Low surface brightness galaxies from BASS+MzLS with Machine Learning
Peng-Liang Du, Wei Du, Bing-Qing Zhang, Zhen-Ping Yi, Min He, Hong Wu

TL;DR
This study identifies and characterizes a large sample of low surface brightness galaxies from BASS+MzLS surveys using machine learning, revealing distinct populations with different colors, morphologies, and spatial distributions.
Contribution
It introduces a new, extensive sample of LSBGs from BASS+MzLS, extending previous SDSS-based studies to fainter and lower surface brightness regimes using machine learning techniques.
Findings
LSBGs show bimodal color distribution indicating two populations.
Red LSBGs are more clustered and spheroidal, blue are more uniform and disk-like.
Sample extends LSBG studies to lower brightness and fainter magnitudes.
Abstract
From 5000 deg of the combination of the Beijing-Arizona Sky Survey (BASS) and Mayall -band Legacy Survey (MzLS) which is also the northern sky region of the Dark Energy Spectroscopic Instrument (DESI) Legacy Imaging Surveys, we selected a sample of 31,825 candidates of low surface brightness galaxies (LSBGs) with the mean effective surface brightness 24.2 28.8 mag arcsec and the half-light radius 2.5 20 based on the released photometric catalogue and the machine learning model. The distribution of the LSBGs is of bimodality in the - color, indicating the two distinct populations of the blue ( - 0.60) and the red ( - 0.60) LSBGs. The blue LSBGs appear spiral, disk or irregular while the red LSBGs are spheroidal or ellipitcal and spatially clustered. This…
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