Quarkyonic equation of state with a momentum dependent interaction and neutron star structure

TL;DR

This paper introduces a phenomenological model for quarkyonic matter with momentum-dependent interactions, affecting the neutron star equation of state and structure, and compares it with observational data for validation.

Contribution

It proposes a novel momentum-dependent interaction model for quarkyonic matter, enhancing the flexibility and realism of neutron star EOS modeling.

Findings

The model significantly influences the shape of the EOS.

Comparison with observational data constrains model parameters.

The approach offers new insights into neutron star properties.

Abstract

The structure and basic properties of dense nuclear matter still remain one of the open problems of Physics. In particular, the composition of the matter that composes neutron stars is under theoretical and experimental investigation. Among the theories that have been proposed, apart from the classical one where the composition is dominated by hadrons, the existence or coexistence of free quark matter is a dominant guess. An approach towards this solution is the phenomenological view according to which the existence of quarkyonic matter plays a dominant role in the construction of the equation of state (EOS). According to it the structure of the EOS is based on the existence of the quarkyonic particle which is a hybrid state of a particle that combines properties of hadronic and quark matter with a corresponding representation in momentum space. In this paper we propose a phenomenological model for quarkyonic matter, borrowed from corresponding applications in hadronic models, where the interaction in the quarkyonic matter depends not only on the position but also on the momentum of the quarkyonic particles. This consideration, as we demonstrate, can have a remarkable consequence on the shape of the EOS and thus on the properties of neutron stars, offering a sufficiently flexible model. Comparison with recent observational data can place constraints on the parameterization of the particular model and help improve its reliability.