Machine Learning for Galaxy Morphology Classification

TL;DR

This paper explores machine learning algorithms like decision trees and fuzzy logic to classify galaxy types using data from Galaxy Zoo and SDSS DR7, aiming for reliable galaxy morphology classifiers.

Contribution

It applies and compares decision tree and fuzzy logic algorithms for galaxy classification, introducing reliable classifiers based on extensive astronomical datasets.

Findings

Decision tree algorithms effectively classify galaxy morphologies.

Fuzzy logic systems provide robust classification performance.

The study demonstrates the applicability of machine learning in astronomy.

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.