Exploring Type Ia Supernova Systematics: the Host Galaxy Bias and Intrinsic Variability

Abstract:  Once calibrated and standardized, type Ia supernova samples are powerful cosmological probes, especially for measuring dark energy. Rising tensions between independent cosmological measurements underscores the importance of accounting related systematic errors. This is particularly true for type Ia supernovae, where observations are first fit to empirical models with little to no incorporated physics, and where the underlying population potentially varies with redshift.

This dissertation explores two topics in supernova cosmology systematics. First, the type Ia supernova host galaxy bias and its dependence on observation methods or fitting techniques is considered .Host galaxy stellar mass and star formation rates are estimated from photometry or spectroscopy using different galaxy property fitting software, from which different estimates of the host bias are calculated. We find no evidence that the choice in method or technique influences host bias measurements. Next, a new model providing extended phase-independent flux variation modeling is introduced.

With it, we find sufficient signal-to-noise in available data sets to constrain models beyond the ubiquitous two parameter counterparts. The results also indicate that intrinsic flux variation can be misidentified as dust-like, highlighting the difficulties inherent to estimating type Ia supernova dust properties.