Predicting the Stability of Hierarchical Triple Systems with Convolutional Neural Networks

Abstract

Understanding the long-term evolution of hierarchical triple systems is challenging due to its inherent chaotic nature, and it requires computationally expensive simulations. Here we propose a convolutional neural network model to predict the stability of hierarchical triples by looking at their evolution during the first $5\times 10^5$ inner binary orbits. We employ the regularized few-body code TSUNAMI to simulate $5\times 10^6$ hierarchical triples, from which we generate a large training and test dataset. We develop twelve different network configurations that use different combinations of the triples' orbital elements and compare their performances. Our best model uses 6 time-series, namely, the semimajor axes ratio, the inner and outer eccentricities, the mutual inclination and the arguments of pericenter. This model achieves an area under the curve of over $95\%$ and informs of the relevant parameters to study triple systems stability. All trained models are made publicly available, allowing to predict the stability of hierarchical triple systems $200$ times faster than pure $N$-body methods.

12 pages, 6 figures, accepted for publication in ApJ

References in corpus (6)

• Satellite dynamics on the Laplace surface
• Generalized Hill-Stability Criteria for Hierarchical Three-Body Systems at Arbitrary Inclinations
• Multiple Stellar Evolution: a population synthesis algorithm to model the stellar, binary, and dynamical evolution of multiple-star systems
• Compact Object Mergers in Hierarchical Triples from Low-Mass Young Star Clusters
• Algebraic and machine learning approach to hierarchical triple-star stability
• A Simple Random-Walk Model Explains the Disruption Process of Hierarchical, Eccentric 3-Body Systems

Cited by in corpus (8)

• Merging Hierarchical Triple Black Hole Systems with Intermediate-mass Black Holes in Population III Star Clusters
• Dynamical disruption timescales and chaotic behavior of hierarchical triple systems
• Tracing the evolution of short-period binaries with super-synchronous fast rotators
• Lagrange vs. Lyapunov stability of hierarchical triple systems: dependence on the mutual inclination between inner and outer orbits
• A hybrid approach for solving the gravitational N-body problem with Artificial Neural Networks
• Interactions among binary black holes in star clusters: Eccentric gravitational wave captures and triple formation
• Exploring the parameter space of hierarchical triple black hole systems
• Sparse logistic regression for RR Lyrae vs binaries classification