Machine Learning for Galaxy Morphology Classification

Abstract

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

References in corpus (3)

• Spectroscopic Target Selection in the Sloan Digital Sky Survey: The Main Galaxy Sample
• Galaxy Zoo : Morphologies derived from visual inspection of galaxies from the Sloan Digital Sky Survey
• Galaxy Zoo: the dependence of morphology and colour on environment

Cited by in corpus (6)

• Galaxy Morphology Classification with Deep Convolutional Neural Networks
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• Galaxy And Mass Assembly: Automatic Morphological Classification of Galaxies Using Statistical Learning
• Machine Learning in Compiler Optimisation
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• Galaxy Morphological Classification with Efficient Vision Transformer