Binary neutron star merger offsets from their host galaxies II. Short-duration gamma-ray bursts

Abstract

The mergers of binary neutron stars (BNSs) and neutron star-black holes (NSBHs) binaries have long been linked to short-duration gamma-ray bursts (SGRBs). However, despite their stellar progenitors, SGRBs are often found outside the stellar light of the host galaxy. This is commonly attributed to supernova kicks, which displace the SGRB progenitors from the original stellar population. Our goal is to use stellar population synthesis models to reproduce and interpret the observed offsets of a statistical sample of SGRBs, using realistic galactic models based on the observed host properties. We derive the host galaxy potentials from the observed properties on a case-by-case basis, and simulate the galactic trajectories of synthetic BNSs and NSBHs from the BPASS code using three different kick prescriptions. We compare predicted and observed offsets to investigate the impact of velocity kicks, host galaxy types, and host association criteria. The results confirm that the locations of the SGRB population are consistent with the expectations of kicked BNS or BHNS progenitors, implying that such mergers are the dominant (if not only) progenitor system. Predictions for NSBHs provide a significantly worse fit compared to BNSs, while we find no significant difference when comparing different kick prescriptions. For late-type hosts, we find the best agreement when including hosts with a probability of chance alignment Pch up to 20%, while lower Pch thresholds lead us to overestimate SGRB offsets. We argue that Pch is biased against viable hosts at the largest offsets, and suggest the use of less conservative Pch thresholds for late-type hosts. For early-type hosts, the predictions underestimate SGRB offsets in a few cases regardless of the Pch threshold applied. We argue that this is likely due to the models missing galaxy evolution, or spurious host associations.

14 pages, 5 figures, submitted to A&A, comments are welcome

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