Issue |
A&A
Volume 693, January 2025
|
|
---|---|---|
Article Number | A141 | |
Number of page(s) | 12 | |
Section | Numerical methods and codes | |
DOI | https://doi.org/10.1051/0004-6361/202451734 | |
Published online | 13 January 2025 |
An efficient unsupervised classification model for galaxy morphology: Voting clustering based on coding from ConvNeXt large model
1
Institute of Astronomy and Astrophysics, Anqing Normal University,
Anqing
246133,
PR China
2
Department of Physics, The Chinese University of Hong Kong,
Shatin, N.T.,
Hong Kong S.A.R.,
China
3
School of Engineering, Dali University,
Dali
671003,
PR China
4
Department of Astronomy, University of Science and Technology of China,
Hefei
230026,
China
5
School of Astronomy and Space Science, University of Science and Technology of China,
Hefei
230026,
PR China
6
Tsung-Dao Lee Institute, and Key Laboratory for Particle Physics, Astrophysics and Cosmology, Ministry of Education, Shanghai Jiao Tong University,
Shanghai
200240,
China
★ Corresponding authors; wen@mail.ustc.edu.cn; zhouchichun@dali.edu.cn; xkong@ustc.edu.cn
Received:
31
July
2024
Accepted:
29
November
2024
By combining unsupervised and supervised machine learning methods, we have proposed a framework, called USmorph, to carry out automatic classifications of galaxy morphologies. In this work, we update the unsupervised machine learning (UML) step by proposing an algorithm based on ConvNeXt large model coding to improve the efficiency of unlabeled galaxy morphology classifications. The method can be summarized into three key aspects as follows: (1) a convolutional autoencoder is used for image denoising and reconstruction and the rotational invariance of the model is improved by polar coordinate extension; (2) uthilizing a pre-trained convolutional neural network (CNN) named ConvNeXt for encoding the image data. The features were further compressed via a principal component analysis (PCA) dimensionality reduction; (3) adopting a bagging-based multi-model voting classification algorithm to enhance robustness. We applied this model to I-band images of a galaxy sample with Imag < 25 in the COSMOS field. Compared to the original unsupervised method, the number of clustering groups required by the new method is reduced from 100 to 20. Finally, we managed to classify about 53% galaxies, significantly improving the classification efficiency. To verify the validity of the morphological classification, we selected massive galaxies with M* > 1010M⊙ for morphological parameter tests. The corresponding rules between the classification results and the physical properties of galaxies on multiple parameter surfaces are consistent with the existing evolution model. Our method has demonstrated the feasibility of using large model encoding to classify galaxy morphology, which not only improves the efficiency of galaxy morphology classification, but also saves time and manpower. Furthermore, in comparison to the original UML model, the enhanced classification performance is more evident in qualitative analysis and has successfully surpassed a greater number of parameter tests. The enhanced UML method will support the Chinese space station telescope in the future.
Key words: Galaxy: general / Galaxy: structure / galaxies: statistics
© The Authors 2025
Open Access article, published by EDP Sciences, under the terms of the Creative Commons Attribution License (https://creativecommons.org/licenses/by/4.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
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