Scientific Preparation for CSST: Classification of Galaxy and Nebula/Star Cluster Based on Deep Learning

• URL: http://arxiv.org/abs/2312.04948v1
• Date: Fri, 8 Dec 2023 10:27:40 GMT
• Title: Scientific Preparation for CSST: Classification of Galaxy and Nebula/Star Cluster Based on Deep Learning
• Authors: Yuquan Zhang, Zhong Cao, Feng Wang, Lam, Man I, Hui Deng, Ying Mei, and Lei Tan
• Abstract summary: We develop a deep learning model named HR-CelestialNet for classifying images of the galaxy and NSC. HR-CelestialNet achieved an accuracy of 89.09% on the testing set, outperforming models such as AlexNet, VGGNet and ResNet. The proposed method can enable real-time identification of celestial images during CSST survey mission.
• Score: 8.633077812513074
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: The Chinese Space Station Telescope (abbreviated as CSST) is a future advanced space telescope. Real-time identification of galaxy and nebula/star cluster (abbreviated as NSC) images is of great value during CSST survey. While recent research on celestial object recognition has progressed, the rapid and efficient identification of high-resolution local celestial images remains challenging. In this study, we conducted galaxy and NSC image classification research using deep learning methods based on data from the Hubble Space Telescope. We built a Local Celestial Image Dataset and designed a deep learning model named HR-CelestialNet for classifying images of the galaxy and NSC. HR-CelestialNet achieved an accuracy of 89.09% on the testing set, outperforming models such as AlexNet, VGGNet and ResNet, while demonstrating faster recognition speeds. Furthermore, we investigated the factors influencing CSST image quality and evaluated the generalization ability of HR-CelestialNet on the blurry image dataset, demonstrating its robustness to low image quality. The proposed method can enable real-time identification of celestial images during CSST survey mission.

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