Miles Cranmer | A Bayesian neural network predicts the dissolution of compact planetary systems

4/14/2021 New Technologies in Mathematics Seminar Speaker: Miles Cranmer, Dept. of Astrophysical Sciences, Princeton University Title: A Bayesian neural network predicts the dissolution of compact planetary systems Abstract: Despite over three hundred years of effort, no solutions exist for predicting when a general planetary configuration will become unstable. I will discuss our deep learning architecture (arxiv:) which pushes forward this problem for compact systems. While current machine learning algorithms in this area rely on scientist-derived instability metrics, our new technique learns its own metrics from scratch, enabled by a novel internal structure inspired from dynamics theory. The Bayesian neural network model can accurately predict not only if, but also when a compact planetary system with three or more planets will go unstable. Our model, trained directly from short N-body time series of raw orbital elements, is more than two orders of magnitude more accurate at predicting instab