Direct astrophysical tests of chiral effective field theory at supranuclear densities

Recent observations of neutron stars with gravitational waves and x-ray timing provide unprecedented access to the equation of state (EoS) of cold dense matter at densities difficult to realize in terrestrial experiments. At the same time, predictions for the EoS equipped with reliable uncertainty estimates from chiral effective field theory (χ EFT ) allow us to bound our theoretical ignorance. In this work, we analyze astrophysical data by using a nonparametric representation of the neutron-star EoS conditioned on χ EFT to directly constrain the underlying physical properties of the compact objects without introducing modeling systematics. We discuss how the data alone constrain the EoS at high densities when we condition on χ EFT at low densities. We also demonstrate how to exploit astrophysical data to directly test the predictions of χ EFT for the EoS up to twice nuclear saturation density, in order to estimate the density at which these predictions might break down. We find that the existence of massive pulsars, gravitational waves from GW 170817 , and NICER observations of PSRJ0030 +0451 favor χ EFT predictions for the EoS up to nuclear saturation density over a more agnostic analysis by as much as a factor of seven for the quantum Monte Carlo (QMC) calculations used in this work. While χ EFT predictions using QMC are fully consistent with gravitational-wave data up to twice nuclear saturation density, NICER observations suggest that the EoS stiffens relative to these predictions at or slightly above nuclear saturation density. Additionally, for these QMC calculations, we marginalize over the uncertainty in the density at which χ EFT begins to break down, constraining the radius of a 1.4 M_⊙ neutron star to R_1.4=11 .40_-1.04^+1.38 (12 .54_-0.63^+0.71 ) km and the pressure at twice nuclear saturation density to p (2 n_sat) =14 .2_-8.4^+18.1 (28 .7_-15.0^+15.3 ) MeV /fm³ with massive pulsar and gravitational-wave (and NICER) data.