Quantitative modelling of type Ia supernovae spectral time series II: Exploring the diversity of thermonuclear explosion scenarios

TL;DR

This paper presents an improved machine learning framework called riddler for automated, quantitative spectral fitting of type Ia supernovae, enabling better differentiation of explosion scenarios.

Contribution

The work introduces enhancements to riddler, expanding its training data to include diverse explosion models, and demonstrates its ability to accurately recover supernova parameters and scenarios.

Findings

Riddler accurately recovers input parameters from unseen spectra.

The expanded training dataset covers multiple explosion scenarios.

Riddler successfully fits observed spectra of three different SNe Ia.

Abstract

Observations of type Ia supernovae (SNe Ia) have led to suggestions of multiple progenitor and explosion scenarios. Distinguishing between scenarios and tying specific SNe Ia to individual scenarios however has so far been challenging. Constraints on the explosion physics are often achieved through empirical modelling of SNe Ia spectra and qualitative assessments of the level of agreement. While this approach has provided useful insights, it cannot be scaled up to large numbers of SNe Ia in a robust and systematic way. As a machine learning based framework for automated and quantitative fitting of SNe Ia, riddler is designed to overcome these limitations. Neural networks are used as radiative transfer emulators and, in conjunction with nested sampling, emulated spectra are fit to observations of SNe Ia to determine the best-fitting input parameters and explosion scenario. In this work, we present recent improvements to riddler, including a significantly expanded training dataset covering pure deflagrations, delayed detonations, double detonations, gravitationally confined detonations, and violent mergers. We show that despite the increased complexity and variety of our training data, riddler is able to accurately recover the input parameters and explosion scenario of spectra unseen during training. Using riddler, we fit observations of three SNe Ia covering different sub-classes: SN 2011fe, SN 2005hk, and SN 2018byg. We detail a number of limitations and assumptions that should be considered when applying similar approaches. Nevertheless, the benefits of this approach and riddler will result in automated fitting playing an increasingly important role in the coming years.