USmorph: An Updated Framework of Automatic Classification of Galaxy Morphologies and Its Application to Galaxies in the COSMOS Field

TL;DR

This paper presents an improved two-step machine learning framework for classifying galaxy morphologies in the COSMOS field, validated by multiple morphological parameters and correlations with galaxy properties.

Contribution

The study refines the USmorph framework by integrating robust preprocessing and applying it to classify galaxies into five types using HST/ACS images, with comprehensive morphological analysis.

Findings

Galaxies show increasing effective radius from spherical to irregular types.

Distinct distributions in Gini-M20 and C-A spaces for different morphological categories.

Morphology correlates strongly with redshift and stellar mass.

Abstract

Morphological classification conveys abundant information on the formation, evolution, and environment of galaxies. In this work, we refine the two-step galaxy morphological classification framework ({\tt\string USmorph}), which employs a combination of unsupervised machine learning (UML) and supervised machine learning (SML) techniques, along with a self-consistent and robust data preprocessing step. The updated method is applied to the galaxies with $I_{\rm mag}<25$ at $0.2<z<1.2$ in the COSMOS field. Based on their HST/ACS I-band images, we classify them into five distinct morphological types: spherical (SPH, 15,200), early-type disk (ETD, 17,369), late-type disk (LTD, 21,143), irregular disk (IRR, 28,965), and unclassified (UNC, 17,129). In addition, we have conducted both parametric and nonparametric morphological measurements. For galaxies with stellar masses exceeding $10^{9}M_{\sun}$, a gradual increase in effective radius from SPHs to IRRs is observed, accompanied by a decrease in the S\'{e}rsic index. Nonparametric morphologies reveal distinct distributions of galaxies across the $Gini-M_{20}$ and $C-A$ parameter spaces for different categories. Moreover, different categories exhibit significant dissimilarity in their $G_2$ and $\Psi$ distributions. We find morphology to be strongly correlated with redshift and stellar mass. The consistency of these classification results with expected correlations among multiple parameters underscores the validity and reliability of our classification method, rendering it a valuable tool for future studies.