Review of Machine Learning Models for Solar Energetic Particle Prediction

This arXiv paper reviews machine learning models designed to predict solar energetic particle (SEP) events, which pose radiation risks to aviation, spacecraft, and human space exploration. The study compares ML architectures, training datasets, and methodologies against traditional physics-based approaches, providing recommendations for future research in SEP forecasting.

Solar energetic particle events represent a critical intersection of space weather science and practical infrastructure protection. As space-based technologies proliferate and human missions extend beyond Earth's protective magnetosphere, accurate SEP prediction becomes increasingly vital for mission planning and asset protection. This review synthesizes the emerging role of machine learning in replacing or augmenting traditional physics-based simulations and empirical methods that have historically dominated the field.

The shift toward ML-based approaches reflects a broader trend in astrophysics and space science where data-driven models can identify complex patterns in multidimensional datasets that physics-first approaches might miss. SEP events emerge from intricate physical processes spanning the solar surface, corona, and heliosphere, making them ideal candidates for machine learning applications that can capture nonlinear relationships and temporal dependencies across diverse solar and heliospheric measurements.

For the space technology industry, including satellite operators, launch providers, and aerospace contractors, improved SEP prediction capabilities directly translate to reduced mission risk and operational costs. Better forecasting enables more precise satellite maneuvering, component redundancy decisions, and mission timing optimization. The standardization and comparison of ML models across datasets creates reproducibility and benchmark metrics that could accelerate adoption across the industry.

Looking ahead, the critical factor determining ML's long-term utility in this domain will be sustained data availability and the establishment of standardized validation metrics across models. The paper's emphasis on good practices and recommendations signals a maturation phase in applied ML for space weather, where the community is transitioning from proof-of-concept demonstrations to operationalizable systems.

• →Machine learning is emerging as a viable alternative to traditional physics-based models for predicting solar energetic particle events.
• →SEP prediction accuracy directly impacts safety protocols for aviation, spacecraft electronics, and deep space human missions.
• →Current ML models vary significantly in architecture, input datasets, and output formats, creating challenges for standardization and comparison.
• →The review identifies best practices and future research directions to accelerate ML adoption in operational space weather forecasting.
• →Improved SEP prediction capabilities reduce mission risk and operational costs for the global space technology industry.