2018
DOI: 10.3847/1538-3881/aae647
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KMTNet Nearby Galaxy Survey. I. Optimal Strategy for Low Surface Brightness Imaging with KMTNet

Abstract: In hierarchical galaxy formation models, galaxies evolve through mergers and accretions. Tidally-disrupted debris from these processes can remain as diffuse, faint structures, which can provide useful insight into the assembly history of galaxies. To investigate the properties of the faint structures in outskirts of nearby galaxies, we conduct deep and wide-field imaging survey with KMTNet. We present our observing strategy and optimal data reduction process to recover the faint extended features in the imagin… Show more

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Cited by 8 publications
(11 citation statements)
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“…Meanwhile, the completeness rate appears to be much sensitive to the galaxy size or central surface brightness for M R −11 mag. This result is mainly due to confusion between small dwarf galaxies and massive background galaxies, and also partly induced by the 3σ surface brightness limit of ∼26.5 mag arcsec −2 in the mosaic image (see Byun et al 2018). The right panel of Figure 3 also shows the average completeness rates against central surface brightness and absolute magnitude.…”
Section: Completenessmentioning
confidence: 94%
See 3 more Smart Citations
“…Meanwhile, the completeness rate appears to be much sensitive to the galaxy size or central surface brightness for M R −11 mag. This result is mainly due to confusion between small dwarf galaxies and massive background galaxies, and also partly induced by the 3σ surface brightness limit of ∼26.5 mag arcsec −2 in the mosaic image (see Byun et al 2018). The right panel of Figure 3 also shows the average completeness rates against central surface brightness and absolute magnitude.…”
Section: Completenessmentioning
confidence: 94%
“…This strategy enabled us to obtain blank sky regions for dark-sky flat-fielding and also fill in the CCD gaps in the stacked images. The overall description of KMTNet Nearby Galaxy Survey (KNGS) project is also found in Byun et al (2018).…”
Section: Datamentioning
confidence: 99%
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“…Understanding the formation histories of galaxies is one of the major goals in extragalactic astronomy. Various approaches have been tried to achieve this goal, according to available data sets and more specific purposes: the analysis of star clusters in a galaxy (e.g., Dobbs & Pringle 2010;Forbes et al 2018;Forbes & Remus 2018;Ko et al 2018;Villaume et al 2019;Dolfi et al 2021), photometric profiling of individual galaxies (e.g., Head et al 2015;Kim et al 2015;Herrmann et al 2016;Bouquin et al 2018;Byun et al 2018;Watkins et al 2019;Roman-Oliveira et al 2021), tracing the properties of a bulk of galaxies as a function of cosmic time (e.g., van Dokkum et al 2010;Alberts et al 2016;Fossati et al 2017;Mundy et al 2017;Mowla et al 2019;Chu et al 2021), and so on. Such approaches often rely on statistics, which mainly look for a common history of galaxies in a similar type.…”
Section: Introductionmentioning
confidence: 99%