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Inferring the equation of state from neutron star observables via machine learning
Authors: Patra, N.K.; Malik, T.; Pais, H.; Zhou, K.; Agrawal, B.K.; Providencia, C.
Ref.: Phys. Rev. B 865, 139470 (2025)
Abstract: We have conducted an extensive study using a diverse set of equations of state (EoSs) to uncover relationships between neutron star (NS) observables and the underlying EoS parameters using symbolic regression method. These EoS models, derived from a mix of agnostic and physics-based approaches, considered stars composed of nucleons, hyperons, and other exotic degrees of freedom in beta equilibrium. The maximum mass of a NS is found to be strongly correlated with the pressure and baryon density at an energy density approximately 800 MeV.fm-3. We have also demonstrated that the EoS can be expressed as a function of and tidal deformability within the NS mass range 1-2M circle dot. These insights offer a promising and efficient framework to decode the dense matter EoS directly from the accurate knowledge of NS observables.
