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, are poorly constrained from observation. Recent work applied machine learning tools to analyze GRB light curves observed by {Fermi}/GBM and { Swift}/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⁻³yr⁻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 (fb ∈ [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 θⱼ ∈ [0.8∘, 33.5∘] at $90%$ confidence. We similarly place limits on the fraction of GRB-Bright NSBHs as fB ∈ [1.3%, 63%] (fB ∈ [0.4%, 15%]) with { Fermi}/GBM ({ Swift}/BAT) data.
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Kapadia et al. (2024) studied this question.
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