Algebraic and machine learning approach to hierarchical triple-star stability

• URL: http://arxiv.org/abs/2207.03151v1
• Date: Thu, 7 Jul 2022 08:29:17 GMT
• Title: Algebraic and machine learning approach to hierarchical triple-star stability
• Authors: Pavan Vynatheya, Adrian S. Hamers, Rosemary A. Mardling and Earl P. Bellinger
• Abstract summary: We present two approaches to determine the stability of a hierarchical triple-star system. The first is an improvement on the semi-analytical stability criterion of Mardling & Aarseth (2001), where we introduce a dependence on inner orbital eccentricity. The second involves a machine learning approach, where we use a multilayer perceptron (MLP) to classify triple-star systems as stable' and unstable'
• Score: 0.0
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: We present two approaches to determine the dynamical stability of a hierarchical triple-star system. The first is an improvement on the semi-analytical stability criterion of Mardling & Aarseth (2001), where we introduce a dependence on inner orbital eccentricity and improve the dependence on mutual orbital inclination. The second involves a machine learning approach, where we use a multilayer perceptron (MLP) to classify triple-star systems as `stable' and `unstable'. To achieve this, we generate a large training data set of 10^6 hierarchical triples using the N-body code MSTAR. Both our approaches perform better than the original Mardling & Aarseth (2001) stability criterion, with the MLP model performing the best. The improved stability formula and the machine learning model have overall classification accuracies of 93 % and 95 % respectively. Our MLP model, which accurately predicts the stability of any hierarchical triple-star system within the parameter ranges studied with almost no computation required, is publicly available on Github in the form of an easy-to-use Python script.

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