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.

翻译：土星卫星系统的动力学为研究轨道稳定性与共振相互作用提供了丰富的框架。分析此类系统的传统方法（包括傅里叶分析与稳定性度量）难以应对现代数据集的规模与复杂性。本研究提出了一种基于机器学习的流程，用于对约22,300条模拟卫星轨道进行聚类，通过先进的特征提取与降维技术应对这些挑战。该方法的关键在于使用MiniRocket，它能将400个时间步高效转换为9,996维特征空间，从而捕捉复杂的时间模式。额外的自动化特征提取与降维技术进一步优化了数据，实现了稳健的聚类分析。该流程揭示了土星卫星系统中的稳定区域、共振结构及其他关键行为，为其长期动力学演化提供了新的见解。通过将计算工具与传统天体力学技术相结合，本研究为分析大规模轨道数据集及推进行星动力学探索提供了一种可扩展且可解释的方法论。