A scalable random forest regressor for combining neutron-star equation of state measurements: A case study with GW170817 and GW190425

Gravitational-wave observations of binary neutron star coalescences constrain the neutron-star equation of state by enabling measurement of the tidal deformation of each neutron star. This deformation is well approximated by the tidal deformability parameter Λ, which was constrained using the first binary neutron star gravitational-wave observation, GW170817. Now, with the measurement of the second binary neutron star, GW190425, we can combine different gravitational-wave measurements to obtain tighter constraints on the neutron-star equation of state. In this paper, we combine data from GW170817 and GW190425 to place constraints on the neutron-star equation of state. To facilitate this calculation, we derive interpolated marginalized likelihoods for each event using a machine learning algorithm. These likelihoods, which we make publicly available, allow for results from multiple gravitational-wave signals to be easily combined. Using these new data products, we find that the radius of a fiducial 1.4 M⊙ neutron star is constrained to 11.6+1.6 -0.9 km at 90 per cent confidence and the pressure at twice the nuclear saturation density is constrained to 3.1+3.1 -1.3 × 1034 dyne cm-2 at 90 per cent confidence. Combining GW170817 and GW190425 produces constraints indistinguishable from GW170817 alone and is consistent with findings from other works.