# KMTNet Nearby Galaxy Survey: Overview and a Survey Description

Woowon Byun Korea Astronomy and Space Science Institute, Daejeon 34055,
Republic of Korea  University of Science and Technology, Korea, Daejeon 34113,
Republic of Korea  Yun-Kyeong Sheen Korea Astronomy and Space Science
Institute, Daejeon 34055, Republic of Korea  Kwang-Il Seon Korea Astronomy and
Space Science Institute, Daejeon 34055, Republic of Korea  University of
Science and Technology, Korea, Daejeon 34113, Republic of Korea  Luis C. Ho
Kavli Institute for Astronomy and Astrophysics, Peking University, Beijing
100871, People’s Republic of China  Department of Astronomy, School of
Physics, Peking University, Beijing 100871, People’s Republic of China  Joon
Hyeop Lee Korea Astronomy and Space Science Institute, Daejeon 34055, Republic
of Korea  Hyunjin Jeong Korea Astronomy and Space Science Institute, Daejeon
34055, Republic of Korea  Sang Chul Kim Korea Astronomy and Space Science
Institute, Daejeon 34055, Republic of Korea  University of Science and
Technology, Korea, Daejeon 34113, Republic of Korea  Byeong-Gon Park Korea
Astronomy and Space Science Institute, Daejeon 34055, Republic of Korea
Yongseok Lee Korea Astronomy and Space Science Institute, Daejeon 34055,
Republic of Korea  School of Space Research, Kyung Hee University, Yongin,
Kyeonggi 17104, Republic of Korea  Sang-Mok Cha Korea Astronomy and Space
Science Institute, Daejeon 34055, Republic of Korea  School of Space Research,
Kyung Hee University, Yongin, Kyeonggi 17104, Republic of Korea  Minjin Kim
Department of Astronomy and Atmospheric Sciences, Kyungpook National
University, Daegu 41566, Republic of Korea

###### Abstract

Recently, there has been an increasing demand for deep imaging surveys to
investigate the history of the mass assembly of galaxies in detail by
examining the remnants of mergers and accretions, both of which have very low
surface brightness (LSB). In addition, the nature of star formation in LSB
regions, such as galaxy outer disks, is also an intriguing topic in terms of
understanding the physical mechanisms of disk evolution. To address these
issues, this study conducts a survey project, called the Korea Microlensing
Telescope Network (KMTNet) Nearby Galaxy Survey to construct a deep imaging
data set of nearby galaxies in the southern hemisphere using KMTNet. It
provides deep and wide-field images with a field-of-view of $\sim$12 deg2 for
13 nearby galaxies drawn from the Carnegie-Irvine Galaxy Survey catalog, in
optical broadbands ($BRI$) and an H$\alpha$ narrowband. Through a dedicated
data reduction, the surface brightness limit in 10${}^{\prime\prime}\times
10^{\prime\prime}$ boxes was found to reach as deep as $\mu_{1\sigma}\sim
29$–31 mag arcsec-2 in the optical broadbands and $f_{1\sigma}\sim 1$–$2\times
10^{-18}$ erg s-1 cm-2 arcsec-2 in the H$\alpha$ narrowband. To conclude the
paper, several possible scientific applications for this data set are
described.

Galaxy (573); Galaxy properties (615); Photometry (1234); Astronomy data
reduction (1861); Astronomy databases (83)

††journal: PASP††facilities: KMTNet, AAVSO, IRSA, NED††software: Astropy
(Astropy Collaboration et al., 2013, 2018), Scipy (Virtanen et al., 2020),
Source Extractor (Bertin & Arnouts, 1996), Photutils (Bradley et al., 2021),
SCAMP (Bertin, 2006), SWarp (Bertin et al., 2002), Matplotlib (Hunter, 2007)

##  1 Introduction

In the $\Lambda$CDM model, galaxies are formed and evolve in a hierarchical
manner, in the sense that they grow through mergers and the smooth accretion
of satellite galaxies (e.g., White & Frenk, 1991). In this model, low surface
brightness (LSB) features (e.g., stellar halos, extended star formations, and
dwarf galaxies) in the galaxy outskirts can be used to probe in detail the
history of mass assembly in the host galaxies (e.g., Bullock & Johnston,
2005). Therefore, studying the physical properties of these LSB features will
enable a better understanding of the recent evolution of the corresponding
galaxies. However, it is technically challenging to study LSB features
observationally due to their relative faintness compared with the underlying
sky brightness. For example, reaching a surface brightness of 27–28 mag
arcsec-2 requires suppression of the sky fluctuation down to 0.5%. In
addition, contamination from the point-spread function as well as scattered
light and dust in Galactic cirrus are non-negligible at this level of depth
(e.g., Tal & van Dokkum, 2011; Duc et al., 2015; Gilhuly et al., 2022).

Recently, several survey projects have been carried out to study LSB features
in nearby galaxies. For example, the Dragonfly Nearby Galaxies Survey obtained
deep and wide-field images of nearby galaxies using the Dragonfly Telephoto
Array, reaching up to 30–31 mag arcsec-2 (Abraham & van Dokkum, 2014). Based
on this survey, Merritt et al. (2016) and Zhang et al. (2018) demonstrated
that the stellar halo fractions among the spiral galaxies are diverse,
indicating that their formation history is non-uniform (see also Gilhuly et
al., 2022). In addition, a large number of dwarf galaxies and ultra-diffuse
galaxies have been identified by this survey, and further studies have been
extensively carried out to unveil their physical nature (e.g., van Dokkum et
al., 2015, 2016, 2017; Danieli et al., 2018; Gu et al., 2018; van Dokkum et
al., 2022). The great success of this unprecedented deep survey clearly
reveals that deep images of nearby galaxies are of importance in understanding
their evolutionary history.

Dwarf galaxies in particular can serve as key probes to test the $\Lambda$CDM
model on a small scale. The observed number of dwarf satellite galaxies is
substantially lower than that predicted by the theoretical studies, an issue
referred to as the missing satellites problem (e.g., Klypin et al., 1999;
Moore et al., 1999). To reconcile this discrepancy, several scenarios have
been proposed. One solution suggests that dark matter halos are composed of
warm dark matter rather than cold dark matter, helping to partly prevent the
formation of small scale structures (e.g., Bode et al., 2001; Nadler et al.,
2019). One alternative solution is that star formation (SF) in dwarf galaxies
is simply suppressed due to the ultraviolet (UV) background or supernova
explosions (e.g., Navarro et al., 1997; Dekel & Woo, 2003; Agertz et al.,
2020). Finally, it has long been suggested that this issue may arise from
observational incompleteness (e.g., Irwin et al., 2007; Simon & Geha, 2007;
Fattahi et al., 2020), as a substantial number of ultra-faint dwarf galaxies
have recently been discovered. However, previous studies have mostly relied on
the observational results from the Local Group (i.e., our Galaxy and M31).
Therefore, from an observational point of view, it is desirable to search for
the faint satellite galaxies of various types beyond the Local Group using
deep imaging.

One of the intriguing characteristics in the outskirts of nearby galaxies is
that the SF properties are clearly distinct from those in the inner regions.
For example, based on the Galaxy Evolution Explorer (GALEX) mission, Thilker
et al. (2005, 2007) reported a significant fraction of nearby disk galaxies
exhibiting a diffuse extended UV emission in their outer disks. These are
known as extended UV (XUV)-disk galaxies. This indicates that SF actively
occurs in the outer disk, and may serve as indirect evidence for inside-out
disk formation (e.g., Padave et al., 2021). While the physical origin of this
feature is still under debate (e.g., Gil de Paz et al., 2005; Dong et al.,
2008; Emami et al., 2019; Das et al., 2021), the flux ratio of H$\alpha$ to UV
in the XUV disks appears to be lower than that in typical star-forming regions
(e.g., Zaritsky & Christlein, 2007; Goddard et al., 2010). A similar
discrepancy between UV and H$\alpha$ has also been reported in dwarf galaxies
(e.g., Lee et al., 2009). Such an H$\alpha$ deficit in low-stellar-density
regions can be attributed to poor sampling of the initial mass function (IMF;
e.g., Fumagalli et al., 2011; da Silva et al., 2014), a non-uniform IMF (e.g.,
Meurer et al., 2009; Pflamm-Altenburg et al., 2009; Jeřábková et al., 2018;
Bruzzese et al., 2020; Kauffmann, 2021), a leakage of UV photons (e.g., Oey &
Kennicutt, 1997; Eldridge & Relaño, 2011; Relaño et al., 2012; Choi et al.,
2020), and a diverse SF history (SFH; e.g., Iglesias-Páramo et al., 2004;
Sullivan et al., 2004; Weisz et al., 2012; Emami et al., 2019). A detailed
analysis of the stellar population and SFH in XUV disks, estimated from deep
multi-band images and deep H$\alpha$ images, is crucial to exploring the SF
mechanism in XUV disks.

To extensively study the LSB features in nearby galaxies, this study conducts
a deep and wide-field imaging survey of nearby galaxies from the southern
hemisphere using the Korea Microlensing Telescope Network (KMTNet; Kim et al.,
2016). In this paper, an overview of the survey (the KMTNet Nearby Galaxy
Survey; KNGS) is presented. In Section 2, the KMTNet is briefly introduced and
the sample selection and observation strategy are described. A detailed data
reduction process used to minimize the sky fluctuation is presented in Section
3, and an assessment of the imaging quality is described in Section 4.
Finally, the potential scientific applications of the survey are summarized in
Section 5.

Table 1: Information on the Samples of KMTNet Nearby Galaxy Survey

Name | R.A.a | Decl.a | Distancea |  $m_{B}$a | Morphologya | $t_{B}^{\mathrm{tot}}$ | $t_{R}^{\mathrm{tot}}$ | $t_{I}^{\mathrm{tot}}$ | $t_{\mathrm{H\alpha}}^{\mathrm{tot}}$ | Note
---|---|---|---|---|---|---|---|---|---|---
| (hh:mm:ss) | (dd:mm:ss) | (Mpc) | (mag) |  | (hr) | (hr) | (hr) | (hr) |
NGC 1097 | 02:46:19.05 |  $-$30:16:29.6 | 16.8 | 10.0 | SB(s)b | 4.4 | 2.1 | 3.6 | 3.2 | XUVb
NGC 1291 | 03:17:18.59 |  $-$41:06:29.0 | 9.1 | 9.4 | (R)SB(s)0/a | 2.8 | 1.6 | 1.6 | 2.1 | Peculiarb
NGC 1316 | 03:22:41.72 |  $-$37:12:29.6 | 17.5 | 9.4 | SAB(s)0 | 4.7 | 4.9 | 4.3 | 2.5 |
NGC 1512 | 04:03:54.28 |  $-$43:20:55.9 | 10.4 | 11.3 | SB(r)a | 4.8 | 5.0 | 4.6 | 1.9 | XUV
NGC 1672 | 04:45:42.50 |  $-$59:14:49.9 | 11.4 | 10.8 | SB(s)b | 6.6 | 4.4 | 5.2 | 1.8 | XUV
NGC 2090 | 05:47:01.89 |  $-$34:15:02.2 | 11.3 | 11.8 | SA(rs)c | 4.6 | 4.9 | 4.7 | 1.9 | XUV
ESO 556-012 | 06:17:49.24 |  $-$21:03:38.0 | 49.9 | 14.9 | SB(s)m | 4.8 | 4.7 | 4.1 | - | XUV
ESO 208-021 | 07:33:56.25 |  $-$50:26:34.9 | 13.3 | 12.0 | SAB0 | 4.5 | 4.7 | 4.7 | 1.8 |
NGC 2784 | 09:12:19.50 |  $-$24:10:21.4 | 9.6 | 11.2 | SA0(s) | 4.3 | 3.1c | 3.8 | 2.6 |
NGC 2835 | 09:17:52.91 |  $-$22:21:16.8 | 8.8 | 11.1 | SB(rs)c | 3.7c | 3.1 | 3.8 | 0.7 |
NGC 3621 | 11:18:16.51 |  $-$32:48:50.6 | 6.6 | 9.7 | SA(s)d | 7.0 | 4.6 | 4.5 | 3.5 | XUV
NGC 3923 | 11:51:01.69 |  $-$28:48:21.7 | 21.3 | 10.9 | E4-5 | 2.9 | 5.1 | 4.1 | 1.3 |
NGC 5236 | 13:37:00.95 |  $-$29:51:55.5 | 4.6 | 8.3 | SAB(s)c | 5.4 | 4.5 | 4.7 | 3.7 | XUV

Note. — The total exposure time was calculated from only the images used to
create the final mosaic image.

a NASA Extragalactic Database (NED)

b This was additionally classified by Goddard et al. (2010).

c The chip gap is not completely filled due to the observing condition.

##  2 Sample and Observation

###  2.1 Sample

The KNGS aims to take deep optical images of nearby galaxies to explore faint
structures, which may provide crucial hints on the history of these galaxies’
evolution. This study’s primary sample of nearby galaxies was adopted from the
Carnegie-Irvine Galaxy Survey (CGS; Ho et al., 2011; Li et al., 2011). The CGS
performed optical broadband imaging of a sample of 605 bright southern
galaxies using the du Pont 2.5-m telescope at Las Campanas Observatory. The
KMTNet was able to revisit the target galaxies using new images with a field-
of-view (FoV) of $2^{\circ}\times 2^{\circ}$, wider than the small FoV of the
CGS ($8.^{\prime}9\times 8.^{\prime}9$). Even when observing nearby galaxies,
the large FoV enables greater dithering and secures many more pixels in the
sky area, thereby guaranteeing a more robust estimation of the background sky.
This allowed us to perform flat-fielding using a dark-sky flat and individual
sky subtraction (see Section 3). Indeed, photometric uncertainty can be
introduced by many factors, such as the detector, optics, or even data
processing. This study’s strategy took advantage of a wide FoV to minimize the
uncertainty arising from improper data reduction.

While the CGS catalog includes galaxies with a wide range of morphological
types, this study focused on the outskirts of spiral galaxies with LSB
features. One of the intriguing types of targets was galaxies with an XUV-disk
beyond their optical extent. These XUV disks indicate recent SF at a low
surface density at the outskirts of the galaxy. Thilker et al. (2007) searched
XUV-disk galaxies using the far-UV and near-UV images of the GALEX satellite
(Martin et al., 2005). This study matched the CGS catalog with the XUV-disk
galaxy list and prioritized these galaxies in the survey. To maximize the
merit of the KMTNet’s wide FoV, we preferentially selected observation targets
located at a close distance. Most of the target galaxies were within 20 Mpc,
with ESO 556-012 being the farthest away at 49.9 Mpc. Additionally, a control
sample of non-XUV disks was selected from the CGS catalog with comparable
morphological types to the XUV-disk targets. Based on a comparison between the
XUV disks and non-XUV disks, the aim was to unveil the physical nature of the
XUV disks. The basic information regarding the target galaxies is presented in
Table 1. We make the final mosaic images publicly available on the website:
https://data.kasi.re.kr/vo/KNGS/.

Figure 1: Configuration of the KMTNet CCD imager. It consists of four
$9k\times 9k$ chips labeled M, K, N, and T, each of which is divided into
eight amplifiers. The order and name of each amplifier are noted.

###  2.2 Observations

The KNGS was conducted from 2015 to 2020 using the KMTNet, consisting of three
1.6-m telescopes located at the Cerro Tololo Inter-American Observatory
(CTIO), South African Astronomical Observatory, and Siding Spring Observatory.
Each telescope is equipped with a mosaic CCD camera with a wide FoV of
$2^{\circ}\times 2^{\circ}$ and a pixel scale of 0.′′4 pixel-1. Figure 1
illustrates the configuration of the KMTNet CCD imager, which includes four
CCD chips with eight amplifiers each. Byun et al. (2018) have described the
telescopes in more detail.

Optical images of the target galaxies were taken using $B$, $R$, $I$, and
H$\alpha$ filters with the same exposure time of 120 sec for each image. For
each observation, we placed the target galaxy in the center of a chip and
conducted 7-point dithering to fill the gap between the four chips.111In the
case of NGC 2784 and NGC 2835, the chip gap is not filled properly due to the
observing condition. Additionally, we adopted a 4-point dithering sequence
with a large shift ($\sim 1$ deg) to place the target in the order of M-K-N-T
in a different chip. This allowed us to use the images of the blank sky to
robustly model the global background. Primarily, observations were coordinated
to obtain total exposure times of $\sim$4 hr in the $BRI$ bands and $\sim$2 hr
in the H$\alpha$ band for each target by the end of the survey. The H$\alpha$
narrowband filter had a bandwidth of $\sim$80 $\mathrm{\AA}$ centered at
$\sim$6,570 $\mathrm{\AA}$, allowing for the detection of the H$\alpha$
emission from our targets within 30 Mpc. The total integration times of the
final deep images of the $B$, $R$, $I$, and H$\alpha$ bands are listed in
Table 1. According to the observation strategy, data were taken using the same
telescope for the same filter. This way, we could avoid any issues resulting
from slightly different observation conditions among the three KMTNet
telescopes and achieve more robust calibrations of the images for co-addition.
As the H$\alpha$ narrowband filter was only available in the telescope at the
CTIO, all the H$\alpha$ data were obtained at the KMTNet-CTIO site.

##  3 Data Reduction

To fully exploit the potential of the wide-field images, we had to pay
attention to the complex steps of the data reduction procedure, especially as
they included flat-fielding, sky subtraction, and the determination of
background uncertainties. One of the most important things we aimed to achieve
was to simplify the data reduction procedure as much as possible to prevent
the introduction of any additional photometric uncertainty during the data
processing. As shown in Figure 1, the configuration of the KMTNet imager is
not optimal for the observation of the LSB features. Although the composition
of the amplifiers enabled us to read out large amounts of imaging data faster,
it introduced discontinuities into the background level among the amplifiers.
This led us to search for a method to effectively remove the amplifier pattern
(see details in Byun et al., 2018). Finally, we built an optimal data
reduction procedure, including the steps outlined below:222The numerical
results in this section are based on the $R$ band images of NGC 1291. The
values can vary depending on the targets and bands, but the overall trends do
not change.

  * •

Image sampling – It is desirable that all the object frames that will be co-
added have similar photometric conditions. Therefore, a strict initial
sampling of the raw images was required. For instance, we discarded images
with high background levels due to clouds, artifacts caused by telescope
tracking errors, or abnormal bias levels resulting from some of the detectors.
In addition, we used the data only if at least 15 object frames per day were
guaranteed so that a dark-sky flat of the day could be created.

Figure 2: Variations of bias level for the 32 amplifiers. The two lines
represent the average trend of the evening (blue) and morning (orange),
respectively, and are normalized to median values for each. The variation
trends are not constant from amplifier to amplifier.

  * •

Overscan correction and trimming – The bias levels appeared to change up to
$\pm 1$% over a single night (Figure 2), indicating that applying bias
correction using a single master bias could be unreliable. Because we verified
that the overscan followed the bias variation well (see Figure 2 in Byun et
al. 2018), we used overscan instead of bias for each amplifier. Note that we
did not perform dark subtraction because the dark levels were revealed to be
negligible.

  * •

Flat-field correction – We found that a master flat generated from twilight
flat frames was inappropriate for flat-field correction. Between the evening
and the morning, the level of the twilight flat changed significantly by $\pm
5$%, and the trend even reversed in some amplifiers, possibly due to the
intrinsic sky gradient (Figure 3). Consequently, a dark-sky flat was created
using overscan-subtracted object frames with intensive object masking.333In
fact, the sky level of the object frames also changed over time but it was
less exaggerated than that of twilight frames, thereby providing a more
appropriate flat image. Object masking was carried out in two stages: a
conservative detection with a sigma threshold of $\sim$1.5–3$\sigma$ using
objmasks in IRAF and an additional detection with
Photutils444https://photutils.readthedocs.io/en/stable/ from the Python
library to mask out the diffuse light of bright objects. Although a large
fraction of the pixels in each image were masked, valid pixels representing
the blank sky were secured by the significant dithering and chip-by-chip
movement included in our observational strategy. Flat-field correction with a
dark-sky flat effectively removed discontinuity among the background levels
between the amplifiers in individual images, leaving only an intrinsic sky
gradient with a peak-to-peak deviation of $\sim$3% compared with the original
sky level. Meanwhile, bad pixel correction was applied to each image using
fixpix in IRAF with the bad pixel mask generated from the dark-sky flat.

Figure 3: Variations in flat level for the 32 amplifiers. The other
descriptions are the same as in Figure 2, except for the addition of a dark-
sky flat (black). This shows that the trend of the dark-sky flat, which
yielded the best results for the flat-field correction, is not at all
consistent with either the trend of the evening or morning flats.  Figure 4:
Comparisons of results for different data reduction procedures: (a) overscan
and twilight flat-field corrections, (b) overscan and dark-sky flat-field
corrections, and (c) overscan and dark-sky flat-field corrections and sky
subtraction. Flat-field correction using the dark-sky flat appears to be an
appropriate process that allows for accurate modeling of the sky background by
eliminating the amplifier pattern. The large object on the top-left is NGC
1291.

  * •

Sky subtraction – The co-addition of images with different sky gradients
introduces significant photometric uncertainty. Therefore, careful sky
subtraction by fitting the background before the co-addition was necessary.
First, we merged each object frame with 32 amplifiers into a single image. In
the same manner, the mask frames, obtained through improved processing after
flat-fielding, were also merged. Second, we carried out median binning for the
sky background of each object image with 500$\times$500 pixels to discard any
unmasked hot pixels and diffused light around bright sources. Last, we modeled
a sky background using a two-dimensional (2D) polynomial fit. Here, we adopted
a quadratic function because a sky model fitted to higher-order functions can
be sensitive to substructure rather than the global sky gradient. As a result
of sky subtraction, the peak-to-peak deviation in the single image was reduced
to less than 1% of the original sky levels.

  * •

Co-addition – Astrometric calibration was conducted separately in each eight-
amplifier chip using SExtractor (Bertin & Arnouts, 1996) and SCAMP (Bertin,
2006). The details on this process can be found at http://kmtnet.kasi.re.kr.
Finally, all the images were median-combined using SWarp (Bertin et al.,
2002), providing an FoV of $\sim$ 12 deg2, which was wide enough to cover the
virial radii of the target galaxies in our sample.

##  4 Imaging Quality

###  4.1 Estimates of surface brightness limit

This section describes the surface brightness limit of the processed data.
While we were able to effectively remove the amplifier pattern and flatten the
individual image to the level of $\sim$1% of the sky value (Figure 4), the
local sky fluctuation remained in the co-added images. This background
fluctuation could interfere with the accurate determination of sky level as
well as the detection of LSB features.

Figure 5: IRAS 100$\mu$m maps with an FoV of $\sim 1\times 1$ deg2 around the
sample galaxies. The names and positions (cross) of the galaxies are denoted
in each panel. The grayscale is limited by $\pm 5\sigma$ of the background
deviation of each image to emphasize the local fluctuation, and the units are
MJy sr-1.

Since we carried out the sky subtraction by adopting a second-order polynomial
to effectively fit the global sky gradient, high-order fluctuation may be
caused by Galactic cirrus. In general, the Galactic dust distribution can be
traced by far-infrared (FIR) measurement, in which certain images with high
Galactic cirrus ($F_{100\mu{\rm m}}\geq$ 1–2 MJy sr-1) may be severely
affected. Figure 5 shows the Infrared Astronomical Satellite (IRAS) 100$\mu$m
maps around the sample galaxies with an FoV of $\sim 1$ deg2. These maps
include fine structures and non-negligible amounts of gradient in the cirrus,
which could cause non-uniform background features. Further, saturation trails
and scattered light from nearby bright stars may also contribute to the
fluctuation in crowded regions (see the figures in the Appendix). Therefore,
the practical surface brightness limit might vary depending on the size and
location of the target.

Table 2: The galactic coordinates and the 1$\sigma$ depths of the samples

Name | $l^{\mathrm{a}}$ | $b^{\mathrm{a}}$ | $\langle S_{100\mu m}\rangle^{\mathrm{b}}$ | $\mu_{B,1\sigma}$ | $\mu_{R,1\sigma}$ | $\mu_{I,1\sigma}$ | $f_{\mathrm{H\alpha,1\sigma}}$
---|---|---|---|---|---|---|---
| (deg) | (deg) | (MJy sr-2) | (mag arcsec-2) | (mag arcsec-2) | (mag arcsec-2) | (erg s-1 cm-2 arcsec-2)
NGC 1097 | 226.9 |  $-$64.7 | 1.84 | 31.31 | 30.37 | 29.73 | $1.03\times 10^{-18}$
NGC 1291 | 247.5 |  $-$57.0 | 1.51 | 31.12 | 30.13 | 29.31 | $1.29\times 10^{-18}$
NGC 1316 | 240.2 |  $-$56.7 | 1.78 | 31.20 | 30.63 | 29.40 | $9.34\times 10^{-19}$
NGC 1512 | 248.7 |  $-$48.2 | 1.29 | 31.38 | 30.84 | 29.85 | $1.15\times 10^{-18}$
NGC 1672 | 268.8 |  $-$39.0 | 2.05 | 31.41 | 30.62 | 29.79 | $1.41\times 10^{-18}$
NGC 2090 | 239.5 |  $-$27.4 | 2.59 | 30.65 | 30.75 | 29.69 | $1.23\times 10^{-18}$
ESO 556-012 | 228.5 |  $-$16.6 | 5.41 | 31.19 | 30.49 | 29.64 | -
ESO 208-021 | 262.7 |  $-$14.3 | 12.40 | 30.91 | 29.87 | 29.39 | $2.30\times 10^{-18}$
NGC 2784 | 252.0 | 16.4 | 10.10 | 30.93 | 30.22 | 29.65 | $1.20\times 10^{-18}$
NGC 2835 | 251.4 | 18.5 | 6.03 | 31.17 | 30.41 | 29.72 | $6.98\times 10^{-18}$
NGC 3621 | 281.2 | 26.1 | 5.00 | 31.52 | 30.66 | 29.75 | $1.04\times 10^{-18}$
NGC 3923 | 287.3 | 32.2 | 5.12 | 30.89 | 30.73 | 29.52 | $1.19\times 10^{-18}$
NGC 5236 | 314.6 | 32.0 | 3.76 | 31.18 | 30.64 | 29.73 | $1.01\times 10^{-18}$

Note. — The calculated 1$\sigma$ depth of each band was scaled for an angular
scale of $10\times 10$ arcsec boxes.

a NASA Extragalactic Database (NED)

b Average flux of the background of $\sim$1$\times$1 deg2 images from
NASA/IPAC Infrared Science Archive (IRSA)

Figure 6: Distribution of background values for an object-masked image around
NGC 1291 with a $\sim$$2.5\times 2.5$ deg2 region. The histograms follow a
Gaussian distribution. The upper wings are clipped out to mask bright objects.

To more straightforwardly compare the imaging quality of the data set, we
present the surface brightness limit derived from the total background noise,
which was dominated by the Poisson noise of the data. Due to the dithering
pattern moving between the CCD chips, the borders of the co-added images with
an FoV of $\sim$$3.5\times 3.5$ deg2 usually had poor quality. Thus, we used
only the central $\sim$$2.5\times 2.5$ deg2 regions and applied aggressive
sigma clipping to mask bright objects. Note that the diffuse light around the
bright objects was not completely masked in practice. Unmasked diffuse light
was unlikely to significantly affect the estimation of the global background
noise, but we conservatively extended the mask to the neighboring pixels
within a radius of 15 pixels to minimize possible contamination from the
diffuse light. Figure 6 shows histograms of the pixel values of the sky
background around NGC 1512. It appeared that the histograms in each band could
be described well by a Gaussian distribution once sky values larger than
2$\sigma$ were excluded.

At the same time, the surface brightness limit can be determined in a variety
of ways. For example, it can be measured either directly from 2D images or
from an azimuthally averaged radial profile around the target. The latter
typically yields a significantly lower (fainter) surface brightness limit than
the former. In addition, the surface brightness limit can be estimated from
the unbinned original image or from a binned image. In fact, a fainter surface
brightness limit can be achieved from a binned image compared with an unbinned
one.

Obtaining azimuthally averaged radial profiles for all galaxies within this
data set is beyond the scope of this paper. Thus, we present the surface
brightness limit measured from 2D images. However, it would not be practical
to measure the limit using pixel-to-pixel variation. Because most of our
samples were located within a distance of $\lesssim$20 Mpc, typical LSB
objects, such as dwarf satellite galaxies, could be extended to at least
$10^{\prime\prime}\times 10^{\prime\prime}$ in diameter. This suggested that
optimal scaling (binning) was necessary to properly characterize the detection
capability of the LSB features. Therefore, referring to the definition
described in Appendix A of a study by Román et al. (2020), we calculated the
1$\sigma$ surface brightness limit for an angular scale of $10\times
10$-arcsec boxes as follows:

$\mu_{1\sigma;10^{\prime\prime}\times
10^{\prime\prime}}=-2.5\times\mathrm{log}\left(\frac{1\sigma}{\mathrm{pix}\times
10^{\prime\prime}}\right)+\mathrm{Zp},$ (1)

where pix is the pixel scale of 0.4 arcsec and Zp is the zero point of the
data. The zero points of the individual images were estimated using the AAVSO
Photometric All-Sky Survey (APASS) DR10 catalog.555https://www.aavso.org/apass
As $R$ and $I$ magnitudes are unavailable in the APASS, we computed them using
the equations $R=r-0.1837(g-r)-0.0971$ and $I=r-1.2444(r-i)-0.3820$, as
provided by
Lupton(2005).666https://www.sdss.org/dr17/algorithms/sdssUBVRITransform/#Lupton2005

As presented in Table 2, the calculated surface brightness limit in the
broadbands ranged from $\mu_{1\sigma;10^{\prime\prime}\times
10^{\prime\prime}}\sim 29$ to 31 mag arcsec-2 for different filters. Due to
the high background level and its Poisson noise, the $I$-band images were the
shallowest, as can be identified based on the fact they had the greatest width
(shown in Figure 6). Our data set appeared to satisfy the minimum requirements
for the surface brightness limit ($\sim$27–28 mag arcsec-2) to enable the
detection of tidal features and stellar halos (Courteau et al., 2011; Bakos &
Trujillo, 2012). In addition, it was deep enough to discover ultra-diffuse
galaxies with half-light radii larger than 1.5 kpc and central surface
brightnesses fainter than 24 mag arcsec-2 (van Dokkum et al., 2015).

At the same time, the surface brightness limit would be theoretically
correlated with the integration times since background uncertainty can be
reduced based on the number of co-added frames. However, practically, the
depth may be affected further by a variety of factors, such as the observation
conditions, the presence of bright nearby stars, or possibly the strength of
diffuse light from Galactic cirrus.

Table 2 provides the galactic coordinates of the sample galaxies and the
average FIR fluxes in the sky background within 1 deg2 measured from the IRAS
maps. Overall, galaxies with a galactic latitude of $|b|\lesssim 30$ deg were
likely to be significantly affected by Galactic cirrus as in the case of
$\langle S_{100\mu m}\rangle\gtrsim 2$ MJy sr-2. Intriguingly, the surface
brightness limit appeared to be slightly correlated with the fluxes of the
IRAS 100$\mu$m, especially in the $R$-band (Figure 7). For instance, the
correlation coefficient and $p$-value for the $R$-band data were $-0.63$ and
0.02, respectively. This might constitute evidence that Galactic cirrus is
bright in the $R$ band and introduces additional uncertainty in the sky
estimate. Indeed, this hypothesis is in good agreement with the results of
Román et al. (2020) indicating Galactic cirrus has a bluer $r-i$ color than
extragalactic sources for a given $g-r$ color and exhibits a positive
correlation between $g-r$ color and IRAS 100$\mu$m flux.

Figure 7: Correlation between the average FIR fluxes in the IRAS 100$\mu$m
band and the surface brightness limits in the optical bands. Each band is
color-coded differently, and the dashed lines represent the results of a
linear fit.

To calculate the surface brightness limit of the H$\alpha$ emission,
additional procedures were required. The H$\alpha$ emission images were
created by subtracting the scaled $R$ band images to remove the contribution
of the underlying stellar continuum. Field stars were used to determine the
relative scale between the H$\alpha$ and $R$ band images. The photometric zero
point was also estimated using field stars in the $R$ band image, which may be
insensitive to H$\alpha$ emission. Additionally, we recalibrated the measured
H$\alpha$ fluxes by accounting for the difference between the H$\alpha$ and
$R$-band bandpasses. The details have been presented by Byun et al. (2021). As
a result, the surface brightness limit of H$\alpha$ emission reached
$\sim$1–$2\times 10^{-18}$ erg s-1 cm-2 arcsec-2. This is slightly deeper than
the Local Volume Legacy (LVL) survey data at $\sim$$4\times 10^{-18}$ erg s-1
cm-2 arcsec-2 (see Kennicutt et al., 2008). Although our data were not deep
enough to detect diffuse H$\alpha$ emission from galaxies (see Lee et al.,
2016), they could be useful in analyzing the SF properties of H II regions,
since our survey provides H$\alpha$ images with spatial resolution
approximately twice as good as that of the LVL survey.

Although our images have achieved a reasonably satisfactory photometric depth,
we do note that they contain significant saturation trails from bright stars
(see the figures in the Appendix). These trails can be at least partly
mitigated through local interpolation or modeling. However, the degree to
which this can be achieved and the level of accuracy necessary depend strongly
on the actual science goals. We leave this to the discretion of the user and
do not consider it further in this work.

###  4.2 Comparison with other surveys

It is difficult to quantify the imaging quality of our data set and directly
compare it with that from other deep surveys as the definition of the imaging
quality strongly depends on the ultimate goals of the surveys. Additionally,
the deep surveys have been conducted with a variety of exposure times and
telescope sizes, while the methods used to estimate the surface brightness
limit also differ. Nevertheless, it is worth comparing the surface brightness
limit of our survey with those of other surveys with similar aims to ours.

  1. (i)

Duc et al. (2015) presented deep multi-band images of nearby early-type
galaxies obtained with the MegaCam camera at the Canada-France-Hawaii
Telescope. The exposure times were typically $\sim$40 min in the $g$, $r$, $i$
bands and twice of that in the $u$ band. They achieved a sky flattening of
0.2%, yielding a nominal limit of 28.5 mag arcsec-2 in the $g$ band as an
upper limit.

  2. (ii)

Merritt et al. (2016) investigated eight nearby Milky Way-analog galaxies as
part of the Dragonfly Nearby Galaxies Survey. The total exposure times were
typically $\sim$15–20 hr per galaxy. While the 1$\sigma$ surface brightness
limits of the co-added $g$ band images were revealed to be 29–30 mag arcsec-2
in $60^{\prime\prime}\times 60^{\prime\prime}$ boxes, the one-dimensional (1D)
surface brightness profile reached 30–32 mag arcsec-2 with a 2$\sigma$ depth.
After the full 48-lens array was completely built, Danieli et al. (2020)
achieved 1$\sigma$ depths of 31 mag arcsec-2 on arcmin scales with a total
exposure time of $\sim$5 hr.

  3. (iii)

Mihos et al. (2017) conducted a deep imaging survey of galaxies in the Virgo
cluster using Case Western Reserve University’s 0.6/0.9-m Burrell Schmidt
telescope. They obtained a maximum of 100 images each season with 900 sec in
the $M$ band for five years and 1,200 sec in the $B$ band for two years. They
also obtained a 3$\sigma$ limiting depth of $\mu_{B}=29.5$ and $\mu_{V}=28.5$
mag arcsec-2 in $60^{\prime\prime}\times 60^{\prime\prime}$ boxes.

  4. (iv)

The VST Early-type GAlaxy Survey (VEGAS; Capaccioli et al., 2015) was designed
to obtain deep multi-band photometry in the $g$, $r$, $i$ bands of $\sim$100
nearby galaxies down to $\mu\sim 26$–27 mag arcsec-2. Further, the surface
brightness reached as low as 30 mag arcsec-2 using a 1D profile of galaxies
calculated from images with a longer total exposure time of 4–5 hr (Iodice et
al., 2020; Ragusa et al., 2021).

  5. (v)

Martínez-Delgado et al. (2010) carried out deep wide-field imaging of eight
isolated spiral galaxies in the Local Volume with small (0.1–0.5 m)
telescopes. A luminance filter was equipped onto the telescope, and the total
exposure times ranged from 5 to 18 hr per galaxy. They obtained a surface
brightness limit of $\mu_{V}\sim 28.5$ mag arcsec-2 from background rms in
boxes, several tens to 100 arcsec per side.

  6. (vi)

Rich et al. (2019) aimed to recover the halos of $\sim$120 galaxies in the
Local Volume using the 0.7-m Jeanne Rich Telescope Centurion 28. With a total
exposure time of 1–3 hr, they achieved a surface brightness limit of 28–30 mag
arcsec-2 from 1D profiles.

  7. (vii)

Trujillo et al. (2021) presented the results of a deep imaging survey
exploring the stellar halos and “missing satellites” (if any) of nearby
galaxies using the $2\times 8.4$-m Large Binocular Telescope. The surface
brightness limit reached $\mu_{V}\sim 31$ mag arcsec-2 (3$\sigma$ in
$10^{\prime\prime}\times 10^{\prime\prime}$ boxes) with a total exposure time
of 2 hr.

  8. (viii)

Several surveys mapping the extragalactic sky may be useful in the detection
of LSB features. The Hyper Suprime-Cam (HSC) survey (Aihara et al., 2018) and
Dark Energy Camera Legacy Survey (DECaLS; Dey et al., 2019) are the most
representative and aimed to cover large areas over 1,000 deg2 and 9,000 deg2,
respectively. Li et al. (2021) robustly evaluated the depth of these data and
suggested that both HSC and DECaLS can achieve a surface brightness limit of
$\mu_{r}\sim 28$–29 mag arcsec-2 if customized pipelines are applied.

  9. (ix)

In addition to measuring the integrated light of galaxies, the direct star
count method can be used to study the faint stellar halos of galaxies.
Radburn-Smith et al. (2011) investigated the resolved stellar populations of
14 nearby disk galaxies using the Hubble Space Telescope with a typical
exposure time of 700 sec. The median depth was 2.7 mag below the tip of the
red giant branch, corresponding to $\mu_{V}\sim 30$ mag arcsec-2.

##  5 Scientific Applications

Here, we briefly summarize the potential scientific applications of the KNGS.

###  5.1 Stellar Halos

Studies on the stellar halos of nearby galaxies have demonstrated that the
halo fraction significantly varies from one galaxy to another (e.g., Merritt
et al., 2016; Rich et al., 2019). While stellar halos can be marginally
detected with a depth of 28–29 mag arcsec-2, 30–31 mag arcsec-2 is required
for definitive detection (e.g., Cooper et al., 2013; Merritt et al., 2016;
Trujillo et al., 2021). Therefore, it may be difficult to explore the
photometric properties of stellar halos for all targets in our survey (Byun et
al., 2018). However, for a part of the sample, one would be able to identify
relatively bright substructures, such as stellar streams (e.g., Martínez-
Delgado et al., 2010; van Dokkum et al., 2019). This can be used to probe the
accretion history of dwarf galaxies and to constrain the gravitational
potential of the host galaxy (e.g., Ibata et al., 2002).

###  5.2 Dwarf Satellite Galaxies

With the deep images from the KNGS, it is possible to detect satellite
galaxies with a central surface brightness of $\mu_{R}\leq 26$ mag arcsec-2
(Byun et al., 2020, and see also Carlsten et al. 2022). This reveals that the
KNGS provides an opportunity to search for new dwarf galaxies in different
environments other than the Local Group. Byun et al. (2020) showed that the
dwarf galaxies associated with NGC 1291, which resides in a relatively sparse
environment, tend to be bluer than those in denser environments. Further,
identification of the physical properties of faint companions around XUV-disk
galaxies and comparison with non-XUV-disk counterparts would provide useful
insights into the physical origins of XUV disks (e.g., Thilker et al., 2007).
In combination with knowledge of the distribution of H I gas, at least for XUV
disks in interacting systems, one might be able to detect candidates for tidal
dwarf galaxies (TDGs; e.g., Koribalski & López-Sánchez, 2009). Studying the
stellar populations of TDGs with the deep images from the KNGS will help us to
explore the nature of TDGs.

Figure 8: Comparison of the FUV flux-density and H$\alpha$ line flux of H II
regions in nine galaxies. All the measurements were corrected for foreground
attenuation but not for internal attenuation. The contours contain 90% of
selected data points, and the star-forming regions outside the radial distance
of 10 kpc are highlighted with a solid red line. The dashed black line
indicates the one-to-one relation between the FUV-inferred SFR and
H$\alpha$-inferred SFR. The vectors on each panel show the effect of
correction for continuum attenuation of $A_{V}=1$ with the stellar-to-gas
attenuation ratios of 1 (orange) and 0.44 (blue), respectively.

###  5.3 SF Properties in the outer disk

Byun et al. (2021) demonstrated that broad and narrowband imaging data from
the KNGS, along with complementary UV and IR images, are useful in studying
the physical properties of star-forming regions on the galaxy outskirts. From
an analysis of the spatially resolved spectral energy distribution of NGC 1512
and NGC 2090, Byun et al. (2021) demonstrated that the flux ratio of H$\alpha$
to UV dramatically decreases with galactic radius, indicating that H$\alpha$
flux is deficient in low-mass regions. It appears that the distinctive SF
history may be responsible for this trend. Indeed, this result needs to be
further explored with other targets from the KNGS. Further, the comparison
between XUV and non-XUV-disks in terms of SF among the outskirts will provide
valuable information about the physical nature of XUV disks.

As a preliminary illustration of how the data might be used for a scientific
application, we compared the H$\alpha$ and UV fluxes for the nine galaxies
that have available far-ultraviolet (FUV) data and detectable H$\alpha$
emission. As described in Byun et al. (2021), we measured H$\alpha$ and FUV
fluxes of star-forming regions in galaxies using an array of circular
apertures. Before performing the photometry, we manually masked the galaxy
core, foreground stars, and bleeding features. We measured the fluxes with a
fixed aperture size of radius 6 arcsec, suitable for covering the physical
size of the H II region at a distance of 10 Mpc.777Varying aperture sizes for
a specific physical scale can be used instead. However, this section aims to
determine the FUV and H$\alpha$ flux ratio. In addition, by changing the
aperture size, severe aperture correction for distant galaxies may increase
uncertainties in the measures. The primary condition in selecting a star-
forming region was whether or not the FUV flux was more than the 3$\sigma$
threshold. Because some artifacts in nearby backgrounds were selected
together, we used additional color cuts (e.g., $B-R<1.45$) to effectively
discard data that were not considered the galaxy’s components. Finally,
apertures containing any masked pixels were also excluded. Since investigating
the detailed physical properties of each star-forming region is not the
purpose of this paper, all the measurements were only corrected for foreground
attenuation but not for internal attenuation.

Figure 8 shows a distribution of the FUV flux density and H$\alpha$ flux of
the star-forming regions in the nine galaxies. Because we do not yet know the
local physical properties of these regions, we focus on the outer regions
where dust attenuation is expected to be less significant. Based on the
approximate upper limit of the half-light radii of nearby disk galaxies (Ho et
al., 2011; Gao et al., 2019), we highlight the regions into those that lie
exterior to a physical radius of 10 kpc from the full set of regions for the
entire galaxy. Most of the galaxies show a similar trend: regions with low
flux or low star formation rate (SFR) tend to have deficient H$\alpha$ flux.
NGC 2835 is the only exception, because no deep UV data were available and we
could not detect any star-forming regions with low flux. These variations of
the FUV-to-H$\alpha$ ratio would be difficult to identify using integrated
fluxes, highlighting the utility of spatially resolved observations (see also
Byun et al., 2021). This suggests that spatially resolved analysis is
essential to study the detailed SF properties of galaxies, especially in their
outer regions. In future work, we plan to perform spatially resolved SED
fitting with available multi-wavelength data to explore the SF properties in
XUV- and non-XUV-disk galaxies.

We are grateful to an anonymous referee for constructive comments and
suggestions. This research was supported by the Korea Astronomy and Space
Science Institute under the R&D program(Project No. 2022-1-830-05), supervised
by the Ministry of Science and ICT. This research has made use of the KMTNet
system operated by the Korea Astronomy and Space Science Institute (KASI), and
the data were obtained at CTIO in Chile, SAAO in South Africa, and SSO in
Australia. LCH was supported by the National Science Foundation of China
(11721303, 11991052, 12011540375) and the China Manned Space Project (CMS-
CSST-2021-A04, CMS-CSST-2021-A06). This research was supported by ‘National
Research Council of Science & Technology (NST)’ - ‘Korea Astronomy and Space
Science (KASI)’ Postdoctoral Fellowship Program for Young Scientists at KASI
in South Korea. This work was supported by the National Research Foundation of
Korea (NRF) grant funded by the Korean government (MSIT) (No.2020R1A2C4001753)
and under the framework of the international cooperation program managed by
the National Research Foundation of Korea (NRF-2020K2A9A2A06026245). Y.K.S.
acknowledges support from the National Research Foundation of Korea (NRF)
grant funded by the Ministry of Science and ICT (NRF-2019R1C1C1010279). J.H.L.
and H.J. were supported by the National Research Foundation of Korea(NRF)
grant funded by the Korea government(MSIT) (No. 2022R1A2C1004025).

## References

  * Abraham & van Dokkum (2014) Abraham, R. G., & van Dokkum, P. G. 2014, PASP, 126, 55, doi: 10.1086/674875
  * Agertz et al. (2020) Agertz, O., Pontzen, A., Read, J. I., et al. 2020, MNRAS, 491, 1656, doi: 10.1093/mnras/stz3053
  * Aihara et al. (2018) Aihara, H., Arimoto, N., Armstrong, R., et al. 2018, PASJ, 70, S4, doi: 10.1093/pasj/psx066
  * Astropy Collaboration et al. (2013) Astropy Collaboration, Robitaille, T. P., Tollerud, E. J., et al. 2013, A&A, 558, A33, doi: 10.1051/0004-6361/201322068
  * Astropy Collaboration et al. (2018) Astropy Collaboration, Price-Whelan, A. M., Sipőcz, B. M., et al. 2018, AJ, 156, 123, doi: 10.3847/1538-3881/aabc4f
  * Bakos & Trujillo (2012) Bakos, J., & Trujillo, I. 2012, arXiv e-prints, arXiv:1204.3082.  https://arxiv.org/abs/1204.3082
  * Bertin (2006) Bertin, E. 2006, in Astronomical Society of the Pacific Conference Series, Vol. 351, Astronomical Data Analysis Software and Systems XV, ed. C. Gabriel, C. Arviset, D. Ponz, & S. Enrique, 112
  * Bertin & Arnouts (1996) Bertin, E., & Arnouts, S. 1996, A&AS, 117, 393, doi: 10.1051/aas:1996164
  * Bertin et al. (2002) Bertin, E., Mellier, Y., Radovich, M., et al. 2002, in Astronomical Society of the Pacific Conference Series, Vol. 281, Astronomical Data Analysis Software and Systems XI, ed. D. A. Bohlender, D. Durand, & T. H. Handley, 228
  * Bode et al. (2001) Bode, P., Ostriker, J. P., & Turok, N. 2001, ApJ, 556, 93, doi: 10.1086/321541
  * Bradley et al. (2021) Bradley, L., Sipőcz, B., Robitaille, T., et al. 2021, astropy/photutils: 1.1.0, 1.1.0, Zenodo, doi: 10.5281/zenodo.4624996
  * Bruzzese et al. (2020) Bruzzese, S. M., Thilker, D. A., Meurer, G. R., et al. 2020, MNRAS, 491, 2366, doi: 10.1093/mnras/stz3151
  * Bullock & Johnston (2005) Bullock, J. S., & Johnston, K. V. 2005, ApJ, 635, 931, doi: 10.1086/497422
  * Byun et al. (2018) Byun, W., Sheen, Y.-K., Ho, L. C., et al. 2018, AJ, 156, 249, doi: 10.3847/1538-3881/aae647
  * Byun et al. (2020) Byun, W., Sheen, Y.-K., Park, H. S., et al. 2020, ApJ, 891, 18, doi: 10.3847/1538-4357/ab6f6e
  * Byun et al. (2021) Byun, W., Sheen, Y.-K., Seon, K.-I., et al. 2021, ApJ, 918, 82, doi: 10.3847/1538-4357/ac0f00
  * Capaccioli et al. (2015) Capaccioli, M., Spavone, M., Grado, A., et al. 2015, A&A, 581, A10, doi: 10.1051/0004-6361/201526252
  * Carlsten et al. (2022) Carlsten, S. G., Greene, J. E., Beaton, R. L., Danieli, S., & Greco, J. P. 2022, arXiv e-prints, arXiv:2203.00014.  https://arxiv.org/abs/2203.00014
  * Choi et al. (2020) Choi, Y., Dalcanton, J. J., Williams, B. F., et al. 2020, ApJ, 902, 54, doi: 10.3847/1538-4357/abb467
  * Cooper et al. (2013) Cooper, A. P., D’Souza, R., Kauffmann, G., et al. 2013, MNRAS, 434, 3348, doi: 10.1093/mnras/stt1245
  * Courteau et al. (2011) Courteau, S., Widrow, L. M., McDonald, M., et al. 2011, ApJ, 739, 20, doi: 10.1088/0004-637X/739/1/20
  * da Silva et al. (2014) da Silva, R. L., Fumagalli, M., & Krumholz, M. R. 2014, MNRAS, 444, 3275, doi: 10.1093/mnras/stu1688
  * Danieli et al. (2018) Danieli, S., van Dokkum, P., & Conroy, C. 2018, ApJ, 856, 69, doi: 10.3847/1538-4357/aaadfb
  * Danieli et al. (2020) Danieli, S., Lokhorst, D., Zhang, J., et al. 2020, ApJ, 894, 119, doi: 10.3847/1538-4357/ab88a8
  * Das et al. (2021) Das, M., Yadav, J., Patra, N., et al. 2021, Journal of Astrophysics and Astronomy, 42, 85, doi: 10.1007/s12036-021-09749-9
  * Dekel & Woo (2003) Dekel, A., & Woo, J. 2003, MNRAS, 344, 1131, doi: 10.1046/j.1365-8711.2003.06923.x
  * Dey et al. (2019) Dey, A., Schlegel, D. J., Lang, D., et al. 2019, AJ, 157, 168, doi: 10.3847/1538-3881/ab089d
  * Dong et al. (2008) Dong, H., Calzetti, D., Regan, M., et al. 2008, AJ, 136, 479, doi: 10.1088/0004-6256/136/1/479
  * Duc et al. (2015) Duc, P.-A., Cuillandre, J.-C., Karabal, E., et al. 2015, MNRAS, 446, 120, doi: 10.1093/mnras/stu2019
  * Eldridge & Relaño (2011) Eldridge, J. J., & Relaño, M. 2011, MNRAS, 411, 235, doi: 10.1111/j.1365-2966.2010.17676.x
  * Emami et al. (2019) Emami, N., Siana, B., Weisz, D. R., et al. 2019, ApJ, 881, 71, doi: 10.3847/1538-4357/ab211a
  * Fattahi et al. (2020) Fattahi, A., Navarro, J. F., & Frenk, C. S. 2020, MNRAS, 493, 2596, doi: 10.1093/mnras/staa375
  * Fumagalli et al. (2011) Fumagalli, M., da Silva, R. L., & Krumholz, M. R. 2011, ApJ, 741, L26, doi: 10.1088/2041-8205/741/2/L26
  * Gao et al. (2019) Gao, H., Ho, L. C., Barth, A. J., & Li, Z.-Y. 2019, ApJS, 244, 34, doi: 10.3847/1538-4365/ab3c6a
  * Gil de Paz et al. (2005) Gil de Paz, A., Madore, B. F., Boissier, S., et al. 2005, ApJ, 627, L29, doi: 10.1086/432054
  * Gilhuly et al. (2022) Gilhuly, C., Merritt, A., Abraham, R., et al. 2022, ApJ, 932, 44, doi: 10.3847/1538-4357/ac6750
  * Goddard et al. (2010) Goddard, Q. E., Kennicutt, R. C., & Ryan-Weber, E. V. 2010, MNRAS, 405, 2791, doi: 10.1111/j.1365-2966.2010.16661.x
  * Gu et al. (2018) Gu, M., Conroy, C., Law, D., et al. 2018, ApJ, 859, 37, doi: 10.3847/1538-4357/aabbae
  * Ho et al. (2011) Ho, L. C., Li, Z.-Y., Barth, A. J., Seigar, M. S., & Peng, C. Y. 2011, ApJS, 197, 21, doi: 10.1088/0067-0049/197/2/21
  * Hunter (2007) Hunter, J. D. 2007, Computing in Science & Engineering, 9, 90, doi: 10.1109/MCSE.2007.55
  * Ibata et al. (2002) Ibata, R. A., Lewis, G. F., Irwin, M. J., & Cambrésy, L. 2002, MNRAS, 332, 921, doi: 10.1046/j.1365-8711.2002.05360.x
  * Iglesias-Páramo et al. (2004) Iglesias-Páramo, J., Boselli, A., Gavazzi, G., & Zaccardo, A. 2004, A&A, 421, 887, doi: 10.1051/0004-6361:20034572
  * Iodice et al. (2020) Iodice, E., Cantiello, M., Hilker, M., et al. 2020, A&A, 642, A48, doi: 10.1051/0004-6361/202038523
  * Irwin et al. (2007) Irwin, M. J., Belokurov, V., Evans, N. W., et al. 2007, ApJ, 656, L13, doi: 10.1086/512183
  * Jeřábková et al. (2018) Jeřábková, T., Hasani Zonoozi, A., Kroupa, P., et al. 2018, A&A, 620, A39, doi: 10.1051/0004-6361/201833055
  * Kauffmann (2021) Kauffmann, G. 2021, MNRAS, 506, 4979, doi: 10.1093/mnras/stab1987
  * Kennicutt et al. (2008) Kennicutt, Robert C., J., Lee, J. C., Funes, J. G., et al. 2008, ApJS, 178, 247, doi: 10.1086/590058
  * Kim et al. (2016) Kim, S.-L., Lee, C.-U., Park, B.-G., et al. 2016, Journal of Korean Astronomical Society, 49, 37, doi: 10.5303/JKAS.2016.49.1.037
  * Klypin et al. (1999) Klypin, A., Kravtsov, A. V., Valenzuela, O., & Prada, F. 1999, ApJ, 522, 82, doi: 10.1086/307643
  * Koribalski & López-Sánchez (2009) Koribalski, B. S., & López-Sánchez, Á. R. 2009, MNRAS, 400, 1749, doi: 10.1111/j.1365-2966.2009.15610.x
  * Lee et al. (2016) Lee, J. C., Veilleux, S., McDonald, M., & Hilbert, B. 2016, ApJ, 817, 177, doi: 10.3847/0004-637X/817/2/177
  * Lee et al. (2009) Lee, J. C., Gil de Paz, A., Tremonti, C., et al. 2009, ApJ, 706, 599, doi: 10.1088/0004-637X/706/1/599
  * Li et al. (2021) Li, J., Huang, S., Leauthaud, A., et al. 2021, arXiv e-prints, arXiv:2111.03557.  https://arxiv.org/abs/2111.03557
  * Li et al. (2011) Li, Z.-Y., Ho, L. C., Barth, A. J., & Peng, C. Y. 2011, ApJS, 197, 22, doi: 10.1088/0067-0049/197/2/22
  * Martin et al. (2005) Martin, D. C., Fanson, J., Schiminovich, D., et al. 2005, ApJ, 619, L1, doi: 10.1086/426387
  * Martínez-Delgado et al. (2010) Martínez-Delgado, D., Gabany, R. J., Crawford, K., et al. 2010, AJ, 140, 962, doi: 10.1088/0004-6256/140/4/962
  * Merritt et al. (2016) Merritt, A., van Dokkum, P., Danieli, S., et al. 2016, ApJ, 833, 168, doi: 10.3847/1538-4357/833/2/168
  * Meurer et al. (2009) Meurer, G. R., Wong, O. I., Kim, J. H., et al. 2009, ApJ, 695, 765, doi: 10.1088/0004-637X/695/1/765
  * Mihos et al. (2017) Mihos, J. C., Harding, P., Feldmeier, J. J., et al. 2017, ApJ, 834, 16, doi: 10.3847/1538-4357/834/1/16
  * Moore et al. (1999) Moore, B., Ghigna, S., Governato, F., et al. 1999, ApJ, 524, L19, doi: 10.1086/312287
  * Nadler et al. (2019) Nadler, E. O., Gluscevic, V., Boddy, K. K., & Wechsler, R. H. 2019, ApJ, 878, L32, doi: 10.3847/2041-8213/ab1eb2
  * Navarro et al. (1997) Navarro, J. F., Frenk, C. S., & White, S. D. M. 1997, ApJ, 490, 493, doi: 10.1086/304888
  * Oey & Kennicutt (1997) Oey, M. S., & Kennicutt, R. C., J. 1997, MNRAS, 291, 827, doi: 10.1093/mnras/291.4.827
  * Padave et al. (2021) Padave, M., Borthakur, S., Gim, H. B., et al. 2021, ApJ, 923, 199, doi: 10.3847/1538-4357/ac2c01
  * Pflamm-Altenburg et al. (2009) Pflamm-Altenburg, J., Weidner, C., & Kroupa, P. 2009, MNRAS, 395, 394, doi: 10.1111/j.1365-2966.2009.14522.x
  * Radburn-Smith et al. (2011) Radburn-Smith, D. J., de Jong, R. S., Seth, A. C., et al. 2011, ApJS, 195, 18, doi: 10.1088/0067-0049/195/2/18
  * Ragusa et al. (2021) Ragusa, R., Spavone, M., Iodice, E., et al. 2021, A&A, 651, A39, doi: 10.1051/0004-6361/202039921
  * Relaño et al. (2012) Relaño, M., Kennicutt, R. C., J., Eldridge, J. J., Lee, J. C., & Verley, S. 2012, MNRAS, 423, 2933, doi: 10.1111/j.1365-2966.2012.21107.x
  * Rich et al. (2019) Rich, R. M., Mosenkov, A., Lee-Saunders, H., et al. 2019, MNRAS, 490, 1539, doi: 10.1093/mnras/stz2106
  * Román et al. (2020) Román, J., Trujillo, I., & Montes, M. 2020, A&A, 644, A42, doi: 10.1051/0004-6361/201936111
  * Simon & Geha (2007) Simon, J. D., & Geha, M. 2007, ApJ, 670, 313, doi: 10.1086/521816
  * Sullivan et al. (2004) Sullivan, M., Treyer, M. A., Ellis, R. S., & Mobasher, B. 2004, MNRAS, 350, 21, doi: 10.1111/j.1365-2966.2004.07649.x
  * Tal & van Dokkum (2011) Tal, T., & van Dokkum, P. G. 2011, ApJ, 731, 89, doi: 10.1088/0004-637X/731/2/89
  * Thilker et al. (2005) Thilker, D. A., Bianchi, L., Boissier, S., et al. 2005, ApJ, 619, L79, doi: 10.1086/425251
  * Thilker et al. (2007) Thilker, D. A., Bianchi, L., Meurer, G., et al. 2007, ApJS, 173, 538, doi: 10.1086/523853
  * Trujillo et al. (2021) Trujillo, I., D’Onofrio, M., Zaritsky, D., et al. 2021, A&A, 654, A40, doi: 10.1051/0004-6361/202141603
  * van Dokkum et al. (2016) van Dokkum, P., Abraham, R., Brodie, J., et al. 2016, ApJ, 828, L6, doi: 10.3847/2041-8205/828/1/L6
  * van Dokkum et al. (2017) van Dokkum, P., Abraham, R., Romanowsky, A. J., et al. 2017, ApJ, 844, L11, doi: 10.3847/2041-8213/aa7ca2
  * van Dokkum et al. (2019) van Dokkum, P., Gilhuly, C., Bonaca, A., et al. 2019, ApJ, 883, L32, doi: 10.3847/2041-8213/ab40c9
  * van Dokkum et al. (2022) van Dokkum, P., Shen, Z., Keim, M. A., et al. 2022, Nature, 605, 435, doi: 10.1038/s41586-022-04665-6
  * van Dokkum et al. (2015) van Dokkum, P. G., Abraham, R., Merritt, A., et al. 2015, ApJ, 798, L45, doi: 10.1088/2041-8205/798/2/L45
  * Virtanen et al. (2020) Virtanen, P., Gommers, R., Oliphant, T. E., et al. 2020, Nature Methods, 17, 261, doi: 10.1038/s41592-019-0686-2
  * Weisz et al. (2012) Weisz, D. R., Johnson, B. D., Johnson, L. C., et al. 2012, ApJ, 744, 44, doi: 10.1088/0004-637X/744/1/44
  * White & Frenk (1991) White, S. D. M., & Frenk, C. S. 1991, ApJ, 379, 52, doi: 10.1086/170483
  * Zaritsky & Christlein (2007) Zaritsky, D., & Christlein, D. 2007, AJ, 134, 135, doi: 10.1086/518238
  * Zhang et al. (2018) Zhang, J., Abraham, R., van Dokkum, P., Merritt, A., & Janssens, S. 2018, ApJ, 855, 78, doi: 10.3847/1538-4357/aaac81

##  Appendix A Co-added images of the samples with a full FoV

Section 4 provides the surface brightness limits calculated from the
background noise of individual images for a quantitative assessment of the
imaging quality of the data set. At the same time, we need to be careful about
accepting the limits at face value. Our images contain a variety of artifacts,
such as saturation trails, crosstalks, and stray light from stars. In
particular, the last two elements may be mistakenly considered as LSB features
or overlapped with actual LSB features. This means that the completeness and
reliability of the LSB detection may vary for each image regardless of the
surface brightness limit. Therefore, a subsequent visual assessment is
necessary to understand the imaging quality of the data. For this purpose, we
also present final mosaic images of all the samples. We also provide H$\alpha$
emission maps overlaid on the $R$-band images to highlight the spatial
distribution of star-forming regions. The nuclear regions in some galaxies
might be affected by imperfect continuum subtraction.

Figure 9: Final mosaic image of NGC 1097 in the $R$ band. The upper panels
show zoomed-in images with a FoV of $30^{\prime}\times 30^{\prime}$ (left) and
$10^{\prime}\times 10^{\prime}$ (right), respectively. The color-coded regions
in the upper-left panel represent the structures with a surface brightness of
$\mu_{R}\lesssim 25$ mag arcsec-2, while the ones in the upper-right panel
depict regions with H$\alpha$ flux levels 1$\sigma$ (yellow) and 10$\sigma$
(green) above the background.  Figure 10: Final mosaic image of NGC 1291 in
the $R$ band. The other descriptions are the same as in Figure 9, except that
the FoV of the upper-right panel is $15^{\prime}\times 15^{\prime}$. Figure
11: Final mosaic image of NGC 1316 in the $R$ band. The other descriptions are
the same as in Figure 9, except that the FoV of the upper right panel is
$15^{\prime}\times 15^{\prime}$. Figure 12: Final mosaic image of NGC 1512 in
the $R$ band. The other descriptions are the same as in Figure 9. Figure 13:
Final mosaic image of NGC 1672 in the $R$ band. The other descriptions are the
same as in Figure 9. Figure 14: Final mosaic image of NGC 2090 in the $R$
band. The other descriptions are the same as in Figure 9. Figure 15: Final
mosaic image of ESO 556-012 in the $R$ band. The other descriptions are the
same as in Figure 9, except that the upper-right panel shows a zoomed-in
$R$-band image with a FoV of $5^{\prime}\times 5^{\prime}$. Figure 16: Final
mosaic image of ESO 208-021 in the $R$ band. The other descriptions are the
same as in Figure 9. Figure 17: Final mosaic image of NGC 2784 in the $R$
band. The other descriptions are the same as in Figure 9. Figure 18: Final
mosaic image of NGC 2835 in the $R$ band. The other descriptions are the same
as in Figure 9. Figure 19: Final mosaic image of NGC 3621 in the $R$ band. The
other descriptions are the same as in Figure 9, except that the FoV of the
upper right panel is $15^{\prime}\times 15^{\prime}$. Figure 20: Final mosaic
image of NGC 3923 in the $R$ band. The other descriptions are the same as in
Figure 9, except that the FoV of the upper right panel is $15^{\prime}\times
15^{\prime}$. Figure 21: Final mosaic image of NGC 5236 in the $R$ band. The
other descriptions are the same as in Figure 9, except that the FoV of the
upper right panel is $15^{\prime}\times 15^{\prime}$.