发表机构
Institute for Astrophysics, School of Physics, Zhengzhou University; School of Physics and Astronomy, Beijing Normal University; Department of Physics “E. Pancini”, University Federico II; School of Physics and Astronomy, Sun Yat-sen University; School of Mechanical, Electrical and Information Engineering, Shandong University; Yunnan Observatories, Chinese Academy of Sciences（郑州大学物理学院天体物理研究所; 北京师范大学物理与天文学院; 那不勒斯费德里科二世大学E. Pancini物理系; 中山大学物理与天文学院; 山东大学机械电气信息工程学院; 中国科学院云南天文台）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
针对大规模巡天中星系形态分类依赖大量人工标注的问题，提出一种标签高效的自监督学习框架，利用305,583张未标注图像进行对比学习，仅用5,000张标注图像训练分类器，在KiDS数据上达到91.0%准确率，并生成首个包含310,583个星系的公开形态星表。
AI 中文摘要
星系形态分类是理解星系形成与演化的基础。大规模巡天的出现产生了前所未有的星系图像数量，使得传统的人工分类变得不切实际。尽管有监督深度学习可以达到高精度，但它需要大量标注数据集，而这些数据集的构建非常耗时。相比之下，无监督方法通常表现出有限的分类性能。为了解决这一局限，我们提出了一种标签高效的自监督学习框架用于星系形态分类。我们的方法首先通过对比学习从305,583张未标注的KiDS星系图像中学习稳健的形态表征，然后仅使用5,000张人工标注图像训练分类器。该分类器将星系分为五类：椭圆星系、旋涡星系、透镜盘星系、不规则星系和“其他”星系。使用裁剪尺寸为64x64像素的ResNet-50模型，我们的方法在人工分类星表上达到了高达91.0%的总体测试准确率（平均90.5% ± 0.2%）。椭圆、旋涡、不规则、透镜盘和“其他”星系对应的F1分数分别为0.96、0.86、0.86、0.95和0.92。我们将此流程应用于千度巡天数据发布5（Kilo-Degree Survey Data Release 5），并生成了一个包含310,583个星系的公开形态星表。这是首个针对KiDS星系的形态星表，为未来星系演化的研究提供了宝贵资源。我们的结果表明，自监督学习可以在保持高分类精度的同时大幅减少对人工标注的需求，使其在大规模巡天时代成为一种有前景且可扩展的自动星系形态分类方法。
英文摘要
Galaxy morphology classification is fundamental to understanding galaxy formation and evolution. The advent of large-scale sky surveys has produced an unprecedented volume of galaxy images, making traditional manual classification impractical. Although supervised deep learning can achieve high accuracy, it requires large labeled datasets that are time-consuming to construct. In contrast, unsupervised methods often show limited classification performance. To address this limitation, we propose a label-efficient self-supervised learning framework for galaxy morphology classification. Our method first learns robust morphological representations from 305,583 unlabeled KiDS galaxy images through contrastive learning, and then trains a classifier using only 5,000 human-labeled images. The classifier separates galaxies into five categories: elliptical, spiral, lenticular-disk, irregular, and "other." Using a ResNet-50 model with a crop size of 64x64 pixels, our approach achieves an overall test accuracy of up to 91.0% (90.5% +/- 0.2% on average) on the human-classified catalog. The corresponding F1 scores for elliptical, spiral, irregular, lenticular-disk, and "other" galaxies are 0.96, 0.86, 0.86, 0.95, and 0.92, respectively. We apply this pipeline to the Kilo-Degree Survey Data Release 5 and produce a publicly available morphology catalog of 310,583 galaxies. This is the first morphology catalog for KiDS galaxies and provides a valuable resource for future studies of galaxy evolution. Our results show that self-supervised learning can substantially reduce the need for manual labels while maintaining high classification accuracy, making it a promising and scalable approach for automated galaxy morphology classification in the era of large-scale surveys.
Comments20 pages, 9 figures
