Automatic Classification of Galaxy Morphology: a rotationally invariant supervised machine learning method based on the UML-dataset

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-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 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.

18 pages, 13 figures, To be published in AJ

References in corpus (13)

• 3D-HST+CANDELS: The Evolution of the Galaxy Size-Mass Distribution since $z=3$
• 3D-HST WFC3-selected Photometric Catalogs in the Five CANDELS/3D-HST Fields: Photometry, Photometric Redshifts and Stellar Masses
• Detection of quiescent galaxies in a bicolor sequence from z=0-2
• Rotation-invariant convolutional neural networks for galaxy morphology prediction
• Galaxy Zoo 1 : Data Release of Morphological Classifications for nearly 900,000 galaxies
• The Evolution of Galaxy Structure over Cosmic Time
• A robust morphological classification of high-redshift galaxies using support vector machines on seeing limited images. I Method description
• Deep-HiTS: Rotation Invariant Convolutional Neural Network for Transient Detection
• Galaxy Zoo: Quantitative Visual Morphological Classifications for 48,000 galaxies from CANDELS
• Galaxy Morphological Classification Catalogue of the Dark Energy Survey Year 3 data with Convolutional Neural Networks
• Pushing automated morphological classifications to their limits with the Dark Energy Survey
• The Morphology-Density relationship in 1<z<2 clusters
• Automatic morphological classification of galaxies: convolutional autoencoder and bagging-based multiclustering model

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• COSMOS2025: Machine Learning Classification of Early- and Late-type Galaxies at 0 < z < 3
• Neural network ensemble for computing cross sections for rotational transitions in H${}_2$O + H${}_2$O collisions