The Classification of Galaxy Morphology in H-band of COSMOS-DASH Field: a combination-based machine learning clustering model
Yao Dai, Jun Xu, Jie Song, Guanwen Fang, Chichun Zhou, Shuo Ba, Yizhou, Gu, Zesen Lin, and Xu Kong

TL;DR
This paper presents a comprehensive galaxy morphology catalog for the COSMOS-DASH field using a novel combination of unsupervised and supervised machine learning methods, achieving reliable classification of over 17,000 galaxies.
Contribution
The study introduces a two-step machine learning scheme combining clustering and deep learning for galaxy morphology classification, improving completeness and accuracy.
Findings
Successfully classified 48\% of galaxies with clustering
Galaxy morphologies correlate with Sersic index and effective radius
Galaxies are well separated in G--M20 space
Abstract
By applying our previously developed two-step scheme for galaxy morphology classification, we present a catalog of galaxy morphology for H-band selected massive galaxies in the COSMOS-DASH field, which includes 17292 galaxies with stellar mass at . The classification scheme is designed to provide a complete morphology classification for galaxies via a combination of two machine-learning steps. We first use an unsupervised machine learning method (i.e., bagging-based multi-clustering) to cluster galaxies into five categories: spherical (SPH), early-type disk (ETD), late-type disk (LTD), irregular (IRR), and unclassified (UNC). About 48\% of galaxies (8258/17292) are successfully clustered during this step. For the remaining sample, we adopt a supervised machine learning method (i.e., GoogLeNet) to classify them, during which galaxies that are…
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Taxonomy
TopicsData Visualization and Analytics · Face and Expression Recognition
