Livermore, P. orcid.org/0000-0001-7591-6716, de Ridder, S., Wu, L. et al. (1 more author) (2024) Reconstructions of Jupiter's magnetic field using physics informed neural networks. [Preprint]
Abstract
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. In this paper 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. Compared with other methods, our reconstructions of Jupiter’s magnetic field both on and above the surface are similar, and we achieve a similar fit to the Juno data. However, our models are not hampered by noise at depth, and so 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 the great blue spot appears to be rooted in neighbouring structures of oppositely signed flux.
Metadata
Item Type: | Preprint |
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Authors/Creators: |
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Copyright, Publisher and Additional Information: | This preprint is made available here with the permission of the authors. |
Dates: |
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Institution: | The University of Leeds |
Academic Units: | The University of Leeds > Faculty of Environment (Leeds) > School of Earth and Environment (Leeds) > Inst of Geophysics and Tectonics (IGT) (Leeds) |
Depositing User: | Symplectic Publications |
Date Deposited: | 13 Mar 2024 15:45 |
Last Modified: | 14 Mar 2024 02:36 |
Open Archives Initiative ID (OAI ID): | oai:eprints.whiterose.ac.uk:210188 |