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Abstract Whistler‐mode hiss waves are crucial to the dynamics of Earth's radiation belts, particularly in the scattering and loss of energetic electrons and forming the slot region between the inner and outer belts. The generation of hiss waves involves multiple potential mechanisms, which are under active research. Understanding the role of hiss waves in radiation belt dynamics and their generation mechanisms requires analyzing their temporal and spatial evolutions, especially for strong hiss waves. Therefore, we developed an Imbalanced Regressive Neural Network (IR‐NN) model for predicting hiss amplitudes. This model addresses the challenge posed by the data imbalance of the hiss data set, which consists of predominantly quiet‐time background samples and fewer but significant active‐time intense hiss samples. Notably, the IR‐NN hiss model excels in predicting strong hiss waves (>100pT). We investigate the temporal and spatial evolution of hiss wave during a geomagnetic storm on 24–27 October 2017. We show that hiss waves occur within the nominal plasmapause, and follow its dynamically evolving shape. They exhibit intensifications with 1 and 2 hr timescale similar to substorms but with a noticeable time delay. The intensifications begin near dawn and progress toward noon and afternoon. During the storm recovery phase, hiss intensifications may occur in the plume. Additionally, we observe no significant latitudinal dependence of the hiss waves within |MLAT| < 20°. In addition to describing the spatiotemporal evolution of hiss waves, this study highlights the importance of imbalanced regressive methods, given the prevalence of imbalanced data sets in space physics and other real‐world applications.
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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 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.
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- Award ID(s):
- 1847818
- PAR ID:
- 10468007
- Publisher / Repository:
- DOI PREFIX: 10.1029
- Date Published:
- Journal Name:
- Space Weather
- Volume:
- 21
- Issue:
- 10
- ISSN:
- 1542-7390
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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