EDP Sciences
Free Access
Volume 475, Number 3, December I 2007
Page(s) 1159 - 1183
Section Astronomical instrumentation
DOI https://doi.org/10.1051/0004-6361:20077638
Published online 24 September 2007

A&A 475, 1159-1183 (2007)
DOI: 10.1051/0004-6361:20077638

Automated supervised classification of variable stars

I. Methodology
J. Debosscher1, L. M. Sarro2, 3, C. Aerts1, 4, J. Cuypers5, B. Vandenbussche1, R. Garrido6, and E. Solano7, 3

1  Instituut voor Sterrenkunde, KU Leuven, Celestijnenlaan 200B, 3001 Leuven, Belgium
2  Dpt. de Inteligencia Artificial , UNED, Juan del Rosal, 16, 28040 Madrid, Spain
3  Spanish Virtual Observatory, INTA, Apartado de Correos 50727, 28080 Madrid, Spain
4  Department of Astrophysics, Radbout University Nijmegen, PO Box 9010, 6500 GL Nijmegen, The Netherlands
5  Royal Observatory of Belgium, Ringlaan 3, 1180 Brussel, Belgium
6  Instituto de Astrofísica de Andalucía-CSIC, Apdo 3004, 18080 Granada, Spain
7  Laboratorio de Astrofísica Espacial y Física Fundamental, INSA, Apartado de Correos 50727, 28080 Madrid, Spain

(Received 13 April 2007 / Accepted 7 August 2007)

Context.The fast classification of new variable stars is an important step in making them available for further research. Selection of science targets from large databases is much more efficient if they have been classified first. Defining the classes in terms of physical parameters is also important to get an unbiased statistical view on the variability mechanisms and the borders of instability strips.
Aims.Our goal is twofold: provide an overview of the stellar variability classes that are presently known, in terms of some relevant stellar parameters; use the class descriptions obtained as the basis for an automated "supervised classification" of large databases. Such automated classification will compare and assign new objects to a set of pre-defined variability training classes.
Methods.For every variability class, a literature search was performed to find as many well-known member stars as possible, or a considerable subset if too many were present. Next, we searched on-line and private databases for their light curves in the visible band and performed period analysis and harmonic fitting. The derived light curve parameters are used to describe the classes and define the training classifiers.
Results.We compared the performance of different classifiers in terms of percentage of correct identification, of confusion among classes and of computation time. We describe how well the classes can be separated using the proposed set of parameters and how future improvements can be made, based on new large databases such as the light curves to be assembled by the CoRoT and Kepler space missions.
Conclusions.The derived classifiers' performances are so good in terms of success rate and computational speed that we will evaluate them in practice from the application of our methodology to a large subset of variable stars in the OGLE database and from comparison of the results with published OGLE variable star classifications based on human intervention. These results will be published in a subsequent paper.

Key words: stars: variables: general -- stars: binaries: general -- techniques: photometric -- methods: statistical -- methods: data analysis

© ESO 2007

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