Optimal Classification and Outlier Detection for Stripped-envelope Core-collapse Supernovae

Abstract

Abstract In the current era of time-domain astronomy, it is increasingly important to have rigorous, data-driven models for classifying transients, including supernovae. We present the first application of principal component analysis to the photospheric spectra of stripped-envelope core-collapse supernovae. We use one of the largest compiled optical data sets of stripped-envelope supernovae, containing 160 SNe and 1551 spectra. We find that the first five principal components capture 79% of the variance of our spectral sample, which contains the main families of stripped supernovae: Ib, IIb, Ic, and broad-lined Ic. We develop a quantitative, data-driven classification method using a support vector machine, and explore stripped-envelope supernovae classification as a function of phase relative to V-band maximum light. Our classification method naturally identifies “transition” supernovae and supernovae with contested labels, which we discuss in detail. We find that the stripped-envelope supernovae types are most distinguishable in the later phase ranges of 10 ± 5 days and 15 ± 5 days relative to V-band maximum, and we discuss the implications of our findings for current and future surveys such as Zwicky Transient Factory and Large Synoptic Survey Telescope.