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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. 

Abstract Solar flare prediction studies have been recently conducted with the use of Space-Weather MDI (Michelson Doppler Imager on board Solar and Heliospheric Observatory) Active Region Patches (SMARPs) and Space-Weather HMI (Helioseismic and Magnetic Imager on board Solar Dynamics Observatory) Active Region Patches (SHARPs), which are two currently available data products containing magnetic field characteristics of solar active regions (ARs). The present work is an effort to combine them into one data product, and perform some initial statistical analyses in order to further expand their application in space-weather forecasting. The combined data are derived by filtering, rescaling, and merging the SMARP and SHARP parameters, which can then be spatially reduced to create uniform multivariate time series. The resulting combined MDI–HMI data set currently spans the period between 1996 April 4 and 2022 December 13, and may be extended to a more recent date. This provides an opportunity to correlate and compare it with other space-weather time series, such as the daily solar flare index or the statistical properties of the soft X-ray flux measured by the Geostationary Operational Environmental Satellites. Time-lagged cross correlation indicates that a relationship may exist, where some magnetic field properties of ARs lead the flare index in time. Applying the rolling-window technique makes it possible to see how this leader–follower dynamic varies with time. Preliminary results indicate that areas of high correlation generally correspond to increased flare activity during the peak solar cycle.