Morphological Classification of Galaxies From the SDSS Using Machine Learning Techniques

Abstract

[Abstract]: Given the vast amount of labeled data from surveys like SDSS and Galaxy Zoo 2, AI has become essential for galaxy morphology classification. Challenges such as redshift, low resolution, and similar shapes make even expert labeling difficult. We developed a hierarchical two-stage CNN model to classify galaxy images. The first model categorized them into four classes, merging the underrepresented and visually similar cigar-shaped and edge-on types. A second submodel was then trained to distinguish between these two classes, using data augmentation to address class imbalance. Using these approaches, we achieved 95\% accuracy, outperforming other research models using the same classes and demonstrating better classification accuracy and generalization on imbalanced datasets.