Three-Dimensional Dust Distribution in the Jovian System from Juno/Waves Observations: Insights into the Halo Ring and Magnetospheric Dust

Three-Dimensional Dust Distribution in the Jovian System from Juno/Waves Observations: Insights into the Halo Ring and Magnetospheric Dust
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)
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.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.