Thermal Quarkyonic Matter and Its Implications for Neutron Star Structure

TL;DR

This paper introduces a phenomenological model of hot quarkyonic matter, exploring its impact on the equation of state and neutron star properties, with implications for understanding dense nuclear matter.

Contribution

It proposes a new momentum-dependent interaction model for quarkyonic matter, extending previous hadronic models to better describe hot dense matter in neutron stars.

Findings

The model significantly alters the equation of state shape.

Temperature effects are crucial for neutron star property predictions.

Comparison with observations constrains model parameters.

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 deconfined 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 hot 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, especially in those for which the effect of temperature is significant, 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.