Scientific Preparation for CSST: Classification of Galaxy and
  Nebula/Star Cluster Based on Deep Learning
Yuquan Zhang Zhong Cao Feng Wang Lam Man I Hui Deng Ying Mei Lei Tan
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.