AI 中文总结
该研究利用物理信息正则化条件可逆神经网络，通过模拟质量-半径观测优化研究，发现约束高密度状态方程能力与观测位置有关，最优策略可降不确定性，确立此网络能从多信使观测中快速且一致地推断致密物质属性。
AI 中文摘要
NICER对中子星精确的质量和半径测量显著提升了我们约束超核密度下物质属性的能力。本文中，我们开发了一种物理信息正则化条件可逆神经网络（cINN），它将质量-半径后验分布双射地直接映射到相应的中心能量密度和压力上，无需显式的高维参数采样。物理信息正则化确保所有推断解满足因果性和热力学稳定性，无需显式正向建模就能确保物理上一致的预测。我们证明该框架能准确重建类似NICER观测的中心状态方程后验，同时保留宏观恒星可观测量与致密物质微观属性之间的映射。利用cINN的计算效率，我们对62400次模拟质量-半径观测进行了系统优化研究，以确定约束高密度状态方程最具信息量的目标。我们发现约束能力强烈依赖于质量-半径平面中观测的位置，最优策略是在紧凑的高质量恒星和扩展的中等质量恒星之间交替，相对于当前NICER基线，将推断状态方程的不确定性降低约9%-10%。这些结果确立了物理信息可逆神经网络作为一个强大框架，可从当前和未来的多信使观测中快速且物理一致地推断致密物质属性。
英文摘要
The precise mass and radius measurements of neutron stars by NICER have significantly advanced our ability to constrain the properties of matter at supranuclear densities. In this work, we develop a physics-informed regularized conditional Invertible Neural Network (cINN) that bijectively maps mass--radius posterior distributions directly onto the corresponding central energy density and pressure, eliminating the need for explicit high-dimensional parameter sampling. The physics-informed regularisation guarantees that all inferred solutions satisfy causality and thermodynamic stability, ensuring physically consistent predictions without explicit forward modelling. We demonstrate that the framework accurately reconstructs central EoS posteriors for NICER-like observations while preserving the mapping between macroscopic stellar observables and the microscopic properties of dense matter. Exploiting the computational efficiency of the cINN, we perform a systematic optimisation study of 62,400 simulated mass--radius observations to identify the most informative targets for constraining the high-density EoS. We find that the constraining power depends strongly on the location of the observation in the mass--radius plane, with an optimal strategy that alternates between compact high-mass stars and extended intermediate-mass stars, reducing the uncertainty in the inferred EoS by up to $\sim 9\%-10\%$ relative to the current NICER baseline. These results establish physics-informed invertible neural networks as a powerful framework for rapid, physically consistent inference of dense-matter properties from present and future multi-messenger observations.
Comments18 pages, 13 figures
