# Diversity in Fermi/GBM Gamma Ray Bursts: New insights from Machine Learning

Source: [Diversity in Fermi/GBM Gamma Ray Bursts: New insights from Machine Learning](<https://www.sota2.com/research/paper/68fa47ca1e209131ce92acb2>)

Collection index: [llms.txt](<https://www.sota2.com/research/llms.txt>)

Data updated: 2025-10-25T06:31:37.124Z

Authors: Dimple, K. Misra, K. G. Arun

Published: 2024-06-07T15:19:53.000Z

## Abstract

Classification of gamma-ray bursts (GRBs) has been a long-standing puzzle in high-energy astrophysics. Recent observations challenge the traditional short vs. long viewpoint, where long GRBs are thought to originate from the collapse of massive stars and short GRBs from compact binary mergers. Machine learning (ML) algorithms have been instrumental in addressing this problem, revealing five distinct GRB groups within the Swift/BAT light curve data, two of which are associated with kilonovae (KNe). In this work, we extend our analysis to the Fermi/GBM catalog and identify five clusters using unsupervised ML techniques, consistent with the Swift/BAT results. These five clusters are well separated in fluence-duration plane, hinting at a potential link between fluence, duration and complexities (or structures) in the light curves of GRBs. Further, we confirm two distinct classes of KN-associated GRBs. The presence of GRB 170817A in one of the two KNe-associated clusters lends evidence to the hypothesis that this class of GRBs could potentially be produced by binary neutron star (BNS) mergers. The second KN-associated GRB cluster could potentially originate from NS-BH mergers. Future multimessenger observations of compact binaries in gravitational waves (GWs) and electromagnetic waves can be paramount in understanding these clusters better.

## Recorded benchmark results

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## Original sources and implementation

[Original paper](<https://arxiv.org/abs/2406.05005>)

[PDF](<https://arxiv.org/pdf/2406.05005.pdf>)