Machine Learning for Galaxy Morphology Classification

• DOI: 10.48550/arxiv.1005.0390
• Publication Date: 2010-01-01
• Authors: Adam Gauci; Kristian Zarb Adami; John Abela; Alessio Magro
• Keywords: Morphology

In this work, decision tree learning algorithms and fuzzy inferencing systems are applied for galaxy morphology classification. particular, the CART, C4.5, Random Forest logic studied reliable classifiers developed to distinguish between spiral galaxies, elliptical galaxies or star/unknown galactic objects. Morphology information training testing datasets is obtained from Galaxy Zoo project while corresponding photometric spectra parameters downloaded SDSS DR7 catalogue.