一种用于预测重大耀斑期间太阳极紫外辐射的深度学习框架
A Deep Learning Framework for Predicting Solar EUV Irradiance During Significant Flares
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- Sam Houston State University（萨姆休斯顿州立大学）
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中文总结 AI 辅助
该研究提出FlareEUV框架，利用NASA的SDO多仪器观测，通过轻量级基于注意力的架构从原始成像数据学习磁结构与日冕发射关系，用于预测重大太阳耀斑期间连续三天6.5纳米处每日EUV辐照度，实验证明其性能优于基线方法。
中文摘要 AI 辅助
我们提出了FlareEUV，这是一个多模态深度学习框架，用于利用美国国家航空航天局太阳动力学天文台（SDO）的多仪器观测，预测重大太阳耀斑期间连续三天6.5纳米处的每日极紫外（EUV）辐照度。我们考虑了第24太阳活动周期2011年至2014年期间的33次重大耀斑。SDO观测包括13幅共对准的全日面图像，包括8个AIA EUV/UV和5个HMI磁/连续体产品。FlareEUV使用轻量级基于注意力的架构从原始成像数据中学习磁结构与日冕发射之间的关系。我们的实验结果证明了FlareEUV在重大耀斑期间短期EUV辐照度预测中的良好性能及其相对于基线方法的优越性。
英文摘要
We present FlareEUV, a multimodal deep learning framework for predicting daily extreme ultraviolet (EUV) irradiance at 6.5 nm over three consecutive days during significant solar flares, using multi-instrument observations from NASA's Solar Dynamics Observatory (SDO). We consider 33 significant flares in the period between 2011 and 2014 in Solar Cycle 24. The SDO observations include 13 co-aligned full-disk images, comprising eight AIA EUV/UV and five HMI magnetic/continuum products. FlareEUV learns the relationship between magnetic structure and coronal emission from the raw imaging data using a lightweight attention-based architecture. Our experimental results demonstrate the good performance of FlareEUV in short-term EUV irradiance forecasting during the significant flares and its superiority over baseline methods.
