Relativistic Mean-Field Hadronic Models under Nuclear Matter Constraints

TL;DR

This study evaluates 263 relativistic mean-field models against nuclear matter constraints, identifying the most consistent parameterizations and highlighting the impact of specific constraints on model acceptance.

Contribution

It provides a comprehensive analysis of RMF models under various nuclear matter constraints, pinpointing the models that best satisfy these conditions and examining the influence of specific constraints.

Findings

2 models satisfy all constraints in SET1

4 models are consistent in SET2a

3 models meet criteria in SET2b

Abstract

Relativistic mean-field (RMF) models have been widely used in the study of many hadronic frameworks because of several important aspects not always present in nonrelativistic models, such as intrinsic Lorentz covariance, automatic inclusion of spin, appropriate saturation mechanism for nuclear matter, causality and, therefore, no problems related to superluminal speed of sound. With the aim of identifying the models which best satisfy well known properties of nuclear matter, we have analyzed $263$ parameterizations of seven different types of RMF models under three different sets of constraints related to symmetric nuclear matter, pure neutron matter, symmetry energy, and its derivatives. One of these (SET1) is formed of the same constraints used in a recent work [M. Dutra et al., Phys. Rev. C 85, 035201 (2012)] in which we analyzed $240$ Skyrme parameterizations. The results pointed to $2$ models consistent with all constraints. By using another set of constraints, namely, SET2a, formed by the updated versions of the previous one, we found $4$ models approved simultaneously. Finally, in the third set, named SET2b, in which the values of the constraints are more restrictive, we found $3$ consistent models. Another interesting feature of our analysis is that the results change dramatically if we do not consider the constraint regarding the volume part of the isospin incompressibility ($K_{\tau,\rm v}$). In this case, we have $35$ approved models in SET2a and $30$ in SET2b.