Planetary systems architecture based on a conditional generative model

Understanding the diversity and structure of planetary systems requires capturing not only the properties of individual planets but also the statistical relationships between planets within the same system and their interaction with the host star. 

Traditional population synthesis models, such as the Bern model, provide physically motivated insights into these correlations, but their computational cost limits the applications. To address this, we introduce a conditional generative model that produces synthetic planetary systems with high fidelity and efficiency, while explicitly incorporating host star and protoplanetary disk properties such as the stellar metallicity, disk lifetime, mass and so on.

The model architecture builds on a previous transformer framework (Alibert, Davoult and Marques in review, see http://ai4exoplanets.com). Through conditioning, the new model is expected to capture system-level features such as planet multiplicity, orbital distribution, but also to link such properties to the host Star and disk. The training is performed using Bern model synthetic simulations to avoid observational bias and the model is capable of producing rapidly new planetary systems that remain statistically consistent with those from the simulations.