Predicting the Stability of Hierarchical Triple Systems with Convolutional Neural Networks
This work addresses a computational bottleneck in astrophysics for researchers studying triple star systems, offering a significant speed-up but is incremental as it applies an existing machine learning method to a new domain-specific problem.
The authors tackled the challenge of predicting the stability of hierarchical triple systems, which is computationally expensive due to chaotic dynamics, by developing a convolutional neural network model that achieves over 95% area under the curve and predicts stability 200 times faster than traditional N-body methods.
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.