Solar Energetic Particle Forecasting with Multi-Task Deep Learning: SEPNET

TL;DR

SEPNET is a multi-task deep learning model that improves solar energetic particle forecasting by jointly predicting solar eruptions and SEPs, demonstrating higher accuracy and real-time applicability.

Contribution

This work introduces SEPNET, a novel multi-task neural network utilizing LSTM and transformer architectures for enhanced SEP prediction using diverse solar activity data.

Findings

SEPNET outperforms classical machine learning and current models in detection rates and skill scores.

Incorporating SHARP magnetic field parameters improves SEP forecast accuracy.

SEPNET maintains real-time operational capability despite data class imbalance.

Abstract

Solar energetic particle (SEP) events pose severe threats to spacecraft, astronaut safety, and aviation operations. Accurate SEP forecasting remains a critical challenge in space weather research due to their complex origins and highly variable propagation. In this work, we built SEPNET, an innovative multi-task neural network that jointly predicts future solar eruptive events, including solar flares and coronal mass ejections (CMEs) and SEPs, incorporating long short-term memory and transformer architectures that capture contextual dependencies. SEPNet is a machine learning framework for SEP prediction that utilizes an extensive set of predictors, including solar flares, CMEs, and space-weather HMI active region patches (SHARP) magnetic field parameters. SEPNET is rigorously evaluated on the SEPVAL SEP dataset (Whitman, 2025b), which is used to evaluate the performance of the current SEP prediction models. The performance of SEPNet is compared with classical machine learning methods and current state-of-the-art pre-eruptive SEP prediction models. The results show that SEPNET, particularly with SHARP parameters, achieves higher detection rates and skill scores while maintaining suitable for real-time space weather alert operations. Although class imbalance in the data leads to relatively high false alarm rates, SEPNET consistently outperforms reference methods and provides timely SEP forecasts, highlighting the capability of deep multi-task learning for next-generation space weather prediction. All data and code are available on GitHub at https://github.com/yuyian/SEP-Prediction.git.