#  Physical Characterization of Early Galaxies in the Webb’s First Deep Field
SMACS J0723.3$-$7323

T. Morishita IPAC, California Institute of Technology, MC 314-6, 1200 E.
California Boulevard, Pasadena, CA 91125, USA  M. Stiavelli Space Telescope
Science Institute, 3700 San Martin Drive, Baltimore, MD 21218, USA

###### Abstract

This paper highlights initial photometric analyses of JWST NIRCam imaging data
in the sightline of SMACS0723, aiming to identify galaxies at redshift $z>7$.
By applying a conservative Lyman-break selection followed by photometric
redshift analysis and visual inspection, we identify four F090W-dropout and
two F150W-dropout sources, three of which were recently confirmed in an
independent spectroscopic analysis to $z=7.663$, 7.665, and 8.499. We then
supplement our sample with a photometric-redshift selection, and identify five
additional candidates at $7<z_{\rm phot}<13$. The NIRCam images clearly
resolve all sources and reveal their sub-galactic components that were not
resolved/detected in the previous imaging by Hubble Space Telescope. Our
spectral energy distribution analysis reveals that the selected galaxies are
characterized by young stellar populations (median age of $\sim 50$ Myr) of
sub-solar metallicity ($\sim 0.2\,Z_{\odot}$) and little dust attenuation
($A_{V}\sim 0.5$). In several cases, we observe extreme H$\beta$+[O III] lines
being captured in the F444W band and seen as color excess, which is consistent
with their observed high star formation rate surface density. Eight of the 11
sources identified in this study appear in at least one of the recent studies
of the same fields (Adams et al., 2022; Atek et al., 2022; Donnan et al.,
2022; Harikane et al., 2022a; Yan et al., 2022), implying the high fidelity of
our selection. We cross-match all high-$z$ galaxy candidates presented in the
five studies with our catalog and discuss the possible causes of discrepancy
in the final lists.

††journal: ApJL††software: EaZY (Brammer et al., 2008), SExtractor (v2.25.0,
Bertin & Arnouts, 1996), gsf (Morishita et al., 2018b, 2019), fsps (Conroy et
al., 2009)

##  1 Introduction

Our exploration of galaxies in the early universe has been enabled by the
Hubble Space Telescope (HST). Since the installation of Wide Field Camera 3
(WFC3), our redshift limit has been pushed toward $z\,\hbox to0.0pt{\lower
3.5pt\hbox{$\mathchar 0\relax\sim$}\hss}\raise 1.0pt\hbox{$>$}\,6$, the era
known as the epoch of reionization (Gunn & Peterson, 1965; Madau et al., 1999;
Robertson et al., 2015). A tremendous amount of effort has been invested in
the photometric search of early galaxies via Lyman break technique (Steidel et
al., 1996), revealing hundreds of galaxy candidates from various HST surveys
(e.g., Bradley et al., 2012; McLeod et al., 2015; Bouwens et al., 2015; Oesch
et al., 2018; Bowler et al., 2020). Spectroscopic followups then have
successfully confirmed $\sim 20$ of those candidates at $z>7$ (e.g., Vanzella
et al., 2011; Shibuya et al., 2012; Finkelstein et al., 2013; Stark et al.,
2015; Oesch et al., 2015; Hashimoto et al., 2018; Roberts-Borsani et al.,
2022b) up to $\sim 12$ (Oesch et al., 2016; Jiang et al., 2021).

The James Webb Space Telescope (JWST) has started revolutionizing our
understanding of galaxies and stellar populations in the era. With its
exquisite sensitivity coverage up to $\sim 5\,\mu$m, Near-Infrared Camera
(NIRCam) plays the key role in pushing the redshift front of galaxy search,
which enables the identification of sources beyond the previous limit (Naidu
et al., 2022; Castellano et al., 2022; Finkelstein et al., 2022) up to
$z\,\hbox to0.0pt{\lower 3.5pt\hbox{$\mathchar 0\relax\sim$}\hss}\raise
1.0pt\hbox{$>$}\,20$. Identification of sources at such high redshifts is
crucial to our understanding of the formation of the first galaxies and stars
(Stiavelli & Trenti, 2010; Harikane et al., 2022b; Pacucci et al., 2022).

Figure 1:  (Left): Composite RGB image of the Webb’s First Deep Field, in the
sightline of SMACS0723 (cluster field). The JWST NIRCam and NIRISS, and HST
ACS and WFC3-IR images are combined (blue:
F435W+F606W+F814W+F090W+F105W+F115W, green: F125W+F140W+F150W+F160W+F200W,
red: F277W+F356W+F444W). The locations of the final high-$z$ galaxy candidates
(Sec. 4.1) are marked. (Right): Same but for the parallel field (blue:
F090W+F150W, green: F200W+F277W, red: F356W+F444W).

As part of the Early Release Observations (ERO; Pontoppidan et al., 2022),
JWST pointed to a field of SMACS0723, a massive galaxy cluster at $z=0.390$,
dubbed as the Webb’s First Deep Field. This observing program, by utilizing
all four instruments onboard, provides a new glimpse of the universe. The new
imaging data revealed a number of potentially interesting high-redshift
sources both in the cluster center and parallel fields (Fig. 1). Here we
present our initial identification of galaxy candidates identified by Lyman-
break dropout selection at $z>7$. The simultaneous imaging of the NIRCam’s
dual modules allows us to explore early galaxies to: i) search for
intrinsically faint-but-strongly magnified sources by gravitational lensing,
and ii) provides a reference to the cluster field, as well as a glimpse of
galaxy search with JWST in normal fields. This paper is structured as follows:
in Sec. 2, we present our analyses on the ERO data, including initial
reduction and additional photometric flux calibration. We present our
selection of high-$z$ sources in Sec. 3 and analyses of their physical
properties in Sec. 4. In Sec. 5, we estimate the number density of the final
candidates and discuss the fidelity of those sources by comparing identified
high-$z$ sources in the same filed by other studies. Lastly, we discuss future
prospects and provide the summary of this study in Sec. 6. Throughout, we
adopt the AB magnitude system (Oke & Gunn, 1983; Fukugita et al., 1996),
cosmological parameters of $\Omega_{m}=0.3$, $\Omega_{\Lambda}=0.7$,
$H_{0}=70\,{\rm km~{}s^{-1}}\,{\rm Mpc}^{-1}$, and the Chabrier (2003) initial
mass function.

Table 1:  $5\,\sigma$-limiting magnitudes.

ID | WDF-C | WDF-P
---|---|---
ACS F435W | 27.2 | –
ACS F606W | 28.1 | –
ACS F814W | 27.5 | –
WFC3 F105W | 28.1 | –
WFC3 F125W | 27.9 | –
WFC3 F140W | 27.9 | –
WFC3 F160W | 27.8 | –
NIRCam F090W | 28.4 | 28.5
NIRCam F150W | 28.5 | 28.8
NIRCam F200W | 28.6 | 28.9
NIRCam F277W | 29.0 | 29.3
NIRCam F356W | 29.2 | 29.4
NIRCam F444W | 28.9 | 29.0
NIRISS F115W | 28.0 | –
NIRISS F200W | 27.9 | –

Note. — Limiting magnitudes measured in $r=0.\\!^{\prime\prime}16$ apertures.
WDF-C and WDF-P represent the cluster and parallel fields, respectively.

Figure 2:  Final color-cut sample (red circles) and phot-$z$ sample (orange
circles), shown in two color-color diagrams for F090W-dropout sources (left)
and F150W-dropout sources (right). Color tracks of sources at various
redshifts are also shown — young galaxy template of different UV
$\beta_{\lambda}$ slopes (blue dashed lines; square symbols mark redshift,
starting from $z=7.0$ and 11.8 for the left and right panels, respectively, at
an increment of $\Delta z=0.1$), low-$z$ early-type galaxy template (red
dashed lines), and foreground dwarfs (gray dots).

##  2 Data and Analyses

###  2.1 JWST Early Release Observations Program

We focus our primary analysis on the NIRCam data taken as part of the JWST
Early Release Observations Program, in the sightline of SMACS0723. The NIRCam
imaging data consist of F090/150/200/277/356/444W filters, with $\sim 2$ hr
exposure on each. We also include Near Infrared Imager and Slitless
Spectrograph (NIRISS) imaging data (F115W and F200W), taken as part of the
wide slitless spectroscopic observation in the cluster field.

We retrieve raw data (_uncal.fits) from a dedicated AWS
storage111https://outerspace.stsci.edu/display/MASTDATA/JWST+AWS+Bulk+Download+Scripts
placed by the MAST team at STScI. We reduce raw images by using the official
pipeline (ver.1.7.2 along with the reference file context of jwst_0988.pmap)
for detector calibration (DETECTOR) and photometric calibration (IMAGE STEP2),
and then combine those in a common pixel grid (IMAGE STEP3).

We made a few changes to the default pipeline processes. First, we replaced
the flat-field reference files for NIRCam images with those processed and
published by Brammer
(2022).222https://s3.amazonaws.com/grizli-v2/NIRCAMSkyflats/flats.html We find
that those new flats, created with in-flight data, improve removing artifacts
in the reduced images. We also adopt the magnitude zeropoints for NIRCam
published by the same author.333https://github.com/gbrammer/grizli/pull/107
While the latest version of the official reference files includes a similarly
improved set of magnitude zeropoints (Boyer et al., 2022), our choice of
adopting those by Brammer (2022) was made for the consistency with the flat
files adopted above, though the difference is small ($\sim$ a few percent)
across the filters between the two studies. Lastly, during STEP2 and STEP3, we
include an extra step to subtract $1/f$-noise in _cal.fits images by using
bbpn444https://github.com/mtakahiro/bbpn, which follows the procedure
presented in Schlawin et al. (2021). The final pixel scale is set to
$0.\\!^{\prime\prime}0315$.

Images are aligned to the common astrometric WCS in multiple steps. First, we
align the single F444W image to the GAIA-DR3 WCS frame. The rest of the NIRCam
images and the NIRISS images are then aligned to the F444W image, by using
both point sources and compact galaxies. We refine the pixel grid of all
images to the one of F444W
reproject555https://reproject.readthedocs.io/en/stable/.

To homogenize the PSF of the NIRCam and NIRISS images to the one of F444W, we
generate convolution kernels by following the same procedure in Morishita
(2021). Bright stars are identified by cross-matching our sources with the
GAIR DR3 catalog. We visually inspect each of them and exclude those saturated
or contaminated by neighboring objects. We then resample each PSF to align
those at a sub-pixel level and stacked stars of each filter to generate a
median PSF. We then provide the median-stacked stars to pypher (Boucaud et
al., 2016) and generate convolution kernels for each filter. The quality of
convolved PSFs is excellent, with $\ll 1\,\%$ agreement in the encircled flux
at radius $r=0.\\!^{\prime\prime}16$, within which our photometric fluxes are
extracted.

We also include the HST data available in the cluster field, originally taken
in the RELICS program (Coe et al., 2019; Salmon et al., 2020), to supplement
our selection. We retrieve raw data from MAST, which consist of seven filters
(F435/606/814/105/125/140/160W) and reduce those by using borgpipe (Morishita,
2021). It is noted that the HST coverage is only for the cluster field. For
the HST images, we repeat a similar analysis as for NIRCam, but convolve
images to the WFC3-IR F160W PSF, which has a similar FWHM as of F444W. This
choice was made because of significantly different PSF shapes in JWST and HST
images. While this may leave systematic offsets between the two instruments,
those will be removed in the following analysis (Sec.2.2).

For photometric flux extraction, we run borgpipe, a photometric pipeline
designed for HST and JWST data reduction and photometric analyses. borgpipe
runs SExtractor (ver.2.25.0; Bertin & Arnouts, 1996) for image detection and
flux extraction, while it includes extra steps for sophisticated error
estimate of images, aperture correction, and Lyman-break dropout selection of
high-$z$ candidates, as done in similar previous studies (Trenti et al., 2012;
Morishita et al., 2018a). We create infrared stacks of the F277/356/444W
filters as the detection image for both fields. We set configuration
parameters of SExtractor as follows: DETECT_MINAREA 0.0162 arcsec2,
DETECT_THRESH 1.5, DEBLEND_NTHRESH 64, DEBLEND_MINCONT 64, BACK_SIZE 20, and
BACK_FILTSIZE 3. The photometric magnitude limits measured with
$r=0.\\!^{\prime\prime}16$ apertures are listed in Table 1.

###  2.2 Zero-point correction across filters and instruments

Next, we aim to calibrate flux zeropoints of imaging data, including offsets
caused by the different PSF profiles of F444W and F160W. We first correct the
relative flux offset between the two telescopes by scaling pseudo F150W
fluxes, interpolated from the observed HST F140W and F160W fluxes, to NIRCam
F150W. We use bright and isolated objects with $S/N>20$ taken from the
photometric catalog constructed in the previous section. For the pseudo F150W
fluxes, we assume a linear slope between the two HST filters and use the value
at the wavelength center of the NIRCam F150W filter. We adopt the median value
of the correcting factor, $f_{150}/f_{140+160}=1.264$. We then run eazypy, a
python wrapper of photometric redshift code EaZY (Brammer et al., 2008), to
fine-tune magnitude zeropoints across all filters. We run redshift fitting on
those with spectroscopic redshifts (Mahler et al., 2022), both cluster member
galaxies and background emission line galaxies. The derived correction factor
is $-1.5$–$4.4\,\%$ relative to F150W as the pivot (Table 2), requiring only
minor correction.666The derived correction factors were $30$–$40\%$ in the
NIRCam filters in our original analysis using the calibration context of
jwst_0916.pmap and the ver.1.6.0 pipeline and consistent with those reported
in Rigby et al. (2022). We apply the correction to the photometric catalog.
For the NIRCam images in the parallel field, where no spectroscopic
measurement or HST images are available, we apply the same zeropoint-offsets
derived for the cluster field.

Table 2:  Magnitude-zeropoint correction factors (Sec. 2.2).

Instrument | Filter | Correction factor
---|---|---
ACS | F435W | 1.025
ACS | F606W | 1.009
ACS | F814W | 1.032
WFC3 | F105W | 1.017
WFC3 | F125W | 1.000
WFC3 | F140W | 1.044
WFC3 | F160W | 0.994
NIRCam | F090W | 1.016
NIRCam | F150W | 1.000
NIRCam | F200W | 1.029
NIRCam | F277W | 1.016
NIRCam | F356W | 1.016
NIRCam | F444W | 1.032
NIRISS | F115W | 0.985
NIRISS | F200W | 0.985

Note. — NIRCam F150W filter is used as the pivot point. For HST filters, the
correction factors here are applied after the universal correction of $1.264$
for aperture correction (Sec. 2).

##  3 Selection of high-redshift galaxy candidates

In what follows, we present our selection of galaxy candidates at $z\,\hbox
to0.0pt{\lower 3.5pt\hbox{$\mathchar 0\relax\sim$}\hss}\raise
1.0pt\hbox{$>$}\,7$. To make our identification of high-$z$ candidates as
comprehensive as possible, we adopt two selection methods — conventional
color-cut selection and supplemental photometric-redshift (phot-$z$)
selection. The former provides a sample of high purity, whereas the latter
covers fainter sources that fall outside the color-cut boundary.

###  3.1 Color-cut Selection

Our first selection consists of three steps — Lyman break color-cut selection,
photometric redshift analysis, and visual inspection. For the filters
available in this study, we explore four different redshift ranges.

####  3.1.1 Lyman-break color cut

We select candidate galaxies in the following four redshift ranges enabled by
six NIRCam filters available in both fields:

$F090W-dropouts$:

$S/N_{\rm 150}>3.0$ $m_{090}-m_{150}>1.7$ $m_{150}-m_{200}<0.2$ $S/N_{\rm
090(,435,606,814)}<2.0$

$F150W-dropouts$:

$S/N_{\rm 200}>3.0$ $m_{150}-m_{200}>1.0$ $m_{200}-m_{277}<0.3$ $S/N_{\rm
090(,435,606,814,105,115,125)}<2.0$

$F200W-dropouts$:

$S/N_{\rm 277}>3.0$ $m_{200}-m_{277}>1.6$ $m_{277}-m_{356}<0.25$ $S/N_{\rm
090,150(,435,606,814,105,115,125,140,160)}<2.0$

$F277W-dropouts$:

$S/N_{\rm 356}>3.0$ $m_{277}-m_{356}>1.4$ $m_{356}-m_{444}<0.25$ $S/N_{\rm
090,150,200(,435,606,814,105,115,125,140,160)}<2.0$

In addition, we also impose $S/N>4$ in detection images for all selections
above, to minimize the fraction of artifacts.

When NIRISS or HST images are available (i.e. the cluster field), we
supplement our selection by adding those to the non-detection filters. The
color boundary of each selection is designed to effectively capture young,
relatively dust-free galaxies at $7\,\hbox to0.0pt{\lower
3.5pt\hbox{$\mathchar 0\relax\sim$}\hss}\raise 1.0pt\hbox{$<$}\,z\,\hbox
to0.0pt{\lower 3.5pt\hbox{$\mathchar 0\relax\sim$}\hss}\raise
1.0pt\hbox{$<$}\,11$, $11\,\hbox to0.0pt{\lower 3.5pt\hbox{$\mathchar
0\relax\sim$}\hss}\raise 1.0pt\hbox{$<$}\,z\,\hbox to0.0pt{\lower
3.5pt\hbox{$\mathchar 0\relax\sim$}\hss}\raise 1.0pt\hbox{$<$}\,15$,
$15\,\hbox to0.0pt{\lower 3.5pt\hbox{$\mathchar 0\relax\sim$}\hss}\raise
1.0pt\hbox{$<$}\,z\,\hbox to0.0pt{\lower 3.5pt\hbox{$\mathchar
0\relax\sim$}\hss}\raise 1.0pt\hbox{$<$}\,20$, and $21\,\hbox to0.0pt{\lower
3.5pt\hbox{$\mathchar 0\relax\sim$}\hss}\raise 1.0pt\hbox{$<$}\,z\,\hbox
to0.0pt{\lower 3.5pt\hbox{$\mathchar 0\relax\sim$}\hss}\raise
1.0pt\hbox{$<$}\,28$, respectively.

It is noted that in the F090W-dropout selection, we require non-detection in
F090W ($z$-band dropout) while we calculate the color of Lyman break by using
the flux upper limit estimated in the image in the photometric step above;
thus, F090W$-$F150W color of the sources selected in this selection is an
upper limit (Fig. 2).

In Fig. 2, we show color trajectories of three power-law UV slopes,
$\beta_{\lambda}=-1.5,-2,-2.5$, and $-3$). Our color-cut selection is designed
to effectively minimize low-$z$ interlopers, such as foreground dwarfs and
low-$z$ galaxy populations. Colors of dwarf stars, taken from the IRTF
spectral library (Rayner et al., 2003) and the SpeX prism library (Burgasser,
2014), and galaxies at $0<z<4.5$ are shown.

####  3.1.2 Photometric-redshift cut

We apply photometric redshift cut to those selected in the previous color-cut
section, in a similar same way as performed in previous work (Morishita et
al., 2018a; Roberts-Borsani et al., 2022a; Ishikawa et al., 2022). This is to
minimize the fraction of low-$z$ interlopers such as galaxies with old stellar
populations (e.g., Oesch et al., 2016) and foreground dwarfs (e.g., Morishita
et al., 2020) that may migrate to the color boundary box due to photometric
scatters. We run EaZY with the default magnitude prior set to the filter that
covers the wavelength of Lyman break. The redshift range is set to $0<z<30$,
with a step size of $\log(1+z)=0.01$. By following Morishita et al. (2018a),
we exclude those with $p(z<6.5)>0.2$, i.e. total redshift probability at
$z<6.5$ is greater than $20\,\%$.

Table 3: List of our final candidates.

ID | R.A. | Decl. | $m_{\rm UV}$ | $z_{\rm phot.}$ | $p(z>6.5)$ |  $\mu$† | Comments | Claimed‡
---|---|---|---|---|---|---|---|---
| degree | degree | mag |  |  |  |  |
F090W-dropouts ($7\,\hbox to0.0pt{\lower 3.5pt\hbox{$\mathchar
0\relax\sim$}\hss}\raise 1.0pt\hbox{$<$}\,z\,\hbox to0.0pt{\lower
3.5pt\hbox{$\mathchar 0\relax\sim$}\hss}\raise 1.0pt\hbox{$<$}\,11$)
WDF-C-769 / ZD1 | 110.8343506 | -73.4345093 | 26.6 | $7.34_{-0.07}^{+0.05}$ | 1.00 | 1.54 | $z_{\rm spec.}=7.663$ | [1],[2]
WDF-C-1045 / ZD2 | 110.8449173 | -73.4350433 | 26.0 | $8.55_{-0.18}^{+0.15}$ | 1.00 | 1.56 | $z_{\rm spec.}=7.666$ | [1],[2]
WDF-C-3186 / ZD3 | 110.8598175 | -73.4491272 | 27.1 | $9.03_{-0.24}^{+0.31}$ | 1.00 | 15.3 | $z_{\rm spec.}=8.498$ | [1],[2],[3]
WDF-P-1762 / ZD4 | 110.6461792 | -73.4758453 | 27.9 | $10.27_{-0.64}^{+0.71}$ | 1.00 | – |  | [1],[3]
F150W-dropouts ($10\,\hbox to0.0pt{\lower 3.5pt\hbox{$\mathchar
0\relax\sim$}\hss}\raise 1.0pt\hbox{$<$}\,z\,\hbox to0.0pt{\lower
3.5pt\hbox{$\mathchar 0\relax\sim$}\hss}\raise 1.0pt\hbox{$<$}\,14$)
WDF-C-1730 / HD1 | 110.8452759 | -73.4404297 | 28.1 | $13.85_{-1.39}^{+1.36}$ | 0.81 | 2.00 |  | [4]
WDF-P-3504 / HD2 | 110.6964722 | -73.4768295 | 28.5 | $13.41_{-1.56}^{+1.25}$ | 0.98 | – |  |
Phot-z sample
WDF-C-1622 / ZD5p | 110.8616486 | -73.4362259 | 28.2 | $8.21_{-0.79}^{+1.02}$ | 1.00 | 1.63 |  | [3]
WDF-P-949 / ZD6p | 110.6147919 | -73.4774246 | 27.7 | $10.87_{-0.64}^{+0.65}$ | 1.00 | – | Combined with WDF-P-963. | [1]
WDF-P-963 / ZD6p | 110.6146545 | -73.4773712 | 28.2 | $7.35_{-0.18}^{+3.65}$ | 0.99 | – | Combined with WDF-P-949. | [1]
WDF-P-1095 / ZD7p | 110.6909027 | -73.4629288 | 29.0 | $10.58_{-1.35}^{+0.97}$ | 0.97 | – |  | [3]
WDF-P-3004 / ZD8p | 110.7211685 | -73.4687576 | 28.6 | $10.02_{-1.51}^{+1.01}$ | 0.84 | – |  |
WDF-C-1152 / HD3p | 110.7653885 | -73.4514236 | 27.9 | $12.70_{-1.31}^{+2.05}$ | 0.81 | 4.72 | Close to a dusty galaxy at $z\sim 2.5$ |

Note. — For the color cut selection, no dropout sources other than
F090W-dropouts were identified. Photometric redshift uncertainty is estimated
by only using the probability distribution at $z>6.5$. WDF-P-949 and WDF-P-963
are treated as a single system in the main text (Sec. 4.1.2). $\dagger$ Median
magnification of various lens models (Jullo et al., 2007; Oguri, 2010; Caminha
et al., 2022; Mahler et al., 2022) calculated at the position of the source.
$\ddagger$ Cross-appearance of the source in other studies. 1:Adams et al.
(2022) 2:Atek et al. (2022) 3:Donnan et al. (2022) 4:Yan et al. (2022). None
of our samples appears in Harikane et al. (2022a).

####  3.1.3 Visual Inspection

Lastly, we visually inspect the sources remaining after the previous two
steps. In this step, we exclude those near diffraction spikes or possible
artifacts, such as flux residuals of snowballs (Rigby et al., 2022). Sources
are excluded when either of the inspectors (TM, MS) raises a flag that the
object is an obvious artifact, blending with bright sources (where flux
measurement would be challenging), or shows a significant amount of positive
pixels in the non-detection filters.

###  3.2 Supplemental phot-$z$ selection

While the source selection based on the color cut is designed to provide a
sample of high purity, it may not cover all possible high-$z$ sources,
especially those 1. near the color selection window or 2. faint, whose Lyman
break color is a relatively weak upper limit. To improve the completeness, we
here aim to identify possible sources as a supplemental sample.

We run EaZY as in Sec. 2.2 on those detected at $S/N>4$ in the IR-detection
filter and then select those with $p(z>6.5)>0.8$. To further eliminate low-$z$
interlopers, we impose non-detection ($S/N<2$) in all filters that are
blueward to the Lyman break inferred from the derived redshift. We then
visually inspect individual candidates, to exclude any suspicious objects and
artifacts.

Figure 3:  Postage stamps of the final color-cut sample (F090W-dropouts for
the first four and F150W-dropouts for the following two). For each object, the
top panel shows the image stamps ($3.2\times 3.2$ arcsec2). ‘DET’ represents
the IR-stacked image. The source detection segmentation map and rgb composite
(NIRCam F090W/F150W/F200W) image are also shown in the two right panels.

Figure 4:  Same as Fig. 3 but for the phot-$z$ sample. IDs 949 and 963 are
analyzed and presented as a single system, ZD6p.

##  4 Results

From the color-cut selection, we identified four F090W-dropouts, two
F150W-dropouts, and none from the higher redshift bins (Fig. 3). From the
phot-$z$ selection, we identify additional five sources at $7<z<11$ (Fig. 4).
Our final candidates are summarized in Table 3.

In what follows, we provide an overview of the final candidates and their
physical properties obtained by spectral energy distribution (SED) and
morphological analyses. For those in the cluster field, magnification factors
are estimated by taking the median value of various lens models (Jullo et al.,
2007; Oguri, 2010; Caminha et al., 2022; Mahler et al., 2022) at the position
of each source.

###  4.1 Overview of selected candidate galaxies

####  4.1.1 The color-cut sample

In Fig. 3, we show postage stamps of the final color-cut samples. For the five
sources in the cluster field, they are not detected in the optical filters of
HST in addition to their clear dropout in the F090W band. Indeed, all three of
those galaxies are spectroscopically confirmed in recent work (Carnall et al.,
2022; Tacchella et al., 2022), to $z=7.663$ (ZD1), 7.665 (ZD2), and 8.498
(ZD3).

The NIRCam images resolve all candidates and reveal their morphology in detail
in rest-frame UV ($\sim 0.15\,\mu$m in F090W) to optical ($\sim 0.44\,\mu$m in
F444W). The comparison of the NIRCam images to WFC3-IR images clearly
showcases the resolving power of JWST. Aside from multi-band postage stamps
above, in Fig. 5 we also show the zoom-in image of each candidate in WFC3
F160W and NIRCam images.

ZD1 is isolated from other neighboring sources. The source consists of one
primary core and a small component to its right. To provide a detailed view of
this sub-component, we perform single component Sérsic fit in the F150W image
(Fig. 5) using galfit (Peng et al., 2002), where we clearly see non-negligible
residuals. In the stamp of ZD2, there are two sources to the lower-right
direction but they are faint and reasonably apart from ZD2, and their flux
contamination to ZD2 is considered small. Same as for ZD1, single Sérsic fit
reveals multiple components, including a diffuse extended component. ZD3 has
two compact companions that align in one direction. SExtractor successfully
deblends each of the three sources. Among the three sources, ZD3 is the
brightest and thus the flux contamination is also considered small. Indeed,
Sérsic fit to ZD3, with the two close companions included simultaneously,
reveals smoother residuals compared to the other two. Thus, ZD3 is
characterized as compact, despite its higher magnification. We present the
measured sizes in Table 4. The middle component (ID 3198 in the segmentation
map) exhibits similar dropout color as for ZD3. However, we find that this
object is detected in F105W, whereas ZD3 is undetected in the same band. In
addition, while uncertainty is large, the photometric redshift of ID 3198 is
$z\sim 1.8$. We thus conclude that, despite the proximity and the similarity
in color, this object is not likely at the same redshift.

ZD4 is identified in the parallel field, with photometric redshift of $z_{\rm
phot.}\sim 10.3\pm 0.7$. While the photometric redshift distribution extends a
relatively wider range, the solution is unique to high-$z$, with $p(z<6.5)\ll
1\%$. ZD4 shows extended morphology, elongated to the vertical direction.
Interestingly, it shows two separate components in the F150W and F200W
filters, while it is barely separated in the long channel of NIRCam, possibly
due to lower spatial resolution. Since our detection is based on the F444W
image, these (possible) two components are not deblended in our catalog.

In the F150W-dropout selection, we identify two candidates. HD1 shows clear
non-detection in F090W, F115W, and F150W filters. The object is resolved and
shows multiple components, while those components are not deblended in our
detection configuration. HD2 is a faint object and flux measurements come with
relatively large errors. Despite, the measured F150W-F200W color satisfies the
color criterion, with the redshift probability of $p(z>6.5)=0.98$. It is noted
that while the F150W image shows some positive pixels at the location of the
object, non-detection in the band is not required in this selection.

Among the color-cut objects identified in the cluster field, ZD3, HD2, and
HD3p were not detected in the public photometric catalog by the RELICS
team777https://relics.stsci.edu. ZD1 and ZD2 are detected and listed in the
catalog (with their IDs 62 and 75) but with photometric redshift of $z\sim
1.6$ and $2.0$. Thus, none of the four galaxies was identified as a candidate
high-$z$ source from the HST-only dataset. It is worth mentioning that the two
objects are flagged as detected in the ACS F606W filter in the RELICS catalog,
with $2.5$ and 3.1 $\sigma$, respectively, while not in the other two ACS
filters (F435W and F814W). By revisiting their original F606W image, we could
not confirm any convincing detection but only a small fraction of positive
pixels near the position in both cases. The false detection might have
rejected those objects as a high-$z$ candidate in their original selection.

Figure 5:  Zoom-in image ($3.\\!^{\prime\prime}2\times 3.\\!^{\prime\prime}2$)
of the final candidates at $z<12$ (in NIRCam F150W) and $z>12$ (NIRCam F200W).
HST WFC3-IR F160W is shown for those in the cluster field. In the right panel
we show the residual image after subtracting a single Sérsic component in the
NIRCam image. Surrounding objects are either fitted simultaneously or masked.

Figure 6:  Spectral energy distribution of the final candidates. The best-fit
and the 16-84th percentile range are shown (gray line and hatched region).
$2\,\sigma$ error is shown along with the photometric fluxes (red symbols).
Blue diamonds represent the model fluxes of the corresponding filter.
Photometric redshift probability distribution (gray for EaZY and red for gsf)
is shown in the inset. For those with spectroscopic redshift available (yellow
vertical lines in the $p(z)$ panel), redshift is fixed during the SED fitting
process. It is noted that fluxes and best-fit parameters are not corrected for
lens magnification in the figure.  Table 4: Physical properties of the final
candidates.

ID | $z^{\dagger}$ | $M_{\rm UV}$ | $\beta_{\lambda}$ | $\log M_{*}$ | SFR | $\log t_{*}$ | $\log Z_{*}$ | $A_{V}$ | $r_{e}$ | $n$
---|---|---|---|---|---|---|---|---|---|---
|  | mag |  | $M_{\odot}$ |  $M_{\odot}$ / yr | Gyr | $Z_{\odot}$ | mag | pc |
ZD1 | $7.663$ | $-20.05_{-0.07}^{+0.05}$ | $-2.25_{-0.05}^{+0.03}$ | $8.40_{-0.20}^{+0.18}$ | $1.15_{-0.45}^{+0.54}$ | $-1.54_{-0.69}^{+0.53}$ | $-0.43_{-0.43}^{+0.23}$ | $0.53_{-0.18}^{+0.14}$ | $119\pm 7$ | $1.57\pm 0.37$
ZD2 | $7.665$ | $-20.51_{-0.05}^{+0.05}$ | $-1.77_{-0.03}^{+0.02}$ | $9.03_{-0.14}^{+0.13}$ | $5.74_{-1.69}^{+1.72}$ | $-1.57_{-0.30}^{+0.35}$ | $-0.23_{-0.20}^{+0.07}$ | $0.79_{-0.15}^{+0.23}$ | $282\pm 8$ | $0.37\pm 0.06$
ZD3 | $8.499$ | $-17.22_{-0.09}^{+0.08}$ | $-1.88_{-0.07}^{+0.09}$ | $7.75_{-0.15}^{+0.19}$ | $0.27_{-0.07}^{+0.11}$ | $-1.94_{-0.78}^{+0.82}$ | $-0.78_{-0.37}^{+0.37}$ | $1.02_{-0.19}^{+0.13}$ | $10\pm 1$ | $0.20\pm 0.27$
ZD4 | $9.78_{-1.15}^{+1.14}$ | $-19.43_{-0.12}^{+0.12}$ | $-1.99_{-0.12}^{+0.28}$ | $8.55_{-0.90}^{+0.80}$ | $0.30_{-0.18}^{+0.66}$ | $-0.80_{-0.70}^{+0.30}$ | $-0.48_{-0.48}^{+0.59}$ | $0.20_{-0.15}^{+0.11}$ | $237\pm 22$ | $0.31\pm 0.27$
ZD5p | $8.65_{-0.72}^{+0.45}$ | $-18.19_{-0.41}^{+0.28}$ | $-1.45_{-0.26}^{+0.42}$ | $8.62_{-1.03}^{+0.51}$ | $1.14_{-1.06}^{+4.21}$ | $-1.33_{-0.58}^{+0.68}$ | $-0.81_{-0.47}^{+0.49}$ | $0.67_{-0.43}^{+1.09}$ | $184\pm 12$ | $0.20\pm 0.17$
ZD6p | $7.58_{-0.40}^{+0.43}$ | $-19.87_{-0.09}^{+0.10}$ | $-1.72_{-0.00}^{+0.06}$ | $8.74_{-0.07}^{+0.09}$ | $3.07_{-0.44}^{+0.62}$ | $-1.47_{-0.27}^{+0.23}$ | $-0.09_{-0.05}^{+0.09}$ | $0.49_{-0.08}^{+0.13}$ | $176\pm 23$ | $1.55\pm 0.91$
ZD7p | $10.50_{-0.96}^{+1.06}$ | $-18.52_{-0.26}^{+0.16}$ | $-1.89_{-0.08}^{+0.19}$ | $8.87_{-0.67}^{+0.32}$ | $0.64_{-0.47}^{+1.87}$ | $-0.74_{-0.95}^{+0.25}$ | $-0.90_{-0.74}^{+0.72}$ | $0.58_{-0.38}^{+0.60}$ | $101\pm 18$ | $0.20\pm 0.78$
ZD8p | $10.01_{-1.15}^{+0.96}$ | $-18.51_{-0.35}^{+0.22}$ | $-1.93_{-0.11}^{+0.32}$ | $8.54_{-0.42}^{+0.34}$ | $1.27_{-0.99}^{+1.39}$ | $-1.13_{-0.67}^{+0.47}$ | $-1.17_{-0.52}^{+0.96}$ | $0.67_{-0.50}^{+0.54}$ | $217\pm 31$ | $0.40\pm 0.44$
HD1 | $13.62_{-1.22}^{+1.27}$ | $-19.18_{-0.21}^{+0.19}$ | $-1.99_{-0.11}^{+0.20}$ | $8.51_{-0.40}^{+0.43}$ | $1.25_{-0.68}^{+1.70}$ | $-1.27_{-0.61}^{+0.55}$ | $-1.06_{-0.68}^{+0.91}$ | $0.45_{-0.27}^{+0.34}$ | $160\pm 29$ | $0.55\pm 0.37$
HD2 | $13.16_{-0.91}^{+1.36}$ | $-18.99_{-0.30}^{+0.22}$ | $-1.47_{-0.07}^{+0.04}$ | $8.56_{-0.86}^{+1.04}$ | $0.65_{-0.57}^{+3.48}$ | $-0.89_{-0.66}^{+0.32}$ | $-0.43_{-1.24}^{+0.59}$ | $0.41_{-0.22}^{+0.66}$ | $129\pm 13$ | $0.20\pm 0.50$
HD3p | $13.45_{-1.40}^{+1.12}$ | $-18.23_{-0.28}^{+0.19}$ | $-2.30_{-0.12}^{+0.16}$ | $7.94_{-0.45}^{+0.47}$ | $0.30_{-0.19}^{+0.49}$ | $-1.29_{-0.80}^{+0.59}$ | $-1.03_{-0.68}^{+0.96}$ | $0.52_{-0.37}^{+0.47}$ | $29\pm 6$ | $0.20\pm 0.76$
Median value
$7<z<14$ (all) | $9.78$ | $-18.99$ | $-1.89$ | $8.55$ | $1.14$ | $-1.29$ | $-0.78$ | $0.53$ | $160$ | $0.31$
$7<z<10$ ($N=6$) | $8.08$ | $-19.65$ | $-1.82$ | $8.59$ | $1.15$ | $-1.50$ | $-0.45$ | $0.60$ | $180$ | $0.34$
$10<z<14$ ($N=5$) | $13.16$ | $-18.52$ | $-1.93$ | $8.54$ | $0.65$ | $-1.13$ | $-1.03$ | $0.52$ | $129$ | $0.20$

Note. — $\dagger$: posterior redshift derived by gsf. ZD1, ZD2, and ZD3 are
fixed to spectroscopic redshift during the fit. $r_{e}$: circularized
effective radius. $n$: Sérsic index. $t_{*}$: mass-weighted age. $Z_{*}$:
mass-weighted metallicity. Measurements are corrected for magnification.

####  4.1.2 The phot-z sample

In Fig. 4, we show six galaxies (two of which are treated as a single system;
see below) identified in the phot-$z$ selection. Most of them are located near
the boundary of the color selection window. Phot-$z$ samples are in general
fainter than the color-cut sample; all our phot-$z$ samples have Lyman-break
color (or upper limit) that is not red enough to be in the selection window.

HD3p and ZD5p are identified in the cluster field. HD3p is located next to a
bright galaxy at $z_{\rm phot}=2.5$, showing an elongated morphology. The lens
model indicates strong stretch at $\sim 45$ deg direction, which aligns with
the observed elongation. It is noted that while the F150W image shows some
positive pixels at the location of the object, detection is marginal
($S/N_{150}\sim 1.8$). ZD5p is an isolated system but shows multiple
components, clearly resolved in the NIRCam F200W stamp image. All components
clearly drop out in F090W.

In the parallel field, three galaxies are identified. Among those, IDs 949 and
963 are of particular interest sources, being located next to each other.
While SExtractor deblends those into two different objects, for their
proximity and similarity in color (including clear dropout in F090W), we treat
the two objects (949+963; ZD6p) as a single system in the SED fitting analysis
below. It is noted that to its northern part, there is another object (ID 793)
of a similar color. We did not include this segment in our photometric
analysis, because of some residual flux that appears in the dropout band. We
attribute this segment to the galaxy at its west (ID 942), whose redshift is
$z_{\rm phot.}\sim 5$.

The remaining two phot-$z$ candidates, ZD7p and ZD8p, are isolated and
compact. ZD8p shows a small sub-component to its west in three filters (F200W,
F277W, and F356W), but this small component is not visible in F150W. None of
the phot-$z$ candidates in the cluster field (ZD5p and HD3p) are listed in the
RELICS catalog.

###  4.2 Spectral energy distribution fitting

To investigate the physical properties of the selected candidates, we conduct
spectral energy distribution fitting. We use SED fitting code
gsf888https://github.com/mtakahiro/gsf (Morishita et al., 2018b, 2019). For
those with spec-$z$, we set redshift as a fixed parameter during fitting. For
the others, we set redshift as a free parameter within the 16-84 th percentile
range derived by EaZY (Table 3). We use a template library generated by fsps
(Conroy et al., 2009), with the dust attenuation law derived for the SMC
(Gordon et al., 2003). We adopt a non-functional form for the star formation
history as presented in Morishita et al. (2019); Morishita (2021), with the
age pixels set to [1, 3, 10, 30, 100, 300] Myrs. This configuration allows
flexibility in determining the star formation history and provides a clear
insight into the stellar populations that consists of the observed SED. During
our initial test, we found that the inclusion of emission lines is crucial to
successfully fit the observed fluxes. We add an emission component to our
fitting model, with two free parameters; ionization parameter set to $\log
U\in[-3:-1]$, and the amplitude of this component.

We show the results of SED analyses in Fig. 6 and summarize the derived
physical parameters in Table 4. All galaxies are in general fitted well with
young stellar populations. The unprecedented coverage up to $5\,\mu$m by
NIRCam reveals flux excess in the F444W filter, which is attributed to
significant emission lines. For galaxies at $7<z<9$, strong emission lines
such as [O III] doublet and ${\rm H\beta}$ fall in the wavelength range of
F444W, whereas galaxies at higher redshift show flux excess in the same
filter, which is likely by strong [O ii]. The observed excess in the $m_{\rm
F356W}-m_{\rm F444W}$ color of those galaxies clearly indicates intense star
formation and hot ISM (e.g., Roberts-Borsani et al., 2020; Morishita et al.,
2020; Leethochawalit et al., 2022) and provides clues to metallicity (e.g.,
Schaerer et al., 2022; Tacchella et al., 2022). While direct inference of
their emission properties is limited to the three spec-$z$ sources, the
observed blue UV slope, $\beta_{\rm UV}$, of our sample is large in negative,
with the median value of $\sim-1.9$, which is consistent with strong line
emitters found in the literature (Bouwens et al., 2014; Yamanaka & Yamada,
2019; Nanayakkara et al., 2022). It is also known that $\beta_{\rm UV}$ has a
negative trend both with redshift and $M_{\rm UV}$ (Bouwens et al., 2014);
however, we do not observe such a trend among our sample, likely due to the
limited number of galaxies.

To further investigate the physical properties of the candidate galaxies, we
derive star-formation rate surface density, $\Sigma_{\rm SFR}={\rm SFR}/2\pi
r_{e}^{2}$, which is a good proxy for star formation activity within the
system (e.g., Oesch et al., 2010; Ono et al., 2013). We use half-light radius
derived by galfit and star-formation rate calculated by averaging the last 100
Myr of the posterior star formation history. In Fig. 7, we show $\Sigma_{\rm
SFR}$ as a function of stellar mass, along with low-$z$ galaxies taken from
the 3DHST catalog (Skelton et al., 2014; van der Wel et al., 2014; Whitaker et
al., 2014). As expected from the shape of SEDs and the flux excesses in F444W,
our candidates are located at the high-locus of the distribution at the
similar stellar mass. The observed increase in $\Sigma_{\rm SFR}$ compared to
lower-$z$ sources is likely driven both by compact morphologies (see also Yang
et al., 2022) and intense star formation at high redshifts. The observed
increase in star-formation surface density indicates even stronger feedback in
individual systems at the redshift range. Among our sample, a weak trend of
$\Sigma_{\rm SFR}$ with stellar mass is seen, with the measured slope of $\sim
0.38$. The Spearman’s test indicates a moderate correlation, $R=0.54$ with
$p$-value of 0.08. A followup investigation with a larger sample is of
particular interest, as it may hint at the efficiency of star formation in
systems of different stellar (and halo) masses.

Figure 7:  Star formation rate surface density of our final galaxy candidates
plotted as a function of stellar mass (red circles). For comparison, galaxies
at $0.2<z<4$ taken from the 3DHST catalog are shown. Our sample shows a
moderate correlation with stellar mass. The measured linear slope ($\sim
0.38$; dashed line) is shown.  Table 5:  Number densities of high-$z$
candidates.

$M_{\rm UV}$ | Number density
---|---
mag | $\log{\rm Mpc^{-3}\,mag^{-1}}$
$z\sim 9$
$-21.34$ | $<-4.13$
$-20.34$ | $-4.40_{-0.94}^{+0.57}$
$-19.34$ | $-3.87_{-0.94}^{+0.57}$
$-18.34$ | $-2.82_{-0.50}^{+0.37}$
$-17.34$ | $-3.08_{-1.64}^{+0.75}$
$-16.34$ | $<-2.07$
$z\sim 12$
$-21.90$ | $<-3.92$
$-20.90$ | $<-3.93$
$-19.90$ | $<-3.89$
$-18.90$ | $-3.78_{-0.94}^{+0.57}$
$-17.90$ | $-3.70_{-1.64}^{+0.75}$
$-16.90$ | $<-2.63$
$-15.90$ | $<-2.14$
$z\sim 18$
$-20.37$ | $<-3.85$
$-18.37$ | $<-3.07$
$-16.37$ | $<-2.16$
$z\sim 24$
$-20.80$ | $<-3.58$
$-18.80$ | $<-3.11$
$-16.80$ | $<-1.84$

Note. — $2\,\sigma$ uncertainties are quoted.

##  5 Discussion

In the previous sections, we present our high-$z$ galaxy candidates identified
in the first deep images of JWST. Despite the limited search volume with a
single pointing, we have identified 11 photometric candidates (including three
spectroscopically confirmed), two of which are $z\sim 13$, beyond the previous
redshift limit enabled by Hubble (e.g., Oesch et al., 2018; Bouwens et al.,
2021). Several teams report independent identification of high-$z$ galaxy
candidates in the same field (Adams et al., 2022; Atek et al., 2022; Donnan et
al., 2022; Harikane et al., 2022a; Yan et al., 2022). High-$z$ source
selection is extremely sensitive to detailed processes during selection and
the final list may vary from one to another. Therefore, it is of particular
interest to investigate if our identification of 11 sources is consistent with
predictions by theoretical/empirical models and if our candidates are
consistent with those identified in other studies.

Figure 8:  Number density estimates of the F090W-dropout ($z\sim 9$, top left
panel) and F150W-dropout ($z\sim 12$, top right) candidates. Upper limits for
the number density of F200W-dropout and F277W-dropout sources, none of which
are identified here, are also shown ($z\sim 18$, bottom left and $z\sim 24$,
bottom right). Error bars and upper limits represent $2\,\sigma$
uncertainties. Predicted luminosity functions at the corresponding redshift
(Mason et al., 2015, 2022, dashed and dotted lines for the models with and
without dust, respectively) shown in each panel.

###  5.1 Number densities of high-redshift candidates

In Fig. 8, we show our estimates of source number density at three redshift
ranges, $z\sim 9$ (F090W-dropout), $z\sim 12$ (F150W-dropout), and $z\sim 17$
(F200W-dropout). The number density is calculated as a function of absolute UV
magnitude, $M_{\rm UV}$. The effective volume is calculated by running
simulations, in the same way as in (Morishita et al., 2018a), for each of the
color-cut selections. We calculate the detection completeness as a function of
apparent F444W magnitude, $C(m)$, and the source selection function as a
function of magnitude and redshift, $S(z,m)$, by analyzing artificial sources
buried in the NIRCam images (and HST$+$NIRISS images in the cluster pointing).
The effective comoving volume is then calculated as

$V_{\rm eff}(m)=\int_{0}^{\infty}S(z,m)C(m){dV\over{dz}}dz,$ (1)

where $dV/dz$ represents differential comoving volume at redshift $z$. We also
corrected the volume estimate in the cluster field for magnification by using
the latest version of public magnification maps, provided by Zitrin’s team
through the RELICS collaboration. To estimate a number density at each
magnitude bin, we use the best-fit redshift and absolute UV magnitude derived
by gsf reported in Table 4. The number densities of galaxies in the four
redshift bins are reported in Table 5.

Our number density estimate at $z\sim 9$ is in good agreement with previous
studies in the literature (Stefanon et al., 2017; Morishita et al., 2018a), as
well as recent those in JWST fields (Donnan et al., 2022; Harikane et al.,
2022a). We also see agreement with the empirical prediction by Mason et al.
(2015, 2022) for the Sheth et al. 2001 halo mass function. At $z\sim 9$, the
model that includes dust seems to better fit the number densities estimates
here and in the literature. At higher redshift, those two models are
inseparable mostly due to the lack of data points at the bright end ($M_{\rm
UV}<-20$).

At $z\sim 12$, where we identified three candidates, our number density
estimate is slightly larger than the two JWST studies (Donnan et al., 2022;
Harikane et al., 2022a) and the prediction by Mason et al. (2022) but is still
consistent within the uncertainty. It is noted that the two JWST studies
explore not only the SMACS field but also CEERS (four pointings), GLASS (one
pointing), and the Stephan’s Quinted field (only in Harikane et al., 2022a),
resulting in $\,\hbox to0.0pt{\lower 3.5pt\hbox{$\mathchar
0\relax\sim$}\hss}\raise 1.0pt\hbox{$>$}\,5\times$ larger volume than explored
here. For the limited volume, our finding of two candidates in a single
pointing may be attributed to cosmic variance. In fact, Harikane et al.
(2022a) identify three $z\sim 12$ candidates in one field while none in
another field. The same is true in Donnan et al. (2022), where they identify
two candidates at $z\sim 12$ in the SMACS field, while none in the three of
the CEERS’s pointings. It is also worth mentioning that that none of our
$z\sim 13$ candidates are identified in either of the two studies (but one,
HD1, is identified by Yan et al. 2022; see below).

At $z\sim 18$ and $\sim 24$, where we identified no candidate, only upper
limits are shown. For the volume investigated here, it is reasonable not to
identify any candidates, as the prediction goes by an order of $\,\hbox
to0.0pt{\lower 3.5pt\hbox{$\mathchar 0\relax\sim$}\hss}\raise
1.0pt\hbox{$>$}\,3$ below the volume here, requiring $\sim 100\times$ more
volume to find one.

###  5.2 Comparison to candidates from other studies

Here we investigate the consistency of the final list in multiple studies and
what makes a difference by visiting all published candidates. All candidates
from the literature studies are summarized in Table 6, along with the IDs
cross-matched with our catalog. It is noted that as of the writing of this
manuscript, all but one of the five studies bases their analyses on the
reference files in the context of jwst_916.pmap. As we have found in this
study, the latest set of the reference files changed flux estimates for as
high as $\sim 40$ % from the original release of the data. We thus caution the
readers that their future updates with the latest reference files may change
their final list of high-$z$ candidates and may cause mismatch in our
discussion here.

####  5.2.1 Adams et al.

Adams et al. (2022) identify seven objects at $9<z<12$ via photometric
redshift selection using six NIRCam filters in the SMACS0723 field. Their
selection also imposes a conservative magnitude limit ($m>28.4$), which
includes only candidates with strong Lyman break color. All of their objects
are detected in our catalog, 5 of which remain as the final candidates in our
study. Despite the difference in photometric redshift code used, their
redshifts are broadly consistent with ours. The only exception is ID 1514
(ZD6p), whose redshift is estimated to $9.85$. While our phot-$z$ probability
distribution also shows a secondary peak at $z\sim 10$, we find that the best-
fit template at $z=7.7$ consistently explains the observed flux ‘bumps’ at
$>2\,\mu$m, likely caused by a negative Balmer break between F277W and F356W
and strong line emissions captured in F444W.

The other two objects are not selected in our final list of high-$z$
candidates, primarily because of a strong low-$z$ peak (their IDs 6115 and
10234) and suspicious positive noise in F090W (ID 10234). The SED of ID 6115
is fit well with an old galaxy template at $z\sim 2.1$. The SED of ID 10234 is
fit well with a strong line emitter at $z\sim 3.1$, where we find that its
flux excess in F200W is attributed to strong ${\rm H\beta}$+[O III] emission.
They limit their study to $z<12$, and thus none of our three galaxies at
$z>12$ appear in their study.

####  5.2.2 Atek et al.

Atek et al. (2022) present two $z\sim 16$ candidates, two at $z\sim 12$, and
seven at $9<z<11$, from color-cut selection, and four additional photo-$z$
candidates at $z\sim 11$, from the SMACS0723 field. In addition to the NIRCam
filters, they combined the two NIRISS filters (F115W and F200W) to improve the
redshift accuracy. Among 15 of their candidates, only three are found in our
final list. The majority of their objects (10) are rejected here because of
significant low-$z$ probability. Despite clean non-detection in F090W,
SMACS_z16a barely misses our phot-$z$ selection ($p(z>6.5)=0.74<0.8$). The
relatively high low-$z$ probability is due to the faintness of the source,
resulting in its flux estimate being relatively uncertain. Use of multiple
phot-$z$ estimators may recover such sources at the boundary. The other two
(SMACS_z12b, SMACS_z16b) have suspicious positive noise in the non-detection
filters for their redshift solutions (F090W and F150W, respectively).

####  5.2.3 Donnan et al.

Donnan et al. (2022) present identification of galaxies in three public
fields, SMACS0723, CEERS (Finkelstein et al., 2022), and GLASS-ERS (Treu et
al., 2022). From the SMACS0723 field, they identify 16 candidates at $z>8.5$
via spectral energy distribution fitting supplemented with $2\,\sigma$ non-
detection in blue filters. Thus, their candidate list does not include ZD1 and
ZD2. Among their 16 candidates, four remain in our final list, including ZD3
with $z_{\rm spec}=8.5$. Nine of their candidates are excluded here due to
high phot-$z$ probabilities at $z<6.5$. While one (their ID 38681) has
relatively high probability, $p(z>6.5)=75\%$, its peak redshift is $z=6.7$
with a tail extending to $z<6.5$, making itself at lower $z$ compared to the
galaxies in our final list. Two (38697, 12682) were excluded by our visual
inspection. The former object is in a crowded region of two merging galaxies,
likely a part of the merging remnants. The latter object shows a suspicious
noise structure in the non-detection filter F090W. Lastly, ID 6486 is a faint
object ($m_{200}=29.7$), and does not satisfy our detection limit ($S/N_{\rm
det}=3.8<4$).

####  5.2.4 Harikane et al.

Harikane et al. (2022a) perform identification of galaxies in four public
fields, SMACS0723, CEERS, GLASS-ERS, and the Stephan’s Quintet field. They
applied two color-cut selections for $z\sim 12$ (F150W-dropout) and $z\sim 17$
(F200W-dropout) in the SMACS0723 fields, and identify one candidate at $z\sim
11.5$ and one possible candidate at $z\sim 15.6$.

SM-z12-1 is detected in our catalog and satisfies non-detection criteria in
F200W and bluer bands. However, the object is faint ($m_{277}=29.4$) and does
not satisfy our detection limit ($S/N_{277}=2.5<3$). The Lyman break is also
not large enough to satisfy our criterion (F200W$-$F277W$=1.2<1.6$). SM-z17-1
is also detected in our catalog and satisfies non-detection criteria in F200W
and bluer bands. The Lyman break is not large enough to pass the limit
(F200W$-$F277W$=1.2$). We note, however, that both objects have relatively
high $p(z)$ values ($0.77$ and 0.78, respectively) and barely miss our
phot-$z$ selection. As for SMACS_z16a in 5.2.2, phot-$z$ estimates by another
phot-$z$ code may recover those two objects as final candidates.

It is worth noting that none of our two $z\sim 13$ color-cut candidates are
selected in Harikane et al. (2022a). While they adopt different color-cut
criteria for F150W-dropout sources, we confirm that both candidates satisfy
their conditions. One possible remaining factor is their additional constraint
by $\Delta\chi^{2}=\chi_{\rm low-z}^{2}-\chi_{\rm high-z}^{2}>4$ from their
SED analyses.

####  5.2.5 Yan et al.

Yan et al. (2022) present 88 objects as galaxy candidates at $11<z<20$ in the
SMACS0723 field. They select their candidates via color-cut using six NIRCam
filters, starting F150W as a dropout band; thus their selection does not
overlap with our F090W-dropout selection. Out of their 88 sources, eight
objects are found not to be listed in our parent catalog.

The majority ($N=77$) of the remaining sources are not selected in this study
due to high phot-$z$ probabilities at $z<6.5$. One of the objects (ID
F150DB-082) remains in our list of the phot-$z$ sample. One of the other two
objects with $p(z>6.5)>0.8$, ID F150DB-C4, shows a significantly red SED. Our
SED analysis, with redshift fixed to $z=10.4$, Yan et al. (2022)’s value,
indicates a significant dust attenuation, $A_{V}\sim 4$ mag, which is unlikely
at this redshift. We thus conclude that, despite being $p(z>6.5)=0.83$, this
object is a low-$z$ interloper, likely at $z\sim 4.5$, where we observe a
narrow redshift peak. The other object, F200DA-034 at $z\sim 19.8$, is very
faint ($m_{356}=29.3$) and does not pass the detection cut ($S/N_{\rm
det}=2.5<4$).

##  6 Summary

Already on the basis of this first data set it is clear that detections with
WFC3-IR with few bands shorter than 1.6 µm are not a robust way to identify
high-$z$ galaxies as at least the two sources in the cluster field had been
assigned much lower redshifts. These data also allow us to begin testing the
reliability of phot-$z$ at previously untested redshifts. More will be needed
but it is comforting that three candidates identified in the cluster field on
the basis of photometry alone were spectroscopically confirmed. This gives us
hope that photometric selection is not seriously plagued by false detection.
We cannot say at this time anything about its completeness given that the
NIRSpec MSA slits were placed on photometrically selected objects.

We repeated the selection analyses for the cluster field with the NIRCam
imaging data only. This provided the same set of color dropout galaxies
selected in the first step of Sec 3.1. This implies that our selection of
candidates in the parallel field, despite the absence of HST coverage, is more
or less of similar quality. This is also encouraging for future searches of
high-$z$ sources with JWST alone (e.g., pure-parallel imaging programs).

The comparison with other studies over the same fields provides a cautionary
tale on how to interpret results derived only through photometry: different
studies with different but equally reasonable criteria can yield significant
differences in the selected samples.

A final note is that our analysis may still not be complete in terms of source
detection. The (in)famous hexagonal PSF spikes from bright stars severely
contaminate surrounding pixels and make it challenging to detect all
background sources. In addition, the cluster field is even more challenging,
being affected by brightest cluster galaxies. A dedicated analysis, such as
one presented in Bouwens et al. (2021), will be required for detection
completeness in crowded fields.

SMACS0723 is not only the deepest NIR imaging field at this time, but also has
rich spectroscopic data obtained by NIRSPEC and NIRISS as well as mid-IR
imaging by MIRI collected during the ERO campaign. Future follow-up studies
with those data will further reveal the nature of the three galaxies in the
cluster field. While the additional candidates newly identified in this study
are not as exceptionally bright as those in the cluster field, they are still
within the reach of sensitive NIRSPEC spectroscopy for its strong emission
lines revealed in our study. For future spectroscopic study and planning, we
publish the best-fit templates of our final galaxies on a dedicated
website.999https://github.com/mtakahiro/SMACS0723_highz_SED_templates

## Acknowledgements

We would like to thank the anonymous referee for providing extremely valuable
comments, which improved the manuscript significantly. The Early Release
Observations and associated materials were developed, executed, and compiled
by the ERO production team: Hannah Braun, Claire Blome, Matthew Brown,
Margaret Carruthers, Dan Coe, Joseph DePasquale, Néstor Espinoza, Macarena
Garcia Marin, Karl Gordon, Alaina Henry, Leah Hustak, Andi James, Ann Jenkins,
Anton Koekemoer, Stephanie LaMassa, David Law, Alexandra Lockwood, Amaya Moro-
Martin, Susan Mullally, Alyssa Pagan, Dani Player, Klaus Pontoppidan, Charles
Proffitt, Christine Pulliam, Leah Ramsay, Swara Ravindranath, Neill Reid,
Massimo Robberto, Elena Sabbi, Leonardo Ubeda. The EROs were also made
possible by the foundational efforts and support from the JWST instruments,
STScI planning and scheduling, and Data Management teams. Some of the data
presented in this paper were obtained from the Mikulski Archive for Space
Telescopes (MAST) at the Space Telescope Science Institute. The specific
observations analyzed can be accessed via https://doi.org/10.17909/67ft-nb86
(catalog 10.17909/67ft-nb86). TM is grateful to Guido Roberts-Borsani,
Benedetta Vulcani, and Xin Wang for our constructive discussion at UCLA and
their insights into the new dataset from this brand-new observatory, and to
Tommaso Treu for his kind and generous support.

Table 6:  List of high-$z$ candidates identified in the literature

IDliter. | R.A. | Decl. | $z_{\rm liter.}$ | ID | $z$ | $p(z>6.5)$ | Comments
---|---|---|---|---|---|---|---
| degree | degree |  |  |  |  |
Adams et al.
1696 | 1.1083435e+02 | -7.3434509e+01 | 9.59 | WDF-C-769 | $7.34_{-0.07}^{+0.05}$ | 1.00 |
2462 | 1.1084492e+02 | -7.3435043e+01 | 9.50 | WDF-C-1045 | $8.55_{-0.18}^{+0.15}$ | 1.00 |
6878 | 1.1085982e+02 | -7.3449127e+01 | 9.59 | WDF-C-3186 | $9.03_{-0.25}^{+0.31}$ | 1.00 |
1514 | 1.1061509e+02 | -7.3477554e+01 | 9.85 | WDF-P-963 | $7.35_{-0.18}^{+3.65}$ | 0.99 |
2779 | 1.1064618e+02 | -7.3475845e+01 | 9.51 | WDF-P-1762 | $10.26_{-0.66}^{+0.72}$ | 1.00 |
6115 | 1.1071698e+02 | -7.3464973e+01 | 10.94 | WDF-P-2289 | $2.05_{-1.12}^{+8.04}$ | 0.17 | $p(z)$
10234 | 1.1066535e+02 | -7.3501740e+01 | 11.42 | WDF-P-6576 | $3.05_{-2.81}^{+0.27}$ | 0.03 |  $p(z)$. Suspicious noise in F090W.
Atek et al.
SMACS_z10a | 1.1085982e+02 | -7.3449127e+01 | 9.92 | WDF-C-3186 | $9.03_{-0.25}^{+0.31}$ | 1.00 |
SMACS_z10b | 1.1084492e+02 | -7.3435043e+01 | 9.79 | WDF-C-1045 | $8.55_{-0.18}^{+0.15}$ | 1.00 |
SMACS_z10c | 1.1083435e+02 | -7.3434509e+01 | 9.94 | WDF-C-769 | $7.34_{-0.07}^{+0.05}$ | 1.00 |
SMACS_z11c | 1.1075713e+02 | -7.3448654e+01 | 11.22 | WDF-C-629 | $3.02_{-1.56}^{+7.79}$ | 0.34 | $p(z)$
SMACS_z16a | 1.1086060e+02 | -7.3467911e+01 | 15.97 | WDF-C-5261 | $9.84_{-7.33}^{+1.38}$ | 0.74 |  $p(z)$.
SMACS_z10d | 1.1069465e+02 | -7.3478012e+01 | 9.98 | WDF-P-3632 | $2.34_{-1.27}^{+2.30}$ | 0.07 | $p(z)$
SMACS_z10e | 1.1068899e+02 | -7.3491814e+01 | 10.44 | WDF-P-6786 | $4.62_{-2.91}^{+6.02}$ | 0.32 | $p(z)$
SMACS_z10f | 1.1073891e+02 | -7.3487900e+01 | 10.47 | WDF-P-6546 | $1.92_{-1.01}^{+1.07}$ | 0.02 | $p(z)$
SMACS_z11a | 1.1066482e+02 | -7.3494514e+01 | 10.75 | WDF-P-5279 | $3.70_{-1.73}^{+7.78}$ | 0.34 | $p(z)$
SMACS_z11b | 1.1072444e+02 | -7.3473129e+01 | 11.22 | WDF-P-3753 | $1.99_{-0.65}^{+0.93}$ | 0.01 | $p(z)$
SMACS_z11d | 1.1065319e+02 | -7.3469231e+01 | 11.28 | WDF-P-893 | $0.72_{-0.40}^{+1.56}$ | 0.00 | $p(z)$
SMACS_z11e | 1.1070534e+02 | -7.3462379e+01 | 11.52 | WDF-P-1486 | $3.51_{-2.07}^{+7.80}$ | 0.24 | $p(z)$
SMACS_z12a | 1.1069765e+02 | -7.3500481e+01 | 12.03 | WDF-P-5896 | $12.68_{-10.04}^{+1.91}$ | 0.68 | $p(z)$
SMACS_z12b | 1.1071779e+02 | -7.3465393e+01 | 12.35 | WDF-P-2356 | $13.83_{-9.35}^{+1.72}$ | 0.82 | suspicious noise in F090W
SMACS_z16b | 1.1066457e+02 | -7.3502274e+01 | 15.70 | WDF-P-6055 | $15.95_{-2.15}^{+0.89}$ | 0.91 | detection in F150W
Donnan et al.
38681 | 1.1086747e+02 | -7.3438889e+01 | 8.57 | WDF-C-2143 | $7.31_{-2.21}^{+0.91}$ | 0.75 |  $p(z)$. $z_{\rm peak}\sim 6.7$
39556 | 1.1086165e+02 | -7.3436226e+01 | 8.86 | WDF-C-1622 | $8.20_{-0.78}^{+1.03}$ | 1.00 |
28093 | 1.1090845e+02 | -7.3455986e+01 | 9.16 | WDF-C-4967 | $2.78_{-2.11}^{+7.26}$ | 0.32 | $p(z)$
34086 | 1.1085982e+02 | -7.3449127e+01 | 9.36 | WDF-C-3186 | $9.03_{-0.25}^{+0.31}$ | 1.00 |
38697 | 1.1086647e+02 | -7.3438789e+01 | 9.36 | WDF-C-2064 | $8.50_{-1.11}^{+1.18}$ | 0.98 | merger remnant?
35470 | 1.1076282e+02 | -7.3446548e+01 | 12.03 | WDF-C-512 | $9.47_{-7.31}^{+3.57}$ | 0.64 | $p(z)$
40079 | 1.1080821e+02 | -7.3434746e+01 | 14.28 | WDF-C-237 | $5.54_{-3.17}^{+7.50}$ | 0.45 | $p(z)$
9544 | 1.1066106e+02 | -7.3479889e+01 | 9.06 | WDF-P-2590 | $2.06_{-0.26}^{+0.29}$ | 0.00 | $p(z)$
12682 | 1.1066240e+02 | -7.3475121e+01 | 9.47 | WDF-P-2194 | $9.44_{-2.37}^{+1.56}$ | 0.86 | Suspicious noise in F090W.
22480 | 1.1069090e+02 | -7.3462929e+01 | 9.47 | WDF-P-1095 | $10.53_{-1.55}^{+1.00}$ | 0.97 |
12218 | 1.1064618e+02 | -7.3475845e+01 | 9.68 | WDF-P-1762 | $10.26_{-0.66}^{+0.72}$ | 1.00 |
15019 | 1.1074287e+02 | -7.3472061e+01 | 9.68 | WDF-P-4203 | $7.28_{-5.41}^{+3.42}$ | 0.53 | $p(z)$
3763 | 1.1070474e+02 | -7.3492020e+01 | 9.78 | WDF-P-6064 | $3.51_{-2.94}^{+8.19}$ | 0.24 | $p(z)$
6200 | 1.1067314e+02 | -7.3486275e+01 | 9.78 | WDF-P-4247 | $8.28_{-6.05}^{+3.05}$ | 0.60 | $p(z)$
21901 | 1.1069484e+02 | -7.3463715e+01 | 12.16 | WDF-P-1363 | $3.16_{-0.56}^{+1.06}$ | 0.15 | $p(z)$
6486 | 1.1072211e+02 | -7.3485748e+01 | 12.56 | WDF-P-6773 | $14.11_{-9.39}^{+1.89}$ | 0.81 | $S/N_{\rm det}=3.8<4$
Harikane et al.
SM-z12-1 | 1.1070145e+02 | -7.3476830e+01 | 11.52 | WDF-P-3651 | $12.20_{-7.58}^{+7.25}$ | 0.77 |  $S/N_{\rm F277W}=2.5$, F200W$-$F277W$=1.2$
SM-z17-1† | 1.1087592e+02 | -7.3458427e+01 | 15.63 | WDF-C-5499 | $14.73_{-10.57}^{+4.52}$ | 0.78 | F200W$-$F277W$=1.2$

Note. — Sources that remained in our final list are listed without comments.
$\dagger$: Listed as a potential candidate.

Table 7:  (Continued) List of high-$z$ candidates identified in the literature

IDliter. | R.A. | Decl. | $z_{\rm liter.}$ | ID | $z$ | $p(z>6.5)$ | Comments
---|---|---|---|---|---|---|---
| degree | degree |  |  |  |  |
Yan et al.†
F150DB-004 | 1.1081004e+02 | -7.3469292e+01 | 14.00 | WDF-C-5150 | $5.61_{-0.22}^{+0.24}$ | 0.00 | $p(z)$
F150DB-007 | 1.1085056e+02 | -7.3466286e+01 | 14.60 | WDF-C-5204 | $10.41_{-9.35}^{+2.20}$ | 0.59 | $p(z)$
F150DB-011 | 1.1086408e+02 | -7.3466446e+01 | 11.60 | WDF-C-5176 | $1.52_{-1.06}^{+3.22}$ | 0.06 | $p(z)$
F150DB-013 | 1.1077367e+02 | -7.3464104e+01 | 11.40 | WDF-C-2937 | $2.90_{-2.67}^{+0.39}$ | 0.00 | $p(z)$
F150DB-021 | 1.1080333e+02 | -7.3462578e+01 | 11.80 | WDF-C-3474 | $2.84_{-1.14}^{+5.61}$ | 0.44 | $p(z)$
F150DB-023 | 1.1077446e+02 | -7.3462074e+01 | 7.20 | WDF-C-2724 | $6.07_{-4.23}^{+3.95}$ | 0.46 | $p(z)$
F150DB-026 | 1.1084926e+02 | -7.3461319e+01 | 11.40 | WDF-C-4828 | $2.91_{-2.57}^{+0.45}$ | 0.00 | $p(z)$
F150DB-031 | 1.1083992e+02 | -7.3460083e+01 | 11.60 | WDF-C-4061 | $3.90_{-2.46}^{+7.10}$ | 0.25 | $p(z)$
F150DB-033 | 1.1087781e+02 | -7.3459175e+01 | 14.80 | WDF-C-5690 | $3.36_{-1.89}^{+10.80}$ | 0.23 | $p(z)$
F150DB-040 | 1.1080025e+02 | -7.3456947e+01 | 10.80 | WDF-C-2728 | $2.80_{-2.59}^{+0.39}$ | 0.01 | $p(z)$
F150DB-041 | 1.1077812e+02 | -7.3457100e+01 | 16.00 | WDF-C-2145 | $0.53_{-0.15}^{+3.42}$ | 0.03 | $p(z)$
F150DB-044 | 1.1091432e+02 | -7.3456139e+01 | 11.60 | WDF-C-5213 | $2.61_{-2.36}^{+0.67}$ | 0.01 | $p(z)$
F150DB-048 | 1.1075723e+02 | -7.3455078e+01 | 15.00 | WDF-C-1268 | $6.12_{-3.90}^{+6.47}$ | 0.46 | $p(z)$
F150DB-050 | 1.1085293e+02 | -7.3454163e+01 | 11.60 | WDF-C-3629 | $2.93_{-2.66}^{+0.80}$ | 0.01 | $p(z)$
F150DB-052 | 1.1086780e+02 | -7.3453796e+01 | 15.00 | WDF-C-3964 | $12.42_{-9.49}^{+2.01}$ | 0.73 | $p(z)$
F150DB-054 | 1.1080293e+02 | -7.3452637e+01 | 11.40 | WDF-C-2235 | $0.48_{-0.19}^{+0.08}$ | 0.00 | $p(z)$
F150DB-056 | 1.1078082e+02 | -7.3452866e+01 | 7.20 | WDF-C-1658 | $11.53_{-8.51}^{+3.21}$ | 0.72 | $p(z)$
F150DB-058 | 1.1085042e+02 | -7.3452530e+01 | 15.20 | WDF-C-3407 | $0.27_{-0.14}^{+0.12}$ | 0.00 | $p(z)$
F150DB-069 | 1.1076826e+02 | -7.3448433e+01 | 11.80 | WDF-C-865 | $12.48_{-9.39}^{+1.90}$ | 0.77 | $p(z)$
F150DB-075 | 1.1075976e+02 | -7.3444901e+01 | 11.40 | WDF-C-284 | $2.89_{-2.59}^{+0.31}$ | 0.00 | $p(z)$
F150DB-076 | 1.1087300e+02 | -7.3444359e+01 | 11.60 | WDF-C-2959 | $0.93_{-0.47}^{+2.77}$ | 0.00 | $p(z)$
F150DB-079 | 1.1080526e+02 | -7.3441589e+01 | 13.80 | WDF-C-931 | $3.36_{-0.79}^{+9.25}$ | 0.38 | $p(z)$
F150DB-082 | 1.1084528e+02 | -7.3440430e+01 | 11.60 | WDF-C-1730 | $13.39_{-9.37}^{+1.65}$ | 0.81 |
F150DB-084 | 1.1078188e+02 | -7.3439972e+01 | 11.60 | WDF-C-226 | $4.00_{-3.33}^{+0.92}$ | 0.10 | $p(z)$
F150DB-088 | 1.1080888e+02 | -7.3438148e+01 | 11.60 | WDF-C-626 | $3.58_{-1.28}^{+8.92}$ | 0.34 | $p(z)$
F150DB-090 | 1.1085972e+02 | -7.3437157e+01 | 11.40 | WDF-C-1643 | $2.95_{-1.16}^{+0.68}$ | 0.00 | $p(z)$
F150DB-095 | 1.1085355e+02 | -7.3433678e+01 | 11.60 | WDF-C-1144 | $2.77_{-2.01}^{+1.86}$ | 0.07 | $p(z)$
F150DB-C4 | 1.1085859e+02 | -7.3444389e+01 | 10.40 | WDF-C-2420 | $15.69_{-10.25}^{+1.10}$ | 0.83 | Likely a dusty interloper at $z\sim 4.5$.
F200DB-015 | 1.1078262e+02 | -7.3467125e+01 | 16.00 | WDF-C-3520 | $9.68_{-6.40}^{+10.66}$ | 0.62 | $p(z)$
F200DB-045 | 1.1084516e+02 | -7.3461304e+01 | 20.40 | WDF-C-4322 | $0.40_{-0.26}^{+0.15}$ | 0.00 | $p(z)$
F200DB-086 | 1.1077728e+02 | -7.3455536e+01 | 15.40 | WDF-C-1932 | $14.60_{-9.97}^{+5.76}$ | 0.76 | $p(z)$
F200DB-109 | 1.1090538e+02 | -7.3453957e+01 | 15.80 | WDF-C-5512 | $0.05_{-0.03}^{+0.04}$ | 0.00 | $p(z)$
F200DB-159 | 1.1085593e+02 | -7.3445839e+01 | 16.00 | WDF-C-2731 | $1.97_{-1.59}^{+0.32}$ | 0.01 | $p(z)$
F200DB-175 | 1.1079664e+02 | -7.3443909e+01 | 16.20 | WDF-C-999 | $5.07_{-3.46}^{+4.32}$ | 0.34 | $p(z)$
F200DB-181 | 1.1080309e+02 | -7.3442154e+01 | 15.80 | WDF-C-946 | $4.21_{-2.69}^{+1.03}$ | 0.13 | $p(z)$
F277DB-001 | 1.1082343e+02 | -7.3473969e+01 | -99.00 | WDF-C-5008 | $7.66_{-5.05}^{+5.95}$ | 0.55 | $p(z)$

Note. — $\dagger$: 80 objects that are detected in our catalog, out of 88 in
the original study by Yan et al. (2022), are shown.

Table 8:  (Continued) List of high-$z$ candidates identified in the literature

IDliter. | R.A. | Decl. | $z_{\rm liter.}$ | ID | $z$ | $p(z>6.5)$ | Comments
---|---|---|---|---|---|---|---
| degree | degree |  |  |  |  |
Yan et al.
F150DB-013 | 1.1073157e+02 | -7.3465691e+01 | 11.40 | WDF-P-2859 | $1.64_{-1.41}^{+1.64}$ | 0.01 | $p(z)$
F150DA-008 | 1.1067804e+02 | -7.3498581e+01 | 13.40 | WDF-P-6060 | $4.07_{-2.67}^{+3.78}$ | 0.20 | $p(z)$
F150DA-015 | 1.1064458e+02 | -7.3491646e+01 | 11.80 | WDF-P-4197 | $7.17_{-5.18}^{+4.08}$ | 0.53 | $p(z)$
F150DA-018 | 1.1069163e+02 | -7.3490501e+01 | 6.40 | WDF-P-4934 | $1.81_{-0.41}^{+0.43}$ | 0.00 | $p(z)$
F150DA-019 | 1.1062254e+02 | -7.3489227e+01 | 11.60 | WDF-P-2702 | $1.27_{-0.18}^{+0.20}$ | 0.00 | $p(z)$
F150DA-020 | 1.1069055e+02 | -7.3488899e+01 | 11.20 | WDF-P-5172 | $4.15_{-3.54}^{+0.47}$ | 0.00 | $p(z)$
F150DA-026 | 1.1064958e+02 | -7.3486969e+01 | 11.00 | WDF-P-3551 | $0.41_{-0.16}^{+0.20}$ | 0.00 | $p(z)$
F150DA-027 | 1.1071288e+02 | -7.3486656e+01 | 7.40 | WDF-P-5532 | $0.67_{-0.37}^{+0.25}$ | 0.00 | $p(z)$
F150DA-031 | 1.1062753e+02 | -7.3484474e+01 | 11.40 | WDF-P-2555 | $3.36_{-0.96}^{+8.78}$ | 0.41 | $p(z)$
F150DA-036 | 1.1071104e+02 | -7.3481148e+01 | 11.00 | WDF-P-4620 | $2.88_{-2.32}^{+1.35}$ | 0.03 | $p(z)$
F150DA-038 | 1.1072043e+02 | -7.3480797e+01 | 13.40 | WDF-P-4785 | $0.71_{-0.27}^{+0.52}$ | 0.00 | $p(z)$
F150DA-039 | 1.1071104e+02 | -7.3481148e+01 | 7.40 | WDF-P-4620 | $2.88_{-2.32}^{+1.35}$ | 0.03 | $p(z)$
F150DA-047 | 1.1066683e+02 | -7.3481430e+01 | 7.40 | WDF-P-3289 | $8.00_{-5.54}^{+7.32}$ | 0.56 | $p(z)$
F150DA-050 | 1.1064584e+02 | -7.3478386e+01 | 13.40 | WDF-P-2190 | $4.60_{-3.51}^{+5.22}$ | 0.34 | $p(z)$
F150DA-054 | 1.1062034e+02 | -7.3476067e+01 | 11.40 | WDF-P-930 | $7.93_{-5.55}^{+8.37}$ | 0.57 | $p(z)$
F150DA-057 | 1.1070329e+02 | -7.3475700e+01 | 11.40 | WDF-P-3537 | $3.84_{-3.24}^{+0.71}$ | 0.02 | $p(z)$
F150DA-058 | 1.1065908e+02 | -7.3475243e+01 | 13.40 | WDF-P-2102 | $2.98_{-2.61}^{+0.71}$ | 0.00 | $p(z)$
F150DA-060 | 1.1062808e+02 | -7.3473839e+01 | 11.40 | WDF-P-822 | $6.97_{-5.09}^{+4.58}$ | 0.53 | $p(z)$
F150DA-062 | 1.1068426e+02 | -7.3474068e+01 | 11.40 | WDF-P-2694 | $1.47_{-0.55}^{+0.75}$ | 0.00 | $p(z)$
F150DA-063 | 1.1068298e+02 | -7.3474297e+01 | 7.40 | WDF-P-2691 | $3.51_{-2.21}^{+6.45}$ | 0.20 | $p(z)$
F150DA-066 | 1.1062359e+02 | -7.3470810e+01 | 11.40 | WDF-P-178 | $13.82_{-9.12}^{+9.88}$ | 0.77 | $p(z)$
F150DA-077 | 1.1067607e+02 | -7.3466461e+01 | 13.40 | WDF-P-1210 | $15.00_{-10.20}^{+10.19}$ | 0.78 | $p(z)$
F150DA-078 | 1.1066418e+02 | -7.3464508e+01 | 11.80 | WDF-P-430 | $2.79_{-1.51}^{+2.46}$ | 0.00 | $p(z)$
F150DA-081 | 1.1066294e+02 | -7.3463394e+01 | 13.40 | WDF-P-237 | $1.67_{-1.60}^{+0.52}$ | 0.00 | $p(z)$
F150DA-082 | 1.1067789e+02 | -7.3463089e+01 | 7.60 | WDF-P-658 | $8.38_{-6.63}^{+9.25}$ | 0.58 | $p(z)$
F200DA-033 | 1.1064064e+02 | -7.3488998e+01 | 6.40 | WDF-P-3653 | $7.33_{-5.24}^{+6.28}$ | 0.57 | $p(z)$
F200DA-034 | 1.1073029e+02 | -7.3488083e+01 | 19.80 | WDF-P-6862 | $16.58_{-11.24}^{+7.92}$ | 0.80 | $S/N_{\rm det}=2.5<4$
F200DA-040 | 1.1072467e+02 | -7.3485855e+01 | 20.00 | WDF-P-6327 | $7.47_{-5.45}^{+6.90}$ | 0.54 | $p(z)$
F200DA-056 | 1.1061219e+02 | -7.3479179e+01 | 15.60 | WDF-P-1195 | $2.91_{-1.90}^{+6.64}$ | 0.19 | $p(z)$
F277DA-001 | 1.1065731e+02 | -7.3502014e+01 | -99.00 | WDF-P-7484 | $0.32_{-0.18}^{+0.16}$ | 0.00 | $p(z)$
F277DA-033 | 1.1070955e+02 | -7.3476181e+01 | -99.00 | WDF-P-3715 | $1.55_{-0.34}^{+0.80}$ | 0.00 | $p(z)$
F277DA-040 | 1.1060577e+02 | -7.3473579e+01 | -99.00 | WDF-P-50 | $0.66_{-0.25}^{+0.26}$ | 0.00 | $p(z)$
F277DA-044 | 1.1069387e+02 | -7.3470459e+01 | -99.00 | WDF-P-2421 | $0.09_{-0.06}^{+0.22}$ | 0.00 | $p(z)$
F277DA-045 | 1.1067736e+02 | -7.3469704e+01 | -99.00 | WDF-P-1729 | $3.81_{-1.01}^{+1.34}$ | 0.01 | $p(z)$

Note. —

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