Bayesian Inference of Fine-Features of Nuclear Equation of State from Future Neutron Star Radius Measurements to 0.1km 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 meta-model 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 Δ R ranging from about 1.0 km to 0.1 km. We found that (1) the Δ R has little effect on inferring the stiffness of SNM at suprasaturation densities, (2) smaller Δ R 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 anti-correlations is revealed when Δ R is less than about 0.2 km, (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) times the saturation density of SNM.