Poster abstract details

In the new era of time-domain astronomy, it will become increasingly important to have rigorous, data driven models for classifying transients, including exploding stars (SNe). We present the first application of Principal Component Analysis (PCA) to stripped-envelope core-collapse supernovae (SESNe). Previous studies of SNe types Ib, IIb, Ic and broad-line Ic (Ic-BL) focus only on specific spectral features, while our PCA algorithm uses all of the information contained in each spectrum. We use one of the largest compiled datasets of SESNe, containing over 150 SNe, each with spectra taken at multiple phases. Our work focuses on spectra taken 15\pm 5 days after maximum V-band light where better distinctions can be made between SNe type Ib and Ic spectra. We find that spectra of SNe type IIb and Ic BL are separable from the other types in PCA space, indicating that PCA is a promising option for developing a purely data driven model for SESNe classification.