AT2017gfo: Bayesian inference and model selection of multicomponent kilonovae and constraints on the neutron star equation of state
Abstract The joint detection of the gravitational wave GW170817, of the short γ-ray burst GRB170817A and of the kilonova AT2017gfo, generated by the the binary neutron star (NS) merger observed on 2017 August 17, is a milestone in multimessenger astronomy and provides new constraints on the NS equation of state. We perform Bayesian inference and model selection on AT2017gfo using semi-analytical, multicomponents models that also account for non-spherical ejecta. Observational data favour anisotropic geometries to spherically symmetric profiles, with a log-Bayes’ factor of ∼104, and favour multicomponent models against single-component ones. The best-fitting model is an anisotropic three-component composed of dynamical ejecta plus neutrino and viscous winds. Using the dynamical ejecta parameters inferred from the best-fitting model and numerical–relativity relations connecting the ejecta properties to the binary properties, we constrain the binary mass ratio to q < 1.54 and the reduced tidal parameter to $120\lt \tilde{\Lambda }\lt 1110$. Finally, we combine the predictions from AT2017gfo with those from GW170817, constraining the radius of a NS of 1.4 M⊙ to 12.2 ± 0.5 km (1σ level). This prediction could be further strengthened by improving kilonova models with numerical-relativity information.
This is a pre-copyedited, author-produced PDF of an article accepted for publication in Monthly Notices of the Royal Astronomical Society following peer review. The version of record [AT2017gfo: Bayesian inference and model selection of multicomponent kilonovae and constraints on the neutron star equation of state. Monthly Notices of the Royal Astronomical Society 505, 2 p1661-1677 (2021)] is available online at: https://doi.org/10.1093/mnras/stab1287.
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Work Title | AT2017gfo: Bayesian inference and model selection of multicomponent kilonovae and constraints on the neutron star equation of state |
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License | In Copyright (Rights Reserved) |
Work Type | Article |
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Publication Date | May 12, 2021 |
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Deposited | May 27, 2022 |
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