外辐射带增强与耗散事件的机器学习可解释性
Machine Learning Interpretability of Outer Radiation Belt Enhancement \& Depletion Events
AI总结：
使用可解释机器学习方法研究外辐射带电子通量对太阳风和地磁指数的响应，提出SHESEA方法发现亚暴序列决定通量增强或耗散，太阳风压力驱动初始骤降。
摘要：
我们使用一种可解释的机器学习方法，研究了外辐射带电子通量对不同太阳风和地磁指数的响应。我们重建了19个增强事件和7个耗散事件期间的电子通量变化，并首次对叠加纪元结果进行了特征归因分析。我们发现，初始骤降之后的亚暴序列的强度和持续时间决定了电子通量的整体增强或耗散，而太阳风压力在两类事件中均驱动了初始骤降。来自包含71个事件的数据集的进一步统计结果证实了这一点，并表明最终通量水平与平均 AL 指数之间存在显著相关性，这表明观测到的“耗散”事件可以更准确地描述为“非增强”事件。我们新颖的 SHAP 增强叠加纪元分析（SHESEA）方法可作为一种在多种物理系统中使用的洞察发现工具。
英文摘要：
We investigate the response of outer radiation belt electron fluxes to different solar wind and geomagnetic indices using an interpretable machine learning method. We reconstruct the electron flux variation during 19 enhancement and 7 depletion events and demonstrate a feature attribution analysis on the superposed epoch results for the first time. We find that the intensity and duration of the substorm sequence following an initial dropout determine the overall enhancement or depletion of electron fluxes, while the solar wind pressure drives the initial dropout in both types of events. Further statistical results from a dataset with 71 events confirm this and show a significant correlation between the resulting flux levels and the average AL index, indicating that the observed "depletion" event can be more accurately described as a "non-enhancement" event. Our novel SHAP-Enhanced Superposed Epoch Analysis (SHESEA) method can be used as an insight discovery tool in various physical systems.
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。
