Machine Learning Interpretability of Outer Radiation Belt Enhancement \& Depletion Events

TL;DR

This study uses an interpretable machine learning approach to analyze how solar wind and geomagnetic factors influence outer radiation belt electron fluxes during enhancement and depletion events, revealing key drivers and correlations.

Contribution

It introduces the SHESEA method, combining SHAP and superposed epoch analysis, to interpret physical system responses and identify key factors affecting radiation belt dynamics.

Findings

Substorm sequence intensity and duration determine flux enhancement or depletion.

Solar wind pressure causes initial flux dropout in both event types.

Significant correlation between flux levels and average AL index.

Abstract

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.