Solar flares, especially C, M, and X class, pose significant risks to satellite operations, communication systems, and power grids. We present a novel approach for predicting extreme solar flares using HMI intensitygrams and magnetograms. By detecting sunspots from intensitygrams and extracting magnetic field patches from magnetograms, we train a Residual Network (ResNet) to classify extreme class flares. Our model demonstrates high accuracy, offering a robust tool for predicting extreme solar flares and improving space weather forecasting. Additionally, we show that HMI magnetograms provide more useful data for deep learning compared to other SDO AIA images by better capturing features critical for predicting flare magnitudes. This study underscores the importance of identifying magnetic fields in solar flare prediction, marking a significant advancement in solar activity prediction with practical implications for mitigating space weather impacts.

翻译：太阳耀斑，尤其是C、M和X级耀斑，对卫星运行、通信系统和电网构成重大风险。我们提出了一种利用HMI强度图和磁图预测极端太阳耀斑的新方法。通过从强度图检测太阳黑子并从磁图提取磁场斑块，我们训练了一个残差网络（ResNet）来对极端级别耀斑进行分类。我们的模型展现出高精度，为预测极端太阳耀斑和改进空间天气预报提供了一个稳健的工具。此外，我们证明，与其他SDO AIA图像相比，HMI磁图通过更好地捕捉预测耀斑强度的关键特征，为深度学习提供了更有用的数据。本研究强调了识别磁场在太阳耀斑预测中的重要性，标志着太阳活动预测领域的重大进展，对减轻空间天气影响具有实际意义。