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.

此论文提出了一种使用机器学习进行形态分类的方法，并在 SDSS DR6 目录中测试了该方法的效果，并发现利用自适应形状参数、浓度和纹理等参数可以改善算法分类效果，并证明使用 Galaxy Zoo 目录可以为下一代广域成像调查提供宝贵的训练集。