Supervised classification of CoRoT variables

We present the classification results obtained for the first four measured fields, which represent a one-year in-orbit operation. 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. 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.