生成式人工智能用于重建中子星物质
Generative artificial intelligence for reconstructing neutron-star matter
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中文总结 AI 辅助
该研究用去噪扩散模型重建中子星物态方程，结合先验与精确物理约束，得到1.4倍太阳质量下的半径和潮汐形变结果，支持强子-夸克交叉过渡，为不适定逆问题提供模板。
中文摘要 AI 辅助
中子星核心包含宇宙中已知唯一同时处于低温、强相互作用且被压缩至超过核密度的物质，其组成未知。物态方程将恒星质量、半径和潮汐形变与该状态关联，但从稀疏观测中恢复这一关键量是一个不适定逆问题。现有分析将先验信息嵌入固定函数形式，对可接受解的权重不均，导致结果有偏差。我们采用去噪扩散模型重建物态方程，该模型将先验、物理和数据分离：学习可解释的、基于第一性原理核理论的物理驱动先验，同时精确引入微扰量子色动力学（Perturbative-QCD）和天体物理约束。未来测量将仅通过重新加权更新后验，无需重新训练或重采样。在1.4倍太阳质量下，推断得到的半径为12.6 km、潮汐形变为469，尽管先验范围更广，仍与高斯过程和重离子 informed 的推断结果一致。我们发现最重恒星中的物质近共形但仍较刚硬，与逐渐的强子-夸克交叉过渡一致，不支持强一阶相变。更广泛而言，将学习到的先验与精确强制执行的物理相结合，为理论和数据约束不同区域的不适定逆问题建立了模板。
英文摘要
Neutron-star cores hold the only known matter in the universe that is simultaneously cold and strongly interacting, compressed beyond nuclear density into a state of unknown composition. The equation of state links stellar masses, radii and tidal deformabilities to this regime, but recovering this key quantity from sparse observations is an ill-posed inverse problem. Existing analyses bury a prior in a fixed functional form, unevenly weighting admissible solutions and biasing the result. We reconstruct the equation of state with a denoising diffusion model that keeps prior, physics and data separate: it learns an inspectable, physically motivated prior anchored to first-principles nuclear theory, while perturbative-QCD and astrophysical constraints are imposed exactly. Future measurements therefore will update the posterior by reweighting alone, without retraining or resampling. The inferred radius of 12.6 km and tidal deformability of 469 at 1.4 solar masses reproduce Gaussian-process and heavy-ion-informed inferences despite a far broader prior. We find near-conformal but still stiff matter in the heaviest stars, consistent with a gradual hadron-quark crossover and disfavouring a strong first-order phase transition. More broadly, coupling a learned prior to exactly enforced physics establishes a template for ill-posed inverse problems where theory and data constrain different regions.
