A MACHINE LEARNS to PREDICT the STABILITY of TIGHTLY PACKED PLANETARY SYSTEMS

Daniel Tamayo, Ari Silburt, Diana Valencia, Kristen Menou, Mohamad Ali-Dib, Cristobal Petrovich, Chelsea X. Huang, Hanno Rein, Christa Van Laerhoven, Adiv Paradise, Alysa Obertas, Norman Murray

    Research output: Contribution to journalArticlepeer-review

    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.

    Original languageEnglish (US)
    Article numberL22
    JournalAstrophysical Journal Letters
    Volume832
    Issue number2
    DOIs
    StatePublished - Jan 12 2016

    Keywords

    • celestial mechanics
    • chaos
    • planets and satellites: dynamical evolution and stability

    ASJC Scopus subject areas

    • Astronomy and Astrophysics
    • Space and Planetary Science

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