Morphological Classification of Galaxies From the SDSS Using Machine Learning Techniques

• Clara Valle Gómez ^[1] ; Manuel Francisco González Penedo ^[1] ; Minia Manteiga Outeiro ^[1]
  1. [1] Universidade da Coruña

     Universidade da Coruña

     A Coruña, España

     
• Localización: Proceedings XoveTIC 2025: Impulsando el talento científico / coord. por Manuel Lagos Rodríguez, Hilda Romero-Velo, Álvaro Leitao , Javier Pereira-Loureiro , Manuel Penedo , 2026, ISBN 978-84-9749-925-5, págs. 407-413
• Idioma: inglés
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• Resumen

  • 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.