Reconstructing the neutron star equation of state from observational data via automatic differentiation

TL;DR

This paper introduces a deep learning approach using automatic differentiation to reconstruct the neutron star equation of state from observational data, providing consistent results with traditional methods and gravitational wave constraints.

Contribution

It presents a novel neural network-based method that optimizes the neutron star EoS directly within an automatic differentiation framework for inverse problems.

Findings

Neural network EoS matches conventional results

Narrow pressure and sound speed bands as functions of density

Results align with gravitational wave observations from GW170817

Abstract

Neutron star observables like masses, radii, and tidal deformability are direct probes to the dense matter equation of state~(EoS). A novel deep learning method that optimizes an EoS in the automatic differentiation framework of solving inverse problems is presented. The trained neural network EoS yields narrow bands for the relationship between the pressure and speed of sound as a function of the mass density. The results are consistent with those obtained from conventional approaches and the observational bound on the tidal deformability inferred from the gravitational wave event, GW170817.