Rewinding a supernova with machine learning

Abstract

This thesis focuses on supernova (SN) spectra. It begins by examining SN 2019hcc, an unusual SN which displays a 'w'-shape in its early spectrum characteristic of a certain class of SN (the ultra-bright and exotic Type I superluminous supernovae, SLSNe I) but, by all other criteria, appears to be an ordinary core-collapse Type II. This work is expanded upon in a subsequent chapter by investigating this 'w'-shape via a quantitative analysis of these lines' properties for a sample of SLSNe I, and their correlation to other physical quantities. This analysis also includes spectral modelling of SN spectra for various elemental compositions, in order to better understand the contributions to the 'w'-shape by different ions. This work has significance in expanding our understanding of the mechanisms involved in producing the ultra-bright SLSNe. The study of SN spectra takes another angle in the final chapter on machine learning to predict SN spectra, which takes a large sample of publicly available core-collapse Type II SNe as the training sample for an algorithm to create synthetic spectra in order to augment and supplement existing datasets. This work allows us to make use of the massive volume of astronomical data available in augmenting our existing data and could allow for applications to population studies, spectral template libraries, and cosmology