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 ∼26 000 stars. We found many eclipsing binaries as well as classical nonradial pulsators, such as slowly pulsating B stars, γ Doradus, β Cephei and δ Scuti stars. Also a few classical radial pulsators were found.

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Cite this paper

@inproceedings{Blomme2011ImprovedMF, title={Improved methodology for the automated classification of periodic variable stars}, author={Jonas Blomme and Luis M. Sarro and F. T. O’Donovan and J. Debosscher and Thomas M. Brown and M. L{\'o}pez and P. Dubath and Lorenzo Rimoldini and Didier Charbonneau and Edward W. Dunham and G. Mandushev and David R. Ciardi and J. De Ridder and Conny Aerts}, year={2011} }