4.2 Article

Cold uniform matter and neutron stars in the quark-meson-coupling model

期刊

NUCLEAR PHYSICS A
卷 792, 期 3-4, 页码 341-369

出版社

ELSEVIER
DOI: 10.1016/j.nuclphysa.2007.05.011

关键词

-

向作者/读者索取更多资源

A new density dependent effective baryon-baryon interaction has been recently derived from the quark-meson-coupling (QMC) model, offering impressive results in application to finite nuclei and dense baryon matter. This self-consistent, relativistic, quark-level approach is used to construct the Equation of State (EoS) and to calculate key properties of high density matter and cold, slowly rotating neutron stars. The results include predictions for the maximum mass of neutron-star models, together with the corresponding radius and central density, as well the properties of neutron stars with mass of order 1.4 M-circle dot. Some conditions related to the direct URCA process are explored for the QMC EoS and the parameters relevant to slow rotation, namely the moment of inertia and the period of rotation, are investigated. The results of the calculation, which are found to be in good agreement with available observational data, are compared with the predictions of several more traditional EoS. The QMC EoS provides cold neutron-star models with maximum mass in the range 1.9-2.1 M-circle dot, with central density less than 6 times nuclear saturation density (n(0) = 0.16 fm(-3)) and offers a consistent description of the stellar mass up to this density limit. In contrast with other models, QMC predicts no hyperon contribution at densities lower than 3n(0), for matter in beta-equilibrium. At higher densities, Xi(-.0) and A hyperons are present, with consequent lowering of the maximum mass. The absence of lighter Sigma(+/-.0) hyperons is understood as consequence of including the color hyperfine interaction in the response of the quark bag to the nuclear scalar field. (c) 2007 Elsevier B.V. All rights reserved.

作者

我是这篇论文的作者
点击您的名字以认领此论文并将其添加到您的个人资料中。

评论

主要评分

4.2
评分不足

次要评分

新颖性
-
重要性
-
科学严谨性
-
评价这篇论文

推荐

暂无数据
暂无数据