Towards Precision Photometric Type Ia Supernova Cosmology with Machine Learning

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 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+ 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 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 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.