arXiv 2511.094752025-11-12cs.LG

增强太阳高能粒子事件预测的可解释性：一种全局特征映射方法

Enhancing Explainability in Solar Energetic Particle Event Prediction: A Global Feature Mapping Approach

Dept. of Computer Science, California State University, Fullerton, CA, USA（加州州立大学富尔顿分校计算机科学系）；Dept. of Computer Science, Georgia State University, Atlanta, GA, USA（佐治亚州立大学计算机科学系）；Dept. of Computer Science, Texas Christian University, Fort Worth, TX, USA（德克萨斯基督教大学计算机科学系）；Applied Physics Laboratory, Johns Hopkins University, Laurel, MD, USA（约翰霍普金斯大学应用物理实验室）

Anli Ji, Pranjal Patil, Chetraj Pandey, Manolis K. Georgoulis, Berkay Aydin

备注：: 10 pages, 3 Figures. This is a pre-print of an accepted paper at ICDMW: SABID 2025

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AI总结：

针对现有SEP预测数据驱动方法多为黑箱模型、难以解释结果及理解物理成因的问题，提出整合全局解释与临时特征映射的新框架，经覆盖3个太阳活动周的341个SEP事件数据集验证，可提升模型透明度并促进物理依据理解。

摘要：

太阳高能粒子（SEP）事件是太阳活动最显著的表现之一，当被太阳耀斑或日冕物质抛射（CME）旁形成的冲击波加速时，会产生严重的有害辐射。然而，大多数现有的SEP预测数据驱动方法以黑箱模型运行，这使得太阳物理学家难以解释结果、理解此类事件的潜在物理原因，而非仅获得预测结果。为应对这一挑战，我们提出一种整合全局解释和临时特征映射的新框架，以增强模型透明度并提供对决策过程的更深入洞察。我们使用包含341个SEP事件的数据集验证方法，其中244个是超过空间天气预测中心S1阈值的显著（≥10 MeV）质子事件，覆盖太阳活动周22、23和24。此外，我们呈现了针对重大SEP事件的可解释性案例研究，展示了我们的方法如何提升可解释性并促进对SEP事件预测的更具物理依据的理解。

英文摘要：

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.

机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。