Type Ia Supernovae are calibrated standard light beacons that enable us to measure distances across cosmic time. These distances encode the expansion history of the Universe; however, one of the biggest challenges is finding a “pure” sample of these supernovae, given that many things explode in the night sky, and only some of those are useful cosmological probes. The Vera C Rubin Observatory is a telescope that takes images of the sky and will find hundreds of thousands of these objects, contaminated by other light sources.  Our group is working on a fully Bayesian supernova cosmology analysis pipeline to process the incoming Rubin data.

There are many aspects to this analysis, including parametrizing supernova rates over time, modelling supernova spectra, and more practical considerations such as optimizing the analytic and numerical runtime, and performing coverage tests. Depending on the SUDS Scholar’s interests and strengths, your tasks could include developing statistical tests to determine the accuracy of the Bayesian model, using conformal prediction or similar methods to improve quantified uncertainties, performing an independent analysis on an alternate supernova dataset, or optimizing the code for accuracy or performance.