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arXiv 2602.16264cs.LGastro-ph.SR
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利用可解释的类依赖奖励框架预测主要日冕层暴：结合活跃区域磁图与领域知识

Prediction of Major Solar Flares Using Interpretable Class-dependent Reward Framework with Active Region Magnetograms and Domain Knowledge

School of Computer Science, Jiangsu University of Science and Technology（江苏科技大学计算机科学学院）；State Key Laboratory of Space Weather, Chinese Academy of Sciences（中国科学院空间天气状态重点实验室）；School of software, Southeast University（东南大学软件学院）；Yunnan Astronomical Observatory, Chinese Academy of Sciences（中国科学院云南天文台）；Radio Cosmology Lab, Centre for Astronomy and Astrophysics Research, Department of Physics, Faculty of Science, Universiti Malaya（马来亚大学科学学院物理系射电天文学实验室）；National Centre for Particle Physics, Universiti Malaya（马来亚大学粒子物理国家研究中心）；Department of Electrical Engineering, Faculty of Engineering, Universiti Malaya（马来亚大学工程学院电气工程系）
Zixian Wu, Xuebao Li, Yanfang Zheng, Rui Wang, Shunhuang Zhang, Jinfang Wei, Yongshang Lv, Liang Dong, Zamri Zainal Abidin, Noraisyah Mohamed Shah, Hongwei Ye, … 展开作者

Zixian Wu, Xuebao Li, Yanfang Zheng, Rui Wang, Shunhuang Zhang, Jinfang Wei, Yongshang Lv, Liang Dong, Zamri Zainal Abidin, Noraisyah Mohamed Shah, Hongwei Ye, Pengchao Yan, Xuefeng Li, Xiaojia Ji, Xusheng Huang, Xiaotian Wang, Honglei Jin

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24 pages,12 figures
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AI总结：

本文提出一种可解释的类依赖奖励框架，结合活跃区域磁图和领域知识，通过深度学习模型预测日冕层暴，CDR-Transformer在性能和可解释性上均优于传统方法。

摘要：

在本工作中，我们首次开发了一种带有类依赖奖励（CDR）的监督分类框架，用于在24小时内预测≥MM级的日冕层暴。我们构建了多个数据集，涵盖知识引导的特征和视线磁图（LOS磁图）。我们还应用了三种深度学习模型（CNN、CNN-BiLSTM和Transformer）以及三种CDR对应模型（CDR-CNN、CDR-CNN-BiLSTM和CDR-Transformer）。首先，我们使用Transformer分析LOS磁力场参数的重要性，然后比较其在仅使用LOS、仅使用矢量和结合磁力场参数时的性能。其次，我们比较基于CDR模型和深度学习模型的耀斑预测性能。第三，我们对CDR模型的奖励工程进行敏感性分析。第四，我们使用SHAP方法进行模型可解释性分析。最后，我们比较我们的模型与NASA/CCMC的性能。主要发现是：（1）在LOS特征组合中，R_VALUE和AREA_ACR始终产生最佳结果。（2）Transformer在使用结合的LOS和矢量磁力场数据时比单独使用时表现更好。（3）使用知识引导特征的模型优于使用磁图的模型。（4）在使用磁图时，CNN和CNN-BiLSTM优于其CDR对应模型，但CDR-Transformer在使用知识引导特征时略优于其深度学习对应模型。（5）CDR模型的预测性能对奖励选择不敏感。（6）通过SHAP分析，CDR模型倾向于认为TOTUSJH更重要，而Transformer则优先考虑R_VALUE。（7）在相同预测时间和活跃区域（AR）数量下，CDR-Transformer的预测能力优于NASA/CCMC。

英文摘要：

In this work, we develop, for the first time, a supervised classification framework with class-dependent rewards (CDR) to predict $\geq$MM flares within 24 hr. We construct multiple datasets, covering knowledge-informed features and line-of sight (LOS) magnetograms. We also apply three deep learning models (CNN, CNN-BiLSTM, and Transformer) and three CDR counterparts (CDR-CNN, CDR-CNN-BiLSTM, and CDR-Transformer). First, we analyze the importance of LOS magnetic field parameters with the Transformer, then compare its performance using LOS-only, vector-only, and combined magnetic field parameters. Second, we compare flare prediction performance based on CDR models versus deep learning counterparts. Third, we perform sensitivity analysis on reward engineering for CDR models. Fourth, we use the SHAP method for model interpretability. Finally, we conduct performance comparison between our models and NASA/CCMC. The main findings are: (1)Among LOS feature combinations, R_VALUE and AREA_ACR consistently yield the best results. (2)Transformer achieves better performance with combined LOS and vector magnetic field data than with either alone. (3)Models using knowledge-informed features outperform those using magnetograms. (4)While CNN and CNN-BiLSTM outperform their CDR counterparts on magnetograms, CDR-Transformer is slightly superior to its deep learning counterpart when using knowledge-informed features. Among all models, CDR-Transformer achieves the best performance. (5)The predictive performance of the CDR models is not overly sensitive to the reward choices.(6)Through SHAP analysis, the CDR model tends to regard TOTUSJH as more important, while the Transformer tends to prioritize R_VALUE more.(7)Under identical prediction time and active region (AR) number, the CDR-Transformer shows superior predictive capabilities compared to NASA/CCMC.

机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。

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