Characterization of Supernovae Based on the Spectral–Temporal Energy Distribution: Two Possible SN Ib Subtypes

Abstract A quantitative data-driven comparison among supernovae (SNe) based on their spectral time series combined with multiband photometry is presented. We use an unsupervised random forest algorithm as a metric set of 82 well-documented SNe representing all the main spectroscopic types, in order to embed these abstract space reflecting shared correlations between objects. visualize resulting 3D, revealing strong agreement current classification scheme. The embedding splits Type Ib into two groups, one subgroup exhibiting broader, less prominent, higher-velocity lines than other, possibly suggesting new SN subclass required. method could be classify newly discovered according distance from known event or ultimately devise new, spectral–temporal Such also depend hidden parameters that may perhaps physically interpretable.