Clustering Astronomical Orbital Synthetic Data Using Advanced Feature Extraction and Dimensionality Reduction Techniques

2026-03-13

AI Summary

Traditional approaches struggle to efficiently process the large-scale, high-dimensional orbital data of Saturn’s satellite system, hindering a deeper understanding of orbital stability and resonance structures. This work proposes a machine learning–based clustering framework that, for the first time, integrates advanced time-series feature extraction methods such as MiniRocket into astronomical orbital data analysis. By combining automated feature extraction with dimensionality reduction techniques (UMAP/t-SNE) and clustering algorithms (HDBSCAN/K-means), the method effectively characterizes approximately 22,300 simulated orbits. The approach successfully uncovers stable regions and resonant configurations within the system, offering an interpretable and scalable new paradigm for investigating the long-term dynamical evolution of Saturn’s satellites.

Technology Category

• Machine Learning: Clustering
• Data Mining & Knowledge Management: Anomaly/Outlier Detection
• Constraint Satisfaction and Optimization: Satisfiability Modulo Theories

Application Category

• Web Mining and Content Analysis: Normalization, clustering, classification, and summarization of Web text
• Graph Algorithms and Modeling for the Web: Representation, reconstruction, and subgraph or motif discovery in Web-related graphs
• Search and Retrieval-Augmented AI: Web query analysis, representation and understanding

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.

Problem

• orbital dynamics
• satellite system
• clustering
• dimensionality reduction
• feature extraction

Innovation

• MiniRocket
• feature extraction
• dimensionality reduction
• orbital clustering
• satellite dynamics