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Forecasting Solar Energetic Particle Events During Solar Cycles 23 and 24 Using Interpretable Machine Learning
- Source :
- The Astrophysical Journal, Vol 974, Iss 1, p 131 (2024)
- Publication Year :
- 2024
- Publisher :
- IOP Publishing, 2024.
-
Abstract
- The prediction of solar energetic particle (SEP) events garners 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 data set that combines the Solar Dynamics Observatory/Space-weather Helioseismic and Magnetic Imager Active Region Patches and the Solar and Heliospheric Observatory/Space-weather Michelson Doppler Imager Active Region Patches. We employ a suite of machine learning strategies, including support vector machines (SVMs) 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 (experimental setting), 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 hr 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.
Details
- Language :
- English
- ISSN :
- 15384357
- Volume :
- 974
- Issue :
- 1
- Database :
- Directory of Open Access Journals
- Journal :
- The Astrophysical Journal
- Publication Type :
- Academic Journal
- Accession number :
- edsdoj.fae320139cdd4a99b1842596fc4503d0
- Document Type :
- article
- Full Text :
- https://doi.org/10.3847/1538-4357/ad6f0e