Study of Jupiter's Interior with Quadratic Monte Carlo Simulations
Burkhard Militzer

TL;DR
This paper introduces a novel quadratic Monte Carlo method for modeling Jupiter's interior, improving efficiency over traditional methods, and applies it to generate models matching gravity data, revealing insights into the planet's composition and structure.
Contribution
The paper presents a new quadratic Monte Carlo technique that enhances sampling efficiency for planetary interior models, and applies it to Jupiter to incorporate winds and dilute core features.
Findings
Quadratic Monte Carlo outperforms affine invariant methods in efficiency.
Models successfully match Jupiter's gravity data including odd and even harmonics.
Temperature and equation of state variations significantly impact inferred heavy element distribution.
Abstract
We construct models for Jupiter's interior that match the gravity data obtained by the Juno and Galileo spacecrafts. To generate ensembles of models, we introduce a novel quadratic Monte Carlo technique that is more efficient in confining fitness landscapes than affine invariant method that relies on linear stretch moves. We compare how long it takes the ensembles of walkers in both methods to travel to the most relevant parameter region. Once there, we compare the autocorrelation time and error bars of the two methods. For a ring potential and the 2d Rosenbrock function, we find that our quadratic Monte Carlo technique is significantly more efficient. Furthermore we modified the walk moves by adding a scaling factor. We provide the source code and examples so that this method can be applied elsewhere. Here we employ our method to generate five-layer models for Jupiter's interior that…
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