Galaxy morphology classification based on Convolutional vision Transformer (CvT)^★

¹ School of Mathematics and Computer Science, Yunnan Minzu University, Kunming, Yunnan 650504, PR China
e-mail: xutingting@ymu.edu.cn; linhua.deng@ymu.edu.cn; ynzwh@163.com
² Key Laboratory for the Structure and Evolution of Celestial Objects, Chinese Academy China of Sciences, Kunming, Yunnan 650011, PR China

Received: 10 November 2023
Accepted: 2 January 2024

Abstract

Context. The classification of galaxy morphology is among the most active fields in astronomical research today. With the development of artificial intelligence technology, deep learning is a useful tool in the classification of the morphology of galaxies and significant progress has been made in this domain. However, there is still some room for improvement in terms of classification accuracy, automation, and related issues.

Aims. Convolutional vision Transformer (CvT) is an improved version of the Vision Transformer (ViT) model. It improves the performance of the ViT model by introducing a convolutional neural network (CNN). This study explores the performance of the CvT model in the area of galaxy morphology classification.

Methods. In this work, the CvT model was applied, for the first time, in a five-class classification task of galaxy morphology. We added different types and degrees of noise to the original galaxy images to verify that the CvT model achieves good classification performance, even in galaxy images with low signal-to-noise ratios (S/Ns). Then, we also validated the classification performance of the CvT model for galaxy images at different redshifts based on the low-redshift dataset GZ2 and the high-redshift dataset Galaxy Zoo CANDELS. In addition, we visualized and analyzed the classification results of the CvT model based on the t-distributed stochastic neighborhood -embedding (t-SNE) algorithm.

Results. We find that (1) compared with other five-class classification models of galaxy morphology based on CNN models, the average accuracy, precision, recall, and F1_score evaluation metrics of the CvT classification model are all higher than 98%, which is an improvement of at least 1% compared with those based on CNNs; (2) the classification visualization results show that different categories of galaxies are separated from each other in multi-dimensional space.

Conclusions. The application of the CvT model to the classification study of galaxy morphology is a novel undertaking that carries important implications for future studies.