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

The joint detection of the gravitational wave GW170817, of the short gamma-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 similar to 10(4), 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 . Finally, we combine the predictions from AT2017gfo with those from GW170817, constraining the radius of a NS of 1.4 M-circle dot to 12.2 +/- 0.5 km (1 sigma level). This prediction could be further strengthened by improving kilonova models with numerical-relativity information.

Automated summary

The joint detection of the binary neutron star merger on August 17, 2017, through the gravitational wave GW170817, short gamma-ray burst GRB170817A, and kilonova AT2017gfo, marks a milestone in multimessenger astronomy and provides new constraints on the neutron star equation of state. Observational data favor non-spherical geometries and multi-component models, resulting in constraints on the binary mass ratio and reduced tidal parameter. The predictions from AT2017gfo combined with those from GW170817 constrain the radius of a NS of 1.4 solar masses at 12.2 +/- 0.5 km (1 sigma level), with potential for further strengthening through improvements in kilonova models using numerical-relativity information.