# The JWST Advanced Deep Extragalactic Survey:
Discovery of an Extreme Galaxy Overdensity at $z=5.4$ with JWST/NIRCam in
GOODS-S

Jakob M. Helton Steward Observatory, University of Arizona, 933 N. Cherry
Ave., Tucson, AZ 85721, USA  Fengwu Sun Steward Observatory, University of
Arizona, 933 N. Cherry Ave., Tucson, AZ 85721, USA  Charity Woodrum Steward
Observatory, University of Arizona, 933 N. Cherry Ave., Tucson, AZ 85721, USA
Kevin N. Hainline Steward Observatory, University of Arizona, 933 N. Cherry
Ave., Tucson, AZ 85721, USA  Christopher N. A. Willmer Steward Observatory,
University of Arizona, 933 N. Cherry Ave., Tucson, AZ 85721, USA  George H.
Rieke Steward Observatory, University of Arizona, 933 N. Cherry Ave., Tucson,
AZ 85721, USA  Marcia J. Rieke Steward Observatory, University of Arizona, 933
N. Cherry Ave., Tucson, AZ 85721, USA  Sandro Tacchella Kavli Institute for
Cosmology, University of Cambridge, Madingley Road, Cambridge, CB3 OHA, UK
Cavendish Laboratory, University of Cambridge, 19 JJ Thomson Avenue, Cambridge
CB3 0HE, UK  Brant Robertson Department of Astronomy and Astrophysics,
University of California, Santa Cruz, 1156 High Street, Santa Cruz, CA 95064,
USA  Benjamin D. Johnson Center for Astrophysics $|$ Harvard & Smithsonian, 60
Garden St., Cambridge MA 02138, USA  Stacey Alberts Steward Observatory,
University of Arizona, 933 N. Cherry Ave., Tucson, AZ 85721, USA  Daniel J.
Eisenstein Center for Astrophysics $|$ Harvard & Smithsonian, 60 Garden St.,
Cambridge MA 02138, USA  Ryan Hausen Department of Physics and Astronomy, The
Johns Hopkins University, 3400 N. Charles St., Baltimore, MD 21218, USA  Nina
R. Bonaventura Steward Observatory, University of Arizona, 933 N. Cherry
Avenue, Tucson, AZ 85721, USA  Andrew Bunker Department of Physics, University
of Oxford, Denys Wilkinson Building, Keble Road, Oxford OX1 3RH, UK  Stephane
Charlot Sorbonne Université, CNRS, UMR 7095, Institut d’Astrophysique de
Paris, 98 bis bd Arago, 75014 Paris, France  Mirko Curti Kavli Institute for
Cosmology, University of Cambridge, Madingley Road, Cambridge, CB3 OHA, UK
Cavendish Laboratory, University of Cambridge, 19 JJ Thomson Avenue, Cambridge
CB3 0HE, UK  European Southern Observatory, Karl-Schwarzschild-Strasse 2,
D-85748 Garching bei Muenchen, Germany  Emma Curtis-Lake Centre for
Astrophysics Research, Department of Physics, Astronomy and Mathematics,
University of Hertfordshire, Hatfield AL10 9AB, UK  Tobias J. Looser Kavli
Institute for Cosmology, University of Cambridge, Madingley Road, Cambridge,
CB3 OHA, UK  Cavendish Laboratory, University of Cambridge, 19 JJ Thomson
Avenue, Cambridge CB3 0HE, UK  Roberto Maiolino Kavli Institute for Cosmology,
University of Cambridge, Madingley Road, Cambridge, CB3 OHA, UK  Cavendish
Laboratory, University of Cambridge, 19 JJ Thomson Avenue, Cambridge CB3 0HE,
UK  Department of Physics and Astronomy, University College London, Gower
Street, London WC1E 6BT, UK  Chris Willott NRC Herzberg, 5071 West Saanich Rd,
Victoria, BC V9E 2E7, Canada  Joris Witstok Kavli Institute for Cosmology,
University of Cambridge, Madingley Road, Cambridge, CB3 OHA, UK  Cavendish
Laboratory, University of Cambridge, 19 JJ Thomson Avenue, Cambridge CB3 0HE,
UK  Kristan Boyett School of Physics, University of Melbourne, Parkville 3010,
VIC, Australia  ARC Centre of Excellence for All Sky Astrophysics in 3
Dimensions (ASTRO 3D), Australia  Zuyi Chen Steward Observatory, University of
Arizona, 933 N. Cherry Ave., Tucson, AZ 85721, USA  Eiichi Egami Steward
Observatory, University of Arizona, 933 N. Cherry Ave., Tucson, AZ 85721, USA
Ryan Endsley Department of Astronomy, University of Texas at Austin, 2515
Speedway Blvd Stop C1400, Austin, TX 78712, USA  Raphael E. Hviding Steward
Observatory, University of Arizona, 933 N. Cherry Ave., Tucson, AZ 85721, USA
Daniel T. Jaffe Department of Astronomy, University of Texas at Austin, 2515
Speedway Blvd Stop C1400, Austin, TX 78712, USA  Zhiyuan Ji Steward
Observatory, University of Arizona, 933 N. Cherry Ave., Tucson, AZ 85721, USA
Jianwei Lyu Steward Observatory, University of Arizona, 933 N. Cherry Ave.,
Tucson, AZ 85721, USA  Lester Sandles Kavli Institute for Cosmology,
University of Cambridge, Madingley Road, Cambridge, CB3 OHA, UK  Cavendish
Laboratory, University of Cambridge, 19 JJ Thomson Avenue, Cambridge CB3 0HE,
UK Jakob M. Helton<EMAIL_ADDRESS>
###### Abstract

We report the discovery of an extreme galaxy overdensity at $z=5.4$ in the
GOODS-S field using JWST/NIRCam imaging from JADES and JEMS alongside
JWST/NIRCam wide field slitless spectroscopy from FRESCO. We identified
potential members of the overdensity using HST+JWST photometry spanning
$\lambda=0.4-5.0\ \mu\mathrm{m}$. These data provide accurate and well-
constrained photometric redshifts down to $m\approx 29-30\,\mathrm{mag}$. We
subsequently confirmed $N=96$ potential members of the overdensity using JWST
slitless spectroscopy over $\lambda=3.9-5.0\ \mu\mathrm{m}$ through a targeted
line search for $\mathrm{H}\alpha$ around the best-fit photometric redshift.
We verified that $N=53$ galaxies reside in the field at $z=5.2-5.5$ while
$N=43$ galaxies reside in an overdensity at $z=5.4$ around $\sim 10-12$ times
that of a random volume. Stellar populations for these galaxies were inferred
from the photometry and used to construct the star-forming main sequence,
where protocluster members appeared more massive and exhibited earlier star
formation (and thus older stellar populations) when compared to their field
galaxy counterparts. We estimate the total halo mass of this large-scale
structure to be
$13.0\lesssim\mathrm{log}_{10}\left(M_{\mathrm{halo}}/M_{\odot}\right)\lesssim
13.5$ using an empirical stellar mass to halo mass relation, although this is
likely an underestimate as a result of incompleteness. Our discovery
demonstrates the power of JWST at constraining dark matter halo assembly and
galaxy formation at very early cosmic times.

Early universe (435); Galaxy evolution (594); Galaxy formation (595);
High-redshift galaxies (734); High-redshift galaxy clusters (2007)

††facilities: HST (ACS), JWST (NIRCam)††software: AstroPy (Astropy
Collaboration et al., 2013, 2018), Cloudy (Byler et al., 2017), dynesty
(Speagle, 2020), FSPS (Conroy et al., 2009; Conroy & Gunn, 2010), Matplotlib
(Hunter, 2007), NumPy (van der Walt et al., 2011; Harris et al., 2020), Pandas
(The Pandas Development Team, 2022), photutils (Bradley et al., 2022),
Prospector (Johnson et al., 2021), python-FSPS (Foreman-Mackey et al., 2014),
SciPy (Virtanen et al., 2020), seaborn (Waskom, 2021), TinyTim (Krist et al.,
2011), WebbPSF (Perrin et al., 2014)

tablenum SIXtablenum

##  1 Introduction

In the local Universe, galaxy clusters represent the largest and most massive
gravitationally bound structures, consisting of up to thousands of individual
galaxies contained within a virialized or virializing dark matter halo, and
representing the most extreme matter overdensities allowed by the standard
cosmological paradigm of hierarchical structure formation (White & Rees,
1978). In the early Universe, the structures that eventually evolved into the
galaxy clusters seen today are referred to as “protoclusters”, which consist
of fewer individual galaxies contained within more complex dark matter halos
that are yet to be virialized (for a review of protoclusters, see Overzier,
2016).

Observations have suggested that the majority of the stellar mass in the local
Universe resides in massive elliptical galaxies, which are preferentially
found within galaxy clusters (Dressler, 1980). Additionally, the average
formation timescales for galaxies in clusters is shorter than that for
analogous galaxies in the field (Webb et al., 2020). These results suggest
that the physical processes associated with extreme matter overdensities
induce earlier star formation, earlier stellar mass assembly, and earlier
quenching. However, massive clusters at relatively high redshift ($z=1-2$)
have also been observed to have large amounts of star formation, on par with
field populations (Alberts et al., 2014, 2016, 2021). Quantifying these
effects across the protocluster to cluster boundary remains an important open
problem for extragalactic astronomy (Wang et al., 2013).

The impact of environment on galaxy formation and evolution is best understood
in the local Universe, where we can observe the effects of transformational
processes such as dynamical relaxation, tidal interactions, and mergers
(Zabludoff et al., 1996). However, these transformational processes make it
difficult to ascertain important evidence (e.g., both the initial relative
positions and velocities of the constituent galaxies) related to the early
formation and evolution of the most massive gravitationally bound structures.
For this reason, searching for protoclusters in the early Universe offers our
best chance of understanding the initial formation and subsequent evolution of
galaxy clusters today (e.g., Li et al., 2022; Brinch et al., 2022; Morishita
et al., 2022).

In this paper, we present the discovery of an extreme galaxy overdensity at
$z=5.4$ in the GOODS-S field using data from the Near Infrared Camera (NIRCam;
Rieke et al., 2005, 2022) on JWST. The powerful combination of deep imaging
and wide field slitless spectroscopy (WFSS) provided by JWST/NIRCam allows us
to identify this overdensity, characterize the stellar populations of galaxies
both inside and outside this large-scale structure, and estimate the dark
matter halo mass associated with this protocluster. These observations provide
important insights into the impact of environment on galaxy formation and
evolution immediately after the epoch of reionization (EoR; $z>6$) when the
Universe was approximately a billion years old.

This paper proceeds as follows. In Section 2, we describe the various data and
observations that are used in our analysis, including the photometric redshift
determination and emission line detection. In Section 3, we present our
analysis and results, including the stellar population modeling and halo mass
inference. In Section 4, we summarize our findings and their implications for
galaxy evolution in the early Universe. All magnitudes are in the AB system
(Oke & Gunn, 1983). Throughout this work, we report wavelengths in vacuum and
adopt the standard flat $\Lambda$CDM cosmology from Planck18 with $H_{0}=67.4\
\mathrm{km/s/Mpc}$ and $\Omega_{m}=0.315$ (Planck Collaboration et al., 2020).

##  2 Data & Observations

In this work, we use deep optical imaging from the Advanced Camera for Surveys
(ACS) on HST alongside deep infrared imaging and WFSS from JWST/NIRCam in the
Great Observatories Origins Deep Survey South (GOODS-S; Giavalisco et al.,
2004) field. The imaging data and photometry from HST/ACS and JWST/NIRCam are
described in Section 2.1. The photometric redshifts and sample selection are
described in Section 2.2. The spectral data and line detection from
JWST/NIRCam WFSS are described in Section 2.3.

###  2.1 Imaging Data & Photometry

Our imaging data consist of: (1) deep optical imaging taken with HST/ACS in
five photometric bands (F435W, F606W, F775W, F814W, and F850LP) and (2) deep
infrared imaging taken with JWST/NIRCam in fourteen photometric bands (F090W,
F115W, F150W, F182M, F200W, F210M, F277W, F335M, F356W, F410M, F430M, F444W,
F460M, and F480M).

The HST/ACS mosaics used here were produced as part of the Hubble Legacy
Fields (HLF) project v2.0 and include observations covering a
$25^{\prime}\times 25^{\prime}$ area over the GOODS-S field (Illingworth et
al., 2016; Whitaker et al., 2019). The JWST/NIRCam data were obtained by the
JWST Advanced Deep Extragalactic Survey (JADES; Eisenstein et al., in
preparation; PID: 1180) and the JWST Extragalactic Medium-band Survey (JEMS;
Williams et al., 2023, PID: 1963) in September and October of 2022. The JADES
observations consist of a deep mosaic covering a $4.4^{\prime}\times
6.2^{\prime}$ area with nine filters (F090W, F115W, F150W, F200W, F277W,
F335M, F356W, F410M, and F444W) and a medium region covering an additional
$6.1^{\prime}\times 6.5^{\prime}$ area with eight filters (F090W, F115W,
F150W, F200W, F277W, F356W, F410M, and F444W). The JEMS observations consist
of two $2.2^{\prime}\times 2.2^{\prime}$ regions with five filters (F182M,
F210M, F430M, F460M, and F480M), all lying in JADES coverage. For all of the
subsequent analysis, we do not require any of our objects to have JEMS
observations, but we use these data when available.

A detailed description of the JWST/NIRCam imaging data reduction and mosaicing
will be presented in a forthcoming paper from the JADES Collaboration
(Tacchella et al., in preparation). We briefly summarize here the main steps
of the reduction and mosaicing process. The data are initially processed with
the standard JWST calibration
pipeline111https://github.com/spacetelescope/jwst. Customized steps are
included to aid in the removal of “1/f” noise, “wisp” artifacts, “snowball”
artifacts, and persistence from previous observations (see also Rigby et al.,
2022). The JWST Calibration Reference Data System (CRDS) context map
jwst_1008.pmap is used, including the flux calibration for JWST/NIRCam from
Cycle 1. The background from the sky is modeled and removed using the
BackGround2D class from photutils (Bradley et al., 2022). Finally, the image
mosaics for each of the fourteen JWST/NIRCam filters are registered to the
Gaia DR3 frame (Gaia Collaboration et al., 2022) and resampled onto the same
world coordinate system (WCS) with a $30\,\mathrm{mas/pixel}$ grid. Assuming a
circular aperture with a diameter of $0.3^{\prime\prime}$, the $5\sigma$
point-source detection limit in the F200W filter is $m\approx
30.0\,\mathrm{AB\,mag}$ and $m\approx 29.0\,\mathrm{AB\,mag}$ for the deep and
medium regions, respectively.

A detailed description of the JWST/NIRCam source detection was outlined in
Robertson et al. (2022) and will be presented in detail in another forthcoming
paper from the JADES Collaboration (Robertson et al., in preparation). We
briefly summarize here the main steps of the source detection process. Six
image mosaics (F200W, F277W, F335M, F356W, F410M, and F444W) are initially
stacked using the corresponding error images and inverse-variance weighting to
produce a single detection image. In this detection image, we construct a
source catalog by selecting contiguous regions with signal-to-noise ratios
$\mathrm{S/N}>3$ and applying a standard Source Extractor (SExtractor; Bertin
& Arnouts, 1996) deblending algorithm with parameters nlevels = 32 and
contrast = 0.001 using photutils (Bradley et al., 2022). Finally, we perform
forced photometry at the source centroids in all HST/ACS and JWST/NIRCam
photometric bands, assuming both circular apertures with diameters of
$0.3^{\prime\prime}$ and elliptical Kron apertures with parameter = 2.5. Using
model point spread functions (PSFs) from the TinyTim (Krist et al., 2011)
package for HST/ACS and the WebbPSF (Perrin et al., 2014) package for
JWST/NIRCam, we apply aperture corrections assuming point source morphologies.
Uncertainties are estimated by placing random apertures across regions of the
image mosaics to compute a flux variance (e.g., Labbé et al., 2005; Quadri et
al., 2007; Whitaker et al., 2011), which is summed in quadrature with the
associated Poisson uncertainty for each detected source.

###  2.2 Photometric Redshifts & Sample Selection

Using the previously described photometry, we measure photometric redshifts
with the template-fitting code EAZY (Brammer et al., 2008). A more detailed
description of this procedure will be discussed in a forthcoming paper from
the JADES Collaboration (Hainline et al., in preparation). We briefly
summarize here the main steps of the photometric redshift process. EAZY uses a
chi-square ($\chi^{2}$) minimization technique to model the broadband spectral
energy distributions (SEDs) for galaxies using linear combinations of galaxy
templates. It is designed to be both fast and flexible, and has been used
extensively in the literature to model the photometric redshifts of galaxies
(e.g., Newman et al., 2013; Skelton et al., 2014; Bouwens et al., 2015).

We fit all of the available photometry for each object with EAZY, assuming
circular apertures with diameters of $0.3^{\prime\prime}$. For objects in the
UDF, this includes photometry in nineteen filters. For objects in the JADES
deep region but not in the UDF, this includes photometry in fourteen filters.
For objects in the JADES medium region, this includes photometry in thirteen
filters. We utilize sixteen templates in total to perform the fitting, which
includes the nine EAZY “v1.3” templates, two additional templates for simple
stellar populations with ages of $5\,\mathrm{Myr}$ and $25\,\mathrm{Myr}$, and
five more templates with strong nebular continuum emission that were created
using the Flexible Stellar Population Synthesis code (FSPS; Conroy et al.,
2009; Conroy & Gunn, 2010). These templates span a large range of stellar
population properties and include contributions from both nebular continuum
and line emission, as well as obscuration from dust.

While the photometric calibration of JWST/NIRCam has improved significantly
over the course of the last few months, there is still some uncertainty with
these calibrations that needs to be taken into account. To this end, we
iteratively calculate the photometric offset from the EAZY templates compared
to the true JWST/NIRCam photometry. These photometric offsets are relatively
small (on the order of a few percent for both HST/ACS and JWST/NIRCam) and are
subsequently applied to the entire photometric catalog. We choose not to adopt
any apparent magnitude priors, but we do make use of the template error file
“template_error.v2.0.zfourge”.

The primary measurements used here are the EAZY “z_a” redshifts, which
correspond to the fit with the minimum $\chi^{2}$ value. We allow EAZY to fit
across the redshift range of $z=0.2-22$. To test the accuracy of these
photometric redshifts, we compare these predictions with existing
spectroscopic redshifts in the GOODS-S field from the Multi Unit Spectroscopic
Explorer (MUSE; Inami et al., 2017; Urrutia et al., 2019). While the MUSE
spectroscopic redshifts are biased toward the brightest objects detected by
JWST at $z<7$, we found catastrophic outlier fractions of only $5.7\%$ when
comparing to the highest quality spectroscopic redshifts available with MUSE.
The catastrophic outlier fraction is defined to be the fraction of objects
that satisfy Equation 1:

$\frac{|z_{\,\mathrm{spec}}-z_{\,\mathrm{phot}}|}{1+z_{\,\mathrm{spec}}}>0.15.$
(1)

To perform an accurate and efficient targeted emission line search within the
available spectroscopic data, we require a sample of relatively bright
objects, since these are the only objects for which we expect to detect an
emission line (see Section 2.3). We also require these objects to have narrow
photometric redshift constraints, which allow for spectroscopic redshift
confirmation using only a single line detection. Most objects with narrow
photometric redshift constraints are those with emission lines that fall in
one of the medium band filters (e.g., F410M) which allows for tight
constraints when paired with the broad band filter coverage of JADES (e.g.,
F444W). Our selection criteria for the final photometric catalog consist of
the following: $m<29\ \mathrm{mag}$ in F444W assuming circular apertures with
diameters of $0.3^{\prime\prime}$, $5<z_{\,\mathrm{phot}}<11$, and $\Delta
z_{\,\mathrm{phot}}<1$. The EAZY confidence interval ($\Delta
z_{\,\mathrm{phot}}$) is defined to be the difference between the 16th and
84th percentiles of the photometric redshift posterior distribution and is
roughly twice the standard deviation.

###  2.3 Spectroscopic Data & Emission Line Detection

Our spectroscopic data consist of WFSS taken with JWST/NIRCam in the F444W
filter ($\lambda=3.9-5.0\ \mu\mathrm{m}$). These data were obtained by the
First Reionization Epoch Spectroscopic COmplete Survey (FRESCO; PI: Oesch;
PID: 1895) in November of 2022. The FRESCO observations cover a
$8.2^{\prime}\times 8.6^{\prime}$ area using the row-direction grisms on both
modules of JWST/NIRCam (Grism R; $R\approx 1600$). The total overlapping area
between the JADES and FRESCO footprints is $\approx 41$ square arcminutes. The
total spectroscopic observing time for FRESCO in GOODS-S is $\approx 16$ hours
with a typical on-source time of $\approx 2$ hours. The $3\sigma$ unresolved
emission line detection limit around $4.2\,\mu\mathrm{m}$ in the F444W filter
is $\sim 1.2\times 10^{-18}\,\mathrm{erg/s/cm^{2}}$, which corresponds to a
star formation rate (SFR) detection limit of $\sim 2.1\,M_{\odot}/\mathrm{yr}$
at $z=5.4$ using the conversion factor from Kennicutt & Evans (2012).

A detailed description of the JWST/NIRCam grism data reduction can be found in
Sun et al. (2022). We briefly summarize here the main steps of the reduction
process. The data are initially processed with the standard JWST calibration
pipeline222https://github.com/spacetelescope/jwst. We assign WCS to the rate
files, perform flat-fielding, and subtract out the sigma-clipped median sky
background from each individual exposure after the “ramp-to-slope” fitting in
the calibration pipeline. Because we are interested in conducting a targeted
emission line search, and we do not expect any of our sources to have a strong
continuum, we utilize a median-filtering technique to subtract out any
remaining continuum or background on a row-by-row basis following the
methodology of Kashino et al. (2022). This produces emission line maps for
each grism exposure that are void of any continuum. Although this median-
filtering technique is able to properly remove continuum contamination, it
sometimes over-subtracts signal in the spectral regions immediately
surrounding the brightest emission lines (e.g., emission from [N II] on either
side of H$\alpha$). The full widths at half maximum (FWHMs) of these emission
lines are relatively small and typically on the order of a few pixels, which
means that the H$\alpha$ line flux is preserved by the nine pixel central gap
of the median filter and is not affected by the aforementioned over-
subtraction. We further remove the “1/f” noise using the tshirt/roeba
algorithm333https://github.com/eas342/tshirt in both the row and column
directions.

We extract two-dimensional (2d) grism spectra using the reduced emission-line
maps for all of the objects that are part of the final photometric catalog
discussed in Section 2.2. Short wavelength (SW) parallel observations were
conducted in two photometric bands (F182M and F210M) and are used for both
astrometric and wavelength calibration of the long wavelength (LW)
spectroscopic data. We use the spectral tracing and grism dispersion models
(Sun et al., 2022) that were produced using the JWST/NIRCam commissioning data
of the Large Magellanic Cloud (LMC; PID: 1076), which are also outlined in
Wang et al., in preparation. We additionally use the flux calibration models
that were produced using JWST/NIRCam Cycle-1 absolute flux calibration
observations (PID: 1536/1537/1538).

Figure 1: The distribution of spectroscopic redshifts for the $N=96$ objects
at $z=5.2-5.5$ that are part of the final spectroscopic sample identified in
Section 2.3. As defined in Section 3.1, the grey histograms represent the
$N=53$ confirmed members of the field while the turquoise histograms represent
the $N=43$ confirmed members of the overdensity. The median redshift of the
overdensity is given by the solid magenta line. Compared to the field members,
the overdensity members appear much more clustered, representing a $\sim 10$
times overdensity at $z=5.4$.

Using the already extracted 2d spectra, we further extract one-dimensional
(1d) grism spectra using a boxcar aperture, assuming a height of five pixels
($0.31^{\prime\prime}$). We subsequently identify $>3\sigma$ peaks
automatically in the 1d spectra, assuming various bin sizes (integer units of
$\mathrm{nm}$ from one to eight) and fit these detected peaks with Gaussian
profiles. For each of these lines, we tentatively assign a spectroscopic
redshift which minimizes the difference between the estimated photometric
redshift and the tentative spectroscopic redshift. Visual inspection is
performed on each of these tentative spectroscopic redshift solutions in order
to remove spurious detections caused by either noise or contamination. For
sources that pass our visual inspection and have secure line detections, we
optimally re-extract the 1d spectra using the F444W surface brightness profile
(Horne, 1986) and once again fit these detected peaks with Gaussian profiles.
According to the grism wavelength calibration uncertainty, the typical
absolute uncertainties of our spectroscopic redshifts are $\Delta
z_{\mathrm{spec}}=0.001$.

Figure 2: The on-sky distribution in angular units for the $N=96$ objects at
$z=5.2-5.5$ that are part of the final spectroscopic sample. The grey points
represent the $N=53$ confirmed members of the field while the turquoise pluses
represent the $N=43$ confirmed members of the overdensity. The median position
of the overdensity is given by the magenta point. The JADES deep (medium)
footprint is illustrated by the dashed (solid) red line, the JEMS footprint by
the solid orange line, and the FRESCO footprint by the solid yellow line. It
is apparent that the overdensity falls near the edge of the JADES medium and
FRESCO footprints, which means we cannot rule out the overdensity extending
well beyond the region for which we currently have data.

Our final spectroscopic sample includes $N=96$ objects at $z=5.2-5.5$ with
$>3\sigma$ detections of H$\alpha$ from the FRESCO spectra. This redshift
range was chosen to ensure that H$\alpha$ would fall in the F410M filter,
which is the only medium-band filter for which we have uniform coverage,
providing a sanity check for the derived emission line fluxes through a
comparison with the F410M excess relative to F444W. Our final spectroscopic
sample represents a subset of a larger spectroscopic sample of galaxies from
both GOODS fields across a much broader redshift range (Sun et al., in
preparation.). For the majority of galaxies in our final spectroscopic sample,
neither of the [N II] lines were detected, partially as a result of the
aforementioned median-filtering technique (Kashino et al., 2022) but primarily
because the line ratio [N II]/H$\alpha$ is typically low at these redshifts
(e.g., Cameron et al., 2023). The NIRCam cutout images alongside the 2d and 1d
extracted spectra for these objects are shown in Appendix A. Figure 1 shows
the distribution of spectroscopic redshifts for these $N=96$ objects while
Figure 2 shows the on-sky distribution in angular units. These distributions
enabled us to visually identify an overdensity of galaxies around $z=5.4$.

##  3 Analysis & Results

Using the data and observations from Section 2, we perform various analyses on
the $N=96$ galaxies in our final spectroscopic sample and present the results.
Identification of the extreme galaxy overdensity is described in Section 3.1.
Detailed physical modeling of the stellar populations, presentation of the
star-forming main sequence, and comparison of inferred SFRs are described in
Section 3.2. Determining the dynamic state, estimating the dark matter halo
mass, and predicting the future evolution of the overdensity are described in
Section 3.3. Placing this overdensity in context with previous works is
described in Section 3.4.

###  3.1 Overdensity Identification

Following the technique described in Calvi et al. (2021), we use a Friends-of-
Friends (FoF) algorithm to identify the overdensity after looking for three-
dimensional (3d; two spatial, one spectral) structural groupings (see also
Huchra & Geller, 1982; Eke et al., 2004; Berlind et al., 2006). This algorithm
iteratively selects groups, which consist of one or more galaxies that have
projected separations and line-of-sight (LOS) velocity dispersions below the
adopted linking parameters (projected separation
$d_{\mathrm{link}}=500\,\mathrm{kpc}$, chosen to be roughly the virial radius
of a typical galaxy cluster; LOS velocity dispersion
$\sigma_{\mathrm{link}}=500\,\mathrm{km/s}$, chosen to be roughly the velocity
dispersion of a typical galaxy cluster). We identify one large-scale structure
consisting of $N=43$ galaxies out of the $N=96$ galaxies that are part of our
final spectroscopic sample. This structure is an extreme overdensity at
$z=5.4$, $\sim 10$ times more dense in 1d and $\sim 12$ times more dense in 3d
than the $N=53$ analogous field galaxies at $z=5.2-5.5$. In this case and
throughout the rest of this work, analogous means that the field galaxies were
selected in the same manner as the clustered galaxies. These field galaxies
consist of (1) isolated galaxies and (2) those in smaller groups as determined
by the FoF algorithm444The smaller groups include three groups of two, a group
of three, a group of four, a group of five, and a group of six. We consider
galaxies that fall into these groups as field galaxies since they are not part
of the extreme galaxy overdensity, which is the primary focus of this paper. A
more complete clustering analysis will be the subject of future work.. These
groupings do not depend strongly on the adopted linking parameters, producing
similar results when varying either the projected separation or the LOS
velocity dispersion by a factor of a few. The median redshift of the
overdensity is $z=5.387$, spanning a relatively narrow redshift range of
$5.374<z<5.398$.

Figure 3: Left panel: The on-sky distribution in physical units for the $N=43$
confirmed members of the overdensity, color-coded by their spectroscopic
redshift. The color-coded crosses (pluses) represent members of the first
(second) component of the overdensity. The median position of the first
(second) component is given by the black cross (plus). Right panel: The
observed phase-space diagram for the $N=43$ confirmed members of the
overdensity. The median velocity offset of the first (second) component of the
overdensity is given by the green (blue) dashed line. The $1\sigma$ confidence
interval of the velocity offsets for each of the groups are given by the
shaded regions.

Throughout the rest of Section 3, the distinction between $N=43$ “confirmed
members of overdensity” and $N=53$ “confirmed members of field” will be used.
The confirmed members of the overdensity are shown in Figure 1 (Figure 2) by
the turquoise histograms (turquoise pluses) while the confirmed members of the
field are shown by the grey histograms (grey points). The median redshift or
position of the overdensity is given by the solid magenta line or the magenta
point. In both of these figures, the overdensity members appear much more
clustered when compared to the field members. The confirmed members of the
overdensity are additionally shown in the left panel of Figure 3, color-coded
by their spectroscopic redshift. We identify a spatial and kinematic
bimodality within the overdensity at $z=5.4$, which we return to in Section
3.3. To assign objects between the two components of the bimodality, we adopt
an iterative process that minimizes the 3d separation within each of these
components, finding $N=17$ galaxies that are part of the first component
($\Delta v=-320^{+90}_{-120}\,\mathrm{km/s}$) and $N=26$ galaxies that are
part of the second component ($\Delta v=+150^{+120}_{-150}\,\mathrm{km/s}$).
The median positions of these two groups are given by the black plus and cross
in the left panel of Figure 3. Velocity offsets are calculated relative to the
median spectroscopic redshift of the overdensity.

###  3.2 Stellar Population Modeling

Following the methodology outlined in Tacchella et al. (2022), we utilize the
SED fitting code Prospector (v1.1.0; Johnson et al., 2021) to infer the
stellar populations for the $N=96$ objects that are part of our final
spectroscopic sample. Fits are performed on the photometry described in
Section 2.1 assuming elliptical Kron apertures with parameter = 2.5, while the
redshift is fixed at the spectroscopic redshift determined in Section 2.2.
Prospector uses a Bayesian inference framework and we choose to sample
posterior distributions with the dynamic nested sampling code dynesty (v1.2.3;
Speagle, 2020).

We use the Flexible Stellar Population Synthesis code (FSPS; Conroy et al.,
2009; Conroy & Gunn, 2010) via python-FSPS (Foreman-Mackey et al., 2014) with
the Modules for Experiments in Stellar Astrophysics Isochrones and Stellar
Tracks (MIST; Choi et al., 2016; Dotter, 2016), which makes use of the Modules
for Experiments in Stellar Astrophysics (MESA) stellar evolution package
(Paxton et al., 2011, 2013, 2015, 2018). We assume the MILES stellar spectral
library (Falcón-Barroso et al., 2011; Vazdekis et al., 2015) and adopt a
Chabrier (2003) initial mass function (IMF). Absorption by the intergalactic
medium (IGM) is modeled after Madau (1995), where the overall scaling of the
IGM attenuation curve is set to be a free parameter. Dust attenuation is
modeled using a two-component dust attenuation model (Charlot & Fall, 2000)
with a flexible attenuation curve where the slope is tied to the strength of
the ultraviolet (UV) bump (Kriek & Conroy, 2013). Nebular emission (both from
emission lines and continuum) is self-consistently modeled with the spectral
synthesis code Cloudy (Byler et al., 2017).

In order to test the robustness of the inferred stellar populations and SFHs,
we assume three different models for the SFH in an attempt to see how our
results depend on the assumed prior. Two of these models are non-parametric
(one with the “continuity” prior, the other with the “bursty continuity”
prior) while the third is parametric (with the shape of a delayed-tau
function). For each of the non-parametric models, we assume that the SFH can
be described by $N_{\mathrm{SFR}}$ distinct time bins of constant star-
formation. The time bins are specified in units of lookback time and the
number of distinct bins is fixed at $N_{\mathrm{SFR}}=6$. The first two bins
are fixed at $0-30\ \mathrm{Myr}$ and $30-100\ \mathrm{Myr}$ while the last
bin is fixed between $0.15\,t_{\mathrm{univ}}$ and $t_{\mathrm{univ}}$, where
$t_{\mathrm{univ}}$ is the age of the Universe at the galaxy’s spectroscopic
redshift, measured with respect to the formation redshift
$z_{\mathrm{form}}=20$. The rest of the bins are spaced equally in logarithmic
time between $100\,\mathrm{Myr}$ and $0.85\,t_{\mathrm{univ}}$. Both the total
stellar mass and the ratios of adjacent time bins are set to be free
parameters. A summary of the parameters and priors associated with this
Prospector model is presented in Table B1.

Figure 4 shows the star-forming main sequence for the $N=96$ objects at
$z=5.2-5.5$ that are part of the final spectroscopic sample identified in
Section 2.3. The confirmed members of the field are given by the grey points
while the confirmed members of the overdensity are given by the turquoise
points. The reported stellar masses and SFRs are derived from the Prospector
fits using the non-parametric SFH with “continuity” prior, where the SFRs are
averaged over the last $100\ \mathrm{Myr}$ of lookback time. These stellar
masses and SFRs are reported in Table C1, which gives a summary of the
physical properties for our final spectroscopic sample. To be used as a point
of comparison, the empirical star-forming main sequence at $z=5.4$ derived by
Popesso et al. (2022) is given by the solid black line. Additionally, the
maximum allowed SFR assuming all of the stellar mass was formed in the last
$100\ \mathrm{Myr}$ of lookback time is given by the solid magenta line.

Figure 4: The star-forming main sequence for the $N=96$ objects at $z=5.2-5.5$
that are part of the final spectroscopic sample. The stellar masses and SFRs
reported here are derived from the Prospector fits. The SFRs are averaged over
the last $100\ \mathrm{Myr}$ of lookback time. The grey points represent the
$N=53$ confirmed members of the field while the turquoise points represent the
$N=43$ confirmed members of the overdensity. The empirical star-forming main
sequence at $z=5.4$ derived by Popesso et al. (2022) is given by the solid
black line. The maximum allowed SFR assuming all of the stellar mass was
formed in the last $100\ \mathrm{Myr}$ of lookback time is given by the solid
magenta line. The median values of the inferred stellar masses and SFRs for
the confirmed members of the field (overdensity) are given by the grey
(turquoise) dashed lines in the twin axes to the top and right. The $1\sigma$
confidence interval for these parameters are given by the shaded regions in
the same axes. The overdensity members appear to have larger inferred stellar
masses and SFRs when compared to the field members. Additionally, nearly all
of these objects agree with the empirically derived star-forming main sequence
at $z=5.4$ within $1\sigma$.

We find that nearly all of the $N=96$ objects at $z=5.2-5.5$ that are part of
our final spectroscopic sample agree with the empirically derived star-forming
main sequence at $z=5.4$ given by Popesso et al. (2022) within $1\sigma$,
despite our sample being biased as a result of our requirement to detect the
$\mathrm{H}\alpha$ emission line at greater than $3\sigma$. If we were to
instead use $\mathrm{H}\alpha$-based SFRs derived with the conversion factor
from Kennicutt & Evans (2012), the objects within our final spectroscopic
sample would be shifted upward by $\Delta\sim 0.5\,\mathrm{dex}$. However,
this is likely because the canonical hydrogen ionizing photon production
efficiency ($\xi_{\mathrm{ion}}\propto L_{\mathrm{H}\alpha}/L_{\mathrm{UV}}$)
used in Kennicutt & Evans (2012) is only $\xi_{\mathrm{ion}}\sim
10^{25.1}\,\mathrm{erg}^{-1}\mathrm{Hz}$, lower than measurements at $z=5-6$
by $\Delta\xi_{\mathrm{ion}}\sim 0.5\,\mathrm{dex}$ (e.g., Bouwens et al.,
2016; Ning et al., 2022; Sun et al., 2022). The use of UV-based SFRs derived
with the conversion factor from Kennicutt & Evans (2012) does not change the
distribution of our final spectroscopic sample on Figure 4.

The most notable examples of galaxies disagreeing with the empirical relation
include one well above the main sequence at high stellar mass
(JADES$-$GS$+$53.13858$-$27.79024) and two somewhat below the main sequence at
intermediate stellar mass (JADES$-$GS$+$53.08901$-$27.84191 and
JADES$-$GS$+$53.08901$-$27.84191) despite large uncertainties in the inferred
SFRs. We note that the galaxy well above the main sequence at high stellar
mass is likely an active galactic nucleus (AGN) and will be the subject of
future work. Combined with our relatively low SFR detection limit ($\sim
2.1\,M_{\odot}/\mathrm{yr}$, see Section 2.3), the fact that our sample agrees
with the empirical relation suggests that we are sampling the bulk of the
star-forming population at these redshifts despite our selection criteria.
However, we should mention that we are likely missing some amount of dusty
star-forming galaxies (DSFGs), which historically have been used as tracers to
identify protocluster candidates at these redshifts (for a review of
environmental galaxy evolution, see Alberts & Noble, 2022).

To test the impact of the assumed SFH, we compare the Prospector derived
stellar masses and SFRs for the three different SFH models. We remind the
reader that for Figure 4 and Table C1, the assumed SFH is non-parametric with
“continuity” prior. Compared to the non-parametric SFH with “bursty
continuity” prior, the derived stellar masses are a bit larger ($\Delta\approx
0.1\,\mathrm{dex}$) with large scatter ($\sigma\approx 0.7\,\mathrm{dex}$)
while the derived SFRs are broadly consistent ($\Delta\approx
0.0\,\mathrm{dex}$) with large scatter ($\sigma\approx 0.8\,\mathrm{dex}$).
Compared to the parametric SFH, both the derived stellar masses and SFRs are
broadly consistent ($\Delta\approx 0.0\,\mathrm{dex}$) with large scatter
($\sigma\approx 0.8\,\mathrm{dex}$ and $\sigma\approx 1.0\,\mathrm{dex}$,
respectively). We find that the inferred Prospector parameters considered here
do not depend strongly on the assumed SFH.

Figure 5 shows the comparison of SFRs in the two most recent time bins
(corresponding to lookback times of $0-30\ \mathrm{Myr}$ and $30-100\
\mathrm{Myr}$, respectively) for the $N=96$ objects at $z=5.2-5.5$. Once
again, the confirmed members of the field are given by the grey points while
the confirmed members of the overdensity are given by the turquoise points. A
constant SFH is given by the solid black line, which assumes that the average
SFRs in the two most recent time bins are equal. Values above this line
represent a falling SFH over the last $100\ \mathrm{Myr}$, while values below
this line represent a rising SFH. The twin axes to the top and right are
similar to those shown in Figure 4.

Figure 5: The comparison of SFRs in the two most recent time bins for the
$N=96$ objects at $z=5.2-5.5$ that are part of the final spectroscopic sample.
The stellar masses and SFRs reported here are derived from the Prospector
fits. The grey points represent the $N=53$ confirmed members of the field
while the turquoise points represent the $N=43$ confirmed members of the
overdensity. A constant SFH is given by the solid black line. Values above
(below) this line represent a falling (rising) SFH over the last $100\
\mathrm{Myr}$ of lookback time. The twin axes to the top and right are similar
to those shown in Figure 4. The overdensity (field) members appear to have
non-parametric SFHs consistent with a constant (rising) SFH with relatively
small (large) scatter.

By comparing members of the overdensity with members of the field in Figures 4
and 5, we can begin to explore the impact of environment on galaxy formation
and evolution at $z=5.2-5.5$. Table 1 gives a summary of percentiles for some
of the Prospector inferred physical parameters for both the members of the
field and members of the overdensity. In the star-forming main sequence,
overdensity members appear to have larger inferred stellar masses and SFRs
when compared to the field members. However, in order to fairly compare these
two samples, the stellar mass distributions must be similar such that any
observed differences can be attributed to external processes (e.g.,
environment) rather than internal processes (e.g., feedback). To create a
mass-matched sample of field galaxies, we select the field galaxy that is
closest in stellar mass to each overdensity galaxy. The percentiles for the
mass-matched members of the field also appear in Table 1.

Table 1: A summary of percentiles for some of the Prospector inferred physical
parameters.

Parameter Field Overdensity Mass-Matched Field 16th 50th 84th 16th 50th 84th
16th 50th 84th $A_{V}/\mathrm{mag}$ $0.06$ $0.16$ $0.53$ $0.08$ $0.23$ $0.48$
$0.07$ $0.20$ $0.74$ $\mathrm{log}_{10}(M_{\ast}/M_{\odot})$ $8.23$ $8.68$
$9.36$ $8.83$ $9.14$ $9.59$ $8.84$ $9.16$ $9.60$
$\mathrm{log}_{10}(\mathrm{SFR_{\,0-100\,Myr}}/\left[M_{\odot}/\mathrm{yr}\right])$
$-0.17$ $+0.19$ $+1.01$ $+0.31$ $+0.51$ $+1.19$ $+0.23$ $+0.68$ $+1.09$
$\mathrm{log}_{10}\left(\mathrm{SFR_{\,30-100\,Myr}}/\mathrm{SFR_{\,0-30\,Myr}}\right)$
$-0.66$ $-0.16$ $+0.16$ $-0.21$ $+0.02$ $+0.30$ $-0.40$ $-0.16$ $+0.17$

When compared to the mass-matched field members, the overdensity members have
marginally lower inferred SFRs. The ratio of SFRs in the two most recent time
bins (corresponding to lookback times of $0-30\ \mathrm{Myr}$ and $30-100\
\mathrm{Myr}$, respectively) also show an interesting trend, where overdensity
members have SFHs consistent with constant while mass-matched field members
have SFHs consistent with rising. These results suggest the physical processes
associated with this extreme galaxy overdensity at $z=5.4$ have induced
earlier star formation and earlier stellar mass assembly relative to the
field, although there are large uncertainties associated with all of these
parameters.

###  3.3 Dark Matter Halo Mass Estimates

To further understand the dynamical state of the overdensity identified in
Section 3.1, the right panel of Figure 3 shows the phase-space diagram for the
$N=43$ confirmed members of the overdensity. Velocity offsets are calculated
from the median redshift of the overdensity (see Figure 1) while spatial
offsets are calculated from the median on-sky positions of the two groups that
make up the bimodality (see the left panel of Figure 3). The median velocity
offset of the overdensity is given by the grey dashed line while the median
velocity offsets of the first and second groups are given by the green and
blue dotted lines, respectively. For each of the groups, the $1\sigma$
confidence interval of the velocity offsets are given by the shaded regions
(for group one, $\Delta v=-320^{+90}_{-120}\,\mathrm{km/s}$; for group two,
$\Delta v=+150^{+120}_{-150}\,\mathrm{km/s}$).

Following the methodology described in Long et al. (2020), we derive two
different estimates of the dark matter halo mass for the overdensity at
$z=5.4$. For both of these methods, we use the stellar-to-halo abundance
matching relation described in Behroozi et al. (2013) to convert stellar
masses into halo masses. Uncertainties on our estimates are calculated by
adopting the mean stellar-to-halo abundance matching relation from Behroozi et
al. (2013) and propagating the uncertainties on the stellar masses for each
individual galaxy. This is an underestimate of the true uncertainties since
there is scatter in the stellar-to-halo abundance matching relation which we
do not account for.

The first halo mass estimate is calculated by summing the halo masses for each
individual galaxy that is part of the overdensity. This estimate assumes that
(1) each galaxy is formed in a separate dark matter halo and (2) the
individual galaxies are closer to virialization than the large-scale structure
associated with the overdensity. We find that the first method gives a total
halo mass of
$\mathrm{log}_{10}\left(M_{\mathrm{halo}}/M_{\odot}\right)=12.96\pm 0.05$. The
second halo mass estimate is determined by summing the stellar masses for each
individual galaxy that is part of the overdensity and converting to a halo
mass. This estimate instead assumes that each galaxy is formed in the same
dark matter halo. We find that the second method gives a total halo mass of
$\mathrm{log}_{10}\left(M_{\mathrm{halo}}/M_{\odot}\right)=13.50\pm 0.28$.

Weighing this kind of large-scale structure in the early Universe is
challenging and requires a variety of assumptions. Typical methods for
weighing galaxy clusters include (1) gravitational lensing, (2) the Sunyaev-
Zel’dovich effect, and (3) using X-ray observations of the hot gas in the
intracluster medium (ICM). However, current X-ray and sub-millimeter
observations are not sensitive enough to properly weigh this extreme
overdensity at $z=5.4$. For this reason, we use the above dark matter halo
mass estimates to infer a probable halo mass range of
$13.0\lesssim\mathrm{log}_{10}\left(M_{\mathrm{halo}}/M_{\odot}\right)\lesssim
13.5$ for the overdensity identified in Section 3.1.

We note that none of these methods account for additional members of the
overdensity that were not identified and included in the final spectroscopic
sample, including objects that fall outside either the JADES or the FRESCO
footprints. This is a non-negligible effect since the first component of the
overdensity (see Figure 3) falls right at the edge of the JADES footprint
($\theta\ll 1^{\prime}$). Additionally, since our final spectroscopic sample
only includes galaxies with narrow photometric redshift constraints and secure
$\mathrm{H}\alpha$ line detections, we are likely missing some additional
subset of objects with relatively unconstrained photometric redshifts and/or
low levels of star formation (e.g., DSFGs and/or obscured AGNs). Given that
clusters induce earlier quenching for their constituent members, we cannot
rule out the existence of a significant population of these kinds of objects.
For these reasons, the halo mass range quoted above is likely an underestimate
of the true halo mass associated with this extreme galaxy overdensity at
$z=5.4$.

Figure 6: The dark matter halo mass distribution for groups and protoclusters
at $z<8$. The two halo mass estimates derived in Section 3.3 are given by the
turquoise triangles. For comparison, groups and protoclusters at $z<6$ from Li
et al. (2022) are given in grayscale while protoclusters at $z>6$ from Brinch
et al. (2022) are given by the grey points. The magenta shaded region shows
the expected halo mass evolution of a Coma-like cluster (Chiang et al., 2013).
The black horizontal dashed line represents the typical threshold for shock
stability assuming a spherical infall, below which the flows are predominantly
cold and above which a shock-heated ICM is expected (Dekel & Birnboim, 2006).
The black diagonal dashed line represents the typical threshold for
penetrating cold gas flows. The overdensity at $z=5.4$ is expected to evolve
into a Coma-like cluster with
$\mathrm{log}_{10}(M_{\mathrm{halo}}/M_{\odot})>15$ by $z=0$.

Figure 6 shows the dark matter halo mass distribution for some representative
groups and protoclusters at $z<8$. The two halo mass estimates derived here in
Section 3 are given by the turquoise triangles, where the triangles indicate
that these are likely underestimates of the true halo mass. Groups and
protoclusters at $z<6$ from Li et al. (2022) are given in grayscale while
protocluster candidates in the COSMOS field at $z>6$ from Brinch et al. (2022)
are given by the grey points for comparison, both selected based on
photometric redshifts, with dark matter halo mass estimates that assume the
same stellar-to-halo abundance matching relation used here. The magenta shaded
region shows the expected halo mass evolution of a Coma-like cluster (Chiang
et al., 2013) assuming a smooth evolution at $z>6$. The grey dashed line
represents the typical threshold for shock stability assuming a spherical
infall, below which the flows are predominantly cold and above which a shock-
heated ICM is expected (Dekel & Birnboim, 2006). The black diagonal dashed
line represents the typical threshold for penetrating cold gas flows. The
overdensity identified in Section 3.1 is expected to eventually evolve into a
Coma-like cluster with $\mathrm{log}_{10}(M_{\mathrm{halo}}/M_{\odot})>15$ by
$z=0$, rivaling some of the most massive galaxy clusters found in the local
Universe (Ruel et al., 2014; Buddendiek et al., 2015). For a Coma-like cluster
at $z=0$, the effective radius at $z=5.4$ would be $R_{e}\approx
10\,\mathrm{cMpc}$ (Chiang et al., 2013). This value is much larger than the
physical size of this extreme galaxy overdensity (see Figure 3). Thus, we
conclude that the classification of this large-scale structure as a
protocluster is justified.

###  3.4 Comparison with Previous Works

A number of previous works have found overdensities at similar redshifts to
the one reported here. The earliest one identified at $z>5$ is from Ouchi et
al. (2005), who used narrow-band imaging with Subaru-Suprime Cam to identify
galaxies with strong $\mathrm{Ly}\alpha$ emission lines at $z=5.7$ ($\Delta
z=0.05$). The distribution of sources was described as “clumpy” with one
prominent overdensity significant at the $4.8\sigma$ level, and a second one
at the $2.2\sigma$ level. Follow-up spectroscopy confirmed the narrow redshift
range for these clumps ($\Delta z=0.03$), each being about $2^{\prime}$ in
diameter with a separation of about $9^{\prime}$. The narrow redshift range
combined with the small on-sky separations suggests that these two
overdensities may be clumps within a large-scale structure analogous to the
one reported here.

Similar structures have been reported more recently (e.g., Jiang et al., 2018;
Chanchaiworawit et al., 2019; Harikane et al., 2019; Wang et al., 2021), with
similar techniques in the first three cases, and based on overdensities in the
sub-millimeter found with the South Pole Telescope in the fourth. Jiang et al.
(2018) found two overdensities near that which was identified by Ouchi et al.
(2005), one at $z=5.68$ with a diameter of $\sim 15^{\prime}$ and a smaller
one at $z=5.75$. These both are presumably related to the overdensities
reported by Ouchi et al. (2005), given their proximity in redshift and on-sky.
Chanchaiworawit et al. (2019) and Harikane et al. (2019) identified two
similar structures at $z=6.5$. Since the search method for these works starts
with narrow-band imaging, the samples are from a narrow redshift range and the
actual density of such overdensities on the sky must be significantly higher
than implied by the existing detections. Thus, massive clusters of galaxies
must be well on the way to formation by $z=6$, which is something that we
expect based on simulations (Chiang et al., 2013, 2017) and more recent
observations (Laporte et al., 2022; Morishita et al., 2022).

One of the key breakthroughs illustrated here with respect to these previous
works is the efficient spectroscopic confirmation of such a large sample of
galaxies at $z=5.2-5.5$, made possible by the powerful combination of deep
imaging and WFSS provided by JWST/NIRCam. As also presented in Kashino et al.
2022, JWST/NIRCam WFSS enabled the discovery of three galaxy overdensities
along the sight line of quasar $\mathrm{J}0100+2802$ at $z=6.19$,
$z_{\mathrm{quasar}}=6.33$, and $z=6.78$ through the blind detections of [O
III]-emitting galaxies. It is also worth mentioning that a similar $z=5.2$
galaxy overdensity was identified in the GOODS-N field with a probable halo
mass range of
$12.3<\mathrm{log}_{10}\left(M_{\mathrm{halo}}/M_{\odot}\right)<12.9$ through
$\mathrm{Ly}\alpha$ and sub-millimeter spectroscopy (Walter et al., 2012;
Calvi et al., 2021), which was also partially observed by FRESCO in February
2023. Future observations with JWST will certainly find more structures
similar to those highlighted here, allowing a more complete look at the
progenitors of the most massive gravitationally bound structures in the local
Universe: galaxy clusters.

##  4 Summary & Conclusions

We have presented the discovery of an extreme galaxy overdensity at $z=5.4$ in
the GOODS-S field using data from the Near Infrared Camera (NIRCam) on JWST.
These data consist of JWST/NIRCam imaging from the JWST Advanced Deep
Extragalactic Survey (JADES) and JWST/NIRCam wide field slitless spectroscopy
from the First Reionization Epoch Spectroscopic COmplete Survey (FRESCO). Our
findings can be summarized as follows.

  1. 1.

Galaxies were initially selected using HST+JWST photometry spanning
$\lambda=0.4-5.0\ \mu\mathrm{m}$. These data provide well-constrained
photometric redshifts down to $m\approx 29-30\,\mathrm{mag}$, particularly at
$z=5.2-5.5$, where $\mathrm{H}\alpha$ excess can be traced by comparing
photometry in the F410M and F444W filters. Galaxies were subsequently selected
using slitless spectroscopy over $\lambda=3.9-5.0\ \mu\mathrm{m}$ via a
targeted emission line search for $\mathrm{H}\alpha$ around the best-fit
photometric redshift. The final spectroscopic sample of galaxies includes
$N=96$ objects at $z=5.2-5.5$.

  2. 2.

A Friends-of-Friends (FoF) algorithm was used to identify this extreme galaxy
overdensity by iteratively looking for three-dimensional (3d) structural
groupings within the final spectroscopic sample. One large-scale structure
consisting of $N=43$ galaxies was discovered, which is $\sim 10$ times more
dense in one-dimension (1d) and $\sim 12$ times more dense in 3d than the
$N=53$ analogous field galaxies at $z=5.2-5.5$.

  3. 3.

The stellar populations for these $N=96$ objects at $z=5.2-5.5$ were inferred
using the HST+JWST photometry spanning $\lambda=0.4-5.0\ \mu\mathrm{m}$, the
spectroscopic redshifts determined by the targeted line search, and the SED
fitting code Prospector (Johnson et al., 2021). We constructed the star-
forming main sequence at $z=5.2-5.5$ and found that nearly all the galaxies in
our sample agree with the empirically derived star-forming main sequence at
$z=5.4$ derived by Popesso et al. (2022). Combined with our relatively low SFR
detection limit, this suggests that we are sampling the bulk of the star-
forming population at these redshifts despite our $\mathrm{H}\alpha$ selection
criteria. By comparing members of the overdensity with a mass-matched sample
of members of the field, we find evidence suggesting that environment has
induced earlier star formation and earlier stellar mass assembly within the
overdensity relative to the field, although there are large uncertainties
associated with these parameters.

  4. 4.

Using two different methods, we estimated the total dark matter halo mass
associated with this extreme galaxy overdensity at $z=5.4$ to be within
$13.0\lesssim\mathrm{log}_{10}\left(M_{\mathrm{halo}}/M_{\odot}\right)\lesssim
13.5$. As a result of our selection criteria, we are potentially missing
objects that fall outside either the JADES or the FRESCO footprints, as well
as some subset of objects with relatively unconstrained photometric redshifts
and/or low levels of star formation. This means the total dark matter halo
mass range quoted above is likely an underestimate of the true halo mass. This
massive large-scale structure is expected to evolve into a Coma-like cluster
with $\mathrm{log}_{10}(M_{\mathrm{halo}}/M_{\odot})>15$ by $z=0$.

In this work, we have demonstrated the powerful combination of JWST/NIRCam
imaging and slitless spectroscopy by efficiently confirming the redshifts for
$N=96$ galaxies at $z=5.2-5.5$, inferring the physical properties of these
galaxies, and assessing the large-scale structure in which these galaxies
reside. Follow-up spectroscopic observations using JWST and/or the Atacama
Large Millimeter/sub-millimeter Array (ALMA) will (1) inform us about the
chemical compositions of these (and similar) galaxies, (2) provide insight
into the formation and evolution of extreme galaxy overdensities in the early
Universe, and (3) constrain the total number of these kinds of large-scale
structures immediately after the epoch of reionization (EoR; $z>6$) when the
Universe was less than a billion years old.

## Acknowledgements

This work is based on observations made with the NASA/ESA/CSA James Webb Space
Telescope. The data were obtained from the Mikulski Archive for Space
Telescopes at the Space Telescope Science Institute, which is operated by the
Association of Universities for Research in Astronomy, Inc., under NASA
contract NAS 5-03127 for JWST. These observations are associated with program
#1180, 1210, 1895 and 1963. The authors sincerely thank the FRESCO team (PI:
Pascal Oesch) for developing their observing program with a zero-exclusive-
access period. Additionally, this work made use of the lux supercomputer at UC
Santa Cruz which is funded by NSF MRI grant AST 1828315, as well as the High
Performance Computing (HPC) resources at the University of Arizona which is
funded by the Office of Research Discovery and Innovation (ORDI), Chief
Information Officer (CIO), and University Information Technology Services
(UITS).

We respectfully acknowledge the University of Arizona is on the land and
territories of Indigenous peoples. Today, Arizona is home to 22 federally
recognized tribes, with Tucson being home to the Oodham and the Yaqui.
Committed to diversity and inclusion, the University strives to build
sustainable relationships with sovereign Native Nations and Indigenous
communities through education offerings, partnerships, and community service.

FS, CNAW, GHR, MJR, BR, BDJ, DJE, and EE acknowledge support from the
JWST/NIRCam contract to the University of Arizona, NAS5-02015. DJE is
supported as a Simons Investigator. RH ackowledges funding from by the Johns
Hopkins University, Institute for Data Intensive Engineering and Science
(IDIES). NB acknowledges support from the Cosmic Dawn Center (DAWN), which is
funded by the Danish National Research Foundation under grant No. 140. AB
acknowledges funding from the “FirstGalaxies” Advanced Grant from the European
Research Council (ERC) under the European Union’s Horizon 2020 research and
innovation programme (Grant agreement No. 789056). ECL acknowledges support of
an STFC Webb Fellowship (ST/W001438/1). TJL, RM, and JW acknowledge support
from the ERC Advanced Grant 695671, “QUENCH”. TJL and RM acknowledge support
by the Science and Technology Facilities Council (STFC) and funding from a
research professorship from the Royal Society. JW acknowledges support from
the Fondation MERAC. KB acknowledges support in part by the Australian
Research Council Centre of Excellence for All Sky Astrophysics in 3 Dimensions
(ASTRO 3D), through project number CE170100013. REH acknowledges support from
the National Science Foundation Graduate Research Fellowship Program under
Grant No. DGE-1746060.

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##  Appendix A Cutout Images and Grism Spectra for Final Spectroscopic Sample

Figures A1 and A2 show the cutout images (see Section 2.1) alongside the
continuum subtracted 2d and 1d extracted spectra (see Section 2.3) for the
$N=96$ objects that are part of the final spectroscopic sample described in
Section 2.3. The $N=43$ confirmed members of the overdensity are given by
Figure A1 while the $N=53$ confirmed members of the field are given by Figure
A2. For each galaxy, the upper-left panel shows the $1.2^{\prime\prime}\times
1.2^{\prime\prime}$ F444W-F277W-F150W RGB thumbnail. The upper-right panel
shows the extracted 2d spectrum around the $\mathrm{H}\alpha$ emission line
detection, indicated by the solid red line. The lower-right panel instead
shows the extracted 1d spectrum around the $\mathrm{H}\alpha$ emission line
detection alongside the best-fit Gaussian profile given by the solid red line.
The JADES ID and confirmed spectroscopic redshift are given in the lower-right
panel for each galaxy.

Figure A1: The cutout images alongside the continuum subtracted 2d and 1d
extracted spectra for the $N=43$ confirmed members of the overdensity as
defined in Section 3.1. For each galaxy, the upper-left panel shows the
$1.2^{\prime\prime}\times 1.2^{\prime\prime}$ F444W-F277W-F150W RGB thumbnail.
The upper-right panel shows the extracted 2d spectrum around the
$\mathrm{H}\alpha$ emission line detection, indicated by the solid red line.
The lower-right panel instead shows the extracted 1d spectrum around the
$\mathrm{H}\alpha$ emission line detection alongside the best-fit Gaussian
profile given by the solid red line. The JADES ID and confirmed spectroscopic
redshift are given in the lower-right panel for each galaxy.

Figure A1: Continued.

Figure A1: Continued.

Figure A1: Continued.

Figure A2: The cutout images alongside the continuum subtracted 2d and 1d
extracted spectra, just like in Figure A1, now for the $N=53$ confirmed
members of the field as defined in Section 3.1.

Figure A2: (Continued).

Figure A2: (Continued).

Figure A2: (Continued).

Figure A2: (Continued).

##  Appendix B Assumed Physical Model for Stellar Population Modeling

Table B1 gives a summary of the parameter and priors assumed in the Prospector
model that was used for the stellar population modeling described in Section
3.2.

Table B1: A summary of the parameters and priors used in our Prospector model
(see Section 3.3).

Parameter Description Prior $z$ Redshift Fixeda
$\mathrm{log}_{10}(Z_{\ast}/Z_{\odot})$ Stellar metallicity Uniformb
$\mathrm{log}_{10}(M_{\ast}/M_{\odot})$ Total stellar mass formed Uniformc
Non-Parametric SFH (Secondary) Ratio of the SFRs in adjacent time bins with
Student’s t-distributiond five ($N_{\mathrm{SFR}}-1$) free parameters Non-
Parametric SFH (Primary) Ratio of the SFRs in adjacent time bins with
Student’s t-distributione five ($N_{\mathrm{SFR}}-1$) free parameters
Parametric SFH Delayed-tau model with one free parameter Log uniformf $n$
Power-law modifier to the shape of the Uniformg Calzetti et al. (2000) diffuse
dust attenuation curve $\tau_{\mathrm{dust},\,1}$ Birth-cloud dust optical
depth Clipped normalh $\tau_{\mathrm{dust},\,2}$ Diffuse dust optical depth
Clipped normali $\mathrm{log}_{10}(Z_{\mathrm{gas}}/Z_{\odot})$ Gas-phase
metallicity Uniformj $\mathrm{log}_{10}(U)$ Ionization parameter for nebular
emission Uniformk $f_{\mathrm{IGM}}$ Scaling of the IGM attenuation curve
Clipped normall • Notes a Fixed value at $z=z_{\mathrm{spec}}$. b Uniform
prior with min$=-2.00$, max$=+0.19$. c Uniform prior with min$=+6$, max$=+12$.
d Student’s t-distribution prior with $\sigma=+1.0$, $\nu=+2.0$. e Student’s
t-distribution prior with $\sigma=+0.3$, $\nu=+2.0$. f Log uniform prior with
min$=+0.1$, max$=+30.0$. g Uniform prior with min$=-1.0$, max$=+0.4$. h
Clipped normal prior in $\tau_{\mathrm{dust},\,1}/\tau_{\mathrm{dust},\,2}$
with min$=+0$, max$=+2$, $\mu=+1.0$, $\sigma=+0.3$. i Clipped normal prior
with min$=+0$, max$=+4$, $\mu=+0.3$, $\sigma=+1.0$. j Uniform prior with
min$=-2.0$, max$=+0.5$. k Uniform prior with min$=-4.0$, max$=-1.0$. l Clipped
normal prior with min$=+0$, max$=+2$, $\mu=+1.0$, $\sigma=+0.3$.

##  Appendix C Physical Properties of Final Spectroscopic Sample

Table C1 gives a summary of the physical properties for the $N=96$ objects
that are part of the final spectroscopic sample described in Section 2.3.

Table C1: A summary of the physical properties for the $N=96$ objects in our
final spectroscopic sample.

Index R.A. Decl. $z_{\mathrm{spec}}$ $M_{\mathrm{UV}}$ $f_{\mathrm{H}\alpha}$
$\mathrm{log}_{10}(M_{\ast}/M_{\odot})$ $\mathrm{SFR}_{0-100\,\mathrm{Myr}}$
Type (J2000) (J2000) (mag) ($10^{-19}\ \mathrm{erg/s/cm^{2}}$)
($M_{\odot}/\mathrm{yr}$) 1 53.06177 -27.84238 $5.207$ $-18.96\pm 0.03$
$12.9\pm 2.6$ $8.34\pm 0.22$ $1.0\pm 1.1$ Field 2 53.07460 -27.85648 $5.208$
$-18.71\pm 0.05$ $14.6\pm 3.8$ $8.86\pm 0.19$ $2.3\pm 1.7$ Field 3 53.11272
-27.83827 $5.213$ $-21.04\pm 0.01$ $24.5\pm 4.2$ $9.06\pm 0.29$ $6.1\pm 6.1$
Field 4 53.16357 -27.86576 $5.225$ $-20.14\pm 0.05$ $8.5\pm 2.3$ $8.46\pm
0.27$ $1.0\pm 0.9$ Field 5 53.16858 -27.73726 $5.236$ $-18.68\pm 0.07$
$29.2\pm 3.4$ $8.27\pm 0.19$ $1.3\pm 0.8$ Field 6 53.15488 -27.81150 $5.246$
$-19.50\pm 0.01$ $29.6\pm 4.2$ $9.50\pm 0.29$ $12.8\pm 10.7$ Field 7 53.17058
-27.73561 $5.247$ $-18.14\pm 0.08$ $32.3\pm 4.3$ $9.16\pm 0.20$ $4.8\pm 3.6$
Field 8 53.18831 -27.81283 $5.248$ $-19.64\pm 0.02$ $12.6\pm 2.2$ $8.31\pm
0.19$ $0.9\pm 0.8$ Field 9 53.12535 -27.85616 $5.258$ $-17.88\pm 0.10$
$15.1\pm 5.5$ $9.20\pm 0.22$ $2.1\pm 2.8$ Field 10 53.15105 -27.78295 $5.260$
$-20.44\pm 0.04$ $28.2\pm 3.1$ $8.77\pm 0.16$ $1.8\pm 1.1$ Field 11 53.17025
-27.76293 $5.263$ $-18.77\pm 0.07$ $194.1\pm 5.9$ $9.84\pm 0.08$ $69.4\pm
16.9$ Field 12 53.16758 -27.76549 $5.265$ $-19.55\pm 0.08$ $15.2\pm 5.4$
$8.98\pm 0.20$ $7.7\pm 5.0$ Field 13 53.17079 -27.76230 $5.265$ $-18.85\pm
0.06$ $235.5\pm 5.5$ $9.34\pm 0.09$ $20.3\pm 6.7$ Field 14 53.16646 -27.74697
$5.265$ $-18.30\pm 0.06$ $22.5\pm 4.6$ $8.71\pm 0.19$ $2.1\pm 1.5$ Field 15
53.17183 -27.73770 $5.268$ $-18.71\pm 0.06$ $21.8\pm 3.7$ $8.84\pm 0.23$
$2.1\pm 1.7$ Field 16 53.17091 -27.76234 $5.272$ $-20.05\pm 0.02$ $209.4\pm
5.9$ $8.87\pm 0.11$ $6.6\pm 2.3$ Field 17 53.17838 -27.81632 $5.278$
$-20.10\pm 0.01$ $48.5\pm 5.1$ $9.03\pm 0.28$ $2.9\pm 3.5$ Field 18 53.19272
-27.79244 $5.301$ $-17.97\pm 0.04$ $6.7\pm 2.0$ $8.40\pm 0.27$ $0.6\pm 0.7$
Field 19 53.13173 -27.84999 $5.313$ $-18.18\pm 0.09$ $32.1\pm 4.0$ $8.90\pm
0.25$ $1.7\pm 1.8$ Field 20 53.08803 -27.81320 $5.316$ $-19.17\pm 0.04$
$40.4\pm 4.0$ $9.12\pm 0.18$ $2.3\pm 1.8$ Field 21 53.13081 -27.84688 $5.320$
$-17.09\pm 0.09$ $27.3\pm 3.8$ $10.04\pm 0.21$ $17.7\pm 23.5$ Field 22
53.13174 -27.84710 $5.321$ $-19.17\pm 0.01$ $20.3\pm 3.5$ $8.67\pm 0.24$
$1.8\pm 1.5$ Field 23 53.12716 -27.78900 $5.323$ $-18.14\pm 0.03$ $8.4\pm 3.3$
$8.29\pm 0.17$ $1.4\pm 0.8$ Field 24 53.15687 -27.86069 $5.326$ $-17.94\pm
0.11$ $5.5\pm 1.8$ $8.68\pm 0.29$ $1.4\pm 1.5$ Field 25 53.07235 -27.81415
$5.329$ $-19.32\pm 0.03$ $22.2\pm 3.9$ $8.42\pm 0.24$ $0.5\pm 0.6$ Field 26
53.12819 -27.78768 $5.335$ $-19.39\pm 0.04$ $7.5\pm 2.9$ $8.34\pm 0.21$
$0.9\pm 0.7$ Field 27 53.15585 -27.76672 $5.348$ $-18.72\pm 0.06$ $20.4\pm
3.1$ $8.58\pm 0.22$ $1.1\pm 1.0$ Field 28 53.08698 -27.84806 $5.358$
$-18.41\pm 0.05$ $32.3\pm 5.4$ $8.18\pm 0.18$ $1.1\pm 0.7$ Field 29 53.10304
-27.83951 $5.372$ $-19.28\pm 0.01$ $32.5\pm 4.7$ $9.80\pm 0.25$ $7.1\pm 7.9$
Field 30 53.07408 -27.80401 $5.374$ $-19.08\pm 0.05$ $72.6\pm 3.9$ $9.03\pm
0.20$ $9.9\pm 5.0$ Group 1 31 53.06908 -27.80779 $5.374$ $-17.29\pm 0.06$
$14.8\pm 4.9$ $9.27\pm 0.18$ $2.8\pm 3.1$ Group 1 32 53.12644 -27.79200
$5.375$ $-17.53\pm 0.15$ $14.2\pm 2.7$ $8.11\pm 0.20$ $0.5\pm 0.4$ Field 33
53.12775 -27.78098 $5.376$ $-18.61\pm 0.06$ $13.9\pm 3.5$ $8.86\pm 0.19$
$1.4\pm 1.1$ Field 34 53.07417 -27.80382 $5.377$ $-19.39\pm 0.04$ $110.7\pm
7.4$ $9.78\pm 0.30$ $21.3\pm 19.5$ Group 1 35 53.07496 -27.80446 $5.378$
$-18.83\pm 0.02$ $19.6\pm 4.8$ $9.12\pm 0.22$ $2.2\pm 2.5$ Group 1 36 53.06799
-27.80815 $5.378$ $-19.50\pm 0.01$ $18.6\pm 4.3$ $9.02\pm 0.31$ $3.5\pm 3.7$
Group 1 37 53.16163 -27.78707 $5.378$ $-19.08\pm 0.05$ $14.9\pm 4.1$ $9.18\pm
0.23$ $0.6\pm 1.4$ Field 38 53.07486 -27.80462 $5.378$ $-19.07\pm 0.04$
$17.0\pm 5.9$ $8.89\pm 0.28$ $2.3\pm 2.4$ Group 1 39 53.06784 -27.81850
$5.378$ $-19.17\pm 0.04$ $34.8\pm 3.6$ $8.84\pm 0.23$ $1.9\pm 1.7$ Group 1 40
53.07482 -27.80479 $5.379$ $-19.23\pm 0.04$ $276.7\pm 7.1$ $10.07\pm 0.29$
$64.4\pm 44.0$ Group 1 41 53.16730 -27.75273 $5.379$ $-18.42\pm 0.02$ $17.7\pm
4.7$ $8.65\pm 0.21$ $1.5\pm 1.2$ Field 42 53.07496 -27.80453 $5.380$
$-21.24\pm 0.02$ $18.7\pm 6.6$ $9.11\pm 0.24$ $3.2\pm 3.4$ Group 1 43 53.08112
-27.82613 $5.380$ $-18.07\pm 0.03$ $10.9\pm 2.8$ $8.63\pm 0.18$ $2.1\pm 1.2$
Group 1 44 53.07352 -27.81487 $5.381$ $-19.64\pm 0.02$ $16.2\pm 4.1$ $9.22\pm
0.13$ $6.1\pm 4.2$ Group 1 45 53.07624 -27.80607 $5.381$ $-18.40\pm 0.09$
$38.2\pm 7.4$ $8.89\pm 0.25$ $2.6\pm 2.3$ Group 1 46 53.07444 -27.80483
$5.381$ $-17.88\pm 0.10$ $98.4\pm 16.4$ $9.76\pm 0.18$ $7.9\pm 8.2$ Group 1 47
53.07496 -27.80481 $5.381$ $-17.09\pm 0.09$ $104.7\pm 9.3$ $9.30\pm 0.38$
$9.2\pm 13.4$ Group 1 48 53.12557 -27.86562 $5.382$ $-18.71\pm 0.05$ $12.2\pm
4.4$ $8.76\pm 0.28$ $1.4\pm 1.5$ Group 2

Table C1: Continued.

Index R.A. Decl. $z_{\mathrm{spec}}$ $M_{\mathrm{UV}}$ $f_{\mathrm{H}\alpha}$
$\mathrm{log}_{10}(M_{\ast}/M_{\odot})$ $\mathrm{SFR}_{0-100\,\mathrm{Myr}}$
Type (J2000) (J2000) (mag) ($10^{-19}\ \mathrm{erg/s/cm^{2}}$)
($M_{\odot}/\mathrm{yr}$) 49 53.07421 -27.80499 $5.383$ $-18.20\pm 0.11$
$38.4\pm 6.5$ $9.39\pm 0.20$ $9.1\pm 9.1$ Group 1 50 53.08825 -27.79990
$5.383$ $-19.61\pm 0.02$ $18.5\pm 4.4$ $8.83\pm 0.22$ $2.0\pm 1.9$ Group 1 51
53.10304 -27.85386 $5.385$ $-18.18\pm 0.09$ $11.8\pm 3.6$ $8.95\pm 0.19$
$2.0\pm 2.1$ Group 2 52 53.07873 -27.79750 $5.386$ $-19.24\pm 0.03$ $15.7\pm
5.0$ $9.01\pm 0.21$ $2.7\pm 2.3$ Group 1 53 53.09525 -27.82278 $5.386$
$-20.10\pm 0.01$ $8.8\pm 3.0$ $8.33\pm 0.20$ $0.5\pm 0.4$ Group 2 54 53.11872
-27.86656 $5.386$ $-17.94\pm 0.11$ $34.2\pm 3.6$ $9.02\pm 0.34$ $5.3\pm 6.1$
Group 2 55 53.10171 -27.83617 $5.387$ $-18.14\pm 0.03$ $289.1\pm 8.1$ $9.70\pm
0.25$ $14.8\pm 10.4$ Group 2 56 53.10221 -27.82234 $5.387$ $-19.32\pm 0.03$
$18.2\pm 4.0$ $9.03\pm 0.21$ $3.0\pm 2.4$ Group 2 57 53.10414 -27.82042
$5.387$ $-18.30\pm 0.06$ $39.4\pm 4.0$ $8.64\pm 0.20$ $2.5\pm 1.5$ Group 2 58
53.08538 -27.83271 $5.388$ $-18.77\pm 0.07$ $35.1\pm 8.1$ $9.60\pm 0.24$
$17.8\pm 11.8$ Group 2 59 53.10606 -27.83744 $5.389$ $-17.95\pm 0.12$ $25.7\pm
4.1$ $9.48\pm 0.19$ $7.0\pm 6.1$ Group 2 60 53.06769 -27.83671 $5.389$
$-18.38\pm 0.04$ $15.1\pm 3.7$ $9.23\pm 0.25$ $2.0\pm 2.8$ Group 2 61 53.10604
-27.83731 $5.389$ $-17.53\pm 0.15$ $35.4\pm 7.7$ $9.18\pm 0.21$ $5.0\pm 3.6$
Group 2 62 53.10665 -27.82834 $5.389$ $-21.04\pm 0.01$ $40.0\pm 3.3$ $9.01\pm
0.19$ $2.7\pm 2.3$ Group 2 63 53.10905 -27.83919 $5.390$ $-18.96\pm 0.03$
$11.3\pm 3.9$ $8.38\pm 0.21$ $1.0\pm 0.7$ Group 2 64 53.06865 -27.83498
$5.390$ $-18.20\pm 0.07$ $83.2\pm 4.9$ $9.37\pm 0.09$ $18.9\pm 8.0$ Group 2 65
53.10437 -27.84056 $5.390$ $-17.67\pm 0.17$ $22.5\pm 4.6$ $8.86\pm 0.38$
$4.5\pm 5.5$ Group 2 66 53.08872 -27.83333 $5.391$ $-19.55\pm 0.08$ $70.8\pm
5.9$ $9.18\pm 0.27$ $2.2\pm 3.1$ Group 2 67 53.08863 -27.83349 $5.392$
$-17.64\pm 0.12$ $18.2\pm 6.6$ $9.36\pm 0.20$ $4.2\pm 4.5$ Group 2 68 53.10430
-27.84020 $5.392$ $-19.17\pm 0.01$ $134.6\pm 7.0$ $10.22\pm 0.25$ $149.7\pm
57.3$ Group 2 69 53.11063 -27.83967 $5.392$ $-17.97\pm 0.04$ $23.6\pm 9.2$
$9.58\pm 0.10$ $3.2\pm 3.7$ Group 2 70 53.08068 -27.83516 $5.392$ $-18.61\pm
0.06$ $28.0\pm 5.4$ $9.58\pm 0.31$ $17.8\pm 13.3$ Group 2 71 53.10660
-27.82918 $5.392$ $-19.28\pm 0.01$ $25.3\pm 5.3$ $9.26\pm 0.19$ $5.3\pm 4.1$
Group 2 72 53.10476 -27.83228 $5.392$ $-18.42\pm 0.02$ $73.5\pm 3.8$ $9.14\pm
0.27$ $2.1\pm 2.7$ Group 2 73 53.08831 -27.84042 $5.393$ $-18.41\pm 0.05$
$74.3\pm 4.9$ $9.22\pm 0.23$ $6.8\pm 4.3$ Group 2 74 53.10880 -27.81817
$5.395$ $-20.14\pm 0.05$ $19.0\pm 6.2$ $8.79\pm 0.26$ $1.6\pm 1.6$ Group 2 75
53.08072 -27.83532 $5.396$ $-18.26\pm 0.03$ $55.1\pm 6.2$ $10.27\pm 0.16$
$99.2\pm 53.3$ Group 2 76 53.08901 -27.84191 $5.398$ $-19.26\pm 0.03$ $19.6\pm
6.1$ $9.00\pm 0.09$ $0.6\pm 0.9$ Group 2 77 53.14766 -27.84205 $5.401$
$-18.38\pm 0.04$ $44.3\pm 3.1$ $9.37\pm 0.32$ $12.2\pm 9.9$ Field 78 53.11184
-27.84066 $5.410$ $-18.20\pm 0.07$ $21.3\pm 4.1$ $8.61\pm 0.22$ $1.6\pm 1.3$
Field 79 53.12210 -27.80429 $5.429$ $-19.61\pm 0.02$ $6.9\pm 2.3$ $7.91\pm
0.33$ $0.4\pm 0.4$ Field 80 53.12247 -27.79652 $5.443$ $-19.07\pm 0.04$
$9.1\pm 3.4$ $8.47\pm 0.22$ $1.1\pm 0.8$ Field 81 53.16406 -27.79972 $5.444$
$-19.23\pm 0.04$ $99.1\pm 4.7$ $9.60\pm 0.08$ $11.8\pm 5.0$ Field 82 53.12874
-27.79787 $5.444$ $-19.24\pm 0.03$ $10.8\pm 3.5$ $8.35\pm 0.20$ $0.8\pm 0.6$
Field 83 53.11671 -27.79394 $5.448$ $-19.26\pm 0.03$ $13.3\pm 4.8$ $8.20\pm
0.32$ $0.7\pm 0.7$ Field 84 53.11439 -27.79211 $5.449$ $-17.95\pm 0.12$
$29.6\pm 4.2$ $8.35\pm 0.23$ $1.4\pm 1.1$ Field 85 53.08325 -27.80701 $5.455$
$-18.40\pm 0.09$ $10.3\pm 3.5$ $8.64\pm 0.43$ $0.7\pm 1.0$ Field 86 53.18044
-27.77066 $5.455$ $-17.64\pm 0.12$ $15.4\pm 4.8$ $7.78\pm 0.39$ $0.4\pm 0.4$
Field 87 53.15842 -27.85931 $5.460$ $-18.20\pm 0.11$ $11.4\pm 2.6$ $9.84\pm
0.20$ $11.7\pm 12.9$ Field 88 53.16577 -27.78489 $5.464$ $-19.27\pm 0.10$
$15.6\pm 5.1$ $8.12\pm 0.21$ $0.5\pm 0.4$ Field 89 53.16904 -27.78769 $5.464$
$-18.26\pm 0.03$ $15.2\pm 5.8$ $8.15\pm 0.14$ $1.4\pm 0.5$ Field 90 53.16501
-27.78493 $5.466$ $-18.02\pm 0.14$ $18.9\pm 4.3$ $8.13\pm 0.21$ $0.6\pm 0.4$
Field 91 53.16613 -27.78575 $5.467$ $-19.56\pm 0.02$ $72.9\pm 4.3$ $9.21\pm
0.19$ $5.8\pm 3.9$ Field 92 53.13858 -27.79024 $5.480$ $-18.83\pm 0.02$
$123.0\pm 6.1$ $10.65\pm 0.07$ $490.6\pm 19.7$ Field 93 53.13865 -27.79035
$5.480$ $-21.24\pm 0.02$ $23.6\pm 4.4$ $8.72\pm 0.16$ $1.9\pm 1.1$ Field 94
53.11542 -27.83347 $5.484$ $-18.07\pm 0.03$ $19.1\pm 3.6$ $8.11\pm 0.24$
$1.0\pm 0.7$ Field 95 53.09596 -27.81077 $5.485$ $-17.29\pm 0.06$ $12.4\pm
2.2$ $8.56\pm 0.24$ $1.6\pm 1.2$ Field 96 53.06054 -27.84840 $5.496$
$-17.67\pm 0.17$ $16.3\pm 2.3$ $9.46\pm 0.21$ $30.0\pm 6.9$ Field