Probabilistic Reconstruction of Type Ia Supernova SN 2002bo

TL;DR

This paper introduces a probabilistic method to reconstruct the outer ejecta of Type Ia supernova SN 2002bo using spectral data, leveraging machine learning to accelerate spectral synthesis and compare models with observations.

Contribution

It presents a new probabilistic reconstruction technique combining spectral modeling and machine learning, enabling detailed comparison of supernova explosion models with observational data.

Findings

Favors detonation over deflagration explosion scenarios

Reveals complex, degenerate parameter space in supernova modeling

Provides a rapid spectral synthesis method for supernova analysis

Abstract

Manual fits to spectral times series of Type Ia supernovae have provided a method of reconstructing the explosion from a parametric model but due to lack of information about model uncertainties or parameter degeneracies direct comparison between theory and observation is difficult. In order to mitigate this important problem we present a new way to probabilistically reconstruct the outer ejecta of the normal Type Ia supernova SN 2002bo. A single epoch spectrum, taken 10 days before maximum light, is fit by a 13-parameter model describing the elemental composition of the ejecta and the explosion physics (density, temperature, velocity, and explosion epoch). Model evaluation is performed through the application of a novel rapid spectral synthesis technique in which the radiative transfer code, TARDIS, is accelerated by a machine-learning framework. Analysis of the posterior distribution reveals a complex and degenerate parameter space and allows direct comparison to various hydrodynamic models. Our analysis favors detonation over deflagration scenarios and we find that our technique offers a novel way to compare simulation to observation.