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A Bayesian neural network predicts the dissolution of compact planetary systems.

Miles Cranmer¹, Daniel Tamayo², Hanno Rein^3,4

¹Department of Astrophysical Sciences, Princeton University, Princeton, NJ 08 544; mcranmer@princeton.edu.

Proceedings of the National Academy of Sciences of the United States of America

|October 2, 2021

Summary

We developed a Bayesian neural network to predict planetary system instability, accurately forecasting not only if but also when systems become unstable. This AI model significantly improves accuracy and speed over existing methods for celestial dynamics.

Keywords:

Bayesian analysis chaos deep learning planetary dynamics

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Area of Science:

Planetary Science
Astrophysics
Computational Science

Background:

Predicting the long-term stability of planetary systems is crucial for understanding their evolution.
Traditional methods often struggle with accuracy and computational cost for complex systems.

Purpose of the Study:

To introduce a novel Bayesian neural network model for predicting the instability of compact planetary systems.
To enhance the accuracy and efficiency of instability time predictions.

Main Methods:

Training a Bayesian neural network directly on N-body time series data of orbital elements.
Utilizing short time series to predict system instability.
Developing a model that generalizes to various system configurations.

Main Results:

The model achieves over two orders of magnitude greater accuracy in predicting instability times compared to analytical estimators.
It reduces the bias of existing machine learning algorithms by nearly a factor of three.
Predictions are up to [Formula: see text] times faster than numerical integrators, with confidence intervals.

Conclusions:

The Bayesian neural network offers a highly accurate and efficient tool for assessing planetary system stability.
The model demonstrates robust generalization capabilities beyond its training configurations.
Publicly available code facilitates further research in celestial dynamics and AI applications.