The study focuses on reconstructing Jupiter's magnetic field using data from the Juno mission. By employing physics-informed neural networks, the authors present two models based on different numbers of orbits, highlighting a dynamo at a fractional radius of 0.8. These reconstructions provide stable results with reduced noise at depth, offering insights into Jupiter's internal magnetic field structure. The paper discusses the limitations of traditional methods and the advantages of using machine learning techniques for accurate reconstructions.
The research outlines the methodology used to process Juno mission data and develop physics-informed neural network models for Jupiter's magnetic field reconstruction. By comparing these models with existing spherical harmonic-based methods, the study demonstrates improved stability and clarity in imaging Jupiter's internal magnetic field. The results suggest that neural networks can effectively capture local structures and provide valuable insights into Jupiter's dynamo region.
Furthermore, the study delves into detailed analyses of model comparisons, spectral power variations, and implications for understanding Jupiter's internal dynamics. The findings highlight the potential of machine learning approaches in enhancing our understanding of planetary magnetic fields and offer new perspectives on interpreting complex celestial phenomena.
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by Philip W. Li... at arxiv.org 03-13-2024
https://arxiv.org/pdf/2403.07507.pdfDeeper Inquiries