Speaker
Description
We present a unified Bayesian analysis of neutron stars admixed with dark matter, focusing on both fermionic and bosonic candidates. The hadronic equation of state is modeled within a relativistic mean-field framework, consistently calibrated to chiral effective field theory at low densities, finite nuclei and heavy-ion data at intermediate densities, and astrophysical observations at high densities.For the dark sector, we consider a fermionic model with a vector mediator alongside two self-interacting scalar (bosonic) scenarios. Using Bayesian inference, we constrain key dark matter properties, including particle mass, interaction strengths, and the fraction of dark matter accumulated inside neutron stars.Our results show that all considered models remain compatible with current nuclear and astrophysical constraints, while allowing only a small dark matter component (≲10%). The presence of dark matter leads to a mild softening of the equation of state, resulting in modest reductions in neutron star masses, radii, and tidal deformabilities, yet staying consistent with observations from NICER and GW170817.Model comparison based on Bayesian evidence reveals no statistically significant preference between fermionic and bosonic dark matter scenarios. This indicates that current observational data are not yet sensitive enough to distinguish the nature of dark matter within neutron stars. Overall, this work establishes a comprehensive statistical framework that connects neutron star observables with dark matter microphysics, highlighting both the constraining power and current limitations of multimessenger astrophysics.