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Discriminating between Neutron Stars and Black Holes with Imperfect Knowledge of the Maximum Neutron Star Mass
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Discriminating between Neutron Stars and Black Holes with Imperfect Knowledge of the Maximum Neutron Star Mass
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Although gravitational-wave signals from exceptional low-mass compact binary coalescences, like GW170817, may carry matter signatures that differentiate the source from a binary black hole system, only one out of every eight events detected by the current Advanced LIGO and Virgo observatories are likely to have signal-to-noise ratios large enough to measure matter effects, even if they are present. Nonetheless, the systems' component masses will generally be constrained precisely. Constructing an explicit mixture model for the total rate density of merging compact objects, we develop a hierarchical Bayesian analysis to classify gravitational-wave sources according to the posterior odds that their component masses are drawn from different subpopulations. Accounting for current uncertainty in the maximum neutron star mass, and adopting different reasonable models for the total rate density, we examine two recent events from the LIGO-Virgo Collaboration's third observing run, GW190425 and GW190814. For population models with no overlap between the neutron star and black hole mass distributions, we typically find that there is a $\gtrsim 70\%$ chance that GW190425 was a binary neutron star merger rather than a neutron-star--black-hole merger. On the other hand, we find that there is a $\lesssim 6\%$ chance that GW190814 involved a slowly spinning neutron star, regardless of our assumed population model.
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Cited by 5 Pith papers
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