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Galaxy Zoo: reproducing galaxy morphologies via machine learning

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Galaxy Zoo : reproducing galaxy morphologies via machine learning. / 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.

In: Monthly Notices of the Royal Astronomical Society, Vol. 406, No. 1, 2010, p. 342-353.

Research output: Contribution to journalArticle

Harvard

Banerji, M, Lahav, O, Lintott, CJ, Abdalla, FB, Schawinski, K, Bamford, SP, Andreescu, D, Murray, P, Raddick, MJ, Slosar, A, Szalay, A, Thomas, D & Vandenberg, J 2010, 'Galaxy Zoo: reproducing galaxy morphologies via machine learning', Monthly Notices of the Royal Astronomical Society, vol. 406, no. 1, pp. 342-353. https://doi.org/10.1111/j.1365-2966.2010.16713.x

APA

Banerji, M., Lahav, O., Lintott, C. J., Abdalla, F. B., Schawinski, K., Bamford, S. P., Andreescu, D., Murray, P., Raddick, M. J., Slosar, A., Szalay, A., Thomas, D., & Vandenberg, J. (2010). Galaxy Zoo: reproducing galaxy morphologies via machine learning. Monthly Notices of the Royal Astronomical Society, 406(1), 342-353. https://doi.org/10.1111/j.1365-2966.2010.16713.x

Vancouver

Banerji M, Lahav O, Lintott CJ, Abdalla FB, Schawinski K, Bamford SP et al. Galaxy Zoo: reproducing galaxy morphologies via machine learning. Monthly Notices of the Royal Astronomical Society. 2010;406(1):342-353. https://doi.org/10.1111/j.1365-2966.2010.16713.x

Author

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. / Galaxy Zoo : reproducing galaxy morphologies via machine learning. In: Monthly Notices of the Royal Astronomical Society. 2010 ; Vol. 406, No. 1. pp. 342-353.

Bibtex

@article{178f2a892c4e47bdb9c1698b2d79f3bb,
title = "Galaxy Zoo: reproducing galaxy morphologies via machine learning",
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.",
author = "Manda Banerji and Ofer Lahav and Lintott, {Chris J.} and Abdalla, {Filipe B.} and Kevin Schawinski and Bamford, {Steven P.} and Dan Andreescu and Phil Murray and Raddick, {M. Jordan} and Anze Slosar and Alex Szalay and Daniel Thomas and Jan Vandenberg",
note = "{\textcopyright} 2010 The Authors. Journal compilation {\textcopyright} 2010 RAS",
year = "2010",
doi = "10.1111/j.1365-2966.2010.16713.x",
language = "English",
volume = "406",
pages = "342--353",
journal = "MNRAS",
issn = "0035-8711",
publisher = "Oxford University Press",
number = "1",

}

RIS

TY - JOUR

T1 - Galaxy Zoo

T2 - reproducing galaxy morphologies via machine learning

AU - Banerji, Manda

AU - Lahav, Ofer

AU - Lintott, Chris J.

AU - Abdalla, Filipe B.

AU - Schawinski, Kevin

AU - Bamford, Steven P.

AU - Andreescu, Dan

AU - Murray, Phil

AU - Raddick, M. Jordan

AU - Slosar, Anze

AU - Szalay, Alex

AU - Thomas, Daniel

AU - Vandenberg, Jan

N1 - © 2010 The Authors. Journal compilation © 2010 RAS

PY - 2010

Y1 - 2010

N2 - 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.

AB - 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.

U2 - 10.1111/j.1365-2966.2010.16713.x

DO - 10.1111/j.1365-2966.2010.16713.x

M3 - Article

VL - 406

SP - 342

EP - 353

JO - MNRAS

JF - MNRAS

SN - 0035-8711

IS - 1

ER -

ID: 67551