发表机构
Université Paris-Saclay; CEA; Laboratoire Matière en Conditions Extrêmes（巴黎萨克雷大学; 法国原子能和替代能源委员会; 极端条件物质实验室）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
本研究利用结合卷积神经网络和多层感知器的混合深度学习模型，预测恒星条件下铁和镍等离子体的光谱及平均不透明度，在显著降低计算成本的同时与标准方法高度一致，仅镍高温下L、M壳层存在较大偏差。
AI 中文摘要
本研究旨在预测恒星条件下等离子体的不透明度。我们聚焦于铁和镍，因为这些元素已在理论和实验上得到广泛研究。在某些条件下，特别是在非局部热力学平衡下，计算光谱不透明度可能计算量巨大。为缓解这一困难，我们采用深度学习模型来预测精确的不透明度，同时大幅降低计算成本。具体而言，我们开发并训练了一个混合模型，该模型结合了卷积神经网络和多层感知器。所提出的方法提供了0–10,000 eV能量范围内的光谱不透明度，以及针对大范围温度和质量的相应平均不透明度，与标准计算方法结果高度一致。除镍在高温下的L壳层和M壳层能量范围外，一致性非常令人满意，因为在这些范围内大量涌现的跃迁导致了显著差异。
英文摘要
The aim of this work is to predict the opacity of plasmas under stellar conditions. We focus on iron and nickel, as these elements have been extensively investigated both theoretically and experimentally. In certain regimes, notably under non-local thermodynamic equilibrium, calculating the spectral opacity can be computationally demanding. To mitigate this difficulty, we employ deep learning models to predict accurate opacities while achieving a substantial reduction in computational cost. Specifically, we develop and train a hybrid model that combines a convolutional neural network with a multilayer perceptron. The proposed approach provides both the spectral opacity over the 0--10,000 eV energy range, and the corresponding mean opacities for large sets of temperatures and mass densities, in close agreement with standard computational methods. The agreement is very satisfactory, except in the L- and M-shell energy ranges for nickel at high temperatures, where a very large number of emerging transitions leads to substantial discrepancies.
Commentssubmitted to Phys. Rev. E
Journal refPhys. Rev. E 114, 015204 (2026)
