Toward Enhanced Prediction of High-Impact Solar Energetic Particle Events Using Multimodal Time Series Data Fusion Models

Solar energetic particle (SEP) events, originating from solar flares and Coronal Mass Ejections, present significant hazards to space exploration and technology on Earth. Accurate prediction of these high-energy events is essential for safeguarding astronauts, spacecraft, and electronic systems. In this study, we conduct an in-depth investigation into the application of multimodal data fusion techniques for the prediction of high-energy SEP events, particularly similar to 100 MeV events. Our research utilizes six machine learning (ML) models, each finely tuned for time series analysis, including Univariate Time Series (UTS), Image-based model (Image), Univariate Feature Concatenation (UFC), Univariate Deep Concatenation (UDC), Univariate Deep Merge (UDM), and Univariate Score Concatenation (USC). By combining time series proton flux data with solar X-ray images, we exploit complementary insights into the underlying solar phenomena responsible for SEP events. Rigorous evaluation metrics, including accuracy, F1-score, and other established measures, are applied, along with K-fold cross-validation, to ensure the robustness and generalization of our models. Additionally, we explore the influence of observation window sizes on classification accuracy. This study is centered on forecasting solar energetic particle (SEP) events, which can pose serious risks to astronauts and technology. We employed advanced machine learning (ML) techniques to make these predictions. Our research involved integrating various data sources, including information about protons and X-rays emitted by the Sun, to enhance the accuracy of our forecasts. We evaluated the performance of four distinct multimodal time series data fusion models along with two distinct unimodal time series models to determine the most effective approach for predicting these solar events. Additionally, we examined how the choice of time window for predictions influenced their accuracy. To aid in interpreting the results, we utilized visual representations known as heatmaps, which provide a graphical view of the data. Our findings have significant implications for improving the safety and success of space missions. By achieving precise predictions of SEP events, we can better protect astronauts, spacecraft, and vital electronic systems both in space and on Earth. Combining proton flux data and solar X-ray images for precise Solar Energetic Particle event prediction Utilizing six specialized machine learning models to improve SEP event prediction accuracy through time series analysis Exploring the impact of varying observation window sizes on classification accuracy in data fusion