Quadruple-star systems are not always nested triples: a machine learning approach to dynamical stability

TL;DR

This study introduces machine learning models to classify the long-term stability of quadruple-star systems, outperforming traditional nested triple approaches and providing a fast, accurate tool for astrophysical population analysis.

Contribution

The paper presents the first application of machine learning to directly classify the stability of quadruple-star systems, surpassing nested triple models in accuracy.

Findings

MLP models achieve 94% and 93% accuracy for 2+2 and 3+1 quadruples.

MLP models outperform nested triple approaches, especially for 3+1 quadruples.

Models are simple, fast, and publicly available on GitHub.

Abstract

The dynamical stability of quadruple-star systems has traditionally been treated as a problem involving two `nested' triples which constitute a quadruple. In this novel study, we employed a machine learning algorithm, the multi-layer perceptron (MLP), to directly classify 2+2 and 3+1 quadruples based on their stability (or long-term boundedness). The training data sets for the classification, comprised of $5\times10^5$ quadruples each, were integrated using the highly accurate direct $N$-body code MSTAR. We also carried out a limited parameter space study of zero-inclination systems to directly compare quadruples to triples. We found that both our quadruple MLP models perform better than a `nested' triple MLP approach, which is especially significant for 3+1 quadruples. The classification accuracies for the 2+2 MLP and 3+1 MLP models are 94% and 93% respectively, while the scores for the `nested' triple approach are 88% and 66% respectively. This is a crucial implication for quadruple population synthesis studies. Our MLP models, which are very simple and almost instantaneous to implement, are available on GitHub, along with Python3 scripts to access them.