دورية أكاديمية
Classification of Magnetosheath Jets Using Neural Networks and High Resolution OMNI (HRO) Data
| العنوان: | Classification of Magnetosheath Jets Using Neural Networks and High Resolution OMNI (HRO) Data |
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| المؤلفون: | Savvas Raptis, Sigiava Aminalragia-Giamini, Tomas Karlsson, Martin Lindberg |
| المصدر: | Frontiers in Astronomy and Space Sciences, Vol 7 (2020) |
| بيانات النشر: | Frontiers Media S.A., 2020. |
| سنة النشر: | 2020 |
| المجموعة: | LCC:Astronomy LCC:Geophysics. Cosmic physics |
| مصطلحات موضوعية: | magnetosheath jets, neural networks, solar wind, machine learning, bow shock, Astronomy, QB1-991, Geophysics. Cosmic physics, QC801-809 |
| الوصف: | Magnetosheath jets are transient, localized dynamic pressure enhancements found downstream of the Earth's bow shock in the magnetosheath region. Using a pre-existing database of magnetosheath jets we train a neural network to distinguish between jets found downstream of a quasi-parallel bow shock (θBn45o). The initial database was compiled using MMS measurements in the magnetosheath (downstream) to identify and classify them as “quasi-parallel” or “quasi-perpendicular,” while the neural network uses only solar wind (upstream) measurements from the OMNIweb database. To evaluate the results, a comparison with three physics-based modeling approaches is done. It is shown that neural networks are systematically outperforming the other methods by achieving a ~93% agreement with the initial dataset, while the rest of the methods achieve around 80%. The better performance of the neural networks likely is due to the fact that they use information from more solar wind quantities than the physics-based models. As a result, even in the absence of certain upstream properties, such as the IMF direction, they are capable of accurately determining the jet class. |
| نوع الوثيقة: | article |
| وصف الملف: | electronic resource |
| اللغة: | English |
| تدمد: | 2296-987X |
| Relation: | https://www.frontiersin.org/article/10.3389/fspas.2020.00024/full; https://doaj.org/toc/2296-987X |
| DOI: | 10.3389/fspas.2020.00024 |
| URL الوصول: | https://doaj.org/article/898648cb1b8b4e9c910b376f628127d1 |
| رقم الأكسشن: | edsdoj.898648cb1b8b4e9c910b376f628127d1 |
| قاعدة البيانات: | Directory of Open Access Journals |
Full Text Finder | تدمد: | 2296987X |
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| DOI: | 10.3389/fspas.2020.00024 |