| Thermonuclear Supernovae (TN SNe) are an extremely important tool in modern astronomy. In their role as cosmological distance probes, they have revealed the accelerated expansion of the universe and have begun to constrain the nature of the dark energy that may be driving that expansion. The next decade will see a succession of wide-field surveys producing thousands of TNSN detections each year. Traditional methods of SN analysis, rooted in time-intensive spectroscopic follow-up, will become completely impractical. To realize the potential of this coming tide of massive data sets, we will need to extract cosmographic parameters (redshift and luminosity distance) from SN photometry without any spectroscopic support.;In this dissertation, I present the Supernova Ontology with Fuzzy Templates (SOFT) method, an innovative new approach to the analysis of SN light curves. SOFT uses the framework of fuzzy set theory to perform direct comparisons of SN candidates against template light curves, simultaneously producing both classifications and cosmological parameter estimates.;The SOFT method allows us to shed new light on two rich archival data sets. I revisit the IfA Deep Survey and HST GOODS to extract new and improved measurements of the TNSN rate from z=0.2 out to z=1.6. Our new analysis shows a steady increase in the TNSN rate out to z∼1, and adds support for a decrease in the rate at z=1.5. Comparing these rate measurements to theoretical models, I conclude that the progenitor scenario most favored by the collective observational data is a single degenerate model, regulated by a strong wind from the accreting white dwarf.;Using a compilation of SN light curves from five recent surveys, I demonstrate that SOFT is able to derive useful constraints on cosmological models from a data set with no spectroscopic information at all. Looking ahead to the near future, I find that photometric analysis of data sets containing 2,000 SNe will be able to improve our constraints on dark energy models by a factor of 2. With 10,000 objects we can realize an improvement of more than 5-fold over current limits. |
