Interactive Visual Analytics for High-Dimensional Stellar Substructure Identification

One prominent field in astronomy seeks to uncover the formation and evolution of the Milky Way based on the large and high-dimensional surveys, thereby deepening our understanding of the Universe. In this context, stellar streams and star clusters are key tracers of the Galaxy's assembly history. However, they are challenging to isolate in complex, noisy datasets. The Star's Galactic Origin (StarGO) uses a self-organizing map (SOM) with an adaptive clustering algorithm to identify the galactic origins of halo stars in high-dimensional space. Despite its effectiveness in detecting stellar substructures, the original StarGO results are difficult to interpret due to its clustering process. To address these challenges, we developed an interactive visual analytics system that overcomes this limitation by integrating high-dimensional reduction, adaptive grouping, and multi-view dynamic visualization into a unified interactive workflow. Users can examine spatial, kinematic, and chemical features of halo stars step by step while visually tracing StarGO's grouping procedure, making the method far more interpretable and transparent. By transforming StarGO into a visual and exploratory platform, our system enhances its scientific usability and greatly improves the interpretability of machine-learning-based stellar population analysis. This interactive extension enables astronomers to more intuitively investigate the origins of stellar streams and clusters, providing a powerful tool for studying the formation and evolution of the Milky Way.