Exoplanet formation inference using conditional invertible neural networks

TL;DR

This paper introduces a machine learning approach using conditional invertible neural networks to quantitatively infer the formation history of exoplanets from synthetic data, including complex multiplanetary systems.

Contribution

It demonstrates the application of cINN to model exoplanet formation, including systems with gravitational interactions, improving inference capabilities over traditional qualitative methods.

Findings

Training on multiplanetary data shows promise for inference.

Single-planet training data is limited in scope.

More data needed for complex planetary systems.

Abstract

The interpretation of the origin of observed exoplanets is usually done only qualitatively due to uncertainties of key parameters in planet formation models. To allow a quantitative methodology which traces back in time to the planet birth locations, we train recently developed conditional invertible neural networks (cINN) on synthetic data from a global planet formation model which tracks growth from dust grains to evolved final giant planets. In addition to deterministic single planet formation runs, we also include gravitationally interacting planets in multiplanetary systems, which include some measure of chaos. For the latter case, we treat them as individual planets or choose the two or three planets most likely to be discovered by telescopes. We find that training on multiplanetary data, each planet treated as individual point, is promising. The single-planet data only covers a small range of planets and does not extrapolate well to planet properties not included in the training data. Extension to planetary systems will require more training data due to the higher dimensionality of the problem.