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.

利用太阳强度图和磁图来预测太阳耀斑，特别是 C 级、M 级和 X 级，以减轻其对卫星操作、通信系统和电网的风险。该研究强调在太阳活动预测中识别磁场的重要性，为改善空间天气预报提供了一种高准确度的工具，对减轻空间天气影响具有实际意义。