A Machine Learns to Predict the Stability of Tightly Packed Planetary Systems

Tamayo, Daniel and Silburt, Ari and Valencia, Diana and Menou, Kristen and Ali-Dib, Mohamad and Petrovich, Cristobal and Huang, Chelsea X. ORCID: https://orcid.org/0000-0003-0918-7484 and Rein, Hanno and van Laerhoven, Christa and Paradise, Adiv and Obertas, Alysa and Murray, Norman (2016) A Machine Learns to Predict the Stability of Tightly Packed Planetary Systems. The Astrophysical Journal Letters, 832 (2):L22. pp. 1-5. ISSN 2041-8205

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Abstract

The requirement that planetary systems be dynamically stable is often used to vet new discoveries or set limits on unconstrained masses or orbital elements. This is typically carried out via computationally expensive N-body simulations. We show that characterizing the complicated and multi-dimensional stability boundary of tightly packed systems is amenable to machine-learning methods. We find that training an XGBoost machine-learning algorithm on physically motivated features yields an accurate classifier of stability in packed systems. On the stability timescale investigated (107 orbits), it is three orders of magnitude faster than direct N-body simulations. Optimized machine-learning classifiers for dynamical stability may thus prove useful across the discipline, e.g., to characterize the exoplanet sample discovered by the upcoming Transiting Exoplanet Survey Satellite. This proof of concept motivates investing computational resources to train algorithms capable of predicting stability over longer timescales and over broader regions of phase space.


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Item Type: Article (Commonwealth Reporting Category C)
Refereed: Yes
Item Status: Live Archive
Additional Information: Published version deposited in accordance with the copyright policy of the publisher.
Faculty/School / Institute/Centre: No Faculty
Faculty/School / Institute/Centre: No Faculty
Date Deposited: 31 Mar 2022 02:21
Last Modified: 13 Apr 2022 02:35
Uncontrolled Keywords: celestial mechanics; chaos; planets and satellites: dynamical evolution and stability; Astrophysics - Earth and Planetary Astrophysics
Fields of Research (2020): 51 PHYSICAL SCIENCES > 5101 Astronomical sciences > 510109 Stellar astronomy and planetary systems
Identification Number or DOI: https://doi.org/10.3847/2041-8205/832/2/l22
URI: http://eprints.usq.edu.au/id/eprint/47377

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