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

Status Report From: arXiv.org e-Print archivePosted: Wednesday, October 19, 2016 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(Submitted on 17 Oct 2016) 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 a state-of-the-art 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 3 orders of magnitude faster…


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