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Publication Details
AFRICAN RESEARCH NEXUS
SHINING A SPOTLIGHT ON AFRICAN RESEARCH
earth and planetary sciences
Galaxy Zoo: Reproducing galaxy morphologies via machine learning
Monthly Notices of the Royal Astronomical Society, Volume 406, No. 1, Year 2010
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Description
We present morphological classifications obtained using machine learning for objects in the Sloan Digital Sky Survey DR6 that have been classified by Galaxy Zoo into three classes, namely early types, spirals and point sources/artefacts. An artificial neural network is trained on a subset of objects classified by the human eye, and we test whether the machine-learning algorithm can reproduce the human classifications for the rest of the sample. We find that the success of the neural network in matching the human classifications depends crucially on the set of input parameters chosen for the machine-learning algorithm. The colours and parameters associated with profile fitting are reasonable in separating the objects into three classes. However, these results are considerably improved when adding adaptive shape parameters as well as concentration and texture. The adaptive moments, concentration and texture parameters alone cannot distinguish between early type galaxies and the point sources/artefacts. Using a set of 12 parameters, the neural network is able to reproduce the human classifications to better than 90 per cent for all three morphological classes. We find that using a training set that is incomplete in magnitude does not degrade our results given our particular choice of the input parameters to the network. We conclude that it is promising to use machine-learning algorithms to perform morphological classification for the next generation of wide-field imaging surveys and that the Galaxy Zoo catalogue provides an invaluable training set for such purposes. © 2010 The Authors. Journal compilation © 2010 RAS.
Authors & Co-Authors
Banerji, Manda
United Kingdom, London
University College London
United Kingdom, Cambridge
University of Cambridge
Lahav, Ofer
United Kingdom, London
University College London
Lintott, Chris J.
United Kingdom, Oxford
Denys Wilkinson Building
Abdalla, Fillipe Batoni
United Kingdom, London
University College London
Schawinski, Kevin
United States, New Haven
Yale University
Bamford, Steven P.
United Kingdom, Nottingham
University of Nottingham
Slosar, Anže Že
United States, Berkeley
University of California, Berkeley
Szalay, Alexander S.
United States, Baltimore
Johns Hopkins University
Thomas, Daniel B.
United Kingdom, Portsmouth
University of Portsmouth
Statistics
Citations: 152
Authors: 9
Affiliations: 8
Identifiers
Doi:
10.1111/j.1365-2966.2010.16713.x
ISSN:
00358711
Research Areas
Health System And Policy
Study Design
Cross Sectional Study
Case-Control Study
Study Approach
Quantitative