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arXiv · 2508.20214

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

Sharleen N. Espinoza·Nicole M. Lloyd-Ronning·Michela Negro·Roseanne M. Cheng·Nicoló Cibrario
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Abstract

We present an analysis of gamma-ray burst (GRB) progenitor classification, through their positions on a Uniform Manifold Approximation and Projection (UMAP) plot, constructed by Negro et al. 2024, from Fermi-GBM waterfall plots. The embedding plot has a head-tail morphology, in which GRBs with confirmed progenitors (e.g. collapsars vs. 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 ($T_{90} > 100s$) in a distinct region of the plot, possibly warranting further investigation.

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Sharleen N. Espinoza, Nicole M. Lloyd-Ronning, Michela Negro, Roseanne M. Cheng, Nicoló Cibrario. 2025-08-27. Mapping Gamma-Ray Bursts: Distinguishing Progenitor Systems Through Machine Learning. https://arxiv.org/abs/2508.20214

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