Machine learning applications with LSST: From Data Processing to Knowledge Discovery

The Large Synoptic Survey Telescope (LSST; http://lsst.org) will be the most comprehensive optical astronomy project ever undertaken. The LSST will take panoramic images of the entire visible sky twice each week for 10 years, building up the deepest, widest, image of the Universe. The resulting hundreds of petabytes of imaging data for close to 40 billion objects will be used for scientific investigations ranging from the properties of near-Earth asteroids to characterizations of dark matter and dark energy. The volume, quality, and the real-time aspects of the LSST survey present significant research opportunities. They will enable studies of entire populations of objects, detections of faint statistical signals, and real-time discovery and follow-up of rare phenomena. Yet at the same time, these characteristics make it a difficult dataset to process and examine using classical techniques. In this talk, I will discuss the challenges presented by the LSST data set and areas where machine learning techniques are expected to be helpful. This includes the generation of well-characterized alert streams, to applications in data anslysis and knowledge discovery. Present-day surveys such as the PTF, CRTS, and ZTF have already shown how machine learning can be an effective way to extract knowledge from astronomical data sets and streams. In the LSST era, we expect them to continue to grow in importance.