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

Jie Song, GuanWen Fang, Shuo Ba, Zesen Lin, Yizhou Gu, Chichun Zhou, Tao Wang, Cai-Na Hao, Guilin Liu, Hongxin Zhang, Yao Yao, Xu Kong
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Abstract

Morphological classification conveys abundant information on the formation, evolution, and environment of galaxies. In this work, we refine a two-step galaxy morphological classification framework (USmorph), which employs a combination of unsupervised machine-learning and supervised machine-learning techniques, along with a self-consistent and robust data-preprocessing step. The updated method is applied to galaxies with I mag < 25 at 0.2 < z < 1.2 in the COSMOS field. Based on their Hubble Space Telescope/Advanced Camera for Survey I-band images, we classify them into five distinct morphological types: spherical (SPH, 15,200), early-type disk (17,369), late-type disk (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 109 M , a gradual increase in effective radius from SPHs to IRRs is observed, accompanied by a decrease in the Sérsic index. Nonparametric morphologies reveal distinct distributions of galaxies across the Gini−M 20 and CA parameter spaces for different categories. Moreover, different categories exhibit significant dissimilarity in their G 2 and Ψ 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.
USmorph: 更新的星系形态自动分类框架及其在 COSMOS 星场星系中的应用
形态分类传递了大量关于星系形成、演化和环境的信息。在这项工作中,我们改进了一个两步式星系形态分类框架(USmorph),该框架结合使用了无监督机器学习和有监督机器学习技术,以及一个自洽和稳健的数据预处理步骤。更新后的方法适用于 COSMOS 星场中 0.2 < z < 1.2 的 Imag < 25 的星系。根据它们的哈勃太空望远镜/高级巡天照相机 I 波段图像,我们把它们分为五种不同的形态类型:球形(SPH,15 200 个)、早期型盘状(17 369 个)、晚期型盘状(21 143 个)、不规则盘状(IRR,28 965 个)和未分类(UNC,17 129 个)。此外,我们还进行了参数和非参数形态测量。对于恒星质量超过109M☉的星系,我们观察到其有效半径从SPHs到IRRs逐渐增大,同时Sérsic指数下降。非参数形态揭示了不同类别星系在 Gini-M20 和 C-A 参数空间的不同分布。此外,不同类别的星系在其 G2 和 Ψ 分布上也表现出明显的差异。我们发现形态与红移和恒星质量密切相关。这些分类结果与预期的多参数之间的相关性相一致,强调了我们分类方法的有效性和可靠性,使其成为未来研究的宝贵工具。
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