Towards Precision Photometric Type Ia Supernova Cosmology with Machine Learning

TL;DR

This paper develops machine learning methods for photometric classification and redshift estimation of Type Ia supernovae, enabling large-scale cosmological studies without extensive spectroscopic data, and demonstrates improved robustness in astronomical object classification.

Contribution

It introduces SCONE, a machine learning classifier for SN types with 99% accuracy, and Photo-zSNthesis, a host galaxy-independent redshift estimator with 2% accuracy, advancing photometric supernova cosmology.

Findings

SCONE achieves 99+% accuracy in classifying simulated SNe Ia.

Photo-zSNthesis estimates redshifts within 2% accuracy across LSST range.

A general robustness method improves classification in astronomy, wildlife, and medical imaging.

Abstract

The revolutionary discovery of dark energy and accelerating cosmic expansion was made with just 42 type Ia supernovae (SNe Ia) in 1999. Since then, large synoptic surveys, e.g., Dark Energy Survey (DES), have observed thousands more SNe Ia and the upcoming Rubin Legacy Survey of Space and Time (LSST) and Roman Space Telescope promise to deliver millions in the next decade. This unprecedented data volume can be used to test concordance cosmology. However, extracting a pure SN Ia sample with accurate redshifts for such a large dataset will be a challenge. Spectroscopic classification will not be possible for the vast majority of discovered objects, and only 25% will have spectroscopic redshifts. This thesis presents a series of observational and methodological studies designed to address the questions associated with this new era of photometric SN Ia cosmology. First, we present a machine learning (ML) method for SN photometric classification, SCONE. Photometric classification enables SNe with no spectroscopic information to be categorized, a critical step for cosmological analysis. SCONE achieves 99+% accuracy distinguishing simulated SNe Ia from non-Ia SNe, and is a part of DES, LSST, and Roman analysis pipelines. We also show that SCONE can classify 6 SN types with 75% accuracy on the night of initial discovery, comparable to results in the literature for full-phase SNe. Next, we study current methods for estimating SN Ia redshifts and propose an ML alternative that uses SN photometry alone to extract redshift information. Photo-zSNthesis is a host galaxy-independent redshift estimator accurate to within 2% across the redshift range of LSST, a first in the literature. Finally, we focus on ML robustness and demonstrate a general method for improving robustness that achieves new state-of-the-art results on astronomical object classification, wildlife identification, and tumor detection.