Optimal Classification and Outlier Detection for Stripped-Envelope Core-Collapse Supernovae

Understand

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 spectra of stripped-envelope core-collapse supernovae.
• We use one of the largest compiled optical datasets of stripped-envelope supernovae, containing 160 SNe and 1551 spectra.
• We find that the first 5 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.