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. Observational records of the Space We...
| Main Authors: | , , , , , , , , , , , |
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| Format: | Article |
| Language: | English |
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IOP Publishing
2023-01-01
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| Series: | The Astrophysical Journal |
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| Online Access: | https://doi.org/10.3847/1538-4357/acdb65 |
| _version_ | 1797698096504766464 |
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| author | Russell D. Marroquin Viacheslav Sadykov Alexander Kosovichev Irina N. Kitiashvili Vincent Oria Gelu M. Nita Egor Illarionov Patrick M. O’Keefe Fraila Francis Chun Jie Chong Paul Kosovich Aatiya Ali |
| author_facet | Russell D. Marroquin Viacheslav Sadykov Alexander Kosovichev Irina N. Kitiashvili Vincent Oria Gelu M. Nita Egor Illarionov Patrick M. O’Keefe Fraila Francis Chun Jie Chong Paul Kosovich Aatiya Ali |
| author_sort | Russell D. Marroquin |
| collection | DOAJ |
| description | 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. Observational records of the Space Weather Prediction Center NOAA enable the creation of time-indexed databases containing information about ARs and particle flux enhancements, most widely known as solar energetic particle (SEP) events. 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 (≈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 a δ component. The resulting database containing information about SEP events and ARs is publicly available and can be used for the development of machine learning models to predict the occurrence of SEPs. |
| first_indexed | 2024-03-12T03:49:20Z |
| format | Article |
| id | doaj.art-46b956273bf34d71881c022a50d32cd3 |
| institution | Directory Open Access Journal |
| issn | 1538-4357 |
| language | English |
| last_indexed | 2024-03-12T03:49:20Z |
| publishDate | 2023-01-01 |
| publisher | IOP Publishing |
| record_format | Article |
| series | The Astrophysical Journal |
| spelling | doaj.art-46b956273bf34d71881c022a50d32cd32023-09-03T12:30:48ZengIOP PublishingThe Astrophysical Journal1538-43572023-01-0195229710.3847/1538-4357/acdb65Statistical Study of the Correlation between Solar Energetic Particles and Properties of Active RegionsRussell D. Marroquin0https://orcid.org/0000-0002-3364-7463Viacheslav Sadykov1https://orcid.org/0000-0002-4001-1295Alexander Kosovichev2https://orcid.org/0000-0003-0364-4883Irina N. Kitiashvili3https://orcid.org/0000-0003-4144-2270Vincent Oria4Gelu M. Nita5https://orcid.org/0000-0003-2846-2453Egor Illarionov6https://orcid.org/0000-0002-2858-9625Patrick M. O’Keefe7Fraila Francis8Chun Jie Chong9Paul Kosovich10Aatiya Ali11https://orcid.org/0000-0003-3196-3822Department of Physics, University of California San Diego , La Jolla, CA 92093, USA; Physics & Astronomy Department, Georgia State University , Atlanta, GA 30303, USA ; vsadykov@gsu.eduPhysics & Astronomy Department, Georgia State University , Atlanta, GA 30303, USA ; vsadykov@gsu.eduPhysics Department, New Jersey Institute of Technology , Newark, NJ 07102, USA; NASA Ames Research Center , Moffett Field, CA 94035, USANASA Ames Research Center , Moffett Field, CA 94035, USAComputer Science Department, New Jersey Institute of Technology , Newark, NJ 07102, USAPhysics Department, New Jersey Institute of Technology , Newark, NJ 07102, USADepartment of Mechanics and Mathematics, Moscow State University , Moscow, 119991, Russia; Moscow Center of Fundamental and Applied Mathematics , Moscow, 119234, RussiaComputer Science Department, New Jersey Institute of Technology , Newark, NJ 07102, USAComputer Science Department, New Jersey Institute of Technology , Newark, NJ 07102, USAComputer Science Department, New Jersey Institute of Technology , Newark, NJ 07102, USAPhysics Department, New Jersey Institute of Technology , Newark, NJ 07102, USAPhysics & Astronomy Department, Georgia State University , Atlanta, GA 30303, USA ; vsadykov@gsu.eduThe 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. Observational records of the Space Weather Prediction Center NOAA enable the creation of time-indexed databases containing information about ARs and particle flux enhancements, most widely known as solar energetic particle (SEP) events. 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 (≈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 a δ component. The resulting database containing information about SEP events and ARs is publicly available and can be used for the development of machine learning models to predict the occurrence of SEPs.https://doi.org/10.3847/1538-4357/acdb65SunspotsSolar active regionsSolar activitySolar particle emissionSolar-terrestrial interactions |
| spellingShingle | Russell D. Marroquin Viacheslav Sadykov Alexander Kosovichev Irina N. Kitiashvili Vincent Oria Gelu M. Nita Egor Illarionov Patrick M. O’Keefe Fraila Francis Chun Jie Chong Paul Kosovich Aatiya Ali Statistical Study of the Correlation between Solar Energetic Particles and Properties of Active Regions The Astrophysical Journal Sunspots Solar active regions Solar activity Solar particle emission Solar-terrestrial interactions |
| title | Statistical Study of the Correlation between Solar Energetic Particles and Properties of Active Regions |
| title_full | Statistical Study of the Correlation between Solar Energetic Particles and Properties of Active Regions |
| title_fullStr | Statistical Study of the Correlation between Solar Energetic Particles and Properties of Active Regions |
| title_full_unstemmed | Statistical Study of the Correlation between Solar Energetic Particles and Properties of Active Regions |
| title_short | Statistical Study of the Correlation between Solar Energetic Particles and Properties of Active Regions |
| title_sort | statistical study of the correlation between solar energetic particles and properties of active regions |
| topic | Sunspots Solar active regions Solar activity Solar particle emission Solar-terrestrial interactions |
| url | https://doi.org/10.3847/1538-4357/acdb65 |
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