Global stellar variability study in the field-of-view of the Kepler satellite
J. Debosscher, J. Blomme, C. Aerts, J. De Ridder

TL;DR
This study automates the analysis of Kepler data to classify stellar variability, identifying new variable stars and eclipsing binaries, and expanding understanding of stellar behavior with a comprehensive catalog.
Contribution
It introduces an improved automated classification pipeline for Kepler data, including new variability classes and detection methods for eclipsing binaries and rotational variables.
Findings
Analyzed 150,000 light curves for stellar variability.
Discovered numerous new candidate non-radial pulsators.
Provided a comprehensive online catalog of classified variables.
Abstract
We present the results of an automated variability analysis of the Kepler public data measured in the first quarter (Q1) of the mission. In total, about 150 000 light curves have been analysed to detect stellar variability, and to identify new members of known variability classes. We also focus on the detection of variables present in eclipsing binary systems, given the important constraints on stellar fundamental parameters they can provide. The methodology we use here is based on the automated variability classification pipeline which was previously developed for and applied successfully to the CoRoT exofield database and to the limited subset of a few thousand Kepler asteroseismology light curves. We use a Fourier decomposition of the light curves to describe their variability behaviour and use the resulting parameters to perform a supervised classification. Several improvements have…
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