Image classification of retrograde resonance in the planar circular restricted three-body problem

The study of resonances in celestial mechanics is crucial for understanding the dynamics of planetary or stellar systems. This study focuses on presenting a method for investigating the topology and resonant structures of a dynamical system. To illustrate the strength of the method, we have applied our method to retrograde resonances in the planar circular restricted three-body problem within binary star systems. Because of the high mass ratio systems, the techniques based on perturbation of the two-body orbit are not the ideal to analyze the system. Consequently, resonant angles could be meaningless, necessitating alternative methods for resonance identification. To address this challenge, an image classification-based machine learning model is implemented to identify resonances based on the shape of orbits in the rotating frame. Initially, the model is trained on empirical cases with low mass ratios using the resonant angle as a starting point for resonance identification. The model’s performance is validated against existing literature results. The model results demonstrate successful classification and identification of retrograde resonances in both empirical and non-empirical cases. The model accurately captures the resonance patterns and provides initial insights into the short-term stability of the corresponding resonances.