A Machine Learning Approach to Understanding the Physical Properties of Magnetic Flux Ropes in the Solar Wind at 1 au
Interplanetary magnetic flux ropes (MFRs) are commonly observed structures in the solar wind, categorized as magnetic clouds (MCs) and small-scale MFRs (SMFRs) depending on whether they are associated with coronal mass ejections. We apply machine learning to systematically compare SMFRs, MCs, and am...
| Main Authors: | , , , , , , , |
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| Format: | Article |
| Language: | English |
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IOP Publishing
2024-01-01
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| Series: | The Astrophysical Journal |
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| Online Access: | https://doi.org/10.3847/1538-4357/ad0c52 |
| _version_ | 1827380577629634560 |
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| author | Hameedullah Farooki Yasser Abduallah Sung Jun Noh Hyomin Kim George Bizos Youra Shin Jason T. L. Wang Haimin Wang |
| author_facet | Hameedullah Farooki Yasser Abduallah Sung Jun Noh Hyomin Kim George Bizos Youra Shin Jason T. L. Wang Haimin Wang |
| author_sort | Hameedullah Farooki |
| collection | DOAJ |
| description | Interplanetary magnetic flux ropes (MFRs) are commonly observed structures in the solar wind, categorized as magnetic clouds (MCs) and small-scale MFRs (SMFRs) depending on whether they are associated with coronal mass ejections. We apply machine learning to systematically compare SMFRs, MCs, and ambient solar wind plasma properties. We construct a data set of 3-minute averaged sequential data points of the solar wind’s instantaneous bulk fluid plasma properties using about 20 years of measurements from Wind. We label samples by the presence and type of MFRs containing them using a catalog based on Grad–Shafranov (GS) automated detection for SMFRs and NASA's catalog for MCs (with samples in neither labeled non-MFRs). We apply the random forest machine learning algorithm to find which categories can be more easily distinguished and by what features. MCs were distinguished from non-MFRs with an area under the receiver-operator curve (AUC) of 94% and SMFRs with an AUC of 89%, and had distinctive plasma properties. In contrast, while SMFRs were distinguished from non-MFRs with an AUC of 86%, this appears to rely solely on the 〈 B 〉 > 5 nT threshold applied by the GS catalog. The results indicate that SMFRs have virtually the same plasma properties as the ambient solar wind, unlike the distinct plasma regimes of MCs. We interpret our findings as additional evidence that most SMFRs at 1 au are generated within the solar wind. We also suggest that they should be considered a salient feature of the solar wind’s magnetic structure rather than transient events. |
| first_indexed | 2024-03-08T13:35:56Z |
| format | Article |
| id | doaj.art-034a72a18fa3469b9e2b1e27d4f2884b |
| institution | Directory Open Access Journal |
| issn | 1538-4357 |
| language | English |
| last_indexed | 2024-03-08T13:35:56Z |
| publishDate | 2024-01-01 |
| publisher | IOP Publishing |
| record_format | Article |
| series | The Astrophysical Journal |
| spelling | doaj.art-034a72a18fa3469b9e2b1e27d4f2884b2024-01-16T16:12:28ZengIOP PublishingThe Astrophysical Journal1538-43572024-01-0196118110.3847/1538-4357/ad0c52A Machine Learning Approach to Understanding the Physical Properties of Magnetic Flux Ropes in the Solar Wind at 1 auHameedullah Farooki0https://orcid.org/0000-0001-7952-8032Yasser Abduallah1https://orcid.org/0000-0003-0792-2270Sung Jun Noh2https://orcid.org/0000-0002-8032-7833Hyomin Kim3https://orcid.org/0000-0002-6350-405XGeorge Bizos4Youra Shin5https://orcid.org/0000-0002-0815-9855Jason T. L. Wang6https://orcid.org/0000-0002-2486-1097Haimin Wang7https://orcid.org/0000-0002-5233-565XInstitute for Space Weather Sciences, New Jersey Institute of Technology, University Heights , Newark, NJ, USA ; haf5@njit.edu; Department of Computer Science, New Jersey Institute of Technology, University Heights , Newark, NJ, USA; Center for Solar-Terrestrial Research, New Jersey Institute of Technology, University Heights , Newark, NJ, USAInstitute for Space Weather Sciences, New Jersey Institute of Technology, University Heights , Newark, NJ, USA ; haf5@njit.edu; Department of Computer Science, New Jersey Institute of Technology, University Heights , Newark, NJ, USACenter for Solar-Terrestrial Research, New Jersey Institute of Technology, University Heights , Newark, NJ, USA; Now at Los Alamos National Laboratory , Los Alamos, NM, USAInstitute for Space Weather Sciences, New Jersey Institute of Technology, University Heights , Newark, NJ, USA ; haf5@njit.edu; Center for Solar-Terrestrial Research, New Jersey Institute of Technology, University Heights , Newark, NJ, USADepartment of Computer Science, New Jersey Institute of Technology, University Heights , Newark, NJ, USACenter for Solar-Terrestrial Research, New Jersey Institute of Technology, University Heights , Newark, NJ, USAInstitute for Space Weather Sciences, New Jersey Institute of Technology, University Heights , Newark, NJ, USA ; haf5@njit.edu; Department of Computer Science, New Jersey Institute of Technology, University Heights , Newark, NJ, USAInstitute for Space Weather Sciences, New Jersey Institute of Technology, University Heights , Newark, NJ, USA ; haf5@njit.edu; Center for Solar-Terrestrial Research, New Jersey Institute of Technology, University Heights , Newark, NJ, USA; Big Bear Solar Observatory, New Jersey Institute of Technology , 40386 North Shore Lane, Big Bear City, CA 92314, USAInterplanetary magnetic flux ropes (MFRs) are commonly observed structures in the solar wind, categorized as magnetic clouds (MCs) and small-scale MFRs (SMFRs) depending on whether they are associated with coronal mass ejections. We apply machine learning to systematically compare SMFRs, MCs, and ambient solar wind plasma properties. We construct a data set of 3-minute averaged sequential data points of the solar wind’s instantaneous bulk fluid plasma properties using about 20 years of measurements from Wind. We label samples by the presence and type of MFRs containing them using a catalog based on Grad–Shafranov (GS) automated detection for SMFRs and NASA's catalog for MCs (with samples in neither labeled non-MFRs). We apply the random forest machine learning algorithm to find which categories can be more easily distinguished and by what features. MCs were distinguished from non-MFRs with an area under the receiver-operator curve (AUC) of 94% and SMFRs with an AUC of 89%, and had distinctive plasma properties. In contrast, while SMFRs were distinguished from non-MFRs with an AUC of 86%, this appears to rely solely on the 〈 B 〉 > 5 nT threshold applied by the GS catalog. The results indicate that SMFRs have virtually the same plasma properties as the ambient solar wind, unlike the distinct plasma regimes of MCs. We interpret our findings as additional evidence that most SMFRs at 1 au are generated within the solar wind. We also suggest that they should be considered a salient feature of the solar wind’s magnetic structure rather than transient events.https://doi.org/10.3847/1538-4357/ad0c52Solar windHeliosphereRandom Forests |
| spellingShingle | Hameedullah Farooki Yasser Abduallah Sung Jun Noh Hyomin Kim George Bizos Youra Shin Jason T. L. Wang Haimin Wang A Machine Learning Approach to Understanding the Physical Properties of Magnetic Flux Ropes in the Solar Wind at 1 au The Astrophysical Journal Solar wind Heliosphere Random Forests |
| title | A Machine Learning Approach to Understanding the Physical Properties of Magnetic Flux Ropes in the Solar Wind at 1 au |
| title_full | A Machine Learning Approach to Understanding the Physical Properties of Magnetic Flux Ropes in the Solar Wind at 1 au |
| title_fullStr | A Machine Learning Approach to Understanding the Physical Properties of Magnetic Flux Ropes in the Solar Wind at 1 au |
| title_full_unstemmed | A Machine Learning Approach to Understanding the Physical Properties of Magnetic Flux Ropes in the Solar Wind at 1 au |
| title_short | A Machine Learning Approach to Understanding the Physical Properties of Magnetic Flux Ropes in the Solar Wind at 1 au |
| title_sort | machine learning approach to understanding the physical properties of magnetic flux ropes in the solar wind at 1 au |
| topic | Solar wind Heliosphere Random Forests |
| url | https://doi.org/10.3847/1538-4357/ad0c52 |
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