كيف قرأ الذكاء الاصطناعي الخط المغناطيسي للتوهجات الشمسية ✛

الملخص الأصلي

كما كتبه المؤلفون، بالإنجليزية arXiv:2607.15719v1

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.