Affiliation:
1. School of Mechanical, Electrical and Information Engineering, Shandong University , 180 Wenhua Xilu, Weihai, 264209 Shandong , China
2. CAS Key Laboratory of Optical Astronomy, National Astronomical Observatories , Beijing 100101 , China
3. School of Mathematics and Statistics, Shandong University , 180 Wenhua Xilu, Weihai 264209, Shandong , China
Abstract
ABSTRACT
The Chinese Space Station Telescope (CSST) is scheduled to launch soon, which is expected to provide a vast amount of image potentially containing low-surface brightness galaxies (LSBGs). However, detecting and characterizing LSBGs is known to be challenging due to their faint surface brightness, posing a significant hurdle for traditional detection methods. In this paper, we propose LSBGnet, a deep neural network specifically designed for automatic detection of LSBGs. We established LSBGnet-SDSS model using data set from the Sloan Digital Sky Survey (SDSS). The results demonstrate a significant improvement compared to our previous work, achieving a recall of 97.22 per cent and a precision of 97.27 per cent on the SDSS test set. Furthermore, we use the LSBGnet-SDSS model as a pre-training model, employing transfer learning to retrain the model with LSBGs from Dark Energy Survey (DES), and establish the LSBGnet-DES model. Remarkably, after retraining the model on a small DES sample, it achieves over 90 per cent precision and recall. To validate the model’s capabilities, we utilize the trained LSBGnet-DES model to detect LSBG candidates within a selected 5 sq. deg area in the DES footprint. Our analysis reveals the detection of 204 LSBG candidates, characterized by a mean surface brightness range of $23.5\ \mathrm{ mag}\ \mathrm{ arcsec}^{-2}\le \bar{\mu }_{\text{eff}}(g)\le 26.8\ \mathrm{ mag}\ \mathrm{ arcsec}^{-2}$ and a half-light radius range of 1.4 arcsec ≤ r1/2 ≤ 8.3 arcsec. Notably, 116 LSBG candidates exhibit a half-light radius ≥2.5 arcsec. These results affirm the remarkable performance of our model in detecting LSBGs, making it a promising tool for the upcoming CSST.
Funder
Shandong Province Natural Science Foundation
National Natural Science Foundation of China
Youth Innovation Promotion Association
Chinese Academy of Sciences
CAS
Natural Science Foundation of Shandong Province
Alfred P. Sloan Foundation
National Science Foundation
NASA
Max Planck Society
Higher Education Funding Council for England
U.S. Department of Energy
National Center for Supercomputing Applications
Financiadora de Estudos e Projetos
Conselho Nacional de Desenvolvimento Científico e Tecnológico
Ministério da Ciência, Tecnologia e Inovação
Deutsche Forschungsgemeinschaft
Publisher
Oxford University Press (OUP)