http://arxiv.org/abs/2211.17238
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. We pursue a classification of Galactic and extragalactic objects that are detected as variable by Gaia across the whole sky. 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, BP, and RP bands. 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 from 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, microlensing), with amplitudes from a few mmag to several magnitudes.
L. Rimoldini, B. Holl, P. Gavras, et. al.
Thu, 1 Dec 22
34/85
Comments: 105 pages, 511 figures, 4 tables. Submitted to Astronomy & Astrophysics
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