# Enhanced Sub-kpc Scale Star-formation: Results From A JWST Size Analysis of
339 Galaxies At $5<z<14$

Takahiro Morishita IPAC, California Institute of Technology, MC 314-6, 1200 E.
California Boulevard, Pasadena, CA 91125, USA  Massimo Stiavelli Space
Telescope Science Institute, 3700 San Martin Drive, Baltimore, MD 21218, USA
Ranga-Ram Chary IPAC, California Institute of Technology, MC 314-6, 1200 E.
California Boulevard, Pasadena, CA 91125, USA  Michele Trenti School of
Physics, University of Melbourne, Parkville 3010, VIC, Australia  ARC Centre
of Excellence for All Sky Astrophysics in 3 Dimensions (ASTRO 3D), Australia
Pietro Bergamini Dipartimento di Fisica, Università degli Studi di Milano, Via
Celoria 16, I-20133 Milano, Italy  INAF - OAS, Osservatorio di Astrofisica e
Scienza dello Spazio di Bologna, via Gobetti 93/3, I-40129 Bologna, Italy
Marco Chiaberge Space Telescope Science Institute for the European Space
Agency (ESA), ESA Office, 3700 San Martin Drive, Baltimore, MD 21218, USA  The
William H. Miller III Department of Physics and Astronomy, Johns Hopkins
University, Baltimore, MD 21218, USA  Nicha Leethochawalit National
Astronomical Research Institute of Thailand (NARIT), Mae Rim, Chiang Mai,
50180, Thailand  Guido Roberts-Borsani Department of Physics and Astronomy,
University of California, Los Angeles, 430 Portola Plaza, Los Angeles, CA
90095, USA  Department of Astronomy, University of Geneva, Chemin Pegasi 51,
1290 Versoix, Switzerland  Xuejian Shen TAPIR, California Institute of
Technology, Pasadena, CA 91125, USA  Department of Physics & Kavli Institute
for Astrophysics and Space Research, Massachusetts Institute of Technology,
Cambridge, MA 02139, USA  Tommaso Treu Department of Physics and Astronomy,
University of California, Los Angeles, 430 Portola Plaza, Los Angeles, CA
90095, USA

###### Abstract

We present a comprehensive search and analysis of high-redshift galaxies in a
suite of nine public JWST extragalactic fields taken in Cycle 1, covering a
total effective search area of $\sim 358\,{\rm arcmin^{2}}$. Through
conservative ($8\,\sigma$) photometric selection, we identify 339 galaxies at
$5<z<14$, with 109 having spectroscopic redshift measurements from the
literature, including recent JWST NIRSpec observations. Our regression
analysis reveals that the rest-frame UV size-stellar mass relation follows
$R_{\rm eff}\propto M_{*}^{0.20\pm 0.03}$, similar to that of star-forming
galaxies at $z\sim 3$, but scaled down in size by $\sim 0.7$ dex. We find a
much slower rate for the average size evolution over the redshift range,
$R_{\rm eff}\propto(1+z)^{-0.4\pm 0.2}$, than that derived in the literature.
A fraction ($\sim 13\,\%$) of our sample are marginally resolved even in the
NIRCam imaging ($\,\hbox to0.0pt{\lower 3.5pt\hbox{$\mathchar
0\relax\sim$}\hss}\raise 1.0pt\hbox{$<$}\,100$ pc), located at $\,\hbox
to0.0pt{\lower 3.5pt\hbox{$\mathchar 0\relax\sim$}\hss}\raise
1.0pt\hbox{$>$}\,1.5\,\sigma$ below the derived size-mass slope. These compact
sources exhibit a high star formation surface density $\Sigma_{\rm
SFR}>10\,M_{\sun}\,{\rm yr^{-1}\,kpc^{-2}}$, a range in which only $<0.01\,\%$
of the local star-forming galaxy sample is found. For those with available
NIRSpec data, no evidence of ongoing supermassive black hole accretion is
observed. A potential explanation for the observed high [O iii]-to-${\rm
H\beta}$ ratios could be high shock velocities, likely originating within
intense star-forming regions characterized by high $\Sigma_{\rm SFR}$. Lastly,
we find that the rest-frame UV and optical sizes of our sample are comparable.
Our results are consistent with these early galaxies building up their
structures inside-out and yet to exhibit the strong color gradient seen at
lower redshift.

††journal: ApJ

##  1 Introduction

In a hierarchical universe, dark matter starts collapsing at initial density
peaks, giving rise to the underlying structure. Baryons then start accreting
in the dark matter potential wells and forming stars. Depending on the initial
conditions of gas and dark matter halos, the appearance of the resulting
system may differ dramatically (e.g., Mo et al., 1998; Bullock et al., 2001b).

Within this context, the size of galaxies is a fundamental and essential proxy
for understanding galaxy formation and evolution. Galaxies occupy a relative
narrow portion of the size and stellar mass/luminosity plane. The distribution
of galaxies within this so-called size–stellar mass/luminosity relation, the
average size growth rate across cosmic time, and the distribution of other
structural parameters, such as the Sérsic index and axis ratio, are key
diagnostics of early galaxy formation.

In the local universe, the large statistics enabled by the Sloan Digital Sky
survey revealed a fundamental relation of galaxy structures and sizes with
mass (e.g., Kauffmann et al., 2003). For example, it has been found that local
early-type and late-type galaxies follow different slopes in the size-mass
plane (e.g., Shen et al., 2003; Guo et al., 2009; Simard et al., 2011;
Cappellari, 2013). Such differences are believed to arise from a combination
of initial conditions, evolutionary paths, and environmental influences.

In contrast, observations of galaxies at higher redshifts have revealed a
variety of galaxy morphologies (Conselice et al., 2004; Wuyts et al., 2011;
Guo et al., 2012; Szomoru et al., 2012). These observations indicate active
physical processes within and between galaxies, establishing the structural
sequences seen in the local universe. Prior to JWST, the Hubble Space
Telescope (HST) pushed the frontier of the fundamental galaxy size-mass
relation to $z\sim 7$ (Bruce et al., 2012; Mosleh et al., 2012; van der Wel et
al., 2012, 2014; Morishita et al., 2014; Allen et al., 2017; Yang et al.,
2021). Beyond that redshift, however, the investigation has been severely
limited by spatial resolution ($\sim 1$ kpc), as well as the limited number of
infrared filters at $>2\,\mu$m, which is critical to robustly infer galaxy
stellar masses. As such, the effort beyond the redshift has been largely
limited to small samples of relatively luminous galaxies (Oesch et al., 2010;
Ono et al., 2013; Holwerda et al., 2015) or a small volume through strong
gravitational lens (Kawamata et al., 2015, 2018; Yang et al., 2022a)

These observable properties are believed to reflect the initial conditions of
the gas and dark matter halos from which galaxies form. However, over time,
such fundamental properties are susceptible to contamination through a
sequence of stochastic, non-linear physical processes, including mergers.
Therefore, a detailed characterization of galaxy size becomes imperative, as
they may offer insights into not only the physical mechanisms on act, but also
their interplay with interstellar medium (e.g., Marshall et al., 2022; Roper
et al., 2022a) and the nature of dark matter (e.g., Shen et al., 2023).

Early results from Cycle 1 have already demonstrated the remarkable
capabilities of JWST, with its red sensitivity and resolution, revealing early
galaxy morphologies down to scales of $\,\hbox to0.0pt{\lower
3.5pt\hbox{$\mathchar 0\relax\sim$}\hss}\raise 1.0pt\hbox{$<$}\,100$ pc,
throughout rest-frame UV to optical wavelengths (e.g., Yang et al., 2022b;
Naidu et al., 2022; Finkelstein et al., 2022; Treu et al., 2023; Morishita &
Stiavelli, 2023; Huertas-Company et al., 2023). Given the substantial number
of observations completed in Cycle 1, this study aims to undertake a
comprehensive analysis of galaxy sizes, offering the first large scale
systematic study of the galaxy size-mass relation at $z>5$. To achieve this,
we perform an analysis based on consistently reduced data from several
publicly available extragalactic fields observed during Cycle-1, encompassing
a total effective area of $\sim 358$ arcmin2. This extensive coverage enables
us to construct a robust sample comprising 339 galaxies within the redshift
range of $5<z<14$.

The paper is structured as follows: we present our data reduction in Sec. 2,
followed by our photometric analyses in Sec. 3. We then characterize the
structure of identified galaxies and infer the distributions of their
structural parameters in Sec. 4. We investigate the inferred physical
properties and discuss the origin of early galaxies in comparison with
lower-$z$ galaxies in Sec. 5. We summarize our key conclusions in Sec. 6.
Where relevant, 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. Distances are in proper units unless
otherwise stated.

##  2 Data

We base our analysis on nine public deep fields from JWST Cycle 1. For all
fields, except for the GLASS/UNCOVER and JADES-GDS fields where the final
mosaic images are made publicly available by the teams, we retrieve the raw-
level images from the MAST archive and reduce those with the official JWST
pipeline, with several customized steps as detailed below. We then apply our
photometric pipeline, borgpipe (Morishita et al., 2021), on all mosaic images
to consistently extract sources and measure fluxes. Our final high-$z$ source
candidates are selected by applying the Lyman-break dropout technique (Steidel
et al., 1998) supplemented by photometric-redshift selection, as implemented
by Morishita & Stiavelli (2022).

###  2.1 Uniform data reduction of NIRCam images

In each field, we start with raw (uncal.fits) images retrieved from MAST. We
use Grizli (ver 1.8.3) to reduce raw images to generate calibrated images
(cal.fits). In this step, in addition to the official pipeline’s DETECTOR
step, Grizli includes additional processes for flagging artifacts, such as
snowball halos, claws, and wisps.

We then apply bbpn111https://github.com/mtakahiro/bbpn on the calibrated
images, to subtract $1/f$-noise. The tool follows the procedure proposed by
Schlawin et al. (2021). Briefly, bbpn first creates object masks; then it
calculates background level in each of the four detector segments (each
corresponds to the detector amplifiers) and subtracts the estimated
background; it then runs through the detector in the vertical direction and
again subtracts the background estimated in each row (that consists of 2048
pixels minus masked pixels); lastly, to compensate for any local over-
subtraction of sky near e.g., bright stars or large foreground galaxies, bbpn
estimates spatially varying background and subtract from the entire image.

After the $1/f$-noise subtraction step, we align the calibrated images using
the tweakreg function of the JWST pipeline. For large mosaic fields (i.e.
PRIMER and CEERS), we divide the images into subgroups beforehand and process
each of those separately, to optimize computing speed and memory usage. In
those fields, images are split into subgroups based on the distance of each
image to the other images. We here set the maximum distance of $6\,\arcmin$
for images to be in the same subgroup. We ensure that the images taken in the
same visits (i.e. eight detector images for the blue channel and two detector
images for the red channel of NIRCam) are grouped together, as their relative
distance should remain consistent in the following alignment step.

Images in each subgroup are aligned on a filter-by-filter basis. We provide
tweakreg a set of images associated with source catalogs, generated by running
SExtractor (Bertin & Arnouts, 1996) on each image. This enables us to
eliminate potential artifacts (such as stellar spikes and saturated stars),
which are often included by the automated algorithm by tweakreg, and secure
alignment calculation by using only reliable sources. For all subgroups, each
image is aligned to, when available, the WCS reference of a single, contiguous
source catalog taken from a large FoV ground-based imaging (see below). This
is to avoid alignment issues in some fields and/or subregions, e.g.,
misalignment in overlapping regions caused by insufficient reference stars. It
is noted that tweakreg estimates a single alignment solution for images that
are taken in the same visit and applies it coherently to those images, such
that the distance between the imaging detectors remains the same for all
visits.

Once the images are aligned to the global WCS reference, we drizzle and
combine the images into a (sub)mosaic using the pipeline step IMAGE3. The
pixel scale and pixel frac for drizzling are set to 0.0315 and 0.7 for all
filters. For the fields that have multiple subgroups, we then create a single
mosaic using a python function reproject. Lastly, to eliminate any residual
shifts, we once again apply tweakreg to a set of multi-band mosaics, but based
on the source catalog generated by using the F444W mosaic. The images are
resampled after the final alignment in the same pixel grid using reproject.

###  2.2 JWST NIRCam Extragalactic Fields

To ensure our selection of high-redshift sources is as consistent as possible,
we consider fields that have images in at least six filters (F115W, F150W,
F200W, F277W, F356W, F444W). Some fields have additional blue filters (F070W,
F090W) and several medium bands (F300M, F335M, F410M, F430M, F480M), that
extend the search range toward lower redshift and improve photometric redshift
estimates. When spectroscopic redshift measurements are available (from either
ground or recent JWST observations), we include and use them for photometric
flux calibration (Sec. 3.1), as well as for sample selection (spec-z supercede
dropout or photo-z).

####  2.2.1 PAR1199

The PAR1199 field (11:49:47.31, +22:29:32.1) was taken as part of a Cycle1 GTO
program (PID 1199, PI Stiavelli) in May and June 2023, attached as coordinated
parallel to the NIRSpec primary, and released immediately without a
proprietary period. The NIRCam imaging consists of eight filters, including
F090W and F410M, with a total science exposure of $\sim 16$ hrs with the
MEDIUM8 readout mode. Due toscheduling constraints, the parallel field falls
in a field where only shallow (1–2 orbits) HST ACS F606W and F814W images are
available. In addition, because of a bright star that is located near the edge
of Module A, the sensitivity limit of the blue channel is slightly shallower
in this module compared to the other Module due to the increased scatter light
and artifacts. The impact is found to be less in red filters. Galactic
reddening is ${E(B-V)}=0.024$ mag (Schlegel et al., 1998). The images are
aligned to bright sources in the Pan-STARRS DR2 catalog (Chambers et al.,
2016). The effective area in the detection image is $10.1$ arcmin2.

####  2.2.2 J1235

The J1235 field (12:35:54.4631, +04:56:8.50) is one of the low-ecliptic
latitude fields that were observed as part of a commissioning program used for
NIRCam flatfield (PID 1063, PI Sunnquist). Among the fields available from
this program, the J1235 field offers deep NIRCam coverage by 10 filters,
including F070W, F090W, F300M, and F480M. Two separate visits were made in
March-April 2022 and May 2022. During the first visit, the telescope mirror
alignment was not complete, and thus we exclude the data taken during that
visit. With the second visit, the exposure time goes as deep as 5.8 hrs in a
single filter (with 50.9 hrs in total for the entire field), making it one of
the deepest NIRCam multi-band fields. Galactic reddening is ${E(B-V)}=0.024$
mag.

The total field coverage extends to $\sim 34\,{\rm arcmin}^{2}$ in the
effective detection area, about $\sim 3.7$ NIRCam footprints; however, some
filter images are shifted from others, making the effective area for dropout
selection dependent on target redshift. The NIRCam images were aligned to
bright sources in the Pan-STARRS DR2 catalog.

####  2.2.3 North Ecliptic Pole Time-Domain Field

The NIRCam imaging in the North Ecliptic Pole Time-domain field (17:22:47.896,
+65:49:21.54; Jansen & Windhorst, 2018) was taken as part of a GTO program
(PID 2738, Windhorst et al., 2023). The imaging data used here was taken and
immediately released after the first epoch of the visit, consisting of eight
filters, including F090W and F410M. For the WCS alignment of our JWST data, we
use a publicly available catalog that consists of sources observed with the
Subaru/HSC instrument (Miyazaki et al., 2018) as part of the Hawaii EROsita
Ecliptic pole Survey (HEROES, PI G. Hasinger & E.
Hu).222http://lambda.la.asu.edu/jwst/neptdf/Subaru/index.html Galactic
reddening is ${E(B-V)}\sim 0.028$ mag. The effective area in the detection
image is $16.9$ arcmin2.

####  2.2.4 Primer-UDS and COSMOS

Two large NIRCam mosaics are scheduled in a Cycle 1 GO program, Public Release
Imaging For Extragalactic Research (PRIMER, PID 1837, PI Dunlop). PRIMER
observed two extragalactic fields, the CANDELS UDS and COSMOS fields (Grogin
et al., 2011; Koekemoer et al., 2011). The visits are configured with a
consistent set of filters (eight filters, including F090W and F410M) and
exposure time. In this study, we use the data taken during the first visit in
both fields, which covers most of the entire planned fields. However, several
images were identified as failed guide star acquisitions and were removed from
our reduction.

The UDS mosaics are aligned to the SPLAXH SXDS catalog (Mehta et al., 2018).
We include spec-$z$ measurements available in the same catalog. The COSMOS
mosaics is aligned to the COSMOS2020 catalog (Weaver et al., 2022). We include
spec-$z$ measurements made by the Keck/DEIMOS instrument and published in
Hasinger et al. (2018). Galactic reddening is ${E(B-V)}\sim 0.023$ mag and
$0.017$ mag, respectively. The effective area in the detection images are
$128.2$ arcmin2 and $93.9$ arcmin2.

####  2.2.5 New Generation Deep Field

The Next Generation Deep Extragalactic Exploratory Public survey, NGDEEP (PID
2079, PI Finkelstein; Bagley et al., 2023a), is a deep spectroscopic+imaging
program using the NIRISS WFSS as the primary mode and the NIRCam imaging
attached as coordinated parallel in the HUDF-Par2 field (Stiavelli, 2005;
Illingworth, 2009). In this study, we use the epoch1 NIRCam imaging data,
whereas the epoch2 imaging is currently scheduled in early 2024.

The NIRCam field consists of six filters. Due to the use of the DEEP8 readout
mode for many of the NIRCam exposures, a small portion of the final images are
severely contaminated by CR hits, which moderately reduces the effective field
area and increases the contamination fraction in the high-$z$ source
selection. We include spec-$z$ measurements made by the VANDELS collaboration
(Pentericci et al., 2018). Galactic reddening is ${E(B-V)}\sim 0.007$ mag. The
effective area in the detection image is $10.2$ arcmin2.

####  2.2.6 CEERS

The Cosmic Evolution Early Release Science Survey, or CEERS (Bagley et al.,
2023b; Finkelstein et al., 2023), is an ERS program (PID 1345, PI
Finkelstein). The data set consists of eight NIRCam filters in the EGS field,
previously studied with HST, including as part of CANDELS. The images are
aligned to the WCS of the HST F606W image released by the CEERS team (HDR1),
which is originally aligned to the GAIA-EDR3 WCS.

The CEERS observations had two separate visits, one in June 2022 (for four
sub-regions, #1, 2, 3, 6) and the other in December 2022 (#4, 5, 7, 8, 9, 10).
We reduce the NIRCam images separately in each subfield for the reason
described in Sec. 2.1. Galactic reddening is ${E(B-V)}\sim 0.010$ mag.

We include spec-$z$ measurements from multiple studies (Skelton et al., 2014;
Momcheva et al., 2016; Roberts-Borsani et al., 2016; Larson et al., 2022), as
well as recent JWST spectroscopy studies (Arrabal Haro et al., 2023a, b;
Harikane et al., 2023a, b; Fujimoto et al., 2023; Larson et al., 2023;
Kocevski et al., 2023; Nakajima et al., 2023; Sanders et al., 2023; Tang et
al., 2023). The effective area in the detection image is $117.5$ arcmin2.

####  2.2.7 GLASS-JWST/UNCOVER

Multiple Cycle 1 programs observed the Abell 2744 field (00:14:21, -30:24:03),
including the GLASS-JWST Early Release Science Program (PID 1324; Treu et al.,
2022), a Treasury Survey program the Ultradeep NIRSpec and NIRCam ObserVations
before the Epoch of Reionization (UNCOVER, PID 2561; Bezanson et al., 2022),
and a JWST DDT program (PID 2756; PI. W. Chen; Roberts-Borsani et al., 2022a).
In this study, we utilize the public imaging data made available by the GLASS-
JWST team (Merlin et al., 2022; Paris et al., 2023). Their reduction processes
include several customized steps, to eliminate detector artifacts. The data
set consists of eight NIRCam filters, including F090W and F410M. We use the
public lens model by Bergamini et al. (2023a) to correct lens magnification of
the background sources. We include spectroscopic measurements made available
in the literature (Braglia et al., 2009; Owers et al., 2011; Schmidt et al.,
2014; Richard et al., 2021; Bergamini et al., 2023b), including those from
recent JWST observations (Roberts-Borsani et al., 2022b, a; Morishita et al.,
2023; Mascia et al., 2023; Jones et al., 2023). Galactic reddening is
$E(B-V)\sim 0.013$ mag. The effective area in the detection image is $48.4$
arcmin2. We hereafter refer to the field as A2744 for the sake of simplicity.

####  2.2.8 JADES-GDS

We include a deep field from the JWST Advanced Deep Extragalactic Survey
(JADES; Robertson et al., 2023; Tacchella et al., 2023; Eisenstein et al.,
2023). As of the time of writing, NIRCam imaging data in one of the deep
fields in the GOODS-South field (3:32:39.3, -27:46:59) are publicly available
(Hainline et al., 2023; Rieke & the JADES Collaboration, 2023). We retrieve
the fully processed images and spectroscopic catalogs made available by the
team. The data set consists of nine NIRCam filters, including F090W, F335M,
and F410M. We include spectroscopic sources listed in the MSA spectroscopic
catalog (Bunker et al., 2023), as well as those from the VANDELS survey
(Pentericci et al., 2018). Galactic reddening is ${E(B-V)}\sim 0.007$ mag. The
effective area in the detection image is $26.7$ arcmin2.

Table 1:  $5\,\sigma$-limiting magnitudes (in ABmag).

Field ID | F070W | F090W | F115W | F150W | F200W | F277W | F300M | F335M | F356W | F410M | F444W | F480M
---|---|---|---|---|---|---|---|---|---|---|---|---
PAR1199 | – | 29.0 | 29.0 | 29.0 | 29.2 | 29.4 | – | – | 29.5 | 28.9 | 28.9 | –
J1235 | 28.6 | 28.6 | 28.6 | 28.3 | 28.9 | 29.1 | 28.8 | – | 29.2 | – | 28.3 | 27.7
NEP | – | 28.4 | 28.5 | 28.6 | 28.7 | 29.0 | – | – | 29.2 | 28.4 | 28.6 | –
PRIMERUDS† | – | 27.8 | 27.8 | 28.0 | 28.1 | 28.4 | – | – | 28.4 | 27.6 | 27.9 | –
PRIMERCOS† | – | 27.8 | 27.8 | 28.0 | 28.1 | 28.4 | – | – | 28.5 | 27.8 | 28.2 | –
NGDEEP | – | – | 29.8 | 29.6 | 29.7 | 29.6 | – | – | 29.7 | – | 29.2 | –
CEERS | – | – | 28.7 | 28.6 | 28.8 | 29.0 | – | – | 29.0 | 28.2 | 28.5 | –
A2744 | – | 29.2 | 28.9 | 28.8 | 28.8 | 29.0 | – | – | 29.1 | 28.5 | 28.9 | –
JADESGDS | – | 29.6 | 29.9 | 29.8 | 29.9 | 30.2 | – | 29.6 | 30.1 | 29.6 | 29.8 | –

Note. — Limiting magnitudes measured in empty regions of the image with
$r=0.\\!^{\prime\prime}16$ apertures. †: Mosaic images have been created using
the first epoch data that are available as of June 2023.

Table 2:  Numbers of the final sources in fields.

Field | F070W-d | F090W-d | F115W-d | F150W-d
---|---|---|---|---
PAR1199 | 0 (0) | 7 (0) | 0 (0) | 0 (0)
J1235 | 9 (0) | 1 (0) | 0 (0) | 0 (0)
NEP | 0 (0) | 11 (0) | 0 (0) | 0 (0)
PRIMERUDS | 0 (0) | 50 (0) | 12 (0) | 0 (0)
PRIMERCOS | 2 (2) | 24 (0) | 3 (0) | 1 (0)
NGDEEP | 1 (1) | 0 (0) | 1 (0) | 0 (0)
CEERS | 33 (33) | 8 (8) | 12 (2) | 0 (0)
A2744 | 19 (19) | 15 (9) | 10 (1) | 0 (0)
JADESGDS | 26 (26) | 84 (5) | 9 (2) | 1 (1)
ALL | 90 (81) | 200 (22) | 47 (5) | 2 (1)

Note. — “-d” represents dropout. Numbers of spectroscopically confirmed
sources are shown in brackets.

##  3 Analysis

###  3.1 Photometry

The photometric catalog in each field is constructed following Morishita &
Stiavelli (2022), using borgpipe (Morishita et al., 2021). Briefly, we first
prepare a detection image for each field by stacking the F277W, F356W, and
F444W filters weighted by each of their RMS maps. Source identification is
made in the detection image using SExtractor (Bertin & Arnouts, 1996). Fluxes
are then estimated for the detected sources with a $r=0.\\!^{\prime\prime}16$
aperture. For the aperture flux measurement, images are PSF-matched to the PSF
size of F444W beforehand. The image convolution kernels are generated by using
pypher (Boucaud et al., 2016) on the psf images generated by webbpsf (Perrin
et al., 2014).

We follow the standard procedure used in the literature (Trenti et al., 2012b;
Bradley et al., 2012; Calvi et al., 2016; Morishita et al., 2018; Morishita &
Stiavelli, 2022), including correction for Galactic extinction and RMS scaling
that accounts for correlation noise in drizzled images. Limiting magnitudes of
the images are measured by using the same aperture size and reported in Table
1. Aperture fluxes of individual sources are then corrected by applying the
correction factor $C=f_{\rm auto,F444W}/f_{\rm aper,F444W}$ universally to all
filters, where $f_{\rm auto,F444W}$ is FLUX_AUTO of SExtractor, measured for
individual sources. With this approach, colors remain those measured in
apertures, whereas the total measurements derived in the following analyses
(such as stellar mass, and star formation rate) represent the total fluxes
(see also Sec 3.3).

Lastly, since several fields have a number of spectroscopic objects across a
wide redshift range, we run eazypy, a python wrapper of photometric redshift
code eazy (Brammer et al., 2008), to fine-tune fluxes across all filters. A
set of correction factors for all filters is derived in each field, from the
redshift fitting results on those with spectroscopic redshift. The derived
correction factors are found to be $<2\,\%$ relative to the pivot filter, here
set to F150W in all fields, only requiring minor correction.

###  3.2 Selection of high-redshift galaxy candidates

In what follows, we present our selection of high-redshift galaxies and galaxy
candidates. To construct a robust photometric sample, we adopt a two-fold
selection method, which has been established in our previous studies
(Morishita et al., 2018; Roberts-Borsani et al., 2022b; Ishikawa et al.,
2022), and is described in the following two subsections.

####  3.2.1 Lyman-break dropout selection

We identify dropout sources in four redshift ranges, by applying one of the
following criteria:

F070W-dropouts ($5.0<z<7.2$)

$S/N_{\rm 115}>8$ $S/N_{\rm 070}<2.0$ $z_{\rm set}=4.5$

F090W-dropouts ($7.2<z<9.7$)

$S/N_{\rm 150}>8$ $S/N_{\rm 090,070}<2.0$ $z_{\rm set}=6$

F115W-dropouts ($9.7<z<13.0$)

$S/N_{\rm 200}>8$ $S/N_{\rm 115,090,070}<2.0$ $z_{\rm set}=8$

F150W-dropouts ($13.0<z<18.6$)

$S/N_{\rm 277}>8$ $S/N_{\rm 150,115,090,070}<2.0$ $z_{\rm set}=10$

We note that S/N is measured in a $r=0.\\!^{\prime\prime}16$ aperture. In each
redshift range, we ensure secure selection by requiring a $S/N>8$ detection in
a filter that covers rest-frame UV ($\sim 1600\,\mathrm{\AA}$, but does not
include the blue side of Lyman break). We require non-detection of fluxes
($S/N<2$) in at least one of the dropout filters (listed as subscripts above)
for the source to be selected as dropout. To secure the non-detection, we
repeat the non-detection step with a smaller aperture,
$r=0.\\!^{\prime\prime}08$ ($\sim 2.5$ pixels).

A major difference from the conventional Lyman break technique in the
literature (e.g., Bouwens et al., 2023a) is that our selection method above
does not cut samples based on the color of the rest-frame UV but only on the
strength of Lyman break. The choice is made to preserve as many potential
sources as possible and make the selection comprehensive — for example, in a
conventional color-cut selection, sources may be dismissed for their color
that barely miss the selection window, even if the color is consistent within
the photometric uncertainty. This also means that the fraction of low-$z$
interlopers misidentified as high-$z$ sources is likely increased with regard
to the standard technique. Therefore, we further secure the sample in the
following step.

####  3.2.2 Photometric-redshift selection

We here secure the dropout sources by applying photometric-redshift selection
to the dropout sources selected above. This is to minimize the fraction of
low-$z$ interlopers, such as galaxies of relatively old stellar populations
(e.g., Oesch et al., 2016) and with dust extinction, or foreground dwarfs
(e.g., Morishita et al., 2020). Such interlopers are often distinguished by
their distinctive red color, readily discernible in our wavelength coverage
with NIRCam.

To estimate photometric redshifts, we run eazy with the default magnitude
prior (Fig. 4 in Brammer et al., 2008). The fitting redshift range is set to
$0<z<30$, with a step size of $\log(1+z)=0.01$. By comparing photometric
redshifts with spectroscopic ones, we find that the template library provided
by Hainline et al. (2023) offers an improved photometric redshift accuracy
over the default (v1.3) template library, and thus in this work we adopt the
former.

Following Morishita et al. (2018), we exclude sources that satisfy $p(z<z_{\rm
set})>0.2$, i.e. total redshift probability at $z<z_{\rm set}$ is greater than
$20\,\%$, where $z_{\rm set}$ is set separately for each selection redshift
range (see Sec. 3.2.1). To eliminate potential contamination by cool
(T/L/M-type) stars (i.e. brown dwarfs), we follow Morishita et al. (2021) and
repeat the phot-$z$ analysis with dwarf templates. A set of dwarf templates
taken from the IRTF spectral Library (Rayner et al., 2003) is provided to eazy
and fit to the data with redshift fixed to 0. The fitting result is inferred
for every source that is unresolved (see Sec. 4.4), and the source is removed
if the $\chi^{2}/\nu$ value is smaller than the one from the galaxy template
fitting above.

Lastly, we visually inspect all sources that pass the two selections above. In
this step, we exclude any suspicious sources whose flux measurements may be
significantly affected, including those with residuals of CRs, close
to/overlapping with a brighter galaxy (caused by deblending), misidentified
stellar spikes, and those near the detector edge where a part of the source is
truncated.

###  3.3 Size measurements

Our primary analysis is based on the size measurement of galaxies. Following
standard practice, we adopt the S$\acute{\mathrm{e}}$rsic profile (Sérsic,
1963):

$\displaystyle I(r)\propto\exp[-b_{n}(\frac{r}{R_{\mathrm{e}}})^{1/n}-1]$ (1)

where size is characterized by the effective radius, $R_{\mathrm{e}}$, which
encloses half of the total light of the galaxy. $n$ is the
S$\acute{\mathrm{e}}$rsic index and $b_{n}$ is an $n$-dependent normalization
parameter. We model the two-dimensional light profile of each galaxy using
galfit (Peng et al., 2002, 2010).

We follow Morishita et al. (2014, 2017) for detailed procedures, with a few
modifications to accommodate efficiency and accuracy. Briefly, for each
galaxy, we first generate image cutouts (here set to $151\times 151$ pixels in
size, equivalent to $4.\\!^{\prime\prime}8\times 4.\\!^{\prime\prime}8$) of
original (i.e. pre-psf matched) science map, rms map, and segmentation map. We
fix Sérsic index $n$ to 1, a value that is found to offer reasonable fit to
high-$z$ Lyman-break galaxies in the literature (Shibuya et al., 2015; Yang et
al., 2022a; Ono et al., 2022). As a test, we repeated the analysis with $n$ as
a free parameter and indeed found its distribution to be centered around $\sim
1$. However, this led to an increased fraction of unsuccessful fits, where the
solutions either did not converge or converged to unrealistic parameters. To
mitigate potential bias from this constraint, we reevaluate the uncertainty of
each size measurement by adding the difference in $R_{e}$ resulting from the
two procedures ($n$-fixed and $n$-free) in quadrature. Consequently, those
with $n$ deviating significantly from 1 incur a larger uncertainty in the size
measurement.

Figure 1:  Radial flux profiles of webbpsf with various $\sigma_{\rm jitter}$
values (dashed lines) generated for the JADESGDS F115W image, where the
adopted profile ($\sigma_{\rm jitter}=0.022$) is color-coded in cyan. The
profile of an example bright star taken from the image is shown for comparison
(red solid line). The inset shows the distribution of $\chi^{2}/\nu$ of the
same star fitted by the webbpsf for different $\sigma_{\rm jitter}$ values.

The psf image generated by webbpsf of the corresponding filter is fed to
galfit, for convolution of the model profile at each iteration. The psf image
is generated for each field by retrieving the Optical Path Difference files of
the observed date
333https://webbpsf.readthedocs.io/en/latest/available_opds.html. As reported
in several studies (Ono et al., 2022; Tacchella et al., 2023; Ito et al.,
2023), we find that the default output of webbpsf exhibits a narrower psf
profile compared to observed stars in our reduced images. This is potentially
caused by e.g., drizzling/resampling of the actual images, as well as jitter
in pointing, which could affect more significantly in a longer exposure. We
therefore find an optimal psf that describes the actual psf size of our image
by tweaking the jitter_sigma parameter of webbpsf. To do this, we visually
identify unsaturated, bright stars that do not have any companion within $<50$
pixels. We then fit these stars with various psf models by using galfit and
select a model that offers the minimum $\chi^{2}$ value. While it is ideal to
repeat the analysis and determine the jitter value in each field, some fields
do not have sufficient number of stars that can be used for this. We thus
adopt the median value that is determined by stars in all fields for each
filter. Figure 1 shows an example radial flux profile of an actual star,
compared with those of webbpsf generated with different $\sigma_{\rm jitter}$
values. The final jitter_sigma value is set $\sim 0.022$ arcsec for the blue-
channel filters and $\sim 0.034$ arcsec for the redder-channel.

Neighboring sources that are close to and sufficiently bright compared to the
target galaxy are fit simultaneously, while the rest of the sources in the
stamp are masked using the segmentation map derived by SExtractor above. We
run galfit in the order of the target source magnitudes; the fitting results
of the primary galaxy are continuously stored, so that the parameters for the
repeated galaxies are fixed to the previously determined values when they
appear in the cutout of a fainter galaxy later in the fitting session.

For each galaxy, we repeat the fit in two filters that corresponds to rest-
frame UV and optical wavelengths. We then inspect all fitting results to
ensure the measurements. We have flagged 20 sources that show significant
residuals, e.g., from multiple clumps within the defined segment region and/or
clear features of interaction with nearby sources. These flagged sources are
excluded from statistical analyses in the following sections. The measured
sizes are presented in Appendix.

Figure 2:  Sample distribution in the star formation rate–stellar mass plane
(circles). Those with spectroscopic redshift (blue square) and flagged as
compact (magenta; Sec 4.4) are marked accordingly. Those that are flagged in
galfit results (Sec. 3.3) are shown by open symbols. The SFMS slope at $z=5$
(black dashed lines; Speagle et al. 2014), the linear regression slope derived
for our entire sample, $\log{\rm SFR}=0.81\log(M_{*}/10^{8}\,M_{\odot})+0.32$
(with the slope fixed to the Speagle et al. 2014’s at $z=5$), and the derived
range for the intrinsic scatter ($0.36$ dex; shaded regions) are shown.

###  3.4 Physical Properties inferred by spectral energy distribution analysis

We infer the spectral energy distribution (SED) of the individual galaxies
through SED fitting using photometric data that covers 0.6–5 $\mu$m. We use
the SED fitting code gsf (ver1.8; Morishita et al., 2019), which allows
flexible determinations of the SED by adopting binned star formation
histories, also known as non-parametric. gsf determines an optimal combination
of stellar and interstellar medium (ISM) templates among the template library.
For this study, we generate templates of different ages, [10, 30, 100, 300,
1000, 3000] Myrs, and metallicities $\log Z_{*}/Z_{\odot}\in[-2,0]$ at an
increment of 0.1 by using fsps (Conroy et al., 2009; Foreman-Mackey et al.,
2014). A nebular component (emission lines and continuum) that is
characterized by an ionization parameter $\log U\in[-3,-1]$ is also generated
by fsps (see also Byler et al., 2017) and added to the template after
multiplication by an amplitude parameter. Dust attenuation and metallicity of
the stellar templates are treated as free parameters during the fit, whereas
the metallicity of the nebular component is synchronized with the metallicity
of the stellar component during the fitting process.

The posterior distribution of the parameters is sampled by using emcee for
$10^{4}$ iterations with the number of walkers set to 100. The final posterior
is collected after excluding the first half of the realizations (known as
burn-in). The resulting physical parameters (such as stellar mass, star
formation rate, rest-frame UV slope $\beta_{\rm UV}$, metallicity, dust
attenuation $A_{V}$, and mass-weighted age) are quoted as the median of the
posterior distribution, along with uncertainties measured from the 16 th to 84
th percentile range. The star-formation rate of individual galaxies is
calculated with the rest-frame UV luminosity ($\sim 1600\,\rm{\AA}$) using the
posterior SED. The UV luminosity is corrected for dust attenuation using the
$\beta_{\rm UV}$ slope, which is measured by using the posterior SED, as in
Smit et al. (2016):

$A_{1600}=4.43+1.99\,\beta_{\rm UV}$ (2)

The attenuation corrected UV luminosity is then converted to SFR via the
relation in Kennicutt (1998):

${\rm SFR\,[M_{\odot}\,yr^{-1}]}=1.4\times 10^{-28}L_{\rm UV}\,[{\rm
erg\,s^{-1}\,Hz^{-1}}].$ (3)

Lastly, we correct both stellar mass and star formation rate measurements to
the total model magnitude derived by galfit, as in Morishita et al. (2014), by
multiplying the correction factor:

$C_{\rm galfit}=10^{-0.4(m_{\rm galfit}-m_{\rm total})}$ (4)

where $m_{\rm galfit}$ is the best-fit total magnitude derived by galfit and
$m_{\rm total}$ is the total magnitude derived in Sec. 3.1, both measured in
the rest-frame UV filter of interest for the target redshift range. Sources
flagged in the galfit results are set $C_{\rm galfit}=1$. The inferred
physical properties are presented in Appendix.

Figure 3:  Example of galfit fitting results in 2D-images (left: original,
middle: model, right: residual). (top row): PRIMERCOS-38203, one of the
highest redshift sources in our sample. (middle row): JADESGDS-30934
(spectroscopically confirmed to $z=13.2$; Curtis-Lake et al. 2023), an example
of those classified as compact (Sec. 4.4). (bottom row): F090W-dropout source
that is flagged in galfit fitting analysis. Flagged objects are not included
in our statistical discussion.  Figure 4:  Distribution of our sample galaxies
at $5<z<14$ in the stellar mass-size plane (gray circles), in four redshift
panels. The linear slope derived by regression analysis for the full sample
(orange solid lines, with the shaded regions covering the $\pm
1.5\,\sigma_{\log R_{e}}$ range from the median slope; Table 3) is shown. For
comparison, the slope derived for late-type galaxies at $z\sim 2.7$ (van der
Wel et al., 2014, black dashed lines) is shown. Those classified as compact
(Sec. 4.4) are shown by magenta symbols. Those with spectroscopic redshift are
marked by open blue squares. Two horizontal hatched regions show the physical
size of FWHM/2 for NIRCam (dark gray) and HST/WFC3-IR F160W (light gray) at
the median redshift of the sources in each redshift window.  Table 3:
Size–Mass Relations of Galaxies at $5<z<14$: Linear Regression Best-Fit
Coefficients

All sample | Resolved sample ($R_{e}>{\rm FWHM/2}$)
---|---
$N$ | $\alpha$ | $\beta_{z}$ | $\alpha_{z}$ | $\sigma_{\mathrm{\log R_{e}}}$ | $N$ | $\alpha$ | $\beta_{z}$ | $\alpha_{z}$ | $\sigma_{\mathrm{\log R_{e}}}$
316 | $0.20_{-0.03}^{+0.03}$ | $-0.28_{-0.20}^{+0.20}$ | $-0.37_{-0.21}^{+0.21}$ | $0.30_{-0.01}^{+0.01}$ | 272 | [$0.20$] | $-0.31_{-0.18}^{+0.18}$ | $-0.26_{-0.19}^{+0.19}$ | $0.25_{-0.01}^{+0.01}$

Note. — Best-fit coefficients for the single slope regression, $\log R_{\rm
e}/{\rm kpc}=\alpha\log(M_{*}/10^{8}M_{\odot})+B(z)$, where
$B(z)=\beta_{z}+\alpha_{z}\log(1+z)$. Note that $\sigma_{\log
R_{e}}=\sigma_{\ln R_{e}}/\ln(10)$.

##  4 Results

###  4.1 Overview of the final sample

In Table 2, we report the number of our final sources selected in each field
and dropout selection window. From all fields, we identify 90 F070W-dropout
sources (81 of which are spec-$z$ confirmed), 200 F090W-dropout sources (22),
47 F115W-dropout sources (5), and 2 F150W-dropout sources (1). As we present
in the following sections, the sample spans a wide range of stellar mass
($6.8\,\hbox to0.0pt{\lower 3.5pt\hbox{$\mathchar 0\relax\sim$}\hss}\raise
1.0pt\hbox{$<$}\,\log M_{*}/M_{\odot}\,\hbox to0.0pt{\lower
3.5pt\hbox{$\mathchar 0\relax\sim$}\hss}\raise 1.0pt\hbox{$<$}\,10.4$) and
absolute UV magnitude ($-23\,\hbox to0.0pt{\lower 3.5pt\hbox{$\mathchar
0\relax\sim$}\hss}\raise 1.0pt\hbox{$<$}\,M_{\rm UV}\,/\,{\rm mag}\,\hbox
to0.0pt{\lower 3.5pt\hbox{$\mathchar 0\relax\sim$}\hss}\raise
1.0pt\hbox{$<$}\,-17$).

In Fig. 2, we show the distribution of the final sample in the stellar mass-
star formation rate plane. Despite the wide mass (more three orders of
magnitude) and redshift ($5<z<14$) ranges, our galaxies are found to
distribute along a sequence, suggesting that most of our sample galaxies are a
typical star-forming population. To compare the location of our galaxies with
those at lower redshift, we derive a linear regression, with the slope fixed
to 0.81 i.e. the one for $z=5$ using the formulae of Speagle et al. (2014).
The regression is derived to be $\log{\rm
SFR}=0.81\log(M_{*}/10^{8}\,M_{\odot})+0.32$, with the intrinsic scatter of
$0.36$ dex.

While the full details of the selected sources will be presented in a
forthcoming paper, we highlight two F150W-dropout galaxies ($z\,\hbox
to0.0pt{\lower 3.5pt\hbox{$\mathchar 0\relax\sim$}\hss}\raise
1.0pt\hbox{$>$}\,13$). One, JADESGDS-30934, is spectroscopically confirmed to
be at $z=13.2$ (Curtis-Lake et al., 2023). The other object, PRIMERCOS-38203,
is a newly identified photometric candidate source at $z=13.8_{-1.0}^{+1.1}$
in the PRIMER-COSMOS field (Fig. 3). Despite the relatively shallow depth in
the field, the source exhibits clean non-detection in F090W, F115W, and F150W
and high-S/N detection in F200W (S/N=9.2) and in the IR-detection band
(S/N=16.1). The observed UV magnitude ($M_{\rm UV}=-20$ mag) and the derived
stellar mass ($\log M_{*}/M_{\odot}=8.0$) are both moderate and comparable to
other galaxy candidates at these redshift (e.g., Morishita & Stiavelli, 2023;
Finkelstein et al., 2023; Bouwens et al., 2023b). While the F200W-dropout
selection covers up to $z\sim 18.6$, none is identified at $z>14$ in our
selection. The number density estimates of the identified sources will be
presented in a forthcoming paper.

Figure 5:  Same as in Fig. 4 but for redshift-corrected size ($=R_{\rm
eff,maj}/(1+z)^{\alpha_{z}}$) of all sources (gray dots) along the determined
regression slope (orange solid line, with shades for the $\pm
1.5\,\sigma_{\log R_{e}}$ range). In the inset, the distribution of the
normalized size, $\Delta R_{\rm e}/\sigma_{\rm\log R_{e}}$ is shown, where
$\Delta R_{\rm e}$ is the distance from the regression for a given stellar
mass. The distribution of the compact sources (open magenta) is shown
separately from the remaining extended sources. The Gaussian fit to the
extended sources (solid red line) highlights the excess.

###  4.2 Size-Stellar Mass Distribution of Galaxies at $5<z<14$

In Fig. 4, we show the distribution of galaxies in the size-mass plane for the
four redshift ranges. In each redshift panel, we show the size measured in the
filter that corresponds to rest-frame $\sim 1600\,{\rm\AA}$ i.e. F115W for the
F070W-dropout, F150W for the F090W-dropout, F200W for the F115W-dropout, and
F277W for the F200W-dropout selection. We adopt the effective radius measured
along the major axis, to mitigate the effect by inclination.

The measured size spans a broad range, $\log R_{e}\,/\,{\rm kpc}\sim-2$ –
$0.3$. Remarkably, at $\log M_{*}/M_{\odot}<9$, many galaxies are
characterized by $R_{e}<0.3$ kpc ($<0.\\!^{\prime\prime}7$), which is below
the resolution limit afforded by HST/WFC3-IR. Thus, the spatial resolution of
NIRCam, $\sim 0.11{-}0.14$ kpc, is essential to study typical star-forming
galaxies at these redshift (see also Sec. 4.4).

We investigate their distribution on the size-mass plane by linear regression
analyses. By following Shen et al. (2003, also ), we parameterize size by a
log-normal distribution as a function of stellar mass and redshift:

$\log R_{e}(M_{*},z)/{\rm kpc}=\alpha\log(M_{*}/M_{\rm 0})+B(z),$ (5)

where we describe the intercept as

$B(z)=\beta_{z}+\alpha_{z}\log(1+z).$ (6)

We adopt a pivot mass $M_{\rm 0}=10^{8}\,M_{\odot}$. The model distribution,
$N(\log R_{e}(M_{*}),\sigma_{\rm\log R_{e}})$, prescribes the probability
distribution for observing $\log R_{e}$ for a galaxy with the stellar mass
$M_{*}$ with an intrinsic scatter of $\sigma_{\rm\log R_{e}}$. By making the
intercept $B(z)$ a function of redshift, we are able to evaluate the size
distribution with a consistent slope determined by the entire sample. Although
the redshift evolution of the slope is of interest, previous studies have
reported little changes over a much broader cosmic time range than what is
explored in this study (e.g., van der Wel et al., 2014; Shibuya et al., 2015).
As we demonstrate below, our findings also reveal no significant evolution in
slope from these studies, supporting our employing a single slope across the
entire redshift range.

The regression is determined by using emcee (Foreman-Mackey et al., 2014),
with the number of walker is set to $N=50$ and the iteration to $10^{4}$. We
only include those not flagged in structural fitting analysis in Sec 3.3.
Measurements uncertainties in size, stellar mass, and redshift are used in the
calculation of likelihood. The derived regression is shown in Fig. 4 along
with the measured sizes in four redshift panels. In Fig. 5, we also show the
redshift-corrected size, $R_{e}/(1+z)^{\alpha_{z}}$, as this represents the
actual variants evaluated in the fitting. We report the determined parameters
in Table 3.

The derived slope, $\sim 0.20\pm 0.03$, is similar to what was found in Mosleh
et al. (2011, $\sim 0.3$) for Lyman Break galaxies at $z\sim 3.5$ and van der
Wel et al. (2014, 0.18–0.25) for late-type galaxies at $z\sim 0.3$ – $3$,
despite the latter being observed in different rest-frame wavelengths (but see
the following and Sec 5.3). The slope of the size-mass relation would reflect
the interplay between the intrinsic compactness and concentrated dust
attenuation in the core of massive star-forming galaxies at high redshift
(Roper et al., 2022b). In fact, negative slopes of the size-mass and size-
luminosity relations have been found in cosmological simulations, e.g.
BlueTides (Marshall et al., 2022), IllustrisTNG (Popping et al., 2022;
Costantin et al., 2023), FLARES (Roper et al., 2022a), and THESAN (Shen et al.
2023, in prep.) simulations. Our finding of a positive slope is consistent
with the idea that some massive galaxies in our sample may already possess a
moderate amount of dust in its core.

We note that the derived intrinsic scatter, $\sigma_{\log R_{e}}$, is
relatively large ($\sim 0.3$) compared to those at lower redshift in the
literature ($\sim 0.2$). The scatter in size distribution is ought to reflect
the initial condition of dark matter halos, such as the distribution of spin
parameter (e.g., Bullock et al., 2001a). Thus, the observed larger scatter
would imply the presence of galaxies that experienced non-linear behavior,
such as merger and other dissipative processes, and deviate from the
distribution predicted by a simple galactic disc formation model (e.g., Mo et
al., 1998). In fact, we observed a number of galaxies that are barely resolved
in the NIRCam images, some of which are located far below ($>1.5\,\sigma$) the
derived regression (Sec. 4.4). We repeat the regression analysis by excluding
these unresolved sources but with the slope fixed to the one derived above.
The derived scatter from this regression is $\sim 0.25$, moderately reduced
from the analysis of the full sample (Table 3).

###  4.3 Redshift Evolution of Galaxy Sizes

In Fig. 6, we show the redshift trend of the UV sizes derived through the
regression analysis above. We note that the redshift evolution represent, by
design, for the mass-corrected size, $R_{e}/(M_{*}/M_{0})^{\alpha}$. We find
that the derived redshift evolution, $\propto(1+z)^{\alpha_{z}}$ with
$\alpha_{z}\sim-0.4\pm 0.2$, is much less significant than the one derived for
rest-frame UV ($\sim 2100\,{\rm\AA}$) size of Lyman-break galaxies at $0<z<7$,
with $\alpha_{z}=-1.2\pm 0.1$ (Mosleh et al., 2012, also Oesch et al. 2010;
Shibuya et al. 2015 who found a similar value).

The primary cause of the discrepancy can be attributed to the redshift range
of the sample. The aforementioned studies derived the redshift evolution by
including lower-redshift galaxies ($z\,\hbox to0.0pt{\lower
3.5pt\hbox{$\mathchar 0\relax\sim$}\hss}\raise 1.0pt\hbox{$<$}\,1$ in Mosleh
et al. 2012; Shibuya et al. 2015 and $2<z<8$ in Oesch et al. 2010). Indeed,
Curtis-Lake et al. (2016) found a much slower evolution, $\alpha_{z}$, for the
LBG sample at $4<z<8$, and argued that the discrepancy is partially attributed
to a stronger evolution in UV sizes at $z<5$ (see also Oesch et al., 2010, who
found little size evolution from $z\sim 7$ to $6$).

To investigate this, we derive the redshift evolution by combining the median
sizes presented in Fig. 6 and in Mosleh et al. (2012, for $1<z<7$), and we do
find a stronger evolution ($\alpha_{z}\sim-1.2$). This supports the idea that
the redshift evolution of the average UV size is much slower at $z>5$ than
lower redshifts. However, we note that the outcomes is likely subject to a
range of systematic factors, such as the weighting of each size measurement,
the inclusion or exclusion of specific data points, and potential sample
mismatches.

In Fig. 6, we also show the evolution of rest-frame optical sizes derived for
low-mass ($\log M_{*}/M_{\odot}\sim 9$) late-type galaxies at $0<z<2$ (van der
Wel et al., 2014). Interestingly, the extrapolated sizes from the fit to our
redshift range exhibit a significant offset, even after accounting for the
mass difference between the two samples. Although the offset might be
attributed to differences in the bands used for size measurements (i.e. rest-
frame $5000\,{\rm\AA}$ in van der Wel et al. 2014), we find that our galaxies,
on average, do not show a significant offset between the two bands. This
reinforces our earlier interpretation that size evolution could be more
pronounced at $z\,\hbox to0.0pt{\lower 3.5pt\hbox{$\mathchar
0\relax\sim$}\hss}\raise 1.0pt\hbox{$<$}\,5$, primarily driven by the build-up
of massive bulges and/or outskirts, which would enhance color gradients, as
seen at lower redshifts. We discuss this in more detail in Sec. 5.3.

###  4.4 Identifying Blue Compact Sources

In our size analysis above, we have identified a number of compact sources
that are near the resolution limit of NIRCam. Previous studies using NIRCam
data also reported a few of such compact sources (Yang et al., 2022a;
Castellano et al., 2022; Naidu et al., 2022; Ono et al., 2022). We define
those that satisfy either of the following as compact:

  * $i$) $R_{e}<{\rm FWHM}/2$ in the measured band;

  * $ii$) $\Delta{R_{e}}<-1.5\,\sigma_{\log R_{e}}$

where $\Delta R$ is the difference of the measured size from the inferred size
by the linear regression for the stellar mass and redshift of the source. Note
that $R_{e}$ used here is apparent size.

With the criteria, we find 45 compact sources from our sample ($\sim 13\,\%$),
including 15 that are spectroscopically confirmed. We then exclude five of the
classified compact sources, whose SED is better fitted with a brown dwarf
template over galaxy templates (Sec. 3.2). The classified compact sources are
marked in Fig. 4. Individual cutout images are presented in Appendix Appendix
C: Image Cutouts of Compact Sources.

In Fig. 5, we show the distribution of the normalized size, $\Delta
R_{e}/\sigma_{\log R_{e}}$. The distribution clearly shows an excess at
smaller size when compared to the distribution of the remaining non-compact
sources. We note that the fraction of the identified compact sources is well
above the number expected at $1.5\,\sigma$ for a normal distribution (i.e.
$6.7\,\%$). The compact sources follow a similar distribution as other
extended sources in physical properties, such as stellar mass, star formation
rate (see also Fig. 2), $\beta_{\rm UV}$, except for star formation surface
density (Sec. 5.1). The ISM properties of the compact sources are further
investigated in Sec. 5.2.

Figure 6:  Redshift evolution of rest-frame UV size of our galaxies (gray
circles). The size of individual galaxies shown here is corrected to the pivot
mass (Sec. 4.2). The redshift evolution of the intercept,
$\propto(1+z)^{\alpha_{z}}$, is shown (orange dashed line), along with those
derived in two previous studies (green line for Mosleh et al. 2012 and black
line for van der Wel et al. 2014). We note that the size trend of both
previous studies is also scaled to the same pivot mass. Median sizes (large
symbols) are derived in each redshift window. Those identified as compact
(Sec. 4.4) are shown by magenta symbols.

##  5 Discussion

Figure 7:  ($Left$): Star formation surface density ($\Sigma_{\rm SFR}$) of
our final sample (gray circles) as a function of redshift. The median values
at each dropout redshift window are shown by larger symbols (squares). The
upper limits for $\Sigma_{\rm SFR}$ by spatial resolution limit, when assuming
SFR of $10\,M_{\odot}\,/\,{\rm yr}$, are shown for the corresponding JWST
NIRCam filters (solid curved lines) and the HST WFC3-IR F160W (black dotted
line). The fit derived for LBGs in HST data (Shibuya et al., 2015, cyan dashed
line) is shown. ($Right$): $\Sigma_{\rm SFR}$ as a function of stellar mass.
In the background, we show the density contour for the distribution of
galaxies at $0.3<z<3.5$, taken from the 3DHST catalog (Skelton et al., 2014;
van der Wel et al., 2014). Those outside the lowest contour level are shown
individually (crosses). Only $\sim 6\,\%$ of the low-$z$ sample is found at
$\Sigma_{\rm SFR}>1\,{\rm M_{\odot}/yr/kpc^{2}}$; the majority of our sample
is located above the same value.  Figure 8:  Same as in Fig. 5 but symbols are
color-coded by age ($\log t$) derived in our SED analysis. The median age in
each grid is shown by larger symbols (squares), with the symbol size scaled by
the number of galaxies in the grid. In the inset, the scatters in size
measured at each mass bin are shown. The error of each scatter measurement is
estimated by bootstrapping. Clearly, the radius and age of the stellar
population increase, while the scatter in sizes decrease, with increasing
stellar mass.

###  5.1 High Star Formation Efficiency Revealed by NIRCam Imaging

Star formation (rate) surface density,

$\Sigma_{\rm SFR}\,/\,{\rm M_{\odot}\,yr^{-1}\,kpc^{-2}}={\rm
0.5\,SFR}\,/\,\pi R_{e}^{2},$ (7)

is known as an excellent proxy for inferring the current mode of star
formation. In Fig. 7, we show the distribution of $\Sigma_{\rm SFR}$ of our
sample. Overall, the median values of our galaxies are consistent with
previous studies with HST (Oesch et al., 2010; Shibuya et al., 2015).

However, we observe a large scatter along the vertical axis. In fact, we find
a large fraction of galaxies of $\log\Sigma_{\rm SFR}\,\hbox to0.0pt{\lower
3.5pt\hbox{$\mathchar 0\relax\sim$}\hss}\raise 1.0pt\hbox{$>$}\,1.5$,
comparable to local ULIRG/starburst (Scoville et al., 2000; Dopita et al.,
2002; Kennicutt & Evans, 2012) and high-$z$ sub-millimetre galaxies (e.g.,
Daddi et al., 2010). Such high-$\Sigma_{\rm SFR}$ galaxies were not reported
in previous HST studies at similar redshift (Oesch et al., 2010; Ono et al.,
2013; Holwerda et al., 2015), except for a few cases in cluster lensing fields
(e.g., Kawamata et al., 2015; Bouwens et al., 2022). Obviously, $\Sigma_{\rm
SFR}$ estimates are limited by imaging resolution, and thus the previous
estimate for smaller galaxies identified in HST data often remained as lower
limits (e.g., Morishita et al., 2021; Fujimoto et al., 2022; Ishikawa et al.,
2022).

The observations in Fig. 7 also demonstrate the potential for JWST NIRCam
observations to probe star formation activity at low stellar masses
($M\lesssim 10^{8}M_{\odot}$). This had been so far limited to indirect
probes, such as studies based on follow-up of Gamma Ray Burst afterglows to
quantify the fraction of detected host galaxies (e.g, Tanvir et al. 2012;
Trenti et al. 2012a; McGuire et al. 2016). Those studies hinted the existence
of the population of sources now directly probed by NIRCam.

####  5.1.1 Implications for Star-formation Efficiency

The compact sources identified in Sec. 4.4 dominate the upper range,
$\log\Sigma_{\rm SFR}\,\hbox to0.0pt{\lower 3.5pt\hbox{$\mathchar
0\relax\sim$}\hss}\raise 1.0pt\hbox{$>$}\,1.5$. We note that this does not
stem from their star formation rates, which are, on average, comparable to
those of more extended sources. Instead, it is attributed to their compact
nature. These compact sources’ physical characteristics are particularly
intriguing, as negative feedback is likely more effective within confined
systems. The observed high values thus imply efficient gas fueling within
these compact sources, potentially facilitated by processes such as the loss
of angular momentum through mergers (see also Sec. 5.2).

In addition, the mass-dependence of $\Sigma_{\rm SFR}$ could hint at the
efficiency of star formation, as the system’s mass is tightly linked to the
regulation of star formation. In the right panel of Fig. 7, we show the
distribution of $\Sigma_{\rm SFR}$ as a function of stellar mass. Of
particular interest is the high mass range, $\sim 10^{9}\,M_{\odot}$. In this
mass range, the shocked gas remains hot (e.g., Birnboim & Dekel, 2003; Dekel &
Birnboim, 2006; Stern et al., 2021), resulting in a reduced star formation
efficiency, as seen at lower redshifts. The observed high values for our
sample thus imply that our sources still hold a high efficiency within this
mass regime. We calculate the median mass-doubling time by $t=M_{*}/{\rm SFR}$
and find $\sim 15$–$90$ Myr for our sample. These considerably small mass-
doubling times suggest that some galaxies in our sample could evolve to $\log
M_{*}/M_{\odot}\sim 11$ by $z\sim 5$, provided that the efficiency remains
similar in the following $\sim 0.5$–1 Gyr.

####  5.1.2 Implications for the Growth of Galaxies

At these early cosmic time, comparing the radius-mass relationship of galaxies
as a function of the age of the stellar population can reveal how galaxies
grow (Fig. 8). In hierarchical structure formation, galaxies grow through the
merger of smaller mass galaxies. The merger process results in the infall of
stars but also allows new star-formation to occur through shocking of the
infalling gas. If galaxies grow predominantly by cold gas accretion (e.g.
Dekel & Birnboim, 2006), one would expect their size to evolve predominantly
through secular evolution which would be driven by dynamical friction between
the stars and the accreted gas i.e. galaxies would become smaller as they
built up their stellar mass. We find two clear trends in our sample of
high-$z$ galaxies:

  * 1.

Both radius and age increase with stellar mass. High mass galaxies harbor
older ($\sim$ several 100 Myr) stellar populations compared to lower mass
galaxies, which harbor populations of age $\sim$10-100 Myr.

  * 2.

The scatter in radii at fixed mass is larger by 0.2 dex for low mass galaxies
than for high mass galaxies.

Since there is no selection effect that prevents the selection of small, high
mass galaxies, the implication of these two observational trends is that
galaxies form inside out. Small, low-mass galaxies have a large scatter in
their sizes likely due to a combination of gas accretion and merger events. As
they form stars, the stars get scattered due to three-body interactions,
resulting in a growth in size and stellar mass. That is consistent both with
the size evolution with cosmic time and the radius and stellar age evolution
with mass. Although the sensitivity of the data at the present time are not
adequate to constrain minor merger rates at these redshifts, future deeper
surveys will help develop this hypothesis further.

####  5.1.3 Comparison with Local Lyman-Break Analogs

It is illustrative to compare the luminosity surface density of these galaxies
with similar objects in the local Universe (Hoopes et al., 2007; Overzier et
al., 2009; Shim & Chary, 2013). Although objects with such high surface
densities of star-formation exist at $z\sim 0$, less than 0.01% of galaxies in
the Sloan sample have $\Sigma_{\rm SFR}>1$ ${\rm
M_{\odot}\,yr^{-1}\,kpc^{-2}}$. Even at higher redshifts, the fraction remains
small ($\sim 6\,\%$ at $0.3<z<3.5$; Skelton et al., 2014). In contrast, the
fraction is $\sim$100% in our sample. The $z\sim 0$ objects have a median mass
of 108.9 M⊙ and span the full range of metallicity from sub-solar to super-
solar. They are not particularly biased towards young ages as determined from
the strength of the Balmer break. Morphologically, we see some evidence of
mergers in the high-$z$ sample with $\sim 20\,\%$ of galaxies having nearby
companions (besides the 20 galaxies flagged in the size analysis). Taken
together, the implication therefore is that the high surface densities are
gas-rich galaxies with the gas being relatively concentrated towards the
nucleus of the galaxy indicating that the late stages of the merging process
is driving the inflow of gas towards the nucleus of the galaxies.

Figure 9:  (Left): Distribution of 51 galaxies at $5<z<9.5$ that have robust
[O iii]λ5007/${\rm H\beta}$ measurements by NIRSpec MSA, in the mass-
excitation (MEx) diagram (circles). For those with ${\rm H\beta}$ detected at
$S/N<3$, lower limits for the ratio are shown (triangles). Compact galaxies
(as defined in Sec. 4.4; magenta) and two sources from Larson et al. (2023,
cyan stars); Kocevski et al. (2023, cyan stars) are marked separately. The
solid lines are shown to indicate the regions dominated by star forming
galaxies (left bottom) and AGN (right top) of $z\sim 2$ (Juneau et al., 2014).
(Right): Distribution of the normalized size ($\Delta R_{e}/\sigma_{\log
R_{e}}$, an indicator that is used to define compactness in Sec. 4.4) as a
function of the distance to the MEx border separation line.

###  5.2 Nature of the Compact Sources — NIRSpec Spectroscopic Analysis

We here investigate the nature of the compact sources identified in Sec. 4.2,
specifically aiming to observe if they exhibit any evidence of AGN.
Traditionally, point-source-like morphologies of UV bright sources have been
considered as evidence of AGN. However, it has been shown that the compactness
does not always indicate the presence of AGN (e.g., Morishita et al., 2020),
and vice versa (see, e.g., Matthee et al., 2023, for faint AGN with extended
features). As such, a comprehensive approach is required to answer the
question.

A subset of our sample galaxies have spectroscopic coverage by NIRSpec/MSA,
taken as part of CEERS, GLASS-ERS, and JADES. Following Morishita et al.
(2023), we reduce the MSA spectra, using
msaexp444https://github.com/gbrammer/msaexp. For the extracted 1d spectrum of
each source, we fit the line profiles of ${\rm H\beta}$ and [O iii]-doublets
with a Gaussian, after subtracting the underlying continuum spectrum inferred
by gsf in Sec. 3.4. For each line, the total flux of each line is estimated by
integrating the flux over the wavelength range of 2$\times$FWHM derived from
the gaussian fit. In the following analysis, we include sources with measured
line S/Ns above 3 for the [O iii]λ5007 line (N=51); when ${\rm H\beta}$ is not
detected above the same significance, we quote the $3\,\sigma$ flux limit
measured at the wavelength of the line over the same line width derived for [O
iii]λ5007.

In Fig. 9, we show the sample in the mass-excitation (MEx) diagram, a
conventional diagnostic for AGN and star-forming galaxies (Juneau et al.,
2011). Among the 51 objects, we find eight sources located within the MEx AGN
region defined by Juneau et al. (2014). One of the sources, CEERS7-18822, was
previously reported to have a tentative ($\sim 2.5\,\sigma$) broad component
in ${\rm H\beta}$ (Larson et al., 2023), agreeing with the classification
here. CEERS7-18822 exhibits extended structures in F115W, which confirms our
classification of this source not to be compact.

None of our compact sources is classified as MEx-AGN. While JADESGDS-18784 is
located near the MEx border, with $\log\,$[O iii]/${\rm H\beta}$ $=0.83\pm
0.34$ (classified as MEx-AGN within the uncertainty range), we do not confirm
any features that immediately supports the presence of AGN (i.e. broad line
features or high-ionization lines). None of the other compact sources show AGN
signatures either, except for CEERS6-7832 which was reported to have a broad
($\sim 2000$ km/s) component in its ${\rm H\alpha}$ line (Kocevski et al.,
2023, as CEERS_1670555The other object reported by Kocevski et al. (2023),
CEERS_3210, does not pass our $S/N>8$ selection criterion and is not included
in our final sample.).

However, this does not completely rule out the absence of AGN. Firstly, the
discriminating power of the MEx diagram could be lower near the transition
region. As shown by Juneau et al. (2014), for the range of $\log$ [O
iii]/${\rm H\beta}$ line ratios probed here ($\,\hbox to0.0pt{\lower
3.5pt\hbox{$\mathchar 0\relax\sim$}\hss}\raise 1.0pt\hbox{$>$}\,0.4$) there
could be 10–30 $\%$ of AGN present even inside the MEx-SF region at $\log
M_{*}/M_{\odot}\sim 10$. For example, the aforementioned CEERS_7832 is found
in the MEx-SF region. The accuracy of the AGN classification at a lower mass
range is not known due to the lack of data in the previous study. We also note
that the observed ratio for our sample ($\log$ [O iii]/${\rm H\beta}$ $\,\hbox
to0.0pt{\lower 3.5pt\hbox{$\mathchar 0\relax\sim$}\hss}\raise
1.0pt\hbox{$>$}\,0.6$) is relatively high, compared to the star-forming
galaxies of a comparable mass in Juneau et al. (2014, also ). Such a high
ratio can still be achieved by a stellar-only configuration, but requires,
e.g., high electron density (Reddy et al., 2023).

We note that, given the evolving ISM properties at these redshifts, there is
likely a shift of the MEx boundary toward a higher line ratio, as is the case
for $z\sim 0$ to $z\sim 2$. As such, it is still possible that any of our MEx-
SF sources near the border may host an AGN, if not a broad line AGN. Vice
versa, some lower-mass MEx-AGN sources here could turn to be MEx-SF due to the
potential redshift evolution of the boundary.

Nonetheless, the absence of clear AGN evidence implies that the observed high
ratios are driven by high surface density of star formation. When we fit high
values of [O iii]/${\rm H\beta}$ $>3$ with radiative shock models (e.g.,
MAPPING III; Allen et al., 2008), we find that shock velocities of a median of
$500$ km/s are required. Ratios of $\sim$10 can only be achieved with high
shock velocities which could be correlated with high surface density of star-
formation (or strong AGN activity). Achieving the observed high star formation
surface density ($>100\,M_{\odot}/{\rm yr/kpc^{2}}$) is considered
challenging, due to the presence of negative feedback. Such high density is
expected only in an extreme environment, where an abundant of gas is
available, and/or in (post-)merging systems where gas could rapidly fall in.
By comparing with a numerical simulation, Ono et al. (2022) found that such a
compact galaxy is in a temporary compact star-forming phase triggered by
recent major mergers. Roper et al. (2022a) found a large fraction of blue
compact ($\sim 100$–300 pc) galaxies and these galaxies to have little
contribution by AGN in the FLARES simulations (also, Marshall et al., 2022,
Shen, X., in prep.). Future work will compare derived physical parameters from
the emission lines, with the measured star-formation rate density.

Figure 10:  Distributions of rest-frame UV-to-optical size ratio for three
redshift bins. The first two panels compares rest-frame UV to optical ($\sim
5000\,{\rm\AA}$) sizes, whereas the last panel at a shorter wavelength ($\sim
3700\,{\rm\AA}$) due to the filter availability. Only resolved galaxies in the
two filters are included. The F150W-dropouts are not shown, as neither of
their size is resolved in F444W. Each distribution is fit with Gaussian (red
lines), with the mean position indicated by vertical dashed lines. The
difference to the extrapolated size in rest-frame optical of van der Wel et
al. (2014) (observed in Fig. 6) is shown in each panel (dotted vertical
lines); the difference is measured to be 3.4, 2.6, and 3.0 $\sigma$,
respectively.

###  5.3 Comparison of sizes in rest-frame UV and optical

In this study, we have analyzed the sizes of galaxies at a rest-frame
wavelength of $\sim 1600\,{\rm\AA}$. In Sec. 4.2, we found that the average
sizes of our galaxies are much smaller than those predicted from the
extrapolation of van der Wel et al. (2014) at the corresponding redshift. A
possible explanation for the discrepancy could be the rest-frame wavelengths
where the size is measured in the previous study (i.e. $\sim 5000\,{\rm\AA}$).
We investigate this by repeating our size analysis but in a filter that
corresponds to $\sim 5000\,{\rm\AA}$, i.e. F356W for the F070W-dropouts, and
F444W for the F090W-dropouts. For the other two ranges at higher redshift, we
use the reddest filter available (F444W), which corresponds to $\sim
4000\,{\rm\AA}$ and $\sim 3000\,{\rm\AA}$, respectively. In Fig. 10, we show
the distribution of the size difference in these two wavelengths. We find that
the difference of size in the two filters is negligible on average, and thus
conclude that the wavelength difference cannot explain the offset seen in the
extrapolated size of van der Wel et al. (2014) observed in Fig. 6. Instead, we
speculate that the extrapolation of their relation may not persist in the
redshift range far beyond their probed redshift range $z<3$.

In fact, we have seen in Sec. 4.2 that the size evolution is much slower
($\alpha_{z}=-0.4$) than the one found in previous studies of rest-frame UV
size ($\sim-1.2$; Mosleh et al., 2012; Shibuya et al., 2015). While a
comprehensive analysis covering a wide redshift range would be necessary, we
attribute the observed conflict to the build-up of complicated structures in
galaxies, such as a central massive bulge and young star forming disk. At
lower redshifts, we observe a more pronounced difference in sizes between
different wavelengths, driven by radial color gradients within the systems
(e.g., Vulcani et al., 2014). Similarly, Shibuya et al. (2015) found that at
$z=1.2{-}2.1$ the average UV size is smaller than the optical size by $\sim
20\%$ in the low-mass regime ($\sim 10^{9}\,M_{\odot}$), while the trend is
reversed at the high-mass end ($\,\hbox to0.0pt{\lower 3.5pt\hbox{$\mathchar
0\relax\sim$}\hss}\raise 1.0pt\hbox{$>$}\,10^{11}\,M_{\odot}$; also see
Szomoru et al. 2012; van der Wel et al. 2014).

On the other hand, the resemblance of galaxy UV and optical sizes of high-
redshift galaxies is not unexpected given the rapid assembly of the stellar
content (also see Yang et al., 2022b; Treu et al., 2023). For our galaxies, we
have estimated the mass-doubling time to be $\,\hbox to0.0pt{\lower
3.5pt\hbox{$\mathchar 0\relax\sim$}\hss}\raise 1.0pt\hbox{$<$}\,100$ Myr in
Sec. 5.1. This time scale is comparable to or even smaller than the star
formation time scale that the UV tracer is sensitive to (Murphy et al., 2011;
Flores Velázquez et al., 2021). Consequently, the stellar content detected by
the UV will predominate over the total content integrated over the full star
formation history, which is probed by observations at optical wavelengths. The
majority of our galaxies are actively building their structures inside-out,
developing rapidly enough to maintain coherence.

##  6 Summary

In this study, we have identified 339 galaxies at $5<z<14$ in legacy fields of
JWST and analysed their rest-frame UV and optical sizes through JWST NIRCam
images. The imaging data used here were collected from several public programs
in Cycle 1, resulting in a combined effective area of 358 arcmin2. With a
robust ($8\,\sigma$) selection of 339 galaxies, made possible by the
unprecedented area coverage provided by JWST, we have conducted the first
systematic exploration of the size-mass relation of galaxies in the first
billion years. The key findings are as follows:

  * •

The slope of the size-mass relation was derived via linear regression analyses
and found to be $\alpha\sim 0.2$, similar to those of star forming galaxies at
$z<3$ but scaled down in size by 0.4 dex. The derived intercept was found to
evolve with $\propto(1+z)^{-0.4}$, much slower evolution than those found in
previous studies of rest-frame UV sizes of galaxies at lower redshifts.

  * •

By using the results from our linear regression analysis, we identified 45
compact sources that are marginally resolved in NIRCam imaging. These compact
sources account for $\sim 13\,\%$ of the full sample presented here.

  * •

We found that our sources overall have high star formation surface density
($\,\hbox to0.0pt{\lower 3.5pt\hbox{$\mathchar 0\relax\sim$}\hss}\raise
1.0pt\hbox{$>$}\,1\,M_{\odot}{\rm yr^{-1}\,kpc^{-2}}$), with the newly
identified compact sources being as high as $\sim 300\,M_{\odot}{\rm
yr^{-1}\,kpc^{-2}}$. We demonstrated that the absence of a clear declining
trend indicates that star formation efficiency may remain high even at the
high mass range; if the observed high efficiency remains similar in the
following $\sim 0.5$–1 Gyr, some of our sources would evolve to $\sim
10^{11}\,M_{\odot}$ by $z\sim 5$.

  * •

For 51 sources with available NIRSpec/MSA data, we investigated their ISM via
the [O iii]-to-${\rm H\beta}$ line ratio. None of the compact sources are
confidently classified as AGN in the Mass-Extinction diagram; however, the
nature of the compact sources remains to be conclusively elucidated in a
future study. A potential explanation for the observed high line ratios is
high shock velocities, likely originating within intense star-forming regions
characterized by high $\Sigma_{\rm SFR}$.

  * •

We found that the sizes in rest-frame and optical wavelengths are on average
consistent. We attributed this to the short mass-doubling time (i.e. $1/{\rm
sSFR}<100$ Myr) of our sources, implying that they are actively building their
structure coherently, and are thus dominated by young stars.

With the unprecedented resolution and sensitivity provided by JWST, this work
demonstrated a comprehensive size analysis of galaxies in the first billion
years. Of particular remarks are the newly discovered compact population.
Specifically, the physical mechanisms that maintain the observed high star
formation rate in such compact systems, which are likely under the strong
influence by negative feedback, remains an open question. Recent JWST
observations have identified a number of faint AGN (Onoue et al., 2023;
Matthee et al., 2023) and a complex of dusty AGN + young stellar populations
(Furtak et al., 2023; Labbe et al., 2023; Akins et al., 2023), suggesting a
greater prevalence of AGN in high-$z$ galaxies than previously thought. These
emerging findings raise a caveat that the estimated star formation rate from
our SED analysis may not accurately represent the intrinsic value, even though
our spectroscopic analysis on the subsample did not find any immediate
signatures of AGN. Future spectroscopic followups of the compact sources will
provide further insight into their nature and their potential impact in a
broad cosmological context.

## Acknowledgements

We would like to thank the teams of the JWST observation programs # 1063,
1180, 1210, 1345, 1837, 2079, and 2738, which a large part of this study is
based on, for their countless efforts in carefully designing and planning
their programs and their generous consideration of making their valuable data
publicly available immediately after the observations or before the expiration
of the original proprietary periods. We thank Zhaoran Liu for kindly providing
a spectroscopic catalog compiled from literature studies in the CEERS field.
We acknowledge support for this work under NASA grant 80NSSC22K1294. The data
presented in this paper were obtained from the Mikulski Archive for Space
Telescopes (MAST) at the Space Telescope Science Institute.

Software: Astropy (Astropy Collaboration et al., 2022), EAzY (Brammer et al.,
2008), EMCEE (Foreman-Mackey et al., 2013), gsf (Morishita et al., 2019),
numpy (Harris et al., 2020), python-fsps (Foreman-Mackey et al., 2014).

Figure 11:  Same as Fig. 4 but the distribution in the absolute UV magnitude
($M_{\rm UV}$)-size plane. For comparison, the slope fit to LBGs at $7<z<12$
(Yang et al., 2022b, black dashed lines) is shown. The predicted slope from
our size-stellar mass relation (Eq. 5) by using the $M_{\rm UV}$–stellar mass
conversion (Eq. 10) is shown (red dashed lines).  Figure 12:  Distribution of
our sample in the absolute UV magnitude–stellar mass plane (circles). Symbols
are color-coded by the value of $\beta_{\rm UV}$. The regression slopes at
various $\beta_{\rm UV}$ values, determined using Eqs. 10 and 11, are shown
(solid lines).

## Appendix A: Comparison with the $M_{\rm UV}$-Size relation

In Fig. 11, we show the distribution of our sample galaxies in the absolute UV
magnitude ($M_{\rm UV}$)-size plane. We follow the regression analysis in Sec.
4.2 using the following formulae:

$\log R_{e}(M_{\rm UV},z)=\alpha(M_{\rm UV}-M_{\rm UV,0})+B(z),$ (8)
$B(z)=\beta_{z}+\alpha_{z}\log(1+z).$ (9)

We adopt the pivot magnitude $M_{\rm UV,0}=-20$ mag. The regression analysis
gives a similar slope ($\alpha=-0.08\pm 0.02$) to those found for in the
literature at $z\sim 5$ ($-0.1$; Huang et al., 2013) and at similar redshift
($-0.11$–$-0.15$; Shibuya et al., 2015; Kawamata et al., 2018). The intercept
is very similar to Yang et al. (2022b), who derived the intercept for $7<z<12$
galaxies using a fixed slope of the Huang et al. (2013)’s value. The
consistent intercept supports that our size measurement is comparable to the
literature studies (see also Ono et al., 2022, who compare their sizes with
Yang et al. 2022b). The derived parameters are reported in Table 4.

In Fig. 12, we show the distribution of our final sample in the $M_{\rm
UV}$-$\log M_{*}$ plane. The distribution shows a weak trend with $\beta_{\rm
UV}$ but not with redshift. We thus characterize the distribution by:

$\log M_{*}(M_{\rm UV},\beta_{\rm UV})=\alpha(M_{\rm UV}-M_{\rm
UV,0})+B(\beta_{\rm UV}),$ (10) $B(\beta_{\rm
UV})=\beta_{c}+\alpha_{c}(\beta_{\rm UV}-\beta_{\rm UV,0})$ (11)

with the same pivot magnitude $M_{\rm UV,0}=-20$ mag as above, and $\beta_{\rm
UV,0}=-2$. The regression slope is determined but with a relatively large
scatter ($\sigma\sim 0.47$) By using this conversion and the size-stellar mass
relation in the main text, we can predict the size-$M_{\rm UV}$ relation (and
vice versa). In Fig. 12, along with the regression derived above, we show the
predicted size-$M_{\rm UV}$ relation for the sample, by using the median
$\beta_{\rm UV}$ value, in each redshift window. The predicted slope is
shallower but overall in agreement with the one derived above within the
uncertainty. We provide the determined parameters in Table 4.

Table 4: Linear Regression Best-Fit Coefficients

$M_{\rm UV}$–Size relation |  $M_{\rm UV}$–Stellar mass relation
---|---
$N$ | $\alpha$ | $\beta_{z}$ | $\alpha_{z}$ | $\sigma_{\mathrm{\log R_{e}}}$ | $N$ | $\alpha$ | $\beta_{c}$ | $\alpha_{c}$ | $\sigma_{\mathrm{\log M_{*}}}$
316 | $-0.08_{-0.02}^{+0.02}$ | $-0.06_{-0.21}^{+0.22}$ | $-0.45_{-0.23}^{+0.22}$ | $0.32_{-0.01}^{+0.01}$ | 316 | $-0.28_{-0.03}^{+0.03}$ | $8.82_{-0.04}^{+0.04}$ | $0.72_{-0.09}^{+0.09}$ | $0.47_{-0.02}^{+0.02}$

Note. — Coefficients determined in the linear regression analysis in Appendix
A.

## Appendix B: Physical Properties of the Final Sample

We report the physical properties of the final 339 sources at $z>5$ in Table
Appendix C: Image Cutouts of Compact Sources. The table contains coordinates,
apparent magnitudes (Sec. 3.1), spectroscopic or photometric redshifts (Sec.
3.2.2), size measurements and flags (Sec. 3.3 and 4.4), and SED parameters
(Sec. 3.4).

## Appendix C: Image Cutouts of Compact Sources

Cutout images of the compact sources identified in Sec. 4.4 are shown in Fig.
13. The only F150W-dropout that is classified as compact is shown in the main
text (Fig. 3).

Figure 13:  Sources that are classified as compact. ($left$): original image,
in the UV band where the size is measured. ($middle$): galfit single Sérsic
model image (with Sérsic index fixed to $1$). ($right$): residual image. Those
with spectroscopic redshift measurements are color-coded its redshift label in
cyan.

Figure 14: Continued.

Table 5: Physical properties of the final candidates.

ID | R.A. | Decl. | $m_{\rm UV}$ | $z^{\dagger}$ | $M_{\rm UV}$ | $M_{*}$ | SFR | $\beta_{\rm UV}$ | $R_{e,maj}$ | [O iii]/${\rm H\beta}$ | $f_{gal}^{\ast}$ | $f_{com}^{\ast}$
---|---|---|---|---|---|---|---|---|---|---|---|---
| deg. | deg. | mag |  | mag | $\log M_{\odot}$ |  $\log M_{\odot}$/yr |  | $\log{\rm kpc}$ | $\log$ |  |
F070W-dropout
J1235-2632 | 188.92749 | 4.8717985 | $26.7$ | $6.0_{-0.2}^{+0.1}$ | $-19.9_{-0.1}^{+0.1}$ | $8.7_{-0.8}^{+0.8}$ | $0.8_{-0.1}^{+0.1}$ | $-2.7_{-0.1}^{+0.1}$ | $-0.61_{-0.01}^{+0.01}$ | – | 0 | 0
J1235-4628 | 188.96568 | 4.8757343 | $25.7$ | $5.2_{-0.1}^{+0.1}$ | $-21.0_{-0.1}^{+0.1}$ | $9.9_{-0.1}^{+0.1}$ | $1.6_{-0.1}^{+0.1}$ | $-1.6_{-0.1}^{+0.1}$ | $-0.27_{-0.02}^{+0.02}$ | – | 0 | 0
J1235-5704 | 188.94789 | 4.8993416 | $26.7$ | $6.6_{-0.1}^{+0.1}$ | $-21.5_{-0.1}^{+0.1}$ | $8.5_{-0.4}^{+0.4}$ | $0.9_{-0.1}^{+0.1}$ | $-2.4_{-0.2}^{+0.1}$ | $-0.84_{-0.01}^{+0.01}$ | – | 0 | 0
J1235-7600 | 188.94092 | 4.915246 | $26.5$ | $5.7_{-0.2}^{+0.2}$ | $-20.1_{-0.1}^{+0.1}$ | $9.4_{-0.1}^{+0.1}$ | $0.8_{-0.1}^{+0.1}$ | $-2.3_{-0.1}^{+0.2}$ | $0.08_{-0.02}^{+0.02}$ | – | 0 | 0
J1235-9040 | 188.95932 | 4.9153123 | $26.4$ | $6.0_{-0.1}^{+0.1}$ | $-20.6_{-0.1}^{+0.1}$ | $8.4_{-0.1}^{+0.1}$ | $0.9_{-0.1}^{+0.1}$ | $-2.3_{-0.1}^{+0.1}$ | $-0.50_{-0.13}^{+0.13}$ | – | 0 | 0
J1235-9171 | 188.95187 | 4.9199114 | $26.4$ | $6.0_{-0.1}^{+0.1}$ | $-20.0_{-0.1}^{+0.1}$ | $9.6_{-0.1}^{+0.1}$ | $1.0_{-0.1}^{+0.1}$ | $-2.2_{-0.1}^{+0.1}$ | $-0.07_{-0.01}^{+0.01}$ | – | 0 | 0
J1235-15534 | 188.97287 | 4.9570713 | $26.9$ | $5.9_{-0.2}^{+0.1}$ | $-19.7_{-0.1}^{+0.1}$ | $8.9_{-0.2}^{+0.2}$ | $0.7_{-0.1}^{+0.1}$ | $-2.5_{-0.1}^{+0.1}$ | $-0.25_{-0.02}^{+0.02}$ | – | 0 | 0
J1235-17888 | 188.97598 | 4.9694805 | $27.2$ | $6.6_{-0.1}^{+0.1}$ | $-19.8_{-0.1}^{+0.1}$ | $8.6_{-0.4}^{+0.4}$ | $0.6_{-0.1}^{+0.1}$ | $-2.6_{-0.1}^{+0.2}$ | $-0.61_{-0.03}^{+0.03}$ | – | 0 | 0
J1235-18627 | 188.97969 | 4.9720902 | $27.0$ | $6.4_{-0.1}^{+0.1}$ | $-19.7_{-0.1}^{+0.1}$ | $8.4_{-0.1}^{+0.1}$ | $0.7_{-0.1}^{+0.1}$ | $-2.4_{-0.1}^{+0.1}$ | $-0.94_{-0.03}^{+0.03}$ | – | 0 | 0
A2744-5240 | 3.6025238 | -30.419222 | $27.6$ | $6.23$ | $-19.2_{-0.1}^{+0.1}$ | $8.5_{-0.2}^{+0.2}$ | $0.6_{-0.1}^{+0.1}$ | $-2.0_{-0.2}^{+0.1}$ | $-0.71_{-0.05}^{+0.05}$ | $0.8\pm 0.1$ | 0 | 0
A2744-6031 | 3.5769939 | -30.415525 | $27.1$ | $5.13$ | $-18.8_{-0.1}^{+0.1}$ | $8.7_{-0.1}^{+0.1}$ | $0.5_{-0.1}^{+0.1}$ | $-1.9_{-0.1}^{+0.1}$ | $0.05_{-0.06}^{+0.06}$ | – | 1 | –
A2744-6323 | 3.5706301 | -30.414635 | $26.7$ | $5.76$ | $-19.8_{-0.1}^{+0.1}$ | $8.9_{-0.2}^{+0.2}$ | $1.0_{-0.1}^{+0.1}$ | $-1.8_{-0.1}^{+0.1}$ | $-0.05_{-0.27}^{+0.27}$ | – | 1 | –
A2744-7284 | 3.5879247 | -30.411587 | $29.4$ | $5.28$ | $-17.9_{-0.1}^{+0.1}$ | $7.8_{-0.2}^{+0.2}$ | $-0.2_{-0.2}^{+0.2}$ | $-2.0_{-0.2}^{+0.3}$ | $-0.93_{-0.60}^{+0.60}$ | $0.5\pm 0.1$ | 0 | 0
A2744-7728 | 3.6006 | -30.410273 | $29.9$ | $6.38$ | $-16.9_{-0.1}^{+0.1}$ | $7.1_{-0.3}^{+0.3}$ | $-0.4_{-0.1}^{+0.1}$ | $-2.6_{-0.2}^{+0.2}$ | $-1.39_{-0.22}^{+0.22}$ | $0.5\pm 0.3$ | 0 | 1
A2744-7965 | 3.5965643 | -30.40897 | $29.2$ | $5.28$ | $-16.5_{-0.1}^{+0.1}$ | $8.0_{-0.2}^{+0.2}$ | $-0.1_{-0.3}^{+0.3}$ | $-1.9_{-0.3}^{+0.2}$ | $-0.81_{-0.50}^{+0.50}$ | – | 0 | 0
A2744-9029 | 3.550838 | -30.405872 | $27.1$ | $5.04$ | $-19.5_{-0.1}^{+0.1}$ | $8.5_{-0.3}^{+0.3}$ | $0.6_{-0.1}^{+0.1}$ | $-2.6_{-0.2}^{+0.3}$ | $-0.68_{-0.25}^{+0.25}$ | $1.1\pm 0.1$ | 0 | 0
A2744-9421 | 3.6000569 | -30.404362 | $28.8$ | $5.28$ | $-17.6_{-0.1}^{+0.1}$ | $7.8_{-0.1}^{+0.1}$ | $-0.1_{-0.1}^{+0.1}$ | $-2.2_{-0.1}^{+0.1}$ | $-0.66_{-0.10}^{+0.10}$ | – | 0 | 0
A2744-9436 | 3.5761104 | -30.404465 | $27.4$ | $5.66$ | $-19.4_{-0.1}^{+0.1}$ | $8.1_{-0.2}^{+0.2}$ | $0.5_{-0.1}^{+0.1}$ | $-2.8_{-0.1}^{+0.1}$ | $-0.59_{-0.05}^{+0.05}$ | – | 0 | 0
A2744-9642 | 3.6010537 | -30.403976 | $29.2$ | $6.33$ | $-17.9_{-0.1}^{+0.1}$ | $7.4_{-0.2}^{+0.2}$ | $-0.2_{-0.1}^{+0.1}$ | $-2.6_{-0.2}^{+0.2}$ | $-0.94_{-0.10}^{+0.10}$ | – | 0 | 0
A2744-9694 | 3.5700579 | -30.403687 | $27.8$ | $5.55$ | $-18.7_{-0.1}^{+0.1}$ | $8.7_{-0.1}^{+0.1}$ | $0.8_{-0.2}^{+0.2}$ | $-1.6_{-0.2}^{+0.2}$ | $-0.65_{-0.05}^{+0.05}$ | $0.8\pm 0.3$ | 0 | 0
A2744-9701 | 3.5529106 | -30.403868 | $27.9$ | $5.02$ | $-17.9_{-0.2}^{+0.2}$ | $8.3_{-0.3}^{+0.3}$ | $0.2_{-0.1}^{+0.1}$ | $-2.4_{-0.4}^{+0.4}$ | $-0.38_{-0.35}^{+0.35}$ | $0.4\pm 0.2$ | 0 | 0
A2744-11854 | 3.6054316 | -30.396582 | $28.7$ | $5.77$ | $-18.6_{-0.1}^{+0.1}$ | $6.8_{-0.1}^{+0.1}$ | $0.0_{-0.1}^{+0.1}$ | $-2.8_{-0.1}^{+0.1}$ | $-0.94_{-0.07}^{+0.07}$ | $>0.8$ | 0 | 0
A2744-11960 | 3.5907068 | -30.395544 | $27.3$ | $5.66$ | $-19.9_{-0.1}^{+0.1}$ | $8.4_{-0.1}^{+0.1}$ | $0.7_{-0.1}^{+0.1}$ | $-2.8_{-0.1}^{+0.1}$ | $-1.07_{-0.09}^{+0.09}$ | – | 1 | –
A2744-12496 | 3.5651999 | -30.394262 | $27.4$ | $5.77$ | $-20.0_{-0.1}^{+0.1}$ | $8.8_{-0.1}^{+0.1}$ | $0.7_{-0.1}^{+0.1}$ | $-1.9_{-0.1}^{+0.1}$ | $-0.82_{-0.04}^{+0.04}$ | $0.8\pm 0.1$ | 0 | 0
A2744-14994 | 3.5957074 | -30.386774 | $27.7$ | $5.05$ | $-18.4_{-0.1}^{+0.1}$ | $8.5_{-0.1}^{+0.1}$ | $0.1_{-0.1}^{+0.1}$ | $-2.5_{-0.1}^{+0.2}$ | $-0.46_{-0.43}^{+0.43}$ | – | 1 | –
A2744-15060 | 3.5954387 | -30.386791 | $26.9$ | $5.05$ | $-19.6_{-0.1}^{+0.1}$ | $8.9_{-0.1}^{+0.1}$ | $0.8_{-0.1}^{+0.1}$ | $-1.9_{-0.1}^{+0.1}$ | $-0.29_{-0.03}^{+0.03}$ | – | 1 | –
A2744-15949 | 3.5947585 | -30.384432 | $29.1$ | $5.61$ | $-17.6_{-0.1}^{+0.1}$ | $8.1_{-0.1}^{+0.1}$ | $-0.1_{-0.1}^{+0.1}$ | $-2.6_{-0.1}^{+0.3}$ | $-1.47_{-0.16}^{+0.16}$ | – | 0 | 1
A2744-37546 | 3.4833336 | -30.332148 | $25.8$ | $5.07$ | $-20.9_{-0.1}^{+0.1}$ | $9.3_{-0.1}^{+0.1}$ | $1.3_{-0.1}^{+0.1}$ | $-2.0_{-0.1}^{+0.1}$ | $-0.06_{-0.01}^{+0.01}$ | – | 1 | –
PRIMERCOS-9711 | 150.14299 | 2.2567828 | $25.2$ | $5.12$ | $-21.3_{-0.1}^{+0.1}$ | $9.3_{-0.4}^{+0.4}$ | $1.5_{-0.1}^{+0.1}$ | $-1.9_{-0.1}^{+0.1}$ | $0.10_{-0.01}^{+0.01}$ | – | 1 | –
PRIMERCOS-10934 | 150.13754 | 2.2596548 | $26.5$ | $5.67$ | $-20.2_{-0.1}^{+0.1}$ | $7.6_{-0.1}^{+0.1}$ | $0.8_{-0.1}^{+0.1}$ | $-2.7_{-0.1}^{+0.1}$ | $-0.26_{-0.03}^{+0.03}$ | – | 0 | 0
NGDEEP-31979 | 53.225365 | -27.821129 | $26.0$ | $5.16$ | $-21.1_{-0.1}^{+0.1}$ | $10.5_{-0.1}^{+0.1}$ | $1.6_{-0.1}^{+0.1}$ | $-1.7_{-0.1}^{+0.1}$ | $0.09_{-0.01}^{+0.01}$ | – | 1 | –
JADESGDS-6734 | 53.126537 | -27.81809 | $27.9$ | $5.92$ | $-18.3_{-0.1}^{+0.1}$ | $9.1_{-0.1}^{+0.1}$ | $0.7_{-0.1}^{+0.1}$ | $-1.6_{-0.1}^{+0.1}$ | $-0.88_{-0.05}^{+0.05}$ | $0.7\pm 0.1$ | 0 | 1
JADESGDS-7592 | 53.145054 | -27.816431 | $28.2$ | $5.77$ | $-18.0_{-0.1}^{+0.1}$ | $8.6_{-0.1}^{+0.1}$ | $0.1_{-0.1}^{+0.1}$ | $-2.3_{-0.1}^{+0.1}$ | $-0.09_{-0.02}^{+0.02}$ | $>0.2$ | 0 | 0
JADESGDS-8179 | 53.155796 | -27.815203 | $28.5$ | $6.71$ | $-18.4_{-0.1}^{+0.1}$ | $7.4_{-0.1}^{+0.1}$ | $0.1_{-0.1}^{+0.1}$ | $-2.4_{-0.1}^{+0.1}$ | $-1.07_{-0.15}^{+0.15}$ | $0.7\pm 0.1$ | 0 | 1
JADESGDS-8408 | 53.115379 | -27.814774 | $27.4$ | $5.76$ | $-19.2_{-0.1}^{+0.1}$ | $7.6_{-0.1}^{+0.1}$ | $0.2_{-0.1}^{+0.1}$ | $-2.5_{-0.1}^{+0.1}$ | $-0.53_{-0.01}^{+0.01}$ | $0.8\pm 0.1$ | 0 | 0
JADESGDS-9326 | 53.118526 | -27.812975 | $27.2$ | $5.12$ | $-19.3_{-0.1}^{+0.1}$ | $8.2_{-0.1}^{+0.1}$ | $0.9_{-0.1}^{+0.1}$ | $-1.7_{-0.1}^{+0.1}$ | $-0.33_{-0.01}^{+0.01}$ | $0.8\pm 0.2$ | 0 | 0
JADESGDS-10902 | 53.14946 | -27.809797 | $28.4$ | $5.05$ | $-17.8_{-0.1}^{+0.1}$ | $7.8_{-0.2}^{+0.2}$ | $0.0_{-0.1}^{+0.1}$ | $-2.3_{-0.1}^{+0.1}$ | $-0.36_{-0.01}^{+0.01}$ | $0.7\pm 0.2$ | 0 | 0
JADESGDS-11449 | 53.11042 | -27.808928 | $28.1$ | $5.94$ | $-18.6_{-0.1}^{+0.1}$ | $7.5_{-0.2}^{+0.2}$ | $0.2_{-0.1}^{+0.1}$ | $-2.6_{-0.1}^{+0.1}$ | $-0.75_{-0.02}^{+0.02}$ | $0.7\pm 0.1$ | 0 | 0
JADESGDS-11841 | 53.129726 | -27.80818 | $28.6$ | $5.56$ | $-18.5_{-0.1}^{+0.1}$ | $8.2_{-0.2}^{+0.2}$ | $0.0_{-0.1}^{+0.1}$ | $-2.5_{-0.1}^{+0.1}$ | $-0.79_{-0.05}^{+0.05}$ | $0.7\pm 0.2$ | 0 | 0
JADESGDS-12147 | 53.130581 | -27.807722 | $28.3$ | $5.62$ | $-18.2_{-0.1}^{+0.1}$ | $8.8_{-0.1}^{+0.1}$ | $0.2_{-0.1}^{+0.1}$ | $-2.2_{-0.1}^{+0.1}$ | $-0.23_{-0.11}^{+0.11}$ | $0.1\pm 0.3$ | 0 | 0
JADESGDS-14986 | 53.167301 | -27.802874 | $28.8$ | $5.82$ | $-17.3_{-0.1}^{+0.1}$ | $8.1_{-0.1}^{+0.1}$ | $-0.1_{-0.1}^{+0.1}$ | $-2.5_{-0.1}^{+0.2}$ | $-0.46_{-0.04}^{+0.04}$ | $0.8\pm 0.2$ | 0 | 0
JADESGDS-16254 | 53.156235 | -27.800737 | $29.4$ | $5.06$ | $-17.0_{-0.1}^{+0.1}$ | $7.9_{-0.1}^{+0.1}$ | $-0.3_{-0.2}^{+0.2}$ | $-2.1_{-0.2}^{+0.1}$ | $-0.99_{-0.08}^{+0.08}$ | $>0.7$ | 0 | 1
JADESGDS-17941 | 53.128735 | -27.797876 | $28.6$ | $5.44$ | $-17.7_{-0.1}^{+0.1}$ | $9.0_{-0.1}^{+0.1}$ | $0.0_{-0.1}^{+0.1}$ | $-2.5_{-0.1}^{+0.1}$ | $-0.33_{-0.06}^{+0.06}$ | $0.7\pm 0.2$ | 0 | 0
JADESGDS-18169 | 53.121758 | -27.797638 | $27.0$ | $5.94$ | $-19.5_{-0.1}^{+0.1}$ | $7.5_{-0.1}^{+0.1}$ | $0.6_{-0.1}^{+0.1}$ | $-2.6_{-0.1}^{+0.1}$ | $-1.04_{-0.03}^{+0.03}$ | $0.8\pm 0.1$ | 0 | 1
JADESGDS-18204 | 53.115837 | -27.797552 | $27.9$ | $5.12$ | $-18.6_{-0.1}^{+0.1}$ | $8.5_{-0.1}^{+0.1}$ | $0.4_{-0.1}^{+0.1}$ | $-2.0_{-0.1}^{+0.1}$ | $-0.71_{-0.01}^{+0.01}$ | $>0.1$ | 0 | 0
JADESGDS-18784 | 53.123005 | -27.796614 | $28.5$ | $5.44$ | $-17.7_{-0.1}^{+0.1}$ | $9.1_{-0.1}^{+0.1}$ | $0.0_{-0.1}^{+0.1}$ | $-2.5_{-0.1}^{+0.1}$ | $-1.02_{-0.04}^{+0.04}$ | $0.8\pm 0.3$ | 0 | 1
JADESGDS-29538 | 53.169044 | -27.778835 | $28.4$ | $6.63$ | $-18.6_{-0.1}^{+0.1}$ | $8.0_{-0.1}^{+0.1}$ | $0.1_{-0.1}^{+0.1}$ | $-2.2_{-0.1}^{+0.1}$ | $-0.83_{-0.02}^{+0.02}$ | $0.6\pm 0.1$ | 0 | 0
JADESGDS-29878 | 53.130047 | -27.778389 | $26.1$ | $5.57$ | $-20.0_{-0.1}^{+0.1}$ | $8.1_{-0.1}^{+0.1}$ | $0.8_{-0.1}^{+0.1}$ | $-2.4_{-0.2}^{+0.1}$ | $0.47_{-0.16}^{+0.16}$ | – | 1 | –
JADESGDS-30683 | 53.127663 | -27.776951 | $28.8$ | $5.04$ | $-17.8_{-0.1}^{+0.1}$ | $7.8_{-0.1}^{+0.1}$ | $-0.1_{-0.1}^{+0.1}$ | $-2.3_{-0.1}^{+0.1}$ | $-0.37_{-0.05}^{+0.05}$ | – | 0 | 0
JADESGDS-31651 | 53.156101 | -27.775881 | $26.8$ | $6.10$ | $-19.6_{-0.1}^{+0.1}$ | $9.7_{-0.1}^{+0.1}$ | $1.3_{-0.1}^{+0.1}$ | $-1.5_{-0.1}^{+0.1}$ | $0.01_{-0.02}^{+0.02}$ | $0.3\pm 0.2$ | 0 | 0
JADESGDS-32171 | 53.175819 | -27.774473 | $27.7$ | $6.33$ | $-19.0_{-0.1}^{+0.1}$ | $8.7_{-0.1}^{+0.1}$ | $0.6_{-0.1}^{+0.1}$ | $-2.0_{-0.1}^{+0.1}$ | $-0.62_{-0.02}^{+0.02}$ | $0.8\pm 0.1$ | 0 | 0
JADESGDS-33170 | 53.134914 | -27.772709 | $26.9$ | $6.33$ | $-19.8_{-0.1}^{+0.1}$ | $7.8_{-0.1}^{+0.1}$ | $0.4_{-0.1}^{+0.1}$ | $-2.6_{-0.1}^{+0.1}$ | $-0.62_{-0.01}^{+0.01}$ | $0.8\pm 0.1$ | 0 | 0
JADESGDS-33350 | 53.166595 | -27.772406 | $28.1$ | $6.33$ | $-18.8_{-0.1}^{+0.1}$ | $7.6_{-0.1}^{+0.1}$ | $0.2_{-0.1}^{+0.1}$ | $-2.2_{-0.1}^{+0.1}$ | $-0.26_{-0.02}^{+0.02}$ | $0.4\pm 0.1$ | 0 | 0
JADESGDS-33803 | 53.160625 | -27.771608 | $28.2$ | $5.97$ | $-18.7_{-0.1}^{+0.1}$ | $7.4_{-0.2}^{+0.2}$ | $0.2_{-0.1}^{+0.1}$ | $-2.6_{-0.1}^{+0.1}$ | $-0.48_{-0.03}^{+0.03}$ | $1.1\pm 0.2$ | 0 | 0
JADESGDS-34065 | 53.176529 | -27.77109 | $28.5$ | $5.89$ | $-18.1_{-0.1}^{+0.1}$ | $7.0_{-0.1}^{+0.1}$ | $-0.3_{-0.1}^{+0.1}$ | $-2.7_{-0.1}^{+0.1}$ | $-0.53_{-0.02}^{+0.02}$ | – | 0 | 0
JADESGDS-34138 | 53.176556 | -27.771128 | $27.6$ | $5.89$ | $-18.0_{-0.1}^{+0.1}$ | $8.1_{-0.2}^{+0.2}$ | $0.5_{-0.1}^{+0.1}$ | $-2.7_{-0.1}^{+0.1}$ | $0.05_{-0.01}^{+0.01}$ | $0.7\pm 0.1$ | 0 | 0
JADESGDS-35365 | 53.134232 | -27.768915 | $28.1$ | $7.04$ | $-18.3_{-0.1}^{+0.1}$ | $7.7_{-0.1}^{+0.1}$ | $0.3_{-0.1}^{+0.1}$ | $-2.5_{-0.1}^{+0.1}$ | $-0.40_{-0.01}^{+0.01}$ | $0.6\pm 0.2$ | 0 | 0
CEERS1-21407 | 215.00113 | 53.011265 | $27.5$ | $7.10$ | $-19.5_{-0.1}^{+0.1}$ | $7.8_{-0.1}^{+0.1}$ | $0.5_{-0.1}^{+0.1}$ | $-2.4_{-0.1}^{+0.1}$ | $-0.92_{-0.03}^{+0.03}$ | – | 0 | 0
CEERS1-23089 | 215.01086 | 53.013317 | $25.9$ | $5.10$ | $-20.6_{-0.1}^{+0.1}$ | $9.6_{-0.1}^{+0.1}$ | $1.3_{-0.1}^{+0.1}$ | $-1.9_{-0.1}^{+0.1}$ | $0.03_{-0.01}^{+0.01}$ | $0.8\pm 0.3$ | 0 | 0
CEERS2-1767 | 214.87251 | 52.875961 | $27.7$ | $5.67$ | $-18.4_{-0.2}^{+0.2}$ | $7.4_{-0.2}^{+0.2}$ | $0.3_{-0.1}^{+0.1}$ | $-2.4_{-0.3}^{+0.2}$ | $-0.84_{-0.03}^{+0.03}$ | $0.6\pm 0.3$ | 0 | 0
CEERS2-7005 | 214.88797 | 52.888271 | $26.9$ | $5.27$ | $-19.5_{-0.1}^{+0.1}$ | $9.0_{-0.1}^{+0.1}$ | $1.0_{-0.1}^{+0.1}$ | $-1.8_{-0.1}^{+0.1}$ | $-0.31_{-0.01}^{+0.01}$ | $0.7\pm 0.3$ | 0 | 0
CEERS3-9221 | 214.81299 | 52.83424 | $26.9$ | $7.18$ | $-20.0_{-0.1}^{+0.1}$ | $8.2_{-0.1}^{+0.1}$ | $0.8_{-0.1}^{+0.1}$ | $-2.4_{-0.1}^{+0.1}$ | $-0.55_{-0.02}^{+0.02}$ | – | 0 | 0
CEERS3-9625 | 214.81163 | 52.837231 | $26.8$ | $5.28$ | $-19.9_{-0.1}^{+0.1}$ | $8.4_{-0.1}^{+0.1}$ | $1.0_{-0.1}^{+0.1}$ | $-1.8_{-0.1}^{+0.1}$ | $-0.31_{-0.02}^{+0.02}$ | – | 0 | 0
CEERS3-15767 | 214.80647 | 52.878826 | $27.0$ | $6.11$ | $-19.4_{-0.1}^{+0.1}$ | $7.8_{-0.3}^{+0.3}$ | $0.7_{-0.1}^{+0.1}$ | $-2.6_{-0.1}^{+0.4}$ | $-0.28_{-0.02}^{+0.02}$ | $0.8\pm 0.1$ | 0 | 0
CEERS3-18419 | 214.82896 | 52.875694 | $26.5$ | $5.77$ | $-20.4_{-0.2}^{+0.1}$ | $9.5_{-0.1}^{+0.1}$ | $0.9_{-0.1}^{+0.1}$ | $-2.3_{-0.1}^{+0.6}$ | $-0.32_{-0.03}^{+0.03}$ | $0.8\pm 0.1$ | 0 | 0
CEERS3-20932 | 214.83617 | 52.882668 | $25.6$ | $6.00$ | $-21.3_{-0.1}^{+0.1}$ | $9.1_{-0.1}^{+0.1}$ | $1.3_{-0.1}^{+0.1}$ | $-2.6_{-0.1}^{+0.1}$ | $-0.33_{-0.01}^{+0.01}$ | $0.8\pm 0.1$ | 0 | 0
CEERS3-21899 | 214.86159 | 52.876141 | $27.5$ | $7.17$ | $-19.9_{-0.1}^{+0.1}$ | $7.5_{-0.1}^{+0.1}$ | $0.5_{-0.1}^{+0.1}$ | $-2.7_{-0.1}^{+0.1}$ | $-0.23_{-0.06}^{+0.06}$ | – | 0 | 0
CEERS4-2257 | 214.75339 | 52.74099 | $26.6$ | $5.45$ | $-19.9_{-0.1}^{+0.1}$ | $9.8_{-0.1}^{+0.1}$ | $1.2_{-0.1}^{+0.1}$ | $-1.6_{-0.1}^{+0.1}$ | $-0.46_{-0.02}^{+0.02}$ | – | 0 | 0
CEERS4-9402 | 214.74466 | 52.750313 | $25.9$ | $5.21$ | $-20.6_{-0.1}^{+0.1}$ | $9.7_{-0.1}^{+0.1}$ | $1.7_{-0.1}^{+0.1}$ | $-1.3_{-0.1}^{+0.1}$ | $-0.29_{-0.01}^{+0.01}$ | $0.5\pm 0.2$ | 0 | 0
CEERS4-12992 | 214.77391 | 52.780613 | $27.1$ | $5.43$ | $-19.8_{-0.1}^{+0.1}$ | $9.3_{-0.1}^{+0.1}$ | $0.6_{-0.1}^{+0.1}$ | $-2.7_{-0.1}^{+0.2}$ | $-0.67_{-0.02}^{+0.02}$ | – | 0 | 0
CEERS5-20468 | 214.89146 | 52.867447 | $27.3$ | $5.15$ | $-19.4_{-0.1}^{+0.1}$ | $8.5_{-0.1}^{+0.1}$ | $0.5_{-0.1}^{+0.1}$ | $-2.2_{-0.1}^{+0.2}$ | $-0.41_{-0.02}^{+0.02}$ | – | 0 | 0
CEERS6-7832 | 214.82341 | 52.830288 | $26.7$ | $5.25$ | $-19.6_{-0.1}^{+0.1}$ | $8.6_{-0.1}^{+0.1}$ | $1.3_{-0.1}^{+0.1}$ | $-1.4_{-0.1}^{+0.1}$ | $-0.87_{-0.02}^{+0.02}$ | $0.7\pm 0.1$ | 0 | 0
CEERS6-8960 | 214.83157 | 52.831512 | $27.8$ | $7.06$ | $-20.5_{-0.1}^{+0.1}$ | $8.2_{-0.2}^{+0.2}$ | $0.5_{-0.1}^{+0.1}$ | $-2.2_{-0.2}^{+0.2}$ | $-0.48_{-0.29}^{+0.29}$ | – | 0 | 0
CEERS6-10587 | 214.8672 | 52.83675 | $27.2$ | $6.06$ | $-19.9_{-0.1}^{+0.1}$ | $9.1_{-0.2}^{+0.2}$ | $0.6_{-0.1}^{+0.1}$ | $-2.5_{-0.1}^{+0.4}$ | $-0.26_{-0.02}^{+0.02}$ | – | 0 | 0
CEERS6-12682 | 214.87643 | 52.839428 | $27.1$ | $6.05$ | $-19.1_{-0.1}^{+0.1}$ | $8.6_{-0.3}^{+0.3}$ | $0.7_{-0.1}^{+0.1}$ | $-2.7_{-0.1}^{+0.2}$ | $-0.06_{-0.03}^{+0.03}$ | – | 0 | 0
CEERS6-14083 | 214.85913 | 52.8536 | $27.5$ | $7.12$ | $-19.5_{-0.1}^{+0.1}$ | $8.4_{-0.3}^{+0.3}$ | $0.6_{-0.1}^{+0.1}$ | $-2.3_{-0.2}^{+0.1}$ | $-0.27_{-0.03}^{+0.03}$ | – | 0 | 0
CEERS6-14120 | 214.85907 | 52.853653 | $27.4$ | $7.12$ | $-19.1_{-0.1}^{+0.1}$ | $8.6_{-0.1}^{+0.1}$ | $0.9_{-0.1}^{+0.1}$ | $-1.9_{-0.1}^{+0.1}$ | $0.03_{-0.03}^{+0.03}$ | – | 0 | 0
CEERS6-15136 | 214.86436 | 52.853672 | $25.7$ | $5.67$ | $-20.9_{-0.1}^{+0.1}$ | $9.5_{-0.1}^{+0.1}$ | $1.2_{-0.1}^{+0.1}$ | $-2.4_{-0.1}^{+0.1}$ | $0.17_{-0.06}^{+0.06}$ | – | 1 | –
CEERS6-15334 | 214.89635 | 52.838524 | $27.0$ | $5.27$ | $-18.8_{-0.2}^{+0.1}$ | $9.4_{-0.1}^{+0.1}$ | $0.7_{-0.1}^{+0.1}$ | $-2.1_{-0.2}^{+0.4}$ | $0.17_{-0.03}^{+0.03}$ | – | 0 | 0
CEERS6-22118 | 214.89142 | 52.86747 | $27.2$ | $5.15$ | $-18.9_{-0.2}^{+0.1}$ | $8.8_{-0.3}^{+0.3}$ | $0.8_{-0.2}^{+0.2}$ | $-1.9_{-0.2}^{+0.3}$ | $-0.35_{-0.02}^{+0.02}$ | – | 0 | 0
CEERS7-3779 | 215.03642 | 52.94175 | $26.8$ | $5.10$ | $-19.2_{-0.1}^{+0.1}$ | $8.7_{-0.2}^{+0.2}$ | $0.7_{-0.1}^{+0.1}$ | $-2.2_{-0.1}^{+0.3}$ | $-0.70_{-0.08}^{+0.08}$ | – | 0 | 0
CEERS7-18822 | 215.09093 | 52.951546 | $26.9$ | $5.50$ | $-19.4_{-0.1}^{+0.1}$ | $9.7_{-0.1}^{+0.1}$ | $1.0_{-0.2}^{+0.2}$ | $-1.8_{-0.2}^{+0.2}$ | $-0.33_{-0.02}^{+0.02}$ | $1.0\pm 0.2$ | 0 | 0
CEERS7-19909 | 215.091 | 52.954319 | $25.3$ | $6.99$ | $-19.6_{-0.1}^{+0.1}$ | $8.6_{-0.1}^{+0.1}$ | $0.6_{-0.1}^{+0.1}$ | $-2.2_{-0.1}^{+0.2}$ | $0.97_{-0.53}^{+0.53}$ | – | 1 | –
CEERS8-18483 | 215.03053 | 52.902603 | $27.0$ | $5.01$ | $-20.2_{-0.1}^{+0.1}$ | $8.5_{-0.1}^{+0.1}$ | $0.6_{-0.1}^{+0.1}$ | $-2.2_{-0.1}^{+0.1}$ | $-0.77_{-0.02}^{+0.02}$ | – | 0 | 0
CEERS9-23526 | 214.89163 | 52.815952 | $27.9$ | $5.67$ | $-18.6_{-0.1}^{+0.1}$ | $7.0_{-0.1}^{+0.1}$ | $0.3_{-0.1}^{+0.1}$ | $-2.6_{-0.1}^{+0.1}$ | $-1.03_{-0.06}^{+0.06}$ | – | 0 | 1
CEERS10-4066 | 214.7991 | 52.725136 | $27.6$ | $6.09$ | $-18.8_{-0.1}^{+0.1}$ | $7.3_{-0.2}^{+0.2}$ | $0.5_{-0.1}^{+0.1}$ | $-2.5_{-0.1}^{+0.1}$ | $-0.23_{-0.03}^{+0.03}$ | – | 0 | 0
CEERS10-8944 | 214.78981 | 52.730808 | $27.1$ | $6.74$ | $-19.4_{-0.1}^{+0.1}$ | $9.1_{-0.1}^{+0.1}$ | $0.7_{-0.1}^{+0.1}$ | $-2.4_{-0.1}^{+0.1}$ | $-0.43_{-0.02}^{+0.02}$ | – | 0 | 0
CEERS10-10494 | 214.78569 | 52.731571 | $27.6$ | $5.29$ | $-19.2_{-0.1}^{+0.1}$ | $8.8_{-0.1}^{+0.1}$ | $0.6_{-0.1}^{+0.1}$ | $-1.9_{-0.1}^{+0.1}$ | $-1.02_{-0.06}^{+0.06}$ | – | 0 | 1
CEERS10-11869 | 214.81262 | 52.75486 | $27.1$ | $5.43$ | $-19.3_{-0.2}^{+0.1}$ | $9.3_{-0.1}^{+0.1}$ | $0.6_{-0.1}^{+0.1}$ | $-2.6_{-0.2}^{+0.4}$ | $-0.27_{-0.02}^{+0.02}$ | – | 0 | 0
CEERS10-13801 | 214.80548 | 52.754742 | $27.6$ | $5.86$ | $-18.6_{-0.1}^{+0.1}$ | $8.8_{-0.3}^{+0.3}$ | $0.4_{-0.1}^{+0.1}$ | $-2.6_{-0.1}^{+0.1}$ | $-0.35_{-0.04}^{+0.04}$ | – | 0 | 0
F090W-dropout
J1235-15721 | 188.96646 | 4.9614086 | $27.4$ | $7.4_{-0.2}^{+0.3}$ | $-19.6_{-0.1}^{+0.1}$ | $8.2_{-0.3}^{+0.3}$ | $0.7_{-0.1}^{+0.1}$ | $-2.5_{-0.1}^{+0.2}$ | $-0.67_{-0.02}^{+0.02}$ | – | 0 | 0
NEP-2312 | 260.68854 | 65.714592 | $26.1$ | $7.3_{-0.1}^{+0.1}$ | $-20.8_{-0.1}^{+0.1}$ | $8.9_{-0.1}^{+0.1}$ | $1.3_{-0.1}^{+0.1}$ | $-2.0_{-0.1}^{+0.1}$ | $-0.28_{-0.01}^{+0.01}$ | – | 0 | 0
NEP-4843 | 260.6767 | 65.720131 | $27.5$ | $7.3_{-0.2}^{+0.4}$ | $-19.4_{-0.1}^{+0.1}$ | $8.5_{-0.2}^{+0.2}$ | $0.7_{-0.1}^{+0.1}$ | $-2.1_{-0.2}^{+0.2}$ | $-0.32_{-0.04}^{+0.04}$ | – | 0 | 0
NEP-15634 | 260.70917 | 65.740883 | $27.2$ | $7.3_{-0.1}^{+0.1}$ | $-19.5_{-0.1}^{+0.1}$ | $8.3_{-0.1}^{+0.1}$ | $0.9_{-0.1}^{+0.1}$ | $-2.0_{-0.1}^{+0.1}$ | $-0.37_{-0.02}^{+0.02}$ | – | 0 | 0
NEP-24767 | 260.69934 | 65.760391 | $27.5$ | $8.2_{-0.3}^{+0.3}$ | $-19.8_{-0.1}^{+0.1}$ | $8.6_{-0.2}^{+0.2}$ | $0.8_{-0.1}^{+0.1}$ | $-2.1_{-0.1}^{+0.1}$ | $-0.21_{-0.01}^{+0.01}$ | – | 1 | –
NEP-38016 | 260.69568 | 65.785934 | $26.6$ | $8.3_{-0.2}^{+0.2}$ | $-20.8_{-0.1}^{+0.1}$ | $8.1_{-0.1}^{+0.1}$ | $1.0_{-0.1}^{+0.1}$ | $-2.6_{-0.1}^{+0.1}$ | $-1.05_{-0.03}^{+0.03}$ | – | 0 | 1
NEP-41263 | 260.70978 | 65.790955 | $27.9$ | $7.2_{-0.3}^{+0.5}$ | $-19.0_{-0.1}^{+0.1}$ | $8.7_{-0.1}^{+0.1}$ | $0.5_{-0.1}^{+0.1}$ | $-2.3_{-0.1}^{+0.2}$ | $-0.50_{-0.04}^{+0.04}$ | – | 0 | 0
NEP-44420 | 260.7374 | 65.795807 | $27.0$ | $7.3_{-0.1}^{+0.1}$ | $-20.9_{-0.1}^{+0.1}$ | $8.5_{-0.1}^{+0.1}$ | $0.9_{-0.1}^{+0.1}$ | $-2.1_{-0.1}^{+0.1}$ | $-0.35_{-0.02}^{+0.02}$ | – | 0 | 0
NEP-48900 | 260.72177 | 65.805756 | $25.9$ | $8.5_{-0.2}^{+0.2}$ | $-20.8_{-0.1}^{+0.1}$ | $9.4_{-0.1}^{+0.1}$ | $1.1_{-0.1}^{+0.1}$ | $-2.6_{-0.1}^{+0.1}$ | $0.07_{-0.05}^{+0.05}$ | – | 1 | –
NEP-55180 | 260.75189 | 65.820541 | $27.4$ | $7.2_{-0.1}^{+0.1}$ | $-20.5_{-0.1}^{+0.1}$ | $8.4_{-0.1}^{+0.1}$ | $0.9_{-0.1}^{+0.1}$ | $-1.9_{-0.1}^{+0.1}$ | $-0.79_{-0.02}^{+0.02}$ | – | 0 | 0
NEP-55261 | 260.73605 | 65.819099 | $27.3$ | $8.4_{-0.3}^{+0.2}$ | $-19.6_{-0.1}^{+0.1}$ | $9.0_{-0.3}^{+0.3}$ | $0.8_{-0.1}^{+0.1}$ | $-2.5_{-0.1}^{+0.1}$ | $-0.40_{-0.03}^{+0.03}$ | – | 0 | 0
NEP-57629 | 260.724 | 65.821655 | $26.8$ | $8.5_{-0.3}^{+0.2}$ | $-20.1_{-0.1}^{+0.1}$ | $8.1_{-0.2}^{+0.2}$ | $1.0_{-0.1}^{+0.1}$ | $-2.6_{-0.1}^{+0.1}$ | $-0.16_{-0.03}^{+0.03}$ | – | 0 | 0
A2744-3796 | 3.6171718 | -30.425545 | $26.2$ | $9.31$ | $-21.7_{-0.1}^{+0.1}$ | $9.3_{-0.1}^{+0.1}$ | $1.5_{-0.1}^{+0.1}$ | $-2.0_{-0.1}^{+0.1}$ | $-0.57_{-0.04}^{+0.04}$ | – | 0 | 0
A2744-5260 | 3.6085184 | -30.418537 | $26.0$ | $7.29$ | $-21.1_{-0.1}^{+0.1}$ | $8.5_{-0.1}^{+0.1}$ | $1.2_{-0.1}^{+0.1}$ | $-2.3_{-0.1}^{+0.1}$ | $-0.39_{-0.02}^{+0.02}$ | $0.9\pm 0.1$ | 0 | 0
A2744-15395 | 3.5960913 | -30.385817 | $27.6$ | $7.89$ | $-19.1_{-0.1}^{+0.1}$ | $8.2_{-0.1}^{+0.1}$ | $0.6_{-0.1}^{+0.1}$ | $-2.2_{-0.1}^{+0.1}$ | $-0.48_{-0.09}^{+0.09}$ | $0.9\pm 0.1$ | 0 | 0
A2744-16933 | 3.6033962 | -30.382244 | $27.7$ | $7.88$ | $-20.6_{-0.1}^{+0.1}$ | $9.4_{-0.1}^{+0.1}$ | $0.7_{-0.1}^{+0.1}$ | $-2.0_{-0.1}^{+0.1}$ | $-0.61_{-0.58}^{+0.58}$ | – | 0 | 0
A2744-16993 | 3.6038527 | -30.382246 | $27.8$ | $7.88$ | $-20.9_{-0.1}^{+0.1}$ | $9.6_{-0.3}^{+0.3}$ | $1.3_{-0.1}^{+0.1}$ | $-1.2_{-0.1}^{+0.1}$ | $-0.55_{-0.12}^{+0.12}$ | $1.0\pm 0.3$ | 0 | 0
A2744-17308 | 3.6064591 | -30.380976 | $27.6$ | $7.88$ | $-20.0_{-0.1}^{+0.1}$ | $9.0_{-0.4}^{+0.4}$ | $0.6_{-0.1}^{+0.1}$ | $-2.2_{-0.1}^{+0.1}$ | $-0.52_{-0.03}^{+0.03}$ | $>0.8$ | 0 | 0
A2744-17424 | 3.6065702 | -30.380907 | $27.5$ | $7.88$ | $-19.3_{-0.1}^{+0.1}$ | $9.1_{-0.1}^{+0.1}$ | $1.1_{-0.1}^{+0.1}$ | $-1.7_{-0.1}^{+0.1}$ | $-0.27_{-0.06}^{+0.06}$ | $>0.8$ | 0 | 0
A2744-17751 | 3.6045153 | -30.380447 | $27.0$ | $7.88$ | $-20.2_{-0.1}^{+0.1}$ | $8.3_{-0.1}^{+0.1}$ | $0.8_{-0.1}^{+0.1}$ | $-2.1_{-0.1}^{+0.1}$ | $-0.97_{-0.20}^{+0.20}$ | $0.9\pm 0.1$ | 0 | 0
A2744-18185 | 3.6013498 | -30.379196 | $27.4$ | $7.88$ | $-19.8_{-0.1}^{+0.1}$ | $8.3_{-0.1}^{+0.1}$ | $0.8_{-0.1}^{+0.1}$ | $-2.1_{-0.1}^{+0.1}$ | $-1.40_{-0.04}^{+0.04}$ | $0.7\pm 0.1$ | 0 | 1
A2744-26748 | 3.4905653 | -30.360378 | $27.9$ | $8.7_{-0.2}^{+0.2}$ | $-19.7_{-0.1}^{+0.1}$ | $8.8_{-0.7}^{+0.7}$ | $0.5_{-0.1}^{+0.1}$ | $-2.5_{-0.1}^{+0.1}$ | $-0.60_{-0.06}^{+0.06}$ | – | 0 | 0
A2744-29120 | 3.4907956 | -30.354845 | $27.2$ | $7.3_{-0.1}^{+0.1}$ | $-19.8_{-0.1}^{+0.1}$ | $8.1_{-0.2}^{+0.2}$ | $0.7_{-0.1}^{+0.1}$ | $-2.4_{-0.1}^{+0.1}$ | $-0.17_{-0.06}^{+0.06}$ | – | 0 | 0
A2744-32915 | 3.511811 | -30.346846 | $28.2$ | $7.8_{-0.3}^{+0.2}$ | $-19.4_{-0.1}^{+0.1}$ | $8.7_{-0.1}^{+0.1}$ | $1.3_{-0.1}^{+0.1}$ | $-1.1_{-0.1}^{+0.1}$ | $-0.88_{-0.08}^{+0.08}$ | – | 0 | 0
A2744-32950 | 3.5119343 | -30.346668 | $27.9$ | $7.7_{-0.5}^{+0.3}$ | $-19.1_{-0.1}^{+0.1}$ | $9.0_{-0.1}^{+0.1}$ | $0.7_{-0.1}^{+0.1}$ | $-1.9_{-0.1}^{+0.1}$ | $-0.37_{-0.05}^{+0.05}$ | – | 0 | 0
A2744-39390 | 3.4519746 | -30.326588 | $27.9$ | $8.3_{-0.3}^{+0.3}$ | $-19.1_{-0.1}^{+0.1}$ | $8.8_{-0.2}^{+0.2}$ | $0.5_{-0.1}^{+0.1}$ | $-2.4_{-0.1}^{+0.1}$ | $-0.56_{-0.07}^{+0.07}$ | – | 0 | 0
A2744-40364 | 3.4806123 | -30.323521 | $27.9$ | $8.5_{-0.2}^{+0.2}$ | $-18.8_{-0.1}^{+0.1}$ | $9.0_{-0.3}^{+0.3}$ | $0.5_{-0.1}^{+0.1}$ | $-2.2_{-0.1}^{+0.1}$ | $-0.71_{-0.04}^{+0.04}$ | – | 0 | 0
PRIMERUDS-200 | 34.245823 | -5.325891 | $26.3$ | $10.2_{-0.9}^{+0.4}$ | $-21.9_{-0.1}^{+0.1}$ | $9.5_{-0.1}^{+0.1}$ | $1.3_{-0.1}^{+0.1}$ | $-2.3_{-0.1}^{+0.1}$ | $-0.40_{-0.13}^{+0.13}$ | – | 0 | 0
PRIMERUDS-1090 | 34.346313 | -5.3226662 | $26.3$ | $7.6_{-0.3}^{+0.3}$ | $-19.8_{-0.1}^{+0.1}$ | $8.8_{-0.1}^{+0.1}$ | $1.1_{-0.1}^{+0.1}$ | $-2.5_{-0.1}^{+0.1}$ | $-0.06_{-0.03}^{+0.03}$ | – | 0 | 0
PRIMERUDS-1498 | 34.317928 | -5.3223157 | $26.7$ | $8.1_{-0.3}^{+0.3}$ | $-19.8_{-0.1}^{+0.1}$ | $8.5_{-0.1}^{+0.1}$ | $1.2_{-0.1}^{+0.1}$ | $-2.0_{-0.1}^{+0.1}$ | $-0.11_{-0.04}^{+0.04}$ | – | 0 | 0
PRIMERUDS-4972 | 34.380459 | -5.3183036 | $26.2$ | $8.6_{-0.2}^{+0.3}$ | $-20.9_{-0.1}^{+0.1}$ | $8.6_{-0.1}^{+0.1}$ | $1.2_{-0.1}^{+0.1}$ | $-2.2_{-0.1}^{+0.1}$ | $-0.28_{-0.03}^{+0.03}$ | – | 0 | 0
PRIMERUDS-8602 | 34.44714 | -5.3152809 | $26.7$ | $8.3_{-0.3}^{+0.2}$ | $-20.0_{-0.1}^{+0.1}$ | $8.5_{-0.1}^{+0.1}$ | $1.2_{-0.1}^{+0.1}$ | $-2.1_{-0.1}^{+0.1}$ | $-0.12_{-0.10}^{+0.10}$ | – | 0 | 0
PRIMERUDS-9713 | 34.319019 | -5.3156524 | $27.3$ | $7.9_{-0.5}^{+0.4}$ | $-19.4_{-0.1}^{+0.1}$ | $8.2_{-0.1}^{+0.1}$ | $0.8_{-0.1}^{+0.1}$ | $-2.3_{-0.1}^{+0.1}$ | $-0.99_{-0.05}^{+0.05}$ | – | 0 | 1
PRIMERUDS-10989 | 34.484421 | -5.3133621 | $26.3$ | $8.0_{-0.3}^{+0.3}$ | $-20.6_{-0.1}^{+0.1}$ | $8.8_{-0.2}^{+0.2}$ | $1.2_{-0.1}^{+0.1}$ | $-2.6_{-0.2}^{+0.1}$ | $0.05_{-0.04}^{+0.04}$ | – | 0 | 0
PRIMERUDS-11193 | 34.49094 | -5.3132 | $26.0$ | $7.2_{-0.1}^{+0.1}$ | $-20.5_{-0.1}^{+0.1}$ | $8.5_{-0.1}^{+0.1}$ | $1.1_{-0.1}^{+0.1}$ | $-2.3_{-0.1}^{+0.1}$ | $-0.15_{-0.02}^{+0.02}$ | – | 0 | 0
PRIMERUDS-12222 | 34.361069 | -5.3136678 | $26.4$ | $7.3_{-0.1}^{+0.1}$ | $-20.4_{-0.1}^{+0.1}$ | $8.3_{-0.1}^{+0.1}$ | $1.0_{-0.1}^{+0.1}$ | $-2.3_{-0.1}^{+0.1}$ | $-0.72_{-0.03}^{+0.03}$ | – | 0 | 0
PRIMERUDS-12379 | 34.322998 | -5.3138881 | $26.4$ | $9.2_{-0.5}^{+0.2}$ | $-20.0_{-0.1}^{+0.1}$ | $8.8_{-0.2}^{+0.2}$ | $1.4_{-0.1}^{+0.1}$ | $-2.0_{-0.1}^{+0.1}$ | $-0.22_{-0.06}^{+0.06}$ | – | 0 | 0
PRIMERUDS-13469 | 34.483696 | -5.3117156 | $27.2$ | $8.0_{-0.7}^{+0.5}$ | $-19.8_{-0.1}^{+0.1}$ | $9.0_{-0.1}^{+0.1}$ | $0.8_{-0.1}^{+0.1}$ | $-2.6_{-0.1}^{+0.2}$ | $-0.22_{-0.05}^{+0.05}$ | – | 0 | 0
PRIMERUDS-16114 | 34.521278 | -5.3095951 | $25.8$ | $8.9_{-0.2}^{+0.1}$ | $-20.9_{-0.1}^{+0.1}$ | $9.1_{-0.6}^{+0.6}$ | $1.1_{-0.1}^{+0.1}$ | $-2.7_{-0.1}^{+0.2}$ | $0.07_{-0.02}^{+0.02}$ | – | 1 | –
PRIMERUDS-21862 | 34.451874 | -5.3065 | $27.0$ | $8.8_{-0.3}^{+0.4}$ | $-19.6_{-0.1}^{+0.1}$ | $9.1_{-0.5}^{+0.5}$ | $0.9_{-0.1}^{+0.1}$ | $-2.6_{-0.1}^{+0.1}$ | $-0.22_{-0.05}^{+0.05}$ | – | 0 | 0
PRIMERUDS-23042 | 34.301533 | -5.3071699 | $26.3$ | $7.9_{-0.4}^{+0.2}$ | $-20.4_{-0.1}^{+0.1}$ | $9.6_{-0.1}^{+0.1}$ | $1.2_{-0.1}^{+0.1}$ | $-2.1_{-0.1}^{+0.1}$ | $-0.24_{-0.02}^{+0.02}$ | – | 0 | 0
PRIMERUDS-30574 | 34.483742 | -5.300765 | $27.1$ | $8.1_{-0.3}^{+0.3}$ | $-19.3_{-0.1}^{+0.1}$ | $8.2_{-0.4}^{+0.4}$ | $0.8_{-0.1}^{+0.1}$ | $-2.8_{-0.1}^{+0.1}$ | $-0.31_{-0.04}^{+0.04}$ | – | 0 | 0
PRIMERUDS-31216 | 34.419689 | -5.30094 | $27.2$ | $7.3_{-0.1}^{+0.1}$ | $-19.4_{-0.1}^{+0.1}$ | $9.0_{-0.4}^{+0.4}$ | $0.8_{-0.1}^{+0.1}$ | $-2.1_{-0.1}^{+0.1}$ | $-0.58_{-0.05}^{+0.05}$ | – | 0 | 0
PRIMERUDS-31414 | 34.364422 | -5.3012862 | $27.2$ | $8.5_{-0.4}^{+0.3}$ | $-19.7_{-0.1}^{+0.1}$ | $8.8_{-0.1}^{+0.1}$ | $0.8_{-0.1}^{+0.1}$ | $-2.6_{-0.1}^{+0.2}$ | $-0.59_{-0.05}^{+0.05}$ | – | 0 | 0
PRIMERUDS-33464 | 34.293663 | -5.3005662 | $26.2$ | $8.6_{-0.2}^{+0.1}$ | $-21.3_{-0.1}^{+0.1}$ | $8.9_{-0.1}^{+0.1}$ | $1.2_{-0.1}^{+0.1}$ | $-2.5_{-0.1}^{+0.1}$ | $-0.76_{-0.26}^{+0.26}$ | – | 0 | 0
PRIMERUDS-35245 | 34.529091 | -5.2968173 | $27.4$ | $7.3_{-0.1}^{+0.1}$ | $-19.9_{-0.1}^{+0.1}$ | $7.7_{-0.2}^{+0.2}$ | $0.6_{-0.1}^{+0.1}$ | $-2.4_{-0.1}^{+0.1}$ | $-0.89_{-0.06}^{+0.06}$ | – | 0 | 0
PRIMERUDS-38781 | 34.415279 | -5.2941837 | $26.3$ | $7.3_{-0.2}^{+0.2}$ | $-21.1_{-0.1}^{+0.1}$ | $8.8_{-0.3}^{+0.3}$ | $1.3_{-0.1}^{+0.1}$ | $-2.0_{-0.1}^{+0.1}$ | $-0.08_{-0.16}^{+0.16}$ | – | 0 | 0
PRIMERUDS-41711 | 34.260204 | -5.2925024 | $26.0$ | $7.3_{-0.1}^{+0.1}$ | $-20.9_{-0.1}^{+0.1}$ | $8.8_{-0.2}^{+0.2}$ | $1.2_{-0.1}^{+0.1}$ | $-2.2_{-0.1}^{+0.1}$ | $-0.02_{-0.02}^{+0.02}$ | – | 0 | 0
PRIMERUDS-42221 | 34.33025 | -5.2913198 | $26.8$ | $7.2_{-0.2}^{+0.3}$ | $-20.3_{-0.1}^{+0.1}$ | $8.9_{-0.2}^{+0.2}$ | $1.1_{-0.2}^{+0.2}$ | $-1.9_{-0.2}^{+0.2}$ | $-0.62_{-0.03}^{+0.03}$ | – | 0 | 0
PRIMERUDS-42290 | 34.330235 | -5.2912478 | $26.7$ | $7.2_{-0.2}^{+0.2}$ | $-20.3_{-0.1}^{+0.1}$ | $9.0_{-0.2}^{+0.2}$ | $1.2_{-0.2}^{+0.2}$ | $-1.8_{-0.2}^{+0.2}$ | $-0.50_{-0.02}^{+0.02}$ | – | 0 | 0
PRIMERUDS-43150 | 34.323875 | -5.2904172 | $26.2$ | $7.5_{-0.3}^{+0.5}$ | $-21.2_{-0.1}^{+0.1}$ | $9.3_{-0.1}^{+0.1}$ | $1.1_{-0.1}^{+0.1}$ | $-2.3_{-0.1}^{+0.2}$ | $-0.04_{-0.06}^{+0.06}$ | – | 0 | 0
PRIMERUDS-43160 | 34.263737 | -5.2909522 | $26.6$ | $7.3_{-0.1}^{+0.1}$ | $-20.1_{-0.1}^{+0.1}$ | $8.4_{-0.2}^{+0.2}$ | $1.0_{-0.1}^{+0.1}$ | $-2.3_{-0.1}^{+0.1}$ | $-0.74_{-0.03}^{+0.03}$ | – | 0 | 0
PRIMERUDS-43179 | 34.323917 | -5.2903838 | $27.0$ | $7.4_{-0.2}^{+0.5}$ | $-21.0_{-0.1}^{+0.1}$ | $8.6_{-0.1}^{+0.1}$ | $0.8_{-0.1}^{+0.1}$ | $-2.2_{-0.1}^{+0.1}$ | $-0.27_{-0.03}^{+0.03}$ | – | 0 | 0
PRIMERUDS-44767 | 34.494659 | -5.2871685 | $26.8$ | $8.7_{-0.4}^{+0.3}$ | $-20.6_{-0.1}^{+0.1}$ | $9.5_{-0.2}^{+0.2}$ | $1.0_{-0.1}^{+0.1}$ | $-2.2_{-0.2}^{+0.2}$ | $-0.80_{-0.02}^{+0.02}$ | – | 0 | 1
PRIMERUDS-49663 | 34.399944 | -5.2839613 | $25.9$ | $7.3_{-0.1}^{+0.1}$ | $-21.4_{-0.1}^{+0.1}$ | $8.6_{-0.2}^{+0.2}$ | $1.3_{-0.1}^{+0.1}$ | $-2.3_{-0.1}^{+0.1}$ | $-0.24_{-0.05}^{+0.05}$ | – | 0 | 0
PRIMERUDS-66080 | 34.280521 | -5.2683573 | $26.7$ | $7.3_{-0.1}^{+0.1}$ | $-20.3_{-0.1}^{+0.1}$ | $8.0_{-0.2}^{+0.2}$ | $0.9_{-0.1}^{+0.1}$ | $-2.5_{-0.1}^{+0.1}$ | $-0.34_{-0.03}^{+0.03}$ | – | 1 | –
PRIMERUDS-69092 | 34.275448 | -5.26442 | $26.1$ | $8.3_{-0.3}^{+0.3}$ | $-20.4_{-0.1}^{+0.1}$ | $9.4_{-0.1}^{+0.1}$ | $1.2_{-0.1}^{+0.1}$ | $-2.3_{-0.1}^{+0.1}$ | $-0.02_{-0.02}^{+0.02}$ | – | 0 | 0
PRIMERUDS-69462 | 34.30558 | -5.2638092 | $26.0$ | $7.2_{-0.1}^{+0.1}$ | $-20.8_{-0.1}^{+0.1}$ | $9.2_{-0.1}^{+0.1}$ | $1.6_{-0.2}^{+0.2}$ | $-1.7_{-0.2}^{+0.2}$ | $0.03_{-0.02}^{+0.02}$ | – | 0 | 0
PRIMERUDS-70520 | 34.47213 | -5.2609186 | $27.1$ | $7.5_{-0.4}^{+0.5}$ | $-19.3_{-0.1}^{+0.1}$ | $8.9_{-0.2}^{+0.2}$ | $0.8_{-0.1}^{+0.1}$ | $-2.3_{-0.2}^{+0.2}$ | $-0.30_{-0.54}^{+0.54}$ | – | 0 | 0
PRIMERUDS-72310 | 34.422253 | -5.2592807 | $25.7$ | $7.2_{-0.1}^{+0.1}$ | $-20.8_{-0.1}^{+0.1}$ | $9.1_{-0.1}^{+0.1}$ | $1.6_{-0.1}^{+0.1}$ | $-1.9_{-0.1}^{+0.1}$ | $-0.07_{-0.03}^{+0.03}$ | – | 0 | 0
PRIMERUDS-84176 | 34.298401 | -5.2426572 | $27.2$ | $8.7_{-0.3}^{+0.3}$ | $-20.0_{-0.1}^{+0.1}$ | $8.7_{-0.3}^{+0.3}$ | $1.0_{-0.2}^{+0.2}$ | $-2.0_{-0.2}^{+0.2}$ | $-0.70_{-0.04}^{+0.04}$ | – | 0 | 0
PRIMERUDS-84698 | 34.384274 | -5.2413874 | $26.5$ | $9.2_{-0.2}^{+0.3}$ | $-21.3_{-0.1}^{+0.1}$ | $9.2_{-0.2}^{+0.2}$ | $1.1_{-0.1}^{+0.1}$ | $-2.8_{-0.1}^{+0.1}$ | $-0.51_{-0.18}^{+0.18}$ | – | 0 | 0
PRIMERUDS-92176 | 34.407547 | -5.2356758 | $26.7$ | $7.9_{-0.3}^{+0.3}$ | $-19.6_{-0.1}^{+0.1}$ | $8.8_{-0.1}^{+0.1}$ | $1.0_{-0.1}^{+0.1}$ | $-2.5_{-0.1}^{+0.1}$ | $-0.23_{-0.04}^{+0.04}$ | – | 0 | 0
PRIMERUDS-96524 | 34.264969 | -5.2341166 | $26.4$ | $7.5_{-0.4}^{+0.3}$ | $-20.9_{-0.1}^{+0.1}$ | $8.9_{-0.2}^{+0.2}$ | $1.4_{-0.1}^{+0.1}$ | $-1.8_{-0.1}^{+0.1}$ | $-0.10_{-0.06}^{+0.06}$ | – | 0 | 0
PRIMERUDS-97368 | 34.459652 | -5.2315211 | $27.6$ | $8.1_{-0.4}^{+0.4}$ | $-18.9_{-0.1}^{+0.1}$ | $7.6_{-0.1}^{+0.1}$ | $0.6_{-0.1}^{+0.1}$ | $-2.9_{-0.1}^{+0.1}$ | $-0.53_{-0.05}^{+0.05}$ | – | 0 | 0
PRIMERUDS-104244 | 34.268559 | -5.2255492 | $26.9$ | $9.1_{-2.8}^{+0.1}$ | $-19.4_{-0.1}^{+0.1}$ | $9.0_{-0.1}^{+0.1}$ | $1.2_{-0.1}^{+0.1}$ | $-1.9_{-0.1}^{+0.1}$ | $-0.28_{-0.06}^{+0.06}$ | – | 0 | 0
PRIMERUDS-110960 | 34.3675 | -5.2190299 | $26.3$ | $8.8_{-0.1}^{+0.1}$ | $-21.0_{-0.1}^{+0.1}$ | $9.4_{-0.6}^{+0.6}$ | $1.2_{-0.1}^{+0.1}$ | $-2.2_{-0.1}^{+0.1}$ | $-0.49_{-0.02}^{+0.02}$ | – | 0 | 0
PRIMERUDS-118212 | 34.367577 | -5.2153816 | $26.4$ | $8.7_{-0.4}^{+0.3}$ | $-19.2_{-0.1}^{+0.1}$ | $8.5_{-0.1}^{+0.1}$ | $1.2_{-0.1}^{+0.1}$ | $-2.6_{-0.1}^{+0.1}$ | $0.15_{-0.04}^{+0.04}$ | – | 0 | 0
PRIMERUDS-121593 | 34.323589 | -5.21416 | $26.9$ | $8.4_{-0.3}^{+0.3}$ | $-20.1_{-0.1}^{+0.1}$ | $9.0_{-0.8}^{+0.8}$ | $0.9_{-0.1}^{+0.1}$ | $-2.2_{-0.1}^{+0.1}$ | $-0.35_{-0.09}^{+0.09}$ | – | 0 | 0
PRIMERUDS-123434 | 34.491253 | -5.2122388 | $27.3$ | $7.2_{-0.1}^{+0.2}$ | $-20.0_{-0.1}^{+0.1}$ | $8.0_{-0.2}^{+0.2}$ | $0.7_{-0.1}^{+0.1}$ | $-2.3_{-0.1}^{+0.1}$ | $-0.62_{-0.06}^{+0.06}$ | – | 0 | 0
PRIMERUDS-129005 | 34.219906 | -5.2117076 | $26.9$ | $9.3_{-2.6}^{+0.1}$ | $-19.0_{-0.1}^{+0.1}$ | $8.2_{-0.1}^{+0.1}$ | $0.9_{-0.1}^{+0.1}$ | $-2.5_{-0.2}^{+0.2}$ | $-0.15_{-0.05}^{+0.05}$ | – | 0 | 0
PRIMERUDS-132885 | 34.233913 | -5.2100353 | $26.1$ | $7.6_{-0.4}^{+0.3}$ | $-21.6_{-0.1}^{+0.1}$ | $10.0_{-0.1}^{+0.1}$ | $1.3_{-0.1}^{+0.1}$ | $-2.1_{-0.1}^{+0.1}$ | $-0.28_{-0.04}^{+0.04}$ | – | 0 | 0
PRIMERUDS-133009 | 34.233852 | -5.2100344 | $27.0$ | $7.3_{-0.3}^{+0.5}$ | $-21.3_{-0.1}^{+0.1}$ | $8.9_{-0.1}^{+0.1}$ | $1.5_{-0.1}^{+0.1}$ | $-1.4_{-0.1}^{+0.1}$ | $-0.94_{-0.04}^{+0.04}$ | – | 0 | 1
PRIMERUDS-137776 | 34.317993 | -5.206953 | $26.8$ | $7.3_{-0.1}^{+0.1}$ | $-19.6_{-0.1}^{+0.1}$ | $8.7_{-0.1}^{+0.1}$ | $1.1_{-0.1}^{+0.1}$ | $-1.8_{-0.1}^{+0.1}$ | $-0.53_{-0.03}^{+0.03}$ | – | 0 | 0
PRIMERUDS-141228 | 34.262043 | -5.2058601 | $26.5$ | $8.3_{-0.3}^{+0.3}$ | $-20.6_{-0.1}^{+0.1}$ | $8.9_{-0.1}^{+0.1}$ | $1.1_{-0.1}^{+0.1}$ | $-2.5_{-0.2}^{+0.1}$ | $0.02_{-0.05}^{+0.05}$ | – | 0 | 0
PRIMERUDS-143983 | 34.495827 | -5.2023988 | $27.0$ | $7.4_{-0.1}^{+0.1}$ | $-19.5_{-0.1}^{+0.1}$ | $7.9_{-0.1}^{+0.1}$ | $0.9_{-0.1}^{+0.1}$ | $-2.8_{-0.1}^{+0.1}$ | $-0.50_{-0.31}^{+0.31}$ | – | 0 | 0
PRIMERUDS-146539 | 34.367455 | -5.2022929 | $26.3$ | $9.0_{-0.3}^{+0.3}$ | $-20.3_{-0.1}^{+0.1}$ | $8.6_{-0.3}^{+0.3}$ | $1.2_{-0.1}^{+0.1}$ | $-2.7_{-0.2}^{+0.1}$ | $0.17_{-0.04}^{+0.04}$ | – | 0 | 0
PRIMERCOS-1876 | 150.17801 | 2.2393584 | $27.0$ | $7.5_{-0.1}^{+0.1}$ | $-19.1_{-0.1}^{+0.1}$ | $7.7_{-0.2}^{+0.2}$ | $0.8_{-0.1}^{+0.1}$ | $-2.6_{-0.1}^{+0.1}$ | $-1.06_{-0.05}^{+0.05}$ | – | 0 | 1
PRIMERCOS-4537 | 150.15474 | 2.2460606 | $26.6$ | $8.1_{-0.2}^{+0.3}$ | $-21.3_{-0.1}^{+0.1}$ | $8.2_{-0.2}^{+0.2}$ | $1.1_{-0.1}^{+0.1}$ | $-2.7_{-0.1}^{+0.2}$ | $-0.07_{-0.05}^{+0.05}$ | – | 0 | 0
PRIMERCOS-5617 | 150.18694 | 2.2494292 | $26.9$ | $7.3_{-0.1}^{+0.1}$ | $-19.5_{-0.1}^{+0.1}$ | $8.1_{-0.3}^{+0.3}$ | $0.9_{-0.1}^{+0.1}$ | $-2.5_{-0.1}^{+0.1}$ | $-0.29_{-0.29}^{+0.29}$ | – | 0 | 0
PRIMERCOS-8110 | 150.1423 | 2.2542021 | $26.8$ | $8.8_{-0.4}^{+0.3}$ | $-19.5_{-0.1}^{+0.1}$ | $8.8_{-0.1}^{+0.1}$ | $0.9_{-0.1}^{+0.1}$ | $-2.3_{-0.1}^{+0.1}$ | $-0.30_{-0.03}^{+0.03}$ | – | 0 | 0
PRIMERCOS-10530 | 150.14932 | 2.2591827 | $27.2$ | $9.0_{-0.3}^{+0.3}$ | $-19.9_{-0.1}^{+0.1}$ | $9.2_{-0.1}^{+0.1}$ | $0.9_{-0.1}^{+0.1}$ | $-2.6_{-0.1}^{+0.1}$ | $-0.49_{-0.05}^{+0.05}$ | – | 0 | 0
PRIMERCOS-12668 | 150.11729 | 2.2635334 | $26.9$ | $8.2_{-0.2}^{+0.3}$ | $-20.0_{-0.1}^{+0.1}$ | $8.7_{-0.1}^{+0.1}$ | $0.9_{-0.1}^{+0.1}$ | $-2.3_{-0.1}^{+0.1}$ | $-0.35_{-0.04}^{+0.04}$ | – | 0 | 0
PRIMERCOS-21719 | 150.17093 | 2.2768395 | $25.8$ | $8.8_{-0.2}^{+0.4}$ | $-21.3_{-0.1}^{+0.1}$ | $8.9_{-0.1}^{+0.1}$ | $1.5_{-0.1}^{+0.1}$ | $-2.1_{-0.1}^{+0.1}$ | $0.08_{-0.03}^{+0.03}$ | – | 0 | 0
PRIMERCOS-22886 | 150.10645 | 2.2780333 | $26.6$ | $7.3_{-0.2}^{+0.2}$ | $-19.5_{-0.1}^{+0.1}$ | $9.5_{-0.1}^{+0.1}$ | $1.1_{-0.2}^{+0.2}$ | $-1.9_{-0.2}^{+0.1}$ | $-0.22_{-0.03}^{+0.03}$ | – | 0 | 0
PRIMERCOS-40689 | 150.20164 | 2.3149087 | $26.7$ | $8.7_{-0.2}^{+0.2}$ | $-20.9_{-0.1}^{+0.1}$ | $9.3_{-0.1}^{+0.1}$ | $1.0_{-0.1}^{+0.1}$ | $-2.7_{-0.1}^{+0.1}$ | $-1.02_{-0.06}^{+0.06}$ | – | 0 | 1
PRIMERCOS-50439 | 150.10057 | 2.3322499 | $27.0$ | $7.2_{-0.1}^{+0.1}$ | $-19.5_{-0.1}^{+0.1}$ | $7.7_{-0.2}^{+0.2}$ | $0.7_{-0.1}^{+0.1}$ | $-2.7_{-0.1}^{+0.2}$ | $-0.31_{-0.49}^{+0.49}$ | – | 0 | 0
PRIMERCOS-59070 | 150.09526 | 2.3492894 | $26.5$ | $8.9_{-0.3}^{+0.2}$ | $-19.7_{-0.1}^{+0.1}$ | $8.6_{-0.1}^{+0.1}$ | $1.2_{-0.1}^{+0.1}$ | $-2.1_{-0.3}^{+0.1}$ | $-0.17_{-0.04}^{+0.04}$ | – | 0 | 0
PRIMERCOS-76099 | 150.11259 | 2.3792171 | $26.9$ | $7.5_{-0.2}^{+0.3}$ | $-19.8_{-0.1}^{+0.1}$ | $8.8_{-0.1}^{+0.1}$ | $0.9_{-0.1}^{+0.1}$ | $-2.2_{-0.1}^{+0.1}$ | $-0.35_{-0.04}^{+0.04}$ | – | 0 | 0
PRIMERCOS-92684 | 150.09816 | 2.412853 | $25.9$ | $8.0_{-0.1}^{+0.1}$ | $-21.3_{-0.1}^{+0.1}$ | $8.3_{-0.1}^{+0.1}$ | $1.3_{-0.1}^{+0.1}$ | $-2.8_{-0.1}^{+0.1}$ | $-1.12_{-0.03}^{+0.03}$ | – | 0 | 1
PRIMERCOS-95321 | 150.17979 | 2.4195976 | $26.6$ | $8.3_{-0.3}^{+0.2}$ | $-20.3_{-0.1}^{+0.1}$ | $8.5_{-0.3}^{+0.3}$ | $1.1_{-0.1}^{+0.1}$ | $-2.6_{-0.1}^{+0.1}$ | $-0.76_{-0.02}^{+0.02}$ | – | 0 | 0
PRIMERCOS-101351 | 150.11674 | 2.4329758 | $27.2$ | $8.3_{-0.4}^{+0.3}$ | $-20.0_{-0.1}^{+0.1}$ | $7.9_{-0.1}^{+0.1}$ | $0.8_{-0.1}^{+0.1}$ | $-3.0_{-0.1}^{+0.2}$ | $-0.93_{-0.04}^{+0.04}$ | – | 0 | 0
PRIMERCOS-102047 | 150.2037 | 2.4356833 | $26.8$ | $8.6_{-0.4}^{+0.3}$ | $-20.3_{-0.1}^{+0.1}$ | $8.4_{-0.3}^{+0.3}$ | $0.9_{-0.1}^{+0.1}$ | $-2.9_{-0.1}^{+0.1}$ | $-0.41_{-0.15}^{+0.15}$ | – | 0 | 0
PRIMERCOS-102710 | 150.14003 | 2.4360871 | $26.2$ | $8.6_{-0.2}^{+0.2}$ | $-21.2_{-0.1}^{+0.1}$ | $9.1_{-0.1}^{+0.1}$ | $1.2_{-0.1}^{+0.1}$ | $-2.2_{-0.1}^{+0.1}$ | $-0.69_{-0.02}^{+0.02}$ | – | 0 | 0
PRIMERCOS-103947 | 150.10559 | 2.437932 | $26.6$ | $8.6_{-0.3}^{+0.5}$ | $-20.4_{-0.1}^{+0.1}$ | $8.4_{-0.1}^{+0.1}$ | $1.1_{-0.1}^{+0.1}$ | $-2.3_{-0.1}^{+0.1}$ | $-0.38_{-0.18}^{+0.18}$ | – | 0 | 0
PRIMERCOS-111899 | 150.10913 | 2.4536574 | $26.1$ | $9.3_{-0.1}^{+0.1}$ | $-21.4_{-0.1}^{+0.1}$ | $9.0_{-0.2}^{+0.2}$ | $1.4_{-0.1}^{+0.1}$ | $-2.1_{-0.1}^{+0.1}$ | $-0.12_{-0.04}^{+0.04}$ | – | 0 | 0
PRIMERCOS-113554 | 150.19943 | 2.4583545 | $26.3$ | $7.3_{-0.1}^{+0.1}$ | $-20.3_{-0.1}^{+0.1}$ | $8.7_{-0.1}^{+0.1}$ | $1.2_{-0.1}^{+0.1}$ | $-2.1_{-0.1}^{+0.1}$ | $-0.35_{-0.02}^{+0.02}$ | – | 0 | 0
PRIMERCOS-119614 | 150.13 | 2.4686878 | $26.6$ | $8.9_{-0.2}^{+0.3}$ | $-19.5_{-0.1}^{+0.1}$ | $8.4_{-0.1}^{+0.1}$ | $1.1_{-0.1}^{+0.1}$ | $-2.5_{-0.1}^{+0.1}$ | $-0.24_{-0.03}^{+0.03}$ | – | 0 | 0
PRIMERCOS-122002 | 150.13895 | 2.4743896 | $26.9$ | $7.3_{-0.2}^{+2.5}$ | $-19.0_{-0.1}^{+0.1}$ | $8.4_{-0.3}^{+0.3}$ | $0.8_{-0.2}^{+0.2}$ | $-2.0_{-0.2}^{+0.2}$ | $-0.21_{-0.03}^{+0.03}$ | – | 0 | 0
PRIMERCOS-122961 | 150.16527 | 2.4770103 | $26.8$ | $7.2_{-0.1}^{+0.1}$ | $-20.1_{-0.1}^{+0.1}$ | $8.9_{-0.2}^{+0.2}$ | $1.1_{-0.2}^{+0.2}$ | $-1.9_{-0.2}^{+0.3}$ | $-0.21_{-0.04}^{+0.04}$ | – | 0 | 0
PRIMERCOS-124364 | 150.13078 | 2.4806907 | $25.6$ | $7.7_{-0.6}^{+0.3}$ | $-21.6_{-0.1}^{+0.1}$ | $9.1_{-0.1}^{+0.1}$ | $1.7_{-0.1}^{+0.1}$ | $-1.9_{-0.1}^{+0.1}$ | $0.12_{-0.03}^{+0.03}$ | – | 0 | 0
JADESGDS-788 | 53.157093 | -27.837086 | $26.9$ | $8.0_{-0.2}^{+0.2}$ | $-20.1_{-0.1}^{+0.1}$ | $9.5_{-0.1}^{+0.1}$ | $0.8_{-0.1}^{+0.1}$ | $-2.4_{-0.1}^{+0.1}$ | $0.09_{-0.03}^{+0.03}$ | – | 1 | –
JADESGDS-1337 | 53.158615 | -27.834078 | $27.9$ | $8.9_{-0.1}^{+0.1}$ | $-20.5_{-0.1}^{+0.1}$ | $8.2_{-0.2}^{+0.2}$ | $0.3_{-0.1}^{+0.1}$ | $-2.5_{-0.1}^{+0.1}$ | $-1.15_{-0.11}^{+0.11}$ | – | 0 | 1
JADESGDS-2304 | 53.182175 | -27.829906 | $28.8$ | $8.9_{-0.2}^{+0.2}$ | $-18.4_{-0.1}^{+0.1}$ | $8.2_{-0.7}^{+0.7}$ | $0.2_{-0.1}^{+0.1}$ | $-2.8_{-0.1}^{+0.1}$ | $-0.98_{-0.04}^{+0.04}$ | – | 0 | 1
JADESGDS-3078 | 53.183949 | -27.827396 | $29.0$ | $7.4_{-0.2}^{+0.2}$ | $-18.8_{-0.1}^{+0.1}$ | $7.7_{-0.2}^{+0.2}$ | $0.0_{-0.1}^{+0.1}$ | $-2.4_{-0.1}^{+0.1}$ | $-0.53_{-0.04}^{+0.04}$ | – | 0 | 0
JADESGDS-4153 | 53.177311 | -27.824068 | $29.0$ | $8.9_{-0.4}^{+0.3}$ | $-17.9_{-0.1}^{+0.1}$ | $8.5_{-0.1}^{+0.1}$ | $0.1_{-0.1}^{+0.1}$ | $-2.6_{-0.1}^{+0.1}$ | $-0.42_{-0.04}^{+0.04}$ | – | 0 | 0
JADESGDS-4667 | 53.145905 | -27.822794 | $29.0$ | $9.0_{-0.2}^{+0.2}$ | $-19.3_{-0.1}^{+0.1}$ | $8.2_{-0.2}^{+0.2}$ | $0.5_{-0.1}^{+0.1}$ | $-1.8_{-0.1}^{+0.1}$ | $-0.97_{-0.07}^{+0.07}$ | – | 0 | 1
JADESGDS-4854 | 53.145844 | -27.822742 | $26.7$ | $8.2_{-0.1}^{+0.1}$ | $-20.1_{-0.1}^{+0.1}$ | $9.0_{-0.2}^{+0.2}$ | $1.3_{-0.1}^{+0.1}$ | $-1.9_{-0.1}^{+0.1}$ | $-0.01_{-0.06}^{+0.06}$ | – | 0 | 0
JADESGDS-5100 | 53.165787 | -27.821783 | $29.1$ | $7.2_{-0.1}^{+0.1}$ | $-18.5_{-0.1}^{+0.1}$ | $7.4_{-0.2}^{+0.2}$ | $-0.1_{-0.1}^{+0.1}$ | $-2.3_{-0.1}^{+0.1}$ | $-0.90_{-0.04}^{+0.04}$ | – | 0 | 1
JADESGDS-5123 | 53.186321 | -27.821741 | $28.5$ | $7.5_{-0.2}^{+0.1}$ | $-18.8_{-0.1}^{+0.1}$ | $7.7_{-0.3}^{+0.3}$ | $0.2_{-0.1}^{+0.1}$ | $-2.6_{-0.1}^{+0.1}$ | $-0.46_{-0.17}^{+0.17}$ | – | 0 | 0
JADESGDS-6492 | 53.187363 | -27.818476 | $29.0$ | $8.8_{-0.3}^{+0.2}$ | $-17.9_{-0.1}^{+0.1}$ | $8.4_{-0.1}^{+0.1}$ | $0.1_{-0.1}^{+0.1}$ | $-2.6_{-0.1}^{+0.1}$ | $-0.59_{-0.04}^{+0.04}$ | – | 0 | 0
JADESGDS-6834 | 53.1768 | -27.817759 | $28.1$ | $8.8_{-0.1}^{+0.1}$ | $-18.9_{-0.1}^{+0.1}$ | $8.5_{-0.1}^{+0.1}$ | $0.5_{-0.1}^{+0.1}$ | $-2.5_{-0.1}^{+0.1}$ | $-0.41_{-0.02}^{+0.02}$ | – | 0 | 0
JADESGDS-7015 | 53.176029 | -27.817396 | $28.7$ | $10.7_{-1.4}^{+0.3}$ | $-18.5_{-0.1}^{+0.1}$ | $9.3_{-0.1}^{+0.1}$ | $0.6_{-0.1}^{+0.1}$ | $-1.9_{-0.1}^{+0.1}$ | $-0.56_{-0.12}^{+0.12}$ | – | 0 | 0
JADESGDS-9528 | 53.167141 | -27.812428 | $27.9$ | $7.4_{-0.1}^{+0.1}$ | $-19.0_{-0.1}^{+0.1}$ | $8.3_{-0.1}^{+0.1}$ | $0.5_{-0.1}^{+0.1}$ | $-2.2_{-0.1}^{+0.1}$ | $-0.23_{-0.02}^{+0.02}$ | – | 0 | 0
JADESGDS-12274 | 53.167358 | -27.807507 | $28.1$ | $9.69$ | $-19.4_{-0.1}^{+0.1}$ | $8.5_{-0.1}^{+0.1}$ | $0.5_{-0.1}^{+0.1}$ | $-2.3_{-0.1}^{+0.1}$ | $-0.81_{-0.02}^{+0.02}$ | – | 0 | 0
JADESGDS-14022 | 53.144043 | -27.804459 | $28.7$ | $8.9_{-0.2}^{+0.2}$ | $-18.5_{-0.1}^{+0.1}$ | $7.6_{-0.1}^{+0.1}$ | $0.3_{-0.1}^{+0.1}$ | $-2.1_{-0.1}^{+0.1}$ | $-0.72_{-0.12}^{+0.12}$ | – | 0 | 0
JADESGDS-15413 | 53.164467 | -27.802185 | $28.6$ | $8.47$ | $-18.2_{-0.1}^{+0.1}$ | $8.6_{-0.5}^{+0.5}$ | $0.4_{-0.2}^{+0.2}$ | $-2.0_{-0.2}^{+0.2}$ | $-1.14_{-0.06}^{+0.06}$ | $0.8\pm 0.2$ | 0 | 1
JADESGDS-15571 | 53.152836 | -27.801945 | $28.8$ | $7.26$ | $-17.6_{-0.1}^{+0.1}$ | $8.6_{-0.1}^{+0.1}$ | $0.1_{-0.1}^{+0.1}$ | $-2.3_{-0.2}^{+0.2}$ | $-0.50_{-0.03}^{+0.03}$ | $>0.6$ | 0 | 0
JADESGDS-15668 | 53.155083 | -27.801775 | $27.9$ | $7.30$ | $-18.6_{-0.1}^{+0.1}$ | $8.8_{-0.1}^{+0.1}$ | $0.5_{-0.1}^{+0.1}$ | $-2.2_{-0.1}^{+0.1}$ | $-0.41_{-0.02}^{+0.02}$ | – | 0 | 0
JADESGDS-15761 | 53.193794 | -27.801634 | $28.4$ | $7.5_{-0.2}^{+0.2}$ | $-18.8_{-0.1}^{+0.1}$ | $8.0_{-0.1}^{+0.1}$ | $0.3_{-0.1}^{+0.1}$ | $-2.5_{-0.1}^{+0.1}$ | $-1.01_{-0.03}^{+0.03}$ | – | 0 | 1
JADESGDS-16719 | 53.183937 | -27.799994 | $27.7$ | $7.4_{-0.1}^{+0.1}$ | $-19.2_{-0.1}^{+0.1}$ | $8.2_{-0.1}^{+0.1}$ | $0.5_{-0.1}^{+0.1}$ | $-2.3_{-0.1}^{+0.1}$ | $-0.54_{-0.02}^{+0.02}$ | – | 0 | 0
JADESGDS-17976 | 53.184048 | -27.797829 | $29.0$ | $7.3_{-0.1}^{+0.1}$ | $-17.9_{-0.1}^{+0.1}$ | $7.4_{-0.3}^{+0.3}$ | $0.0_{-0.1}^{+0.1}$ | $-2.3_{-0.1}^{+0.1}$ | $-0.86_{-0.04}^{+0.04}$ | – | 0 | 0
JADESGDS-18265 | 53.187874 | -27.797451 | $28.5$ | $8.4_{-0.1}^{+0.1}$ | $-18.8_{-0.1}^{+0.1}$ | $8.9_{-0.1}^{+0.1}$ | $0.3_{-0.1}^{+0.1}$ | $-2.2_{-0.1}^{+0.1}$ | $-0.48_{-0.03}^{+0.03}$ | – | 0 | 0
JADESGDS-18325 | 53.190781 | -27.797283 | $29.0$ | $7.4_{-0.3}^{+0.1}$ | $-17.4_{-0.1}^{+0.1}$ | $8.0_{-0.5}^{+0.5}$ | $0.0_{-0.1}^{+0.1}$ | $-2.5_{-0.1}^{+0.2}$ | $-0.38_{-0.03}^{+0.03}$ | – | 0 | 0
JADESGDS-18428 | 53.191059 | -27.797318 | $27.7$ | $7.3_{-0.1}^{+0.1}$ | $-18.9_{-0.1}^{+0.1}$ | $8.3_{-0.1}^{+0.1}$ | $0.8_{-0.1}^{+0.1}$ | $-1.8_{-0.1}^{+0.1}$ | $-0.35_{-0.01}^{+0.01}$ | – | 0 | 0
JADESGDS-18917 | 53.127213 | -27.796421 | $28.3$ | $8.6_{-0.1}^{+0.1}$ | $-19.1_{-0.1}^{+0.1}$ | $8.4_{-0.3}^{+0.3}$ | $0.4_{-0.1}^{+0.1}$ | $-2.7_{-0.1}^{+0.1}$ | $-0.56_{-0.15}^{+0.15}$ | – | 0 | 0
JADESGDS-19434 | 53.186314 | -27.795614 | $28.3$ | $7.3_{-0.2}^{+0.2}$ | $-18.8_{-0.1}^{+0.1}$ | $9.0_{-0.1}^{+0.1}$ | $0.4_{-0.1}^{+0.1}$ | $-2.1_{-0.1}^{+0.1}$ | $-0.71_{-0.02}^{+0.02}$ | – | 0 | 0
JADESGDS-20632 | 53.183746 | -27.793705 | $29.0$ | $7.3_{-0.1}^{+0.1}$ | $-18.0_{-0.1}^{+0.1}$ | $7.3_{-0.2}^{+0.2}$ | $-0.1_{-0.1}^{+0.1}$ | $-2.4_{-0.1}^{+0.1}$ | $-0.89_{-0.04}^{+0.04}$ | – | 0 | 0
JADESGDS-20758 | 53.183754 | -27.793894 | $27.0$ | $7.2_{-0.1}^{+0.1}$ | $-19.0_{-0.1}^{+0.1}$ | $8.3_{-0.1}^{+0.1}$ | $0.9_{-0.1}^{+0.1}$ | $-1.6_{-0.1}^{+0.1}$ | $0.31_{-0.02}^{+0.02}$ | – | 1 | –
JADESGDS-21192 | 53.115772 | -27.792681 | $29.2$ | $7.9_{-0.3}^{+0.3}$ | $-18.8_{-0.1}^{+0.1}$ | $7.5_{-0.1}^{+0.1}$ | $0.1_{-0.1}^{+0.1}$ | $-2.1_{-0.1}^{+0.1}$ | $-0.91_{-0.05}^{+0.05}$ | – | 0 | 0
JADESGDS-23180 | 53.187042 | -27.789448 | $27.2$ | $7.9_{-0.2}^{+0.2}$ | $-20.4_{-0.1}^{+0.1}$ | $8.6_{-0.1}^{+0.1}$ | $0.8_{-0.1}^{+0.1}$ | $-2.4_{-0.1}^{+0.1}$ | $-0.15_{-0.02}^{+0.02}$ | – | 0 | 0
JADESGDS-23858 | 53.149384 | -27.788181 | $28.6$ | $7.3_{-0.1}^{+0.1}$ | $-18.5_{-0.1}^{+0.1}$ | $7.5_{-0.2}^{+0.2}$ | $0.2_{-0.1}^{+0.1}$ | $-2.4_{-0.1}^{+0.1}$ | $-1.10_{-0.36}^{+0.36}$ | – | 1 | –
JADESGDS-23946 | 53.164822 | -27.788261 | $27.1$ | $7.4_{-0.1}^{+0.1}$ | $-19.7_{-0.1}^{+0.1}$ | $8.0_{-0.1}^{+0.1}$ | $0.8_{-0.1}^{+0.1}$ | $-2.1_{-0.1}^{+0.1}$ | $-0.08_{-0.03}^{+0.03}$ | – | 1 | –
JADESGDS-23970 | 53.164684 | -27.788233 | $27.7$ | $7.3_{-0.1}^{+0.1}$ | $-19.3_{-0.1}^{+0.1}$ | $9.3_{-0.1}^{+0.1}$ | $1.1_{-0.1}^{+0.1}$ | $-1.6_{-0.1}^{+0.1}$ | $0.11_{-0.01}^{+0.01}$ | – | 1 | –
JADESGDS-25131 | 53.126896 | -27.78615 | $28.9$ | $7.2_{-0.1}^{+0.1}$ | $-17.7_{-0.1}^{+0.1}$ | $8.6_{-0.3}^{+0.3}$ | $1.0_{-0.2}^{+0.2}$ | $-1.0_{-0.2}^{+0.3}$ | $-1.09_{-0.33}^{+0.33}$ | – | 0 | 1
JADESGDS-25734 | 53.161705 | -27.785393 | $26.8$ | $7.4_{-0.1}^{+0.1}$ | $-20.0_{-0.1}^{+0.1}$ | $8.7_{-0.1}^{+0.1}$ | $1.3_{-0.1}^{+0.1}$ | $-1.7_{-0.1}^{+0.1}$ | $-0.32_{-0.01}^{+0.01}$ | – | 0 | 0
JADESGDS-26008 | 53.164146 | -27.784525 | $29.1$ | $8.0_{-0.3}^{+0.3}$ | $-17.8_{-0.1}^{+0.1}$ | $8.1_{-0.5}^{+0.5}$ | $0.3_{-0.1}^{+0.1}$ | $-2.0_{-0.1}^{+0.2}$ | $-0.50_{-0.14}^{+0.14}$ | – | 0 | 0
JADESGDS-27630 | 53.13805 | -27.781866 | $28.4$ | $7.4_{-0.1}^{+0.1}$ | $-18.1_{-0.1}^{+0.1}$ | $7.5_{-0.1}^{+0.1}$ | $0.2_{-0.1}^{+0.1}$ | $-2.4_{-0.1}^{+0.1}$ | $-0.39_{-0.15}^{+0.15}$ | – | 0 | 0