Building a Better Candle: The Calibration and Classification of Type Ia Supernovae in the Upcoming Legacy Survey of Space and Time

The use of Type Ia Supernovae (SNe~Ia) as astronomical distance indicators relies on their intrinsically bright and homogeneous luminosities. By applying empirical relationships to remove any intrinsic, first-order variation in brightness between individual SNe~Ia, the apparent brightness of these objects is used to determine a relative measure of distance. Upcoming surveys like the Vera C. Rubin Observatory's Legacy Survey of Space and Time (LSST) will observe on order 100,000 SNe~Ia, representing an order of magnitude increase over previous surveys. LSST also promises to provide an impressive sub-percent level of precision between individual measurements. In this work, I present research targeted at two specific challenges faced by SN~Ia research in the LSST era. First, I classify SNe~Ia that exhibit non-standard photometric behavior, such as lower luminosities and faster evolution of brightness over time. With LSST promising on order a million new SNe over a 10-year survey, spectroscopic classifications will be possible for only a small subset of observed targets. As such, photometric classification has become increasingly important in preparing for the next generation of astronomical surveys. Using observations from the Sloan Digital Sky Survey II (SDSS-II) SN Survey, I apply an empirically based classification technique targeted at identifying SN 1991bg-like SNe in photometric data sets and classify 16 previously unidentified 91bg-like SNe. Furthermore, I show that these SNe are preferentially found at a further physical distance from the center of their host galaxies and in host environments with an older average stellar age. Second, I discuss the impact of atmospheric variability on the calibration of LSST observed SNe~Ia. LSST will incorporate multiple calibration systems designed to estimate the atmospheric state and isolate systematic errors, including a GPS to quantify the time-dependent column density of precipitable water vapor (PWV) over the observatory. By combining atmospheric models with near-real-time GPS measurements, I demonstrate that PWV absorption can be removed from observed spectra taken at Kitt Peak National Observatory (KPNO). Using this technique, I use GPS measurements taken at Cerro Tololo Inter-American Observatory (CTIO) to create a model for the PWV absorption over LSST and simulate an LSST-like SN dataset with realistic atmospheric variabilities