Machine and Deep Learning Regression for Compact Object Equations of State

TL;DR

This paper applies advanced machine learning and deep learning techniques to analyze mass-radius data of compact stars, aiming to improve the inference of their dense matter equations of state, which are crucial for understanding neutron stars and quark stars.

Contribution

It introduces a novel application of state-of-the-art machine learning methods to reconstruct the equations of state from observational data of compact objects.

Findings

Machine learning models successfully infer EoS from mass-radius data.

Data-driven approaches refine understanding of matter at supranuclear densities.

The method demonstrates potential for integrating observational constraints into EoS modeling.

Abstract

A central open problem in nuclear physics is the determination of a physically robust equation of state (EoS) for dense nuclear matter, which directly informs our understanding of the internal composition and macroscopic properties of compact objects such as neutron stars and quark stars. Traditional efforts have relied primarily on theoretical modeling grounded in nuclear and particle physics, with subsequent validation against empirical constraints from heavy ion collisions and, increasingly, multimessenger astrophysical observations. Recent developments, however, have introduced complementary analytical strategies that merge theoretical modeling with advanced data driven methodologies. In particular, Bayesian inference, machine learning, and deep learning have emerged as powerful tools for constraining the EoS and extracting physical insight from complex observational datasets. In this work, we employ state of the art machine learning and deep learning techniques to analyze mass radius relations of compact objects with the aim of reconstructing or inferring their underlying equations of state. The analysis is based on an extensive library of physically consistent, multimodal EoSs for neutron stars and a corresponding set for quark stars, each constructed to satisfy established theoretical and observational constraints. By leveraging the predictive capacity of these computational frameworks, we demonstrate the potential of data-driven approaches to provide refined insights into the behavior of matter at supranuclear densities and to contribute to a more unified understanding of the dense matter EoS.