Speaker
Description
Understanding the equation of state (EOS) of dense, neutron-rich matter is a central goal of neutron star physics and is essential for identifying possible exotic phases and phase transitions in compact stars. To more precisely constrain the EOS, 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 which aspects of the EOS will be better constrained and by how much. In this talk, I will discuss a series of recent developments using Bayesian inference and flexible EOS meta-models to connect future high-precision neutron star radius observations with the underlying properties of dense matter. These include constraints on the high-density behavior of nuclear symmetry energy; Bayesian inference of hybrid-star properties; and the precision needed to distinguish hybrid-star branches on the mass-radius diagram statistically. I will then present our recent discovery of nearly universal inverse mappings between the radius of a canonical neutron star and empirical EOS parameters. These mappings reveal a low-dimensional structure underlying Bayesian EOS inference and show that increasing observational precision does not simply narrow posterior distributions: nonlinear filtering through the TOV equations can systematically shift the inferred EOS parameters. In the narrow-distribution limit, this effect reduces to a Jensen-type correction determined by the curvature of the inverse mapping. I will discuss the implications of these results for interpreting forthcoming high-precision neutron star observations.