We present morphological classifications obtained using machine learning for
objects in SDSS DR6 that have been classified by Galaxy Zoo into three classes,
namely early types, spirals and point sources/artifacts. 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/artifacts. Using a set of
twelve parameters, the neural network is able to reproduce the human
classifications to better than 90% 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.
Description
Galaxy Zoo: Reproducing Galaxy Morphologies Via Machine Learning
%0 Generic
%1 banerji2009galaxy
%A Banerji, Manda
%A Lahav, Ofer
%A Lintott, Chris J.
%A Abdalla, Filipe B.
%A Schawinski, Kevin
%A Bamford, Steven P.
%A Andreescu, Dan
%A Murray, Phil
%A Raddick, M. Jordan
%A Slosar, Anze
%A Szalay, Alex
%A Thomas, Daniel
%A Vandenberg, Jan
%D 2009
%K galaxy morphologies zoo
%T Galaxy Zoo: Reproducing Galaxy Morphologies Via Machine Learning
%U http://arxiv.org/abs/0908.2033
%X We present morphological classifications obtained using machine learning for
objects in SDSS DR6 that have been classified by Galaxy Zoo into three classes,
namely early types, spirals and point sources/artifacts. 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/artifacts. Using a set of
twelve parameters, the neural network is able to reproduce the human
classifications to better than 90% 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.
@misc{banerji2009galaxy,
abstract = {We present morphological classifications obtained using machine learning for
objects in SDSS DR6 that have been classified by Galaxy Zoo into three classes,
namely early types, spirals and point sources/artifacts. 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/artifacts. Using a set of
twelve parameters, the neural network is able to reproduce the human
classifications to better than 90% 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.},
added-at = {2014-04-30T01:49:00.000+0200},
author = {Banerji, Manda and Lahav, Ofer and Lintott, Chris J. and Abdalla, Filipe B. and Schawinski, Kevin and Bamford, Steven P. and Andreescu, Dan and Murray, Phil and Raddick, M. Jordan and Slosar, Anze and Szalay, Alex and Thomas, Daniel and Vandenberg, Jan},
biburl = {https://www.bibsonomy.org/bibtex/2b24c9c030e63d5b6736a8dc408f907d5/mattdelhey},
description = {Galaxy Zoo: Reproducing Galaxy Morphologies Via Machine Learning},
interhash = {75de2610cda8a932a6e8aab1185a57f3},
intrahash = {b24c9c030e63d5b6736a8dc408f907d5},
keywords = {galaxy morphologies zoo},
note = {cite arxiv:0908.2033Comment: 13 Pages, 5 figures, 10 tables. Accepted for publication in MNRAS. Revised to match accepted version.},
timestamp = {2014-04-30T01:49:00.000+0200},
title = {Galaxy Zoo: Reproducing Galaxy Morphologies Via Machine Learning},
url = {http://arxiv.org/abs/0908.2033},
year = 2009
}