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| Main Authors: | , , , , |
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| Format: | Preprint |
| Published: |
2025
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| Subjects: | |
| Online Access: | https://arxiv.org/abs/2508.20214 |
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| _version_ | 1866911127080796160 |
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| author | Espinoza, Sharleen N. Lloyd-Ronning, Nicole M. Negro, Michela Cheng, Roseanne M. Cibrario, Nicoló |
| author_facet | Espinoza, Sharleen N. Lloyd-Ronning, Nicole M. Negro, Michela Cheng, Roseanne M. Cibrario, Nicoló |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2508_20214 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Mapping Gamma-Ray Bursts: Distinguishing Progenitor Systems Through Machine Learning Espinoza, Sharleen N. Lloyd-Ronning, Nicole M. Negro, Michela Cheng, Roseanne M. Cibrario, Nicoló High Energy Astrophysical Phenomena 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. |
| title | Mapping Gamma-Ray Bursts: Distinguishing Progenitor Systems Through Machine Learning |
| topic | High Energy Astrophysical Phenomena |
| url | https://arxiv.org/abs/2508.20214 |