Reconstructions of Jupiter's magnetic field using physics informed neural networks
This work addresses the challenge of accurately modeling Jupiter's interior magnetic structure for planetary science, offering improved insights into its dynamo processes, though it is incremental as it applies an existing PINN method to new planetary data.
The researchers tackled the problem of reconstructing Jupiter's internal magnetic field from Juno mission data, which was limited by noise amplification at small scales, by using physics-informed neural networks (PINNs) to provide clearer reconstructions without noise issues and estimate the dynamo boundary at a fractional radius of 0.8.
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.