Speaker
Description
NICER has enabled mass–radius inferences for pulsars using pulse profile modeling (PPM), providing constraints on the equation of state (EOS) of cold, dense matter. To date, PPM and EOS inference have been carried out as two separate steps, with the former using EOS-agnostic priors. This approach has several drawbacks. Ideally, one would perform a fully hierarchical Bayesian inference where the pulse profile and EOS model parameters are jointly fit, but implementing such a framework is complex and computationally demanding.
I present an intermediate solution introducing an EOS-informed prior on mass-radius into the existing PPM pipeline using normalizing flows. This approach both tightens constraints on neutron star parameters while reducing computational costs and requiring minimal additional implementation effort. I will show results for two pulsars, PSR J0740+6620 and PSR J0437-4715, and with two EOS model families: a model based on the speed of sound inside the neutron star interior (CS) and a piecewise-polytropic (PP) model. Both EOS models implement constraints from chiral effective field theory calculations of dense matter.
