Bayesian inference of fine features of the nuclear equation of state from future neutron star radius measurements to 0.1 km accuracy

To more precisely constrain the equation of state (EOS) of supradense neutron-rich nuclear matter, future high-precision x-ray and gravitational wave observatories are proposed to measure the radii of neutron stars (NSs) with an accuracy better than about 0.1 km. However, it remains unclear what particular aspects (other than the stiffness generally spoken of in the literature) of the EOS and to what precision they will be better constrained. In this work, within a Bayesian framework using a metamodel EOS for NSs, we infer the posterior probability distribution functions (PDFs) of incompressibility K0 and skewness J0 of symmetric nuclear matter (SNM) as well as the slope L, curvature Ksym, and skewness Jsym characterizing the density dependence of nuclear symmetry energy Esym(ρ), respectively, from mean values of NS radii consistent with existing observations and an expected accuracy ΔR ranging from about 1.0 to 0.1 km. We found that (1) the ΔR has little effect on inferring the stiffness of SNM at suprasaturation densities, (2) smaller ΔR reveals more accurately not only the PDFs but also pairwise correlations among parameters characterizing high-density Esym(ρ), (3) a double-peak feature of the PDF(Ksym) corresponding to the strong Ksym-Jsym and Ksym-L anticorrelations is revealed when ΔR is less than about 0.2 km, and the locations of the two peaks are sensitive to the maximum value of Jsym reflecting the stiffness of Esym(ρ) above about 3 times the saturation density ρ0 of SNM, and (4) the high-precision radius measurement for canonical NSs is more useful than that for massive ones for constraining the EOS of nucleonic matter around (2–3)ρ0.