Insights into neutron star equation of state by machine learning

TL;DR

This paper demonstrates that a neural network platform can effectively constrain the neutron star equation of state by predicting model parameters and properties of nuclear matter and neutron stars, offering a new data-driven approach.

Contribution

The study introduces a neural network framework to estimate parameters of nuclear matter models and neutron star properties, showcasing its accuracy and efficiency in this domain.

Findings

Neural network can predict model parameters with reasonable precision.

The platform constrains properties of nuclear matter at saturation density.

It accurately estimates global properties of neutron stars.

Abstract

Due to its powerful capability and high efficiency in big data analysis, machine learning has been applied in various fields. We construct a neural network platform to constrain the behaviors of the equation of state of nuclear matter with respect to the properties of nuclear matter at saturation density and the properties of neutron stars. It is found that the neural network is able to give reasonable predictions of parameter space and provide new hints into the constraints of hadron interactions. As a specific example, we take the relativistic mean field approximation in a widely accepted Walecka-type model to illustrate the feasibility and efficiency of the platform. The results show that the neural network can indeed estimate the parameters of the model at a certain precision such that both the properties of nuclear matter around saturation density and global properties of neutron stars can be saturated. The optimization of the present modularly designed neural network and extension to other effective models are straightforward.