Morphology classification for galaxies in the Kilo Degree Survey using a label-efficient self-supervised learning framework
Abstract
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.
Versions
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BibTeX
@misc{huang2026morphology,
title = {{Morphology classification for galaxies in the Kilo Degree Survey using a label-efficient self-supervised learning framework}},
author = {Xu Huang and Rui Li and Liang Gao and Liqing Chen and Hui Li and Huijun Mu and Hao Su and Fucheng Zhong and Zhenping Yi and Xiaoyue Cao and Ran Li and Haicheng Feng and Nicola N. Napolitano and Yue Dong and Yida Deng and Sihan Li and Kang Jiao},
howpublished = {arXiv},
year = {2026},
month = sep,
doi = {10.48550/arxiv.2609.21705},
eprint = {2609.21705},
archivePrefix = {arXiv},
url = {https://arxiv.org/abs/2609.21705},
}
RIS
TY - UNPB
TI - Morphology classification for galaxies in the Kilo Degree Survey using a label-efficient self-supervised learning framework
AU - Xu Huang
AU - Rui Li
AU - Liang Gao
AU - Liqing Chen
AU - Hui Li
AU - Huijun Mu
AU - Hao Su
AU - Fucheng Zhong
AU - Zhenping Yi
AU - Xiaoyue Cao
AU - Ran Li
AU - Haicheng Feng
AU - Nicola N. Napolitano
AU - Yue Dong
AU - Yida Deng
AU - Sihan Li
AU - Kang Jiao
PY - 2026
DA - 2026/09/18
PB - arXiv
DO - 10.48550/arxiv.2609.21705
UR - https://arxiv.org/abs/2609.21705
AB - 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.
ER -