Neural Posterior Estimation of Neutron Star Equations of State

Probing the properties of matter at supranuclear densities is a fundamental challenge in both nuclear physics and astrophysics. In this paper, we present a novel approach to inferring the equation of state (EOS) of neutron stars using neural posterior estimation (NPE) techniques applied to EM and GW observations.

This work is another in a series of projects with Valéria Carvalho, Márcio Ferreira and Constança Providência (see also Conditional variational autoencoder inference of neutron star equation of state from astrophysical observations and Detecting the third family of compact stars with normalizing flows)