发表机构
Institute for Research in Fundamental Sciences (IPM)（基础科学研究所）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
提出基于残差卷积网络的深度学习代理模型，快速预测太阳中微子地球物质效应，精度约2%，加速约60倍，可灵活应用于新物理场景。
AI 中文摘要
我们提出了一种用于太阳中微子地球物质效应的深度学习代理模型。该模型采用以太阳中微子振荡参数为条件的残差卷积网络，并在参数网格上计算的数值解上进行训练。它能够预测地球诱导的跃迁概率随中微子能量和天顶角的变化，为直接数值计算提供快速近似。对于参考电子中微子存活概率，该代理模型在笔记本电脑CPU测试中实现了约2%的逐点相对精度和约60倍的加速。由于网络仅学习地球穿越传播，它可以无需重新训练即可应用于保持该传播不变的新物理场景。该代理模型也可以使用替代数值实现或扩展参数集进行重新训练，为太阳中微子分析提供了一种灵活且计算高效的方法。代码和模拟数据可在\faGithub公开获取。
英文摘要
We present a deep-learning surrogate for the Earth matter effect on solar neutrinos. The model uses a residual convolutional network conditioned on the solar neutrino oscillation parameters and is trained on numerical solutions computed over a grid of parameter values. It predicts the Earth-induced transition probabilities across neutrino energy and zenith angle, providing a fast approximation to the direct numerical calculation. For the reference electron-neutrino survival probability, the surrogate achieves a pointwise relative accuracy of approximately $2\%$ and a speed-up of about a factor of 60 in a laptop-CPU test. Since the network learns only the Earth-crossing propagation, it can be applied without retraining to new-physics scenarios that leave this propagation unchanged. The surrogate can also be retrained using alternative numerical implementations or extended parameter sets, providing a flexible and computationally efficient approach for solar-neutrino analyses. The code and simulation data are publicly available at \href{https://github.com/AI-Driven-HEP/NuMatterSurrogate}{\faGithub}.
Commentscomments are welcome
