Review of Machine Learning Models for Solar Energetic Particle Prediction

2026-06-17

AI Summary

Solar energetic particle (SEP) events pose significant radiation hazards to aviation, spacecraft, and deep-space human missions, underscoring the urgent need for improved forecasting capabilities. This study presents the first systematic review and comparative analysis of existing machine learning approaches applied to SEP prediction, encompassing model architectures, training datasets, feature engineering strategies, and input–output formulations. By synthesizing common challenges and performance limitations across current methodologies, the work identifies key best practices aimed at enhancing reproducibility and standardization. These insights offer methodological guidance and practical recommendations for future research, fostering the development of more reliable and efficient SEP forecasting systems.

Abstract

Solar energetic particle (SEP) events have attracted increasing attention due to their significant radiation hazards for aviation, spacecraft electronics, and human missions beyond Earth's magnetosphere. From a scientific perspective, SEP events are intriguing because they arise from a set of physical processes extending from the solar surface and corona through the heliosphere, offering insight into particle acceleration and transport mechanisms that are widely applicable across astrophysics. Therefore, advancing our ability to understand and predict SEP events is essential both for deepening our knowledge of such mechanisms and for safeguarding space technologies and exploration. Traditionally, researchers have modeled SEPs using physics-based simulations and empirical methods. More recently, machine learning (ML) has emerged as a new tool for understanding and predicting SEP events. The purpose of this manuscript is to review the currently available ML models for SEP prediction, identify the datasets used for training, compare their architectures, inputs, and outputs, and, based on these insights, outline good practices and recommendations for future research.

Problem

• Solar Energetic Particles
• Space Weather Prediction
• Radiation Hazards
• Particle Acceleration
• Machine Learning

Innovation

• Solar Energetic Particles
• Machine Learning
• Space Weather Prediction
• Model Review
• Radiation Hazard Forecasting