发表机构
University of California, Santa Barbara; W. W. Hansen Experimental Physics Laboratory, Stanford University（加州大学圣塔芭芭拉分校; 斯坦福大学W.W.汉森实验物理实验室）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
本研究提出ResNet-Transformer混合模型，结合CNN与Transformer处理太阳图像时空数据，预测24至72小时窗口内耀斑级别与概率，性能优于传统方法。
AI 中文摘要
太阳耀斑是太阳大气中储存的磁能突然释放时产生的强烈电磁辐射爆发。它们被分为五个级别——从最弱到最强：A、B、C、M和X——每一级依次代表能量输出增加十倍。较强级别（C、M和X）发出的电磁辐射能够对地球上的通信系统、卫星和电网造成严重干扰。准确预测太阳耀斑对于减轻其不利影响和确保关键基础设施的功能至关重要。本研究提出了一种名为ResNet-Transformer的新模型，该模型将卷积神经网络（CNN）ResNet50与标准Transformer架构相结合。该混合模型有效处理来自太阳图像的时空数据，以预测24小时、36小时和72小时窗口内太阳耀斑的发生、级别（C、M和X）及概率。该混合深度学习模型是图像基础的多类别太阳耀斑预测领域的首创。我们使用一套全面的指标评估了模型性能，包括加权精确率、召回率和F1分数，以及平衡准确率、马修斯相关系数（MCC）、科恩卡帕系数和受试者工作特征曲线下面积（ROC-AUC）。结果表明，ResNet-Transformer在所有评估指标上均超越了传统机器学习方法，如支持向量机（SVM）和独立CNN模型。本研究凸显了将卷积神经网络与Transformer相结合以增强太阳物理学预测能力的潜力，为更可靠和及时的太阳耀斑预报铺平了道路。
英文摘要
Solar flares are intense bursts of electromagnetic radiation that occur when stored magnetic energy in the Sun's atmosphere is suddenly released. They are categorized into five classes -- from least to most powerful: A, B, C, M, and X -- with each successive class representing a ten-fold increase in energy output. The electromagnetic radiation emitted by the stronger classes (C, M, and X) is capable of causing significant disruptions to communication systems, satellites, and power grids on Earth. Accurate prediction of solar flares is crucial for mitigating their adverse effects and ensuring the functionality of critical infrastructure. This research introduces a novel model named ResNet-Transformer, which combines a convolutional neural network (CNN), ResNet50, with a standard Transformer architecture. The hybrid model effectively processes both spatial and time-series data derived from solar images to predict the occurrence, class (C, M, and X), and probability of solar flares within 24-hour, 36-hour, and 72-hour windows. This hybrid deep learning model represents the first of its kind in the domain of image-based, multiclass solar flare prediction. We evaluated the model's performance using a comprehensive set of metrics, including weighted precision, recall, and F1 score, together with balanced accuracy, the Matthews correlation coefficient (MCC), Cohen's kappa, and the area under the receiver operating characteristic curve (ROC-AUC). Results show that ResNet-Transformer surpasses traditional machine learning methods, such as support vector machines (SVMs) and standalone CNN models, across all evaluated metrics. This study highlights the potential of integrating convolutional neural networks with Transformers to enhance predictive capabilities in solar physics, paving the way for more reliable and timely solar flare forecasting.
Comments12 pages, 7 figures, 5 tables
