标题： 通过结合核物理和天体物理输入以机器学习方法校准状态方程的全局行为 显示英文标题

标题： Calibrating global behaviour of equation of state by combining nuclear and astrophysics inputs in a machine learning approach

摘要： 我们采用了符号回归技术来识别适合的解析函数，这些函数可以将通过求解托尔曼-奥本海默-沃尔科夫（TOV）方程得到的中子星（NSs）的各种性质映射到状态方程（EoS）的几个关键参数上。 然后，这些符号回归模型（SRMs）被用于基于核物理实验和天体物理观测的综合数据集进行贝叶斯推理。 利用SRMs进行贝叶斯推理所获得的EoS参数后验分布与直接从TOV方程解中获得的结果非常接近。 我们的基于SRM的方法大约快100倍，能够在合理的时间范围内高效地对不同数据组合进行贝叶斯分析，探索它们对各种EoS参数的敏感性。 显示英文摘要

摘要： We implemented symbolic regression techniques to identify suitable analytical functions that map various properties of neutron stars (NSs), obtained by solving the Tolman-Oppenheimer-Volkoff (TOV) equations, to a few key parameters of the equation of state (EoS). These symbolic regression models (SRMs) are then employed to perform Bayesian inference with a comprehensive dataset from nuclear physics experiments and astrophysical observations. The posterior distributions of EoS parameters obtained from Bayesian inference using SRMs closely match those obtained directly from the solutions of TOV equations. Our SRM-based approach is approximately 100 times faster, enabling efficient Bayesian analyses across different combinations of data to explore their sensitivity to various EoS parameters within a reasonably short time.