Kara Ponder



Abstract: Type Ia supernovae (SNeIa) led to the discovery that the universe is expanding at an accelerating rate due to dark energy. In the future, surveys such as the ground-based Large Synoptic Survey Telescope (LSST) and the space-based Wide-Field Infrared Survey Telescope (WFIRST) will continue to use SNeIa as a primary dark energy probe. These large surveys will be systematically-limited, which makes it imperative for our insight regarding systematics to dramatically increase over the next decade in order for SNeIa to continue to contribute to precision cosmology. This thesis approaches this problem by improving statistical methods in the likelihood analysis and collecting near infrared (NIR) SNeIa with their host galaxies to improve the nearby data set and search for additional systematics.

We explored more statistically robust methods to account for systematics within likelihood functions to increase accuracy in cosmological parameters with minimal precision loss.  We show that though a sample of at least 10,000 SNeIa is necessary to confirm multiple evolving populations, the bias on cosmology is ~2 sigma with only 2,500 SNeIa. This framework focused on an example systematic (host galaxy correlations), it can be used for any systematic that can be represented as a distribution. 

The SweetSpot survey gathered 114 low redshift SNeIa in the NIR survey that will act as a crucial anchor sample for the future high redshift surveys.  NIR observations are not as affected by dust contamination, which may lead to increased understanding of systematics seen in optical wavelengths.  We obtained spatially-resolved spectra for 32 SweetSpot host galaxies to start understanding local host galaxy correlations. For the first time, we probe global host galaxy correlations with NIR lightcurves from SweetSpot and the current literature sample with host galaxy data from publicly available catalogs. We find that more massive galaxies host SNeIa that are brighter in the NIR than less massive galaxies.




Graduate Advisor

Andrew R Zentner