Low Surface Brightness Galaxies from BASS+MzLS with Machine Learning

From ∼5000 deg^(2) of the combination of the Beijing–Arizona Sky Survey and Mayall z-band Legacy Survey 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<μ_(eff.g)<28.8 mag arcsec^(−2) and the half-light radius 2.″5 < r_(eff)<20″ based on the released photometric catalog and the machine learning model. The distribution of the LSBGs is bimodal in the g−r color, indicating the two distinct populations of the blue (g−r< 0.60) and red (g−r> 0.60) LSBGs. The blue LSBGs appear spiral, disk or irregular while the red LSBGs are spheroidal or elliptical and spatially clustered. This trend shows that the color has a strong correlation with galaxy morphology for LSBGs. In the spatial distribution, the blue LSBGs are more uniformly distributed while the red ones are highly clustered, indicating that red LSBGs preferentially populate a denser environment than the blue LSBGs. Besides, both populations have a consistent distribution of ellipticity (median ∈∼ 0.3), half-light radius (median r_(eff) ∼ 4″) and Sérsic index (median n=1), implying the dominance of the full sample by the round and disk galaxies. This sample has definitely extended the studies of LSBGs to a regime of lower surface brightness, fainter magnitude and broader other properties than the previously Sloan Digital Sky Survey-based samples.

Galaxy properties derived with spectral energy distribution fitting in the Hawaii-Hubble Deep Field-North

We compile multi-wavelength data from ultraviolet to infrared(IR) bands as well as redshift and source-type information, for a large sample of 178 341 sources in the Hawaii-Hubble Deep Field-North field. A total of 145 635 sources among the full sample are classified/treated as galaxies and have redshift information available. We derive physical properties for these sources utilizing the spectral energy distribution fitting code CIGALE that is based on Bayesian analysis. Through various consistency and robustness checks, we find that our stellar-mass and star-formation rate(SFR) estimates are reliable, which is mainly due to two facts. Firstly, we adopt the most up-to-date and accurate redshifts and point spread functionmatched photometry; and secondly, we make sensible parameter choices with the CIGALE code and take into account the influences of mid-IR/far-IR data, star-formation history models, and AGN contribution. We release our catalog of galaxy properties publicly(including, e.g., redshift, stellar mass, SFR, age, metallicity, dust attenuation). It is the largest of its kind in this field and should facilitate future relevant studies on the formation and evolution of galaxies.