Progenitor-Based Classification of Gamma-Ray Bursts with Machine Learning

Gamma-Ray Bursts (GRBs) are among the most energetic events in the universe. Prompt, multiwavelength, and multimessenger observations of these enigmatic transients allow us to probe physics beyond extremes that can be achieved in terrestrial laboratories. A holy grail in GRB studies is progenitor-based classification of GRBs utilizing only prompt duration, aiding follow-up by guiding both observing profiles, prioritization of telescope time, and providing key characterization information for follow-up analysis. Here we present a fast GRB classification tool, based on modern self-supervised deep learning techniques and relying on a set of inputs which contains the core prompt information relevant for GRB classification: the GRB waterfalls.