Magnetic sounding using data collected from the Juno mission can be used to provide constraints on Jupiter's interior. However, inwards continuation of reconstructions assuming zero electrical conductivity and a representation in spherical harmonics are limited by the enhancement of noise at small scales. Here we describe new reconstructions of Jupiter's internal magnetic field based on physics-informed neural networks and either the first 33 (PINN33) or the first 50 (PINN50) of Juno's orbits. The method can resolve local structures, and allows for weak ambient electrical currents. Our models are not hampered by noise amplification at depth, and offer a much clearer picture of the interior structure. We estimate that the dynamo boundary is at a fractional radius of 0.8. At this depth, the magnetic field is arranged into longitudinal bands, and strong local features such as the great blue spot appear to be rooted in neighbouring structures of oppositely signed flux.

翻译：利用朱诺号任务收集的数据进行磁测，可为木星内部结构提供约束。然而，假设零电导率并采用球谐函数表示的重建向内延拓方法，会受到小尺度噪声增强的限制。本文描述了基于物理信息神经网络的新木星内部磁场重建方法，分别使用朱诺号前33次轨道数据（PINN33）或前50次轨道数据（PINN50）。该方法能够解析局部结构，并允许弱环境电流存在。我们的模型不受深度处噪声放大的影响，能更清晰地呈现内部结构。我们估算发电区域边界位于分数半径为0.8处。在此深度，磁场排列成纵向条带状结构，而强烈局部特征（如大红斑）看似源于相邻的相反磁通量结构。