使用可解释机器学习预测第23和第24太阳活动周的SEP事件
Forecasting SEP Events During Solar Cycles 23 and 24 Using Interpretable Machine Learning
AI总结：
本研究整合SHARP与SMARP数据集，采用SVM等机器学习策略预测太阳耀斑后SEP事件，准确率达0.7±0.1，14小时预报准确率较以往小幅提升，凸显了开发物理信息驱动的先进模型的必要性。
摘要：
随着航天任务的活动范围延伸至地球保护磁层之外，太阳高能粒子（Solar Energetic Particle, SEP）事件的预测受到越来越多的关注。这类事件大多是太阳耀斑期间磁重联驱动过程的产物，或是快速日冕物质抛射驱动的冲击波的产物，会对航空、天基电子设备，尤其是太空探索活动构成重大辐射危害。\n在本研究中，我们使用了最新构建的数据集，该数据集整合了太阳动力学天文台/日震与磁成像仪（Solar Dynamics Observatory/Helioseismic and Magnetic Imager, SDO/HMI）的空间天气HMI活动区斑块（Space weather HMI Active Region Patches, SHARP）数据，以及太阳和日球层观测台/迈克尔逊多普勒成像仪（Solar and Heliospheric Observatory/Michelson Doppler Imager, SoHO/MDI）的空间天气MDI活动区斑块（Space Weather MDI Active Region Patches, SMARP）数据。我们采用了包括支持向量机（Support Vector Machines, SVM）和回归模型在内的一系列机器学习策略，评估这一新数据产品对太阳耀斑后SEP事件的预测潜力。\n研究表明，尽管数据量有所增加，预测准确率仍达到0.7±0.1，这一结果与已发表的基准结果持平但并未超越。采用模拟业务运行场景（正负样本不平衡）的训练和测试配置的线性SVM模型显示，与以往研究相比，14小时SEP预报的准确率有小幅提升（+0.04±0.05）。这一结果凸显了开发更先进的、融合物理知识的模型的必要性，以更好地理解导致SEP事件的底层过程。
英文摘要：
Prediction of the Solar Energetic Particle (SEP) events garner increasing interest as space missions extend beyond Earth's protective magnetosphere. These events, which are, in most cases, products of magnetic reconnection-driven processes during solar flares or fast coronal-mass-ejection-driven shock waves, pose significant radiation hazards to aviation, space-based electronics, and particularly, space exploration. In this work, we utilize the recently developed dataset that combines the Solar Dynamics Observatory/Helioseismic and Magnetic Imager's (SDO/HMI) Space weather HMI Active Region Patches (SHARP) and the Solar and Heliospheric Observatory/Michelson Doppler Imager's (SoHO/MDI) Space Weather MDI Active Region Patches (SMARP). We employ a suite of machine learning strategies, including Support Vector Machines (SVM) and regression models, to evaluate the predictive potential of this new data product for a forecast of post-solar flare SEP events. Our study indicates that despite the augmented volume of data, the prediction accuracy reaches 0.7 +- 0.1, which aligns with but does not exceed these published benchmarks. A linear SVM model with training and testing configurations that mimic an operational setting (positive-negative imbalance) reveals a slight increase (+ 0.04 +- 0.05) in the accuracy of a 14-hour SEP forecast compared to previous studies. This outcome emphasizes the imperative for more sophisticated, physics-informed models to better understand the underlying processes leading to SEP events.
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