# Mapping neutron star data to the equation of state using the deep neural network

## TL;DR

This paper introduces a deep neural network approach to estimate the neutron star equation of state from observational data, providing insights consistent with nuclear models and gravitational wave constraints.

## Contribution

The study presents a novel supervised learning method using neural networks to map neutron star observational data to the equation of state, advancing the analysis of dense matter.

## Key findings

- Results align with traditional nuclear model extrapolations
- Consistent with gravitational wave tidal deformability bounds
- Demonstrates the feasibility of neural networks in astrophysical inference

## Abstract

The densest state of matter in the universe is uniquely realized inside central cores of the neutron star. While first-principles evaluation of the equation of state of such matter remains as one of the longstanding problems in nuclear theory, evaluation in light of neutron star phenomenology is feasible. Here we show results from a novel theoretical technique to utilize deep neural network with supervised learning. We input up-to-date observational data from neutron star X-ray radiations into the trained neural network and estimate a relation between the pressure and the mass density. Our results are consistent with extrapolation from the conventional nuclear models and the experimental bound on the tidal deformability inferred from gravitational wave observation.