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Abstract The ever-growing sample of observed supernovae (SNe) enhances our capacity for comprehensive SN population studies, providing a richer data set for understanding the diverse characteristics of Type Ia supernovae (SNe Ia) and possibly those of their progenitors. Here, we present a data-driven analysis of observed SN Ia photometric light curves collected in the Open Supernova Catalog. Where available, we add the environmental information from the host galaxy. We focus on identifying subclasses of SNe Ia without imposing the predefined subclasses found in the literature to date. To do so, we employ an implicit rank-minimizing autoencoder neural network for developing low-dimensional data representations, providing a compact representation of the SN light-curve diversity. When we analyze light curves alone, we find that one of our resulting latent variables is strongly correlated with redshift, allowing us to approximately “de-redshift” the other latent variables describing each event. After doing so, we find that three of our latent variables account for ∼95% of the variance in our sample, and provide a natural separation between 91T and 91bg thermonuclear SNe. Of note, the 02cx subclass is not unambiguously delineated from the 91bg sample in our results, nor do either the overluminous 91T or the underluminous 91bg/02cx samples form a clearly distinct population from the broader sample of “other” SN Ia events. We identify the physical characteristics of SN light curves that best distinguish SNe 91T from SNe 91bg and 02cx, and discuss prospects for future refinements and applications to other classes of SNe as well as other transients.
Bi et al. (Fri,) studied this question.