Evaluating Proton Intensities for the SMILE Mission
Abstract This study introduces five linear regression models developed to accurately predict proton intensities in the critical energy range of 92.2–159.7 keV. To achieve this task we utilized 14 years of data sourced from the Cluster's RAPID experiment and NASA's OMNI database. This data...
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| Format: | Article |
| Language: | English |
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Wiley
2024-12-01
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| Series: | Space Weather |
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| Online Access: | https://doi.org/10.1029/2024SW003934 |
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| author | Simon Mischel Elena A. Kronberg C. P. Escoubet |
| author_facet | Simon Mischel Elena A. Kronberg C. P. Escoubet |
| author_sort | Simon Mischel |
| collection | DOAJ |
| description | Abstract This study introduces five linear regression models developed to accurately predict proton intensities in the critical energy range of 92.2–159.7 keV. To achieve this task we utilized 14 years of data sourced from the Cluster's RAPID experiment and NASA's OMNI database. This data was then aligned with the Solar wind‐Magnetosphere‐Ionosphere Link Explorer (SMILE) mission's trajectory, to increase model accuracy in the relevant regions. Our approach diverges from existing methodologies by offering a user‐friendly model that doesn't require specialized software, making it accessible for broader applications in satellite mission planning and risk assessment. The research segregates the data set into four distinct regions, each analyzed for proton intensity dynamics. In the outer regions (|YGSE|≥6RE) there is a pronounced dependence on radial distance and solar wind speed. In contrast, the inner regions (|YGSE|≤6RE) demonstrate a significant dependence of proton intensities on the absolute value of the z‐coordinate and the magnetic field line topology. Our models achieved a Spearman correlation ranging from 0.57 to 0.72 on the test set, indicating good predictive capabilities. The findings emphasize the role of regional characteristics in space weather prediction and underscore the potential for tailored approaches in future research. |
| format | Article |
| id | doaj-art-64d15f6b13db44129eb0432b223f4613 |
| institution | Kabale University |
| issn | 1542-7390 |
| language | English |
| publishDate | 2024-12-01 |
| publisher | Wiley |
| record_format | Article |
| series | Space Weather |
| spelling | doaj-art-64d15f6b13db44129eb0432b223f46132025-02-01T08:10:32ZengWileySpace Weather1542-73902024-12-012212n/an/a10.1029/2024SW003934Evaluating Proton Intensities for the SMILE MissionSimon Mischel0Elena A. Kronberg1C. P. Escoubet2Department of Earth and Environmental Sciences Ludwig‐Maximilians‐Universität München Munich GermanyDepartment of Earth and Environmental Sciences Ludwig‐Maximilians‐Universität München Munich GermanyEuropean Space Research and Technology Centre Noordwjik The NetherlandsAbstract This study introduces five linear regression models developed to accurately predict proton intensities in the critical energy range of 92.2–159.7 keV. To achieve this task we utilized 14 years of data sourced from the Cluster's RAPID experiment and NASA's OMNI database. This data was then aligned with the Solar wind‐Magnetosphere‐Ionosphere Link Explorer (SMILE) mission's trajectory, to increase model accuracy in the relevant regions. Our approach diverges from existing methodologies by offering a user‐friendly model that doesn't require specialized software, making it accessible for broader applications in satellite mission planning and risk assessment. The research segregates the data set into four distinct regions, each analyzed for proton intensity dynamics. In the outer regions (|YGSE|≥6RE) there is a pronounced dependence on radial distance and solar wind speed. In contrast, the inner regions (|YGSE|≤6RE) demonstrate a significant dependence of proton intensities on the absolute value of the z‐coordinate and the magnetic field line topology. Our models achieved a Spearman correlation ranging from 0.57 to 0.72 on the test set, indicating good predictive capabilities. The findings emphasize the role of regional characteristics in space weather prediction and underscore the potential for tailored approaches in future research.https://doi.org/10.1029/2024SW003934X‐ray telescopesX‐ray detectorsproton intensitieslinear regressionmachine learning |
| spellingShingle | Simon Mischel Elena A. Kronberg C. P. Escoubet Evaluating Proton Intensities for the SMILE Mission Space Weather X‐ray telescopes X‐ray detectors proton intensities linear regression machine learning |
| title | Evaluating Proton Intensities for the SMILE Mission |
| title_full | Evaluating Proton Intensities for the SMILE Mission |
| title_fullStr | Evaluating Proton Intensities for the SMILE Mission |
| title_full_unstemmed | Evaluating Proton Intensities for the SMILE Mission |
| title_short | Evaluating Proton Intensities for the SMILE Mission |
| title_sort | evaluating proton intensities for the smile mission |
| topic | X‐ray telescopes X‐ray detectors proton intensities linear regression machine learning |
| url | https://doi.org/10.1029/2024SW003934 |
| work_keys_str_mv | AT simonmischel evaluatingprotonintensitiesforthesmilemission AT elenaakronberg evaluatingprotonintensitiesforthesmilemission AT cpescoubet evaluatingprotonintensitiesforthesmilemission |