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Gaia Data Release 3: All-sky classification of 12.4 million variable sources into 25 classes

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arxiv 2211.17238 v2 pith:7LNG24IR submitted 2022-11-30 astro-ph.GA astro-ph.SR

classification astro-ph.GAastro-ph.SR
keywords gaiamillionvariablegalaxiesobjectsvariabilityclassificationsources
verification ladder T0 review T1 audit T2 compute T3 formal
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Gaia DR3 contains 1.8 billion sources with G-band photometry, 1.5 billion of which with BP and 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. We pursue a classification of Galactic and extra-galactic 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 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.

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Cited by 3 Pith papers

Reviewed papers in the Pith corpus that reference this work. Sorted by Pith novelty score. Full citation record

  1. Characterising the Kinematics and Evolution of Young Stellar Groups within 1 kpc of the Sun Using Gaia DR3

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    Fine-tuned ResNet50 and vision transformers on polar-hexbin images classify eclipsing binaries as detached or overcontact with high accuracy on real data, but cannot reliably detect starspots.

  3. From stellar light to astrophysical insight: automating variable star research with machine learning

    astro-ph.IM 2025-07 unverdicted

    An invited review of machine learning for automated variable star research, covering data cleaning, variability classification, stellar parameter inference, and foundation models.

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