Neural network reconstruction of the dense matter equation of state from neutron star observables

TL;DR

This paper introduces a deep learning approach using neural networks and automatic differentiation to reconstruct the dense matter equation of state from neutron star mass-radius observations, enabling model-independent insights into ultra-dense QCD matter.

Contribution

It presents a novel neural network-based, model-independent method employing automatic differentiation to reconstruct the neutron star EoS from observational data.

Findings

Successfully reconstructs EoS from mock data with few observations.

Achieves accurate EoS fitting using only 11 mass-radius pairs.

Demonstrates potential for applying to real neutron star observational data.

Abstract

The Equation of State (EoS) of strongly interacting cold and hot ultra-dense QCD matter remains a major challenge in the field of nuclear astrophysics. With the advancements in measurements of neutron star masses, radii, and tidal deformabilities, from electromagnetic and gravitational wave observations, neutron stars play an important role in constraining the ultra-dense QCD matter EoS. In this work, we present a novel method that exploits deep learning techniques to reconstruct the neutron star EoS from mass-radius (M-R) observations. We employ neural networks (NNs) to represent the EoS in a model-independent way, within the range of $\sim$1-7 times the nuclear saturation density. The unsupervised Automatic Differentiation (AD) framework is implemented to optimize the EoS, so as to yield through TOV equations, an M-R curve that best fits the observations. We demonstrate that this method works by rebuilding the EoS on mock data, i.e., mass-radius pairs derived from a randomly generated polytropic EoS. The reconstructed EoS fits the mock data with reasonable accuracy, using just 11 mock M-R pairs observations, close to the current number of actual observations.