Statistical Study of the Correlation between Solar Energetic Particles and Properties of Active Regions

Marroquin, Russell D.; 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; Ali, Aatiya

doi:10.3847/1538-4357/acdb65

Cited by 2 publications

(2 citation statements)

References 19 publications

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“…and (3) using proton and SXR fluxes' time series directly instead of utilizing their statistical moments. We also hypothesize significant contributions to better predictive scores by considering additional input data such as properties and topologies of source ARs (Marroquin et al 2023), parameters of coronal mass ejections (Torres et al 2022), or various dynamic features of the solar corona (Gibson 2015).…”

Section: Discussionmentioning

confidence: 99%

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

Ali,

Sadykov,

Kosovichev

et al. 2024

ApJS

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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 scores2, 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.

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Section: Discussionmentioning

confidence: 99%

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

Ali,

Sadykov,

Kosovichev

et al. 2024

ApJS

Self Cite

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“…A proxy for the solar SXR background flux was then determined by selecting the minimum X-ray flux measurement for each day during Solar Cycle 24. Additionally, we made use of AR Hale classifications provided by Marroquin et al (2023), to study the frequency of complex ARs throughout the solar cycle. We then used these data in conjunction with each other to synthesize our results discussed in Section 4.3.…”

Section: Geostationary Operational Environmental Satellite Sxr Flux A...mentioning

confidence: 99%

Investigating Performance Trends of Simulated Real-time Solar Flare Predictions: The Impacts of Training Windows, Data Volumes, and the Solar Cycle

Goodwin,

Sadykov,

Martens

2024

ApJ

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This study explores the behavior of machine-learning-based flare forecasting models deployed in a simulated operational environment. Using Georgia State University’s Space Weather Analytics for Solar Flares benchmark data set, we examine the impacts of training methodology and the solar cycle on decision tree, support vector machine, and multilayer perceptron performance. We implement our classifiers using three temporal training windows: stationary, rolling, and expanding. The stationary window trains models using a single set of data available before the first forecasting instance, which remains constant throughout the solar cycle. The rolling window trains models using data from a constant time interval before the forecasting instance, which moves with the solar cycle. Finally, the expanding window trains models using all available data before the forecasting instance. For each window, a number of input features (1, 5, 10, 25, 50, and 120) and temporal sizes (5, 8, 11, 14, 17, and 20 months) were tested. To our surprise, we found that, for a window of 20 months, skill scores were comparable regardless of the window type, feature count, and classifier selected. Furthermore, reducing the size of this window only marginally decreased stationary and rolling window performance. This implies that, given enough data, a stationary window can be chosen over other window types, eliminating the need for model retraining. Finally, a moderately strong positive correlation was found to exist between a model’s false-positive rate and the solar X-ray background flux. This suggests that the solar cycle phase has a considerable influence on forecasting.

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Statistical Study of the Correlation between Solar Energetic Particles and Properties of Active Regions

Cited by 2 publications

References 19 publications

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

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

Investigating Performance Trends of Simulated Real-time Solar Flare Predictions: The Impacts of Training Windows, Data Volumes, and the Solar Cycle

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