TY - JOUR
T1 - Gaia Data Release 3
T2 - All-sky classification of 12.4 million variable sources into 25 classes
AU - Rimoldini, Lorenzo
AU - Holl, Berry
AU - Gavras, Panagiotis
AU - Audard, Marc
AU - De Ridder, Joris
AU - Mowlavi, Nami
AU - Nienartowicz, Krzysztof
AU - Jevardat De Fombelle, Grégory
AU - Lecoeur-Taïbi, Isabelle
AU - Karbevska, Lea
AU - Evans, Dafydd W.
AU - Ábrahám, Péter
AU - Carnerero, Maria I.
AU - Clementini, Gisella
AU - Distefano, Elisa
AU - Garofalo, Alessia
AU - García-Lario, Pedro
AU - Gomel, Roy
AU - Klioner, Sergei A.
AU - Kruszyńska, Katarzyna
AU - Lanzafame, Alessandro C.
AU - Lebzelter, Thomas
AU - Marton, Gábor
AU - Mazeh, Tsevi
AU - Molinaro, Roberto
AU - Panahi, Aviad
AU - Raiteri, Claudia M.
AU - Ripepi, Vincenzo
AU - Szabados, László
AU - Teyssier, David
AU - Trabucchi, Michele
AU - Wyrzykowski, Łukasz
AU - Zucker, Shay
AU - Eyer, Laurent
N1 - Publisher Copyright: © The Authors 2023.
PY - 2023/6/1
Y1 - 2023/6/1
N2 - Context. Gaia DR3 contains 1.8 billion sources with G-band photometry, 1.5 billion of which with GBP and GRP photometry, complemented by positions on the sky, parallax, and proper motion. The median number of field-of-view transits in the three photometric bands is between 40 and 44 measurements per source and covers 34 months of data collection. Aims. We pursue a classification of Galactic and extra-galactic objects that are detected as variable by Gaia across the whole sky. Methods. Supervised machine learning (eXtreme Gradient Boosting and Random Forest) was employed to generate multi-class, binary, and meta-classifiers that classified variable objects with photometric time series in the G, GBP, and GRP bands. Results. Classification results comprise 12.4 million sources (selected from a much larger set of potential variable objects) and include about 9 million variable stars classified into 22 variability types in the Milky Way and nearby galaxies such as the Magellanic Clouds and Andromeda, plus thousands of supernova explosions in distant galaxies, 1 million active galactic nuclei, and almost 2.5 million galaxies. The identification of galaxies was made possible by the artificial variability of extended objects as detected by Gaia, so they were published in the galaxy-candidates table of the Gaia DR3 archive, separate from the classifications of genuine variability (in the vari-classifier-result table). The latter contains 24 variability classes or class groups of periodic and non-periodic variables (pulsating, eclipsing, rotating, eruptive, cataclysmic, stochastic, and microlensing), with amplitudes from a few milli-magnitudes to several magnitudes.
AB - Context. Gaia DR3 contains 1.8 billion sources with G-band photometry, 1.5 billion of which with GBP and GRP photometry, complemented by positions on the sky, parallax, and proper motion. The median number of field-of-view transits in the three photometric bands is between 40 and 44 measurements per source and covers 34 months of data collection. Aims. We pursue a classification of Galactic and extra-galactic objects that are detected as variable by Gaia across the whole sky. Methods. Supervised machine learning (eXtreme Gradient Boosting and Random Forest) was employed to generate multi-class, binary, and meta-classifiers that classified variable objects with photometric time series in the G, GBP, and GRP bands. Results. Classification results comprise 12.4 million sources (selected from a much larger set of potential variable objects) and include about 9 million variable stars classified into 22 variability types in the Milky Way and nearby galaxies such as the Magellanic Clouds and Andromeda, plus thousands of supernova explosions in distant galaxies, 1 million active galactic nuclei, and almost 2.5 million galaxies. The identification of galaxies was made possible by the artificial variability of extended objects as detected by Gaia, so they were published in the galaxy-candidates table of the Gaia DR3 archive, separate from the classifications of genuine variability (in the vari-classifier-result table). The latter contains 24 variability classes or class groups of periodic and non-periodic variables (pulsating, eclipsing, rotating, eruptive, cataclysmic, stochastic, and microlensing), with amplitudes from a few milli-magnitudes to several magnitudes.
KW - Catalogs
KW - Galaxies: general
KW - Methods: data analysis
KW - Quasars: general
KW - Stars: variables: general
UR - http://www.scopus.com/inward/record.url?scp=85163524255&partnerID=8YFLogxK
U2 - https://doi.org/10.1051/0004-6361/202245591
DO - https://doi.org/10.1051/0004-6361/202245591
M3 - مقالة
SN - 0004-6361
VL - 674
JO - Astronomy and Astrophysics
JF - Astronomy and Astrophysics
M1 - A14
ER -