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.
Su, Hao, et al. "LSBGnet: an improved detection model for low-surface brightness galaxies." Monthly Notices of the Royal Astronomical Society, vol. 528, no. 1, Jan. 2024. https://doi.org/10.1093/mnras/stae001
Su, Hao, Yi, Zhenping, Liang, Zengxu, Du, Wei, Liu, Meng, Kong, Xiaoming, Bu, Yude, & Wu, Hong (2024). LSBGnet: an improved detection model for low-surface brightness galaxies. Monthly Notices of the Royal Astronomical Society, 528(1). https://doi.org/10.1093/mnras/stae001
Su, Hao, Yi, Zhenping, Liang, Zengxu, et al., "LSBGnet: an improved detection model for low-surface brightness galaxies," Monthly Notices of the Royal Astronomical Society 528, no. 1 (2024), https://doi.org/10.1093/mnras/stae001
@article{osti_2282230,
author = {Su, Hao and Yi, Zhenping and Liang, Zengxu and Du, Wei and Liu, Meng and Kong, Xiaoming and Bu, Yude and Wu, Hong},
title = {LSBGnet: an improved detection model for low-surface brightness galaxies},
annote = {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.},
doi = {10.1093/mnras/stae001},
url = {https://www.osti.gov/biblio/2282230},
journal = {Monthly Notices of the Royal Astronomical Society},
issn = {ISSN 0035-8711},
number = {1},
volume = {528},
place = {United Kingdom},
publisher = {Oxford University Press},
year = {2024},
month = {01}}
Monthly Notices of the Royal Astronomical Society, Journal Name: Monthly Notices of the Royal Astronomical Society Journal Issue: 1 Vol. 528; ISSN 0035-8711