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 data base of photometric time series. The methods are based on multivariate Bayesian statistics and use a multistage approach. We applied our method to the ground-based data of the Trans-Atlantic Exoplanet Survey (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, γ Doradus, β Cephei and δ Scuti stars. Also a few classical radial pulsators were found. © 2011 The Authors Monthly Notices of the Royal Astronomical Society © 2011 RAS.