Gamma-ray bursts (GRBs) are one of the most energetic phenomena in the cosmos, whose study can probe physics extremes beyond the reach of laboratories on Earth.Our quest to unravel the origin of these events and understand their underlying physics is far from complete.Central to this pursuit oval hexagon glass jar is the rapid classification of GRBs to guide follow-up observations and analysis across the electromagnetic spectrum and beyond.Here, we introduce a compelling approach that can set a milestone caramilk latte toward a new and robust GRB prompt classification method.
Leveraging self-supervised deep learning, we pioneer a previously unexplored data product to approach this task: GRB waterfalls.