Low Surface Brightness Galaxies Selected by Different Model Fitting

Bing-Qing Zhang,Hong Wu,Wei Du,Pin-Song Zhao, Min He,Feng-Jie Lei

RESEARCH IN ASTRONOMY AND ASTROPHYSICS(2024)

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摘要
We present a study of low surface brightness galaxies (LSBGs) selection by fitting the images for all the galaxies in alpha.40 SDSS DR7 sample with two kinds of single-component model and two kinds of two-component model (disk+bulge): single exponential, single s & eacute;rsic, exponential+deVaucular (exp+deV), and exponential+s & eacute;rsic (exp+ser). Under the criteria of the B band disk central surface brightness mu (0,disk)(B) >= 22.5 mag arcsec(-2) and the axis ratio b/a > 0.3, we selected four none-edge-on LSBG samples from each of the models which contains 1105, 1038, 207, and 75 galaxies, respectively. There are 756 galaxies in common between LSBGs selected by exponential and s & eacute;rsic models, corresponding to 68.42% of LSBGs selected by exponential model and 72.83% of LSBGs selected by s & eacute;rsic model, the rest of the discrepancy is due to the difference in obtaining mu 0 between the exponential and s & eacute;rsic models. Based on the fitting, in the range of 0.5 <= n <= 1.5, the relation of mu (0) from two models can be written as mu (0,sersic) - mu (0,exp) = -1.345(n - 1). The LSBGs selected by disk+bulge models (LSBG_2comps) are more massive than LSBGs selected by single-component models (LSBG_1comp), and also show a larger disk component. Though the bulges in the majority of our LSBG_2comps are not prominent, more than 60% of our LSBG_2comps will not be selected if we adopt a single-component model only. We also identified 31 giant low surface brightness galaxies (gLSBGs) from LSBG_2comps. They locate at the same region in the color-magnitude diagram as other gLSBGs. After we compared different criteria of gLSBGs selection, we find that for gas-rich LSBGs, M-star > 10(10)M(circle dot) is the best to distinguish between gLSBGs and normal LSBGs with bulge.
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关键词
catalogs,galaxies: spiral,galaxies: bulges,methods: data analysis,methods: statistical
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