  Gamma-ray Bursts (GRBs) are one of the most energetic phenomena in the
cosmos, whose study probes 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 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
for a new and robust GRB prompt classification. Leveraging self-supervised deep
learning, we pioneer a previously unexplored data product to approach this
task: the GRB waterfalls.
