Automated supervised classification of variable stars (original) (raw)

Context. The CoRoT space mission has two main scientific goals: exoplanet searches, and asteroseismology. Detecting planets using the occultation (or transit) method requires continuous monitoring of several thousand stars for a long period and with high photometric precision. As an important consequence, many high-quality light curves are obtained. Among this sample, a large fraction of variable stars is present, most of them previously unknown. This work describes the supervised classification of those newly measured variables, using automated methods. The methods were developed in the framework of the CoRoT mission, but they can easily be applied to other databases. Aims. In this work, we describe the pipeline for the fast supervised classification of light curves observed by the CoRoT exoplanet CCDs. We present the classification results obtained for the first four measured fields, which represent a one-year in-orbit operation. Methods. The basis of the adopted supervised classification methodology has been described in detail in a previous paper, as is its application to the OGLE database. Here, we present the modifications of the algorithms and of the training set, to optimize the performance when applied to the CoRoT data. Results. Classification results are presented for the observed fields IRa01, SRc01, LRc01, and LRa01 of the CoRoT mission. Statistics on the number of variables and the number of objects per class are given and typical light curves of high-probability candidates are shown. We also report on new stellar variability types discovered in the CoRoT data. The full classification results are publicly available.