Volume 41 Issue 4
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Article Navigation >  Chinese Journal of Space Science  > 2021  >  41(4): 654-666
TONG Xin, ZOU Ziming, BAI Xi, ZHONG Jia, HU Zejun, LI Bin. Machine Identification of Throat Aurora[J]. Chinese Journal of Space Science, 2021, 41(4): 654-666. doi: 10.11728/cjss2021.04.654
Citation: TONG Xin, ZOU Ziming, BAI Xi, ZHONG Jia, HU Zejun, LI Bin. Machine Identification of Throat Aurora[J]. Chinese Journal of Space Science, 2021, 41(4): 654-666. doi: 10.11728/cjss2021.04.654
shu

Machine Identification of Throat Aurora

doi: 10.11728/cjss2021.04.654 cstr: 32142.14.cjss2021.04.654

  • 1. National Space Science Center, Chinese Academy of Sciences, Beijing 100190;

  • 2. University of Chinese Academy of Sciences, Beijing 100049;

  • 3. Polar Research Institute of China, Shanghai 200136

  • Received Date: 2020-01-22
  • Rev Recd Date: 2020-12-24
  • Publish Date: 2021-07-15
    Abstract
  • Throat aurora is an auroral form frequently observed nearby the ionospheric convection throat region. Extending from the equatorward edge of the dayside auroral oval, and thus appears to be a north-south aligned discrete auroral structure. It is suggested to be the projection of the magnetopause reconnection process caused by the interaction between magnetopause and high-speed jets in magnetosheath. The investigation on throat aurora is meaningful for understanding solar wind-magnetosphere coupling process. It is the fundamental work of statistic study that identifying accurately and efficiently these special auroral structures from plenty of ASI images. Through preprocessing and labeling ASI images from Yellow River Station during 2003 and 2017, the throat auroral dataset is established, and the feature space of the auroral data is explored by using DenseNet and classified simultaneously based on whether an observation image has a structure of throat aurora, through which machine identification of throat auroral is achieved for the first time. The accuracy of the identification model is close to 96% and it has good generalization performance.

     

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