发表机构
New Jersey Institute of Technology; Big Bear Solar Observatory（新泽西理工学院; 大熊湖太阳观测台）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
提出物理信息神经网络加速多层光谱反演，直接预测参数并保持辐射转移物理，推理提速12-60倍，皮尔逊系数0.933。
AI 中文摘要
强色球吸收线如Hα 6562.8 Å和Ca II 8542.1 Å为太阳色球中的等离子体动力学和热结构提供了重要的诊断信息。多层光谱反演（MLSI）提供了一种物理可解释的框架，使用有限数量的辐射转移层对这些谱线进行建模，但传统的MLSI依赖于逐像素的非线性最小二乘拟合，这使得其在大规模成像光谱数据集上计算成本高昂。在此，我们引入了一种物理信息神经网络（PINN）框架来加速MLSI，同时保留其解析辐射转移公式。该网络直接从观测到的谱线轮廓预测MLSI参数，并通过可微分的MLSI正向模型传递这些参数以合成光谱。训练采用两阶段方法：初始阶段仅通过光谱重建损失进行优化，随后进行微调，将光谱一致性与来自单一参考图像上传统MLSI结果的参数空间监督相结合。该策略消除了对大型预计算训练集的需求，同时保持了物理可解释性。应用于古德太阳望远镜（GST）上的快速成像太阳光谱仪（FISS）观测，目标覆盖宁静区和活动区，MLSI-PINN参数图再现了直接反演的主要空间结构，在所有评估参数上实现了算术平均像素级皮尔逊相关系数0.933。重建的光谱与观测轮廓和传统MLSI拟合结果均高度吻合。训练后，MLSI-PINN处理一幅光栅图像约需5-15秒，而传统MLSI需3-5分钟，推理速度提升约12-60倍，且重建质量无明显损失，从而能够在大规模色球数据集上进行高效的MLSI分析。
英文摘要
Strong chromospheric absorption lines such as H$α$ 6562.8 A and Ca II 8542.1 A provide vital diagnostics of plasma dynamics and thermal structure in the solar chromosphere. Multilayer spectral inversion (MLSI) offers a physically interpretable framework for modeling these lines using a finite number of radiative-transfer layers, but conventional MLSI relies on pixel-by-pixel nonlinear least-squares fitting, making it computationally expensive for large imaging spectroscopic data sets. Here, we introduce a physics-informed neural-network (PINN) framework to accelerate MLSI while preserving its analytic radiative-transfer formulation. The network predicts MLSI parameters directly from observed line profiles and passes them through a differentiable MLSI forward model to synthesize spectra. Training follows a two-stage approach: an initial stage optimized solely via spectral reconstruction loss, followed by fine-tuning that combines spectral consistency with parameter-space supervision from conventional MLSI results on a single reference image. This strategy eliminates the need for large precomputed training sets while maintaining physical interpretability. Applied to Fast Imaging Solar Spectrograph (FISS) observations from the Goode Solar Telescope (GST) targeting both quiet-Sun and active-region regions, MLSI-PINN parameter maps reproduce the primary spatial structures of direct inversions, achieving an arithmetic mean pixel-wise Pearson correlation coefficient of 0.933 across all evaluated parameters. The reconstructed spectra closely match both observed profiles and conventional MLSI fits. Post-training, MLSI-PINN processes a raster in approximately 5-15 seconds compared to 3-5 minutes for conventional MLSI, delivering an inference speedup of about 12-60 times without substantial loss in reconstruction quality, enabling efficient MLSI analysis on large chromospheric data sets.
