Gaia Data Release 3

Summary of the variability processing and analysis

¹ Department of Astronomy, University of Geneva, Chemin Pegasi 51, 1290 Versoix, Switzerland
² Department of Astronomy, University of Geneva, Chemin d’Ecogia 16, 1290 Versoix, Switzerland
³ INAF – Osservatorio Astrofisico di Torino, Via Osservatorio 20, 10025 Pino Torinese (TO), Italy
⁴ INAF – Osservatorio di Astrofisica e Scienza dello Spazio di Bologna, Via Piero Gobetti 93/3, 40129 Bologna, Italy
⁵ Instituut voor Sterrenkunde, KU Leuven, Celestijnenlaan 200D, 3001 Leuven, Belgium
⁶ INAF – Osservatorio Astrofisico di Catania, Via S. Sofia 78, 95123 Catania, Italy
⁷ Institute of Astronomy, University of Cambridge, Madingley Road, Cambridge CB3 0HA, UK
⁸ RHEA for European Space Agency (ESA), Camino bajo del Castillo, s/n, Urbanizacion Villafranca del Castillo, Villanueva de la Cañada, 28692 Madrid, Spain
⁹ School of Physics and Astronomy, Tel Aviv University, Tel Aviv 6997801, Israel
¹⁰ University of Vienna, Department of Astrophysics, Trkenschanzstraße 17, A1180 Vienna, Austria
¹¹ Konkoly Observatory, Research Centre for Astronomy and Earth Sciences, Etvs Loránd Research Network (ELKH), MTA Centre of Excellence, Konkoly Thege Miklós út 15-17, 1121 Budapest, Hungary
¹² INAF – Osservatorio Astronomico di Capodimonte, Via Moiariello 16, 80131 Napoli, Italy
¹³ Astronomical Observatory, University of Warsaw, Al. Ujazdowskie 4, 00-478 Warszawa, Poland
¹⁴ Sednai Sàrl, Geneva, Switzerland
¹⁵ European Space Agency (ESA), European Space Astronomy Centre (ESAC), Camino bajo del Castillo, s/n, Urbanizacion Villafranca del Castillo, Villanueva de la Cañada, 28692 Madrid, Spain
¹⁶ Dipartimento di Fisica e Astronomia “Ettore Majorana”, Università di Catania, Via S. Sofia 64, 95123 Catania, Italy
¹⁷ Porter School of the Environment and Earth Sciences, Tel Aviv University, Tel Aviv 6997801, Israel
¹⁸ ELTE Etvs Loránd University, Institute of Physics, 1117, Pázmány Péter sétány 1A, Budapest, Hungary
¹⁹ Department of Astrophysics/IMAPP, Radboud University, PO Box 9010 6500 GL Nijmegen, The Netherlands
²⁰ Max Planck Institute for Astronomy, Knigstuhl 17, 69117 Heidelberg, Germany
²¹ Dpto. de Inteligencia Artificial, UNED, c/ Juan del Rosal 16, 28040 Madrid, Spain
²² Institute of Physics, Laboratory of Astrophysics, Ecole Polytechnique Fédérale de Lausanne (EPFL), Observatoire de Sauverny, 1290 Versoix, Switzerland
²³ Université de Caen Normandie, Côte de Nacre, Boulevard Maréchal Juin, 14032 Caen, France
²⁴ MTA CSFK Lendlet Near-Field Cosmology Research Group, Konkoly Observatory, MTA Research Centre for Astronomy and Earth Sciences, Konkoly Thege Miklós út 15-17, 1121 Budapest, Hungary
²⁵ Ruđer Bošković Institute, Bijenička cesta 54, 10000 Zagreb, Croatia
²⁶ Villanova University, Department of Astrophysics and Planetary Science, 800 E Lancaster Avenue, Villanova, PA 19085, USA
²⁷ Department of Particle Physics and Astrophysics, Weizmann Institute of Science, Rehovot 7610001, Israel
²⁸ Department of Physics and Astronomy G. Galilei, University of Padova, Vicolo dell’Osservatorio 3, 35122 Padova, Italy
²⁹ Institute of Global Health, University of Geneva, Chemin des Mines 9, 1202 Genève, Switzerland

Received: 10 June 2022
Accepted: 24 February 2023

Abstract

Context. Gaia has been in operations since 2014, and two full data releases (DR) have been delivered so far: DR1 in 2016 and DR2 in 2018. The third Gaia data release expands from the early data release (EDR3) in 2020, which contained the five-parameter astrometric solution and mean photometry for 1.8 billion sources by providing 34 months of multi-epoch observations that allowed us to systematically probe, characterise, and classify variable celestial phenomena.

Aims. We present a summary of the variability processing and analysis of the photometric and spectroscopic time series of 1.8 billion sources carried out for Gaia DR3.

Methods. We used statistical and machine learning methods to characterise and classify the variable sources. Training sets were built from a global revision of major published variable star catalogues. For a subset of classes, specific detailed studies were conducted to confirm their class membership and to derive parameters that are adapted to the peculiarity of the considered class.

Results. In total, 10.5 million objects are identified as variable in Gaia DR3 and have associated time series in G, G_BP, and G_RP and, in some cases, radial velocity time series. The DR3 variable sources subdivide into 9.5 million variable stars and 1 million active galactic nuclei or ‘quasars’. In addition, supervised classification identified 2.5 million galaxies thanks to spurious variability induced by the extent of these objects. The variability analysis output in the DR3 archive amounts to 17 tables, containing a total of 365 parameters. We publish 35 types and subtypes of variable objects. For 11 variable types, additional specific object parameters are published. Here, we provide an overview of the estimated completeness and contamination of most variability classes.

Conclusions. Thanks to Gaia, we present the largest whole-sky variability analysis based on coherent photometric, astrometric, and spectroscopic data. Future Gaia data releases will more than double the span of time series and the number of observations, allowing the publication of an even richer catalogue.