  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.
