Princeton University Thunch Talk

In the era of large-scale astronomical surveys, methods to investigate these data, and especially to classify astronomical objects, are becoming more and more important. For example, various techniques for extracting information, such as structure function fitting and template-based period fitting, can be applied before a subsequent machine-learning classification searches for and classifies variable sources. We give a brief overview of state-of-the-art methods of data handling and machine learning techniques used in astronomy, as well as in more detail describe how to apply specific methods to typical problems occurring from large time-domain surveys such as Pan-STARRS1, ZTF, TESS, and finally the Vera Rubin Observatory (LSST).