In:
The Astrophysical Journal Letters, American Astronomical Society, Vol. 832, No. 2 ( 2016-12-01), p. L22-
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 (10 7 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.
Type of Medium:
Online Resource
ISSN:
2041-8205
,
2041-8213
DOI:
10.3847/2041-8205/832/2/L22
Language:
Unknown
Publisher:
American Astronomical Society
Publication Date:
2016
detail.hit.zdb_id:
7233-3
detail.hit.zdb_id:
2006858-X