发表机构
Yonsei University（延世大学）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
本文提出两种神经后验估计方法（TSNPE+MIS和A-NET+IS）加速中子星状态方程推断，并引入首个证据网络，在保证与UltraNest一致精度的同时大幅提升计算效率。
AI 中文摘要
我们提出了两条互补的神经路径用于微观中子星状态方程（EOS）推断：截断序贯神经后验估计结合混合重要性采样（TSNPE+MIS），针对单一观测；以及摊销神经后验估计结合重要性采样（A-NET+IS），可复用于不同核输入和NICER源。两者均针对原始先验和完整似然进行校正，并以有效样本量作为诊断指标。我们使用核数据、NICER、GW170817和pQCD测试了核子和超子相对论平均场模型。在基准分析中，两者在参数后验以及90%质量-半径和潮汐形变率带方面均与UltraNest一致。TSNPE+MIS的带边差异仅为0.024-0.033 km和1.02-1.03%，而A-NET+IS的差异为0.019-0.050 km和0.86-1.13%。TSNPE+MIS将计算时间减少了1.6-2.3倍；训练后，A-NET+IS每个配置快15-240倍。冻结的A-NET还分析了训练中未包含的三个NICER源，包括新报道的高质量紧凑PSR J1614-2230后验。对于该源，推断的$R_{1.4}$在核子物质中与UltraNest的差异在0.010 km以内，在超子物质中为0.009 km。我们还引入了一种格林函数证据网络（EN），这是证据网络的回归变体，据我们所知，这是首个应用于中子星EOS推断的证据网络。在一次成本之后，其网络查询对于新核数据返回绝对证据约需0.03秒，对于新的NICER源约需3.5秒；所有16个与UltraNest的差异均低于$1\sigma$，且独立重要性采样证据在所述EN不确定性范围内一致。在所采用的似然下，两种证据计算均通过约22:1-27:1的贝叶斯因子支持核子物质。
英文摘要
We present two complementary neural routes for microscopic neutron-star EOS inference: truncated sequential neural posterior estimation with mixture importance sampling (TSNPE+MIS), which targets one observation, and amortized neural posterior estimation with importance sampling (A-NET+IS), which is reused across nuclear inputs and NICER sources. Both are corrected against the original prior and full likelihood, with the effective sample size as a diagnostic. We test nucleonic and hyperonic relativistic mean-field models with nuclear data, NICER, GW170817, and pQCD. For the fiducial analyses, both agree with UltraNest in the parameter posteriors and 90% mass-radius and tidal-deformability bands. TSNPE+MIS band-edge differences are only 0.024-0.033 km and 1.02-1.03%, while A-NET+IS differences are 0.019-0.050 km and 0.86-1.13%. TSNPE+MIS reduces the computing time by factors of 1.6-2.3; after training, A-NET+IS is 15-240 times faster per configuration. The frozen A-NET also analyzes three NICER sources absent from training, including the newly reported high-mass, compact PSR J1614-2230 posterior. For this source, the inferred $R_{1.4}$ agrees with UltraNest within 0.010 km for nucleonic matter and 0.009 km for hyperonic matter. We also introduce a Green-function Evidence Network (EN), a regression-based variant of Evidence Networks and, to our knowledge, the first Evidence Network applied to neutron-star EOS inference. After a one-time cost, its network queries return absolute evidences in about 0.03 s for new nuclear data and 3.5 s for a new NICER source; all 16 differences from UltraNest are below $1σ$, and independent importance-sampling evidences agree within the quoted EN uncertainties. Under the adopted likelihood, both evidence calculations favor nucleonic matter by Bayes factors of about 22:1-27:1.
Comments23 pages, 11 figures, 12 tables. Code and data: https://github.com/prashantstar123/NeuronStar
