Speaker
Description
Gamma-ray bursts (GRBs) are among the most energetic phenomena in the Universe and provide a unique laboratory for physics under extreme conditions. Rapid classification of GRBs based on their prompt emission is essential to guide timely multi-wavelength and multi-messenger follow-up observations.
In this work, we present a data-driven pipeline for GRB classification based on observations from the Fermi Gamma-Ray Burst Monitor (GBM). Our method introduces waterfall plots as a novel data representation, encoding a broad set of key prompt emission properties into high-dimensional images. We reduce the dimensionality of these data using a self-supervised deep learning approach, followed by a semi-supervised algorithm that assigns classification probabilities to each event.
Our approach enables near real-time classification of newly detected GRBs, delivering both a predicted progenitor class and a probabilistic estimate, making it well suited for integration into rapid follow-up frameworks.