Characterization of Supernovae Based on the Spectral-Temporal Energy Distribution: Possible two SN Ib Subtypes

TL;DR

This study uses an unsupervised machine learning approach to analyze supernova spectral and photometric data, revealing potential new subtypes within Type Ib supernovae and suggesting a refined classification scheme.

Contribution

Introduces a data-driven, unsupervised method to classify supernovae based on spectral-temporal energy distribution, uncovering possible new subtypes within Type Ib supernovae.

Findings

Supernovae can be embedded in a metric space reflecting shared spectral-temporal features.

Type Ib supernovae split into two distinct groups, indicating potential new subclasses.

The method aligns well with existing classification and offers a basis for new classification schemes.

Abstract

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