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

TL;DR

This study uses clustering and dimensionality reduction on Fermi GBM data to identify short gamma-ray bursts likely associated with binary neutron star mergers, aiding multimessenger astronomy efforts.

Contribution

It introduces a novel clustering approach with UMAP for classifying GRBs, improving identification of merger-related events from gamma-ray data.

Findings

Successfully identified a cluster of sGRBs consistent with merger signatures

Validated candidate selection by comparison with known kilonova-associated sGRBs

Enhanced methods for rapid identification of gravitational wave counterparts

Abstract

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. 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. We employed a clustering technique to classify GRBs based on their observed properties in gamma-rays (T90, Epeak 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. 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.