Automatic Classification of Galaxy Morphology: A Rotationally-invariant Supervised Machine-learning Method Based on the Unsupervised Machine-learning Data Set
Abstract 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...
Saved in:
Published in | The Astronomical journal Vol. 165; no. 2; pp. 35 - 47 |
---|---|
Main Authors | , , , , , , , , , , |
Format | Journal Article |
Language | English |
Published |
Madison
The American Astronomical Society
01.02.2023
IOP Publishing |
Subjects | |
Online Access | Get full text |
Cover
Loading…
Summary: | Abstract
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 data set 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 Sérsic 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. |
---|---|
Bibliography: | Galaxies and Cosmology AAS40014 |
ISSN: | 0004-6256 1538-3881 |
DOI: | 10.3847/1538-3881/aca1a6 |