Towards an agnostic algorithm for sampling empirical structure models: The case of Uranus and Neptune
Stefano Wirth, Luca Morf, Ravit Helled

TL;DR
This paper introduces an efficient, agnostic gradient-descent algorithm for sampling the full space of planetary interior density profiles, demonstrated on Uranus and Neptune, providing comprehensive models consistent with observed data.
Contribution
The authors develop a novel optimization-based sampling method that covers the entire solution space of planetary interior models, avoiding traditional MCMC limitations.
Findings
The algorithm produces density and pressure profiles matching observed planetary properties.
Most solutions have at most one significant density discontinuity based on the steepness classification.
Discontinuities tend to concentrate around specific normalized radii for Uranus and Neptune.
Abstract
We present an algorithm to efficiently sample the full space of planetary interior density profiles. Our approach uses as few assumptions as possible to pursue an agnostic algorithm. The algorithm avoids the common Markov Chain Monte Carlo method and instead uses an optimisation-based gradient-descent approach designed for computational efficiency. In this work, we use Uranus and Neptune as test cases and obtain empirical models that provide density and pressure profiles consistent with the observed physical properties (total mass, radius, and gravitational moments). We compare our findings to other work and find that while other studies are generally in line with our findings, they do not cover the entire space of solutions faithfully. Furthermore, we present guidance for modellers that construct Uranus or Neptune interior models with a fixed number of layers. We provide a statistical…
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Taxonomy
TopicsAstro and Planetary Science · Stellar, planetary, and galactic studies · High-pressure geophysics and materials
