A Bayesian neural network predicts the dissolution of compact planetary systems.
Proc Natl Acad Sci U S A
; 118(40)2021 10 05.
Article
em En
| MEDLINE
| ID: mdl-34599094
ABSTRACT
We introduce a Bayesian neural network model that 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 instability times than analytical estimators, while also reducing the bias of existing machine learning algorithms by nearly a factor of three. Despite being trained on compact resonant and near-resonant three-planet configurations, the model demonstrates robust generalization to both nonresonant and higher multiplicity configurations, in the latter case outperforming models fit to that specific set of integrations. The model computes instability estimates up to [Formula see text] times faster than a numerical integrator, and unlike previous efforts provides confidence intervals on its predictions. Our inference model is publicly available in the SPOCK (https//github.com/dtamayo/spock) package, with training code open sourced (https//github.com/MilesCranmer/bnn_chaos_model).
Texto completo:
1
Coleções:
01-internacional
Base de dados:
MEDLINE
Tipo de estudo:
Prognostic_studies
/
Risk_factors_studies
Idioma:
En
Revista:
Proc Natl Acad Sci U S A
Ano de publicação:
2021
Tipo de documento:
Article
País de publicação:
EEUU
/
ESTADOS UNIDOS
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ESTADOS UNIDOS DA AMERICA
/
EUA
/
UNITED STATES
/
UNITED STATES OF AMERICA
/
US
/
USA