# CEERS Key Paper III: The Diversity of Galaxy Structure and Morphology at
$z=3-9$ with JWST

Jeyhan S. Kartaltepe Laboratory for Multiwavelength Astrophysics, School of
Physics and Astronomy, Rochester Institute of Technology, 84 Lomb Memorial
Drive, Rochester, NY 14623, USA  Caitlin Rose Laboratory for Multiwavelength
Astrophysics, School of Physics and Astronomy, Rochester Institute of
Technology, 84 Lomb Memorial Drive, Rochester, NY 14623, USA  Brittany N.
Vanderhoof Laboratory for Multiwavelength Astrophysics, School of Physics and
Astronomy, Rochester Institute of Technology, 84 Lomb Memorial Drive,
Rochester, NY 14623, USA  Elizabeth J. McGrath Department of Physics and
Astronomy, Colby College, Waterville, ME 04901, USA  Luca Costantin Centro de
Astrobiología (CAB), CSIC-INTA, Ctra. de Ajalvir km 4, Torrejón de Ardoz,
E-28850, Madrid, Spain  Isabella G. Cox Laboratory for Multiwavelength
Astrophysics, School of Physics and Astronomy, Rochester Institute of
Technology, 84 Lomb Memorial Drive, Rochester, NY 14623, USA  L. Y. Aaron Yung
NASA Postdoctoral Fellow  Astrophysics Science Division, NASA Goddard Space
Flight Center, 8800 Greenbelt Rd, Greenbelt, MD 20771, USA  Dale D. Kocevski
Department of Physics and Astronomy, Colby College, Waterville, ME 04901, USA
Stijn Wuyts Department of Physics, University of Bath, Claverton Down, Bath
BA2 7AY, UK  Henry C. Ferguson Space Telescope Science Institute, 3700 San
Martin Drive, Baltimore, MD 21218, USA  Micaela B. Bagley Department of
Astronomy, The University of Texas at Austin, Austin, TX, USA  Steven L.
Finkelstein Department of Astronomy, The University of Texas at Austin,
Austin, TX, USA  Ricardo O. Amorín Instituto de Investigación Multidisciplinar
en Ciencia y Tecnología, Universidad de La Serena, Raul Bitrán 1305, La Serena
2204000, Chile  Departamento de Astronomía, Universidad de La Serena, Av. Juan
Cisternas 1200 Norte, La Serena 1720236, Chile  Brett H. Andrews Department of
Physics and Astronomy, University of Pittsburgh, Pittsburgh, PA 15260, USA
Pittsburgh Particle Physics, Astrophysics, and Cosmology Center (PITT PACC),
University of Pittsburgh, Pittsburgh, PA 15260, USA  Pablo Arrabal Haro NSF’s
National Optical-Infrared Astronomy Research Laboratory, 950 N. Cherry Ave.,
Tucson, AZ 85719, USA  Bren E. Backhaus Department of Physics, 196 Auditorium
Road, Unit 3046, University of Connecticut, Storrs, CT 06269, USA  Peter
Behroozi Department of Astronomy and Steward Observatory, University of
Arizona, Tucson, AZ 85721, USA  Division of Science, National Astronomical
Observatory of Japan, 2-21-1 Osawa, Mitaka, Tokyo 181-8588, Japan  Laura
Bisigello Dipartimento di Fisica e Astronomia “G.Galilei”, Universitá di
Padova, Via Marzolo 8, I-35131 Padova, Italy  INAF–Osservatorio Astronomico di
Padova, Vicolo dell’Osservatorio 5, I-35122, Padova, Italy  Antonello Calabrò
INAF - Osservatorio Astronomico di Roma, via di Frascati 33, 00078 Monte
Porzio Catone, Italy  Caitlin M. Casey Department of Astronomy, The University
of Texas at Austin, Austin, TX, USA  Rosemary T. Coogan CEA, IRFU, DAp, AIM,
Université Paris-Saclay, Université Paris Cité, Sorbonne Paris Cité, CNRS,
91191 Gif-sur-Yvette, France  M. C. Cooper Department of Physics & Astronomy,
University of California, Irvine, 4129 Reines Hall, Irvine, CA 92697, USA
Darren Croton Centre for Astrophysics & Supercomputing, Swinburne University
of Technology, Hawthorn, VIC 3122, Australia  ARC Centre of Excellence for All
Sky Astrophysics in 3 Dimensions (ASTRO 3D)  Alexander de la Vega Department
of Physics and Astronomy, University of California, Riverside, CA 92521, USA
Mark Dickinson NSF’s National Optical-Infrared Astronomy Research Laboratory,
950 N. Cherry Ave., Tucson, AZ 85719, USA  Adriano Fontana INAF - Osservatorio
Astronomico di Roma, via di Frascati 33, 00078 Monte Porzio Catone, Italy
Maximilien Franco Department of Astronomy, The University of Texas at Austin,
Austin, TX, USA  Andrea Grazian INAF–Osservatorio Astronomico di Padova,
Vicolo dell’Osservatorio 5, I-35122, Padova, Italy  Norman A. Grogin Space
Telescope Science Institute, 3700 San Martin Drive, Baltimore, MD 21218, USA
Nimish P. Hathi Space Telescope Science Institute, 3700 San Martin Drive,
Baltimore, MD 21218, USA  Benne W. Holwerda Physics & Astronomy Department,
University of Louisville, 40292 KY, Louisville, USA  Marc Huertas-Company
Instituto de Astrofísica de Canarias, La Laguna, Tenerife, Spain  Universidad
de la Laguna, La Laguna, Tenerife, Spain  Université Paris-Cité, LERMA -
Observatoire de Paris, PSL, Paris, France  Kartheik G. Iyer Dunlap Institute
for Astronomy & Astrophysics, University of Toronto, Toronto, ON M5S 3H4,
Canada  Shardha Jogee Department of Astronomy, The University of Texas at
Austin, Austin, TX, USA  Intae Jung Space Telescope Science Institute, 3700
San Martin Drive, Baltimore, MD 21218, USA  Lisa J. Kewley Center for
Astrophysics, Harvard & Smithsonian, 60 Garden Street, Cambridge, MA 02138,
USA  Allison Kirkpatrick Department of Physics and Astronomy, University of
Kansas, Lawrence, KS 66045, USA  Anton M. Koekemoer Space Telescope Science
Institute, 3700 San Martin Drive, Baltimore, MD 21218, USA  James Liu
Laboratory for Multiwavelength Astrophysics, School of Physics and Astronomy,
Rochester Institute of Technology, 84 Lomb Memorial Drive, Rochester, NY
14623, USA  Jennifer M. Lotz Gemini Observatory/NSF’s National Optical-
Infrared Astronomy Research Laboratory, 950 N. Cherry Ave., Tucson, AZ 85719,
USA  Ray A. Lucas Space Telescope Science Institute, 3700 San Martin Drive,
Baltimore, MD 21218, USA  Jeffrey A. Newman Department of Physics and
Astronomy and PITT PACC, University of Pittsburgh, Pittsburgh, PA 15260, USA
Camilla Pacifici Space Telescope Science Institute, 3700 San Martin Drive,
Baltimore, MD 21218, USA  Viraj Pandya Hubble Fellow  Columbia Astrophysics
Laboratory, Columbia University, 550 West 120th Street, New York, NY 10027,
USA  Casey Papovich Department of Physics and Astronomy, Texas A&M University,
College Station, TX, 77843-4242 USA  George P. and Cynthia Woods Mitchell
Institute for Fundamental Physics and Astronomy, Texas A&M University, College
Station, TX, 77843-4242 USA  Laura Pentericci INAF - Osservatorio Astronomico
di Roma, via di Frascati 33, 00078 Monte Porzio Catone, Italy  Pablo G. Pérez-
González Centro de Astrobiología (CAB), CSIC-INTA, Ctra. de Ajalvir km 4,
Torrejón de Ardoz, E-28850, Madrid, Spain  Jayse Petersen  Laboratory for
Multiwavelength Astrophysics, School of Physics and Astronomy, Rochester
Institute of Technology, 84 Lomb Memorial Drive, Rochester, NY 14623, USA  Nor
Pirzkal Space Telescope Science Institute, 3700 San Martin Drive, Baltimore,
MD 21218, USA  Marc Rafelski Space Telescope Science Institute, 3700 San
Martin Drive, Baltimore, MD 21218, USA  Department of Physics and Astronomy,
Johns Hopkins University, Baltimore, MD 21218, USA  Swara Ravindranath Space
Telescope Science Institute, 3700 San Martin Drive, Baltimore, MD 21218, USA
Raymond C. Simons Space Telescope Science Institute, 3700 San Martin Drive,
Baltimore, MD 21218, USA  Gregory F. Snyder  Space Telescope Science
Institute, 3700 San Martin Drive, Baltimore, MD 21218, USA  Rachel S.
Somerville Center for Computational Astrophysics, Flatiron Institute, 162 5th
Avenue, New York, NY, 10010, USA  Elizabeth R. Stanway Department of Physics,
University of Warwick, Coventry, CV4 7AL, United Kingdom  Amber N. Straughn
Astrophysics Science Division, NASA Goddard Space Flight Center, 8800
Greenbelt Rd, Greenbelt, MD 20771, USA  Sandro Tacchella Kavli Institute for
Cosmology, University of Cambridge, Madingley Road, Cambridge, CB3 0HA, UK
Cavendish Laboratory, University of Cambridge, 19 JJ Thomson Avenue,
Cambridge, CB3 0HE, UK  Jonathan R. Trump Department of Physics, 196
Auditorium Road, Unit 3046, University of Connecticut, Storrs, CT 06269, USA
Jesús Vega-Ferrero Instituto de Astrofísica de Canarias, La Laguna, Tenerife,
Spain  Stephen M. Wilkins Astronomy Centre, University of Sussex, Falmer,
Brighton BN1 9QH, UK  Institute of Space Sciences and Astronomy, University of
Malta, Msida MSD 2080, Malta  Guang Yang Kapteyn Astronomical Institute,
University of Groningen, P.O. Box 800, 9700 AV Groningen, The Netherlands
SRON Netherlands Institute for Space Research, Postbus 800, 9700 AV Groningen,
The Netherlands  Jorge A. Zavala National Astronomical Observatory of Japan,
2-21-1 Osawa, Mitaka, Tokyo 181-8588, Japan

(Received October 27, 2022; Revised November 18, 2022; Accepted November 28,
2022)

###### Abstract

We present a comprehensive analysis of the evolution of the morphological and
structural properties of a large sample of galaxies at $z=3-9$ using early
JWST CEERS NIRCam observations. Our sample consists of 850 galaxies at $z>3$
detected in both HST/WFC3 and CEERS JWST/NIRCam images, enabling a comparison
of HST and JWST morphologies. We conducted a set of visual classifications,
with each galaxy in the sample classified three times. We also measure
quantitative morphologies across all NIRCam filters. We find that galaxies at
$z>3$ have a wide diversity of morphologies. Galaxies with disks make up 60%
of galaxies at $z=3$ and this fraction drops to $\sim$30% at $z=6-9$, while
galaxies with spheroids make up $\sim 30-40$% across the redshift range and
pure spheroids with no evidence for disks or irregular features make up $\sim
20\%$. The fraction of galaxies with irregular features is roughly constant at
all redshifts ($\sim 40-50\%$), while those that are purely irregular
increases from $\sim 12\%$ to $\sim 20\%$ at $z>4.5$. We note that these are
apparent fractions as many observational effects impact the visibility of
morphological features at high redshift. On average, Spheroid Only galaxies
have a higher Sérsic index, smaller size, and higher axis ratio than Disk or
Irregular galaxies. Across all redshifts, smaller spheroid and disk galaxies
tend to be rounder. Overall, these trends suggest that galaxies with
established disks and spheroids exist across the full redshift range of this
study and further work with large samples at higher redshift is needed to
quantify when these features first formed.

††journal: ApJL††facilities: JWST, HST††software: Astropy (Astropy
Collaboration et al., 2013), Drizzle (Fruchter & Hook, 2002), Galapagos-2
(Häußler et al., 2013), Galfit (Peng et al., 2010), GalfitM (Peng et al.,
2010; Häußler et al., 2013), Source Extractor (Bertin & Arnouts, 1996),
Statmorph (Rodriguez-Gomez et al., 2019),

##  1 Introduction

Between the era of early galaxy formation and today, galaxies have undergone
dramatic transformations in all respects. They have produced successive
generations of stars from clouds of molecular gas, continuously building up
their stellar populations, while enriching the interstellar medium with heavy
elements. The gas reservoir within galaxies changed as they converted a
fraction of their supply of cold molecular gas into stars and fresh gas was
replenished via inflow from the intergalactic medium (IGM). The overall star
formation rate (SFR) density of the universe grew until it reached a peak at
$z\sim 2-3$ (Madau & Dickinson, 2014) and then began to decline toward the
present day low levels. The growth in the stellar mass of galaxies coincided
with a change in their physical structure as the overall massive galaxy
population transitioned from disk-dominated spiral galaxies into bulge-
dominated elliptical galaxies. Throughout this assembly process, the central
supermassive black holes (SMBHs) of galaxies grew, leading to an established
relationship between SMBH and stellar mass (e.g., McConnell et al. 2012).
Tracking the evolution of the structural properties of galaxies can provide
key insights into the galaxy evolution pathways responsible for each of these
transformations. Probing the different physical processes driving the
formation of disks and bulges, the growth of SMBHs, the onset of star
formation, and its subsequent cessation during a critical time in the
universe’s history is important for testing theoretical galaxy formation
models.

Deep extragalactic surveys with the Hubble Space Telescope (HST) have
revolutionized our understanding of galaxy evolution between the peak epoch of
galaxy assembly 10 Gyr ago and today, but many open questions remain about the
early phases of evolution within the first 3 Gyr. When do we see the first
disks in galaxies in the early universe? At what point did the first bulges
form and do the physical processes responsible for their formation change with
redshift? Does the quenching of star formation precede or follow the
morphological transformation in these early galaxies?

To robustly address these questions, it is essential to push our observations
into the early universe, since most of our current understanding of galaxies
at high redshift has come from galaxies at $z=1-3$, the period of time
colloquially referred to as “cosmic noon.” Even though this represents a time
period 10 Gyr in the past, many galaxies at this time were already fairly
mature and had structures, such as disks and bulges in star-forming galaxies,
that generally resemble today’s galaxies (e.g., Tacchella et al. 2015;
Costantin et al. 2022a). Previous large morphological studies of galaxies have
typically been limited to galaxies at $z<3$ due to the fact that cosmological
surface brightness dimming makes faint features in high redshift galaxies hard
to detect and because the rest-frame optical emission that traces the broad
stellar populations in galaxies is shifted beyond the wavelength capabilities
of HST at higher redshifts.

Early morphological studies with WFPC2 and ACS on HST were ground-breaking,
quantifying for the first time the fraction of galaxies of various Hubble
types (i.e., barred and unbarred spirals, ellipticals, and irregular galaxies)
as a function of redshift, even beyond $z\sim 1$ (e.g., Abraham et al. 1996;
Giavalisco et al. 1996; Lowenthal et al. 1997; Conselice et al. 2000; Jogee et
al. 2004; Elmegreen et al. 2004; Sheth et al. 2008; Lotz et al. 2006;
Ravindranath et al. 2006). However, at $z>1$, these optical surveys probed the
rest-frame UV light of galaxies and found that very large fractions of distant
galaxies had peculiar or clumpy morphologies, which suggested at the time that
the Hubble sequence had not yet formed at these early times (e.g., Abraham et
al. 1996). Investigations using near-infrared observations with NICMOS,
sensitive to the rest-frame optical structure of galaxies, found that galaxies
beyond $z\sim 1$ presented a wide diversity of morphologies, including many
objects that were compact or irregular, but also those that were
morphologically mature spirals and ellipticals (e.g., van Dokkum & Franx 2001;
Stanford et al. 2004; Papovich et al. 2005; Daddi et al. 2005; Elmegreen et
al. 2005).

With the installation of WFC3 on HST in 2009, large samples of fainter
galaxies at cosmic noon were observed. CANDELS, the Cosmic Assembly Near-
infrared Deep Extragalactic Legacy Survey, (Koekemoer et al., 2011; Grogin et
al., 2011) obtained deep NIR imaging with WFC3 over a total of $\sim 800$
arcmin2. These observations showed that while galaxies at $z\sim 2$ were
overall messier and clumpier, with larger fractions of mergers and generally
irregular galaxies than today’s universe, the general underpinnings of the
Hubble sequence were already in place, i.e., a large fraction of star-forming
galaxies were overall disk-like and passive galaxies are overall compact or
spheroid-like (e.g., Wuyts et al. 2011; Lee et al. 2013; Mortlock et al. 2013;
van der Wel et al. 2014; Kartaltepe et al. 2015; Zhang et al. 2019). This
means that the first disks and spheroids must have begun to form at much
earlier times.

With its unprecedented sensitivity in the infrared, the James Webb Space
Telescope (JWST) is poised to make remarkable discoveries about this
transformative era in galaxy assembly and test key theoretical predictions of
our understanding of the physics of the early universe. The four pointings of
deep multi-band NIRCam observations taken in 2022 June from the Cosmic
Evolution Early Release Science (CEERS) survey (Finkelstein et al., in prep.)
provide the first opportunity for a comprehensive analysis of the structural
evolution of galaxies in the first 3 Gyr of the Universe’s history.

In this paper, we use these first CEERS observations to conduct an early
analysis of the evolution of galaxy morphology and structure for a large
sample of HST/WFC3-selected galaxies at $z=3-9$. This paper is organized as
follows. In Section 2, we describe the basics of the CEERS observations and
our data reduction, along with the ancillary data used to identify our sample
of galaxies at $z>3$. In Section 3, we describe our morphological
measurements, including visual classifications, parametric, and non-parametric
morphologies. We present our results in Section 4 and discuss their
implications in Section 5. Finally, we summarize our findings in Section 6.
Throughout this paper, all magnitudes are expressed in the AB system and we
assume a Chabrier (2003) Initial Mass Function (IMF). We also assume the
following cosmological parameters: $H_{0}=70~{}{\rm km~{}s^{-1}~{}Mpc^{-1}}$,
$\Omega_{tot}=1$, $\Omega_{\Lambda}=0.7$, and $\Omega_{M}=0.3$.

##  2 Data

###  2.1 CEERS Observations and Data Reduction

CEERS (Finkelstein et al., in prep.) is an Early Release Science (ERS) program
(Proposal ID #1345) that will observe the EGS (Extended Groth Strip, Davis et
al. 2007) extragalactic deep field (one of the five CANDELS fields; Koekemoer
et al. 2011; Grogin et al. 2011) early in Cycle 1 with data made available to
the public immediately. CEERS will obtain observations in several different
modes with JWST, including a mosaic of 10 pointings with NIRCam, NIRSpec
multi-object spectroscopic observations in parallel for six pointings, and six
pointings with MIRI in parallel. The NIRCam imaging of CEERS will cover a
total of 100 sq. arcmin with the F115W, F150W, F200W, F277W, F356W, F410M, and
F444W filters down to a 5$\sigma$ depth ranging from 28.8–29.7 (for a typical
total exposure time of 2835 s per filter). The first set of CEERS observations
were taken on 2022 June 21 in four pointings, hereafter referred to as CEERS1,
CEERS2, CEERS3, and CEERS6.

We performed an initial reduction of the NIRCam images in all four pointings
using version 1.7.2 of the JWST Calibration Pipeline111jwst-
pipeline.readthedocs.io with some custom modifications. We used the current
set of NIRCam reference files222jwst-crds.stsci.edu, jwst_0989.pmap,
jwst_nircam_0232.imap, though we note that the majority were created pre-
flight, including the flats. For details on the reduction steps, see Bagley et
al. (2022). To summarize briefly, we applied detector-level corrections using
Stage 1 of the pipeline with default parameters and used custom scripts to
remove $1/f$ noise, wisps, and snowballs from the count-rate maps. We then
processed the cleaned count-rate maps with Stage 2 of the pipeline and then
used a custom version of the TweakReg step to calibrate the astrometry. We
then coadded the images using the drizzle algorithm with an inverse variance
map weighting (Casertano et al., 2000; Fruchter & Hook, 2002) in the Resample
step in the pipeline. The final mosaics for each pointing in all filters have
pixel scales of 0$\farcs$03/pixel. We then used a custom script to background
subtract the images. These final background-subtracted images were used for
the morphology measurements described in Section 3.

###  2.2 CANDELS Images and Catalogs

For the analysis in this paper, before updated catalogs incorporating JWST
photometry are available, we use existing CANDELS v2 redshifts and stellar
masses for the HST F160W-selected galaxies in the EGS field. Here we provide a
brief summary of these measurements; for full details on the photometric
redshift measurements and resulting catalogs, see Kodra et al. (2022).

The v2 photometric redshifts and stellar masses are based on the CANDELS EGS
photometric catalog of Stefanon et al. (2017), which includes broadband TFIT
(Laidler et al., 2007) photometry from the UV to IR imaging from both ground-
and space-based telescopes. For this paper, we adopt the z_best column from
the Kodra et al. (2022) catalog, which provides the overall best estimate of
the redshift. This corresponds to the secure spectroscopic redshift if one is
available or the mFDa4_z_weight photometric redshift otherwise. We use this
photometric redshift value because it produces the most accurate confidence
intervals (see Kodra et al. 2022 for further details).

We then use the Stefanon et al. (2017) photometry and the above estimated
redshift to determine stellar masses for galaxies in the CANDELS EGS field.
This was done using two different codes: Dense
Basis333https://github.com/kartheikiyer/dense_basis and P12. Dense Basis (Iyer
& Gawiser, 2017; Iyer et al., 2019) is a python-based code that uses Flexible
Stellar Population Synthesis (FSPS) to generate model spectra that correspond
to a wide range of physically motivated non-parametric star formation
histories (SFHs), metallicities, and dust attenuation values. P12 (Pacifici et
al., 2012) is a Fortran-based code that uses a Bayesian fitting algorithm and
model SEDs generated using simple stellar population models and the
reprocessing of these models using the photo-ionization code CLOUDY (Ferland
et al., 1998). For more details on each of these two methods and detailed
comparisons, see Iyer & Gawiser (2017), Iyer et al. (2019), Pacifici et al.
(2012), and Pacifici et al. (2022).

We find that the stellar masses using these two methods agree well with one-
another, with the expected level of scatter (e.g., Pacifici et al. 2022). For
this paper, we use the mean of the two measurements. We note here the caveats
that these measurements are highly uncertain at the highest redshifts ($z>6$)
and the faintest magnitudes (F160W$>26$) because they rely on the HST ACS and
WFC3 photometry that do not trace the rest-frame optical light at these
redshifts. Future work will improve on these measurements with the addition of
JWST NIRCam fluxes.

In addition to the JWST CEERS images described above, we also use the existing
CANDELS ACS and WFC3 images to compare the HST and JWST morphologies. Here we
use the mosaics produced by the CEERS
team444https://ceers.github.io/releases.html#hdr1 with updated astrometry tied
to Gaia-EDR3 and a pixel scale of 0$\farcs$03/pixel.

###  2.3 Sample Selection

For the sample analyzed in this paper, we select all galaxies with z_best$>3$
within the CEERS1, CEERS2, CEERS3, and CEERS6 NIRCam SW or LW footprints. We
cross-match these sources with those identified in NIRCam imaging using the
Source Extractor segmentation map (see 3.1 for more details). We remove any
spurious sources in the NIRCam imaging, such as those that result from the
diffraction spikes of stars and those that are so close to the edge of the
images that we cannot obtain reliable morphology measurements. This results in
a total sample of 850 sources with a detection in any of the NIRCam filters.
We note here that at the magnitude limit of the CANDELS WFC3 images
(F160W$<27.6$, 5$\sigma$) almost all of these galaxies have S$/$N in the
NIRCam images high enough to enable morphological classifications, as
discussed further in Section 3.

Figure 1 shows the redshift and mass distribution for this sample of objects.
We note that ten of these objects have existing spectroscopic redshifts from
either the MOSDEF (Kriek et al., 2015) or DEEP2 (Newman et al., 2013)
spectroscopic surveys; we use these spectroscopic redshifts for these ten
objects. Overall, this sample peaks at $z\sim 3$ and has a long tail beyond
$z\sim 6$ out to $z\sim 9$. There are a total of 40 sources in this sample
with a photometric redshift of $z>6$. We note here that the redshifts and
stellar masses for these sources are uncertain, and some of these may turn out
to be at lower redshift. The redshift and stellar mass estimates will be
improved with the addition of JWST data to the SED modeling in the future.

Figure 1: Stellar mass versus redshift for the $z>3$ sample used in this
paper. Above and to the right are the distributions of redshift and stellar
mass, respectively. The horizontal dashed line at log(M$\star$/M⊙)=9
corresponds to the mass cut used for the subsample of objects in Section 4.1.

##  3 Measurements

###  3.1 Source Extractor Setup

Galaxies were detected in the NIRCam images using Source
Extractor555https://sextractor.readthedocs.io/ version 2.25.0 (Bertin &
Arnouts, 1996). The setup was optimized to detect the HST-selected $z>3$
galaxies without over-deblending. We created empirical PSFs for each filter by
stacking stars and then the F115W, F150W, F200W, and F277W images were PSF-
matched to the F356W image using the Python-based code
PyPHER666https://pypher.readthedocs.io/en/latest/ (Boucaud et al., 2016). In
order to detect galaxies that may be very faint in some of the NIRCam filters,
we used an inverse-variance weighted combination of the PSF-matched F150W,
F200W, F277W, and F356W images as the detection image. We first ran Source
Extractor in a “cold” mode to deblend nearby galaxies, then in “hot” mode to
detect faint galaxies, following Stefanon et al. (2017). We then combined the
“cold” and “hot” detections in order to keep all objects that were detected by
at least one mode, using a factor of 2.5 to enlarge the cold isophotes. We
visually inspected the segmentation map for the $z>3$ sources and optimized
the parameters to ensure that the sources were detected and adequately
deblended from nearby neighbors without being shredded. We use the final
segmentation map produced from this process for all of the measurements
presented in this section.

###  3.2 Visual Classifications

Each of the 850 galaxies in our $z>3$ sample were classified by three
different people from among a total of 35 members of our team. We used the
Zooniverse project builder777https://www.zooniverse.org/lab to host images of
each galaxy and designed a workflow of five tasks based on a modified version
of the classification scheme of Kartaltepe et al. (2015). These five tasks ask
classifiers to select options for the galaxy’s main morphology class, their
interaction class, various structural and quality flags, and finally to leave
any specific comments about a particular object. In this paper, we focus on
the first of these five tasks, the main morphology classification, which
roughly corresponds to the typical Hubble type classification. The options for
the main morphology class are: 1) Disk, 2) Spheroid, 3), Irregular / Peculiar,
4) Point Source / Unresolved, and 5) Unclassifiable / Junk. To reflect the
overall diversity seen in high redshift galaxies, these classes are not
mutually exclusive, so a classifier can choose multiple options to best
reflect the overall morphology of the galaxy. For example, a galaxy can have
both a disk and a spheroid in the case that it is a disk galaxy with a bulge
component. A galaxy can be both a disk and irregular, if for example it is an
asymmetric disk or a disk involved in an interaction. The exception is that if
a galaxy is ‘Unclassifiable’ then it cannot also be one of the other classes.
This level of complexity can make the interpretation of the various classes
challenging, but the extra information provides an important level of nuance
to the classifications.

Classifiers are presented with a collection of postage stamps for each galaxy
being classified. These stamps include each of the JWST NIRCam filters (F115W,
F150W, F200W, F277W, F356W, F410M, and F444W) at their native resolution, with
an asinh scaling to bring out low surface brightness features an RGB stamp
made up of the filters that correspond to the rest-frame optical, a version of
that stamp zoomed out by a factor of two, the NIRCam Source Extractor
segmentation map described above, three HST ACS/WFC3 filters (F814W, F125W,
and F160W, also with an asinh scaling), and finally an RGB stamp of these
three HST filters and a similarly zoomed out version. The stamps are scaled by
the size of the galaxy as measured by Source Extractor, following Equations 2
and 3 of Häussler et al. (2007), with a minimum size of 100$\times$100 pixels.
An example set of stamps for one of the galaxies is shown in the Appendix.

The classifiers are asked to make a holistic judgement about the overall
morphology of the galaxy, taking information across the full wavelength range
into account. In a separate task, the classifiers can select flags to indicate
that the morphology changes across the NIRCam filters or differs between JWST
and HST images.

###  3.3 Parametric Fits

We perform parametric fits on the NIRCam images using both Galfit
888https://users.obs.carnegiescience.edu/peng/work/galfit/galfit.html (Peng et
al., 2002, 2010) and GalfitM
999https://www.nottingham.ac.uk/astronomy/megamorph/ (Häußler et al., 2013).
Galfit is a least-squares fitting algorithm that finds the optimum Sérsic fit
to a galaxy’s light profile and GalfitM is a modified version that uses images
at different wavelengths to allow one to constrain the fit parameters that
vary smoothly as a function of wavelength. The benefit of using GalfitM is
that it fits all bands simultaneously and produces more physically consistent
models. We performed fits using both codes to test for self-consistency, but
since we use the rest-frame optical fit throughout this paper, we focus here
on the GalfitM fits and describe the Galfit fits in the Appendix.

GalfitM fits were performed using the IDL program Galapagos-2 from the
MegaMorph
Project101010https://www.nottingham.ac.uk/astronomy/megamorph/(Bamford et al.,
2011; Vika et al., 2013; Häußler et al., 2013, 2022). Galapagos-2 is a wrapper
that enables GalfitM to be run over larger survey images. We used the Source
Extractor setup described above with Galapagos-2. As input, we used all seven
NIRCam filters (F115W, F150W, F200W, F277W, F356W, F410M, and F444W) and used
the NIRCam WHT images produced by the JWST pipeline to create RMS images to be
used as the input sigma image. We used the F200W Source Extractor catalog for
initial guesses and used the NIRCam empirical PSFs. In addition to the final
output catalog with the Sérsic fit parameters, Galapagos-2 also outputs the
original stamp, the GalfitM model, and the residual image for each galaxy in
each filter. Out of the 850 $z>3$ galaxies in our sample, 37 (4%) were flagged
because GalfitM reached one of the constraint limits in one of the filters.

###  3.4 Non-parametric Measurements

We measure non-parametric morphologies using the Python package
Statmorph111111https://statmorph.readthedocs.io/en/latest/ (Rodriguez-Gomez et
al., 2019). For each NIRCam filter, we create 100$\times$100 pixel cutouts of
the 850 galaxies in our $z>3$ sample to use as input to Statmorph, along with
a cutout of the segmentation map, the empirical PSF, and the gain.

Statmorph measures a wide range of morphology statistics commonly used in
astrophysics. The ones that we use for the analysis in this paper are:
concentration ($C$), asymmetry ($A$), and clumpiness/smoothness ($S$)
(Bershady et al., 2000; Conselice et al., 2000; Conselice, 2003); the Gini
coefficient ($G$) and the second moment of the region of the galaxy containing
20% of the total flux ($M_{20}$) (Abraham et al., 2003; Lotz et al., 2004);
the Gini-M20 bulge, and merger statistics (Rodriguez-Gomez et al., 2019), the
signal-to-noise per pixel, and quality flags.

As for the parametric fits, we use the fits for the filter corresponding to
the rest-frame optical emission at the redshift of the galaxy: F277W for
galaxies at $z=3.0-4.0$, F356W for galaxies at $z=4.0-4.5$, and F444W for
galaxies at $z>4.5$. Of the 850 galaxies fit, 81% have a signal-to-noise per
pixel of $>2.5$ in the corresponding rest-frame optical filter; below this
value, the fit results may not be reliable (Lotz et al., 2006). We compare
these commonly used measures of galaxy morphology to our visual
classifications in Section 4.3.

Figure 2: NIRCam F150W+F277W+F356W postage stamp cutouts of a selection of
example galaxies in each of the seven morphological groups described in
Section4.1 at three different redshift bins. Each cutout is 2′′ on a side.

##  4 Results

###  4.1 Visual Classifications

For each of the 850 galaxies in our $z>3$ sample, we assign a galaxy a given
visual classification if two out of three people select a given option as the
main morphological class. There is only one object in our sample for which all
three classifiers disagree, meaning one selected only ‘disk’, one selected
only ‘spheroid’, and one selected only ‘irregular. This object is therefore
not included in any of the figures presented here. As noted above, since the
main morphological classes are not mutually exclusive, various combinations
are possible. Throughout this paper, we break things down into the following
non-exclusive morphological groups (highlighted in Figure 2):

Figure 3: The fraction of $z>3$ galaxies detected by both JWST and HST with
M${}_{\star}>10^{9}$ M⊙ as a function of redshift for each morphology class.
The top row, from left to right shows galaxies with disks, galaxies with
spheroids, galaxies with irregular features, and Point Sources and
Unclassifiable galaxies. The bottom row shows all of the same morphological
groups, but divided in different ways for easy comparison. From left to right,
the combination of all disks, all spheroids, and all irregulars; the
combination of Disk Only, Spheroid Only, and Irregular Only groups; and
finally, the remaining mixed groups. Error bars represent the 1$\sigma$
binomial confidence limits given the number of objects in each category,
following the method of Cameron (2011).

  * •

Galaxies with Disks: The Disk category contains galaxies classified as Disk
Only (without a spheroid or irregular component), Disk+Spheroid (a galaxy with
both a disk and spheroid component; a separate structural flag indicates
whether the disk or the bulge is dominant), Disk+Irregular (a disk galaxy with
irregularities such as asymmetries, a warp, or disturbance by a nearby
companion), Disk+Spheroid+Irregular (a disk galaxy with a spheroid component
that also has some irregularities; note that these are fairly rare). When we
refer to ‘All Disks’ we are referring to the sum of the galaxies in all of
these categories.

  * •

Galaxies with Spheroids: The Spheroid category contains galaxies classified as
Spheroid Only (without a disk or irregular component), Spheroid+Disk (same as
Disk+Spheroid above), Spheroid+Irregular (a spheroid galaxy with
irregularities such as asymmetries or surrounding low surface brightness
features), Spheroid+Disk+Irregular (same as Disk+Spheroid+Irregular above).
When we refer to ‘All Spheroids’ we are referring to the sum of the galaxies
in all of these categories.

  * •

Galaxies with Irregular Features: The Irregular category contains galaxies
classified as Irregular Only (no discernible disk or spheroid component),
Irregular+Disk (same as Disk+Irregular above), Irregular+Spheroid (same as
Spheroid+Irregular above), Irregular+Disk+Spheroid (same as
Disk+Spheroid+Irregular above). When we refer to ‘All Irregulars’ we are
referring to the sum of the galaxies in all of these categories. Note that the
Irregular category may include merging or interacting systems, but also
galaxies that are irregular for other reasons, such as clumpy star formation.
Mergers and Interactions themselves will be discussed in a future paper (Rose
et al., in prep).

Our sample contains the full range of morphological types across all redshift
and stellar masses. Over the entire redshift range, only 16 and 18 galaxies
are classified as Point Source/Unresolved or Unclassifiable, respectively.
Figure 3 shows the fraction of the total number of galaxies that each
morphological class makes up as a function of redshift. For a fair comparison
across redshifts, we limit this to the 666 galaxies with stellar masses
greater than $10^{9}$ M⊙ since the galaxies with lower stellar masses are only
present at the low redshift end of our sample (see Fig. 1). We emphasize that
this represents a mass-selected sample but not a mass-complete sample, as
there are likely to be many galaxies identified by JWST in this mass range
that are undetected by CANDELS HST imaging.

Overall, 56% of the galaxies above this mass cut at $z>3$ have a visually
identifiable disk component, dropping from $\sim 60\%$ at $z=3-4$, to $\sim
45\%$ at $z\sim 5$, to $\sim 30\%$ at $z>6$. Again, we note that the
photometric redshifts at $z>6$ are uncertain, so some fraction of these
sources may actually be at lower redshift; we caution the reader when
interpreting the results at $z>6$. The Disk Only and the Disk+Irregular groups
each make up $\sim 20\%$ (and slightly less at $z>6$) while the Disk+Spheroid
group makes up $\sim 10\%$ and the Disk+Spheroid+Irregular group makes up
$<5\%$. 38% of the galaxies at $z>3$ have a visually identifiable spheroidal
component, decreasing from 42% to 26% between $z=3$ and 4.5, then varying
between $\sim$30-40% beyond $z=4.5$. This is largely driven by the similar
decrease then increase in the Spheroid Only group. Part of this apparent trend
at higher redshifts may be due to small number statistics and part may be due
to a number of selection effects. For example, there is a possibility that we
miss fainter extended features in some of these systems at high redshift. It
is also possible that a larger fraction of galaxies at higher redshift are
small enough to be at the resolution limit of NIRCam, given the expected size
evolution of galaxies, and are therefore more round and compact in appearance.

Figure 4: Stacked histograms illustrating the distributions of the rest-frame
optical Sérsic Index ($n$), effective radius (Re), and axis ratio ($b/a$) for
the $z>3$ galaxy sample. The colors indicate the different combinations of the
main morphological class chosen by two out of three people during the visual
classifications, as described in Section 4.1 and Figure 2.

43% of the galaxies at $z>3$ have irregular features and this fraction remains
roughly constant across the full redshift range due in part to the fraction of
Disk+Irregular galaxies being roughly constant at $20\%$ and then decreasing
while the fraction of Irregular Only galaxies is at roughly $10-15$% and then
increases to $20\%$ by $z=4.5$. Note that the total fractions of objects that
are All Disks, All Spheroids, or All Irregular do not add up to one due to the
overlapping objects in each of these classes.

Finally, we note that the fraction of point sources and unclassifiable objects
remains at $0-2\%$ across most of the redshift range. At $z>6$, 13% of
galaxies are unresolved and 8% are unclassifiable, corresponding to 5 and 3
individual galaxies, respectively, in this redshift bin. We remind the reader
that the above percentages correspond to galaxies that were bright enough to
be detectable with HST CANDELS imaging and may not be representative of the
overall galaxy sample detectable by JWST at these redshifts.

###  4.2 Comparison with Surface Brightness Profile Fits

One of the major advances that JWST NIRCam imaging brings to morphological
analyses of galaxies is that the broad wavelength coverage enables us to probe
the rest-frame optical morphologies of galaxies across a wide redshift range.
As described in Section 3.3, we used GalfitM to perform multiwavelength
parametric fits across all of the NIRCam filters. For a fair comparison of
these parameters at different wavelengths, we select the NIRCam filter closest
to the rest-frame optical at different redshifts. Throughout this section, we
use the F277W filter for galaxies at $3.0<z<4.0$, F356W for galaxies at
$4.0<z<4.5$, and F444W for galaxies at $z>4.5$. We compare these measurements
to the visual classifications in Figures 4 and 5.

Figure 5: The rest-frame optical Sérsic index ($n$) plotted as a function of
the effective radius (Re) in kpc (top left) and the axis ratio ($b/a$) (top
right) for the $z>3$ galaxy sample. The axis ratio plotted as a function of
the effective radius is shown on the bottom left. The colors of each point
indicate the different combinations of the main morphological class chosen by
two out of three people during the visual classifications, as described in
Section 4.1 and Figure 2. The median values for each group are shown as stars
with the error bars representing the 16th-84th percentile range of the
distribution.

Overall, the distribution of Sérsic indices (where $n=0.5$ corresponds to a
Gaussian profile, $n=1$ to an exponential profile, and $n=4$ to a de
Vaucouleurs profile) tracks with the expectations from the visual
classifications. Disk galaxies with no apparent spheroid or irregular features
(Disk Only) peak at low Sérsic indices with a long tail out to higher values,
as expected ($\langle n\rangle=1.16^{+0.88}_{-0.40}$, where the error bar
denotes the 16th-84th percentile range of the distribution). Galaxies that are
pure spheroids (Spheroid Only) have a much broader distribution and peak at
higher $n$ ($\langle n\rangle=2.46^{+2.32}_{-1.41}$) as has been noted at
lower redshift based on HST imaging (e.g., Vika et al. 2015; Kartaltepe et al.
2015). Galaxies with both a disk and spheroidal component (Disk+Spheroids)
peak at intermediate values ($\langle n\rangle=2.39^{+1.06}_{-0.98}$). This
illustrates that a cut at a fixed Sérsic index would not cleanly select disk
or spheroidal dominated galaxies. For example, a dividing line of $n=2$ would
identify 71% of the visually identified disks and only 45% of the visually
identified spheroids. However, it is worth noting that that a fraction of the
objects visually identified as spheroids with low $n$ might have extended low
surface brightness disks that are difficult to pick out by eye.

Irregular galaxies with no disk or spheroid component peak at very low $n$,
with a substantial fraction at $n<1$ and a long tail out to higher values
($\langle n\rangle=1.19^{+1.51}_{-0.82}$). Disk galaxies with irregular
features (Disk+Irregular) peak closer to $n=1$ with a narrower distribution
that more closely resembles that of the Disk Only galaxies. Likewise, the
distribution of Sérsic indices for the spheroidal galaxies with irregular
features (Spheroid+Irregular) closely resembles that of the Spheroid Only
group. A visual inspection of the models and residuals for the irregular
galaxy population reveals that, unsurprisingly, irregular features are not
well-fit by a Sérsic profile. For disks and spheroidal galaxies with irregular
features, the model fits the disk/spheroidal component well and leaves behind
features in the residuals, while the irregular only population are not well-
fit at all. We caution the reader against over-interpreting Sérsic indices for
irregular galaxies and against using Sérsic indices to select disk galaxies
without first checking the images (and residuals) for irregular features.

Figure 6: The rest-frame optical axis ratio ($b/a$) plotted as a function of
the effective radius (Re) in kpc in six different redshift bins. The colors
indicate the different combinations of the main morphological class chosen by
two out of three people during the visual classifications, as described in
Section 4.1 and Figure 2. The individual sources are shown as transparent
circles and the median for each group is shown as a star with the 16th-84th
percentile range of the distribution shown as error bars.

The center panel of Figure 4 shows the distribution of sizes (effective radii,
Re) measured by GalfitM for each of the morphological types. Galaxies with
disks and irregular features generally have larger sizes than those with
spheroids. For example, galaxies with disks only have a median effective
radius of 1.19${}^{+0.61}_{-0.48}$ kpc, irregular only have
$\langle$R${}_{e}\rangle=1.32^{+0.79}_{-0.57}$ kpc, while spheroid only
galaxies have $\langle$R${}_{e}\rangle=0.54^{+0.42}_{-0.32}$ kpc. These trends
are seen more clearly in Figure 5. The Disk+Irregular and
Disk+Spheroid+Irregular groups have size distributions that more closely match
the distribution for the Irregular Only group, while the Spheroid+Irregular
group has a smaller median size
($\langle$R${}_{e}\rangle=0.74^{+0.81}_{-0.34}$ kpc).

The distribution of the axis ratios is shown in the right panel of Figure 4
and in 5 and offers another way to compare our visual morphologies to a
quantitative measurement. A population of disks with exponential profiles and
random orientations is expected to have a relatively flat distribution of axis
ratios that falls off at low values, while triaxial ellipsoids are expected to
have a distribution that is peaked at higher values, $b/a\sim 0.6$, (e.g.,
Elmegreen et al. 2005; Ravindranath et al. 2006; Padilla & Strauss 2008; Law
et al. 2012; Robertson et al. 2022). The mean values we see for the different
morphological groups follows this trend. The Spheroid Only group has the
largest median axis ratio ($0.64^{+0.17}_{-0.18}$), while the Disk+Irregular
group has the smallest (0.36${}^{+0.21}_{-0.11}$).

Figure 6 shows the axis ratio as a function of effective radius split into
several redshift bins. In each redshift bin from $z=3$ to $z=6$, the Spheroid
Only galaxies have the smallest median effective radius and the largest median
axis ratios, suggestive of a population of true triaxial ellipsoids. Overall,
we see the general trend of small galaxies being rounder, for all morphology
types, as seen by Padilla & Strauss (2008) and Zhang et al. (2019), in each of
these redshift bins. We cannot draw any conclusions at $z>6$ due to the small
sample size and the previously mentioned uncertainties.

Figure 7: Top: The rest-frame optical asymmetry value as a function of
concentration for all galaxies at $z>3$ split by all disk galaxies (left),
spheroids (center), and irregulars (right). The dash-dotted lines show the
boundaries between disk galaxies, elliptical galaxies, and intermediate
galaxies from Bershady et al. (2000) and the dashed line is the dividing line
above which nearby galaxies are expected to be major mergers ($A=0.35$;
Conselice 2003). Bottom: The rest-frame optical Gini value as a function of
M20 for all galaxies at $z>3$ split by all disk galaxies (left), spheroids
(center), and irregulars (right). The lines show the boundaries between disk
and elliptical galaxies (dash-dotted), and mergers (dashed) from Lotz et al.
(2008b). The colors indicate the different combinations of the main
morphological class chosen by two out of three people during the visual
classifications, as described in Section 4.1 and Figure 2.

Figure 8: The rest-frame optical asymmetry value as a function of
concentration (left) and The rest-frame optical Gini value as a function of
M20 (right) for all galaxies at $z>3$, color coded by the median Sérsic index
in each bin. The dividing lines are the same as described in Figure 7

###  4.3 Comparison with Non-parametric Measures

As described in Section 3.4, we used statmorph to measure non-parametric image
statistics for all of the HST-selected $z>3$ galaxies in our sample across all
NIRCam filters. We use the same filters corresponding to the rest-frame
optical emission for each galaxy as we did for the above parametric
comparison. In total, 81% of the galaxies in our sample have a reliable fit
from statmorph; Figures 7 and 8 highlight two of the commonly used methods to
separate galaxies into the standard Hubble types and identify mergers based on
these image statistics (e.g., Bershady et al. 2000; Conselice 2003; Lotz et
al. 2004, 2008b).

The top panel of Figure 7 shows the location of each galaxy on the asymmetry-
concentration plane, with the classic lines used to mark the boundaries
between disk galaxies, elliptical galaxies, intermediate galaxies, and mergers
for moderate redshift HST images (Bershady et al., 2000). While these
boundaries do not cleanly separate $z>3$ galaxies into different types
relative to their visual classifications, a few trends can be seen. On
average, galaxies with a spheroid have a higher concentration (C) than those
with disks and irregulars. Similarly, irregular galaxies have a higher
asymmetry value (A), on average. Very few galaxies lie above the classic
demarcation for mergers (Conselice, 2003), and those that do span the full
range of visual morphologies, albeit with a higher fraction of irregulars.
Figure 8 shows the distribution of the same galaxies on this plane, but color
coded by the median Sérsic index for each bin. This distribution highlights
the correlation of the concentration value with the Sérsic index for the
sample.

The bottom panel of Figure 7 shows the location of each galaxy on the Gini-M20
plane. The lines mark the boundary between disk galaxies, ellipticals/S0s, and
mergers based on nearby galaxies and then adjusted for galaxies at $z\sim 1$
(Lotz et al., 2004, 2008b). While there is no discernible difference between
$z>3$ disks and spheroids with this diagnostic (similar to what has been seen
at lower redshift and in simulations, e.g., Lotz et al. 2008a; Kartaltepe et
al. 2010; Pearson et al. 2019), irregular galaxies have higher Gini and M20
values, on average. 28% of galaxies with irregular features lie above the
merger line, whereas only 17% of disks and 15% of spheroids do. The right-hand
panel of Figure 8 shows the distribution of the same galaxies on this plane,
but color coded by the median Sérsic index for each bin. Galaxies that occupy
the ellipticals/S0 portion of this plane have a higher Sérsic index, on
average. A significant fraction of the galaxies in the merger region of the
plane also have a higher average Sérsic index.

##  5 Discussion

###  5.1 Disks and Spheroids in the Early Universe

We find that galaxies detected by both HST and JWST in the $z>3$ universe have
a wide diversity of morphologies. Overall, $\sim$60% of these galaxies have
disks (including those with spheroids and/or irregular features as well) at
$z=3-4$, and this fraction has an apparent downward trend with increasing
redshift. Other early JWST studies have identified candidate disk galaxies at
these redshifts (e.g., Ferreira et al. 2022a, b; Robertson et al. 2022) and
find similar fractions. Galaxies with spheroids make up $\sim$40% over this
redshift range, with some variations in the higher redshift bins that are
likely related to the the small numbers in these bins overall or the
difficulty in identifying low surface brightness features at these redshifts.
Galaxies with a pure spheroid, i.e., without discernible disks or irregular
features, make up $\sim 20\%$ across the full redshift range, roughly
consistent with the findings of Ferreira et al. (2022a). While the fraction of
all galaxies with irregular features is roughly constant at all redshifts
($\sim$40-50%), the fraction of galaxies that are purely irregular (i.e.,
those that have no discernible disk or spheroidal features) increases from
$\sim 12\%$ at $z=3-3.5$ to $\sim 20\%$ at $z>4.5$ This fraction is lower than
that reported by Ferreira et al. (2022a, b). Slight differences among these
early studies likely arise due to the different classification schemes and
mass ranges used.

The distribution of axis ratios and sizes presented in Section 4.2 and Figures
5 and 6 suggest that our $z>3$ sample indeed contains a mix of true disks and
spheroids. The distribution of axis ratios for the galaxies classified as
Spheroid Only is consistent with that expected from a population of triaxial
ellipsoids, while the relatively flat distribution and lower median for the
Disk galaxies is expected for a population of disks with exponential profiles
and random orientations (e.g., Padilla & Strauss 2008). The axis ratio
distribution for the Spheroid Only group peaks at $b/a>0.6$ across the entire
redshift range of our sample, while the median for the Disk Only group remains
at $b/a\sim 0.4$. The Disk+Spheroid and Spheroid+Irregular populations have
axis ratio and size distributions that are intermediate. Previous theoretical
work has found that galaxy shapes have evolved over time from prolate to
oblate as they transition from having dark matter dominated interiors to
baryonic matter interiors following a compaction event (e.g., Ceverino et al.
2015; Tomassetti et al. 2016) and that this transition happens earlier for
more massive galaxies (e.g., Zhang et al. 2019).

Figure 9: The fraction of $z>3$ galaxies detected HST with
M${}_{\star}>10^{9}$ M⊙ as a function of redshift for each morphology class
based on the CANDELS HST visual classifications of Kartaltepe et al. (2015).
The top row, from left to right shows galaxies with disks, galaxies with
spheroids, galaxies with irregular features, and Point Sources and
Unclassifiable galaxies. The bottom row shows all of the same morphological
groups, but divided in different ways for easy comparison. From left to right,
the combination of all disks, all spheroids, and all irregulars; the
combination of Disk Only, Spheroid Only, and Irregular Only groups; and
finally, the remaining mixed groups. Error bars represent the 1$\sigma$
binomial confidence limits given the number of objects in each category,
following the method of Cameron (2011). Figure 10: The fraction of $z>3$
galaxies detected HST with M${}_{\star}>10^{9}$ M⊙ as a function of redshift
for each morphology class based on the CEERS JWST visual classifications (as
in Fig. 3) compared to the CANDELS HST visual classifications of Kartaltepe et
al. (2015) (as in Fig. 9) for the 59 objects from EGS that were bright enough
to be classified. From left to right, the combination of all disks, all
spheroids, and all irregulars; the combination of Disk Only, Spheroid Only,
and Irregular Only groups; and finally, objects that were unclassifiable or
point sources. Error bars represent the 1$\sigma$ binomial confidence limits
given the number of objects in each category, following the method of Cameron
(2011).

It is worth noting that selection effects may be partially responsible for the
axis ratio distributions observed for these objects. For example, it has been
shown that the distribution of axis ratios has a strong dependence on the mass
and luminosity of the galaxy population (e.g., Padilla & Strauss 2008; Zhang
et al. 2019). Galaxy orientation also plays an important role in this
distribution, as face-on disks may be more difficult to detect than edge-on
disks at the magnitude limit (e.g., Elmegreen et al. 2005) and the presence of
dust can impact the measured axis ratios (Padilla & Strauss, 2008). The size
of the current sample does not allow binning by mass, luminosity, or finer
morphology groupings, however, future work with larger sample sizes will allow
greater exploration of this parameter space.

To summarize, we see evidence for galaxies with established disks and
spheroidal morphologies across the full redshift range of our sample. We
emphasize that the fractions quoted here are apparent fractions only and that
several observational effects likely play a role in these measurements (as
discussed in Section 5.2). Further work is needed to quantify our ability to
pick out disk features and resolve spheroidal galaxies in JWST images at
varying image depths in order to quantify the true fraction of galaxies with
disks and spheroids at these redshifts. Likewise, larger samples, particularly
at $z>6$, will be needed to truly establish when the first disks began to
form, when disks grew their bulges, and when spheroids emerged.

###  5.2 Comparison between JWST and HST Morphologies

Figure 9 shows the morphological fractions as a function of redshift based on
CANDELS HST imaging and using the visual classifications of Kartaltepe et al.
(2015) for all $z>3$ galaxies in all five CANDELS fields (1375 galaxies in
total). The HST classifications were limited to galaxies with F150W$<24.5$,
because fainter galaxies could not be reliably classified, and so only 59
galaxies out of the 850 in our sample are bright enough to make that cut. A
comparison of the JWST morphological fractions with those 59 specific galaxies
are shown in Figure 10. Based on the HST imaging alone, a smaller fraction of
galaxies at $z=3.0-4.5$ have disks ($\sim$40%) and a larger fraction are pure
spheroids ($\sim$20% at $z=3.0-5.0$). The fraction of galaxies that are only
irregular is small and drops with redshift, from $\sim$5% at $z>6$. The
fraction of galaxies that are unclassifiable rises sharply, $\sim$5% at
$z=3.5$ to $\sim$35% at $z=5.5$ to $\sim$80% at $z>7$. Likewise, $\sim$30% are
unresolved at $z=5-7$. Among the 59 galaxies with both HST and JWST
classifications, a higher fraction is classified as a spheroid and a lower
fraction is classified as a disk or irregular with HST than with JWST. At
$z>6$, all of the objects were unclassifiable or unresolved with HST.

The large difference seen between the HST and JWST morphologies at these
redshifts is expected and is due to the difference in depth and wavelength
coverage. 488 galaxies were flagged by at least one classifier as having a
different morphology in the JWST images compared to the HST images (159
galaxies were flagged by two out of three classifiers, see Fig. 11 for
examples). A significant number of galaxies with disks were previously
identified as spheroids because of their compact central morphologies, with
low surface brightness disk features that only became visible with deeper
imaging (see, for example, Conselice et al. 2011; Mortlock et al. 2013;
Kartaltepe et al. 2015). This suggests that some fraction of the spheroidal
galaxies observed with JWST, particularly those that are faint and/or at
higher redshifts, possibly have unobserved disks as well. It is not likely
that these disks would previously have been identified as irregular, except
for some at the low redshift end, as these irregular features are also too
faint to be easily identified at these redshifts with HST. At the low redshift
end ($z=3-3.5$), some disks may have been classified as irregular if the the
HST data only only picked up the brighter star forming clumps rather than the
underlying disk structure.

To explore the impact of observed wavelength on the classifications,
classifiers were also asked to flag objects for which their morphology changes
(i.e., they would have selected different main morphology classes) between the
NIRCam short wavelength filters and the long wavelength filters. At least one
classifier chose this flag for 190 galaxies and two out of three chose it for
37 (see Fig. 11 for examples). Note that some of the difference seen across
the different filters may be due in part to the increased resolution of the
short wavelength bands. This flag was rarely chosen, suggesting that the depth
of the JWST images is the primary driver in the morphological differences
observed between JWST and HST.

Figure 11: HST and JWST postage stamps for five example galaxies with
different morphologies in HST F160W images compared to JWST images or
differences across the JWST filters. The F150W, F277W, and F356W filters are
shown along with an RGB combination of these three filters. Each stamp is 2′′
on a side.

###  5.3 Comparison with Expectations from Theory

Figure 12: The distribution of Sérsic index, size, axis ratio, and asymmetry
of the $z>3$ CEERS galaxy in three different redshift bins compared to the
distribution from the CEERS mock catalog derived from the Santa Cruz Semi-
analytic model (blue dotted line; Somerville et al. 2015, 2021; Yung et al.
2019, 2022), measurements from mock images based on IllustrisTNG50 (red dashed
line; Costantin et al. 2022b), and measurements from mock images based on
IllustrisTNG100 (orange dash-dotted line; Rose et al. 2022).

We compare the results of our surface brightness profile fits and non-
parametric fits with the results from mock images and catalogs based on
several different simulations in Figure 12 in three different redshift bins.

First, we use a mock galaxy catalog based on the Santa Cruz Semi-analytic
model (SAM) and publicly available as part of the CEERS simulated data release
SDR3121212https://ceers.github.io/sdr3.html#catalogs. The CEERS mock galaxy
catalog is an augmented version of the EGS lightcone presented by Yung et al.
(2022), which spans 782 arcmin2 between $0<z\lesssim 10$ and contains galaxies
$-16\gtrsim M_{\rm UV}\gtrsim-22$. The physical properties of the galaxies are
modeled with the physics-based Santa Cruz SAM (Somerville et al., 2015, 2021;
Yung et al., 2019). The sizes of the disk components of galaxies are computed
based on the ansatz that the specific angular momentum of the halo gas is
equal to that of the dark matter halo, and that it is conserved during disk
formation (Mo et al., 1998; Somerville et al., 2008).

We also use the publicly
available131313https://ceers.github.io/ancillary_data.html mock images and
derived morphological catalogs of Costantin et al. (2022b) and Rose et al.
(2022), which use the IllustrisTNG cosmological
simulation141414https://www.tng-project.org/. The IllustrisTNG project
(Springel et al., 2018; Naiman et al., 2018; Nelson et al., 2018; Pillepich et
al., 2018; Marinacci et al., 2018) is a series of large cosmological
magnetohydrodynamical simulations of galaxy formation and is an update to the
original Illustris-1 simulation (Vogelsberger et al., 2014). It consists of
three different runs that span a range of cosmological volumes and
resolutions: TNG50, TNG100, and TNG300.

Costantin et al. (2022b) produced synthetic images of TNG50 galaxies and used
MIRaGe151515https://github.com/spacetelescope/mirage (Multi Instrument Ramp
Generator) to simulate raw NIRCam images for the CEERS depth and filter
combination. These images were then reduced using the JWST pipeline.
Morphological measurements were made using statmorph. Rose et al. (2022)
produced noiseless synthetic images with TNG100 galaxies using the public
visualization API (Nelson et al., 2019) in each of the CEERS filters. These
images were then convolved with the model PSF for each filter using WebbPSF
(Perrin et al., 2014). Poisson noise and background noise estimated from the
JWST exposure time calculator (Pontoppidan et al., 2016) were added to create
mock images at the CEERS depth. Parametric models were fit using Galapagos-2
and GalfitM while non-parametric fits were performed using statmorph.

Figure 12 compares the distribution of the Sérsic indices and sizes of
galaxies from the SAM, TNG50, and TNG100 to the distribution measured from
CEERS galaxies (Section 4.2 and Figure 4). The overall distributions from the
SAM have very similar peaks with a narrower distribution, which holds for all
three redshift bins. The Sérsic index for both TNG50 and TNG100 peak at lower
values than the CEERS galaxies and have narrower distributions at all
redshifts. At $z=3-4$, TNG50 galaxies have larger sizes than TNG100 galaxies
and even larger than both the SAM galaxies and the observed CEERS galaxies. At
$z>4$ the distributions match more closely. At all redshifts, the simulations
do not contain the smaller (lower Re) more compact (larger $n$) galaxies that
we observe with JWST CEERS imaging.

Figure 12 also compares the measured axis ratio and asymmetry value for the
TNG50 and TNG100 galaxies to the distribution from CEERS. In all three
redshift bins, the axis ratios of the TNG50 and TNG100 galaxies match each
other well, but peak at higher $b/a$ ($\sim$0.6) and fall off more sharply at
lower values than the observed CEERS galaxies. At $z=3-4$, the asymmetry
distributions for TNG50, TNG100, and CEERS are well-matched, but the TNG50 and
TNG100 distributions shift toward lower (more negative) values at higher
redshift. Negative asymmetry values are unphysical and typically result from
low $S/N$ sources, where the source is very close to the background level that
is being subtracted when making the asymmetry measurement.

Overall, the agreement between our measurements for the $z>3$ JWST CEERS
galaxies and the various simulations is encouraging. The differences seen (for
example, the difference in axis ratio and the lack of small compact galaxies
in the simulations) are worthy of a more in-depth look in order to determine
if there are selection effects impacting the results or if there is an actual
physical difference between galaxies in these simulations and those in the
real observed universe.

##  6 Summary and Conclusion

In this work, we have conducted a comprehensive analysis of 850 $z>3$ galaxies
detected in both HST CANDELS imaging of the EGS field and JWST CEERS NIRCam
imaging. These galaxies were visually classified by three people each, their
parametric morphologies were measured using Galfit and Galapagos-2/GalfitM,
and their non-parametric morphologies were measured using statmorph. Our
visual classification scheme contains classes that are intentionally not
mutually exclusive so that we can track the properties of galaxies with
different components separately. We compare our results to morphology
measurements based on the HST imaging alone, as well as several cosmological
simulations. Our results are summarized as follows:

  1. 1.

Galaxies detected by both HST and JWST in the $z>3$ Universe have a wide
diversity of morphologies. Galaxies that have disks make up a large fraction
of our sample at all redshifts, from $\sim 60\%$ at $z=3-4$ to $\sim 30\%$ at
$z>6$. Galaxies with spheroids make up $\sim 40\%$ across the full redshift
range, while pure spheroids without a disk component or irregular features
make up $\sim 20\%$. The fraction of galaxies with irregular features is
roughly constant at all redshifts ($\sim 40-50\%$), while those that purely
irregular (with no evidence for a disk or spheroidal component) increase from
$\sim 12\%$ at $z=3.0-3.5$ to $\sim 20\%$ at $z>4.5$.

  2. 2.

Significant differences are seen between JWST morphologies and the HST
morphologies for the same galaxies. With only HST imaging, a smaller fraction
of galaxies at $z>3$ have disks, spheroid, or irregular features overall due
to the larger fraction, particularly at $z>4.5$, that are unresolved or
unclassifiable. For resolved classifiable galaxies, the observed difference in
classification is largely driven by low-surface brightness disks being too
faint to capture in the HST imaging.

  3. 3.

The distributions of Sérsic index, size, and axis ratios show significant
differences between the different morphological groups, as expected. The
spheroid population has a broad distribution of Sérsic index, and therefore,
Sérsic index cannot be used to cleanly separate disk-dominated from Spheroid-
dominated galaxies, as has been shown previously based on HST imaging.
Galaxies with a spheroid tend to be smaller, on average, than galaxies with
disks or irregular features.

  4. 4.

The distribution of axis ratios for the Spheroid Only galaxies peaks at high
values and is consistent with a triaxial population. The Disk Only, Irregular
Only, and Disk+Irregular galaxies peak at lower values with an overall broad
distribution, while the Disk+Spheroid and Spheroid+Irregular groups are
intermediate. In general, smaller galaxies tend to be rounder.

  5. 5.

While classical classification boundaries using non-parametric measures such
as concentration, asymmetry, Gini, and M20 do not cleanly separate galaxies by
their morphological type, galaxies with a spheroid have a higher
concentration, on average, than disks and irregulars, while irregular galaxies
have a higher mean asymmetry value. Irregular galaxies also have higher Gini
and M20 values on average and are slightly more likely than disks or spheroids
to lie above the merger selection line.

  6. 6.

The distribution of Sérsic index, size, axis ratio, and asymmetry of the $z>3$
sample is overall well-matched by the distributions from the CEERS mock
catalog derived from the Santa Cruz Semi-analytic model, and measurements from
mock images based on IllustrisTNG50 and IllustrisTNG100 galaxies. The
simulations do not have the small compact galaxies that we observe in CEERS.
The axis ratio distribution for TNG50 and TNG100 galaxies peaks at higher
$b/a$ and drops off more sharply at lower values than the CEERS galaxies.

Overall, these trends suggest that galaxies with established disks and
spheroidal morphologies exist across the full redshift range of this study.
Future work with larger samples that capture many more galaxies at the high
redshift end in conjunction with observations that can probe their dynamical
nature are needed to fully explore the parameter space, understand how these
disks and spheroids compare to today’s, and quantify the emergence of the
first disks and spheroids.

Support for this work was provided by NASA through grant JWST-ERS-01345.015-A
and HST-AR-15802.001-A awarded by the Space Telescope Science Institute, which
is operated by the Association of Universities for Research in Astronomy,
Inc., under NASA contract NAS 5-26555. This research is based in part on
observations made with the NASA/ESA Hubble Space Telescope obtained from the
Space Telescope Science Institute, which is operated by the Association of
Universities for Research in Astronomy, Inc., under NASA contract NAS 5–26555.
JSK would like to acknowledge the important contributions of her cats, T’Pol
and Shran, who diligently attended every telecon and assisted in paper
editing. MHC acknowledges financial support from the State Research Agency
(AEIMCINN) of the Spanish Ministry of Science and Innovation under the grant
and “Galaxy Evolution with Artificial Intelligence” with reference
PGC2018-100852-A-I00, from the ACIISI, Consejería de Economía, Conocimiento y
Empleo del Gobierno de Canarias and the European Regional Development Fund
(ERDF) under grant with reference PROID2020010057, and from IAC project
P/301802, financed by the Ministry of Science and Innovation, through the
State Budget and by the Canary Islands Department of Economy, Knowledge and
Employment, through the Regional Budget of the Autonomous Community The
authors acknowledge Research Computing at the Rochester Institute of
Technology for providing computational resources and support that have
contributed to the research results reported in this publication.
https://doi.org/10.34788/0S3G-QD15. The authors also acknowledge the Texas
Advanced Computing Center (TACC) at The University of Texas at Austin for
providing HPC resources that have contributed to the research results reported
within this paper. http://www.tacc.utexas.edu Some of the data presented in
this paper are available on the Mikulski Archive for Space Telescopes (MAST)
at the Space Telescope Science Institute. The specific observations can be
accessed via https://doi.org/10.17909/qhb4-fy92 (catalog
doi:10.17909/qhb4-fy92) and https://doi.org/10.17909/T94S3X (catalog
doi:10.17909/T94S3X).

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##  Appendix A Additional Details

Here we include some details and additional figures for the visual
classifications and Galfit measurements. Figure 13 highlights the level of
agreement among the three classifiers for the three options in the main
morphological class: disk, spheroid, and irregular. For the visual
classifications, an example set of stamps that was shown to the classifiers
for one of the galaxies is shown in Figure 14.

###  A.1 Galfit Fits

We used Galfit to compute parametric fits on the F150W and F200W images. As
initial guesses, we use the source location, magnitude, size, position angle,
and axis ratios from the Source Extractor catalogs and segmentation maps. We
use cutouts of the error array (ERR extension) produced by the JWST pipeline
as the input sigma images for each source, which includes Poisson noise from
the sources themselves, as well as the usual instrument noise. As input PSFs,
we create empirical PSFs for each filter from stacked stars from all four
CEERS pointings. For each galaxy, the Kron radius measured by Source Extractor
was used to scale the size of the cutout used as input to Galfit. All galaxies
in the cutout within three magnitudes of the primary source were
simultaneously fit, down to a magnitude limit of 27, with all other sources
masked. Based on our testing, we find that sources fainter than this limit
were not reliably fit. We assign each fit a quality flag. A flag of 0
indicates a good fit; a flag of 1 indicates that the fit is suspect, meaning
the resulting Galfit magnitude differs substantially from the input Source
Extractor magnitudes; a flag of 2 indicates a poor fit, where one or more
parameters reached a constraint limit; a flag of 3 indicates thay the fit
failed to find a solution; and a flag of 4 indicates that the source was not
fit either because it was either an artifact or located too close to the edge.

Of the 850 galaxies in the $z>3$ sample, 74% have a Galfit flag of 0, 13% have
a flag of 1, 8% have a flag of 2, 2% have a flag of 3, and none have a flag of
4 (these would already have been removed by our initial sample selection). For
the comparisons discussed below, we use all of the galaxies with a flag of 0
or 1, representing 87% of the total sample.

As a check on the quality of our fits, and for consistency, we compared the
Galfit and GalfitM fits for the F200W filter and visually inspected the model
and residuals. Overall, we see a high level of agreement with no significant
offsets for the Sérsic index, the size, the axis ratio, and the magnitudes
between the two measurements for the sources that do not reach a constraint
limit. Throughout the paper, we use the GalfitM measurements for the filter
closest to the rest-frame optical at the redshift of the galaxy. The median
and 16th percentile and 84th percentile range for the Sérsic index ($n$), the
effective radius ($R_{e}$), and the axis ratio ($b/a$) for each morphological
type are given in Table 1 and are used throughout the text.

Figure 13: A matrix highlighting the level of agreement between the three classifiers for all galaxies with a disk, with a spheroid, or those with irregular features. Overall, classifiers regularly agree when a galaxy has a disk, agree less often about galaxies with a spheroid, and are more likely to disagree about irregular features. Figure 14: An example set of postage stamps for one galaxy (CANDELS ID 16438 with $z=3.8$ and log(M⋆/M⊙)$=10.0$), used for the visual classification of galaxies at $z>3$. The stamps are scaled by the size of the galaxy as measured by Source Extractor, following Equations 2 and 3 of Häussler et al. (2007), with a minimum size of 100$\times$100 pixels. Two of the three classifiers classified this galaxy as having both a disk and a spheroid, while the third classified it as having a disk only. Table 1: Median Properties for Each Morphology Class Morphology Group | # | Redshift | log(Mass [M⊙]) |  $n$aaSersic index ($n$), effective radius (Re in kpc), and axis ratio ($b/a$) as measured in the NIRCam filter that most closely represents the rest-frame optical for the redshift of the galaxy: F277W for $3.0<z<4.0$, F356W for $4.0<z<4.5$, and F444W for $z>4.5$. |  $R_{e}$ [kpc]aaSersic index ($n$), effective radius (Re in kpc), and axis ratio ($b/a$) as measured in the NIRCam filter that most closely represents the rest-frame optical for the redshift of the galaxy: F277W for $3.0<z<4.0$, F356W for $4.0<z<4.5$, and F444W for $z>4.5$. |  $b/a$aaSersic index ($n$), effective radius (Re in kpc), and axis ratio ($b/a$) as measured in the NIRCam filter that most closely represents the rest-frame optical for the redshift of the galaxy: F277W for $3.0<z<4.0$, F356W for $4.0<z<4.5$, and F444W for $z>4.5$.
---|---|---|---|---|---|---
All Disks | 467 | 3.84$\pm$0.87 | 9.31$\pm$0.63 | 1.41${}^{+1.38}_{-0.65}$ | 1.19${}^{+0.75}_{-0.51}$ | 0.40${}^{+0.22}_{-0.14}$
Disks Only | 192 | 3.86$\pm$0.90 | 9.28$\pm$0.47 | 1.16${}^{+0.88}_{-0.40}$ | 1.19${}^{+0.61}_{-0.48}$ | 0.40${}^{+0.21}_{-0.13}$
Disk$+$Spheroids | 88 | 3.86$\pm$0.98 | 9.29$\pm$1.12 | 2.38${}^{+1.06}_{-0.98}$ | 0.83${}^{+0.73}_{-0.33}$ | 0.51${}^{+0.17}_{-0.16}$
Disk$+$Irregulars | 155 | 3.82$\pm$0.82 | 9.32$\pm$0.41 | 1.12${}^{+1.10}_{-0.51}$ | 1.33${}^{+0.78}_{-0.42}$ | 0.36${}^{+0.21}_{-0.11}$
Disk$+$Spheroid$+$Irregulars | 32 | 3.81$\pm$0.60 | 9.49$\pm$0.48 | 2.63${}^{+1.63}_{-1.14}$ | 1.33${}^{+1.37}_{-0.45}$ | 0.39${}^{+0.18}_{-0.10}$
All Spheroids | 323 | 3.94$\pm$1.04 | 9.30$\pm$0.71 | 2.48${}^{+2.12}_{-1.34}$ | 0.72${}^{+0.66}_{-0.36}$ | 0.56${}^{+0.21}_{-0.20}$
Spheroid Only | 156 | 4.03$\pm$1.11 | 9.29$\pm$0.47 | 2.46${}^{+2.32}_{-1.41}$ | 0.54${}^{+0.42}_{-0.32}$ | 0.64${}^{+0.17}_{-0.18}$
Spheroid$+$Irregulars | 47 | 3.90$\pm$1.12 | 9.21$\pm$0.39 | 2.92${}^{+2.30}_{-1.96}$ | 0.72${}^{+0.81}_{-0.34}$ | 0.55${}^{+0.18}_{-0.20}$
All Irregulars | 376 | 3.90$\pm$0.93 | 9.29$\pm$0.43 | 1.37${}^{+1.73}_{-0.80}$ | 1.28${}^{+0.84}_{-0.57}$ | 0.40${}^{+0.21}_{-0.13}$
Irregular Only | 142 | 4.02$\pm$1.02 | 9.24$\pm$0.44 | 1.12${}^{+1.51}_{-0.82}$ | 1.32${}^{+0.79}_{-0.57}$ | 0.42${}^{+0.17}_{-0.13}$
Point Source $/$ Unresolved | 16 | 5.27$\pm$1.82 | 9.66$\pm$0.65 |  $\cdots$ |  $\cdots$ |  $\cdots$
Unclassifiable | 18 | 4.57$\pm$1.54 | 9.47$\pm$0.84 |  $\cdots$ |  $\cdots$ |  $\cdots$


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