plaNETic is a neural network-based Bayesian internal structure modelling framework for small exoplanets with masses between 0.5 and 15 Mearth. The code efficiently computes posteriors of a planet's internal structure based on its observed planetary and stellar parameters. It uses a full grid accept-reject sampling algorithm with neural networks trained on the interior model of the BICEPS code as a forward model. Furthermore, it allows for different choices in priors concerning the expected abundance of water (formation inside vs. outside of iceline) and the planetary Si/Mg/Fe ratios (stellar vs. iron-enriched vs. free).
For a more detailed description of the features of the code, we refer to Egger et al. 2024, where the code was first introduced and applied to a planetary system.
We run the code on a 2021 MacBook Pro with an Apple M1 Pro chip.
For questions or comments, feel free to contact Jo Ann Egger (jo-ann.egger@unibe.ch).
If you use this code, please cite Egger et al. 2024, where the plaNETic framework was introduced for the first time.
If you use the trained neural networks provided, please also cite Haldemann et al. 2024.
git clone plaNETic and run "pip install ."
To infer the internal structure of the planets in an observed planetary system, create a new subfolder in run_grid with the same structure as TOI-469_Egger+:
- Subfolders posteriors, plots
- Parameter file stellar_planetary_parameters.csv with the observed properties of the host star and all planets in the system
- Executable run_grid_TOI-469.py
Then simply adapt and run the executable, which will generate *_posterior.npy files in the subfolder posteriors.