A machine-learning classifier for the postmerger remnant of binary neutron stars
Anna Puecher, Tim Dietrich

TL;DR
This paper develops a machine-learning classifier using gradient boosted decision trees to predict the postmerger remnant of binary neutron star mergers from inspiral gravitational-wave data, aiding in astrophysical understanding and electromagnetic follow-up.
Contribution
It introduces a novel classifier that predicts neutron star merger outcomes solely from inspiral parameters, without requiring postmerger gravitational-wave signals.
Findings
Classified GW170817 as likely forming a hypermassive neutron star.
Classified GW190425 as likely collapsing promptly into a black hole.
Achieved accurate predictions using only inspiral data.
Abstract
Knowing the kind of remnant produced after the merger of a binary neutron star system, e.g., if a black hole forms or not, would not only shed light on the equation of state describing the extremely dense matter inside neutron stars, but also help understand the physical processes involved in the postmerger phase. Moreover, in the event of a gravitational-wave detection, predicting the presence of a neutron star remnant is crucial in order to advise potential electromagnetic follow-up campaigns. In this work, we use Gradient Boosted Decision Trees and publicly available data from numerical-relativity simulations to construct a classifier that predicts the outcome of binary neutron star mergers, based on the binary's parameters inferred from gravitational-wave inspiral signals: total mass, mass-weighted tidal deformability, mass ratio, and effective inspiral spin. Employing parameters…
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Taxonomy
TopicsGeophysics and Gravity Measurements · Pulsars and Gravitational Waves Research · Geological and Geophysical Studies
