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Automated classification of variable stars in the asteroseismology program of the Kepler space mission
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We present the first results of the application of supervised classification methods to the Kepler Q1 long-cadence light curves of a subsample of 2288 stars measured in the asteroseismology program of the mission. The methods, originally developed in the framework of the CoRoT and Gaia space missions, are capable of identifying the most common types of stellar variability in a reliable way. Many new variables have been discovered, among which a large fraction are eclipsing/ellipsoidal binaries unknown prior to launch. A comparison is made between our classification from the Kepler data and the pre-launch class based on data from the ground, showing that the latter needs significant improvement. The noise properties of the Kepler data are compared to those of the exoplanet program of the CoRoT satellite. We find that Kepler improves on CoRoT by a factor 2 to 2.3 in point-to-point scatter.
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From stellar light to astrophysical insight: automating variable star research with machine learning
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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