Distribution and Evolution of Chorus Waves Modeled by a Neural Network: The Importance of Imbalanced Regression
Abstract Whistler‐mode chorus waves play an essential role in the acceleration and loss of energetic electrons in the Earth’s inner magnetosphere, with the more intense waves producing the most dramatic effects. However, it is challenging to predict the amplitude of strong chorus waves due to the im...
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| Format: | Article |
| Language: | English |
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Wiley
2023-10-01
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| Series: | Space Weather |
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| Online Access: | https://doi.org/10.1029/2023SW003524 |
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| author | Xiangning Chu Jacob Bortnik Wen Li Xiao‐Chen Shen Qianli Ma Donglai Ma David Malaspina Sheng Huang |
| author_facet | Xiangning Chu Jacob Bortnik Wen Li Xiao‐Chen Shen Qianli Ma Donglai Ma David Malaspina Sheng Huang |
| author_sort | Xiangning Chu |
| collection | DOAJ |
| description | Abstract Whistler‐mode chorus waves play an essential role in the acceleration and loss of energetic electrons in the Earth’s inner magnetosphere, with the more intense waves producing the most dramatic effects. However, it is challenging to predict the amplitude of strong chorus waves due to the imbalanced nature of the data set, that is, there are many more non‐chorus data points than strong chorus waves. Thus, traditional models usually underestimate chorus wave amplitudes significantly during active times. Using an imbalanced regressive (IR) method, we develop a neural network model of lower‐band (LB) chorus waves using 7‐year observations from the EMFISIS instrument onboard Van Allen Probes. The feature selection process suggests that the auroral electrojet index alone captures most of the variations of chorus waves. The large amplitude of strong chorus waves can be predicted for the first time. Furthermore, our model shows that the equatorial LB chorus’s spatiotemporal evolution is similar to the drift path of substorm‐injected electrons. We also show that the chorus waves have a peak amplitude at the equator in the source MLT near midnight, but toward noon, there is a local minimum in amplitude at the equator with two off‐equator amplitude peaks in both hemispheres, likely caused by the bifurcated drift paths of substorm injections on the dayside. The IR‐based chorus model will improve radiation belt prediction by providing chorus wave distributions, especially storm‐time strong chorus. Since data imbalance is ubiquitous and inherent in space physics and other physical systems, imbalanced regressive methods deserve more attention in space physics. |
| format | Article |
| id | doaj-art-365045ef924a4e65ad8a943ca7462d4a |
| institution | Kabale University |
| issn | 1542-7390 |
| language | English |
| publishDate | 2023-10-01 |
| publisher | Wiley |
| record_format | Article |
| series | Space Weather |
| spelling | doaj-art-365045ef924a4e65ad8a943ca7462d4a2025-01-14T16:31:16ZengWileySpace Weather1542-73902023-10-012110n/an/a10.1029/2023SW003524Distribution and Evolution of Chorus Waves Modeled by a Neural Network: The Importance of Imbalanced RegressionXiangning Chu0Jacob Bortnik1Wen Li2Xiao‐Chen Shen3Qianli Ma4Donglai Ma5David Malaspina6Sheng Huang7Laboratory for Atmospheric and Space Physics University of Colorado Boulder Boulder CO USADepartment of Atmospheric and Oceanic Sciences University of California Los Angeles CA USACenter for Space Physics Boston University Boston MA USACenter for Space Physics Boston University Boston MA USADepartment of Atmospheric and Oceanic Sciences University of California Los Angeles CA USADepartment of Atmospheric and Oceanic Sciences University of California Los Angeles CA USALaboratory for Atmospheric and Space Physics University of Colorado Boulder Boulder CO USACenter for Space Physics Boston University Boston MA USAAbstract Whistler‐mode chorus waves play an essential role in the acceleration and loss of energetic electrons in the Earth’s inner magnetosphere, with the more intense waves producing the most dramatic effects. However, it is challenging to predict the amplitude of strong chorus waves due to the imbalanced nature of the data set, that is, there are many more non‐chorus data points than strong chorus waves. Thus, traditional models usually underestimate chorus wave amplitudes significantly during active times. Using an imbalanced regressive (IR) method, we develop a neural network model of lower‐band (LB) chorus waves using 7‐year observations from the EMFISIS instrument onboard Van Allen Probes. The feature selection process suggests that the auroral electrojet index alone captures most of the variations of chorus waves. The large amplitude of strong chorus waves can be predicted for the first time. Furthermore, our model shows that the equatorial LB chorus’s spatiotemporal evolution is similar to the drift path of substorm‐injected electrons. We also show that the chorus waves have a peak amplitude at the equator in the source MLT near midnight, but toward noon, there is a local minimum in amplitude at the equator with two off‐equator amplitude peaks in both hemispheres, likely caused by the bifurcated drift paths of substorm injections on the dayside. The IR‐based chorus model will improve radiation belt prediction by providing chorus wave distributions, especially storm‐time strong chorus. Since data imbalance is ubiquitous and inherent in space physics and other physical systems, imbalanced regressive methods deserve more attention in space physics.https://doi.org/10.1029/2023SW003524chorus wavesimbalanced regressionneural networkmachine learningradiation beltVan Allen Probe |
| spellingShingle | Xiangning Chu Jacob Bortnik Wen Li Xiao‐Chen Shen Qianli Ma Donglai Ma David Malaspina Sheng Huang Distribution and Evolution of Chorus Waves Modeled by a Neural Network: The Importance of Imbalanced Regression Space Weather chorus waves imbalanced regression neural network machine learning radiation belt Van Allen Probe |
| title | Distribution and Evolution of Chorus Waves Modeled by a Neural Network: The Importance of Imbalanced Regression |
| title_full | Distribution and Evolution of Chorus Waves Modeled by a Neural Network: The Importance of Imbalanced Regression |
| title_fullStr | Distribution and Evolution of Chorus Waves Modeled by a Neural Network: The Importance of Imbalanced Regression |
| title_full_unstemmed | Distribution and Evolution of Chorus Waves Modeled by a Neural Network: The Importance of Imbalanced Regression |
| title_short | Distribution and Evolution of Chorus Waves Modeled by a Neural Network: The Importance of Imbalanced Regression |
| title_sort | distribution and evolution of chorus waves modeled by a neural network the importance of imbalanced regression |
| topic | chorus waves imbalanced regression neural network machine learning radiation belt Van Allen Probe |
| url | https://doi.org/10.1029/2023SW003524 |
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