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Automated Classification of Variable Stars for ASAS Data

Published online by Cambridge University Press:  12 April 2016

Laurent Eyer  and
Cullen Blake
Laurent Eyer
Affiliation:
Princeton University Observatory, Princeton, NJ 08544, USA
Cullen Blake
Affiliation:
Princeton University Observatory, Princeton, NJ 08544, USA

Abstract

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With the advent of surveys generating multi-epoch photometry and their discoveries of large numbers of variable stars, the classification of the obtained time series has to be automated. We have developed a classification algorithm for the periodic variable stars using a Bayesian classifier on a Fourier decomposition of the light curve. This algorithm is applied to ASAS (AII Sky Automated Survey, Pojmanski, 2000). In ASAS 85% of the variables are red giants. A remarkable relation between their period and amplitude is found for a large fraction of those stars.

Type
Part 1.5. General Aspects
Information
International Astronomical Union Colloquium , Volume 185: Radial and Nonradial Pulsations as Probes of Stellar Physics , 2002 , pp. 160 - 161
Copyright
Copyright © Astronomical Society of the Pacific 2002

References

Cheeseman, P., Stutz, J. 1996, in Advances in Knowledge Discovery and Data Mining, eds. Fayyad, U., Piatetsky-Shapiro, G., Smyth, P., & Uthurusamy, R. Google Scholar
Pojmanski, G. 2000, AcA 50, 177 Google Scholar