Automated classification of periodic variable stars{Improved methodology for the automated classification of periodic variable stars}

Abstract

We present a novel automated methodology to detect and classify periodic variable stars in a large database of photometric time series. The methods are based on multivariate Bayesian statistics and use a multi-stage approach. We applied our method to the ground-based data of the TrES Lyr1 field, which is also observed by the Kepler satellite, covering ~26000 stars. We found many eclipsing binaries as well as classical non-radial pulsators, such as slowly pulsating B stars, Gamma Doradus, Beta Cephei and Delta Scuti stars. Also a few classical radial pulsators were found.

11 pages, 6 figures Accepted for publication in MNRAS

References in corpus (2)

• Automated supervised classification of variable stars I. Methodology
• Automated supervised classification of variable stars II. Application to the OGLE database

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