Core-collapse contamination in photometric samples of Type Ia Supernovae

  • Maria Vincenzi

Student thesis: Doctoral Thesis

Abstract

This is an exciting time for cosmology with type Ia supernovae (SNe Ia). The recently concluded Dark Energy Survey SN programme (DES-SN) has obtained the largest and deepest high-redshift cosmological SN Ia sample, and the Vera Rubin Observatory is expected to observe at least one order of magnitude more SNe Ia in the next decade. In both these experiments, only a limited fraction (≲10 per cent) of the SNe can be spectroscopically classified. This leaves us with large ‘photometric’ SN samples, with the potential for significant contamination by core-collapse SNe that may bias SN Ia cosmological measurements. This thesis demonstrates how this contamination can be modelled and accounted for in current and future cosmological analyses.
First, I present state-of-the-art simulations of the SN universe. These are designed to accurately model the population of SNe Ia, peculiar SNe Ia and core-collapse SNe, as well as their host galaxies. To improve the diversity and quality of the simulated corecollapse SNe, I build a new library of core-collapse SN templates using spectroscopic and photometric (optical and near-ultraviolet) data of 67 core-collapse SNe from the literature. I account for our incomplete knowledge of core-collapse SN properties by generating a set of SN simulations (rather than a single one), each exploring different modelling choices and template libraries. I then characterise selection effects in the DES-SN survey and incorporate them in the simulations, thus obtaining a series of DES-like simulated SN samples that can be compared to the observed DES-SN data. The agreement between the simulations and data is excellent across many observed SN
properties, including Hubble residuals. These simulations are the first to reproduce the observed photometric SN and host galaxy properties in high-redshift surveys with no fine-tuning of the input parameters.
I use my simulation framework to train and test the performance of SuperNNova, a photometric SN classifier based on recurrent neural networks. I explore different training and validation strategies and show that, across all the DES-SN simulations tested, SuperNNova reduces core-collapse SN contamination to 0.8–3.5 per cent. I then show that biases due to contamination on the equation-of-state of dark energy, ω, are < 0.008 when using our reference SuperNNova model. This compares to an expected statistical uncertainty on w from the DES-SN sample of 0.039, thus showing that contamination is not a limiting systematic for the cosmological analysis of the DES-SN sample.
The results presented in this thesis are the foundation of the DES SN Ia cosmological analysis; they also provide important implications for the future of SN cosmology, as they demonstrate that contamination is not expected to significantly degrade the cosmological figure of merit of the Rubin SN Ia analysis.
Date of AwardMay 2022
Original languageEnglish
SupervisorBob Nichol (Supervisor), Mark Sullivan (Supervisor) & Christopher Martin Frohmaier (Supervisor)

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