Forecasting SEP Events During Solar Cycles 23 and 24 Using Interpretable Machine Learning
<|reference_start|>Forecasting SEP Events During Solar Cycles 23 and 24 Using Interpretable Machine Learning: Prediction of the Solar Energetic Particle (SEP) events garner increasing interest as space missions extend beyond Earth's protective magnetosphere. These events, which are, in most cases, products of magnetic reconnection-driven processes during solar flares or fast coronal-mass-ejection-driven shock waves, pose significant radiation hazards to aviation, space-based electronics, and particularly, space exploration. In this work, we utilize the recently developed dataset that combines the Solar Dynamics Observatory/Helioseismic and Magnetic Imager's (SDO/HMI) Space weather HMI Active Region Patches (SHARP) and the Solar and Heliospheric Observatory/Michelson Doppler Imager's (SoHO/MDI) Space Weather MDI Active Region Patches (SMARP). We employ a suite of machine learning strategies, including Support Vector Machines (SVM) and regression models, to evaluate the predictive potential of this new data product for a forecast of post-solar flare SEP events. Our study indicates that despite the augmented volume of data, the prediction accuracy reaches 0.7 +- 0.1, which aligns with but does not exceed these published benchmarks. A linear SVM model with training and testing configurations that mimic an operational setting (positive-negative imbalance) reveals a slight increase (+ 0.04 +- 0.05) in the accuracy of a 14-hour SEP forecast compared to previous studies. This outcome emphasizes the imperative for more sophisticated, physics-informed models to better understand the underlying processes leading to SEP events.<|reference_end|>
@article{kasapis2024forecasting,
    title={Forecasting SEP Events During Solar Cycles 23 and 24 Using Interpretable
  Machine Learning},
    author={Spiridon Kasapis, Irina N. Kitiashvili, Paul Kosovich, Alexander G.
  Kosovichev, Viacheslav M. Sadykov, Patrick O'Keefe, Vincent Wang},
    journal={arXiv preprint arXiv:2403.02536},
    year={2024},
    archivePrefix={arXiv},
    eprint={2403.02536},
    primaryClass={astro-ph.SR cs.LG physics.space-ph}
}