Understanding Exoplanets and Other Variable Sources in Sparsely-Sampled Time Domain Surveys

Abstract

As astronomy expands into the era of Big Data, with increased data capture and storage capabilities and enhanced computing power, the large data sets that will become accessible need to be properly understood to maximize their scientific yield. Some of this data will be generated by upcoming missions, such as the Large Synoptic Survey Telescope (LSST). For this telescope we demonstrate that while it was not specifically designed for it, LSST can recover transiting exoplanets for several archetypal planetary systems. We also provide a set of tools built upon the LSST Metric Analysis Framework (MAF) that quantify how the LSST time sampling will impact observing both periodic and non-periodic variable sources. Other data will be the large data sets generated from archival data, such as from Digital Access to a Sky Century @ Harvard (DASCH), a database of digitized photographic plates over a century. We take a sample of F2 stars from the DASCH archive, and use a series of statistical tests to determine that there are large-scale systematics. After accounting for a large break in the data, we find that most F2 stars do not appear to change in brightness on century timescales, however we also provide a handful of stars that do seem to be changing over long timescales.