Forecasting SEP Events During Solar Cycles 23 and 24 Using Interpretable
  Machine Learning
Spiridon Kasapis Irina N. Kitiashvili Paul Kosovich Alexander G. Kosovichev Viacheslav M. Sadykov Patrick O'Keefe Vincent Wang
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.