Speaker
Description
Gravitationally lensed supernovae (glSNe) are powerful local probes of the Hubble parameter ($H_{0}$), as they are independent of the distance ladder and insensitive to the assumed cosmological model. Despite their rarity, the Rubin Observatory’s Legacy Survey of Space and Time (Rubin-LSST) will increase the sample of known glSNe by an order of magnitude. In this talk, we present a comprehensive analysis of follow-up strategies for glSNe discovered by Rubin-LSST based on how well time delays are estimated from an upgraded GausSN2 model. Within a hierarchical Bayesian framework, GausSN2 simultaneously models data in which the multiple images of the glSN are resolved and in which they are unresolved to achieve robust time-delay estimates. The model also accounts for chromatic microlensing, host galaxy dust extinction, and differential dust extinction in the lens galaxy in the statistical error budget. We apply this model to simulated glSN Ia systems with realistic Rubin-LSST data and varying amounts of space- and ground-based follow-up. Whereas without follow-up, the time delay can only be constrained to of order a week, with 4 epochs of resolved space-based data, the time delay constraint improves to of order 1.5 days. Furthermore, with sufficient optical to NIR wavelength coverage, we can constrain differential dust in the lens, and therefore lensing magnification, which is important to break lens modelling degeneracies. This work provides an important framework for best taking advantage of glSNe discovered by Rubin-LSST whilst maximising the impact of valuable and limited space-based follow-up for a precise $H_{0}$ estimate from time-delay cosmography.
