Towards Uncovering Dark Matter Effects on Neutron Star Properties: A Machine Learning Approach

Abstract

In recent years, researchers have become increasingly interested in understanding how dark matter affects neutron stars, helping them to better understand complex astrophysical phenomena. In this paper, we delve deeper into this problem by using advanced machine learning techniques to find potential connections between dark matter and various neutron star characteristics. We employ Random Forest classifiers to analyze neutron star (NS) properties and investigate whether these stars exhibit characteristics indicative of dark matter admixture. Our dataset includes 32,000 sequences of simulated NS properties, each described by mass, radius, and tidal deformability, inferred using recent observations and theoretical models. We explore a two-fluid model for the NS, incorporating separate equations of state for nucleonic and dark matter, with the latter considering a fermionic dark matter scenario. Our classifiers are trained and validated in a variety of feature sets, including the tidal deformability for various masses. Based on confusion matrices, these classifiers can identify NS with admixed dark matter with approximately 17% probability of misclassification. In particular, we find that additional tidal deformability data do not significantly improve the precision of our predictions. This article also delves into the potential of specific NS properties as indicators of the presence of dark matter. Radius measurements, especially at extreme mass values, emerge as particularly promising features. The insights gained from our study will guide future observational strategies and enhance dark matter detection capabilities. According to this study, neutron stars at 1.4 and 2.07 solar masses have radii that strongly suggest dark matter in neutron stars more likely than just hadronic composition, based on NICER data from pulsars PSR J0030+0451 and PSR J0740+6620.