Agyrotropic Electron Distributions in the Terrestrial Foreshock Transients

Chien, Gao; Tang, Binbin; Guo, Xiaocheng; Li, W. Y.; Khotyaintsev, Yuri V.; Graham, Daniel B.; Turner, D. L.; Yang, Zhongwei; Wang, Chi

doi:10.1029/2022gl102235

Cited by 1 publication

(1 citation statement)

References 45 publications

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“…The demagnetized electrons within the electron diffusion region are deflected and accelerated by electric fields resulting in a distinctive “crescent” shape in the observed velocity distribution (e.g., Burch, Torbert, et al., 2016). However, while these crescents are symptomatic of an electron diffusion region they may also be observed in other locations where there are electron‐scale boundaries, such as within the foreshock (Gao et al., 2023).…”

Section: Data and Modelmentioning

confidence: 99%

Automatic Encoding of Unlabeled Two Dimensional Data Enabling Similarity Searches: Electron Diffusion Regions and Auroral Arcs

Smith,

Rae,

Stawarz

et al. 2024

JGR Space Physics

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Critically important phenomena in Earth’s magnetosphere often occur briefly, or in small spatial regions. These processes are sampled with orbiting spacecraft or by fixed ground observatories and so rarely appear in data. Identifying such intervals can be an incredibly time consuming task. We apply a novel, powerful method by which two dimensional data can be automatically processed and embeddings created that contain key features of the data. The distance between embedding vectors serves as a measure of similarity. We apply the state‐of‐the‐art method to two example datasets: MMS electron velocity distributions and auroral all sky images. We show that the technique creates embeddings that group together visually similar observations. When provided with novel example images the method correctly identifies similar intervals: when provided with an electron distribution sampled during an encounter with an electron diffusion region the method recovers similar distributions obtained during two other known diffusion region encounters. Similarly, when provided with an interesting auroral structure the method highlights the same structure observed from an adjacent location and at other close time intervals. The method promises to be a useful tool to expand interesting case studies to multiple events, without requiring manual data labeling. Further, the models could be fine‐tuned with relatively small set of labeled example data to perform tasks such as classification. The embeddings can also be used as input to deep learning models, providing a key intermediary step—capturing the key features within the data.

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Section: Data and Modelmentioning

confidence: 99%