Predicting Solar Proton Events of Solar Cycles 22–24 Using GOES Proton and Soft-X-Ray Flux Features

• Published in: The Astrophysical journal. Supplement series Vol. 270; no. 1; pp. 15 - 32
• Main Authors: Ali, Aatiya, Sadykov, Viacheslav, Kosovichev, Alexander, Kitiashvili, Irina N., Oria, Vincent, Nita, Gelu M., Illarionov, Egor, O’Keefe, Patrick M., Francis, Fraila, Chong, Chun-Jie, Kosovich, Paul, Marroquin, Russell D.
• Format: Journal Article
• Language: English
• Published: Saskatoon The American Astronomical Society 01.01.2024; IOP Publishing
• Subjects: Energetic particles; Fluctuations; GOES satellites; Machine learning; Mathematical models; Soft x rays; Solar activity; Solar cycle; Solar effects; Solar energetic particles; Solar protons; Solar storms; Space weather; Support vector machines; Training; X-ray fluxes
• ISSN: 0067-0049; 1538-4365
• DOI: 10.3847/1538-4365/ad0a6c

Abstract Solar energetic particle (SEP) events and their major subclass, solar proton events (SPEs), can have unfavorable consequences on numerous aspects of life and technology, making them one of the most harmful effects of solar activity. Garnering knowledge preceding such events by studying operational data flows is essential for their forecasting. Considering only solar cycle (SC) 24 in our previous study, we found that it may be sufficient to only utilize proton and soft X-ray (SXR) parameters for SPE forecasts. Here, we report a catalog recording ≥10 MeV ≥10 particle flux unit SPEs with their properties, spanning SCs 22–24, using NOAA’s Geostationary Operational Environmental Satellite flux data. We report an additional catalog of daily proton and SXR flux statistics for this period, employing it to test the application of machine learning (ML) on the prediction of SPEs using a support vector machine (SVM) and extreme gradient boosting (XGBoost). We explore the effects of training models with data from one and two SCs, evaluating how transferable a model might be across different time periods. XGBoost proved to be more accurate than SVMs for almost every test considered, while also outperforming operational SWPC NOAA predictions and a persistence forecast. Interestingly, training done with SC 24 produces weaker true skill statistic and Heidke skill scores 2 , even when paired with SC 22 or SC 23, indicating transferability issues. This work contributes toward validating forecasts using long-spanning data—an understudied area in SEP research that should be considered to verify the cross cycle robustness of ML-driven forecasts.