Princeton University Thunch Talk - Added

The Large Synoptic Survey Telescope (LSST) will begin science operations in 2022 and is expected to increase the discovery rate of extragalactic transients by two orders of magnitude. With this transition comes the important question: how do we classify these events and separate the interesting “needles” from the “haystack” of objects? While it’s necessary to pick out these needles, it is equally important to understand the haystack — or the millions of events which lack any multi wavelength or spectroscopic followup. In this talk, I will discuss ongoing efforts to classify/characterize the future haystack and to understand one particularly exciting type of needle: kilonovae. In particular, I will introduce two methods to classify supernovae based on their optical light curves, which have been trained and tested on data from the Pan-STARRS Medium Deep Survey. The first method combines Bayesian model fitting with a variety of supervised classification methods, while the second method uses a semi-supervised recurrent neural network-based autoencoder. I will then discuss how well we can extract physical insights from LSST-like light curves without additional followup. I will focus on the rare classes of Type I superluminous supernovae and kilonovae as case studies. I will show how target-of-opportunity observations will make LSST an ideal instrument for kilonova discovery.