics and Astronomy, Uppsala University, Box
516, 751 20 Uppsala, Sweden2525institutetext: 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,
Spain2626institutetext: Data Science and Big Data Lab, Pablo de Olavide
University, 41013, Seville, Spain2727institutetext: Department of
Astrophysics, Astronomy and Mechanics, National and Kapodistrian University of
Athens, Panepistimiopolis, Zografos, 15783 Athens, Greece2828institutetext:
Dipartimento di Fisica e Astronomia ””Ettore Majorana””, Università di
Catania, Via S. Sofia 64, 95123 Catania, Italy2929institutetext: LESIA,
Observatoire de Paris, Université PSL, CNRS, Sorbonne Université, Université
de Paris, 5 Place Jules Janssen, 92190 Meudon, France3030institutetext:
Université Rennes, CNRS, IPR (Institut de Physique de Rennes) - UMR 6251,
35000 Rennes, France3131institutetext: Niels Bohr Institute, University of
Copenhagen, Juliane Maries Vej 30, 2100 Copenhagen Ø,
Denmark3232institutetext: DXC Technology, Retortvej 8, 2500 Valby,
Denmark3333institutetext: CIGUS CITIC, Department of Nautical Sciences and
Marine Engineering, University of A Coruña, Paseo de Ronda 51, 15071, A
Coruña, Spain3434institutetext: IPAC, Mail Code 100-22, California Institute
of Technology, 1200 E. California Blvd., Pasadena, CA 91125,
USA3535institutetext: IRAP, Université de Toulouse, CNRS, UPS, CNES, 9 Av.
colonel Roche, BP 44346, 31028 Toulouse Cedex 4, France3636institutetext:
Thales Services for CNES Centre Spatial de Toulouse, 18 avenue Edouard Belin,
31401 Toulouse Cedex 9, France3737institutetext: Institute of Global Health,
University of Geneva3838institutetext: Applied Physics Department,
Universidade de Vigo, 36310 Vigo, Spain3939institutetext: Sorbonne Université,
CNRS, UMR7095, Institut d’Astrophysique de Paris, 98bis bd. Arago, 75014
Paris, France4040institutetext: Departamento de Astrofísica, Centro de
Astrobiología (CSIC-INTA), ESA-ESAC. Camino Bajo del Castillo s/n. 28692
Villanueva de la Cañada, Madrid, Spain4141institutetext: Aix Marseille Univ,
CNRS, CNES, LAM, Marseille, France4242institutetext: Hamburger Sternwarte,
Gojenbergsweg 112, 21029 Hamburg, Germany4343institutetext: INAF-Osservatorio
Astronomico d’Abruzzo, via M. Maggini, sn. 64100, Teramo,
Italy4444institutetext: INFN – Sezione di Pisa, Largo Pontecorvo 3, 56127
Pisa, Italy4545institutetext: European Organisation for Astronomical Research
in the Southern Hemisphere, Alonso de Córdova 3107, Vitacura, 19001 Casilla,
Santiago de Chile, Chile4646institutetext: Institut für Astrophysik, Georg-
August-Universität, D-37077 Göttingen, Germany4747institutetext: Instituto de
Astrofísica de Andalucía (Consejo Superior de Investigaciones Científicas),
E-18008 Granada, Spain

# Gaia Data Release 3: Astrophysical parameters inference system (Apsis) I -
methods and content overview

O.L. Creevey Gaia Data Release 3: Astrophysical parameters inference system
(Apsis) I - methods and content overviewGaia Data Release 3: Astrophysical
parameters inference system (Apsis) I - methods and content overview R. Sordo
Gaia Data Release 3: Astrophysical parameters inference system (Apsis) I -
methods and content overviewGaia Data Release 3: Astrophysical parameters
inference system (Apsis) I - methods and content overview F. Pailler Gaia Data
Release 3: Astrophysical parameters inference system (Apsis) I - methods and
content overviewGaia Data Release 3: Astrophysical parameters inference system
(Apsis) I - methods and content overview Y. Frémat Gaia Data Release 3:
Astrophysical parameters inference system (Apsis) I - methods and content
overviewGaia Data Release 3: Astrophysical parameters inference system (Apsis)
I - methods and content overview U. Heiter Gaia Data Release 3: Astrophysical
parameters inference system (Apsis) I - methods and content overviewGaia Data
Release 3: Astrophysical parameters inference system (Apsis) I - methods and
content overview F. Thévenin Gaia Data Release 3: Astrophysical parameters
inference system (Apsis) I - methods and content overviewGaia Data Release 3:
Astrophysical parameters inference system (Apsis) I - methods and content
overview R. Andrae Gaia Data Release 3: Astrophysical parameters inference
system (Apsis) I - methods and content overviewGaia Data Release 3:
Astrophysical parameters inference system (Apsis) I - methods and content
overview M. Fouesneau Gaia Data Release 3: Astrophysical parameters inference
system (Apsis) I - methods and content overviewGaia Data Release 3:
Astrophysical parameters inference system (Apsis) I - methods and content
overview A. Lobel Gaia Data Release 3: Astrophysical parameters inference
system (Apsis) I - methods and content overviewGaia Data Release 3:
Astrophysical parameters inference system (Apsis) I - methods and content
overview C.A.L. Bailer-Jones Gaia Data Release 3: Astrophysical parameters
inference system (Apsis) I - methods and content overviewGaia Data Release 3:
Astrophysical parameters inference system (Apsis) I - methods and content
overview D. Garabato Gaia Data Release 3: Astrophysical parameters inference
system (Apsis) I - methods and content overviewGaia Data Release 3:
Astrophysical parameters inference system (Apsis) I - methods and content
overview I. Bellas-Velidis Gaia Data Release 3: Astrophysical parameters
inference system (Apsis) I - methods and content overviewGaia Data Release 3:
Astrophysical parameters inference system (Apsis) I - methods and content
overview E. Brugaletta Gaia Data Release 3: Astrophysical parameters inference
system (Apsis) I - methods and content overviewGaia Data Release 3:
Astrophysical parameters inference system (Apsis) I - methods and content
overview A. Lorca Gaia Data Release 3: Astrophysical parameters inference
system (Apsis) I - methods and content overviewGaia Data Release 3:
Astrophysical parameters inference system (Apsis) I - methods and content
overview C. Ordenovic Gaia Data Release 3: Astrophysical parameters inference
system (Apsis) I - methods and content overviewGaia Data Release 3:
Astrophysical parameters inference system (Apsis) I - methods and content
overview P.A. Palicio Gaia Data Release 3: Astrophysical parameters inference
system (Apsis) I - methods and content overviewGaia Data Release 3:
Astrophysical parameters inference system (Apsis) I - methods and content
overview L.M. Sarro Gaia Data Release 3: Astrophysical parameters inference
system (Apsis) I - methods and content overviewGaia Data Release 3:
Astrophysical parameters inference system (Apsis) I - methods and content
overview L. Delchambre Gaia Data Release 3: Astrophysical parameters inference
system (Apsis) I - methods and content overviewGaia Data Release 3:
Astrophysical parameters inference system (Apsis) I - methods and content
overview R. Drimmel Gaia Data Release 3: Astrophysical parameters inference
system (Apsis) I - methods and content overviewGaia Data Release 3:
Astrophysical parameters inference system (Apsis) I - methods and content
overview J. Rybizki Gaia Data Release 3: Astrophysical parameters inference
system (Apsis) I - methods and content overviewGaia Data Release 3:
Astrophysical parameters inference system (Apsis) I - methods and content
overview G. Torralba Elipe Gaia Data Release 3: Astrophysical parameters
inference system (Apsis) I - methods and content overviewGaia Data Release 3:
Astrophysical parameters inference system (Apsis) I - methods and content
overview A.J. Korn Gaia Data Release 3: Astrophysical parameters inference
system (Apsis) I - methods and content overviewGaia Data Release 3:
Astrophysical parameters inference system (Apsis) I - methods and content
overview A. Recio-Blanco Gaia Data Release 3: Astrophysical parameters
inference system (Apsis) I - methods and content overviewGaia Data Release 3:
Astrophysical parameters inference system (Apsis) I - methods and content
overview M.S. Schultheis Gaia Data Release 3: Astrophysical parameters
inference system (Apsis) I - methods and content overviewGaia Data Release 3:
Astrophysical parameters inference system (Apsis) I - methods and content
overview F. De Angeli Gaia Data Release 3: Astrophysical parameters inference
system (Apsis) I - methods and content overviewGaia Data Release 3:
Astrophysical parameters inference system (Apsis) I - methods and content
overview P. Montegriffo Gaia Data Release 3: Astrophysical parameters
inference system (Apsis) I - methods and content overviewGaia Data Release 3:
Astrophysical parameters inference system (Apsis) I - methods and content
overview A. Abreu Aramburu Gaia Data Release 3: Astrophysical parameters
inference system (Apsis) I - methods and content overviewGaia Data Release 3:
Astrophysical parameters inference system (Apsis) I - methods and content
overview S. Accart Gaia Data Release 3: Astrophysical parameters inference
system (Apsis) I - methods and content overviewGaia Data Release 3:
Astrophysical parameters inference system (Apsis) I - methods and content
overview M.A. Álvarez Gaia Data Release 3: Astrophysical parameters inference
system (Apsis) I - methods and content overviewGaia Data Release 3:
Astrophysical parameters inference system (Apsis) I - methods and content
overview J. Bakker Gaia Data Release 3: Astrophysical parameters inference
system (Apsis) I - methods and content overviewGaia Data Release 3:
Astrophysical parameters inference system (Apsis) I - methods and content
overview N. Brouillet Gaia Data Release 3: Astrophysical parameters inference
system (Apsis) I - methods and content overviewGaia Data Release 3:
Astrophysical parameters inference system (Apsis) I - methods and content
overview A. Burlacu Gaia Data Release 3: Astrophysical parameters inference
system (Apsis) I - methods and content overviewGaia Data Release 3:
Astrophysical parameters inference system (Apsis) I - methods and content
overview R. Carballo Gaia Data Release 3: Astrophysical parameters inference
system (Apsis) I - methods and content overviewGaia Data Release 3:
Astrophysical parameters inference system (Apsis) I - methods and content
overview L. Casamiquela Gaia Data Release 3: Astrophysical parameters
inference system (Apsis) I - methods and content overviewGaia Data Release 3:
Astrophysical parameters inference system (Apsis) I - methods and content
overviewGaia Data Release 3: Astrophysical parameters inference system (Apsis)
I - methods and content overviewGaia Data Release 3: Astrophysical parameters
inference system (Apsis) I - methods and content overview A. Chiavassa Gaia
Data Release 3: Astrophysical parameters inference system (Apsis) I - methods
and content overviewGaia Data Release 3: Astrophysical parameters inference
system (Apsis) I - methods and content overview G. Contursi Gaia Data Release
3: Astrophysical parameters inference system (Apsis) I - methods and content
overviewGaia Data Release 3: Astrophysical parameters inference system (Apsis)
I - methods and content overview W.J. Cooper Gaia Data Release 3:
Astrophysical parameters inference system (Apsis) I - methods and content
overviewGaia Data Release 3: Astrophysical parameters inference system (Apsis)
I - methods and content overviewGaia Data Release 3: Astrophysical parameters
inference system (Apsis) I - methods and content overviewGaia Data Release 3:
Astrophysical parameters inference system (Apsis) I - methods and content
overview C. Dafonte Gaia Data Release 3: Astrophysical parameters inference
system (Apsis) I - methods and content overviewGaia Data Release 3:
Astrophysical parameters inference system (Apsis) I - methods and content
overview A. Dapergolas Gaia Data Release 3: Astrophysical parameters inference
system (Apsis) I - methods and content overviewGaia Data Release 3:
Astrophysical parameters inference system (Apsis) I - methods and content
overview P. de Laverny Gaia Data Release 3: Astrophysical parameters inference
system (Apsis) I - methods and content overviewGaia Data Release 3:
Astrophysical parameters inference system (Apsis) I - methods and content
overview T.E. Dharmawardena Gaia Data Release 3: Astrophysical parameters
inference system (Apsis) I - methods and content overviewGaia Data Release 3:
Astrophysical parameters inference system (Apsis) I - methods and content
overview B. Edvardsson Y. Le Fustec Gaia Data Release 3: Astrophysical
parameters inference system (Apsis) I - methods and content overviewGaia Data
Release 3: Astrophysical parameters inference system (Apsis) I - methods and
content overviewGaia Data Release 3: Astrophysical parameters inference system
(Apsis) I - methods and content overviewGaia Data Release 3: Astrophysical
parameters inference system (Apsis) I - methods and content overview P.
García-Lario Gaia Data Release 3: Astrophysical parameters inference system
(Apsis) I - methods and content overviewGaia Data Release 3: Astrophysical
parameters inference system (Apsis) I - methods and content overview M.
García-Torres Gaia Data Release 3: Astrophysical parameters inference system
(Apsis) I - methods and content overviewGaia Data Release 3: Astrophysical
parameters inference system (Apsis) I - methods and content overview A. Gomez
Gaia Data Release 3: Astrophysical parameters inference system (Apsis) I -
methods and content overviewGaia Data Release 3: Astrophysical parameters
inference system (Apsis) I - methods and content overview I. González-
Santamaría Gaia Data Release 3: Astrophysical parameters inference system
(Apsis) I - methods and content overviewGaia Data Release 3: Astrophysical
parameters inference system (Apsis) I - methods and content overview D.
Hatzidimitriou Gaia Data Release 3: Astrophysical parameters inference system
(Apsis) I - methods and content overviewGaia Data Release 3: Astrophysical
parameters inference system (Apsis) I - methods and content overviewGaia Data
Release 3: Astrophysical parameters inference system (Apsis) I - methods and
content overviewGaia Data Release 3: Astrophysical parameters inference system
(Apsis) I - methods and content overview A. Jean-Antoine Piccolo Gaia Data
Release 3: Astrophysical parameters inference system (Apsis) I - methods and
content overviewGaia Data Release 3: Astrophysical parameters inference system
(Apsis) I - methods and content overview M. Kontizas Gaia Data Release 3:
Astrophysical parameters inference system (Apsis) I - methods and content
overviewGaia Data Release 3: Astrophysical parameters inference system (Apsis)
I - methods and content overview G. Kordopatis Gaia Data Release 3:
Astrophysical parameters inference system (Apsis) I - methods and content
overviewGaia Data Release 3: Astrophysical parameters inference system (Apsis)
I - methods and content overview A.C. Lanzafame Gaia Data Release 3:
Astrophysical parameters inference system (Apsis) I - methods and content
overviewGaia Data Release 3: Astrophysical parameters inference system (Apsis)
I - methods and content overviewGaia Data Release 3: Astrophysical parameters
inference system (Apsis) I - methods and content overviewGaia Data Release 3:
Astrophysical parameters inference system (Apsis) I - methods and content
overview Y. Lebreton Gaia Data Release 3: Astrophysical parameters inference
system (Apsis) I - methods and content overviewGaia Data Release 3:
Astrophysical parameters inference system (Apsis) I - methods and content
overviewGaia Data Release 3: Astrophysical parameters inference system (Apsis)
I - methods and content overviewGaia Data Release 3: Astrophysical parameters
inference system (Apsis) I - methods and content overview E.L. Licata Gaia
Data Release 3: Astrophysical parameters inference system (Apsis) I - methods
and content overviewGaia Data Release 3: Astrophysical parameters inference
system (Apsis) I - methods and content overview H.E.P. Lindstrøm Gaia Data
Release 3: Astrophysical parameters inference system (Apsis) I - methods and
content overviewGaia Data Release 3: Astrophysical parameters inference system
(Apsis) I - methods and content overviewGaia Data Release 3: Astrophysical
parameters inference system (Apsis) I - methods and content overviewGaia Data
Release 3: Astrophysical parameters inference system (Apsis) I - methods and
content overviewGaia Data Release 3: Astrophysical parameters inference system
(Apsis) I - methods and content overviewGaia Data Release 3: Astrophysical
parameters inference system (Apsis) I - methods and content overview E.
Livanou Gaia Data Release 3: Astrophysical parameters inference system (Apsis)
I - methods and content overviewGaia Data Release 3: Astrophysical parameters
inference system (Apsis) I - methods and content overview A. Magdaleno Romeo
Gaia Data Release 3: Astrophysical parameters inference system (Apsis) I -
methods and content overviewGaia Data Release 3: Astrophysical parameters
inference system (Apsis) I - methods and content overview M. Manteiga Gaia
Data Release 3: Astrophysical parameters inference system (Apsis) I - methods
and content overviewGaia Data Release 3: Astrophysical parameters inference
system (Apsis) I - methods and content overview F. Marocco Gaia Data Release
3: Astrophysical parameters inference system (Apsis) I - methods and content
overviewGaia Data Release 3: Astrophysical parameters inference system (Apsis)
I - methods and content overview D.J. Marshall Gaia Data Release 3:
Astrophysical parameters inference system (Apsis) I - methods and content
overviewGaia Data Release 3: Astrophysical parameters inference system (Apsis)
I - methods and content overview N. Mary Gaia Data Release 3: Astrophysical
parameters inference system (Apsis) I - methods and content overviewGaia Data
Release 3: Astrophysical parameters inference system (Apsis) I - methods and
content overview C. Nicolas Gaia Data Release 3: Astrophysical parameters
inference system (Apsis) I - methods and content overviewGaia Data Release 3:
Astrophysical parameters inference system (Apsis) I - methods and content
overview L. Pallas-Quintela Gaia Data Release 3: Astrophysical parameters
inference system (Apsis) I - methods and content overviewGaia Data Release 3:
Astrophysical parameters inference system (Apsis) I - methods and content
overview C. Panem Gaia Data Release 3: Astrophysical parameters inference
system (Apsis) I - methods and content overviewGaia Data Release 3:
Astrophysical parameters inference system (Apsis) I - methods and content
overview B. Pichon Gaia Data Release 3: Astrophysical parameters inference
system (Apsis) I - methods and content overviewGaia Data Release 3:
Astrophysical parameters inference system (Apsis) I - methods and content
overview E. Poggio Gaia Data Release 3: Astrophysical parameters inference
system (Apsis) I - methods and content overviewGaia Data Release 3:
Astrophysical parameters inference system (Apsis) I - methods and content
overviewGaia Data Release 3: Astrophysical parameters inference system (Apsis)
I - methods and content overviewGaia Data Release 3: Astrophysical parameters
inference system (Apsis) I - methods and content overview F. Riclet Gaia Data
Release 3: Astrophysical parameters inference system (Apsis) I - methods and
content overviewGaia Data Release 3: Astrophysical parameters inference system
(Apsis) I - methods and content overview C. Robin Gaia Data Release 3:
Astrophysical parameters inference system (Apsis) I - methods and content
overviewGaia Data Release 3: Astrophysical parameters inference system (Apsis)
I - methods and content overview R. Santoveña Gaia Data Release 3:
Astrophysical parameters inference system (Apsis) I - methods and content
overviewGaia Data Release 3: Astrophysical parameters inference system (Apsis)
I - methods and content overview A. Silvelo Gaia Data Release 3: Astrophysical
parameters inference system (Apsis) I - methods and content overviewGaia Data
Release 3: Astrophysical parameters inference system (Apsis) I - methods and
content overview I. Slezak Gaia Data Release 3: Astrophysical parameters
inference system (Apsis) I - methods and content overviewGaia Data Release 3:
Astrophysical parameters inference system (Apsis) I - methods and content
overview R.L. Smart Gaia Data Release 3: Astrophysical parameters inference
system (Apsis) I - methods and content overviewGaia Data Release 3:
Astrophysical parameters inference system (Apsis) I - methods and content
overview C. Soubiran Gaia Data Release 3: Astrophysical parameters inference
system (Apsis) I - methods and content overviewGaia Data Release 3:
Astrophysical parameters inference system (Apsis) I - methods and content
overview M. S ̈uveges Gaia Data Release 3: Astrophysical parameters inference
system (Apsis) I - methods and content overviewGaia Data Release 3:
Astrophysical parameters inference system (Apsis) I - methods and content
overview A. Ulla Gaia Data Release 3: Astrophysical parameters inference
system (Apsis) I - methods and content overviewGaia Data Release 3:
Astrophysical parameters inference system (Apsis) I - methods and content
overview E. Utrilla Gaia Data Release 3: Astrophysical parameters inference
system (Apsis) I - methods and content overviewGaia Data Release 3:
Astrophysical parameters inference system (Apsis) I - methods and content
overview A. Vallenari Gaia Data Release 3: Astrophysical parameters inference
system (Apsis) I - methods and content overviewGaia Data Release 3:
Astrophysical parameters inference system (Apsis) I - methods and content
overview H. Zhao Gaia Data Release 3: Astrophysical parameters inference
system (Apsis) I - methods and content overviewGaia Data Release 3:
Astrophysical parameters inference system (Apsis) I - methods and content
overview J. Zorec Gaia Data Release 3: Astrophysical parameters inference
system (Apsis) I - methods and content overviewGaia Data Release 3:
Astrophysical parameters inference system (Apsis) I - methods and content
overview D. Barrado Gaia Data Release 3: Astrophysical parameters inference
system (Apsis) I - methods and content overviewGaia Data Release 3:
Astrophysical parameters inference system (Apsis) I - methods and content
overview A. Bijaoui Gaia Data Release 3: Astrophysical parameters inference
system (Apsis) I - methods and content overviewGaia Data Release 3:
Astrophysical parameters inference system (Apsis) I - methods and content
overview J.-C. Bouret Gaia Data Release 3: Astrophysical parameters inference
system (Apsis) I - methods and content overviewGaia Data Release 3:
Astrophysical parameters inference system (Apsis) I - methods and content
overview R. Blomme Gaia Data Release 3: Astrophysical parameters inference
system (Apsis) I - methods and content overviewGaia Data Release 3:
Astrophysical parameters inference system (Apsis) I - methods and content
overview I. Brott Gaia Data Release 3: Astrophysical parameters inference
system (Apsis) I - methods and content overviewGaia Data Release 3:
Astrophysical parameters inference system (Apsis) I - methods and content
overview S. Cassisi Gaia Data Release 3: Astrophysical parameters inference
system (Apsis) I - methods and content overviewGaia Data Release 3:
Astrophysical parameters inference system (Apsis) I - methods and content
overviewGaia Data Release 3: Astrophysical parameters inference system (Apsis)
I - methods and content overviewGaia Data Release 3: Astrophysical parameters
inference system (Apsis) I - methods and content overview O. Kochukhov Gaia
Data Release 3: Astrophysical parameters inference system (Apsis) I - methods
and content overviewGaia Data Release 3: Astrophysical parameters inference
system (Apsis) I - methods and content overview C. Martayan Gaia Data Release
3: Astrophysical parameters inference system (Apsis) I - methods and content
overviewGaia Data Release 3: Astrophysical parameters inference system (Apsis)
I - methods and content overviewGaia Data Release 3: Astrophysical parameters
inference system (Apsis) I - methods and content overviewGaia Data Release 3:
Astrophysical parameters inference system (Apsis) I - methods and content
overview D. Shulyak Gaia Data Release 3: Astrophysical parameters inference
system (Apsis) I - methods and content overviewGaia Data Release 3:
Astrophysical parameters inference system (Apsis) I - methods and content
overviewGaia Data Release 3: Astrophysical parameters inference system (Apsis)
I - methods and content overviewGaia Data Release 3: Astrophysical parameters
inference system (Apsis) I - methods and content overview J. Silvester Gaia
Data Release 3: Astrophysical parameters inference system (Apsis) I - methods
and content overviewGaia Data Release 3: Astrophysical parameters inference
system (Apsis) I - methods and content overview

(Version: )

Gaia Data Release 3 contains a wealth of new data products for the community.
Astrophysical parameters are a major component of this release. They were
produced by the Astrophysical parameters inference system (Apsis) within the
Gaia Data Processing and Analysis Consortium. The aim of this paper is to
describe the overall content of the astrophysical parameters in Gaia DR3 and
how they were produced. In Apsis we use the mean BP/RP and mean RVS spectra
along with astrometry and photometry, and we derive the following parameters:
source classification and probabilities for 1.6 billion objects, interstellar
medium characterisation and distances for up to 470 million sources, including
a 2D total Galactic extinction map, 6 million redshifts of quasar candidates
and 1.4 million redshifts of galaxy candidates, along with an analysis of 50
million outlier sources through an unsupervised classification. The
astrophysical parameters also include many stellar spectroscopic and
evolutionary parameters for up to 470 million sources. These comprise
$T_{\mathrm{{eff}}}$, $\log\,g$, and [M/H] (470 million using BP/RP, 6 million
using RVS), radius (470 million), mass (140 million), age (120 million),
chemical abundances (up to 5 million), diffuse interstellar band analysis (0.5
million), activity indices (2 million), H$\alpha$ equivalent widths (200
million), and further classification of spectral types (220 million) and
emission-line stars (50 thousand). This paper is part of a series of three
papers: this Paper I focusses on describing the global content of the
parameters in Gaia DR3. The accompanying papers II and III focus on the
validation and use of the stellar and non-stellar products, respectively. This
catalogue is the most extensive homogeneous database of astrophysical
parameters to date, and it is based uniquely on Gaia data. It will only be
superseded in Gaia Data Release 4. This catalogue will therefore remain a key
reference over the next four years, providing astrophysical parameters
independent of other ground- and space-based data.

###### Key Words.:

methods: data analysis; catalogs; stars: fundamental parameters; ISM: general;
Galaxy: stellar content; galaxies: fundamental parameters

##  1 Introduction

Physical characterisation of astrophysical objects is a key input for
understanding the structure and evolution of astrophysical systems. By
physical characterisation we mean intrinsic properties for a stellar object
such as its effective temperature $T_{\mathrm{{eff}}}$, age, and chemical
element composition, as well as other inferred properties such as redshifts of
distant sources and object classification. All of these parameters,
collectively, we refer to as astrophysical parameters (APs). In the context of
Gaia (Gaia Collaboration et al., 2016; Gaia Collaboration et al., 2018;
Vallenari, 2022), APs are complementary to multi-dimensional position and
velocity information for achieving a better understanding of the dynamical
evolution of the Milky Way. Characterisation of a significant sample of our
Galaxy’s stars also allows studies of individual stellar populations, stellar
systems including planets, and a better understanding of the structure and
properties of stars themselves. Gaia also observes objects both within our own
solar system as well as beyond the Milky Way, and characterising these objects
in a homogeneous way promises to open new windows (Bailer-Jones, 2022; Tanga,
2022; Ducourant, 2022).

The Gaia Data Processing and Analysis Consortium (Gaia-DPAC) is tasked with
the analysis of Gaia data to provide a catalogue of astrometric, photometric,
and spectroscopic data to the public. The role of the Coordination Unit 8
(CU8), ”Astrophysical Parameters”, is to provide a catalogue of derived APs to
the community based on the mean astrometric, photometric and spectroscopic
data. The Astrophysical Parameters Inference System (Apsis) is the pipeline
that was designed and is executed at the Data Processing Center CNES (DPCC),
Toulouse, France, which produces APs for all sources in the Gaia catalogue.
These APs are not only destined for Gaia releases, but they are also used
internally in DPAC systems, e.g. for determining the radial velocity (RV)
template in the RV data reduction and analysis (Sartoretti et al., 2018).

The CU8 Apsis pipeline was first described in Bailer-Jones, C. A. L. et al.
(2013) before the launch of Gaia. Apsis comprises 13 modules that use
different input data and/or models to produce APs for sub-stellar objects,
stars, galaxies, and quasars. In Gaia Data Release 2, only two of the thirteen
modules processed data to produce five stellar parameters
($T_{\mathrm{{eff}}}$, extinction $A_{G}$, colour-excess $E(G_{\rm BP}-G_{\rm
RP})$, radius ${R}$, luminosity ${L}$) based on parallaxes and integrated
photometry (Andrae et al., 2018). Now, in Gaia Data Release 3, all of the 13
Apsis modules processed data and have contributed to the catalogue to provide
43 primary APs along with auxiliary parameters that appear in a total of 538
archive fields.

APs produced by CU8 appear in ten tables of the Gaia archive, with a subset of
these also appearing in gaia_source. These data comprise both individual
parameters (in four tables) and multi-dimensional data (in six tables). The
individual parameters are properties such as atmospheric properties,
evolutionary parameters, chemical element abundances, and extinction
parameters for stars, along with class probabilities and redshifts of distant
sources. The multi-dimensional data comprise a self-organised map of outliers,
with prototype spectra, a 2D total galactic extinction map at four healpix
levels as well as an optimal-level map, and Markov Chain Monte Carlo samples
for two of the Apsis modules.

This paper is the first in a series of three papers. The goal of the present
paper is to describe the production and content of the CU8 data products
available in the Gaia DR3 archive. More details on the validation and use of
the stellar and non-stellar products can be found in the accompanying papers
II (Fouesneau, 2022) and III (Delchambre, 2022) respectively. More complete
descriptions of specific products and methods can be found in the following
papers: Delchambre (2018); Andrae (2022); Lanzafame (2022) and Recio-Blanco
(2022b), and the official online
documentation111https://gea.esac.esa.int/archive/documentation/GDR3. The AP
content of Gaia DR3 represents one of the most extensive homogeneous databases
of APs to date for exploitation in many domains of astrophysics, see e.g.
Bailer-Jones (2022); Creevey (2022); Drimmel (2022); Recio-Blanco (2022a);
Schultheis (2022).

This paper is structured as follows: Sections 2, 3, and 4 describe the input
data, provide an overview of the methods used in Apsis, and describe the
stellar models, respectively. Section 5 describes the general content and
scope of the 10 Gaia DR3 archive tables with CU8 parameters and contains
useful reference tables for guidance, while Section 6 describes all of the APs
from CU8 grouped by astrophysical category. An overview of the validation
process is described in Section 7, while readers are referred to the
accompanying papers II (Fouesneau, 2022) and III (Delchambre, 2022) for
detailed validation of the Apsis results. In Section 8 we describe the main
caveats and known issues, and we conclude in Section 9. The appendix contains
additional information on the empirical methods that were employed, use of the
multi-dimensional tables, selection function information, and some tools that
have been made available to the community to aid in the exploitation of these
products.

##  2 Input data

Figure 1: Apsis workflow showing the input data (colour-code) used by the 13
modules producing APs in Gaia DR3 along with the dependencies among these
modules (arrows). The input BP/RP spectra in Apsis are in the form of sampled
spectra, produced by SMSgen, see Fig. 5.

The results from Apsis in Gaia DR3 are based solely on Gaia input data, and
these are described in this section. Figure 1 illustrates the input data that
are used by the different modules in Apsis.

###  2.1 Input astrometry and photometry

We used the proper motions and parallaxes from Gaia in the processing of some
of the Apsis modules. As some stellar-based modules are sensitive to the
parallax zero-point, we implemented the systematic correction to the
parallaxes as proposed by Lindegren et al. (2021), who reports biases that
vary with magnitude, colour, and ecliptic latitude.

Some of the Apsis modules use integrated photometry in the $G$,
$G_{\mathrm{BP}}$ and/or $G_{\mathrm{RP}}$ bands, using the zero-points
provided directly by the Coordination Unit 5 (CU5) in Gaia eDR3 (Riello et
al., 2021). They also recommended to correct some of the Gaia eDR3 photometry,
and this was implemented in our processing. This same correction to the eDR3
photometry has been fixed in Gaia DR3 (Gaia Collaboration et al., 2021a).
Figure 2 shows the distribution in $G$ magnitude of all of the individual
products from CU8 from the four 1D tables.

Figure 2: $G$-magnitude distribution of the sources processed by CU8. Top: The
distribution of sources that appear in the astrophysical_parameters and
astrophysical_parameters_supp tables grouped by module, illustrated by the
different colours. The astrophysical_parameters_supp contains results from
GSP-Phot, GSP-Spec and FLAME only, and only those from FLAME are indicated in
this top panel by the dashed lines, because the distributions in both tables
are identical for GSP-Phot and GSP-Spec. Lower: The sources with a CU8 result
in the qso_candidate or galaxy_candidate tables (blue/orange) and the sources
in those tables with a redshift from QSOC (green) or UGC (red). Figure 3:
Examples of the observed RVS spectra (black curve) analysed by various modules
of the Apsis pipeline. The effective temperatures estimated by GSP-Spec (upper
panels) and by ESP-HS (lower panels) are given in blue, while the best fitting
synthetic spectrum is shown in orange. Upper left panel: adopting the APs by
GSP-Spec (orange spectrum), ESP-CS derives an activity index from the
residuals (gray lines: residuals vertically shifted by $+$0.2) summed up
around the calcium triplet line cores (shaded green area). Upper right panel:
synthetic and observed (shifted by –0.1 for readability) spectrum
corresponding to the GSP-Spec APs. The spectrum is then used to derive
chemical abundances. Lower panels: determination of APs of stars hotter than 7
500 K, by analysing the RVS and BP/RP data and assuming a solar chemical
composition using ESP-HS. We overplot the $\lambda$862 nm Diffuse Interstellar
Band (DIB) which is also measured by GSP-Spec.

###  2.2 RVS spectra

Some of the products from CU8 are based on the RVS spectra that are processed
by Coordination Unit 6 (CU6, Seabroke & et al. (2022)). The CU6 pipeline
provides wavelength-calibrated epoch spectra using standard spectroscopic
techniques. The mean spectrum can therefore be obtained by a simple stacking
of the spectra. CU8 processed these mean spectra as provided by CU6. A
fraction of these spectra result from a deblending process of overlapping
sources. All spectra were corrected for the stars’ radial velocities. They
were cosmic-ray-clipped, normalized at the local (pseudo-)continuum
($T_{\mathrm{{eff}}}$ $\geq$ 3 500 K), and re-sampled from 846 to 870 nm with
a spacing of 0.01 nm. The median resolving power $R=\lambda/\Delta\lambda$ =
11 500 (Cropper et al., 2018). Fig. 3 shows examples of input spectra (black)
of different $T_{\mathrm{{eff}}}$ and identifies the main spectroscopic
features. Some fits to models are also shown in orange. These figures are
further described in Secs. 3 and 6. A more detailed description of the RVS
data and its treatment is provided by Seabroke & et al. (2022).

While over 37 million combined spectra were available to CU8, the median
signal-to-noise-ratio (SNR) is $\sim 6.5$. The Apsis modules processing RVS
data then applied their own SNR thresholds and quality checks before
processing. Therefore, in practice, there were only about 10 million spectra
processed, while after applying the module specific post-processing filters
$\sim$6.3 million of these led to published astrophysical parameters. The $G$
magnitude range covered by the remaining data varies from 2 to 15.2 mag.

Figure 4: Example BP/RP model spectra (left) and real spectra (right). All
BP/RP spectra have been rescaled to an apparent magnitude of $G=15$ in order
to make their flux levels comparable. Panels (a) and (c) show the variation
with $T_{\mathrm{{eff}}}$, panels (b) and (d) the variation with $A_{0}$.
Panels (a) and (b) show synthetic BP/RP spectra based on MARCS models (see
Sect. 4.1). Panels (c) and (d) show BP/RP spectra obtained by Gaia where the
APs were produced by the GSP-Phot module in the Apsis pipeline. BP spectra
approximately cover the wavelength range from 325 nm to 680 nm and RP spectra
from 610 nm to 1050 nm, see Fig. 5.

###  2.3 BP and RP spectra

Most of the Apsis modules produce APs based on the mean blue and red prism
spectra (BP and RP), which are available for all of the sources in Gaia DR3.
Examples of these prism spectra are shown in Fig. 4 for stars with different
$T_{\mathrm{{eff}}}$ and extinction ($A_{0}$). These mean low resolution
spectra ($20\leq R\leq 60$ for BP, $30\leq R\leq 50$ for RP, see Fig. 18 of
Montegriffo et al., 2022) allow one to extract the atmospheric parameters
($T_{\mathrm{{eff}}}$, $\log\,g$, $A_{G}$, [M/H]), but also contain enough
resolution to explore specific features such as the H$\alpha$ line for
emission-line stars and extragalactic objects. The RP spectra of very cool
stars also show molecular absorption bands from TiO and VO (e.g. Reiners et
al., 2007). The BP and RP spectra are processed by CU5 and then adapted within
the Apsis pipeline in the form of sampled spectra, as explained below.

####  2.3.1 Production of data by CU5

The production of internally calibrated mean BP/RP spectra by CU5 is described
in detail in Carrasco et al. (2021) and De Angeli et al. (2022). We emphasise
that these mean BP/RP spectra are averaged over time, i.e. any intrinsic
variability of sources is lost. One should be aware of this point when using
APs from Apsis for stars with important variability. Due to varying geometry
over the field of view, occasional sub-optimal centering of the window on the
observed target and variations of the instrument response and optics across
the focal plane and in time, the epoch spectra of all transits of a given
source cannot be simply stacked222Other normal ageing effects impacting all
observations are the contamination level, the radiation damage, the changes in
small-scale effects such as hot/cold columns, the changes in CCD and filter
response.. Instead, they need to be carefully calibrated, resulting in each
epoch spectrum having its own pixel sampling. The combined mean spectrum is a
continuous mathematical function that can be evaluated at any pixel position.
For this, CU5 adopts a linear basis representation in terms of Gauss-Hermite
polynomials. The resulting expansion coefficients and their covariance matrix
are the fundamental CU5 data products and are the input to the Apsis pipeline.

####  2.3.2 Sampled Mean Spectrum Generator (SMSgen)

The modules in the Apsis pipeline use the internally calibrated mean BP/RP
spectra in the format of sampled spectra (integrated flux vs. pixel).
Computing these sampled spectra from the CU5 coefficients is the task of the
Sampled Mean Spectrum Generator (hereafter SMSgen). To this end, SMSgen takes
the CU5 definition of the basis functions and integrates the spectral flux
densities for a fixed wavelength grid. This wavelength grid defines 120 pixels
for each BP and RP spectrum that cover the range of non-zero transmission in
each spectrum as shown in Fig. 5333The sampling scheme is available at
https://www.cosmos.esa.int/web/gaia/dr3-aps-wavelength-sampling.. Here, we use
the most recent EDR3
passbands444https://www.cosmos.esa.int/web/gaia/edr3-passbands. The wavelength
sampling is approximately uniform in pixel space but non-uniform in
wavelengths. SMSgen then numerically integrates the flux densities in order to
obtain integrated fluxes in each pixel. Note that BP/RP spectra can exhibit
non-zero flux in pixels which have no transmission due to the LSF smearing
effect of the BP/RP prisms, although this is negligible in practice. Apsis
modules using BP/RP spectra anyway discard several pixels at the edges that
typically have very low flux and very low SNR.

Figure 5: CU8 sampling scheme for BP/RP spectra. Vertical grey lines show the
wavelengths of 121 pixel edges defining 120 pixels for BP (top panel) and RP
(bottom panel). The blue and red lines show the BP and RP transmission curves
from Gaia Early DR3, respectively.

The sampling process of the CU5 basis functions as well as the flux
integration are strictly linear operations. Consequently, SMSgen can easily
propagate the CU5 uncertainty estimates on the coefficients into uncertainties
of the sampled BP/RP spectrum. However, Apsis modules currently ignore any
correlations between pixels, so SMSgen only provides standard deviations for
the flux uncertainties of each pixel. This approximation ensures lower
computational cost, which is a limiting factor during CU8 operations.
Unfortunately, as illustrated in Fig. 6, notable long-range correlations
between pixels do exist in BP and RP spectra (see Babusiaux et al., 2022).
Ignoring these correlations therefore causes several Apsis modules to
systematically underestimate the uncertainties in their parameters, although
most modules have inflated their uncertainties to account for this effect.

Figure 6: Random example (source_id$=$5336426878835464960) of correlation
matrices of pixel flux uncertainties for BP (left panel) and RP (right panel)
for the CU8 sampling scheme shown in Fig. 5.

##  3 Parameter estimation methods

The Apsis chain produces all of the data from CU8 for Gaia DR3. Apsis is
composed of 14 modules, 13 of which produce data for the release. All of the
modules are described individually and in more technical detail in Section 3
of Chapter 11 Astrophysical Parameters in the online documentation for Gaia
DR3. The first module providing the BP/RP spectra in the CU8 format (SMSgen)
is summarised above in Sect. 2.3.2. Here, we provide a brief overview of the
other modules in order for the reader to have a basic understanding of the
underlying methods, along with the dependencies among modules and dependencies
on models and training data. Both Figure 1 and Table 1 provide an overview of
these details, which together, describe the different categories of
parameters, the object type, the CU8 and non-CU8 input data, the dependencies,
the models and training data that are used, the approximate number of sources
in Gaia DR3 and their $G$ magnitude range for which a result can be found, see
also Fig. 2 for the distribution of $G$ magnitude. In addition, Fig. 7 shows a
HR diagram illustrating the parameter spaces in which the different stellar-
based modules are applied. The background HR diagram is a representative
random sample of 10 million $T_{\mathrm{{eff}}}$ and MG from Apsis.

###  3.1 Discrete Source Classifier

The Discrete Source Classifier, DSC (Section 11.3.2 of the online
documentation, Delchambre 2022, Bailer-Jones 2021), classifies sources
probabilistically into five classes – quasar, galaxy, star, white dwarf,
physical binary star – although it is primarily intended to identify
extragalactic sources. DSC comprises three classifiers: (1) Specmod, an
ExtraTrees method using the BP/RP spectrum; (2) Allosmod (Bailer-Jones et al.,
2019), a Gaussian Mixture Model using several photometric and astrometric
features; (3) Combmod, which combines the probabilities from the other two
classifiers. The classes are defined empirically. DSC incorporates a global
class prior that reflects the intrinsic rareness of extragalactic objects. All
classifiers produce posterior class probabilities.

###  3.2 Outlier Analysis

The Outlier Analysis module, OA (Section 11.3.12 of the online documentation),
aims to complement the overall classification performed by the DSC module by
processing those sources with the lowest combined classification probabilities
from DSC. In order to analyse outliers, OA performs an unsupervised
classification (clustering) by means of Self-Organizing Maps (SOM, Kohonen,
2001), grouping similar objects according to their BP/RP spectra. Each group
of similar objects is referred to as a neuron. In addition, OA characterises
each neuron by reporting statistics of various parameters within them, such as
magnitudes, galactic latitudes, parallaxes, and number of transits.

###  3.3 Unresolved Galaxy Classifier

The Unresolved Galaxy Classifier, UGC (Section 11.3.13 of the online
documentation, Delchambre 2022, Bailer-Jones 2022), aims to estimate the
redshift of unresolved galaxies observed by Gaia. The module processes every
source that has a combined probability greater than or equal to 0.25 of being
a galaxy according to DSC, i.e. classprob_dsc_combmod_galaxy $\geq 0.25$, and
which has a magnitude within the range $13\leq{}G\leq 21$ (after
postprocessing there are no results with $G<15$). UGC predicts the redshift of
the source by applying a supervised machine learning model based on Support
Vector Machines (SVM, Cortes & Vapnik 1995) to its sampled BP/RP spectrum. The
module is trained on a set of Gaia spectra of galaxies with redshifts provided
by an external catalogue (see Sect. A.2) and predicts redshifts in the range
$0.0\leq{}z\leq 0.6$.

###  3.4 QSO Classifier

The QSO Classifier, QSOC (Section 11.3.14 of the online documentation,
Delchambre 2022, Bailer-Jones 2022), aims to determine the redshift of the
sources that are classified as quasars by the DSC module, though it uses a
loose cut of classprob_dsc_combmod_quasar $\geq 0.01$ in order to be as
complete as possible. The method is based on a chi-square approach whereby the
cross-correlation function between a rest-frame quasar template and an
observed BP/RP spectrum is evaluated at a range of trial redshifts. The module
predicts redshifts in the range $0.0826<z<6.1295$ and also provides an
uncertainty and quality measurements from which flags are derived.

###  3.5 General Stellar Parametrizer from Photometry

The General Stellar Parametrizer from Photometry, GSP-Phot (Section 11.3.3 of
the online documentation, Andrae 2022; Liu et al. 2012; Bailer-Jones 2011),
estimates effective temperature $T_{\mathrm{{eff}}}$, logarithm of surface
gravity $\log\,g$, metallicity [M/H], absolute magnitude MG, radius ${R}$,
distance $r$, line-of-sight extinctions $A_{0}$, $A_{G}$, $A_{\mathrm{BP}}$
and $A_{\mathrm{RP}}$, as well as the reddening $E(G_{\rm BP}-G_{\rm RP})$ by
forward-modelling the BP/RP spectra, apparent $G$ magnitude and parallax using
a a Markov Chain Monte Carlo (MCMC) method. To this end, GSP-Phot employs
PARSEC 1.2S Colibri S37 models (Tang et al., 2014; Chen et al., 2015;
Pastorelli et al., 2020, and references therein) in a forward-model
interpolation in order to obtain self-consistent temperatures, surface
gravities, metallicities, radii and absolute magnitudes. For full details, we
refer readers to Andrae (2022). GSP-Phot results come from four stellar
synthetic spectra “libraries” using different grids of atmospheric models
(MARCS, PHOENIX, A stars, OB stars, see Table 1) that cover different
temperature ranges. A “best” library is recommended according to which library
achieves the highest mean log-posterior value averaged over the MCMC samples.

###  3.6 General Stellar Parametrizer from Spectroscopy

The General Stellar Parametrizer from spectroscopy, GSP-Spec (Section 11.3.4
of the online documentation, Recio-Blanco 2022b) estimates, from combined RVS
spectra of single stars, stellar atmospheric parameters ($T_{\mathrm{{eff}}}$,
$\log\,g$, [M/H], [$\alpha$/Fe]), individual chemical abundances ([N/Fe],
[Mg/Fe], [Si/Fe], [S/Fe], [Ca/Fe], [Ti/Fe], [Cr/Fe], [Fe/M], [FeII/M],
[Ni/Fe], [Zr/Fe], [Ce/Fe], [Nd/Fe]), Diffuse Interstellar Band (DIB)
parameters, and a CN under/over-abundance proxy with auxiliary parameters. No
additional information (astrometric, photometric or BP/RP data) is considered,
allowing a purely spectroscopic treatment. GSP-Spec uses specific synthetic
spectra grids computed from MARCS models, see Sect. 4.1, and two different
algorithms, Matisse-Gauguin and ANN, which are described in Recio-Blanco et
al. (2016), see also Recio-Blanco et al. (2006) for the Matisse algorithm.
Both algorithms are applied for atmospheric parameter estimates. Individual
abundances and DIB parameters are estimated only from the Matisse-Gauguin
algorithm using the approaches described in Recio-Blanco et al. (2016) and
Zhao et al. (2021), respectively.

Figure 7: HR diagram showing the parameter spaces covered by the different
stellar modules in Apsis. The solid and dashed-dotted lines represent the
modules that derive spectroscopic parameters. GSP-Phot and GSP-Spec are the
general stellar parametrizers that use BP/RP and RVS data, respectively. The
esp modules work in specific stellar regimes: the ultra-cool dwarfs (ESP-UCD),
cool stars (ESP-CS), hot stars (ESP-HS) and emission-line stars (ESP-ELS).
ESP-HS, ESP-ELS and GSP-Phot provide results on stars up to 50 000 K (not
shown). The green dashed-dotted line shows the regime of MSC which analyses
the BP/RP spectrum as a combination of two components of an unresolved binary.
The dashed line shows the parameter space of FLAME that derives evolutionary
parameters only. The grey data are the stellar parameters from GSP-Phot.

###  3.7 Extended Stellar Parametrizer for Emission-Line Stars

The Extended Stellar Parametrizer for Emission-Line Stars, ESP-ELS (Section
11.3.7 of the online documentation), identifies the BP/RP spectra of emission-
line stars brighter than magnitude $G=17.65$. It then proposes a class label
chosen among the following: Be, Herbig Ae/Be, Wolf Rayet (WC or WN), T Tauri,
active M dwarf (dMe) stars, and planetary nebulae (PN). Figure 8 shows typical
BP/RP spectra of some of these classes. The module uses three random forest
classifiers (RFC, Sect. A.4), and a measure of the pseudo-equivalent width
(pEW) of the H$\alpha$ line. A first classifier (ELSRFC1) trained on synthetic
BP/RP spectra is used to get a first coarse temperature estimate and assigns
to each target one of the following spectral type tags: O, B, A, F, G, K, M,
CSTAR (candidate carbon star, see also Creevey 2022). Only non-“CSTAR” targets
that received a spectral type tag are further processed by the module. The
second RFC (ELSRFC2) identifies the spectra of PN and of Wolf Rayet WC and WN
stars. All the targets that are not identified as PN, WC, or WN are further
processed. If significant H$\alpha$ emission is suspected based on the pEW
value, a third RFC (ELSRFC3) is applied to the data in order to identify Be,
Herbig Ae/Be, T Tauri, and dMe stars. In this process, the astrophysical
parameters derived by GSP-Phot are used to help disentangle the candidate
members of the four classes.

Figure 8: BP (blue) and RP (red) spectra typical of planetary nebula (PN),
Wolf-Rayet (WC, WN) stars, and Be stars. The wavelength position of the
strongest features is noted, while the H$\alpha$ line is represented by the
vertical broken line. The wavelength domains considered for ELS classification
are shown with colour shades, see Sects. 6.1.3 and A.4.

###  3.8 Extended Stellar Parametrizer for Hot Stars

The Extended Stellar Parametrizer for Hot Stars, ESP-HS ( Section 11.3.8 of
the online documentation), derives $T_{\mathrm{{eff}}}$, $\log\,g$, $A_{0}$,
$A_{G}$, $E(G_{\rm BP}-G_{\rm RP})$, and $v\sin i$ (broadening) of stars with
$T_{\mathrm{{eff}}}$ between 7 500 K and 50 000 K, based on either BP/RP+RVS
spectra, or BP/RP alone, by assuming solar composition for stars with $G\leq
17.65$. The target selection is based on receiving an A, B or O spectral type
tag derived by ESP-ELS (see Sect. 3.7). The BP/RP spectra (over the range 340
to 800 nm) are compared to synthetic spectra processed by SMSgen and rebinned
into 40 wavelength bins, and fit in a multi-step $\chi^{2}$-minimisation. The
flux uncertainties were multiplied by a factor of five to account for the
amplitude of the systematic differences found between the observations and the
simulations based on synthetic spectra.

Note that gravitational darkening due to rapid rotation in hot stars is
expected to affect the parameter determination based on BP/RP and/or RVS
spectra (e.g. Frémat et al. 2005). It is however beyond the scope of the
automatic pipeline to take these effects into account.

###  3.9 Extended Stellar Parametrizer for Cool Stars

The Extended Stellar Parametrizer for Cool Stars, ESP-CS (Section 11.3.9 of
the online documentation), computes a chromospheric activity index from the
analysis of the Ca ii IRT (calcium infrared triplet) in the RVS spectra. The
activity index is derived by comparing the observed RVS spectrum with a purely
photospheric model (assuming radiative equilibrium) with $T_{\mathrm{{eff}}}$,
$\log\,g$, and [M/H] from either GSP-Spec or GSP-Phot, and from vbroad when
available from CU6 (provided in gaia_source), see Fig. 3 top left panel. An
excess equivalent width factor in the core of the Ca ii IRT lines, computed on
the observed-to-template ratio spectrum in a
$\pm\Delta\lambda=0.15\leavevmode\nobreak\ \mathrm{nm}$ interval around the
core of each of the triplet lines, is taken as an index of the stellar
chromospheric activity or, in more extreme cases, of the mass accretion rate
in pre-main sequence stars.

###  3.10 Extended Stellar Parametrizer for Ultra Cool Dwarfs

The Extended Stellar Parametrizer for Ultra Cool Dwarfs, ESP-UCD (Section
11.3.10 of the online documentation), provides $T_{\mathrm{{eff}}}$ of Gaia
sources cooler than 2500 K. This is an arbitrary definition that includes
stellar objects and brown dwarfs. In practice, $T_{\mathrm{{eff}}}$
predictions up to 2700 K have been included in the catalogue in order to
accommodate uncertainties. As UCDs are detected at very short distances,
typically less than 200 pc, extinction should be very small and therefore we
ignored this parameter for these objects in Gaia DR3. The ESP-UCD module
consists of a Gaussian Process regression module that takes RP spectra as
input and assigns $T_{\mathrm{{eff}}}$ estimates. The RP spectra used as input
to the ESP-UCD module were reconstructed from the continuous representation
using a truncation procedure described in Carrasco et al. (2021). We use $a$=3
where $a$ is the threshold coefficient in Equation 27 of Carrasco et al.
(2021).

###  3.11 Final Luminosity Age Mass Estimator

The Final Luminosity Age Mass Estimator, FLAME (Section 11.3.6 of the online
documentation, Creevey & Lebreton (2022)), aims to produce the stellar mass
and evolutionary parameters for each Gaia source that has been analysed by
GSP-Phot and/or GSP-Spec, that is, FLAME produces two results for some
sources. The FLAME parameters comprise the radius ${R}$, luminosity ${L}$, and
gravitational redshift $rv_{\rm GR}$, along with the mass ${M}$, age $\tau$,
and evolutionary stage $\epsilon$. FLAME uses as input data
$T_{\mathrm{{eff}}}$, $\log\,g$, and [M/H] from GSP-Phot “best library” and,
when available, these same parameters from GSP-Spec Matisse-Gauguin, along
with a distance estimate, $G$-band photometry (Sect. 2), and extinction from
GSP-Phot. A bolometric correction is evaluated on a grid of models, see Sect.
4.3. To infer ${M}$, $\tau$, and $\epsilon$, the BaSTI555http://basti-iac.oa-
abruzzo.inaf.it (Hidalgo et al., 2018) solar-metallicity stellar evolution
models are employed, which consider a mass range from 0.5 – 10
$\,{{M}}_{\odot}$ and evolution stage from Zero-Age-Main-Sequence (ZAMS) until
the tip of the red giant branch Montegriffo et al. (2022).

###  3.12 Multiple Star Classifier

The Multiple Star Classifier, MSC, (Section 11.3.5 of the online
documentation) infers stellar parameters by assuming the BP/RP is a composite
spectrum of an unresolved coeval binary system and that the two components
have a flux ratio in the BP/RP spectrum between 1 and 5. The primary is
defined as the brighter source in the BP+RP spectrum total flux. MSC uses an
empirical BP/RP model (Sect. A.5) within an MCMC method to sample the
posterior over its parameter space: $T_{\mathrm{{eff}}}$ and $\log\,g$ of its
primary and secondary components, as well as a common metallicity, extinction,
and distance. MSC produces results for all sources with BP/RP spectra, a
parallax, and $G\leq 18.25$.

###  3.13 Total Galactic Extinction

The Total Galactic Extinction module, TGE, (Section 11.3.11, Delchambre 2022),
uses a subset of giants with extinction estimates provided by GSP-Phot as
extinction tracers, to construct all-sky maps at various resolutions of the
total foreground extinction from the Milky Way. The maps specify the median
extinction $A_{0}$ of the tracers per HEALPix, where $A_{0}$ is the extinction
parameter of the adopted extinction curve of Fitzpatrick (1999), see Sect. 4.2
for details. Sky coverage is 97.2% at HEALPix level 6 (0.84 square degrees per
HEALPix), with missing extinction estimates for some HEALPixes at galactic
latitude $|b|<5^{\circ}$. Sky coverage is less at higher resolution, due to
the limited number of tracers per HEALPix.

Table 1: Input data, models, training data, data products, and dependencies of
the Apsis algorithms.

module | category | object | input data | inference model | number | $G$
---|---|---|---|---|---|---
|  | type | non-Apsis | Apsis | models | empirical | (millions) |
DSC | probabilities, | SEB | XP, $\varpi$, |  |  | ExtraTrees/ | 1 591 | $<21$
| classification |  | pm, $G$ |  |  | Gauss.Mix |  |
OA | classification | O | XP | (DSC) |  | SOM | 56 | $<21$
UGC | galaxy redshift | E | XP | (DSC) |  | SVM | 2 | $<21$
QSOC | quasar redshift | E | XP | (DSC) |  |  | 6 | $<21$
GSP-Phot | spectroscopic, | SI | XP, $\varpi,G$ |  | MARCS/PHOENIX |  | 470 | $<19$
| interstellar, evolution |  |  $G_{\mathrm{BP}}$,$G_{\mathrm{RP}}$ |  | OB/A/PARSEC |  |  |
GSP-Spec | spectroscopic, | SI | RVS |  | MARCS |  | 6 | $<16$
| abundances, DIB |  |  |  |  |  |  |
MSC | spectroscopic, | BI | XP |  |  | ExtraTrees | 350 | $<18.3$
| interstellar |  |  | (GSP-Phot) |  |  |  |
ESP-ELS | probabilities, | S | XP | GSP-Phot |  | Random | 0.01 | $<17.65$
| classification, |  |  |  |  | Forest |  |
| H$\alpha$ pEW |  |  |  |  |  | 235 |
ESP-HS | spectroscopic | S | XP, RVS | ESP-ELS | A/OB |  | 2 | $<17.6$
ESP-CS | spectroscopic | S | RVS | (GSP-Spec) | MARCS |  | 2 | $<15$
ESP-UCD | spectroscopic | S | RP,$\varpi,G$ |  |  | Gauss.Proc. | 0.1 | $<21$
FLAME | evolution | S | $\varpi,G$ | GSP-Phot | MARCS/BaSTI |  | 280 | $<18.3$
|  |  |  | GSP-Spec | MARCS/BaSTI |  | 5 | $<13$
TGE | map | I |  | GSP-Phot |  |  | – | $<19$

666 The following notation is used: XP = BP/RP spectra (through sampled mean
spectra), RVS = RVS spectra; $\varpi$ = parallax, pm = proper motions, $G$ =
Gaia photometry which implies $G$, $G_{\mathrm{BP}}$, and/or
$G_{\mathrm{RP}}$. Object Type is S, B, E, I, O for star, binary,
extragalactic, interstellar, and outlier, respectively. Under Apsis Input
Data, values in ”()” mean that APs are used to initialise the analysis or as
selection criteria only.

##  4 Models and training data

The Apsis modules require models and training data to infer APs. In this
section we describe these auxiliary data.

###  4.1 Synthetic spectra

Table 2: CU8 synthetic stellar libraries list of BP/RP spectra. Minimum grid
spacing is listed in parentheses.

LIBRARY NAME |  $N$ models |  $T_{\mathrm{{eff}}}$ [K] |  |  $\log\,g$ [dex] |  | [Fe/H] [dex] | provider/contact
---|---|---|---|---|---|---|---
A (LL models) | 2332 |  6000 $-$ 16000 (250) |  | 2.5 $-$ 4.5 (0.25) |  |  $-$1.5 $-$ +0.5 (0.5) | O. Kochukhov/D. Shulyak
MARCS | 27951 | 2800 $-$ 8000 (25) |  |  $-$0.5 $-$ 5.0 (0.5) |  |  $-$5 $-$ +1 (0.25) | B. Edvardsson
PHOENIX | 4651 |  3000 $-$ 10000 (100) |  |  $-$0.5 $-$ 5.5 (0.5) |  |  $-$2.5 $-$ +0.5 (0.5) | I. Brott/P. Hauschildt
OB | 2162 | 15000 $-$ 55000 (1000) |  | 1.75 $-$ 4.75 (0.25) |  |  0.0 $-$ 0.6 (0.1) | Y. Frémat
BTSettl | 2084 |  400 $-$ 3000 |  |  $-$0.5 $-$ 5.5 |  |  $-$4.0 $-$ +0.5 | F. Allard††footnotemark: †/L. Sarro
(HotSpot | 31957 | 3000 $-$ 7000 |  | 3 $-$ 5 |  | -0.5 $-$ +1.0 | A. Lanzafame)
(WD | 186 |  6000 $-$ 90000 |  | 7 $-$ 9 |  | +0.0 | D. Koester)

777 $$\dagger$$$$\dagger$$footnotetext: France Allard (1963-2020)

For the estimation of stellar APs, extensive synthetic spectral libraries
based on atmospheric models have been computed for the $G$, $G_{\mathrm{BP}}$
and $G_{\mathrm{RP}}$ filter ranges and the BP/RP and RVS wavelength ranges.
These libraries were used to simulate Gaia-observed spectra through the Gaia
instrument models, with noise and extinction added (see Section 4.6 and
Montegriffo et al. 2022).

Figure 9: Examples of BP/RP simulations of different type of sources. All
sources have been simulated at $G$=15. Red and black lines show spectra from
MARCS library, having $T_{\mathrm{{eff}}}$ = 3500 and 6000 K. In blue an OB
spectrum with $T_{\mathrm{{eff}}}$ =30000 K, in purple a WDA spectrum having
$T_{\mathrm{{eff}}}$ =15000 K and $\log\,g$ =8.0. In orange and green, an SDSS
QSO and an SDSS Galaxy, having redshift $z$=2.3 and 0.06 respectively
(randomly selected).

####  4.1.1 Synthetic fluxes for BP/RP

Stellar fluxes have been simulated using standard 1D stellar-atmosphere codes,
covering all spectral types of normal stars. Several grids have been produced
by different code families, each different in physics and assumptions, with
large overlaps in the parameter space. The providers of these libraries were
free to compute models following their own expertise and preferences while
paying attention to the challenges of the respective stellar types (e.g. dust
formation, molecular absorption, treatment of convection, chemical
peculiarities, departures from local thermodynamic equilibrium (LTE), stellar
winds). For example, models for OB-type stars take into account non-LTE
effects both in the computation of the model and of the spectrum. For the
MARCS models (Gustafsson et al., 2008), the chemical abundances compared to
the Sun have been varied over several orders of magnitude by enhancing or
reducing all metals (atomic mass $A>4$) with $\alpha$-elements roughly
following the Galactic trend changing linearly from [$\alpha$/Fe] = 0.0 at
[Fe/H] = 0.0 (solar) to [$\alpha$/Fe] = 0.4 below [Fe/H] = $-1.0$. Some
differences in the assumed solar reference composition exist between
individual libraries, reflecting choices of modellers at the time of
computation. Cool stars ($T_{\mathrm{{eff}}}$ $<$ 4500 K) with prominent
molecular bands react sensitively to different assumptions concerning the
chemical mixture. The assumed composition should thus be considered when
comparing results derived using different libraries at this low
$T_{\mathrm{{eff}}}$.

Spacing between grid points also varies, both between and within libraries,
and can be as low as 25 K in $\Delta$$T_{\mathrm{{eff}}}$ for the MARCS models
(see Table 2). GSP-Phot relies on linear interpolation between grid points
(for computational cost reasons). Since the spectral flux does not change
linearly with changes in the parameters, see e.g. Zwitter et al. (2004), finer
grids will result in better performance than coarser ones.

An overview of the parameter space, number of models and stellar model
providers is given in Table 2, and some examples of synthetic spectra are
shown in Fig. 9 for different objects. While several libraries cover the
physical parameter space of horizontal-branch stars, only the ESP-HS module
provides APs for these (Sect. 6.2.3). Libraries ‘HotSpot’ and ‘WD’ were
finally not used for the production of the data in DR3.

The computation of each of the libraries requires basic information such as
input stellar parameters, key individual abundances, mass fractions of H, He
and metals etc. For the MARCS, PHOENIX, A and OB libraries, these parameter
files can be retrieved from the Gaia DR3 auxiliary data web
pages888https://www.cosmos.esa.int/web/gaia/dr3-astrophysical-parameter-
inference.

####  4.1.2 Synthetic spectra for RVS

For the parametrisation of the infrared RVS spectra within the GSP-Spec
module, large grids of synthetic spectra were computed. These spectra were
calculated from MARCS atmospheric models for FGKM-type stars using the
TURBOSPECTRUM code (Plez, 2012) and specific atomic and molecular line lists
(Contursi et al., 2021). The covered parameter space of these grids is: 2600
to 8000 K for $T_{\mathrm{{eff}}}$, $-$0.5 to 5.5 for $\log\,g$ ($g$ in cm/s2)
and $-$5.0 to 1.0 dex for the mean metallicity, with varying $\alpha$-element
enrichment with respect to iron, as explained above. Individual chemical-
abundance variations were also considered to derive abundances of N, Mg, Si,
S, Ca, Ti, Cr, Fe, Ni, Zr, Ce, Nd. The adopted solar abundances are those of
Grevesse et al. (2007). The computation of these grids of synthetic spectra is
discussed in Recio-Blanco (2022b).

For the other modules using RVS data (i.e. ESP-CS and ESP-HS), the same model
atmosphere grids used to prepare the synthetic BP/RP spectra (Table 2) were
adopted to compute the flux in the 846 - 870 nm wavelength domain. The library
used by ESP-HS was prepared assuming a Solar chemical composition for
$T_{\mathrm{{eff}}}$ $>$ 7000 K, while for ESP-CS the MARCS models were
considered for $T_{\mathrm{{eff}}}$ ranging from 3000 to 7000 K, $\log\,g$
from 3 to 5 dex, and [Fe/H] from $-$0.5 to $+$0.75.

###  4.2 Extinction

Observed spectra are attenuated by the amount of interstellar dust present in
the line-of-sight between the observer and the source. In this sense,
extinction can be considered an astrophysical parameter of a given source, and
can be inferred from the spectra. To estimate this parameter from the
algorithms we use simulations of the BP/RP spectra that cover a wide range of
extinction values.

For Apsis simulations, we adopted the wavelength dependent extinction law by
Fitzpatrick (1999), see Section 11.2.3 in the online documentation. We use the
parameter $A_{0}$, which is the monochromatic extinction at $\lambda_{0}$ =
541.4 nm999The wavelength can be derived from Table 3 in Fitzpatrick (1999),
which gives $A(\lambda)/E(B-V)$ as a function of wavelength. $\lambda_{0}$ is
the wavelength $\lambda$ for which $A(\lambda)/E(B-V)/3.1$ is equal to 1.
$A_{0}$ and $A_{\mathrm{V}}$ are often confused in literature, the latter
being the actual extinction computed in the $V$ band, and as such
intrinsically dependent on the spectral shape of the emitting source. This
dependence is often, justifiably, neglected in the Johnson $V$ band, but it is
particularly evident in the very wide Gaia bands and therefore should not be
neglected.

Simulations are provided covering a semi-regular grid of 56 values of $A_{0}$,
from 0 to 10 magnitudes, while the parameter $R$ is kept fixed at 3.1 (see
Fitzpatrick 1999, their Table 3). For each spectrum and for each $A_{0}$ the
extinction in a given band ($A_{G}$, $A_{\mathrm{BP}}$, $A_{\mathrm{RP}}$) is
computed by comparing the un-reddened and the attenuated flux in the given
Gaia passband. The values of extinction in these bands, and in addition in the
$V$-band, for different APs and $A_{0}$ values are made available to the
community in the parameter files (Sect. 4.1) on the Gaia DR3 auxiliary data
web pages101010https://www.cosmos.esa.int/web/gaia/dr3-astrophysical-
parameter-inference.

###  4.3 Bolometric corrections

In order to derive the bolometric luminosity of stars, specifically in the
FLAME module, we complemented the observed photometric $G$ magnitude with a
bolometric correction, $\mathrm{BC}_{G}$. The $\mathrm{BC}_{G}$ was derived
from the MARCS synthetic stellar spectra as a function of
$T_{\mathrm{{eff}}}$, $\log\,g$, [Fe/H] and [$\alpha$/Fe]. For this data
release, we assumed [$\alpha$/Fe] = 0.0 when calculating the correction for
all stars because [$\alpha$/Fe] is only estimated for a small fraction of the
sources. A tool is made available to the community to calculate the
$\mathrm{BC}_{G}$ as a function of $T_{\mathrm{{eff}}}$, $\log\,g$, [M/H], and
[$\alpha$/Fe] and can be found on the Gaia DR3 tools
webpages111111https://www.cosmos.esa.int/web/gaia/dr3-bolometric-correction-
tool.

We extended the $T_{\mathrm{{eff}}}$ range to intermediate-temperature stars
by using the A star models. Their $\mathrm{BC}_{G}$ values show a slight
offset relative to the MARCS grid (due at least in part to different opacities
used in the two sets of models). We therefore added an offset in magnitude
units to achieve continuity at 8 000 K. The adopted value for the bolometric
correction for the Sun is $\mathrm{BC}_{G\odot}$ = +0.08 mag, where $M_{\rm
bol\odot}=4.74$121212The IAU 2015 Resolution B2 can be found here:
https://www.iau.org/static/resolutions/IAU2015_English.pdf which yields an
absolute magnitude of the Sun M${}_{G,\odot}=4.66$ mag. We estimate an
external accuracy on this zeropoint of $\pm 0.015$ mag from comparison with
known solar analogues ($M_{G}=4.63-4.69$ mag), stellar models ($M_{G}=0.465$
mag) and colour transformations using Riello et al. (2021) ($V-G=0.148\pm
0.003$ mag where $M_{V}=4.817$ mag).

To complement this analysis on the solar reference magnitudes, we estimate the
solar colours in Creevey (2022) using a set of solar analogues although we
note that these colours were not used in Apsis processing:
$(G_{\mathrm{BP}}-G_{\mathrm{RP}})_{\odot}=0.818\pm
0.029\,\textrm{mag},(G_{\mathrm{BP}}-G)_{\odot}=0.324\pm 0.016\,\textrm{mag}$,
and $(G-G_{\mathrm{RP}})_{\odot}=0.494\pm 0.020\,\textrm{mag}$.

###  4.4 Stellar evolution models

Stellar evolution models are used in two of the Apsis modules, GSP-Phot and
FLAME. For GSP-Phot the published APs are astrophysically self-consistent
within the PARSEC 1.2S Colibri S37 models (Tang et al., 2014; Chen et al.,
2015; Pastorelli et al., 2020, and references therein). Imposing these
isochrones ensures that GSP-Phot can simultaneously fit the observed apparent
magnitude (using the absolute magnitude) and the amplitude of low-resolution
BP/RP spectra (using the radius, see Andrae, 2022). Moreover, the isochrones
ensure that only astrophysically reasonable parameter combinations are
possible.

For FLAME the mass, age and evolutionary stage are based on the use of the
BASTI stellar models (Hidalgo et al., 2018). In FLAME these models cover the
zero-age main sequence until the tip of the red-giant branch, corresponding to
evolutionary indices of between 100 and 1300 (main sequence $<390$; turn-off =
390; sub-giant: 420 to 490, and giant $>490$), and masses between 0.5 and 10
$\,{{M}}_{\odot}$. We furthermore imposed a solar-metallicity prior, see Sect.
6.4.1 for discussion on this assumption.

###  4.5 Empirical training

One of the drawbacks of training machine learning algorithms on synthetic data
is that good results require (a) adequate source models from which to generate
the synthetic data, (b) sufficient coverage of the parameter space by the
source models, and (c) a good match between the synthetic data (Gaia
simulations) and the real Gaia data of the corresponding objects. For five
Apsis modules, specifically DSC, MSC, UGC, ESP-UCD, and ESP-ELS, one or more
of these conditions could not be achieved, so for these we use empirical
training. This involves training the algorithm on real Gaia data, with classes
or astrophysical parameters for the training data obtained from external
sources. Typically this involves cross-matching Gaia to external catalogues,
such as the Sloan Digital Sky Survey (SDSS), and using class labels or APs
obtained by others, e.g. from higher resolution spectra. Details of the
empirical training used by the five Apsis modules are given in appendix A.

###  4.6 Simulations with MIOG

The Mean Instrument Object Generator (hereafter MIOG) simulates low-resolution
BP/RP spectra from given model spectral energy distributions (SEDs). This was
developed by CU5 and is only available internal to DPAC systems. MIOG
implements the instrument model and the dispersion law, as derived by CU5 as
part of the external calibration process (Montegriffo et al., 2022). This
external calibration relies on the flux calibration of the Spectro-Photometric
Standard Stars by Pancino et al. (2012); Altavilla et al. (2015); Marinoni et
al. (2016).

All synthetic libraries described in Sect. 4.1 were simulated with MIOG. An
example of the simulated spectra for stars of different $T_{\mathrm{{eff}}}$
and $A_{0}$ is shown on the left panels of Fig. 4. The corresponding real
observed spectra are shown on the right panels. In Fig 9, simulated stellar
spectra at different temperatures (and from different libraries) are shown,
together with extra-galactic sources spectra and a white dwarf spectrum.

CU5 have provided a simplified version of this tool to the community,
GaiaXPy131313https://gaia-dpci.github.io/GaiaXPy-website/ that simulates the
low-resolution spectra from model SEDs, which is fully compatible with the
internal DPAC MIOG simulator (Montegriffo et al., 2022).

##  5 Catalogue description

Figure 10: Distribution in colour-magnitude space of the sources with products
from CU8 in Gaia DR3, separated by module. The colours represent the results
per module, and the colour-code is density of sources. The distribution shown
in gray in all panels indicates the whole Gaia DR3 sample for reference. These
products are found in the astrophysical_parameters,
astrophysical_parameters_supp, galaxy_candidates and qso_candidates tables.

The astrophysical parameters produced by CU8 fall under the following
categories: (a) classification products comprising class probabilities and
class labels of objects and emission-line stars, and stellar spectral types,
(b) interstellar medium characterisation and distances, including 2D total
Galactic extinction maps, (c) stellar spectroscopic and evolutionary
properties, including binary star characterisation, (d) redshifts of
extragalactic objects, (e) outlier analysis products, and (f) auxiliary data.
Most of these products are individual parameters produced on a source-by-
source basis. Multi-dimensional (MD) products are also produced, such as the
two 2D total Galactic extinction maps, two dedicated outlier tables, and
Markov Chain Monte Carlo samples from GSP-Phot and MSC containing stellar and
interstellar medium parameters and distances. All of these data products are
found in one of ten tables in the Gaia DR3 archive, with a subset of these
also copied to the main archive table (gaia_source), see Sect. 5.2.

###  5.1 Operations

The operations run to produce data for DR3 took a total of $\sim$92 days of
continuous processing time (1 021 219 CPU hours). This had been preceded by
several month-long testing and validation runs, and allowed for sufficient
post-operation validation time. With a strict delivery date for production and
validation of these data of June 2021, which would ensure Gaia DR3 in the
first half of 2022, we had to impose processing limitations in some of the
modules that produce stellar parameters. This was done either on an observed
$G$ magnitude basis or RVS SNR basis. The processing limits that were imposed
are the following, for GSP-Phot: $G\leq 19$, FLAME: $G\leq 18.25$, MSC: $G\leq
18.25$, ESP-ELS: $G\leq 17.65$, ESP-HS: $G\leq 17.65$, ESP-CS: $G\leq 16.62$,
and for ESP-UCD, in addition to all sources with $G\leq<17.65$, we also
processed a pre-defined list of around 50 million sources with $G>17.65$.
These limits in magnitude ensured roughly the same number of objects in each
magnitude bin ($\sim$130 million) and enabled the schedule to be optimized.
For GSP-Spec we imposed a minimum SNR $=20$ in the RVS spectra. This
information is also provided in Table 1.

For UGC, QSOC, and OA no limit was necessary because they process relatively
few sources. DSC also had no limitations imposed because it was designed to
run fast in order to process all 2 billion141414The word billion implies 1 000
million. sources in Gaia. The TGE module is very quick as it works on a
HEALPix basis, but as it processes sources from GSP-Phot, no sources with $G$
$>19$ were included. In Fig. 10 we show the distribution in observed colour -
magnitude [($G_{\mathrm{BP}}-G_{\mathrm{RP}}$), $G$] space for the 12 modules
producing data on individual sources.

###  5.2 CU8 data tables in Gaia DR3

The names and dimensions of the tables with CU8 parameters are summarised in
Table 3. The first four tables contain APs for which processing was done on a
source-by-source basis, such as $T_{\mathrm{{eff}}}$ or redshifts. Most of the
stellar parameters, classifications, individual extinction measurements, and
auxiliary data are found in the astrophysical_parameters and
astrophysical_parameters_supp tables, which contain only CU8 products. The
former table contains one main result from each of the Apsis modules, while
the latter table provides supplementary results in the form of specific
libraries (GSP-Phot), methods (GSP-Spec), or input source types (FLAME). Some
of the parameters from DSC and GSP-Phot from the astrophysical_parameters are
copied to gaia_source for convenience to the user.

The galaxy_candidates and qso_candidates tables focus on extragalactic objects
and consolidate results from different CUs. In these tables, CU8 was
responsible for the galaxy and QSO redshifts produced by UGC and QSOC,
respectively, along with the extragalactic class probabilities and labels from
DSC.

To supplement the AP estimates from GSP-Phot and MSC, a sample of the MCMC is
also provided as a datalink product. These tables, mcmc_samples_gsp_phot and
mcmc_samples_msc, contain in addition to the sampled APs, the log posterior
and log likelihood, so that the user can re-analyse the samples for their own
use case, see Section 11.3.3 in the online documentation for details. In
Appendix B we provide information on retrieving the MCMC data.

The primary result from the OA analysis is a self-organising map of $30\times
30$ neurons with a statistical description of each neuron, called
oa_neuron_information. Additionally a template spectrum for each neuron is
provided in the oa_neuron_xp_spectra table. For the sources identified as
outliers the astrophysical_parameters table contains the neuron membership
information. Examples of how to exploit these data are given in Appendix C.

Finally, the results from TGE are given in the total_galactic_extinction_map
table in the form of a two-dimensional total Galactic extinction map at 4
HEALPix levels. The additional total_galactic_extinction_map_opt table
contains a HEALPix level 9 map but based on the optimal HEALPix level.

To help the user navigate to the appropriate table in the archive, we provide
in Table 4 an overview of the contents of each table but organised by the six
astrophysical parameter categories mentioned above. For example, if one is
interested in classifications, Table 4 provides the link to three relevant
tables: astrophysical_parameters, galaxy_candidates, and qso_candidates. In
the last column we give an overview of what type of content is found in each
of those tables for that category. As another example, if one is interested in
monochromatic extinction or extinction in the $G$ band, then one should query
the astrophysical_parameters and astrophysical_parameters_supp tables.

###  5.3 Parameters and fields

In the ten archive tables (excluding gaia_source), there are a total of 538
fields produced by CU8 (excluding solution_id and source_id)151515This count
includes two fields that are reproduced in three tables
(classprob_dsc_combmod_quasar, classprob_dsc_combmod_galaxy in
astrophysical_parameters, galaxy_candidates, qso_candidates) and three fields
that are reproduced in two tables (classlabel_dsc, classlabel_dsc_joint,
classlabel_oa in galaxy_candidates, qso_candidates).. Each field has a field
name associated with it, along with a data type, unit, and a simple and
detailed description. Some of these field names are related. For example, for
the chromospheric activity index there are three related activityindex fields,
its value, uncertainty and information pertaining to the input data. For the
stellar mass there is an upper and lower confidence level associated with the
median value (three fields). Also, the mass was derived using two different
sets of input data, to give a total of six mass fields. There are also some
parameters that are produced by more than one Apsis module, such as class
probabilities (classprob), $T_{\mathrm{{eff}}}$ (teff), and [M/H] (mh). We
refer to activityindex, mass, classprob, …as the fieldname root or parameter.
To make it easier for the user to understand the AP content of Gaia DR3 and to
understand how each of these 538 fields has been derived, the fieldname also
includes the name of the Apsis module which was responsible for deriving that
parameter. We have adopted a general approach to naming the individual
parameter fields and these mostly take the form of

parameter_module_variant_detail161616Some exceptions are: equivalent width
fields from GSP-Spec, $T_{\mathrm{{eff}}}$ and $\log\,g$ from MSC,
classlabel_espels_flag

Here, parameter is one of the 43 main parameters (not counting auxiliary data
products) listed in Table 5.3. Next, module is the name of the Apsis module
that derived the AP, see Sect. 3. Then, variant describes a variant of the
method, models, or input data used to derive the AP. This part may be blank if
only one method was used, or if the parameter value comes from the ‘‘best’’ of
several methods. Finally, detail may be blank if the field contains the value
of the AP, otherwise it takes on values such as upper, lower or uncertainty,
where upper and lower imply upper and lower confidence interval (generally
68%, but see data model descriptions), or an emission-line star type in case
of a class probability field, such as ttauristar. As an example,
teff_gspphot_marcs_upper is the upper confidence level of the
$T_{\mathrm{{eff}}}$ value estimated by the module GSP-Phot using the MARCS
library of synthetic stellar spectra; classprob_dsc_combmod_quasar is the
class probability value of being a quasar from the module DSC using the
combmod method; or sife_gspspec_nlines is the number of lines used to estimate
the [Si/Fe] abundance from GSP-Spec.

Table 5.3 describes all of the unique parameters associated with the six
categories (classification, interstellar and distances, stellar-
spectroscopic/evolutionary, extragalactic, outlier, auxiliary). The
description of the unique parameter is given in the first column, and the
field-name root (parameter) used in the archive field name is given in the
second column. The third and fourth columns give the number of variants
associated with a unique parameter and the total number of related fields,
respectively. Using the above example, for mass these numbers are 2 and 6,
respectively. As another example, for classprob there are four variants (three
from DSC and one from ESP-ELS) for a total of 24 related fields171717including
the two fields from DSC reproduced in three archive tables, so these numbers
are 4 and 24, respectively, in the table. The final column gives the maximum
number of sources for which this parameter is available.

In the next section we describe each of the parameter_module_variant_detail
fields grouped by category.

Table 3: Tables in the Gaia DR3 archive with parameters from CU8. The last six
are multi-dimensional data tables. The number of sources is approximate to the
nearest million.

table name | number of | number
---|---|---
| CU8 fields | of sources
|  | [millions]
astrophysical_parameters | 224 | 1 590
astrophysical_parameters_supp | 173 | 470
galaxy_candidates | 8 | 2
qso_candidates | 11 | 6
gaia_source | 29 | 1 800
|  | dimensions
oa_neuron_information | 77 | 30x30
oa_neuron_xp_spectra | 6 | 87x900
total_galactic_extinction_map | 9 | 4xHP
total_galactic_extinction_map_opt | 6 | 1xHP9
mcmc_samples_gsp_phot | 14 | 100x480M
mcmc_samples_msc | 10 | 100x480M

181818 HP = 3 145 728 (= HP9), 786 432, 196 608, and 49 152 for HEALPix level
9, 8, 7, and 6, respectively. gaia_source contains a subset of parameters from
the astrophysical_parameters table. The mcmc_samples_gsp_phot contains 2000
samples for sources with $G<9$ and for a random 1% of all other sources.

Table 4: Overview of the contents of each table in the Gaia archive containing
Apsis products, organised by product type. A subset of the products from DSC
and GSP-Phot also appear in gaia_source.

product type | Gaia DR3 archive tables | overview content
---|---|---
classification | astrophysical_parameters | object class probabilities (quasar, galaxy, star, white dwarf, physical binary star),
|  | emission-line class probabilities and label, spectral types
| galaxy_candidates | galaxy and QSO class probabilities and label, outlier class label
| qso_candidates | galaxy and QSO class probabilities and label, outlier class label
interstellar | astrophysical_parameters |  monochromatic extinction and extinction in $G_{\rm BP}$, $G_{\rm RP}$, $G$,
|  | colour excess, distances, diffuse interstellar band characteristics
| astrophysical_parameters_supp |  monochromatic extinction and extinction in $G_{\rm BP}$, $G_{\rm RP}$, $G$,
|  | colour-excess, distances
|  total_galactic_extinction_mapaaaMulti-dimensional table. | total Galactic extinction 2D map at HEALpix levels 6, 7, 8, 9
|  total_galactic_extinction_map_optaaaMulti-dimensional table. | total Galactic extinction 2D map at HEALpix level 9 based on
|  | the optimal HEALPix level
stellar | astrophysical_parameters | atmospheric parameters for single and binary stars,
|  | chemical abundances, equivalent widths,
|  | rotation and activity parameters, evolutionary parameters
| astrophysical_parameters_supp | atmospheric and evolutionary parameters
|  mcmc_samples_gsp_photaaaMulti-dimensional table. | MCMC samples of stellar parameters from GSP-Phot
|  mcmc_samples_mscaaaMulti-dimensional table. | MCMC samples of stellar parameters from MSC
extragalactic | qso_candidates | redshifts of qso candidates
| galaxy_candidates | redshifts of unresolved galaxy candidates
| astrophysical_parameters | class probabilities of a source being a galaxy or quasar
outlier analysis | astrophysical_parameters | neuron membership and distance between source and neuron
|  | prototype BP/RP spectra
|  oa_neuron_informationaaaMulti-dimensional table. | self-organising map (SOM, 30x30) of outlier sources
|  oa_neuron_xp_spectraaaaMulti-dimensional table. | prototype BP/RP spectra corresponding to each SOM neuron
auxiliary | astrophysical_parameters | flags, convergence indicators, bolometric correction, library name
| astrophysical_parameters_supp | flags, convergence indicators, bolometric correction, library name
| qso_candidates | flags, quality indicators

191919 The links in the table point directly to the online data model
description.

Table 5: Summary of CU8 parameters in Gaia DR3 source-based tables. For
definition of ’variants’ and ’related fields’ see text (Sect. 5.3). aWe use
xfe in this table to indicate abundances with respect to Fe -- these are in
fact abundances of 11 different elements: N, Mg, Si, S, Ca, Ti, Cr, Ni, Zr,
Ce, Nd, where x in the field name is replaced by the corresponding element
symbol in lower-case.

|  |  | total | maximum
---|---|---|---|---
|  | number of | related | sources
parameter description | fieldname root | variants | fields | [million]
classification |  |  |  |
class probabilities |  |  |  |

& 4 24 1 591 class labels classlabel 4 7 7 spectral types spectraltype 1 1 218
interstellar extinction in $G$ band ag 7 20 470 extinction at 541 nm azero 7
20 470 extinction in $G_{\rm BP}$ band abp 5 15 470 extinction in $G_{\rm RP}$
band arp 5 15 470 reddening ebpminrp 6 17 470 diffuse interstellar band dib 1
9 0.5 distance distance 6 18 470 stellar-spectroscopic effective temperature
teff 9 25 470 surface gravity logg 8 23 470 global metallicity mh 8 24 470
$\alpha$-element over iron abundance alphafe 2 6 5 iron and elemental
abundances fem, feiim, xfeaafootnotemark: a 1 65 3 activity index
activityindex 1 3 3 projected rotation velocity vsini 1 2 2 equivalent widths
ew 2 8 235 binary component $T_{\mathrm{{eff}}}$ teff_msc1, teff_msc2 1 6 380
binary component $\log\,g$ logg_msc1, logg_msc2 1 6 380 stellar-evolutionary
luminosity lum 2 6 280 absolute $G$ magnitude mg 5 15 470 radius radius 7 21
470 mass mass 2 6 140 age age 2 6 130 evolutionary stage evolstage 2 2 170
gravitational redshift gravredshift 2 6 280 extragalactic and outlier analysis
redshift redshift 2 6 6 characteristic of outlier neuron neuron 1 3 56
auxiliary logarithm of posterior probability logposterior 6 6 logarithm of
$\chi^{2}$ from fit logchisq 2 2 bolometric correction bc 2 2 processing and
quality flags flags 10 10 MCMC acceptance rate mcmcaccept 6 6 drift of MSC
MCMC chain mcmcdrift 1 1 name of best library libname 1 2 redshift CCF ratio
ccfratio 1 1 redshift quality indicator zscore 1 1

##  6 Catalogue results

We describe all of the APs and other data products produced by CU8 and
available in the Gaia DR3 archive in this section ordered according to their
category: classification (Sect. 6.1), ISM and distances (Sect. 6.2), stellar
spectroscopic (Sect. 6.3) and evolutionary (Sect. 6.4) parameters,
extragalactic redshifts (Sect. 6.5), outliers (Sect. 6.6), and auxiliary
parameters (Sect. 6.7).

###  6.1 Classification

Class probabilities and class labels are provided by three Apsis modules for
three categories of objects: DSC provides the probabilities for all sources to
belong to the classes quasar, galaxy, star, white dwarf, and physical binary
star; OA classifies sources with lower probabilities from DSC; and ESP-ELS
provides a spectral type classification and emission-line star types for
stellar sources.

####  6.1.1 DSC

DSC provides normalized posterior probabilities for five classes from Specmod
and Combmod, and for three classes (not white dwarfs or physical binaries)
from Allosmod. These are all listed in the astrophysical_parameters table. The
Combmod probabilities for quasars and galaxies also appear in the
qso_candidates and galaxy_candidates tables, and the Combmod quasar, galaxy,
and star probabilities for all objects are duplicated in the gaia_source
table. Additionally, two class labels derived from these probabilities
(defined in Section 11.3.2 of the online documentation), classlabel_dsc and
classlabel_dsc_joint, are listed in the qso_candidates and galaxy_candidates
tables.

DSC Combmod and Specmod provide results for 1.59 billion sources. Allosmod has
fewer, 1.37 billion sources, because some sources have only 2-parameter
astrometric solutions (i.e. they have positions but lack parallaxes and proper
motions). Users can classify sources using the probabilities, either by taking
the class with the largest probability or that with the probability above some
threshold (in the latter case multiple classifications or no classification is
possible).

Taking a probability threshold of 0.5 on Combmod, we get around 5.2 million
quasars and 3.6 million galaxies, although these samples have significant
contamination. More complete numbers are given in Table 11.16 in the online
documentation. Most objects in Gaia are of course stars, so the star class is
of little use in practice. Performance on white dwarfs and physical binaries
is poor (the purities are low), and we recommend against using their
probabilities for building samples.

Figure 11: G-band magnitude distribution of the subset of candidates in the
qso_candidates (blue) and galaxy_candidates (orange) tables identified using
the classlabel_dsc_joint field. These subsets comprise around 547 thousand
quasars and 251 thousand galaxies.

The purity and completeness of samples vary with probability threshold, as
well as magnitude and Galactic latitude (and other parameters). Assessments of
the purity and completeness are given in Section 11.3.2 of the online
documentation (summarized in this table), as well as in Bailer-Jones (2022),
and in more detail in Bailer-Jones (2021). We see there that Specmod,
Allosmod, and Combmod have rather different performances, so users may want to
select on one or the other depending on their goals. More advice on the use of
the DSC results and the (non-trivial) interpretation of its performance can be
found in Delchambre (2022) and Section 11.3.2 of the online documentation. The
label classlabel_dsc_joint in the qso_candidates and galaxy_candidates tables
identify a set of extragalactic sources with purities of around 63%,
increasing to around 83% for the subsets more than 11.5∘ from the Galactic
plane. Their magnitude distributions are show in Fig. 11.

####  6.1.2 OA

For Gaia DR3, OA processed around 56 million objects whose $G$ magnitudes
peaked around $20.8$ mag, in general faint stars and extragalactic objects. It
provides an unsupervised classification that complements the one produced by
DSC, by analysing the sources with the lowest classification probability from
DSC. OA produces a self-organising map (SOM, see Sect. 6.6) with 900
($30\times 30$) neurons, see e.g. Fig 12. An object belonging to any of the
900 neurons can be found in the astrophysical_parameters table by the
neuron_oa_id. The associated parameters indicate how close the source is to
the neuron prototype and its ranking in distance to that prototype,
neuron_oa_dist and neuron_oa_dist_percentile_rank. More information on OA and
its multi-dimensional data is given in Sect. 6.6. Some examples of exploiting
these data are given in the Appendix C

Figure 12: SOM map lattice visualised using the GUASOM tool (Álvarez et al.,
2021) representing the specific class labels assigned to each neuron by the OA
module. The OA module analysed the 56 million sources with the poorest
classification probabilities from DSC. Those neurons for which such a label
can not be attributed remain as ‘‘undefined’’.

####  6.1.3 ESP-ELS

ESP-ELS provides for 218 million targets with $G\leq 17.65$ one of the
following spectral type tags spectraltype_esphs202020ESP-HS and ESP-ELS are
related modules, and this field was originally produced by ESP-HS at the time
of the archive data model definition, but it was finally produced by the
upstream ESP-ELS module: CSTAR, M, K, G, F, A, B, and O (see Table 6). An
indicator of the spectral tag quality is stored in the second digit (reading
from left to right) of flags_esphs. In most cases, its value ranges from 1 to
5 (the lower, the better) and is based on the relative value of the first and
second highest probabilities. Value 0 was added during the validation to
identify those candidate carbon stars (CSTAR tag) having BP/RP spectra with
significantly stronger C2 and CN molecular bands than in normal stars
(Creevey, 2022). The distribution of the spectral types according to the
quality flag is shown in Fig. 13. As can be seen, only the ’CSTAR’ type has a
value of 0.

The module also identified 57 511 emission-line stars, for which it suggests a
stellar class (classlabel_espels) based on the combined probabilities (e.g.
classprob_espels_wcstar) provided by two random forest classifiers. The ELS
classes that were considered, as well as the corresponding classlabel are: Be
stars (beStar), Herbig Ae/Be stars (HerbigStar), T Tauri stars (TTauri),
active M dwarf stars (RedDwarfEmStar), Wolf-Rayet WC (wC) and WN (wN), and
planetary nebula (PlanetaryNebula)212121The classprob_espels_* corresponding
names are bestar, herbigstar, ttauri, dmestar, wcstar, wnstar, pne..

Figure 13: Histogram of the distribution of spectraltype_esphs which processed
sources with $G\leq 17.65$. A coloured distinction is made between the
different values taken by the quality assessment flag (second digit of
flags_esphs). Usually the flag takes values ranging from 1 to 5, with the
lower value indicating higher quality. However, for the CSTAR tag it can also
be ‘‘0’’.

###  6.2 Interstellar medium characterisation and distances

The second category of astrophysical parameters concerns the characterisation
of the interstellar medium (ISM) and distances. Source-based ISM
characterisation is provided by GSP-Phot, ESP-HS, and MSC, as one of the
spectroscopic parameters estimated from BP/RP spectra ($A_{0}$, $A_{G}$,
$A_{\mathrm{BP}}$, $A_{\mathrm{RP}}$, $E(G_{\rm BP}-G_{\rm RP})$) and by GSP-
Spec based on the analysis of the $\lambda 862$ nm diffuse interstellar band
(DIB). The TGE module exploits individual source-based extinction from GSP-
Phot to provide a 2D total Galactic extinction map. Both GSP-Phot and MSC
additionally estimate distances. Further details on most of these parameters
is found in Fouesneau (2022), while TGE is discussed in Delchambre (2022).

####  6.2.1 GSP-Phot

GSP-Phot estimates the monochromatic extinction $A_{0}$, called azero_gspphot,
for all processed sources by fitting the observed BP/RP spectrum, parallax,
and apparent $G$ magnitude. GSP-Phot also estimates the broad-band extinctions
$A_{G}$, $A_{\mathrm{BP}}$, and $A_{\mathrm{RP}}$. The latter are not free fit
parameters but instead obtained from integrating an attenuated model SEDs (see
Section 11.2.3 of the online documentation). Using these extinction estimates,
one can also compute reddenings, e.g. $E(G_{\rm BP}-G_{\rm
RP})=A_{\mathrm{BP}}-A_{\mathrm{RP}}$. These extinction and reddening
estimates along with upper and lower confidence levels are available in the
astrophysical_parameters table ($A_{0}$, $A_{G}$, $A_{\mathrm{BP}}$,
$A_{\mathrm{RP}}$, $E(G_{\rm BP}-G_{\rm RP})$) from the best library i.e. the
library that produced the highest posterior probability for that source, see
libname_gspphot. The astrophysical_parameters_supp table contains the five ISM
parameters $A_{0}$, $A_{G}$, $A_{\mathrm{BP}}$, $A_{\mathrm{RP}}$, $E(G_{\rm
BP}-G_{\rm RP})$ for the individual library results (MARCS, PHOENIX, A, OB).
GSP-Phot additionally derives a distance estimate to be consistent with the
inferred parameters. The parameters azero_gspphot, ag_gspphot,
ebpminrp_gspphot, and distance_gspphot, and their upper and lower confidence
levels are copied from the astrophysical_parameters table to the gaia_source
table, for convenience to the user. A sample of the MCMC from GSP-Phot
inference is also made available as a datalink product.

####  6.2.2 MSC

Like GSP-Phot, MSC also estimates the $A_{0}$ parameter but by assuming that
the BP/RP spectrum is a composite of the two components of an unresolved
binary i.e. two stars at the same distance with a common interstellar
extinction. These parameters for sources with $G\leq 18.25$ are found in the
astrophysical_parameters table: azero_msc and distance_msc along with their
upper and lower confidence levels. By assuming that the flux comes from a
combined system, the distances are necessarily larger than the GSP-Phot ones,
see Section 11.4.1 of the online documentation.

####  6.2.3 ESP-HS

For stars hotter than 7 500 K, and using a preliminary classification from
ESP-ELS, ESP-HS measures the $A_{0}$ interstellar extinction by fitting the
observed BP/RP and, where available, also the RVS data, called azero_esphs.
While $A_{0}$ is the free parameter representing interstellar absorption
during the fit, the corresponding extinction in the $G$ band, $A_{G}$, and
interstellar reddening, $E(G_{\rm BP}-G_{\rm RP})$, along with uncertainties
are derived simultaneously and provided as well. These results are found in
the astrophysical_parameters table for hot stars with $G<17.65$. We show in
Fig. 14 a comparison between the $A_{0}$ estimates for the 1 433 932 hot stars
($T_{\mathrm{{eff}}}$ $>$ 7 500 K) in common between GSP-Phot and ESP-HS. The
synthetic spectra adopted by the modules are slightly different, as ESP-HS
makes some corrections to account for systematics between the observations and
simulations, and the wavelength range above 800 nm was not taken into account.
The impact of this is mostly seen in the B- and O-type star
$T_{\mathrm{{eff}}}$ range where ESP-HS estimates tend to be slightly larger
than those obtained by GSP-Phot.

Figure 14:  Distribution of the $A_{0}$ derived by GSP-Phot vs. $A_{0}$
derived by ESP-HS. The gray diagonal is the identity relation, while the blue
and orange lines were fitted through the values obtained for targets cooler
and hotter than 10 000 K, respectively.

####  6.2.4 GSP-Spec

For the sources where an analysis of the DIB in the RVS spectra is possible,
see Fig. 3 lower right panel, we provide a measurement of the DIB $\lambda
862$ nm equivalent width dibew_gspspec, and the modelled depth dibp0_gspspec
and width dibp2_gspspec parameters, together with uncertainties. A quality
flag dibqf_gspspec is also available ranging from 0 (highest quality) to 5
(lowest quality). Results for DIB measurements are available for 476 117
stars, and are found in the astrophysical_parameters table for
$T_{\mathrm{{eff}}}$ ranging from $\sim$3000 - 50 000 K. A comparison between
dibew_gspspec and ebpminrp_gspphot is shown for a high-quality sub-sample in
Fig. 15 for stars with dibqf_gspspec $<2$ , where the median DIB EW increases
with $E(G_{\rm BP}-G_{\rm RP})$. A detailed discussion between the correlation
of the DIB carrier and the dust extinction can be found in (Schultheis, 2022).

Figure 15: Comparison between GSP-Spec DIB equivalent width and GSP-Phot
$E(G_{\rm BP}-G_{\rm RP})$. The red dots are the median values of $E(G_{\rm
BP}-G_{\rm RP})$ taken in EW bins from 0.0 to 0.6 Å with a step of 0.05 Å. The
error bars show the standard deviations of $E(G_{\rm BP}-G_{\rm RP})$ for each
EW bin.

####  6.2.5 TGE

All-sky HEALPix maps of the total Galactic extinction are made available in
two separate tables in the Gaia DR3 archive at various resolutions (HEALPix
levels), namely the tables total_galactic_extinction_map and
total_galactic_extinction_map_opt. The estimation of the total Galactic
extinction in each HEALPix is taken as the median $A_{0}$ of the extinction
tracers, as measured by GSP-Phot, where the tracers are giants outside the ISM
layer of the disc of the Milky Way. The first table,
total_galactic_extinction_map, contains HEALPix maps at levels 6 through 9
(corresponding to pixel sizes of 0.839 to 0.013 deg2), with extinction
estimates for all HEALPixes that have at least three extinction tracers. The
second map is a reduced version of this first map, using a subset of the
pixels to construct a map at variable resolution, using the highest HEALPix
level available (6 through 9) that has at least ten tracers for that HEALPix
level. An example of the TGE map for the Chameleon region is shown in Fig. 16.

Figure 16: Total galactic extinction of the Chamaeleon region from the
total_galactic_extinction_map_opt, showing the extinction at the optimal
HEALPix level (between 6 and 9).

###  6.3 Stellar spectroscopic parameters

The BP/RP and RVS spectra contain information about atmospheric parameters of
stars: $T_{\mathrm{{eff}}}$, $\log\,g$, [M/H], along with chemical abundances,
an activity index, equivalent widths and $v\sin i$. The parameters are derived
by the two general stellar parametrizers: GSP-Phot and GSP-Spec based on the
BP/RP and RVS spectra respectively, assuming a single source. Other estimates
of these parameters are produced by modules working in specific stellar
regimes, and depending on the scientific case the user may prefer to use these
results: ESP-HS, ESP-CS, and ESP-UCD are tailored to analyse hot stars, cool
active stars, and ultra-cool dwarfs, respectively. Finally, MSC provides two
$T_{\mathrm{{eff}}}$, two $\log\,g$, and one [M/H] parameter assuming that the
BP/RP spectra are a combination of two components of an unresolved binary. The
quality, validation and use of the stellar spectroscopic and evolutionary
parameters are described in the accompanying Paper II (Fouesneau, 2022).

####  6.3.1 GSP-Phot

GSP-Phot provides estimates of the $T_{\mathrm{{eff}}}$, $\log\,g$, [M/H], and
upper and lower confidence intervals for 470 million sources. These parameters
are estimated at the same time as extinction, see Sect. 6.2.1 for details.
These parameters are also available in the mcmc_samples_gsp_phot. The values
for the best library are provided in the astrophysical_parameters table, and
these are duplicated to gaia_source for convenience to the user. The auxiliary
parameter logposterior_gspphot indicates how well the data fits the model.
Results from individual libraries (MARCS, PHOENIX, A, OB) are available in the
astrophysical_parameters_supp table. A HR diagram using GSP-Phot
$T_{\mathrm{{eff}}}$ and FLAME ${L}$ is shown in Sect. 3.11 (Fig. 24), colour-
coded by evolutionary stage.

####  6.3.2 GSP-Spec

The GSP-Spec Matisse-Gauguin method provides 23 independent APs in the
astrophysical_parameters table for up to 6 million sources derived from the
RVS spectra, see Fig. 3 top panels. These include: $T_{\mathrm{{eff}}}$, log
g, [M/H], [$\alpha$/Fe], goodness-of-fit over the entire spectral range,
individual chemical abundances of 12 elements, CN equivalent width and its
fitting parameters, DIB equivalent width and its fitting parameters. For each
chemical element abundance, the number of used spectral lines, as well as the
line-to-line scatter are presented. A histogram with the available chemical
abundances and equivalent widths of the CN line and DIB is shown in Fig. 17.

Figure 17: Histogram showing the number of sources for each chemical species
with abundances or equivalent widths in Gaia DR3 produced by the GSP-Spec
Matisse-Gauguin method, in logarithmic scale. [$\alpha$/Fe] is derived at the
same time as the atmospheric parameters ($T_{\mathrm{{eff}}}$, $\log\,g$,
[M/H]) and is available for $\sim$5 million sources. A quality flag
flags_gspspec is provided for the best use of the elemental abundances.

A second method, GSP-Spec-ANN, based on the ANN method (Dafonte et al., 2016;
Manteiga et al., 2010), provides four APs in the astrophysical_parameters_supp
table: teff_gspspec_ann, logg_gspspec_ann, mh_gspspec_ann,
alphafe_gspspec_ann, and their upper and lower confidence values, along with a
goodness-of-fit over the entire spectral range logchisq_gspspec_ann.

Finally, following the results of the internal GSP-Spec validation a long GSP-
Spec catalogue flag has been implemented during the post-processing and
published in both the astrophysical_parameters and the
astrophysical_parameters_supp tables, and the users should therefore check
this flag depending on the use case of the parameters, see flags_gspspec, and
flags_gspspec_ann (more details on the use of these flags are provided in
Recio-Blanco, 2022b). A HR diagram using $T_{\mathrm{{eff}}}$ from GSP-Spec
Matisse-Gauguin and the FLAME luminosity is shown in Sect. 3.11 (Fig. 24),
colour-coded by stellar age.

####  6.3.3 ESP-ELS

The ESP-ELS module identifies emission-line stars (ELS) in the H$\alpha$
wavelength domain. An estimate of the H$\alpha$ pseudo-equivalent width (pEW
H$\alpha$), ew_espels_halpha, for 235 million stars is provided in the
catalogue. For stars having teff_gspphot $\leq$ 5000 K, a correction was
applied to mitigate the impact of blends with spectral lines and molecular
bands present in the spectra of cooler stars as follows

$\mathrm{ew\\_espels\\_halpha}=\begin{cases}\mathrm{pEWH}\alpha&\text{T${}_{\mathrm{eff}}>$
5\,000 K}\\\
\mathrm{pEWH}\alpha-\mathrm{pEWH}\alpha^{\mathrm{model}}&\text{T${}_{\mathrm{eff}}\leq$
5\,000 K}\end{cases}$ (1)

where $\mathrm{pEWH}\alpha^{\mathrm{model}}$ is the pEW H$\alpha$ value as
measured on the simulated/synthetic spectrum that best corresponds to the
astrophysical parameters provided by GSP-Phot. The value of the correction is
provided by ew_espels_halpha_model. When the correction was applied, the value
of the H$\alpha$ quality flag, ew_espels_halpha_flag, was set to one, if not
it was set to zero. We show in Fig. 18 the temperature distribution of the
pseudo-equivalent width (pEW). As expected, when the model estimate is
subtracted for the cooler stars (middle panel), the H$\alpha$ pEW peaks in
absorption (i.e. positive values) at temperatures between 8000 and 9000 K.
When the model estimate is also applied for the hotter $T_{\mathrm{{eff}}}$
(right panel), the negative estimates are expected to belong to emission-line
stars.

Figure 18: Distribution of the H$\alpha$ pseudo-equivalent width (pEW)
obtained by ESP-ELS for 135 258 targets chosen in order to homogeneously cover
the temperature domain as a function of the effective temperature derived by
GSP-Phot and ESP-HS ($T_{\mathrm{{eff}}}$ $>$ 7500 K only for the latter).
Left panel: We report the value obtained before the removal of the model
estimate. Middle panel: We show the result saved in ew_espels_halpha (the
model value is removed for sources with $T_{\mathrm{{eff}}}$ $\leq 5000$ K).
Right panel: We show the result obtained when the model value
(ew_espels_halpha_model) is also removed for stars hotter than 5000 K.

####  6.3.4 ESP-HS

ESP-HS determines the astrophysical parameters $T_{\mathrm{{eff}}}$
(teff_esphs) and $\log\,g$ (logg_esphs) of $\sim$2 million stars hotter than
7500 K according to the spectral type tag provided by ESP-ELS
(spectraltype_esphs). These results are found in the astrophysical_parameters
table. The module assumes a solar chemical composition, and therefore no
corresponding metallicity value is saved in the catalogue. The parameters are
derived by fitting the BP/RP spectra and, when available, the RVS spectra, see
Fig. 3 lower panels. If RVS data are used, ESP-HS also estimates a line
broadening term (i.e. aimed to take into account the broadening mechanisms not
included when preparing the simulated/synthetic spectra) by assuming that it
is only due to the axial rotation of the star ($v\sin i$). Note that an
attempt to measure it can only be made on the RVS data, when the instrumental
broadening does not dominate. Therefore, a value of $v\sin i$ (vsini_esphs) is
provided along with the spectroscopic parameters for the brighter targets
(where RVS spectra was available for processing). The mode adopted to process
the data is stored in the first digit reading from the left of the ESP-HS flag
flags_esphs. Its value is 0 for BP/RP+RVS processing and 1 for BP/RP-only
processing. We show in Fig. 19 the Kiel diagram obtained in both modes. In the
fainter magnitude regime (i.e. BP/RP-only mode), the overdensity perpendicular
to the main sequence is mainly due to hot horizontal branch stars as was
confirmed by a systematic query in the Simbad database (Wenger et al., 2000).

Figure 19: Kiel diagram of ESP-HS results obtained in BP/RP+RVS (left panel)
and BP/RP-only (right panel) processing modes. Left panel: The evolutionary
tracks of Georgy et al. (2013) for solar metallicity, and
$\frac{\omega}{\omega_{\mathrm{c}}}$ = 0.8 (rotation at 80% of its critical
velocity) are shown in blue. The initial mass in solar masses is indicated at
the start of each track. Right panel: The region occupied by hot horizontal
branch (HB) stars is delimited by the expected zero age (ZAHB) and terminal
age (TAHB) HB lines. ZAHB and TAHB boundaries are taken from Dorman et al.
(1993).

####  6.3.5 ESP-UCD

ESP-UCD provides $T_{\mathrm{{eff}}}$ estimates, teff_espucd, and
uncertainties for ultracool dwarfs (UCDs) for $\sim 94\,000$ sources in the
astrophysical_parameters table. An input target list was provided in order to
process UCDs, see Sect. 5.1, and these sources were selected according to the
following criteria: $\varpi>1.7$ mas, $G-G_{\rm RP}>1.0$ mag, $q_{33}>60$,
$q_{50}>71$, and $q_{67}>83$, where $q_{33}$, $q_{50}$ and $q_{67}$ represent
the pixel indices at which the 33.33, 50 and 66.67 percentiles of the total
flux in the RP spectrum are attained. These criteria were defined using the
Gaia Ultra Cool Dwarf Sample and include a safety margin to go as far as M6.

In order for the source to appear in the catalogue, we required a
$T_{\mathrm{{eff}}}$ estimate in the 500 K to 2700 K range, rp_n_transits
$\geq 15$ and $\log_{10}(\sigma_{\varpi})\geq-0.8+1.3\log_{10}(\varpi)$, where
$\varpi$ is the parallax. We also imposed criteria on the RP flux and distance
between the source RP spectrum and its nearest training set template in order
to retain the source in the DR3 catalogue222222These criteria are the
following: the normalized RP spectrum median curvature $\tau<2.0\times
10^{-5}$ (see Section 11.3.10 of the online documentation for definition of
$\tau$), $\log_{10}(d_{TS})<-2.05$ where $d_{TS}$ is the distance to the
template, the sum of negative normalised RP spectrum fluxes $\leq-0.1$, and
the reddest flux corresponding to the 120-th pixel of the (normalized) RP
spectrum is less than 0.015. . Because the $T_{\mathrm{{eff}}}$ is based on a
regression module trained with empirical data, it should be noted by the user
that results may be biased for sources with metallicity and gravity departing
significantly from the training sample values (solar metallicity and
$5.0\lesssim\log g\lesssim 5.5$).

The final catalogue of Gaia UCDs contains a total of 94 158 sources in three
quality categories, flags_espucd = 0 (best), 1, 2 (see Section 11.3.10 of the
online documentation for a more detailed definition). In Fig. 20 we show the
distribution of $T_{\mathrm{{eff}}}$ for each of the quality levels across the
full $T_{\mathrm{{eff}}}$ range of ultra-cool dwarfs. The inset shows the
distribution of these sources in magnitude--parallax space, colour-coded by
teff_espucd.

Figure 20:  Distribution of the $T_{\mathrm{{eff}}}$ of ultra-cool dwarfs from
ESP-UCD according to their quality flag (note the log scale), flags_espucd (0
= 40 633, 1 = 26 795, 2 = 26 730 sources). Inset: Distribution of these same
sources in $G$ and parallax, colour-coded by $T_{\mathrm{{eff}}}$.  Figure 21:
Distribution of difference in surface gravity ($\log g_{1}-\log g_{2}$ versus
effective temperature ratio $T_{\rm eff,1}/T_{\rm eff,2}$ of a half million
random sources with results from MSC. MSC assumes that each source is an
unresolved binary with the same [M/H], distance, and $A_{0}$. The peak
uncertainty in $T_{\rm eff,1}/T_{\rm eff,2}\sim 0.2$ and $\log g_{1}-\log
g_{2}\sim 0.7$.

####  6.3.6 ESP-CS

In Gaia DR3, ESP-CS estimates a stellar activity index activityindex_espcs and
its uncertainties activityindex_espcs_uncertainty, in nm units, from the
calcium infrared triplet (Ca ii IRT, at 849.8, 854.2, and 866.2 nm) in the RVS
spectra, see . These parameters and a further parameter,
activityindex_espcs_input are found in the astrophysical_parameters table for
$\sim$2 million sources. The latter parameter indicates whether the source APs
used in defining the purely photospheric spectrum to which the RVS spectrum is
compared with are from GSP-Spec "M1" or GSP-Phot "M2". During the processing,
the default value is to use the parameters from GSP-Spec because the activity
index is derived from the same data as the atmospheric parameters, but when
they are not available, the ones from GSP-Phot are used.

Figure 22: Examples of the activity index derived by the ESP-CS module. Top
panel: Ca ii IRT RVS spectrum of the chromospherically active star Gaia DR3
4891212046355683328 (HIP 20737) with a measured $R^{\prime}_{HK}=-3.72$ from
FEROS spectra using the Ca H&K doublet. Bottom panel: RVS spectrum for the T
Tauri star Gaia DR3 6243393817024157184 with a mass accretion rate
$\log\dot{M}=-10.51\,\mathrm{M_{\odot}yr^{-1}}$. The same method is applied to
measure these activity indices for both types of excess flux. Black lines are
the observed spectra. Red lines are the purely photospheric spectrum template.
The orange filled spectral regions are the area over which the integral of the
excess flux is evaluated to produce the activity index.

ESP-CS has processed stars with $G\lesssim 15$, $T_{\mathrm{{eff}}}$ in the
range (3000 K, 7000 K), $\log\,g$ in the (3.0, 5.0) range, and [M/H] in the
($-$0.5, 1.0) range. Only results for sources with the RVS spectrum
$\mathrm{SNR}\geq 20$ are found in the archive.

In Fig. 22 two examples of the ESP-CS analysis are shown. One is the case of
the chromospherically active star Gaia DR3 4891212046355683328 (HIP 20737),
with activityindex_espcs $\approx 0.05$ nm. From the analysis of ESO-FEROS
archive spectra, Lanzafame (2022) derive a corresponding activity index
$R^{\prime}_{HK}=-3.72$ (Noyes et al., 1984) from the Ca ii H&K doublet. The
second example is the case of the T Tauri star Gaia DR3,6243393817024157184
with a mass accretion rate $\log\dot{M}=-10.51\,\mathrm{M_{\odot}yr^{-1}}$
(Manara et al., 2020).

The ESP-CS activity index is given as an enhancement factor in the core of Ca
ii IRT lines with respect to a synthetic template representing the spectrum of
an inactive star with the same $T_{\mathrm{{eff}}}$, $\log\,g$, and [M/H], see
Lanzafame (2022) for details. Despite the fact that the method ensures that
the photospheric contribution is removed from the activity index parameter, in
principle, because of the contrast effect with the underlying continuum, the
index derived gives a relative measure of the stellar activity at a given
$T_{\mathrm{{eff}}}$. In practice, it can be used to compare stars with
similar $T_{\mathrm{{eff}}}$ or same spectral type, but it is unsuitable for
comparing stars with very different $T_{\mathrm{{eff}}}$ or spectral type.
Lanzafame (2022) provides a method to derive an index $R^{\prime}_{\rm IRT}$
from the ESP-CS activity index and $T_{\mathrm{{eff}}}$, which is analogous to
the $R^{\prime}_{\rm HK}$ and largely independent from the contrast effect.

In general, a value of the activity index around 0.03 -- 0.05 separates the
regimes in which the chromospheric activity or mass accretion dominate. The
separation in terms of $R^{\prime}_{\rm IRT}$ is discussed in Lanzafame
(2022).

####  6.3.7 MSC

MSC assumes that the BP/RP spectrum is a composition of two unresolved
components of a binary system, and it estimates $T_{\mathrm{{eff}}}$,
teff_msc1 and teff_msc2, and $\log\,g$, logg_msc1 and logg_msc2, for the two
components for 349 million sources, with upper and lower confidence intervals.
It assumes a solar metallicity prior and estimates one unique metallicity for
each source mh_msc. These parameters are inferred at the same time as $A_{0}$
and distance, see Sect. 6.2.2 In Fig. 21 we show the distribution of the
temperature ratio and $\log\,g$ differences of the individual components
according to the MSC assumption of an unresolved binary. The grey dashed lines
indicate where two sources are of equal mass i.e. same $T_{\mathrm{{eff}}}$
and $\log\,g$. Results from MSC are in Gaia DR3 if $G<18.25$. However, a user
will need to construct a binary sample using external literature sources, or
other indicators of binarity, such as classprob_dsc_combmod_binary or the
other tables in the archive indicating binarity, see Arenou (2022).

###  6.4 Stellar evolutionary parameters

By stellar evolutionary parameters we imply the following: mass ${M}$
232323Mass is technically not an evolution parameter but it is the most
important physical property responsible for evolution., luminosity ${L}$,
absolute magnitude MG, radius ${R}$, radial velocity242424The radial_velocity
parameter produced by CU6 is found in gaia_source. In Gaia DR3 it is not
corrected for the gravitational redshift nor the convective shift. correction
for the stellar gravitational redshift, $rv_{\rm GR}$, the age of a star
$\tau$, and the evolutionary stage (evolstage). The parameters $rv_{\rm GR}$
and age are in units of kilometers per second and gigayears, respectively. The
parameter evolstage is an integer from 100 to 1300 indicating the phase the
star is in, in the evolution sequence, see Hidalgo et al. (2018). Most of
these parameters are derived by FLAME but GSP-Phot also derives MG and ${R}$.
These parameters show good agreement between the two modules in most parameter
ranges, see Paper II (Fouesneau, 2022) and Sect. 11.4.5 of the online
documentation for these comparisons.

Figure 23: Comparison of the mass derived from GSP-Phot using $\log\,g$ and
${R}$, Mlogg,phot, and mass_flame for stars of different metallicities. The
histograms are normalised for visual purposes and the relative number of stars
in each sample is indicated in the label.

####  6.4.1 FLAME

FLAME produces all of the evolutionary parameters except for MG, although this
can be derived directly using ${L}$ and $\mathrm{BC}_{G}$. Two separate
results are provided in the archive: the first in the astrophysical_parameters
table is based on the "best" library from GSP-Phot for $\sim$280 million
sources. A second set of results are based on the Matisse-Gauguin GSP-Spec
parameters for approximately 5 million sources, and these are found in the
supplementary table astrophysical_parameters_supp. Not only are the data found
in separate tables, but the field names are also distinguishable with the
latter containing spec e.g. mass_flame and mass_flame_spec.

The values of ${M}$, ${R}$, ${L}$, $rv_{\rm GR}$, and age are accompanied by
an upper and lower confidence level encompassing a confidence interval of 68%
e.g. mass_flame_upper. To derive ${L}$ a bolometric correction for the $G$
band is needed, see Sect. 4.3, and this is provided as an auxiliary parameter
bc_flame(_spec)252525A tool has been provided to calculate this, see
https://gitlab.oca.eu/ordenovic/gaiadr3_bcg. An estimate of the distance is
also needed: for the results in the astrophysical_parameters table either the
parallax or distance_gspphot is used. This processing information is provided
as the second character of flags_flame where ’0’ implies the use of the
parallax, ’1’ is the use of distance_gspphot, while ’2’ is also parallax but
where convergence issues with distance_gspphot have been reported, see the
online documentation for details.

A solar metallicity-prior was assumed for deriving ${M}$, age and evolstage,
due to known but unquantified issues with [M/H] at the time of operations, see
Sect. 8. This assumption does have an impact on the results for non-solar
metallicity stars, in particular for the age, where metal-poor/metal-rich
stars will have a biased age towards younger/older ages, see Creevey &
Lebreton (2022) for further discussion. One should therefore be cautious when
using the age value outside of the --0.5 $<$ [M/H] $<$ +0.5 regime.

As we can derive a mass using logg_gspphot and radius_gspphot we can
investigate the impact of the solar-metallicity assumption on the masses by
comparing these to mass_flame. In Fig. 23 we show the differences in the two
mass determinations, normalised by their joint uncertainties, for solar-
metallicity stars (grey), and then for non-solar metallicity stars (blue,
green, red). The histogram is normalised for visual purposes and the
percentage of stars in each histogram out of the total is indicated on the
label. One can see that for low-metallicity stars ($<-2.0$), the mass from
GSP-Phot differs by typically 2$\sigma$ or more from FLAME. One may prefer to
use such an estimate of mass for the roughly 2% of lower-metallicity stars.

Determining the masses and ages of giants is a delicate task compared to the