AI 中文总结
利用朱诺Waves仪器电场数据，结合卷积神经网络与差分峰值分析开发混合识别框架，绘制木星尘埃环境。识别超15万个尘埃撞击，揭示晕环尘埃密度“空洞”及磁鞘附近尘埃群体证据，为未来任务提供技术基础。
AI 中文摘要
关于木星尘埃环和伽利略卫星尘埃环境的发现不断被轨道器和飞越探测器所完善。利用朱诺Waves仪器的电场数据，我们开发了一个混合识别框架，将Kvammen等人的卷积神经网络（CNN）与基于规则的差分峰值分析相结合，以系统地绘制木星尘埃环境。这个自动化流程成功识别了超过15万个尘埃撞击，有效从强烈的磁层噪声中分离出尘埃信号，提供了木星微尘分布的高分辨率目录，为未来任务提供了坚实技术基础。分析揭示了木星晕环垂直横截面内一个先前未解决的尘埃密度“空洞”，这是先前尘埃动力学模拟所预测的。此外，我们通过识别磁层边界穿越期间背景磁和等离子体数据的即时变化，报告了木星磁鞘附近或内部尘埃群体的持续证据。
英文摘要
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.
