Quantitative modelling of type Ia supernovae spectral time series III: Implications for type Ia supernovae standardisation in cosmology

TL;DR

This study uses machine learning to analyze supernova explosion models, revealing correlations between explosion mechanisms, supernova properties, and host galaxy environments, with implications for improving cosmological distance measurements.

Contribution

It introduces a machine learning framework for spectral modeling of SNe~Ia and investigates the link between explosion mechanisms and observable properties.

Findings

Approximately two thirds of SNe~Ia are best explained by sub-Chandrasekhar explosions.

Fast-evolving SNe~Ia are less likely to be Chandrasekhar mass explosions.

Selecting SNe~Ia in passive galaxies may yield more homogeneous samples of violent mergers.

Abstract

The physics driving type Ia supernovae (SNe~Ia) standardisation in cosmology remains poorly-understood. Recent advances however mean that it is now possible to systematically analyse the explosion properties of large numbers of cosmological SNe~Ia. To that end we use riddler, a machine learning based framework for rapidly modelling SNe~Ia based on realistic explosion simulations, to perform quantitative spectral modelling of the Zwicky Transient Facility SN~Ia DR2 sample and determine their best-fitting explosion mechanism(s). We find that approximately two thirds of our sample is best reproduced by sub-Chandrasekhar mass explosions. Analysing their light curve and host galaxy properties, we find that Chandrasekhar mass explosions are not favoured for the fastest-evolving SNe~Ia, while sub-Chandrasekhar mass explosions are favoured for the reddest SNe~Ia. Due to the differences in their environments, selecting SNe~Ia in massive, passive galaxies could produce a homogeneous sample of violent merger SNe~Ia. We show that standardising each explosion mechanism independently reduces scatter in distance estimates and previously claimed environmental and non-linear light curve shape corrections may be due to changes in the relative populations of different explosion mechanisms. Although a step forward towards understanding SNe~Ia physics in cosmology, we highlight a number of limitations affecting our conclusions, including sample biases and small numbers. We therefore cannot assess the statistical significance of our results and they should be treated with caution. Larger and more uniformly observed samples will be key to determining the significance of any trends hinted at here.