Speaker
Description
When a supernova is located behind a massive galaxy, its light can be gravitationally lensed to form multiple images. Such a strongly lensed supernova is a rare and powerful probe that provides insights into distant supernova explosions, dark matter in galaxies, and even the expansion rate of the Universe. Currently, the lensed supernova field stands at a turning point, as we transition from a handful of known systems to several hundred with the advent of the Vera Rubin Observatory. However, this increase in data volume also introduces complex new challenges. In my talk, I will focus on the problem of performing fast inference of time delays from irregularly sampled, multi-band observations. I handle this heterogeneous data using a transformer with rotary positional embeddings, combined with simulation-based inference. I will share results from this work in progress, and invite discussions about handling irregular, multi-channel time series data across different physics domains.