Abstract
GW170817 is the very first observation of gravitational waves originating from the coalescence of two compact objects in the mass range of neutron stars, accompanied by electromagnetic counterparts, and offers an opportunity to directly probe the internal structure of neutron stars. We perform Bayesian model selection on a wide range of theoretical predictions for the neutron star equation of state. For the binary neutron star hypothesis, we find that we cannot rule out the majority of theoretical models considered. In addition, the gravitational-wave data alone does not rule out the possibility that one or both objects were low-mass black holes. We discuss the possible outcomes in the case of a binary neutron star merger, finding that all scenarios from prompt collapse to long-lived or even stable remnants are possible. For long-lived remnants, we place an upper limit of 1.9 kHz on the rotation rate. If a black hole was formed any time after merger and the coalescing stars were slowly rotating, then the maximum baryonic mass of non-rotating neutron stars is at most 3.05M⊙, and three equations of state considered here can be ruled out. We obtain a tighter limit of 2.67M⊙ for the case that the merger results in a hypermassive neutron star.
Recommended Citation
B. P. Abbott et al., "Model Comparison from LIGO-Virgo Data on GW170817's Binary Components and Consequences for the Merger Remnant," Classical and Quantum Gravity, vol. 37, no. 4, Institute of Physics - IOP Publishing, Jan 2020.
The definitive version is available at https://doi.org/10.1088/1361-6382/ab5f7c
Department(s)
Physics
Keywords and Phrases
Compact Object Mergers; Gravitational Wave Astronomy; Neutron Star Equation of State; Neutron Stars
International Standard Serial Number (ISSN)
0264-9381; 1361-6382
Document Type
Article - Journal
Document Version
Final Version
File Type
text
Language(s)
English
Rights
© 2020 Institute of Physics - IOP Publishing, All rights reserved.
Creative Commons Licensing
This work is licensed under a Creative Commons Attribution 3.0 License.
Publication Date
01 Jan 2020
