Clustering Astronomical Orbital Synthetic Data Using Advanced Feature Extraction and Dimensionality Reduction Techniques
2026-03-13 • Artificial Intelligence
Artificial Intelligence
AI summaryⓘ
The authors studied the orbits of many simulated moons around Saturn to understand how their movements stay stable or get influenced by each other. Because traditional methods struggle with big datasets, they used a machine learning method called MiniRocket to turn the orbit data into detailed features that capture time patterns. Then, they simplified and grouped this data to find patterns showing which orbits are stable and how resonances appear. Their approach blends new computational techniques with classic astronomy methods to handle large amounts of orbital data effectively.
Saturn's satellitesorbital stabilityresonance interactionsmachine learningMiniRocketfeature extractiondimensionality reductionclustering analysiscelestial mechanicsdynamical evolution
Authors
Eraldo Pereira Marinho, Nelson Callegari Junior, Fabricio Aparecido Breve, Caetano Mazzoni Ranieri
Abstract
The dynamics of Saturn's satellite system offer a rich framework for studying orbital stability and resonance interactions. Traditional methods for analysing such systems, including Fourier analysis and stability metrics, struggle with the scale and complexity of modern datasets. This study introduces a machine learning-based pipeline for clustering approximately 22,300 simulated satellite orbits, addressing these challenges with advanced feature extraction and dimensionality reduction techniques. The key to this approach is using MiniRocket, which efficiently transforms 400 timesteps into a 9,996-dimensional feature space, capturing intricate temporal patterns. Additional automated feature extraction and dimensionality reduction techniques refine the data, enabling robust clustering analysis. This pipeline reveals stability regions, resonance structures, and other key behaviours in Saturn's satellite system, providing new insights into their long-term dynamical evolution. By integrating computational tools with traditional celestial mechanics techniques, this study offers a scalable and interpretable methodology for analysing large-scale orbital datasets and advancing the exploration of planetary dynamics.