AI 中文总结
针对太阳耀斑预测模型缺乏物理可解释性的问题，提出定量XAI框架，通过Grad-CAM识别MFRs并分析其磁参数，揭示MFRs与耀斑发生的强关联，提高了深度学习耀斑预测模型的物理可解释性。
AI 中文摘要
太阳耀斑是太阳大气中强烈的能量释放事件，可能造成重大空间天气危害，因此开发可靠的预测模型至关重要。深度学习方法虽有强大预测性能，但缺乏物理可解释性。本文提出定量可解释人工智能（XAI）框架，用梯度加权类激活映射（Grad-CAM）识别太阳磁图中的模型聚焦区域（MFRs），并进行两项关键分析评估其预测能力与磁场复杂性。结果显示MFRs与耀斑发生有很强物理关联，从MFRs提取的磁特征对耀斑有高预测力，产生耀斑的活跃区域具有磁复杂配置，以单一极性为主。这表明CNN在大规模观测训练时能学习到有物理意义的表示，将XAI与定量磁场分析结合可提高深度学习耀斑预测模型的物理可解释性，使其成为太阳物理预测和建模研究的有用工具。
英文摘要
Solar flares are intense energy release events in the solar atmosphere that may pose significant space weather hazards, which makes developing reliable prediction models essential. Although deep learning methods, particularly convolutional neural networks (CNNs), demonstrate strong predictive performance when using solar magnetograms, their scientific credibility is undermined by a lack of physical interpretability. Explainable artificial intelligence (XAI) offers a potential solution. However, current XAI studies in solar flare prediction are largely qualitative and lack systematic, theory-based, quantitative validation. We present a quantitative XAI framework that can decipher the physical basis of CNN-based solar flare prediction models. Using gradient-weighted class activation mapping (Grad-CAM), we identify model-focused regions (MFRs) in solar magnetograms. Then, we perform two key analyses to evaluate the predictive capability of magnetic parameters derived from MFRs and to quantitatively characterize their magnetic complexity. Our results reveal a strong physical correlation between MFRs and flare occurrence. Specifically, magnetic features extracted from MFRs demonstrate high predictive power for flares. Flare-producing active regions are characterized by magnetically complex configurations that are dominated by a single polarity rather than by balanced or purely unipolar structures. This finding is consistent with established physical theories of magnetic systems prone to flares. Our results suggest that CNNs can learn physically meaningful representations when trained on large-scale observations. Integrating XAI with quantitative magnetic field analysis improves the physical interpretability of deep learning-based flare prediction models, making them useful tools for prediction and modeling investigation in solar physics.
CommentsAccepted for publication in The Astrophysical Journal Letters
