Photometric Supernova Classification With Machine Learning
The automated classification of photometric supernovae has become an active field in recent years in light of current and upcoming imaging surveys, including the Dark Energy Survey (DES) and the Large Synoptic Telescope (LSST). Spectroscopic confirmation of type will be impossible for all supernovae discovered with these surveys, making photometric classification an important step for both cosmology and core-collapse studies. With this in mind, we develop a multi-faceted classification pipeline, investigating the use of machine learning algorithms combined with existing and novel methods of extracting features from light curves. In this talk, I will provide an overview of the methods used and discuss the results from applying the pipeline simulated supernova data. I will also discuss the effect of representativeness of training set and show that accurate classification is possible without redshift information.