Feasibility of Dark Matter in Neutron Stars: A Quantitative Analysis

TL;DR

This study assesses how dark matter influences neutron star properties like mass, radius, and oscillation modes, using models supported by gravitational wave and X-ray data, revealing dark matter's significant impact on neutron star characteristics.

Contribution

It introduces a comprehensive analysis combining two-fluid and single-fluid models with Bayesian inference and machine learning to evaluate dark matter effects on neutron stars.

Findings

Dark matter reduces maximum mass, radius, and tidal deformability.

Semi-universal C-Love relation remains valid despite dark matter influence.

Machine learning effectively classifies dark matter-admixed neutron stars.

Abstract

This thesis investigates the impact of dark matter on neutron star properties, focusing on mass, radius, and tidal deformability. Using two-fluid and single-fluid models, dark matter is incorporated into the equation of state (EOS) via a Relativistic Mean Field (RMF) approach. The study finds that increasing dark matter content reduces the maximum mass, radius, and tidal deformability. Bayesian inference, supported by LIGO-Virgo gravitational wave data and NICER mass-radius measurements, refines these models. Despite dark matter's influence, the semi-universal C-Love relation remains valid. Machine learning techniques effectively classify dark matter-admixed neutron stars. The thesis also explores a sigma-cut potential in the EOS, which stiffens the EOS at high densities, favoring larger radii and lower f-mode frequencies. The study of non-radial oscillations, particularly f- and p-modes, highlights their sensitivity to neutron star composition and EOS. These findings enhance our understanding of neutron star interiors and dark matter's role, emphasizing the need for further observational and theoretical advancements.