Cluster Structures with Machine Learning Support in Neutron Star M-R relations

TL;DR

This paper applies machine learning techniques to analyze neutron star mass-radius data, aiming to identify patterns and improve understanding of the star's equation of state amid observational uncertainties.

Contribution

It introduces a machine learning-based approach to analyze neutron star M-R relations, aiding in pattern recognition and EoS understanding.

Findings

Identified patterns in M-R data using machine learning.

Provided tools for better EoS inference from observational data.

Enhanced understanding of neutron star interior physics.

Abstract

Neutron stars (NS) are compact objects with strong gravitational fields, and a matter composition subject to extreme physical conditions. The properties of strongly interacting matter at ultra-high densities and temperatures impose a big challenge to our understanding and modelling tools. Some difficulties are critical, since one cannot reproduce such conditions in our laboratories or assess them purely from astronomical observations. The information we have about neutron star interiors are often extracted indirectly, e.g., from the star mass-radius relation. The mass and radius are global quantities and still have a significant uncertainty, which leads to great variability in studying the micro-physics of the neutron star interior. This leaves open many questions in nuclear astrophysics and the suitable equation of state (EoS) of NS. Recently, new observations appear to constrain the mass-radius and consequently has helped to close some open questions. In this work, utilizing modern machine learning techniques, we analyze the NS mass-radius (M-R) relationship for a set of EoS containing a variety of physical models. Our objective is to determine patterns through the M-R data analysis and develop tools to understand the EoS of neutron stars in forthcoming works.