2024ApJ...976L..10K

Some, if not all, binary neutron star (BNS) coalescences, and a fraction of neutron star-black hole (NSBH) mergers, are thought to produce sufficient mass ejection to power gamma-ray bursts (GRBs). However, this fraction, as well as the distribution of beaming angles of BNS-associated GRBs, is poorly constrained from observation. Recent work applied machine learning tools to analyze GRB light curves observed by Fermi/Gamma-Ray Burst Monitor (GBM) and Swift/Burst Alert Telescope (BAT). GRBs were segregated into multiple distinct clusters, with the tantalizing possibility that one of them (BNS cluster) could be associated with BNSs and another (NSBH cluster) with NSBHs. As a proof of principle, assuming that all GRBs detected by Fermi/GBM and Swift/BAT associated with BNSs (NSBHs) lie in the BNS (NSBH) cluster, we estimate their rates (Gpc^–3 yr^–1). We compare these rates with corresponding BNS and NSBH rates estimated by the LIGO-Virgo-KAGRA (LVK) collaboration from the first three observing runs (O1, O2, O3). We find that the BNS rates are consistent with LVK's rate estimates, assuming a uniform distribution of beaming fractions (f_b ∈ [0.01, 0.1]). Conversely, using the LVK's BNS rate estimates, assuming all BNS mergers produce GRBs, we are able to constrain the beaming angle distribution to θ_j ∈ [0.°8, 33.°5] at 90% confidence. We similarly place limits on the fraction of GRB-bright NSBHs as f_B ∈ [1.3%, 63%] (f_B ∈ [0.4%, 15%]) with Fermi/GBM (Swift/BAT) data.