A Model for the Classification of Supernovae

With data collection projects such as the Dark Energy Survey underway, data from distant supernovae are becoming increasingly available. As the quantity of information increases, the ability to quickly and accurately distinguish between Type Ia and core collapse supernovae has become an essential key to understanding the nature of the evolving universe. Estimating individual supernova light curves is the first step in modern classification attempts. In this research we focus on the use of hierarchical gaussian processes to model light curves both for individual supernova and across supernova type. Properties inherent in this Bayesian non-parametric form of modeling allow curve definition at a specific point to borrow information from neighboring points and allow the data to dominate the selection of model parameters.