IST Lunch Bunch

The new generation of synoptic sky surveys promise unprecedented amounts of data and information and automated processing and analysis is a necessity. Light curves, however, can show tremendous variation in their temporal coverage, sampling rates, errors and missing values, etc., which makes comparisons between them difficult and training classifiers even harder. A common approach to tackling this is to characterize a set of light curves via a set of common features and then use this alternate homogeneous representation as the basis for further analysis or training. Many different types of feature are used in the literature to capture information contained in the light curve: moments, flux and shape ratios, variability indices, periodicity measures, model representations. In this talk, we will review characterization features with particular attention to the problem of determining accurate and reliable periods for astrophysical objects.