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

TL;DR

This paper introduces a global feature mapping approach to improve the explainability of data-driven solar energetic particle event prediction models, enabling better understanding of physical causes behind SEP events.

Contribution

It presents a novel framework combining global explanations and feature mapping to enhance transparency in SEP prediction models, addressing the black-box challenge.

Findings

Improved model interpretability for SEP prediction.

Validated approach on 341 SEP events across three solar cycles.

Case study demonstrating deeper physical insights.

Abstract

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.