综述机器学习在太阳高能粒子预测中的应用与数据方法
Review of Machine Learning Models for Solar Energetic Particle Prediction
- 系统梳理现有机器学习模型的输入、结构与训练数据
- 对比不同模型在预测准确率与响应速度上的表现差异
- 适合空间天气研究者与航天安全决策人员参考
太阳高能粒子（SEP）事件因对航空、航天器电子设备及地磁层外载人任务构成显著辐射威胁而日益受到关注。从科学角度看，SEP事件源自太阳表面至日球层的多种物理过程，为理解粒子加速与传播机制提供了广泛适用的范例。因此，提升对SEP事件的理解与预测能力，不仅有助于深化相关物理机制认知，也对保障空间技术和探索活动至关重要。传统上，研究人员采用基于物理的模拟和经验方法建模SEP。近年来，机器学习（ML）成为理解与预测SEP事件的新工具。本文旨在综述当前可用的机器学习模型在SEP预测中的应用，识别其训练所用数据集，比较模型架构、输入输出特征，并基于此提出未来研究的良好实践与建议。
原文摘要 · Abstract (English)
Solar energetic particle (SEP) events have attracted increasing attention due to their significant radiation hazards for aviation, spacecraft electronics, and human missions beyond Earth's magnetosphere. From a scientific perspective, SEP events are intriguing because they arise from a set of physical processes extending from the solar surface and corona through the heliosphere, offering insight into particle acceleration and transport mechanisms that are widely applicable across astrophysics. Therefore, advancing our ability to understand and predict SEP events is essential both for deepening our knowledge of such mechanisms and for safeguarding space technologies and exploration. Traditionally, researchers have modeled SEPs using physics-based simulations and empirical methods. More recently, machine learning (ML) has emerged as a new tool for understanding and predicting SEP events. The purpose of this manuscript is to review the currently available ML models for SEP prediction, identify the datasets used for training, compare their architectures, inputs, and outputs, and, based on these insights, outline good practices and recommendations for future research.
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