A Transformer-based Generative Model For Planetary Systems

Numerical calculations of planetary system formation are very demanding in terms of computing power.

These synthetic planetary systems can however provide access to correlations, as predicted in a given numerical framework, between the properties of planets in the same system. Such correlations can, in return, be used in order to guide and prioritize observational campaigns aiming at discovering some types of planets, as Earth-like planets. Our goal is to develop a generative model which is capable of capturing correlations and statistical relationships between planets in the same system.

Such a model, trained on the Bern model, offers the possibility to generate large number of synthetic planetary systems with little computational cost, that can be used, for example, to guide observational campaigns. Our generative model is based on the transformer architecture which is well-known to efficiently capture correlations in sequences and is at the basis of all modern Large Language Models.

To assess the validity of the generative model, we perform visual and statistical comparisons, as well as a machine learning driven tests. Finally, as a use case example, we consider the TOI-469 system, in which we aim at predicting the possible properties of planets c and d, based on the properties of planet b (the first that has been detected). We show using different comparison methods that the properties of systems generated by our model are very similar to the ones of the systems computed directly by the Bern model.

We also show in the case of the TOI-469 system, that using the generative model allows to predict the properties of planets not yet observed, based on the properties of the already observed planet. We provide our model to the community on our website this http URL.

Yann Alibert, Jeanne Davoult, Sara Marques

Comments: Accepted in A&A
Subjects: Earth and Planetary Astrophysics (astro-ph.EP); Instrumentation and Methods for Astrophysics (astro-ph.IM); Artificial Intelligence (cs.AI); Machine Learning (cs.LG)
Cite as: arXiv:2509.07226 [astro-ph.EP] (or arXiv:2509.07226v1 [astro-ph.EP] for this version)
https://doi.org/10.48550/arXiv.2509.07226
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Submission history
From: Sara Marques
[v1] Mon, 8 Sep 2025 21:09:14 UTC (2,209 KB)
https://arxiv.org/abs/2509.07226

Astrobiology,