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Title:
Galaxy Zoo: reproducing galaxy morphologies via machine learning
Authors:
Banerji, Manda; Lahav, Ofer; Lintott, Chris J.; Abdalla, Filipe B.; Schawinski, Kevin; Bamford, Steven P.; Andreescu, Dan; Murray, Phil; Raddick, M. Jordan; Slosar, Anze; Szalay, Alex; Thomas, Daniel; Vandenberg, Jan
Affiliation:
AA(Department of Physics and Astronomy, University College London, Gower Street, London WC1E 6BT; Institute of Astronomy, University of Cambridge, Madingley Road, Cambridge CB3 0HA), AB(Department of Physics and Astronomy, University College London, Gower Street, London WC1E 6BT), AC(Department of Physics, Denys Wilkinson Building, Keble Road, Oxford OX1 3RH), AD(Department of Physics and Astronomy, University College London, Gower Street, London WC1E 6BT), AE(Department of Physics, Yale University, New Haven, CT 06511, USA; Yale Center for Astronomy & Astrophysics, Yale University, PO Box 208121, New Haven, CT 06520, USA), AF(Centre for Astronomy and Particle Theory, School of Physics and Astronomy, University of Nottingham, University Park, Nottingham NG7 2RD), AG(LinkLab, 4506 Graystone Avenue, Bronx, NY 10471, USA), AH(Fingerprint Digital Media, 9 Victoria Close, Newtownards, Co. Down, Northern Ireland BT23 7GY), AI(Department of Physics and Astronomy, Johns Hopkins University, 3400 N. Charles Street, Baltimore, MD 21218, USA), AJ(Berkeley Center for Cosmological Physics, Lawrence Berkeley National Laboratory & Physics Department, University of California, Berkeley, CA 94720, USA), AK(Department of Physics and Astronomy, Johns Hopkins University, 3400 N. Charles Street, Baltimore, MD 21218, USA), AL(Institute of Cosmology and Gravitation, University of Portsmouth, Mercantile House, Hampshire Terrace, Portsmouth, Hants PO1 2EG), AM(Department of Physics and Astronomy, Johns Hopkins University, 3400 N. Charles Street, Baltimore, MD 21218, USA)
Publication:
Monthly Notices of the Royal Astronomical Society, Volume 406, Issue 1, pp. 342-353. (MNRAS Homepage)
Publication Date:
07/2010
Origin:
WILEY
Astronomy Keywords:
methods: data analysis, galaxies: general
Abstract Copyright:
(c) Journal compilation © 2010 RAS
DOI:
10.1111/j.1365-2966.2010.16713.x
Bibliographic Code:
2010MNRAS.406..342B

Abstract

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.

This publication has been made possible by the participation of more than 100000 volunteers in the Galaxy Zoo project. Their contributions are individually acknowledged at http://www.galaxyzoo.org/Volunteers.aspx.

E-mail: mbanerji@ast.cam.ac.uk ‡

Einstein Fellow.


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