基于状态方程的NSCool扩展：用于包含强子与夸克自由度的致密星冷却
An EOS-Driven Extension of NSCool for Compact Star Cooling with Hadronic and Quark Degrees of Freedom
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- Università degli Studi della Campania “Luigi Vanvitelli”（坎帕尼亚路易吉范维特利大学）
- Istituto Nazionale di Fisica Nucleare, Sezione di Napoli（意大利国家核物理研究所那不勒斯分部）
- Istituto Nazionale di Fisica Nucleare, Laboratori Nazionali di Frascati（意大利国家核物理研究所弗拉斯卡蒂国家实验室）
- Universidad Rey Juan Carlos（胡安卡洛斯国王大学）
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中文总结 AI 辅助
提出NSCool的EOS驱动扩展，支持强子与夸克自由度，统一处理多种组分并验证冷却计算，作为软件测试。
中文摘要 AI 辅助
我们提出了NSCool热演化代码的一个基于状态方程（EOS）的扩展，该扩展使得在单一基于组分的输入结构中能够处理完整的表格化状态方程。广义的NEW接口包含了额外的重子组分、一个玻色子组分变量、强子体积分数以及夸克分数，从而允许在同一工作流程中处理核子、超子、共振重子以及混合强子-夸克构型。中微子部分也相应扩展，包括更新的核子修正Urca和轫致辐射率、额外的重子直接Urca通道、超子过程、重子对破缺与形成，以及夸克直接Urca、修正Urca、轫致辐射和PBF贡献。在混合相中，发射率和相应的核心热容贡献由相局域量评估，并使用强子体积分数进行组合。该实现针对一个受控的核子基准与原始NSCool计算结果进行了验证，并通过代表性的含强子和含夸克的状态方程进行了演示。这些计算旨在作为广义工作流程的软件验证测试，而非观测拟合或统计EOS推断。
英文摘要
We present an EOS-driven extension of the NSCool thermal evolution code that enables complete tabulated equations of state to be treated within a single composition-based input structure. The generalized NEW interface includes additional baryonic fractions, a bosonic composition variable, the hadronic volume fraction, and quark fractions, allowing nucleonic, hyperonic, resonant-baryonic and hybrid hadron-quark configurations to be handled within the same workflow. The neutrino sector is extended accordingly, including updated nucleonic modified Urca and bremsstrahlung rates, additional baryonic direct Urca channels, hyperonic processes, baryonic pair breaking and formation and quark direct Urca, modified Urca, bremsstrahlung and PBF contributions. In mixed phases, emissivities and the corresponding core heat capacity contributions are evaluated from phase-local quantities and combined using the hadronic volume fraction. The implementation is validated against the original NSCool calculation for a controlled nucleonic benchmark and demonstrated with representative hadronic and quark-containing EOSs. These calculations are intended as software validation tests of the generalized workflow rather than as observational fits or statistical EOS inference.
