Automatic Classification of Galaxy Morphology: a rotationally invariant supervised machine learning method based on the UML-dataset
Classification of galaxy morphology is a challenging but meaningful task for
the enormous amount of data produced by the next-generation telescope. By
introducing the adaptive polar coordinate transformation, we develop a
rotationally invariant supervised machine learning (SML) method that ensures
consistent classifications when rotating galaxy images, which is always
required to be satisfied physically but difficult to achieve algorithmically.
The adaptive polar coordinate transformation, compared with the conventional
method of data augmentation by including additional rotated images in the
training set, is proved to be an effective and efficient method in improving
the robustness of the SML methods. In the previous work, we generated a catalog
of galaxies with well-classified morphologies via our developed unsupervised
machine learning (UML) method. By using this UML-dataset as the training set,
we apply the new method to classify galaxies into five categories
(unclassifiable, irregulars, late-type disks, early-type disks, and spheroids).
In general, the result of our morphological classifications following the
sequence from irregulars to spheroids agrees well with the expected trends of
other galaxy properties, including S\'{e}rsic indices, effective radii,
nonparametric statistics, and colors. Thus, we demonstrate that the
rotationally invariant SML method, together with the previously developed UML
method, completes the entire task of automatic classification of galaxy
morphology.