Feasibility study of a dark matter admixed neutron star based on recent observational constraints

Context. The equation of state (EOS) for neutron stars is modeled using the relativistic mean field (RMF) approach with a mesonic nonlinear (NL) interaction, a modified sigma potential (NL– σ cut) that mimics the effect of an exclusion volume or the onset of a quarkyonic phase, and influences of dark matter in the NL (NL DM). Experimental constraints on the general properties of finite nuclei and heavy ion collisions, as well as astrophysical observations of neutron star radii and tidal deformation are taken into account. Aims. We evaluate the plausibility and implications of each scenario by exploring how modifications to the RMF model, including the NL– σ cut and dark matter influences, affect the neutron star EOS. Additionally, the study examines the tension between the PREX-II experimental data and other constraints, aiming to identify which model is able to optimally reconcile the available experimental and observational evidence. Methods. A Bayesian analysis framework was employed to systematically compare the different EOS scenarios. This approach integrates constraints from nuclear experiments (finite nuclei properties and heavy ion collisions) and astrophysical observations (neutron star radii, tidal deformation, and PSR J0437–4715 measurements) to rigorously assess the viability of each model. Results. The analysis shows that including the PREX–II data renders all models less favorable, indicating significant tension with the other constraints and incompatibility with chiral effective field theory calculations of pure neutron matter. When excluding PREX–II, the NL– σ cut model emerges with the highest Bayes evidence, favoring a stiffening of the EOS at high densities, whereas the model incorporating a dark matter component is the least favorable. Furthermore, new PSR J0437–4715 measurements lead to an approximate 0.2 km reduction in the 90% confidence interval upper boundary for neutron star radii, along with a notable decrease in Bayesian evidence, suggesting potential conflicts with prior data and/or the need for more adaptable models.