NeuralCMS

NeuralCMS is a machine-learning model to compute the gravitational moments and mass of Jupiter given seven chosen parameters setting its interior model. The model is trained on over a million interior model solutions computed with the accurate but computationally demanding concentric Maclaurin spheroid method (CMS; Hubbard 2013 DOI:10.1088/0004-637X/768/1/43).

NeuralCMS receives the following interior features as input: protosolar helium abundance (setting the overall planetary abundance) $Y_{\rm proto}$, temperature at 1 bar $T_{\rm 1 bar}$, atmospheric heavy materials (anything heavier than helium) abundance $Z_1$, transition pressure between the inner and the outer envelopes $P_{12}$, dilute core extent $m_{\rm dilute}$, dilute core maximum heavy materials abundance $Z_{\rm dilute}$, and compact core normalize radius $r_{\rm core}$, and computes the lower even degree gravity moments and mass.

Here, we share the trained models presented in Ziv et al. 2024, which was accepted for publication in A&A (DOI:10.1051/0004-6361/202450223), together with a Python notebook to load the models, compute a single interior model, and perform a grid search for interior models consistent with Nasa's Juno mission measured gravity moments and mass.

Installation using pip

This project uses PyTorch, which requires Python 3.8 or higher.

The required packages:

• python>=3.8
• torch
• numpy
• tqdm
• itertools
• jupyter

Install the requirements:

pip install -r requirements.txt

Getting started

Start working with the NeuralCMS in NeuralCMS_notebook.ipynb.

Acknowledgements

Our numerical interior model (CMS), which NeuralCMS was trained on its results, is based on the model from https://github.com/nmovshov/CMS-planet.