Gaia Data Release 3

Cross-match of Gaia sources with variable objects from the literature^⋆

¹ RHEA for European Space Agency (ESA), Camino bajo del Castillo, s/n, Urbanizacion Villafranca del Castillo, Villanueva de la Cañada, 28692 Madrid, Spain
² Department of Astronomy, University of Geneva, Chemin d’Ecogia 16, 1290 Versoix, Switzerland
³ Sednai Sàrl, Geneva, Switzerland
⁴ Department of Astronomy, University of Geneva, Chemin Pegasi 51, 1290 Versoix, Switzerland
⁵ Konkoly Observatory, Research Centre for Astronomy and Earth Sciences, Eötvös Loránd Research Network, Konkoly Thege 15-17, 1121 Budapest, Hungary
⁶ ELTE Eötvös Loránd University, Institute of Physics, Pázmány Péter sétány 1/A, 1117 Budapest, Hungary
⁷ INAF – Osservatorio Astrofisico di Torino, Via Osservatorio 20, 10025 Pino Torinese, Italy
⁸ INAF – Osservatorio di Astrofisica e Scienza dello Spazio di Bologna, Via Gobetti 93/3, 40129 Bologna, Italy
⁹ Instituut voor Sterrenkunde, KU Leuven, Celestijnenlaan 200D, 3001 Leuven, Belgium
¹⁰ INAF – Osservatorio Astrofisico di Catania, Via S. Sofia 78, 95123 Catania, Italy
¹¹ European Space Agency (ESA), European Space Astronomy Centre (ESAC), Camino Bajo del Castillo s/n, Urb. Villafranca del Castillo, 28692 Villanueva de la Cañada, Spain
¹² Max Planck Institute for Astronomy, Königstuhl 17, 69117 Heidelberg, Germany
¹³ Astronomical Observatory, University of Warsaw, Al. Ujazdowskie 4, 00-478 Warszawa, Poland
¹⁴ School of Physics and Astronomy, Raymond and Beverly Sackler Faculty of Exact Sciences, Tel Aviv University, Tel Aviv 6997801, Israel
¹⁵ INAF – Osservatorio Astronomico di Capodimonte, Via Moiariello 16, 80131 Napoli, Italy
¹⁶ Porter School of the Environment and Earth Sciences, Raymond and Beverly Sackler Faculty of Exact Sciences, Tel Aviv University, Tel Aviv 6997801, Israel

Received: 28 June 2022
Accepted: 24 October 2022

Abstract

Context. In current astronomical surveys with ever-increasing data volumes, automated methods are essential. Objects of known classes from the literature are necessary to train supervised machine-learning algorithms and to verify and validate their results.

Aims. The primary goal of this work is to provide a comprehensive data set of known variable objects from the literature that we cross-match with Gaia DR3 sources, including a large number of variability types and representatives, in order to cover sky regions and magnitude ranges relevant to each class in the best way. In addition, non-variable objects from selected surveys are targeted to probe their variability in Gaia and possible use as standards. This data set can be the base for a training set that can be applied to variability detection, classification, and validation.

Methods. A statistical method that employed astrometry (position and proper motion) and photometry (mean magnitude) was applied to selected literature catalogues in order to identify the correct counterparts of known objects in the Gaia data. The cross-match strategy was adapted to the properties of each catalogue, and the verification of results excluded dubious matches.

Results. Our catalogue gathers 7 841 723 Gaia sources, 1.2 million of which are non-variable objects and 1.7 million are galaxies, in addition to 4.9 million variable sources. This represents over 100 variability (sub)types.

Conclusions. This data set served the requirements of the Gaia variability pipeline for its third data release (DR3) from classifier training to result validation, and it is expected to be a useful resource for the scientific community that is interested in the analysis of variability in the Gaia data and other surveys.