提出全局特征映射方法，让太阳高能粒子预测更可解释。
Enhancing Explainability in Solar Energetic Particle Event Prediction: A Global Feature Mapping Approach
- 结合全局解释与局部特征映射，提升模型透明度。
- 基于341个事件数据集验证，涵盖244个≥10MeV强事件。
- 适合需要物理机制理解的太阳物理研究者使用。
太阳高能粒子（SEP）事件是太阳活动的重要表现形式，当其由太阳耀斑或日冕物质抛射（CME）伴随的激波加速时，会产生严重的有害辐射。然而，现有基于数据驱动的预测方法多为黑箱模型，使太阳物理学家难以解释结果或理解事件背后的物理成因。为此，本文提出一种新框架，融合全局解释与特定特征映射，增强模型可解释性，并深入揭示决策过程。我们利用包含341个SEP事件的数据集进行验证，其中244个为≥10 MeV的强事件，超过太空天气预测中心S1阈值，覆盖太阳周期22、23和24。此外，通过重大事件的可解释性案例研究，展示了该方法如何提升可解释性，促进对SEP事件预测的物理认知。
原文摘要 · Abstract (English)
Solar energetic particle (SEP) events, as one of the most prominent manifestations of solar activity, can generate severe hazardous radiation when accelerated by solar flares or shock waves formed aside from coronal mass ejections (CMEs). However, most existing data-driven methods used for SEP predictions are operated as black-box models, making it challenging for solar physicists to interpret the results and understand the underlying physical causes of such events rather than just obtain a prediction. To address this challenge, we propose a novel framework that integrates global explanations and ad-hoc feature mapping to enhance model transparency and provide deeper insights into the decision-making process. We validate our approach using a dataset of 341 SEP events, including 244 significant (>=10 MeV) proton events exceeding the Space Weather Prediction Center S1 threshold, spanning solar cycles 22, 23, and 24. Furthermore, we present an explainability-focused case study of major SEP events, demonstrating how our method improves explainability and facilitates a more physics-informed understanding of SEP event prediction.
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