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

The flux of energetic particles originating from the Sun fluctuates during the solar cycles. It depends on the number and properties of Active Regions (ARs) present in a single day and associated solar activities, such as solar flares and coronal mass ejections (CMEs). Observational records of the S...

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Bibliographic Details
Published in:arXiv.org 2023-03
Main Authors: Marroquin, Russell D, Sadykov, Viacheslav, Kosovichev, Alexander, Kitiashvili, Irina N, Oria, Vincent, Nita, Gelu M, Illarionov, Egor, O'Keefe, Patrick M, Fraila Francis, Chun-Jie, Chong, Kosovich, Paul, Aatiya Ali
Format: Article
Language:English
Subjects:
Coronal mass ejection
Energetic particles
Machine learning
Solar corona
Solar cycle
Solar flares
Space weather
Statistical analysis
Sunspots
Weather forecasting
Western Hemisphere
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Summary:The flux of energetic particles originating from the Sun fluctuates during the solar cycles. It depends on the number and properties of Active Regions (ARs) present in a single day and associated solar activities, such as solar flares and coronal mass ejections (CMEs). Observational records of the Space Weather Prediction Center (SWPC NOAA) enable the creation of time-indexed databases containing information about ARs and particle flux enhancements, most widely known as Solar Energetic Particle events (SEPs). In this work, we utilize the data available for Solar Cycles 21-24, and the initial phase of Cycle 25 to perform a statistical analysis of the correlation between SEPs and properties of ARs inferred from the McIntosh and Hale classifications. We find that the complexity of the magnetic field, longitudinal location, area, and penumbra type of the largest sunspot of ARs are most correlated with the production of SEPs. It is found that most SEPs (\(\approx\)60\%, or 108 out of 181 considered events) were generated from an AR classified with the 'k' McIntosh subclass as the second component, and these ARs are more likely to produce SEPs if they fall in a Hale class containing \(\delta\) component. The resulting database containing information about SEP events and ARs is publicly available and can be used for the development of Machine Learning (ML) models to predict the occurrence of SEPs.
ISSN:2331-8422
DOI:10.48550/arxiv.2303.06100