Supervised classification of variable stars

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. 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. 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.