Letter to the Editor

NeuralCMS: A deep learning approach to study Jupiter’s interior

¹ Department of Earth and Planetary Sciences, Weizmann Institute of Science, Rehovot 76100, Israel
e-mail: maayan.ziv@weizmann.ac.il
² Université Côte d’Azur, Observatoire de la Côte d’Azur, CNRS, Laboratoire Lagrange, Nice, France
³ Institut für Astrophysik, Universität Zürich, Winterthurerstr. 190, 8057 Zürich, Switzerland

Received: 3 April 2024
Accepted: 6 May 2024

Abstract

Context. NASA’s Juno mission provided exquisite measurements of Jupiter’s gravity field that together with the Galileo entry probe atmospheric measurements constrains the interior structure of the giant planet. Inferring its interior structure range remains a challenging inverse problem requiring a computationally intensive search of combinations of various planetary properties, such as the cloud-level temperature, composition, and core features, requiring the computation of ∼10⁹ interior models.

Aims. We propose an efficient deep neural network (DNN) model to generate high-precision wide-ranged interior models based on the very accurate but computationally demanding concentric MacLaurin spheroid (CMS) method.

Methods. We trained a sharing-based DNN with a large set of CMS results for a four-layer interior model of Jupiter, including a dilute core, to accurately predict the gravity moments and mass, given a combination of interior features. We evaluated the performance of the trained DNN (NeuralCMS) to inspect its predictive limitations.

Results. NeuralCMS shows very good performance in predicting the gravity moments, with errors comparable with the uncertainty due to differential rotation, and a very accurate mass prediction. This allowed us to perform a broad parameter space search by computing only ∼10⁴ actual CMS interior models, resulting in a large sample of plausible interior structures, and reducing the computation time by a factor of 10⁵. Moreover, we used a DNN explainability algorithm to analyze the impact of the parameters setting the interior model on the predicted observables, providing information on their nonlinear relation.