用Transformer模型分析太阳磁数据，提前预测日冕物质抛射
Prediction of Halo Coronal Mass Ejections Using SDO/HMI Vector Magnetic Data Products and a Transformer Model
- 基于太阳磁场数据构建时间序列输入，用Transformer建模
- 在2010-2023年间数据上达到TSS 0.907的预测性能
- 首个将Transformer用于日冕物质抛射预测的研究，适合空间天气领域
我们提出一种名为DeepHalo的Transformer模型，用于预测日冕物质抛射（CME）是否为晕状。模型以活动区（AR）及其前24小时的时间序列数据为输入，该序列由太阳动力学观测台（SDO）上的日震与磁成像仪（HMI）获取的光球矢量磁场数据提取物理特征构成。我们匹配了2010年11月至2023年8月期间的DONKI和LASCO CME目录中的事件，整理出包含晕状与非晕状CME的活动区列表，并据此标注样本标签。实验表明，DeepHalo的真技能统计（TSS）得分为0.907，优于类似结构的长短期记忆网络（TSS=0.821）。据我们所知，这是首次将Transformer应用于晕状CME预测。
原文摘要 · Abstract (English)
We present a transformer model, named DeepHalo, to predict the occurrence of halo coronal mass ejections (CMEs). Our model takes as input an active region (AR) and a profile, where the profile contains a time series of data samples in the AR that are collected 24 hours before the beginning of a day, and predicts whether the AR would produce a halo CME during that day. Each data sample contains physical parameters, or features, derived from photospheric vector magnetic field data taken by the Helioseismic and Magnetic Imager (HMI) on board the Solar Dynamics Observatory (SDO). We survey and match CME events in the Space Weather Database Of Notification, Knowledge, Information (DONKI) and Large Angle and Spectrometric Coronagraph (LASCO) CME Catalog, and compile a list of CMEs including halo CMEs and non-halo CMEs associated with ARs in the period between November 2010 and August 2023. We use the information gathered above to build the labels (positive versus negative) of the data samples and profiles at hand, where the labels are needed for machine learning. Experimental results show that DeepHalo with a true skill statistics (TSS) score of 0.907 outperforms a closely related long short-term memory network with a TSS score of 0.821. To our knowledge, this is the first time that the transformer model has been used for halo CME prediction.
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