用机器学习分析2.2万条卫星轨道，发现稳定区与共振结构。
Clustering Astronomical Orbital Synthetic Data Using Advanced Feature Extraction and Dimensionality Reduction Techniques
- 用MiniRocket将400个时间点转为9996维特征，捕捉轨道动态模式。
- 从22,300条模拟轨道中识别出稳定区域和共振结构。
- 方法可扩展、可解释，适合大规模天体动力学研究。
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框架。传统分析方法如傅里叶分析和稳定性度量，在面对现代大规模数据集时面临挑战。本研究提出一种基于机器学习的管道，用于聚类约22,300条模拟卫星轨道，通过先进特征提取与降维技术应对这些难题。核心方法是使用MiniRocket，将400个时间步高效转换为9,996维特征空间，捕获复杂的时序模式。结合其他自动特征提取与降维技术，优化数据以实现稳健聚类分析。该管道揭示了土星卫星系统中的稳定性区域、共振结构及其他关键行为，为长期动力学演化提供新见解。通过融合计算工具与经典天体力学方法，本研究提供了一种可扩展且可解释的大规模轨道数据分析范式，推动行星动力学探索。
原文摘要 · Abstract (English)
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.
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