# Photometric Selection of Unobscured QSOs in the Ecliptic Poles: KMTNet in
the South Field and Pan-STARRS in the North Field

Woowon Byun Korea Astronomy and Space Science Institute, Daejeon 34055,
Republic of Korea  Minjin Kim Department of Astronomy and Atmospheric
Sciences, Kyungpook National University, Daegu 41566, Republic of Korea  Yun-
Kyeong Sheen Korea Astronomy and Space Science Institute, Daejeon 34055,
Republic of Korea  Dongseob Lee  Department of Earth Science Education,
Kyungpook National University, Daegu 41566, 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
Jongwan Ko Korea Astronomy and Space Science Institute, Daejeon 34055,
Republic of Korea  University of Science and Technology, Korea, Daejeon 34113,
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  Hyunjin Shim Department of Earth Science
Education, Kyungpook National University, Daegu 41566, Republic of Korea
Dohyeong Kim Department of Earth Sciences, Pusan National University, Busan
46241, Republic of Korea  Yongjung Kim Department of Astronomy and Atmospheric
Sciences, Kyungpook National University, Daegu 41566, Republic of Korea  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  Jong-Hak Woo Department of Physics & Astronomy,
Seoul National University, Seoul 08826, Republic of Korea  Woong-Seob Jeong
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  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  Yongseok Lee Korea Astronomy and Space
Science Institute, Daejeon 34055, Republic of Korea  School of Space Research,
Kyung Hee University, 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, Kyeonggi 17104, Republic of
Korea  Hyunmi Song Department of Astronomy and Space Science, Chungnam
National University, Daejeon 34134, Republic of Korea  Donghoon Son Department
of Physics & Astronomy, Seoul National University, Seoul 08826, Republic of
Korea  Yujin Yang Korea Astronomy and Space Science Institute, Daejeon 34055,
Republic of Korea  University of Science and Technology, Korea, Daejeon 34113,
Republic of Korea

(Received May 24, 2023; Revised July 11, 2023; Accepted July 27, 2023)

###### Abstract

We search for quasi-stellar objects (QSOs) in a wide area of the south
ecliptic pole (SEP) field, which has been and will continue to be intensively
explored through various space missions. For this purpose, we obtain deep
broadband optical images of the SEP field covering an area of
$\sim$$14.5\times 14.5$ deg2 with the Korea Microlensing Telescope Network.
The 5$\sigma$ detection limits for point sources in the $BVRI$ bands are
estimated to be $\sim$22.59, 22.60, 22.98, and 21.85 mag, respectively.
Utilizing data from Wide-field Infrared Survey Explorer, unobscured QSO
candidates are selected among the optically point-like sources using the mid-
infrared (MIR) and optical-MIR colors. To further refine our selection and
eliminate any contamination not adequately removed by the color-based
selection, we perform the spectral energy distribution fitting with archival
photometric data ranging from optical to MIR. As a result, we identify a total
of 2,383 unobscured QSO candidates in the SEP field. We also apply a similar
method to the north ecliptic pole field using the Pan-STARRS data and obtain a
similar result of identifying 2,427 candidates. The differential number count
per area of our QSO candidates is in good agreement with those measured from
spectroscopically confirmed ones in other fields. Finally, we compare the
results with the literature and discuss how this work will be implicated in
future studies, especially with the upcoming space missions.

Galaxy (573) — QSO (1319) — Photometry (1234) — Catalogs (205)

††journal: ApJS††facilities: KMTNet, AAVSO, IRSA (Wright et al., 2019;
Skrutskie et al., 2003)††software: Astropy (Astropy Collaboration et al.,
2013, 2018, 2022), Scipy (Virtanen et al., 2020), Source Extractor (Bertin &
Arnouts, 1996), SWarp (Bertin et al., 2002), SCAMP (Bertin, 2006), LePhare
(Arnouts et al., 1999; Ilbert et al., 2006)

##  1 Introduction

Quasi-stellar objects (QSOs), which are luminous active galactic nuclei
(AGNs), are of great importance to understanding the formation and growth of
supermassive black holes (SMBHs) at the center of massive galaxies and the
coevolution between SMBHs and their host galaxies (e.g., Silk & Rees, 1998; Di
Matteo et al., 2005; Yang et al., 2021). In addition, hunting QSOs enables a
broad range of science, such as measuring abundance and clustering as
functions of redshift and luminosity (e.g., Martini & Weinberg, 2001; Shen et
al., 2007), searching quasar lens (e.g., Oguri et al., 2006), and their
changing-look mechanisms (e.g., MacLeod et al., 2016; Yang et al., 2018).

Many attempts have been made to identify them, and the most extensive work has
been done with Sloan Digital Sky Survey (SDSS), which has spectroscopically
confirmed over 750,000 QSOs (Lyke et al., 2020). However, as SDSS covers only
the northern hemisphere, the known QSOs in the southern hemisphere are
relatively lacking (e.g., Wisotzki et al., 1996; Croom et al., 2005). In this
regard, a more comprehensive survey is still necessary.

In addition to identifying QSOs, it is imperative to investigate the
properties of their host galaxies for a comprehensive understanding of their
formation and evolution. Celestial objects, which are less affected by dust
obscuration caused by the Galactic cirrus, will be advantageous, and the
ecliptic poles are one of the favorable target regions. Moreover, the sun-
synchronized orbit of various infrared (IR) satellite survey missions,
including AKARI and Wide-field Infrared Survey Explorer (WISE; Wright et al.,
2010), led to covering these regions repeatedly, resulting in deep IR
photometry data (e.g., Matsuhara et al., 2006; Jarrett et al., 2011; Takagi et
al., 2012). Indeed, extensive studies on the physical properties of dust in
star-forming galaxies have been conducted in combination with multiwavelength
data (e.g., Hanami et al., 2012; Shim et al., 2022). However, the south
ecliptic pole (SEP) has remained relatively unexplored in contrast to the
north ecliptic pole (NEP) due to the lack of a complementary dataset (e.g.,
Małek et al., 2010; Clements et al., 2011; Jeong et al., 2016).

Future satellite missions, such as Euclid and Spectro-Photometer for the
History of the Universe and Ices Explorer (SPHEREx; see Doré et al., 2016,
2018), will also intensively cover the ecliptic poles, providing deep and
multi-epoch photometric data. In particular, SPHEREx will perform forced
photometry on the positions of already-known celestial objects. If we identify
intriguing targets in this area in advance, we can maximize the scientific
outcomes yielded by the survey dataset from future missions. One of the
expected outcomes is that SPHEREx deep fields will enable us to carry out the
reverberation mapping (RM) experiments of QSOs using the multi-epoch
spectroscopic data covering 0.75–$5\ \mu$m (see Kim et al., 2021). Since the
RM method has been widely used to measure the size of the broad line region in
AGNs, identifying unobscured QSOs in the ecliptic poles in advance is strongly
required.

Based on the dataset obtained from Spitzer and WISE, mid-infrared (MIR) colors
have been widely used for hunting QSOs (e.g., Lacy et al., 2004; Stern et al.,
2005; Assef et al., 2018; Poliszczuk et al., 2021). However, this selection
method favors obscured AGNs compared to the optical selection (e.g., Assef et
al., 2015; Hickox & Alexander, 2018) because the MIR continuum is less
affected by the obscuration. To disentangle unobscured AGNs from MIR-selected
AGNs, the optical-MIR color was commonly used (e.g., Polletta et al., 2006;
Hickox et al., 2017). The optical-near-IR (NIR) colors are also useful for
distinguishing obscured/unobscured AGNs (e.g., Kim et al., 2018; Kim & Im,
2018). That is, in addition to IR data, deep optical data is essential for QSO
identification (e.g., Hickox et al., 2017; Chaussidon et al., 2023).

In this study, we obtain deep optical imaging data in a wide area of the SEP
field. Combined with MIR photometric data from WISE, we identify unobscured
QSO candidates, which will be useful for extensively conducting AGN-related
studies based on upcoming SPHEREx survey data. This paper is structured as
follows. In Section 2, we describe the observations and data reduction for
optical data in the SEP field. The source detection and completeness are
discussed in Section 3. In Section 4, we describe the QSO selection based on
the MIR and optical-MIR colors and SED fitting. To validate our results, we
compare the results with the literature in Section 5. Finally, we summarize
this work in Section 6.

Table 1: Detailed information on the SEP observations with KMTNet

Filter | Site | texp | Ntotal | ZP | $m_{\mathrm{limit}}$
---|---|---|---|---|---
|  | (sec) |  | (mag) | (mag)
$B$ | SSO | 85 | $147+18$ | $28.02\pm 0.09$ | 22.59
$V$ | SSO | 85 | $147+25$ | $28.18\pm 0.10$ | 22.60
$R$ | CTIO | 85 | $147-14$ | $28.95\pm 0.04$ | 22.98
$I$ | SAAO | 85 | $147+28$ | $28.44\pm 0.06$ | 21.85

Note. — texp is the exposure time of a single frame, and Ntotal is the total
number of frames used to create the final mosaic image. The frames were
obtained by visiting $7\times 7$ patches sequentially with individual three-
point dithering, yielding a median integration time per patch of 255 s. ZP and
$m_{\mathrm{limit}}$ are the zero point magnitude and 5$\sigma$ detection
limit, respectively.

Figure 1: Illustration of the KMTNet-SEP field observed in the $BVRI$ bands.
Each red box corresponds to an FoV of $\sim$2$\arcdeg\times 2\arcdeg$, and the
entire area covered by $7\times 7$ patches is $\sim$$14.5\times 14.5$ deg2.
The dotted red boxes indicate areas excluded from this study due to the poor
quality of $B$\- and $R$-band data. The blue boxes represent the nine sub-
fields of $1\arcdeg\times 1\arcdeg$ used for standardization and background
estimation.

##  2 Observations and Data

###  2.1 Observations

Aiming to provide the complementary optical data for the SPHEREx mission, we
observed a wide area of the SEP field using the Korea Microlensing Telescope
Network (KMTNet; Kim et al., 2016) from 2019 Dec to 2020 Feb, as a part of the
KMTNet Nearby Galaxy Survey (KNGS; see Byun et al., 2022). The central
position of the full coverage was set to be $\mathrm{R.A.}\approx
4^{\mathrm{h}}44^{\mathrm{m}}$ and $\mathrm{Decl.}\approx-53\arcdeg$ to avoid
the extension of the Large Magellanic Cloud (see Clements et al., 2011; Jeong
et al., 2016).

KMTNet consists of three 1.6 m telescopes located at three different sites:
Cerro-Tololo Inter-American Observatory (CTIO), South African Astronomical
Observatory (SAAO), and Siding Spring Observatory (SSO). The site-seeing is
approximately 1$\farcs$0, and the one measured from the actual images can be
larger. Each telescope is equipped with a mosaic CCD camera composed of four
$9\mathrm{k}\times 9\mathrm{k}$ chips. Its pixel scale is 0$\farcs$4 pixel-1,
and the field of view (FoV) is $\sim$2$\arcdeg\times 2\arcdeg$. Four optical
broadband ($BVRI$) imaging is available in all telescopes by default, while
H$\alpha$ narrowband imaging is only available at KMTNet-CTIO.

We observed the SEP field with four optical broadband filters. To ensure
consistency in data quality, the same bands were assigned to the same sites
throughout the observation period. Figure 1 illustrates the observation design
and coverage. The observations were conducted by sequentially visiting
$7\times 7$ patches to cover the SEP field widely. The exposure time of a
single object frame was 85 s. A three-point dithering was performed for each
visit to fill the gaps between the CCD chips. Since the patches are adjacent
to neighboring ones, the entire area of $\sim$$14.5\times 14.5$ deg2 was
observed in each band. As a result of sequence iteration, some patches were
visited more than once. The relevant details of the observations are listed in
Table 1.

Figure 2: Comparison between $B$ magnitudes of the field stars from the APASS
catalog and their total counts measured using aperture photometry. Each panel
contains hundreds of bright stars (gray), of which only a fraction of the
unsaturated stars (red) were used for linear fitting. The relative positions
of nine sub-fields and resulting zero points are indicated in each panel.

###  2.2 Data reduction

We followed the data reduction pipeline built for the KNGS science (see Byun
et al., 2018, 2022). In short, we first discarded the poor-quality images
caused by bad weather or chip errors. Consequently, some $B$\- and $R$-band
images were forcibly discarded, excluding the corresponding patches indicated
by dotted red boxes in Figure 1. Then, we performed bias subtraction with
overscan and flat-field correction with dark-sky flats. Here, the dark-sky
flats were created by combining several tens of object-masked science images
that observed the SEP field on the same night. This effectively reduces the
amplifier-to-amplifier variation on each chip. Subsequently, bad pixel
correction was applied to science images using the bad pixel mask generated
from the dark-sky flat. Since this work aims to identify compact and bright
objects, unlike the KNGS’s main targets, we skipped global sky subtraction for
individual frames using two-dimensional polynomial models. Instead, we dealt
with it in another way that will be mentioned later. We then conducted
astrometric calibration in each chip using SExtractor (Bertin & Arnouts, 1996)
and SCAMP (Bertin, 2006). More details on the astrometric calibration for
KMTNet data can be found on the website http://kmtnet.kasi.re.kr. Finally, all
processed images were median-combined using SWarp (Bertin et al., 2002).

Since we skipped sky subtraction earlier, each science image may have a global
sky gradient. Therefore, removing the sky background from individual frames
was necessary while combining them to match the background level between
frames. So we activated the background subtraction option in SWarp with a mesh
size of 128 pixels. It is worth mentioning that the choice of mesh size can
affect the resulting image quality. For example, if the mesh size is too
small, the background may be overestimated because of the diffuse light near
bright sources. Therefore, we pre-tested with various sizes ranging from 32 to
1024 pixels to decide on an appropriate mesh size. By applying a mesh size of
128 pixels, we could properly subtract the overall sky background while also
removing the diffuse light in the vicinity of very bright foreground stars.

###  2.3 Standardization

Photometric zero points for the combined mosaic images were determined using
field stars in the SEP field using AAVSO Photometric All-Sky Survey (APASS)
DR10 catalog.111https://www.aavso.org/apass The fluxes of stars were measured
using SExtractor with a detection threshold of 15$\sigma$ and aperture size of
20$\arcsec$ in diameter.222The aperture size was set to the same as the APASS
photometry. Because of its size, blending issues might occur in crowded
regions, but it does not appear to affect estimating zero points
significantly. Since the APASS catalog does not provide $R$ and $I$
magnitudes, 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).

Because the sky-subtracted images were combined without any photometric
calibration for individual images, there would still be non-negligible
photometric uncertainty caused by variations in factors such as weather and
airmass at the time of observation. This is why standardization over such a
wide area could be challenging. For this reason, we performed separate
standardization for nine sub-fields of a $1\arcdeg\times 1\arcdeg$ area to
quantify these systematics, as shown in Figure 1. This also allowed us to take
the deviation between nine sub-fields into account in the error budget.

Figure 2 compares the APASS magnitudes of stars and their total counts in the
$B$ band. For each sub-field, a comparison was made using several hundreds of
stars with coordinate differences of less than 0$\farcs$5 (almost one pixel).
We performed a linear fitting with sigma clipping on unsaturated stars to
derive the zero points. The results show that the overall uncertainty is
relatively insignificant, while the deviation between sub-fields is much
larger in the range of 0.05 to 0.25 mag depending on the band. Therefore, we
took this into account in deriving the representative zero points. The final
zero point magnitudes presented in Table 1 were obtained by averaging the zero
points estimated for nine sub-fields, and their uncertainties were estimated
by combining the standard deviation of the zero points and the median of the
uncertainties using the quadratic sum.

Figure 3: Histograms of the sky background pixel values obtained by random
sampling. The results corresponding to the $BVRI$ bands are displayed in
different colors. The histograms follow a Gaussian distribution with a median
of approximately 0. The standard deviation increases as the wavelength becomes
longer.

###  2.4 Detection limit

Estimating the detection limit requires accurately measuring the sky
background with masking objects. One way to determine whether objects are
properly masked is to examine the histogram of the remaining sky background
pixel values, where the skewness should be close to zero. For this purpose, we
created segmentation maps for nine sub-fields with various detection
thresholds using SExtractor. For each segmentation map of each sub-field, we
measured the sky background using a sub-sampling with 500 random boxes of size
$500\times 500$ pixels. As a result, the skewness was found to approach almost
zero at the 2$\sigma$ threshold, and therefore we conservatively adopted the
1.5$\sigma$ threshold to ensure the detection of faint sources.

Figure 3 shows the histograms of the background pixel values obtained by
random sampling with a detection threshold of 1.5$\sigma$. They are well
represented by a Gaussian function with a median close to zero, revealing that
sky subtraction was performed correctly. Their standard deviations are
$\sim$2.69, 3.08, 4.41, and 7.83 for the $BVRI$ bands, respectively. We
estimated the 5$\sigma$ detection limit as the magnitude equivalent to the
standard deviation of the background within a circular aperture of diameter
5$\arcsec$333The aperture size was adopted from the median size of the point-
like sources detected in SExtractor.:

$m_{\mathrm{limit}}=-2.5\
\mathrm{log}(5\sigma\times\sqrt{\mathrm{N}})+\mathrm{ZP},$ (1)

where N is the number of pixels within the aperture ($\approx$122.7), and ZP
is the zero point magnitude of each band. As a result, the 5$\sigma$ detection
limits were estimated to be $\sim$22.59, 22.60, 22.98, and 21.85 mag for the
$BVRI$ bands, respectively. These are also shown in Table 1.

Figure 4: Histograms of magnitudes of all the detected sources (blue) and
point-like sources with a stellarity index higher than 0.9 (red). The shaded
regions represent the saturated magnitudes, and the dashed vertical lines
represent the 5$\sigma$ detection limit for point sources.

##  3 Source detection and photometry

###  3.1 Detection

Initial source detection was performed using SExtractor with a detection
threshold of 1.5$\sigma$ and other configuration parameters described in Table
2. Since the imaging quality and exact FoV differed slightly from image to
image, we individually performed photometry on each of the four ($BVRI$)
mosaic images. We then matched the initial catalogs using the equatorial
coordinate system, allowing a maximum deviation of radius 0$\farcs$5 and
adopted only the sources detected in all four bands. It is worth mentioning
that this process eliminates many artifacts, such as saturation trails and
crosstalks, caused by the KMTNet’s CCD characteristics. As a result, a total
of 994,037 sources were obtained. Note that all the magnitudes measured from
the KMTNet data in this paper are the Kron-like elliptical aperture magnitude
(MAG_AUTO).

Table 2: SExtractor configuration parameters for initial source detection

Parameter | Value
---|---
DETECT_MINAREA | 5
DETECT_THRESH | 1.5
DEBLEND_NTHRESH | 32
DEBLEND_MINCONT | 0.005
PHOT_AUTOPARAMS | 2.5, 3.5
PIXEL_SCALE | 0.4
SEEING_FWHM | 1.75 ($B$), 1.86 ($V$), 1.51 ($R$), 1.44 ($I$)
BACK_SIZE | 64

Note. — SEEING_FWHM is in a unit of arcsec. Each was measured from the co-
added $BVRI$ images individually.

Figure 4 shows the magnitude histograms of the detected sources. The
magnitudes are distributed from 10 to 25 mag, while those brighter than
12.5–14 mag are saturated. Because QSOs are likely point-like sources, we
separately highlighted sources with optically point-like morphology (i.e.,
stellarity), which were chosen to have the CLASS_STAR in SExtractor higher
than 0.9. The number of point-like sources dramatically drops at $m\sim 22$
mag as shown in Figure 4. This will be discussed in the following section.

###  3.2 Completeness

We performed imaging simulations of mock point sources to estimate the
detection completeness. First, we created 9k $\times$ 9k noise maps imitating
the sky backgrounds of the $BVRI$ mosaic images and injected 1,000 mock point
sources into each. The sources were set to follow the Moffat distribution
(Moffat, 1969) with structural parameters (FWHM and $\beta$) equal to the
values obtained from the observation data. The distribution of their total
magnitudes was nearly uniform in a range of 10 to 25 mag. Then we carried out
the photometry using SExtractor with the same configurations described in
Table 2. To avoid blending issues between sources and estimate the detection
success rate correctly, we conducted the same tests repeatedly by randomly
positioning the sources.

The upper panel of Figure 5 shows the result of the detection completeness
test. The success rates for detection appear to be achieved at more than 90%
up to about 22 mag but drop rapidly afterward. The 50% detection success
limits are estimated to be $\sim$22.56, 22.61, 23.23, and 22.40 mag for the
$BVRI$ bands, respectively. These values are upper limits because they were
derived from a pure noise map with no artifacts.

Figure 5: Top: the result of the detection completeness test as a function of
magnitudes. The detection success rates appear to be over 90% up to $\sim$22
mag, but drop rapidly afterward. Bottom: the success rate at which the
stellarity index is classified as 0.9 or higher. The success rate rapidly
decreases at $\sim$19–21 mag, while the $R$-band data shows the highest rate
at a given magnitude. The results corresponding to the $BVRI$ bands are
displayed in different colors.

Because we will use the stellarity index to pre-selected point-like sources in
Section 4, the practical completeness for QSO candidates can be much lower
when accounting for the success rate of stellarity measurement. The stellarity
classifier may be sensitive to the seeing size and the object’s brightness.
This can be the reason for the rapid drops in the number of sources after
imposing a stellarity cut, as shown in Figure 4. Therefore, we also estimated
the success rate of stellarity measurement for varying magnitudes using the
same mock images.

Figure 6: MIR color-color diagram (left) and optical-MIR color-color diagram
(right) of the KMTNet sources and SDSS QSOs. The filled contours in grayscale
represent the sources from the KMTNet data. The blue contours represent the
type 1 (unobscured) QSOs (Lyke et al., 2020), whereas the red contours
represent the type 2 (obscured) QSOs (Reyes et al., 2008). The contour levels
represent the whole dataset and [1$\sigma$, 2$\sigma$, 3$\sigma$] confidence
levels. The orange lines in the left and right panels represent the AGN
selection wedge (Mateos et al., 2012) and optical-MIR color cut for unobscured
QSO selection, respectively. The y-axis boundaries indicated by the dashed
orange lines in the right panel represent that it is not used for the QSO
selection in practice.

The bottom panel of Figure 5 shows the result of the stellarity measurement
test. The magnitude limits with a 50% chance of being classified by a
stellarity index higher than 0.9 are estimated to be $\sim$19.81, 20.39,
21.43, and 20.84 mag for the $BVRI$ bands, respectively. These are about 1–2
mag brighter than the 50% detection success limits, indicating that the pre-
selection for QSO candidates using a stellarity index higher than 0.9 would be
incomplete for objects fainter than $\sim$20 mag.

In the meantime, the $R$-band data still has the highest success rate of
stellarity measurement at a given magnitude. This is consistent with the
result that point-like sources are classified the most in the $R$-band data as
illustrated in Figure 4. For this reason, we opted to employ a stellarity
index higher than 0.9 in the $R$ band as the primary criterion for pre-
selecting QSO candidates in the next section. However, the slightly varying
seeing sizes in each patch might have led to some point sources being
misclassified as non-point sources even in the $R$ band. To address this
issue, we incorporated sources that did not satisfy the stellarity index
requirement in the $R$ band but met a stellarity cut in at least one other
band into our pre-selection for QSO candidates.

##  4 QSO selection

Before selecting unobscured QSO candidates in earnest, we pre-selected point-
like sources using the stellarity index requirement as mentioned in Section
3.2. In addition, we imposed an $I$-band magnitude cut ($m_{I}\leq 19.5$ mag
and $\mathrm{S/N}\geq 5$) to reject suspicious objects, yielding a sample of
501,614 sources. This magnitude cut meets the single-visit depth of the
SPHEREx survey, enabling the RM experiments for the selected QSOs properly.

In fact, even without pre-selection, most sources are eventually discarded
through color-based selection (see details in Section 4.1). Still, pre-
selection can eliminate unlikely objects related to this study in advance,
enabling an efficient QSO selection process. For example, the difference
between sample sizes with and without pre-selection is approximately three
times based on color-based selection results. Note that pre-selection with the
stellarity index also risks ruling out host-galaxy-dominated QSOs. However,
the photometric selection method alone has limitations in discriminating them
from non-QSOs. Hence, we decided to conduct the pre-selection to increase the
purity.

###  4.1 Color-color diagram

Since unobscured QSOs exhibit a color excess in blue bands, $U$-band
observations are required to select QSO candidates best. In other words, the
color selection based solely on $BVRI$ may be contaminated by the stellar
components and/or obscured QSOs. Hence, we selected unobscured QSO candidates
with two steps to enhance the selection effectiveness: (1) using the MIR
colors and (2) using the optical-MIR colors.

We first cross-correlated the pre-selected sources with the ALLWISE catalog
using a matching radius of 2$\arcsec$. About 87% of the sources appeared to
have counterparts, and we plotted them in the WISE color-color diagram (the
left panel of Figure 6). To clarify the color properties of
obscured/unobscured QSOs, we overlaid the spectroscopically selected SDSS QSOs
adopted from Reyes et al. (2008) and Lyke et al. (2020). It shows that the
majority of type 1 (unobscured) QSOs are selected using the AGN selection
wedge defined by Mateos et al. (2012). Applying it to our dataset with WISE
magnitude errors less than 0.5 mag, we obtained 4,547 sources remaining.
However, as previously known, MIR selection is preferentially biased toward
the obscured AGNs. This can be seen by the fact that the type 2 (obscured)
QSOs are also located on the wedge. Hence, we subsequently utilized an
optical-MIR color to exclude the obscured QSOs. The right panel of Figure 6
shows that type 1 QSOs are pretty well distinguished from type 2 QSOs in the
W1-W2 and $B$-W3 color diagrams.444The $B$-band magnitudes of SDSS QSOs were
estimated using the transformation equation of Jester et al. (2005). Based on
this experiment, we employed an optical-MIR color cut of $6<B-\mathrm{W3}\leq
10$. As a result, a sample of 4,318 sources was selected as unobscured QSO
candidates.

It is worth noting that including the W3 data restricts the magnitude range
for QSO selection because of its shallow depth, limiting it to 1–2 magnitudes
brighter compared to the case where only W1 and W2 from unWISE data were used.
However, since we focused on relatively bright QSOs, it is unlikely to affect
the result significantly. In addition, one of the main objectives of this
paper is to select unobscured QSOs. Hence, it was vital to use appropriate
selection criteria even if the completeness is slightly reduced. We conducted
several tests for QSO selection with other parameters (e.g., Chehade et al.,
2016; Eltvedt et al., 2023), but the $B-\mathrm{W3}$ color was the most
effective in differentiating between obscured and unobscured QSOs.

###  4.2 SED fitting

Utilizing the photometric data obtained with KMTNet, we performed spectral
energy distribution (SED) fitting with the LePhare code (Arnouts et al., 1999;
Ilbert et al., 2006) to ensure whether the SEDs of our QSO candidates is well-
matched with those of known AGNs. To conduct fitting more robustly, we also
utilized the archival photometric data555http://cdsxmatch.u-strasbg.fr from
WISE (W1, W2, W3, and W4) and the Two Micron All Sky Survey (2MASS; Skrutskie
et al., 2006) ($JHK$) if present. Because the 2MASS data is significantly
shallow compared to other datasets, with only $\sim$7% of our sample detected,
we adopted the VISTA Hemisphere Survey (VHS) $JHK$ data to supplement the NIR
data.

We constructed AGN SED templates by combining different kinds of libraries:
host component including stellar continuum and far-IR continuum from cold
dust, and semi-empirical AGN continuum. First, we mainly adopted the stellar
template from Lyu & Rieke (2018). In detail, the optical and NIR continuum was
produced by assuming a simple stellar population with an age of 7 Gyr from
Bruzual & Charlot (2003) and the MIR continuum from dust was empirically
determined by combining the Spitzer/IRS spectra of local early-type galaxies
with negligible SF activity. To account for the young stellar population in
the host galaxies (see Kim & Ho, 2019; Xie et al., 2021; Zhao et al., 2021),
we additionally employed the normal (S0/Sa/Sb/Sc/Sd) galaxy templates from the
SWIRE survey (Polletta et al., 2007). Next, the AGN continuum comprises the
UV/optical emissions from the accretion disk and the IR emission from the
dusty torus. Here, the IR emission is known to be complex due to the
structural diversity of the torus. For example, its shape is highly dependent
on the presence of hot and polar dust (see Braatz et al., 1993; Mor et al.,
2009; Son et al., 2022). To account for this complexity, we adopted the semi-
empirical AGN templates from Lyu et al. (2017) that include three kinds of
AGNs: normal, warm-dust-deficient, and hot-dust-deficient ones. These
templates were empirically generated from normal and dust-deficient Palomar-
Green QSO photometric data. In addition, the emission from the polar dust was
theoretically modeled in line with the intrinsic extinction of the accretion
disk (see Lyu & Rieke, 2018). Note that the SED templates constructed by this
method were validated by successfully modeling the SEDs of the low-redshift
QSOs selected from SDSS (Son et al., 2023).

Our QSO candidates can be further refined by comparing them with non-AGN SEDs
as well as AGN SEDs. For this purpose, we additionally adopted the SED
templates of inactive galaxies from COSMOS (Ilbert et al., 2009), which covers
elliptical/spiral/starburst galaxies. In this comparison, some host-galaxy-
dominated QSOs might be eliminated mainly due to the IR emission associated
with the star formation activity.

The final selection for QSO candidates was made with the Bayesian information
criterion (BIC). Under the assumption that the measurement error follows the
normal distribution, BIC is defined as $\mathrm{BIC}=\chi^{2}+k\ln(n)$, where
$k$ is the number of free parameters in the model fit and $n$ is the number of
data. Here, $\mathrm{BIC_{galaxy}}$ and $\mathrm{BIC_{QSO}}$ represent the
values for the best-fit models in inactive galaxies and QSOs, respectively. If
$\Delta\mathrm{BIC}=\mathrm{BIC_{galaxy}}-\mathrm{BIC_{QSO}}$ is larger than
10, we considered the observed SED to be better fitted with QSOs rather than
inactive galaxies (see Liddle, 2007). At the same time, using the extinction
value from the SED fitting, we attempted to sort out obscured QSOs, which
might not have been adequately excluded by the color-based selection. We
assumed only QSO candidates with $\tau_{V}<1.0$ to be unobscured ones. Figure
7 shows an example of the SED fitting for one of the QSO candidates, clearly
distinguished from the SED of inactive galaxies, especially in the MIR regime.
Based on all these criteria, a total of 2,383 sources remained as the final
unobscured QSO candidates. The full list of them is given online.

Figure 7: Example of the SED fitting for a QSO candidate. The blue circles
represent the observational data. The solid red and black lines represent the
best-fit models in the QSO and inactive galaxy templates, respectively. The
dashed green line is the best-fit model in stellar templates from the LePhare
library, which shows how different the SEDs of QSOs and stars are.

Table 3 demonstrates the form and content of the final catalog of this study.
It contains magnitudes in sixteen filters if present: KMTNet $BVRI$, Pan-
STARRS $grizy$, 2MASS $JHK$, VHS $JHK$, and four WISE bands. Alternate names
for targets identified in previous research are also included.

###  4.3 Application to the NEP field

One way to ensure the reliability of the QSO selection result obtained from a
particular data is to conduct an independent experiment with different data
but the same method. Hence, we targeted the NEP field, another favorable
region mentioned in Section 1. To select QSO candidates in the NEP field, we
utilized the Pan-STARRS1 archival data (Kaiser et al., 2002), which surveyed
the northern hemisphere of $\mathrm{Decl.}>-30\arcdeg$ using $grizy$ filters
(see Tonry et al., 2012). To ensure that we fully cover the wide area of the
SPHEREx deep field, we collected the photometric data of relatively bright
objects ($m_{i}\leq 20$ mag and $\mathrm{S/N}\geq 5$) within a $6\fdg 5$
radius circle centered at $\mathrm{R.A.}=270\arcdeg$ and
$\mathrm{Decl.}=66\fdg 5$. As a result, more than 1 million sources were
retrieved in the area of 132.7 deg2.

Since the stellarity information is unavailable in the Pan-STARRS1 data, we
selected point-like sources using the difference between the PSF magnitude and
Kron magnitude in the $r$ band instead
($m_{\mathrm{PSF}}-m_{\mathrm{Kron}}<0.05$; see Farrow et al., 2014). This
yielded a sample of 836,824 sources. We cross-correlated the list of point-
like sources with the ALLWISE catalog, and $\sim$80% of the sample had WISE
counterparts. We selected unobscured QSO candidates by adopting the same
criteria for MIR and optical-MIR colors666The $B$-band magnitudes were
estimated using the transformation equation $B=g+0.10(g-r)+0.12$ adopted from
Jester et al. (2005) as in the SEP field, resulting in 3,309 QSO candidates
remaining. Finally, we performed SED fitting and identified a total of 2,427
sources as the final unobscured QSO candidates. It is similar to the result of
the SEP field, suggesting that the QSO selection with the KMTNet data is
pretty trustworthy. The full list of them is also given online.

Table 3: Contents for the catalog of QSO candidates in the SEP and NEP fields

Column | Unit | Description
---|---|---
ID |  | Identification number in the catalog
RA | deg | R.A. (J2000) of the object’s centroid
DEC | deg | Decl. (J2000) of the object’s centroid
Bmag | mag | KMTNet $B$-band AUTO magnitude
err_Bmag | mag | Uncertainty on Bmag
Vmag | mag | KMTNet $V$-band AUTO magnitude
err_Vmag | mag | Uncertainty on Vmag
Rmag | mag | KMTNet $R$-band AUTO magnitude
err_Rmag | mag | Uncertainty on Rmag
Imag | mag | KMTNet $I$-band AUTO magnitude
err_Imag | mag | Uncertainty on Imag
gmag | mag | Pan-STARRS $g$-band magnitude
err_gmag | mag | Uncertainty on gmag
rmag | mag | Pan-STARRS $r$-band magnitude
err_rmag | mag | Uncertainty on rmag
imag | mag | Pan-STARRS $i$-band magnitude
err_imag | mag | Uncertainty on imag
zmag | mag | Pan-STARRS $z$-band magnitude
err_zmag | mag | Uncertainty on zmag
ymag | mag | Pan-STARRS $y$-band magnitude
err_ymag | mag | Uncertainty on ymag
Jmag | mag | 2MASS $J$-band magnitude
err_Jmag | mag | Uncertainty on Jmag
Hmag | mag | 2MASS $H$-band magnitude
err_Hmag | mag | Uncertainty on Hmag
Kmag | mag | 2MASS $K_{S}$-band magnitude
err_Kmag | mag | Uncertainty on Kmag
VJmag | mag | VHS $J$-band magnitude
err_VJmag | mag | Uncertainty on VJmag
VHmag | mag | VHS $H$-band magnitude
err_VHmag | mag | Uncertainty on VHmag
VKmag | mag | VHS $K_{S}$-band magnitude
err_VKmag | mag | Uncertainty on VKmag
W1mag | mag | WISE W1 (3.4$\mu$m) band magnitude
err_W1mag | mag | Uncertainty on W1mag
W2mag | mag | WISE W2 (4.6$\mu$m) band magnitude
err_W2mag | mag | Uncertainty on W2mag
W3mag | mag | WISE W3 (12$\mu$m) band magnitude
err_W3mag | mag | Uncertainty on W3mag
W4mag | mag | WISE W4 (22$\mu$m) band magnitude
err_W4mag | mag | Uncertainty on W4mag
flag1 |  | Object in the SEP field (“S”) or in the NEP field (“N”)
flag2 |  | Object identified by Flesch (2021) (“F”) and/or Yang & Shen (2023) (“Y”)

Note. — All the magnitudes are given on the AB magnitude system.

(This table is available in machine-readable format, including full data.)

##  5 External validation

###  5.1 Number counts

To confirm the completeness and reliability of our QSO selection, we compared
the number counts of our candidates with that of spectroscopically confirmed
SDSS QSOs (Richards et al., 2006). For directly corresponding to the SDSS
photometric system, we used the $i$-band magnitudes.777The $i$-band magnitudes
were estimated using the transformation equation $i=V-0.19(B-V)-0.9(R-I)+0.18$
adopted from Jester et al. (2005) Because the number counts of QSOs can vary
depending on the redshift, we limited the redshift less than 1.8 for SDSS QSOs
to mimic those of our samples that derived by the SED fitting. This can also
mitigate the risk of varying magnitudes due to the $k$-correction, which is
not currently available for our samples. Note that the $i$-band magnitudes of
SDSS QSOs were corrected for dust reddening.

Figure 8: Differential $i$-band number counts of the final QSO candidates. The
red and blue circles represent the QSO candidates from this study, whereas
gray squares represent the spectroscopically-confirmed unobscured QSOs from
SDSS (Richards et al., 2006).

Figure 8 presents the differential number-counts distributions of SDSS QSOs
and our candidates. We estimated the number of QSOs per 0.25 mag deg-2, where
an effective area of $\approx$1622 (SDSS), 190 (SEP), and 133 (NEP) deg2. The
overall trend appears to be consistent with each other, indicating that our
QSO candidates identified by the color-based selection method and SED fitting
seemingly reproduce the abundance of the spectroscopically confirmed QSOs
pretty well. At the bright end ($16<m_{i}<19$), the power-law slopes appeared
to be $0.82\pm 0.02$, $0.82\pm 0.04$, and $0.86\pm 0.04$ for SDSS QSOs and our
candidates in the SEP/NEP fields, respectively. The power-law slope for NEP
QSOs is slightly steeper than others. Compared with the number counts of SDSS
QSOs in varying redshift ranges, it seems to be caused by a lack of low-
redshift bright QSO candidates in the NEP field. However, the redshifts of our
candidates derived from the SED fitting might be uncertain. To confirm the
number counts distribution and clearly address the discrepancy between them,
spectroscopic follow-up observations are necessary.

Meanwhile, this work presents numerous QSO candidates nearly one magnitude
fainter than the SDSS QSOs since the photometric QSO selection is generally
deeper than the spectroscopic confirmation. These faint QSO candidates can be
fully detected by a single visit of the SPHEREx survey. This indicates that
our work can have a broad impact on the SPHEREx mission, for example, in
studying RM and IR variability (see Kim et al., 2021).

###  5.2 Comparison with the Literature

Finally, we cross-matched our QSO candidates with the literature. Compared
with the Million Quasars (Milliquas) catalog v7.9 (Flesch, 2021), $\sim$14% of
our samples were found to have counterparts in the catalog; the rest are newly
identified ones in this work. It decreased to $\sim$4% only considering
confirmed unobscured QSOs, demonstrating that our work greatly contributes to
increasing QSO samples in those fields. On the contrary, regarding the
completeness, we could identify one-third of the unobscured QSOs of the
Milliquas catalog in the same area. It can be attributed that different QSO
selection methods cause this incompleteness since the Milliquas catalog
contains all quasars in various papers published until late 2022. Indeed, we
found that red unobscured QSOs in the Milliquas catalog tend to be missing in
our candidates. This indicates that the $B-\mathrm{W3}$ color cut cannot
perfectly discriminate between the obscured and red unobscured QSOs.

Recently, Yang & Shen (2023, hereafter YS23) presented an extensive QSO
catalog using the photometric data from Dark Energy Survey Data Release 2
(Abbott et al., 2021), which includes the SEP field. They selected QSO
candidates with a Bayesian approach using optical-to-MIR colors, quasar
luminosity function, redshift, and proper-motion significance. We compared our
QSO candidates with the YS23 catalog and found that $\sim$98% of our
candidates are cross-matched. Although most of our samples are redundant to
them, it is still significant in that it provides cross-validation and
independent photometric estimates.

Meanwhile, the YS23 sample contains 20 times more QSO candidates than ours
within the same KMTNet-SEP field. To explore the cause of this discrepancy, we
compared the $i$-band magnitude distributions of QSO candidates selected in
the same area. As shown in Figure 9, the YS23 sample contains a large number
of faint QSO candidates, which are primarily responsible for the observed
discrepancy. However, considering the number density for magnitude bins, there
is still evident overestimation, even for bright QSO candidates. To avoid any
misinterpretation that may arise from different QSO selection methods, we
applied strict criteria to the YS23 sample by mimicking those employed in this
work. For example, we additionally imposed criteria of high confidence in
point-like morphology, high probability to the QSO model, definite WISE
detections, and a similar redshift range. The resulting number density
distribution, represented by the black histogram in Figure 9, is now
consistent with ours. The discrepancy in abundance is reduced by a factor of
five; the YS23 sample still contains many faint QSO candidates compared to
ours.

Figure 9: $i$-band magnitude distributions of QSO candidates selected within
the KMTNet-SEP field. The red histogram represents the QSO candidates from
this study. The gray histogram represents the sample of Yang & Shen (2023),
whereas the black one represents the strictly-sampled result (see the text for
details). The blue line represents the QLF model from Palanque-Delabrouille et
al. (2016).

Finally, we compared the number density distributions with the “pure
luminosity-function plus luminosity and density evolution” quasar luminosity
function (QLF) model over $z<1.5$ adopted from Table 6 of Palanque-
Delabrouille et al. (2016).888We converted the $g$-band magnitude QLF to the
$i$-band magnitude assuming $g-i=0.4$ for convenience. As shown in Figure 9,
the distributions of QSO candidates from this study are in broad agreement
with that predicted from the QLF model, supporting that our QSO selection is
reliable. Further in-depth analysis and discussion are beyond the scope of
this paper because most of the candidates have not yet been confirmed. We
expect that future space missions will be able to address this issue by
conducting spectroscopic surveys.

##  6 Summary

We searched unobscured QSOs in the SEP field through the photometric selection
method and conducted the same work in the NEP field. While both ecliptic poles
have been and will continue to be intensively covered by satellite space
missions, the SEP field has yet to be thoroughly surveyed. Therefore,
exploring the SEP field in advance is suitable for implementing research with
space missions, e.g., SPHEREx, in the future.

We obtained deep $BVRI$ images of the SEP field with KMTNet. The observation
covers the SEP field using $7\times 7$ patches, resulting in a total FoV of
$\sim$$14.5\times 14.5$ deg2. The exposure time of a single image is 85 s, and
the typical integration time per patch is 255 s. We determined the sky
background dedicatedly with a segmentation map test, and the 5$\sigma$
detection limits for point sources were estimated to be $\sim$22.59, 22.60,
22.98, and 21.85 mag for the $BVRI$ bands, respectively.

The initial source detection was carried out with a threshold of 1.5$\sigma$
to minimize missing faint objects, resulting in around a million sources
obtained. We used the criteria of optical stellarity, MIR colors, and optical-
MIR color to select unobscured QSO candidates among them. As a result, only
$\sim$0.46% of them remained. To robustly remove contaminations, such as non-
AGN galaxies or obscured QSOs, we performed SED fitting together with the
photometric data from 2MASS, VHS, and WISE based on the self-constructed AGN
SED templates. A total of 2,383 sources were finally identified as unobscured
QSO candidates. We conducted a similar identification process on the NEP field
using Pan-STARRS data and obtained a very similar result.

To validate our QSO selection, we utilized the differential number counts of
the QSO candidates in the SEP/NEP fields. The results appeared to be
consistent with that of spectroscopically confirmed QSOs. Furthermore, we
compared our QSO candidates with the previous unobscured QSO catalogs. It
revealed that this work is meaningful in providing larger QSO samples in the
ecliptic poles and cross-validating with independent photometric estimates.
Unfortunately, however, confirming QSOs with photometric data alone still has
limitations, thereby requiring spectroscopic follow-up observations. We expect
that our work will be used to provide complementary data for future space
missions, which will also lead to the confirmation of QSOs.

We are grateful to an anonymous referee for constructive comments and
suggestions. We thank Byeongha Moon for assisting with the catalog production.
This research has made use of the KMTNet system operated by the Korea
Astronomy and Space Science Institute (KASI) at three host sites of CTIO in
Chile, SAAO in South Africa, and SSO in Australia. Data transfer from the host
site to KASI was supported by the Korea Research Environment Open NETwork
(KREONET). LCH was supported by the National Science Foundation of China
(11721303, 11991052, 12011540375, 12233001) and the China Manned Space Project
(CMS-CSST-2021-A04, CMS-CSST-2021-A06). This work was supported by the
National Research Foundation of Korea (NRF) grant funded by the Korean
government (MSIT) (No. 2020R1A2C4001753 and 2022R1A4A3031306) and under the
framework of the international cooperation program managed by the National
Research Foundation of Korea (NRF-2020K2A9A2A06026245). YKS acknowledges
support from the National Research Foundation of Korea (NRF) grant funded by
the Ministry of Science and ICT (NRF-2019R1C1C1010279). D.L. and H.S.
acknowledge the support from the National Research Foundation of Korea grant
No.2021R1A2C4002725, funded by the Korea government (MSIT). D.K. acknowledges
the support by the National Research Foundation of Korea (NRF) grant (No.
2021R1C1C1013580 and 2022R1A4A3031306) funded by the Korean government (MSIT).
JHL acknowledges support from the National Research Foundation of Korea (NRF)
grant funded by the Korea government (MSIT) (No. 2022R1A2C1004025). This
research was supported by the Korea Astronomy and Space Science Institute
under the R&D program (Project No. 2023-1-830-00), supervised by the Ministry
of Science and ICT. 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 research was made possible through the use of the AAVSO Photometric All-
Sky Survey (APASS), funded by the Robert Martin Ayers Sciences Fund and NSF
AST-1412587. This publication makes use of data products from the Wide-field
Infrared Survey Explorer, which is a joint project of the University of
California, Los Angeles, and the Jet Propulsion Laboratory/California
Institute of Technology, funded by the National Aeronautics and Space
Administration. This publication makes use of data products from the Two
Micron All Sky Survey, which is a joint project of the University of
Massachusetts and the Infrared Processing and Analysis Center/California
Institute of Technology, funded by the National Aeronautics and Space
Administration and the National Science Foundation. This publication makes use
of data products based on observations obtained as part of the VISTA
Hemisphere Survey, ESO Progam, 179.A-2010 (PI: McMahon).

## References

  * Abbott et al. (2021) Abbott, T. M. C., Adamów, M., Aguena, M., et al. 2021, ApJS, 255, 20, doi: 10.3847/1538-4365/ac00b3
  * Arnouts et al. (1999) Arnouts, S., Cristiani, S., Moscardini, L., et al. 1999, MNRAS, 310, 540, doi: 10.1046/j.1365-8711.1999.02978.x
  * Assef et al. (2018) Assef, R. J., Stern, D., Noirot, G., et al. 2018, ApJS, 234, 23, doi: 10.3847/1538-4365/aaa00a
  * Assef et al. (2015) Assef, R. J., Eisenhardt, P. R. M., Stern, D., et al. 2015, ApJ, 804, 27, doi: 10.1088/0004-637X/804/1/27
  * 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
  * Astropy Collaboration et al. (2022) Astropy Collaboration, Price-Whelan, A. M., Lim, P. L., et al. 2022, ApJ, 935, 167, doi: 10.3847/1538-4357/ac7c74
  * 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
  * Braatz et al. (1993) Braatz, J. A., Wilson, A. S., Gezari, D. Y., Varosi, F., & Beichman, C. A. 1993, ApJ, 409, L5, doi: 10.1086/186846
  * Bruzual & Charlot (2003) Bruzual, G., & Charlot, S. 2003, MNRAS, 344, 1000, doi: 10.1046/j.1365-8711.2003.06897.x
  * 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. (2022) Byun, W., Sheen, Y.-K., Seon, K.-I., et al. 2022, PASP, 134, 094104, doi: 10.1088/1538-3873/ac8ed4
  * Chaussidon et al. (2023) Chaussidon, E., Yèche, C., Palanque-Delabrouille, N., et al. 2023, ApJ, 944, 107, doi: 10.3847/1538-4357/acb3c2
  * Chehade et al. (2016) Chehade, B., Shanks, T., Findlay, J., et al. 2016, MNRAS, 459, 1179, doi: 10.1093/mnras/stw616
  * Clements et al. (2011) Clements, D. L., Bendo, G., Pearson, C., et al. 2011, MNRAS, 411, 373, doi: 10.1111/j.1365-2966.2010.17689.x
  * Croom et al. (2005) Croom, S. M., Boyle, B. J., Shanks, T., et al. 2005, MNRAS, 356, 415, doi: 10.1111/j.1365-2966.2004.08379.x
  * Di Matteo et al. (2005) Di Matteo, T., Springel, V., & Hernquist, L. 2005, Nature, 433, 604, doi: 10.1038/nature03335
  * Doré et al. (2016) Doré, O., Werner, M. W., Ashby, M., et al. 2016, arXiv e-prints, arXiv:1606.07039.  https://arxiv.org/abs/1606.07039
  * Doré et al. (2018) Doré, O., Werner, M. W., Ashby, M. L. N., et al. 2018, arXiv e-prints, arXiv:1805.05489.  https://arxiv.org/abs/1805.05489
  * Eltvedt et al. (2023) Eltvedt, A. M., Shanks, T., Metcalfe, N., et al. 2023, MNRAS, 521, 3384, doi: 10.1093/mnras/stad516
  * Farrow et al. (2014) Farrow, D. J., Cole, S., Metcalfe, N., et al. 2014, MNRAS, 437, 748, doi: 10.1093/mnras/stt1933
  * Flesch (2021) Flesch, E. W. 2021, arXiv e-prints, arXiv:2105.12985, doi: 10.48550/arXiv.2105.12985
  * Hanami et al. (2012) Hanami, H., Ishigaki, T., Fujishiro, N., et al. 2012, PASJ, 64, 70, doi: 10.1093/pasj/64.4.70
  * Hickox & Alexander (2018) Hickox, R. C., & Alexander, D. M. 2018, ARA&A, 56, 625, doi: 10.1146/annurev-astro-081817-051803
  * Hickox et al. (2017) Hickox, R. C., Myers, A. D., Greene, J. E., et al. 2017, ApJ, 849, 53, doi: 10.3847/1538-4357/aa8c77
  * Ilbert et al. (2006) Ilbert, O., Arnouts, S., McCracken, H. J., et al. 2006, A&A, 457, 841, doi: 10.1051/0004-6361:20065138
  * Ilbert et al. (2009) Ilbert, O., Capak, P., Salvato, M., et al. 2009, ApJ, 690, 1236, doi: 10.1088/0004-637X/690/2/1236
  * Jarrett et al. (2011) Jarrett, T. H., Cohen, M., Masci, F., et al. 2011, ApJ, 735, 112, doi: 10.1088/0004-637X/735/2/112
  * Jeong et al. (2016) Jeong, W.-S., Ko, K., Kim, M., et al. 2016, Journal of Korean Astronomical Society, 49, 225, doi: 10.5303/JKAS.2016.49.5.225
  * Jester et al. (2005) Jester, S., Schneider, D. P., Richards, G. T., et al. 2005, AJ, 130, 873, doi: 10.1086/432466
  * Kaiser et al. (2002) Kaiser, N., Aussel, H., Burke, B. E., et al. 2002, in Society of Photo-Optical Instrumentation Engineers (SPIE) Conference Series, Vol. 4836, Survey and Other Telescope Technologies and Discoveries, ed. J. A. Tyson & S. Wolff, 154–164, doi: 10.1117/12.457365
  * Kim & Im (2018) Kim, D., & Im, M. 2018, A&A, 610, A31, doi: 10.1051/0004-6361/201731963
  * Kim et al. (2018) Kim, D., Im, M., Canalizo, G., et al. 2018, ApJS, 238, 37, doi: 10.3847/1538-4365/aadfd5
  * Kim & Ho (2019) Kim, M., & Ho, L. C. 2019, ApJ, 876, 35, doi: 10.3847/1538-4357/ab11cf
  * Kim et al. (2021) Kim, M., Jeong, W.-S., Yang, Y., et al. 2021, Journal of Korean Astronomical Society, 54, 37, doi: 10.5303/JKAS.2021.54.2.37
  * 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
  * Lacy et al. (2004) Lacy, M., Storrie-Lombardi, L. J., Sajina, A., et al. 2004, ApJS, 154, 166, doi: 10.1086/422816
  * Liddle (2007) Liddle, A. R. 2007, MNRAS, 377, L74, doi: 10.1111/j.1745-3933.2007.00306.x
  * Lupton (2005) Lupton, R. H. 2005, Transformations between SDSS magnitudes and other systems.  https://www.sdss.org/dr17/algorithms/sdssubvritransform/#Lupton(2005)
  * Lyke et al. (2020) Lyke, B. W., Higley, A. N., McLane, J. N., et al. 2020, ApJS, 250, 8, doi: 10.3847/1538-4365/aba623
  * Lyu & Rieke (2018) Lyu, J., & Rieke, G. H. 2018, ApJ, 866, 92, doi: 10.3847/1538-4357/aae075
  * Lyu et al. (2017) Lyu, J., Rieke, G. H., & Shi, Y. 2017, ApJ, 835, 257, doi: 10.3847/1538-4357/835/2/257
  * MacLeod et al. (2016) MacLeod, C. L., Ross, N. P., Lawrence, A., et al. 2016, MNRAS, 457, 389, doi: 10.1093/mnras/stv2997
  * Małek et al. (2010) Małek, K., Pollo, A., Takeuchi, T. T., et al. 2010, A&A, 514, A11, doi: 10.1051/0004-6361/200913419
  * Martini & Weinberg (2001) Martini, P., & Weinberg, D. H. 2001, ApJ, 547, 12, doi: 10.1086/318331
  * Mateos et al. (2012) Mateos, S., Alonso-Herrero, A., Carrera, F. J., et al. 2012, MNRAS, 426, 3271, doi: 10.1111/j.1365-2966.2012.21843.x
  * Matsuhara et al. (2006) Matsuhara, H., Wada, T., Matsuura, S., et al. 2006, PASJ, 58, 673, doi: 10.1093/pasj/58.4.673
  * Moffat (1969) Moffat, A. F. J. 1969, A&A, 3, 455
  * Mor et al. (2009) Mor, R., Netzer, H., & Elitzur, M. 2009, ApJ, 705, 298, doi: 10.1088/0004-637X/705/1/298
  * Oguri et al. (2006) Oguri, M., Inada, N., Pindor, B., et al. 2006, AJ, 132, 999, doi: 10.1086/506019
  * Palanque-Delabrouille et al. (2016) Palanque-Delabrouille, N., Magneville, C., Yèche, C., et al. 2016, A&A, 587, A41, doi: 10.1051/0004-6361/201527392
  * Poliszczuk et al. (2021) Poliszczuk, A., Pollo, A., Małek, K., et al. 2021, A&A, 651, A108, doi: 10.1051/0004-6361/202040219
  * Polletta et al. (2007) Polletta, M., Tajer, M., Maraschi, L., et al. 2007, ApJ, 663, 81, doi: 10.1086/518113
  * Polletta et al. (2006) Polletta, M. d. C., Wilkes, B. J., Siana, B., et al. 2006, ApJ, 642, 673, doi: 10.1086/500821
  * Reyes et al. (2008) Reyes, R., Zakamska, N. L., Strauss, M. A., et al. 2008, AJ, 136, 2373, doi: 10.1088/0004-6256/136/6/2373
  * Richards et al. (2006) Richards, G. T., Strauss, M. A., Fan, X., et al. 2006, AJ, 131, 2766, doi: 10.1086/503559
  * Shen et al. (2007) Shen, Y., Strauss, M. A., Oguri, M., et al. 2007, AJ, 133, 2222, doi: 10.1086/513517
  * Shim et al. (2022) Shim, H., Lee, D., Kim, Y., et al. 2022, MNRAS, 514, 2915, doi: 10.1093/mnras/stac1105
  * Silk & Rees (1998) Silk, J., & Rees, M. J. 1998, A&A, 331, L1, doi: 10.48550/arXiv.astro-ph/9801013
  * Skrutskie et al. (2003) Skrutskie, M. F., Cutri, R. M., Stiening, R., et al. 2003, 2MASS All-Sky Point Source Catalog, IPAC, doi: 10.26131/IRSA2
  * Skrutskie et al. (2006) —. 2006, AJ, 131, 1163, doi: 10.1086/498708
  * Son et al. (2022) Son, S., Kim, M., & Ho, L. C. 2022, ApJ, 927, 107, doi: 10.3847/1538-4357/ac4dfc
  * Son et al. (2023) —. 2023, arXiv e-prints, arXiv:2306.12927, doi: 10.48550/arXiv.2306.12927
  * Stern et al. (2005) Stern, D., Eisenhardt, P., Gorjian, V., et al. 2005, ApJ, 631, 163, doi: 10.1086/432523
  * Takagi et al. (2012) Takagi, T., Matsuhara, H., Goto, T., et al. 2012, A&A, 537, A24, doi: 10.1051/0004-6361/201117759
  * Tonry et al. (2012) Tonry, J. L., Stubbs, C. W., Lykke, K. R., et al. 2012, ApJ, 750, 99, doi: 10.1088/0004-637X/750/2/99
  * 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
  * Wisotzki et al. (1996) Wisotzki, L., Koehler, T., Groote, D., & Reimers, D. 1996, A&AS, 115, 227
  * Wright et al. (2010) Wright, E. L., Eisenhardt, P. R. M., Mainzer, A. K., et al. 2010, AJ, 140, 1868, doi: 10.1088/0004-6256/140/6/1868
  * Wright et al. (2019) —. 2019, AllWISE Source Catalog, IPAC, doi: 10.26131/IRSA1
  * Xie et al. (2021) Xie, Y., Ho, L. C., Zhuang, M.-Y., & Shangguan, J. 2021, ApJ, 910, 124, doi: 10.3847/1538-4357/abe404
  * Yang et al. (2021) Yang, J., Wang, F., Fan, X., et al. 2021, ApJ, 923, 262, doi: 10.3847/1538-4357/ac2b32
  * Yang & Shen (2023) Yang, Q., & Shen, Y. 2023, ApJS, 264, 9, doi: 10.3847/1538-4365/ac9ea8
  * Yang et al. (2018) Yang, Q., Wu, X.-B., Fan, X., et al. 2018, ApJ, 862, 109, doi: 10.3847/1538-4357/aaca3a
  * Zhao et al. (2021) Zhao, Y., Ho, L. C., Shangguan, J., et al. 2021, ApJ, 911, 94, doi: 10.3847/1538-4357/abe8d4