Automatic detection of low surface brightness galaxies from Sloan Digital Sky Survey images

Yi, Zhenping; Li, Jia; Du, Wei; Liu, Meng; Liang, Zengxu; Xing, Yongguang; Pan, Jingchang; Bu, Yude; Kong, Xiaoming; Wu, Hong

doi:10.1093/mnras/stac775

Title: Automatic detection of low surface brightness galaxies from Sloan Digital Sky Survey images

Journal Article · Wed Mar 23 00:00:00 UTC 2022 · Monthly Notices of the Royal Astronomical Society

DOI: https://doi.org/10.1093/mnras/stac775 · OSTI ID:1868131

Yi, Zhenping; Li, Jia; Du, Wei; Liu, Meng; Liang, Zengxu; Xing, Yongguang; Pan, Jingchang; Bu, Yude; Kong, Xiaoming; Wu, Hong

ABSTRACT Low surface brightness (LSB) galaxies are galaxies with central surface brightness fainter than the night sky. Due to the faint nature of LSB galaxies and the comparable sky background, it is difficult to search LSB galaxies automatically and efficiently from large sky survey. In this study, we established the low surface brightness galaxies autodetect (LSBG-AD) model, which is a data-driven model for end-to-end detection of LSB galaxies from Sloan Digital Sky Survey (SDSS) images. Object-detection techniques based on deep learning are applied to the SDSS field images to identify LSB galaxies and estimate their coordinates at the same time. Applying LSBG-AD to 1120 SDSS images, we detected 1197 LSB galaxy candidates, of which 1081 samples are already known and 116 samples are newly found candidates. The B-band central surface brightness of the candidates searched by the model ranges from 22 to 24 mag arcsec−2, quite consistent with the surface brightness distribution of the standard sample. A total of 96.46 per cent of LSB galaxy candidates have an axial ratio (b/a) greater than 0.3, and 92.04 per cent of them have $$fracDev\_r$$ < 0.4, which is also consistent with the standard sample. The results show that the LSBG-AD model learns the features of LSB galaxies of the training samples well, and can be used to search LSB galaxies without using photometric parameters. Next, this method will be used to develop efficient algorithms to detect LSB galaxies from massive images of the next-generation observatories.

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Research Organization:: US Department of Energy (USDOE), Washington, DC (United States). Office of Science, Sloan Digital Sky Survey (SDSS)

Sponsoring Organization:: National Science Foundation (NSF); USDOE

OSTI ID:: 1868131

Journal Information:: Monthly Notices of the Royal Astronomical Society, Journal Name: Monthly Notices of the Royal Astronomical Society Journal Issue: 3 Vol. 513; ISSN 0035-8711

Publisher:: Oxford University PressCopyright Statement

Country of Publication:: United Kingdom

Language:: English

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