AI 中文总结
研究针对核物质和中子星物理中约束致密物质状态方程需处理大量数据及高维耦合空间微调难的问题，提出NNStar这一端到端人工智能代理，可自动化工作流程，为相关观测分析提供新的AI驱动框架。
AI 中文摘要
约束致密物质的状态方程需要将有效模型与大量数据进行对比，这些数据在规模上跨越多个数量级，从亚饱和核物质性质到中子星的质量、半径和潮汐形变。探索此类模型的高维耦合空间并根据所有这些约束进行微调是一项耗费人力和时间的任务。我们提出了NNStar，一个端到端的人工智能代理，它能使这个工作流程自动化。NNStar作为一种可移植的“技能”提供给一个开放的大语言模型代理平台，它能直接从拉格朗日量构建相对论平均场模型，求解平均场运动方程并评估饱和性质，构建β平衡状态方程并与地壳拼接，积分托尔曼 - 奥本海默 - 沃尔科夫方程，通过贝叶斯联合分析对核物质和天体物理观测结果进行评分。该代理可以读取模型、拟合参数并报告全套核物质和中子星可观测量，无需人工干预。因此，NNStar为分析核物质和中子星观测提供了一个新的、由人工智能驱动的框架。
英文摘要
Constraining the equation of state of dense matter requires confronting effective models with massive data that spans many orders of magnitude in scale, from sub-saturation nuclear matter properties to the masses, radii, and tidal deformabilities of neutron stars. Exploring the high-dimensional coupling space of such a model and fine tuning it against all of these constraints is a labor- and time-intensive task. We present \textsc{NNStar}, an end-to-end artificial-intelligence agent that automates this workflow. Rather than a bespoke application, \textsc{NNStar} is delivered as a portable \emph{skill} for an open large-language-model (LLM) agent platform -- a self-describing module that pairs worked usage conventions with symbolic and numerical physics engines that (i) build a relativistic mean-field model directly from a Lagrangian, (ii) solve the mean-field equations of motion and evaluate the saturation properties, (iii) construct the $β$-equilibrium equation of state, splice it to a crust, and integrate the Tolman--Oppenheimer--Volkoff equations, and (iv) score the resulting predictions through a Bayesian joint analysis against nuclear matter and astrophysical observations. The agent can read a model, fit its parameters, and report the full set of nuclear matter and neutron star observables without human intervention. \textsc{NNStar} therefore provides a new, AI-driven framework for analyzing nuclear matter and neutron-star observations.
Comments14 pages; comments are welcome
