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.

生成讲解博客的中文版本

生成AI智能校验的中文版本