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Gaia Data Release 2: Variable stars in the colour-absolute magnitude diagram

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arxiv 1804.09382 v2 pith:3HUAVDG4 submitted 2018-04-25 astro-ph.SR

classification astro-ph.SR
keywords variablegaiastarsvariabilitycamdstargalacticacross
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The ESA Gaia mission provides a unique time-domain survey for more than 1.6 billion sources with G ~ 21 mag. We showcase stellar variability across the Galactic colour-absolute magnitude diagram (CaMD), focusing on pulsating, eruptive, and cataclysmic variables, as well as on stars exhibiting variability due to rotation and eclipses. We illustrate the locations of variable star classes, variable object fractions, and typical variability amplitudes throughout the CaMD and illustrate how variability-related changes in colour and brightness induce `motions' using 22 months worth of calibrated photometric, spectro-photometric, and astrometric Gaia data of stars with significant parallax. To ensure a large variety of variable star classes to populate the CaMD, we crossmatch Gaia sources with known variable stars. We also used the statistics and variability detection modules of the Gaia variability pipeline. Corrections for interstellar extinction are not implemented in this article. Gaia enables the first investigation of Galactic variable star populations across the CaMD on a similar, if not larger, scale than previously done in the Magellanic Clouds. Despite observed colours not being reddening corrected, we clearly see distinct regions where variable stars occur and determine variable star fractions to within Gaia's current detection thresholds. Finally, we show the most complete description of variability-induced motion within the CaMD to date. Gaia enables novel insights into variability phenomena for an unprecedented number of stars, which will benefit the understanding of stellar astrophysics. The CaMD of Galactic variable stars provides crucial information on physical origins of variability in a way previously accessible only for Galactic star clusters or external galaxies.

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  1. 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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