Inferring the Equation of State from Neutron Star Observables via Machine Learning

TL;DR

This paper uses machine learning, specifically symbolic regression, to uncover relationships between neutron star observables and their underlying equations of state, enabling more direct inference of dense matter properties.

Contribution

It introduces a novel application of symbolic regression to connect neutron star observables with EoS parameters, including diverse models with exotic matter.

Findings

Maximum neutron star mass correlates with pressure at high energy density.

EoS can be expressed as a function of radius and tidal deformability.

The approach provides an efficient framework to decode dense matter EoS.

Abstract

We have conducted an extensive study using a diverse set of equations of state (EoSs) to uncover strong relationships between neutron star (NS) observables and the underlying EoS parameters using symbolic regression method. These EoS models, derived from a mix of agnostic and physics-based approaches, considered neutron stars composed of nucleons, hyperons, and other exotic degrees of freedom in beta equilibrium. The maximum mass of a NS is found to be strongly correlated with the pressure and baryon density at an energy density of approximately 800 MeV.fm$^{-3}$. We have also demonstrated that the EoS can be expressed as a function of radius and tidal deformability within the NS mass range 1-2$M_\odot$. These insights offer a promising and efficient framework to decode the dense matter EoS directly from the accurate knowledge of NS observables.