Identifying potential binary neutron star merger events from the Fermi GBM Gamma-Ray Burst Catalog

¹ Università degli Studi di Napoli Federico II, Dipartimento di Fisica “Ettore Pancini”, Naples, Italy
² INAF, Osservatorio Astronomico di Capodimonte, Salita Moiariello 16, I-80131 Naples, Italy
³ DARK, Niels Bohr Institute, University of Copenhagen, Jagtvej 128, 2200 Copenhagen, Denmark
⁴ Mashfrog Group, Via Giacomo Peroni 400, 00131 Rome, Italy
⁵ INAF, Osservatorio Astronomico di Padova, vicolo dell’Osservatorio 5, 35122 Padova, Italy
⁶ INFN, Sezione di Napoli, I-80126 Napoli, Italy
⁷ ICRANet, Piazza della Repubblica 10, I-65122 Pescara, Italy

^⋆ Corresponding author: ines.giudice@inaf.it

Received: 14 February 2025
Accepted: 9 July 2025

Abstract

Context. Short gamma-ray bursts are expected to be associated with compact object mergers, such as binary neutron star or neutron star-black hole systems, and are key high-energy multimessenger events. The detection of GRB 170817A, coinciding with the gravitational wave signal GW170817 from a BNS merger, confirmed the link between sGRBs and compact object mergers. Similarly, GRB 150101B displayed remarkable similarities to GRB 170817A, further supporting its association with compact binary mergers.

Aims. The objective of this study is to uncover the intrinsic properties that differentiate merger-associated sGRBs from other GRBs by analyzing the Fermi GBM Burst Catalog and using GRB 170817A and GRB 150101B as reference events, enhancing our ability to select events from this class and promptly to search for their electromagnetic counterpart.

Methods. We employed a clustering technique to classify GRBs based on their observed properties in gamma-rays (T₉₀, E_peak and fluence). Prior to clustering, we tested three dimensionality reduction techniques, among which Uniform Manifold Approximation and Projection demonstrated the best performance making it the preferred technique for our analysis. This combination of dimensionality reduction and clustering analysis allowed us to group GRBs with similar characteristics, with a focus on identifying those most likely associated with BNS mergers.

Results. Our analysis successfully identified a cluster of sGRBs events with characteristics consistent with sGRB merger-associated. A comparison between our sample of candidates and known kilonova candidates associated with sGRBs, identified through other methodologies, further validated our approach.