A metric space for type Ia supernova spectra: a new method to assess explosion scenarios

TL;DR

This paper introduces a PCA-based metric space to systematically compare supernova spectra with models, revealing that multiple progenitor scenarios are plausible but models struggle to replicate observed spectral-light curve correlations.

Contribution

The paper presents a novel PCA and PLS-based method to compare supernova models with observations, addressing data noise and missing data issues.

Findings

Different progenitor models have observed counterparts.

Models face challenges in reproducing spectral-light curve correlations.

Support for multiple progenitor scenarios.

Abstract

Over the past years type Ia supernovae (SNe Ia) have become a major tool to determine the expansion history of the Universe, and considerable attention has been given to, both, observations and models of these events. However, until now, their progenitors are not known. The observed diversity of light curves and spectra seems to point at different progenitor channels and explosion mechanisms. Here, we present a new way to compare model predictions with observations in a systematic way. Our method is based on the construction of a metric space for SN Ia spectra by means of linear Principal Component Analysis (PCA), taking care of missing and/or noisy data, and making use of Partial Least Square regression (PLS) to find correlations between spectral properties and photometric data. We investigate realizations of the three major classes of explosion models that are presently discussed: delayed-detonation Chandrasekhar-mass explosions, sub-Chandrasekhar-mass detonations, and double-degenerate mergers, and compare them with data. We show that in the PC space all scenarios have observed counterparts, supporting the idea that different progenitors are likely. However, all classes of models face problems in reproducing the observed correlations between spectral properties and light curves and colors. Possible reasons are briefly discussed.