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Published March 10, 2020 | Published + Submitted
Journal Article Open

Saturn's Probable Interior: An Exploration of Saturn's Potential Interior Density Structures

Abstract

The gravity field of a giant planet is typically our best window into its interior structure and composition. Through comparison of a model planet's calculated gravitational potential with the observed potential, inferences can be made about interior quantities, including possible composition and the existence of a core. Necessarily, a host of assumptions go into such calculations, making every inference about a giant planet's structure strongly model dependent. In this work, we present a more general picture by setting Saturn's gravity field, as measured during the Cassini Grand Finale, as a likelihood function driving a Markov Chain Monte Carlo exploration of the possible interior density profiles. The result is a posterior distribution of the interior structure that is not tied to assumed composition, thermal state, or material equations of state. Constraints on interior structure derived in this Bayesian framework are necessarily less informative, but are also less biased and more general. These empirical and probabilistic constraints on the density structure are our main data product, which we archive for continued analysis. We find that the outer half of Saturn's radius is relatively well constrained, and we interpret our findings as suggesting a significant metal enrichment, in line with atmospheric abundances from remote sensing. As expected, the inner half of Saturn's radius is less well constrained by gravity, but we generally find solutions that include a significant density enhancement, which can be interpreted as a core, although this core is often lower in density and larger in radial extent than typically found by standard models. This is consistent with a dilute core and/or composition gradients.

Additional Information

© 2020 The American Astronomical Society. Received 2019 July 23; revised 2020 January 25; accepted 2020 January 29; published 2020 March 11. We would like to thank Dan Foreman-Mackey, Nadine Nettlemann, Bill Hubbard, Burkhard Militzer, Sean Wahl, Daniele Durante, and Luciano Iess for helpful advice on several aspects of this work. We thank Tristan Guillot for his thorough and thoughtful review. J.J.F. acknowledges the support of NASA Cassini Participating Science grant NNX16AI43G and the University of California grant A17-0633-001 to the Center for Frontiers in High Energy Density Science. Resources supporting this work were provided by the NASA High-End Computing (HEC) Program through the NASA Advanced Supercomputing (NAS) Division at Ames Research Center, as well as the lux supercomputer at UC Santa Cruz, funded by NSF MRI grant AST 1828315. Software: emcee (Foreman-Mackey et al. 2013).

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Published - Movshovitz_2020_ApJ_891_109.pdf

Submitted - 1912.02137.pdf

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Created:
August 22, 2023
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October 20, 2023