Data-driven Photometric Templates for Stripped Envelope Supernovae

Stripped envelope supernovae (SESNe) are a family of supernovae for which progenitor and explosion channels are still poorly constrained. While their spectroscopic classification scheme is clear, photometric differences between classes remain elusive. Even though we know that they originate from massive stars that lost their envelopes of Hydrogen and Helium, a detailed mapping of each SESN subtype (IIb, Ib, Ic, Ic-BL) to its stellar progenitor remain uncertain. Additionally, the mechanism for their envelope loss, the relationships among subtypes and their connections with long duration Gamma Ray Bursts (GRB) that are occasionally seen in conjunctions with Ic-BL are still unclear.

Photometric surveys, like Vera C. Rubin’s Legacy Survey of Space and Time, discovering tens of thousands of transients each night, offer an incredible opportunity to improve our knowledge of these supernova subtypes. However, increasing emphasis has to be placed on photometric classification and characterization, as spectroscopic resources will only enable follow up of a small fraction of observed transients. We have generated data-driven photometric templates for SESNe subtypes using machine learning techniques (Gaussian processes) and a comprehensive multi-survey dataset of all well-sampled open-access light curves from the Open Supernova Catalog. We assess the photometric diversity of SESNe, among and within subtypes, setting the stage for studies aimed at relating the explosion properties to their stellar progenitors. Our templates can help evaluate the current photometric simulations used to develop classification methods and identify peculiar behavior.