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

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 × 105 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.