Event Discovery in Astronomical Time Series

The discovery of events in astronomical time series data is a non-trival problem. Existing methods address the problem by requiring a fixed-sized sliding window which, given the varying lengths of events and sampling rates, could overlook important events. In this work, we develop probability models for finding the significance of an arbitrary-sized sliding window, and use these probabilities to find areas of significance. In addition, we present our analyses of major surveys archived at the Time Series Center, part of the Initiative in Innovative Computing at Harvard University. We applied our method to the time series data in order to discover events such as microlensing or any non-periodic events in the MACHO, OGLE and TAOS surveys. The analysis shows that the method is an effective tool for filtering out nearly 99% of noisy and uninteresting time series from a large set of data, but still provides full recovery of all known variable events (microlensing, blue star events, supernovae etc.). Furthermore, due to its efficiency, this method can be performed on-the-fly and will be used to analyze upcoming surveys, such as Pan-STARRS.