Dr. Dimple Panchal Jha

Gamma-ray bursts (GRBs) exhibit extraordinary diversity in their light curve morphologies and spectral characteristics, reflecting the complex physics of relativistic outflows and radiation mechanisms operating under extreme conditions. Traditionally, this diversity has been reduced to a small number of phenomenological classes (e.g., long vs. short, Type I vs. Type II); however, mounting observational evidence challenges these binary classification schemes and exposes their physical limitations. In this talk, I will explore whether GRB diversity can be leveraged as a diagnostic of underlying physical processes rather than treated as observational scatter, using a data-driven framework that combines unsupervised machine learning with physical modeling of prompt emission pulse structures. I will focus in particular on ongoing efforts to interpret the feature-space clusters identified by dimensionality reduction and clustering algorithms, and to assess whether these groupings trace meaningful physical differences in jet physics, radiation mechanisms, or progenitor properties, or instead arise from degeneracies in the observational parameter space.