GaiaData Release 3

Abstract

Context. Gaia DR3 contains 1.8 billion sources with G-band photometry, 1.5 billion of which with G BP and G RP 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, G BP , and G RP 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.

Variability Catalog of Stars Observed during the TESS Prime Mission

“…These include light curves from Quick Look Pipeline Lastly, there are a number of efforts underway to classify photometric variables with much finer physical and observational categories than has been done in this work. Notable examples are the OGLE project (Udalski et al 2008), the Gaia DR3 variability search (Gaia Collaboration et al 2021;Rimoldini et al 2023), specific types of variable stars observed with TESS (e.g., Antoci et al 2019;Howard et al 2020b;Hon et al 2021;Avallone et al 2022;Barac et al 2022;Holcomb et al 2022;Kurtz 2022;Prša et al 2022a;Saunders et al 2022), and many others. The TESS Asteroseismic Science Operations Center (TASOC) is engaged in much more detailed and individualized analysis of certain types of stellar variability (Audenaert et al 2021).…”

Quaia, the Gaia-unWISE Quasar Catalog: An All-sky Spectroscopic Quasar Sample

“…DSC is estimated to have a completeness of over 90% and a purity of around 24% for quasars. Another machine learning model selected over 1 million sources based on their variability, as active nuclei have time-variable accretion; the model inputs were statistics of time series data in all Gaia bands as well as photometric and astrometric quantities, as detailed in Rimoldini et al (2023). Additionally, a set of nearly 1 million sources was selected based on their surface brightness profile; this selection used existing major quasar catalogs to compile an initial list of sources, which were then processed by the Gaia surface brightness profile module (Ducourant et al 2023).…”

Impact of the Gaia ESA mission on the primary Period–Luminosity Calibrators in the Milky Way: Cepheids and RR Lyrae

“…Statistical and Machine Learning methods are used to identify, characterise and classify the candidate variable sources into different types (Eyer et al 2017(Eyer et al , 2023Rimoldini et al 2023). Candidate variables of the different types are then ingested into the Specific Object Study (SOS) pipelines, which are specialised to validate the classification into types and derive specific attributes for the variable sources, which are confirmed to actually belong to the classes they have been assigned to.…”