Bayesian Inference of High-density Nuclear Symmetry Energy from Radii of Canonical Neutron Stars

Abstract

Abstract The radius R 1.4 of neutron stars (NSs) with a mass of 1.4 M ⊙ has been extracted consistently in many recent studies in the literature. Using representative R 1.4 data, we infer high-density nuclear symmetry energy E sym(ρ) and the associated nucleon specific energy E 0(ρ) in symmetric nuclear matter (SNM) within a Bayesian statistical approach using an explicitly isospin-dependent parametric equation of state (EOS) for nucleonic matter. We found the following. (1) The available astrophysical data can already significantly improve our current knowledge about the EOS in the density range of ρ 0 − 2.5ρ 0. In particular, the symmetry energy at twice the saturation density ρ 0 of nuclear matter is determined to be E sym(2ρ 0)= MeV at a 68% confidence level. (2) A precise measurement of R 1.4 alone with a 4% 1σ statistical error but no systematic error will not greatly improve the constraints on the EOS of dense neutron-rich nucleonic matter compared to what we extracted from using the available radius data. (3) The R 1.4 radius data and other general conditions, such as the observed NS maximum mass and causality condition, introduce strong correlations for the high-order EOS parameters. Consequently, the high-density behavior of E sym(ρ) inferred depends strongly on how the high-density SNM EOS E 0(ρ) is parameterized, and vice versa. (4) The value of the observed maximum NS mass and whether it is used as a sharp cutoff for the minimum maximum mass or through a Gaussian distribution significantly affects the lower boundaries of both E 0(ρ) and E sym(ρ) only at densities higher than about 2.5ρ 0.