TY - JOUR
T1 - Gaia Data Release 3
T2 - Cross-match of Gaia sources with variable objects from the literature
AU - Gavras, Panagiotis
AU - Rimoldini, Lorenzo
AU - Nienartowicz, Krzysztof
AU - De Fombelle, Grégory Jevardat
AU - Holl, Berry
AU - Ábrahám, Péter
AU - Audard, Marc
AU - Carnerero, Maria I.
AU - Clementini, Gisella
AU - De Ridder, Joris
AU - Distefano, Elisa
AU - Garcia-Lario, Pedro
AU - Garofalo, Alessia
AU - Kóspál, Ágnes
AU - Kruszyńska, Katarzyna
AU - Kun, Mária
AU - Lecoeur-Taïbi, Isabelle
AU - Marton, Gábor
AU - Mazeh, Tsevi
AU - Mowlavi, Nami
AU - Raiteri, Claudia M.
AU - Ripepi, Vincenzo
AU - Szabados, László
AU - Zucker, Shay
AU - Eyer, Laurent
N1 - Publisher Copyright:
© 2023 EDP Sciences. All rights reserved.
PY - 2023/6/1
Y1 - 2023/6/1
N2 - 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.
AB - 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.
KW - Catalogs
KW - Galaxies: general
KW - Methods: data analysis
KW - Stars: variables: general
KW - Surveys
UR - http://www.scopus.com/inward/record.url?scp=85164262788&partnerID=8YFLogxK
U2 - 10.1051/0004-6361/202244367
DO - 10.1051/0004-6361/202244367
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AN - SCOPUS:85164262788
SN - 0004-6361
VL - 674
JO - Astronomy and Astrophysics
JF - Astronomy and Astrophysics
M1 - A22
ER -