AI 中文总结
本研究开发两种神经网络代理模型，可快速准确预测中子星结构，其评估速度比直接数值积分提升约两个数量级，适合大规模致密物质EOS贝叶斯推断与种群研究。
AI 中文摘要
对大量物态方程（EOS）样本求解托尔曼-奥本海默-沃尔科夫（TOV）方程及潮汐扰动方程，是致密物质EOS贝叶斯推断中的主要计算瓶颈，随着下一代观测台提供更大更精确的数据集，这一瓶颈将愈发明显。我们开发正向TOV映射的神经网络代理模型，可直接从EOS参数和中心密度同时预测中子星质量、半径和潮汐形变。我们训练并对比两种架构：传统前馈网络和残差网络，据我们所知，残差网络此前未被用于TOV代理建模。在分段多方EOS参数空间上训练后，两种网络均以高精度重现数值解，三个可观测量的决定系数均超过0.999，且相比直接数值积分，恒星可观测量的评估速度提升约两个数量级。我们发现，在本文考虑的网络规模下，两种架构均达到出色的预测精度，残差网络相比前馈网络有适度的精度提升，但代价是推理时间略长；总体性能差异仍较小，说明前馈网络已具备足够容量完成该映射，而残差连接仅提供增量增益。不过，残差架构为未来扩展到更丰富的EOS参数化或更高维回归任务提供了可靠基线。生成的代理模型非常适合大规模贝叶斯EOS推断和种群研究，否则重复TOV评估将占主导计算成本。
英文摘要
Solving the Tolman--Oppenheimer--Volkoff (TOV) equations, together with the tidal perturbation equations, for large numbers of equation-of-state (EOS) samples is a major computational bottleneck in Bayesian inference of the dense-matter EOS, and this will become increasingly limiting as next-generation observatories deliver far larger and more precise datasets. We develop neural-network surrogates for the forward TOV mapping that predict neutron star mass, radius, and tidal deformability simultaneously and directly from the EOS parameters and central density. We train and compare two architectures: a conventional feedforward network and a residual network, the latter of which, to our knowledge, has not previously been explored for TOV surrogate modeling. Trained on a piecewise polytropic EOS parameter space, both networks reproduce the numerical solutions to high accuracy, with the coefficient of determination exceeding 0.999 for all three observables, while accelerating the evaluation of stellar observables by roughly two orders of magnitude relative to direct numerical integration. We find that both architectures achieve excellent predictive accuracy at the network sizes considered here, with the residual network providing a modest improvement in accuracy over the feedforward network at the expense of slightly longer inference times. The overall performance differences remain small, indicating that a feedforward network already has sufficient capacity for this mapping while residual connections offer only incremental gains. Nevertheless, the residual architecture provides a robust baseline for future extensions to richer EOS parameterizations or higher-dimensional regression tasks. The resulting surrogates are well-suited to large-scale Bayesian EOS inference and population studies, where repeated TOV evaluations would otherwise dominate the computational cost.
Comments13 pages, 11 figures
