Photometric Classification of Astronomical Transients and Variable Stars

Joseph Richards
UC Berkeley


Next-generation photometric surveys need sophisticated statistical tools that accurately classify variable sources for optimal allocation of follow-up resources.  I will discuss some of the challenges in building such a classifier, including estimation of class-predictive features from photometric light curves and classification modeling using flexible, robust methods.  I describe my work on two classification problems: supernova typing using simulations from the DES Supernova Classification Challenge and variable star classification using light curves from the OGLE and Hipparcos surveys.  I will detail my use of cutting-edge statistical methodology such as diffusion maps, random forests and structured classification for these problems.