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

TL;DR

This study applies machine learning to classify Fermi/GBM gamma-ray bursts, revealing five distinct groups linked to different astrophysical origins, including neutron star mergers, and confirming previous Swift/BAT findings.

Contribution

The paper extends ML-based classification of GRBs to the Fermi/GBM catalog, identifying five clusters consistent with Swift/BAT results and linking them to specific astrophysical phenomena.

Findings

Five clusters identified in Fermi/GBM data consistent with Swift/BAT results.

Two clusters associated with kilonovae, linked to neutron star mergers.

GRB 170817A supports BNS merger origin hypothesis.

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.