Universal EOS-Radius Inverse Mappings Govern Precision-Dependent Inference of the Neutron Star Equation of State

Abstract

Bayesian inference of the neutron star (NS) equation of state (EOS) generally assumes that improved observations primarily reduce posterior uncertainties while leaving inferred EOS parameters unchanged. Using mock measurements of the radius of a canonical $1.4\,M_\odot$ NS with identical central values but varying observational precisions, we show that the inferred posterior means of EOS parameters can shift systematically as the measurement uncertainty changes. We demonstrate that this behavior originates from previously unidentified nearly universal inverse mappings between the NS radius $R_{1.4}$ and empirical EOS parameters. Across a broad range of observational precisions, posterior samples collapse onto nearly unique functions. These mappings are largely independent of observational precision and define a low-dimensional EOS manifold underlying Bayesian inference. We show that the precision dependence of inferred EOS parameters arises from nonlinear filtering of the posterior radius distribution through these mappings. In the narrow-distribution limit this effect reduces to a Jensen-type correction proportional to the local curvature of the inverse mapping, while for presently realistic uncertainties the full nonlinear-filtering relation accurately reproduces the posterior means. Our results reveal a geometric origin of precision-dependent inference in NS EOS studies and provide a new framework for connecting astrophysical observations directly to microscopic nuclear many-body theories.