The Variable Universe with the Gaia mission and AI methods

TL;DR

The paper discusses how the Gaia mission's extensive dataset of stellar observations, combined with machine learning techniques, has enabled the creation of the largest catalog of classified variable celestial sources across the entire sky.

Contribution

It introduces a novel application of AI methods to process Gaia's vast data, resulting in a comprehensive catalog of variable stars and objects.

Findings

Largest catalog of classified variable sources created

Effective machine learning techniques for variability detection

Successful crossmatching and classification of celestial sources

Abstract

The Gaia mission has observed over 2 billion stars repeatedly across the entire sky over 10 years, revealing the many astronomical objects that vary on human timescales from seconds to years. Its repeated astrometric, photometric, spectrophotometric and spectroscopic measurements create an unprecedented dataset to probe the variable celestial sources down to G ~ 21 mag. To extract meaningful results from these many time series for so many sources, we have used machine learning techniques for crossmatching, variability detection, and variability classification. This approach has now led to the largest catalogue of classified variable sources ever produced over the entire celestial sphere.