Predicting Long-Term stability of Extrasolar Systems with Machine Learning

Determining the long-term stability of planetary systems traditionally relies on direct orbital integrations, which are computationally intensive. Recent advances have demonstrated that machine learning (ML) models, when combined with short-timescale simulations, can reach desirable performance for robustly classifying stability in extrasolar systems, while significantly reducing computational cost. In this work, we explore multiple ML strategies: (i) decision tree-based ensembles incorporating features derived from analytical understanding of resonant dynamics in two-planet systems and (ii) deep learning models tailored for time series data in which we use latent representations learned by the model directly from the time evolution of the initial orbital conditions. We show the efficiency of each strategy, access feature importance for model interpretability, and emphasize the contribution of chaos indicators to stability prediction.