本文利用朱诺号探测器的数据，结合卷积神经网络与规则分析构建了自动化识别框架，系统地绘制了木星系统的尘埃分布图。研究揭示了木星晕环的精细结构，并为理解木星磁层边界的尘埃分布提供了新的证据。
arXiv:2607.19304v2 Announce Type: replace Abstract: Discoveries regarding the dusty rings of Jupiter and the Galilean satellites' dust environment have been continuously refined by orbiters and flybys. Leveraging Juno Waves instrument electric field data, we developed a hybrid recognition framework, coupling Kvammen's Convolutional Neural Network (CNN) with rule-based differential peak analysis, to systematically map the Jovian dust environment. This automated pipeline successfully identified over 150,000 dust impacts, effectively isolating dust signals from intense magnetospheric noise, providing a high-resolution catalog of Jovian microdust distribution and offering a robust technical foundation for future missions. Analysis of the vertical cross-section of the Jovian halo ring reveals a more detailed dust distribution structure, with a distinct number density enhancement near the center of the halo ring. Moreover, we report the continued evidence of dust populations near or in the Jovian magnetosheath through identification of background magnetic and plasma data instant variations during magnetospheric boundary crossings.
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本站仅展示标题与摘要，正文版权归原作者及原发布机构所有。 原作者：Yuqi Zhang (Southern University of Science and Technology, Shenzhen, China), Shengyi Ye (Southern University of Science and Technology, Shenzhen, China), Yuting Li (Southern University of Science and Technology, Shenzhen, China), Wenyue Li (Southern University of Science and Technology, Shenzhen, China), Guangzhou Wang (Southern University of Science and Technology, Shenzhen, China), Xinya Duanmu (Southern University of Science and Technology, Shenzhen, China)。 原始页面