Discriminating between Neutron Stars and Black Holes with Imperfect Knowledge of the Maximum Neutron Star Mass
Reed Essick, Philippe Landry

TL;DR
This paper develops a hierarchical Bayesian method to classify gravitational-wave sources as neutron star or black hole mergers, considering uncertainties in neutron star maximum mass, and applies it to recent LIGO-Virgo events.
Contribution
It introduces a novel Bayesian classification framework that accounts for uncertain neutron star maximum mass and applies it to real gravitational-wave data.
Findings
High probability (over 70%) that GW190425 is a neutron star merger.
Low probability (under 6%) that GW190814 involved a neutron star.
Method effectively distinguishes source types despite limited matter effect signals.
Abstract
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…
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