Presentation of a new method for classifying stripped-envelope supernovae spectra using principal component analysis and support vector machine. Provides a quantitative, continuous method for characterizing transition supernovae and outliers. Finds that the best time to distinguish the major spectral types is two weeks after peak brightness.
Currently working with Wolfgang Kerzendorf to model the chemical abundances and velocity structure of stripped-envelope supernovae ejecta using TARDIS, a fast 1D radiation transport code. Working on the first Bayesian framework for understanding uncertainties and degeneracies associated with ejecta model parameters.
Recently fast rising, luminous, blue transients have challenged our standard picture of stripped-envelope supernovae types. We present spectra and photometry for a particularly interesting object, SN2018gep, and quantify its difference from standard stripped-envelope supernovae. (Image Credit: Ho et al. 2019)