Mapping Gamma-Ray Bursts: Distinguishing Progenitor Systems Through Machine Learning

We present an analysis of gamma-ray burst (GRB) progenitor classification through their positions on a Uniform Manifold Approximation and Projection plot, constructed by M. Negro et al., from Fermi-GBM waterfall plots. The embedding plot has a head–tail morphology, in which GRBs with confirmed progenitors (e.g., collapsars versus binary neutron star mergers) fall in distinct regions. We investigate the positions of various proposed sub-populations of GRBs, including those with and without radio afterglow emission, those with the lowest intrinsic luminosity, and those with the longest lasting prompt gamma-ray duration. The radio-bright and radio-dark GRBs fall in the head region of the embedding plot with no distinctive clustering, although the sample size is small. Our low luminosity GRBs fall in the head/collapsar region. A continuous duration gradient reveals an interesting cluster of the longest GRBs ( T 90 > 100 s) in a distinct region of the plot, possibly warranting further investigation.