Galactic Component Mapping of Galaxy UGC 2885 by Machine Learning Classification
Machine learning models, especially Support Vector Machine (SVM) and Random Forest (RF), were used to classify components within high-resolution Hubble imagery of galaxy UGC 2885. Distance and texture parameters proved most important for accurate classification. This method successfully maps the galaxy's spatial structure using HST data, suggesting ML is a useful tool for automated galactic component mapping.