AT2017gfo: Bayesian inference and model selection of multi-component kilonovae and constraints on the neutron star equation of state

Matteo Breschi; Albino Perego; Sebastiano Bernuzzi; Walter Del Pozzo; Vsevolod Nedora; David Radice; Diego Vescovi

doi:10.1093/mnras/stab1287

paper

AT2017gfo: Bayesian inference and model selection of multi-component kilonovae and constraints on the neutron star equation of state

arXiv:2101.01201 · doi:10.1093/mnras/stab1287

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 merger observed on August 17, 2017, is a milestone in multimessenger astronomy and provides new constraints on the neutron star equation of state. We perform Bayesian inference and model selection on AT2017gfo using semi-analytical, multi-components models that also account for non-spherical ejecta. Observational data favor anisotropic geometries to spherically symmetric profiles, with a log-Bayes' factor of $\sim 1 0^{4}$ , and favor multi-component 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 $120 < Λ ~ < 1110$ . Finally, we combine the predictions from AT2017gfo with those from GW170817, constraining the radius of a neutron star of $1.4 M_{⊙}$ to $12.2 \pm 0.5 km$ ( $1 σ$ level). This prediction could be further strengthened by improving kilonova models with numerical-relativity information.

AT2017gfo: Bayesian inference and model selection of multi-component kilonovae and constraints on the neutron star equation of state

Abstract

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