# Webb’s PEARLS: Prime Extragalactic Areas for Reionization and Lensing
Science:
Project Overview and First Results

Rogier A. Windhorst School of Earth and Space Exploration, Arizona State University, Tempe, AZ 85287-1404, USA  Seth H. Cohen School of Earth and Space Exploration, Arizona State University, Tempe, AZ 85287-1404, USA  Rolf A. Jansen School of Earth and Space Exploration, Arizona State University, Tempe, AZ 85287-1404, USA  Jake Summers School of Earth and Space Exploration, Arizona State University, Tempe, AZ 85287-1404, USA  Scott Tompkins School of Earth and Space Exploration, Arizona State University, Tempe, AZ 85287-1404, USA  Christopher J. Conselice Jodrell Bank Centre for Astrophysics, Alan Turing Building, University of Manchester, Oxford Road, Manchester M13 9PL, UK  Simon P. Driver International Centre for Radio Astronomy Research (ICRAR) and the International Space Centre (ISC), The University of Western Australia, M468, 35 Stirling Highway, Crawley, WA 6009, Australia  Haojing Yan Department of Physics and Astronomy, University of Missouri, Columbia, MO 65211, USA  Dan Coe AURA for the European Space Agency (ESA), Space Telescope Science Institute, 3700 San Martin Drive, Baltimore, MD 21210, USA  Brenda Frye Steward Observatory, University of Arizona, 933 N Cherry Ave, Tucson, AZ, 85721-0009, USA  Norman Grogin Space Telescope Science Institute, 3700 San Martin Drive, Baltimore, MD 21210, USA  Anton Koekemoer Space Telescope Science Institute, 3700 San Martin Drive, Baltimore, MD 21210, USA  Madeline A. Marshall National Research Council of Canada, Herzberg Astronomy & Astrophysics Research Centre, 5071 West Saanich Road, Victoria, BC V9E 2E7, Canada  ARC Centre of Excellence for All Sky Astrophysics in 3 Dimensions (ASTRO 3D), Australia  Rosalia O’Brien School of Earth and Space Exploration, Arizona State University, Tempe, AZ 85287-1404, USA  Nor Pirzkal Space Telescope Science Institute, 3700 San Martin Drive, Baltimore, MD 21210, USA  Aaron Robotham International Centre for Radio Astronomy Research (ICRAR) and the International Space Centre (ISC), The University of Western Australia, M468, 35 Stirling Highway, Crawley, WA 6009, Australia  Russell E. Ryan, Jr. Space Telescope Science Institute, 3700 San Martin Drive, Baltimore, MD 21210, USA  Christopher N. A. Willmer Steward Observatory, University of Arizona, 933 N Cherry Ave, Tucson, AZ, 85721-0009, USA  Timothy Carleton School of Earth and Space Exploration, Arizona State University, Tempe, AZ 85287-1404, USA  Jose M. Diego Instituto de Física de Cantabria (CSIC-UC). Avenida. Los Castros s/n. 39005 Santander, Spain  William C. Keel Dept. of Physics and Astronomy, University of Alabama, Box 870324, Tuscaloosa, AL 35404, USA  Paolo Porto School of Earth and Space Exploration, Arizona State University, Tempe, AZ 85287-1404, USA  Caleb Redshaw School of Earth and Space Exploration, Arizona State University, Tempe, AZ 85287-1404, USA  Sydney Scheller Yale University, New Haven, CT 06511, USA  Andi Swirbul School of Earth and Space Exploration, Arizona State University, Tempe, AZ 85287-1404, USA  Stephen M. Wilkins Astronomy Centre, Department of Physics and Astronomy, University of Sussex, Brighton, BN1 9QH, UK  S. P. Willner Center for Astrophysics | Harvard & Smithsonian, 60 Garden St., Cambridge, MA 02138, USA  Adi Zitrin Physics Department, Ben-Gurion University of the Negev, P.O. Box 653, Beer-Sheva 8410501, Israel  Nathan J. Adams Jodrell Bank Centre for Astrophysics, Alan Turing Building, University of Manchester, Oxford Road, Manchester M13 9PL, UK  Duncan Austin Jodrell Bank Centre for Astrophysics, Alan Turing Building, University of Manchester, Oxford Road, Manchester M13 9PL, UK  Richard G. Arendt UMBC/CRESST2, NASA Goddard Space Flight Center, Greenbelt, MD 21771, USA  John F. Beacom Center for Cosmology and AstroParticle Physics (CCAPP), Department of Physics, Ohio State University, 191 W. Woodruff Ave., Columbus, OH 43210, USA  Rachana A. Bhatawdekar European Space Agency, ESA/ESTEC, Keplerlaan 1, 2201 AZ Noordwijk, The Netherlands  Larry D. Bradley Space Telescope Science Institute, 3700 San Martin Drive, Baltimore, MD 21210, USA  Tom Broadhurst Department of Theoretical Physics, University of the Basque Country UPV-EHU, 48040 Bilbao, Spain  Donostia International Physics Center (DIPC), 20018 Donostia, The Basque Country  IKERBASQUE, Basque Foundation for Science, Alameda Urquijo, 36-5 48008 Bilbao, Spain  Cheng Cheng Chinese Academy of Sciences, National Astronomical Observatories, CAS, Beijing 100101, China  Francesca Civano Center for Astrophysics | Harvard & Smithsonian, 60 Garden St., Cambridge, MA 02138, USA  Liang Dai Department of Physics, 366 Physics North MC 7300, University of California, Berkeley, CA 94720, USA  Hervé Dole Université Paris-Saclay, CNRS, Institut d’Astrophysique Spatiale, 91405, Orsay, France  Jordan C. J. D’Silva International Centre for Radio Astronomy Research (ICRAR) and the International Space Centre (ISC), The University of Western Australia, M468, 35 Stirling Highway, Crawley, WA 6009, Australia  Kenneth J. Duncan Institute for Astronomy, Royal Observatory, Blackford Hill, Edinburgh, EH9 3HJ, UK  Giovanni G. Fazio Center for Astrophysics | Harvard & Smithsonian, 60 Garden St., Cambridge, MA 02138, USA  Giovanni Ferrami School of Physics, University of Melbourne, Parkville, VIC 3010, Australia  ARC Centre of Excellence for All Sky Astrophysics in 3 Dimensions (ASTRO 3D), Australia  Leonardo Ferreira University of Nottingham, School of Physics & Astronomy, Nottingham, NG7 2RD, UK  Steven L. Finkelstein Department of Astronomy, The University of Texas at Austin, Austin, TX 78712, USA  Lukas J. Furtak Physics Department, Ben-Gurion University of the Negev, P.O. Box 653, Beer-Sheva 84105, Israel  Alex Griffiths University of Nottingham, School of Physics & Astronomy, Nottingham, NG7 2RD, UK  Heidi B. Hammel Associated Universities for Research in Astronomy, Inc., 1331 Pennsylvania Avenue NW, Suite 1475, Washington, DC 20004, USA  Kevin C. Harrington European Southern Observatory, Alonso de Córdova 3107, Vitacura, Casilla 19001, Santiago de Chile, Chile  Nimish P. Hathi Space Telescope Science Institute, 3700 San Martin Drive, Baltimore, MD 21210, USA  Benne W. Holwerda Department of Physics and Astronomy, University of Louisville, Louisville KY 40292, USA  Jia-Sheng Huang National Astronomical Observatories of China, A20 Datun Road, Beijing, China  Minhee Hyun SNU Astronomy Research Center, Astronomy program, Dept. of Physics & Astronomy, Seoul National University, 1 Gwanak-ro, Gwanak-gu, Seoul 08826, Republic of Korea  Korea Astronomy and Space Science Institute, 776 Daedeok-daero, Yuseong-gu, Daejeon 34055, Republic of Korea  Myungshin Im SNU Astronomy Research Center, Astronomy program, Dept. of Physics & Astronomy, Seoul National University, 1 Gwanak-ro, Gwanak-gu, Seoul 08826, Republic of Korea  Bhavin A. Joshi Center for Astrophysical Sciences, Department of Physics and Astronomy, The Johns Hopkins University, 3400 N Charles St., Baltimore, MD 21218, USA  Patrick S. Kamieneski Department of Astronomy, University of Massachusetts, Amherst, MA 01003, USA  Patrick Kelly School of Physics and Astronomy, University of Minnesota, 116 Church Street SE, Minneapolis, MN 55455, USA  Rebecca L. Larson Department of Astronomy, The University of Texas at Austin, Austin, TX 78712, USA  Juno Li International Centre for Radio Astronomy Research (ICRAR) and the International Space Centre (ISC), The University of Western Australia, M468, 35 Stirling Highway, Crawley, WA 6009, Australia  Jeremy Lim Department of Physics, The University of Hong Kong, Pokfulam Road, Hong Kong  Zhiyuan Ma Department of Astronomy, University of Massachusetts, Amherst, MA 01003, USA  Peter Maksym Center for Astrophysics | Harvard & Smithsonian, 60 Garden St., Cambridge, MA 02138, USA  Giorgio Manzoni Department of Physics, The Hong Kong University of Science and Technology, Clear Water Bay, Kowloon, Hong Kong  Ashish Kumar Meena Physics Department, Ben-Gurion University of the Negev, P.O. Box 653, Beer-Sheva 8410501, Israel  Stefanie N. Milam Astrochemistry Laboratory, NASA Goddard Space Flight Center, Code 691, Greenbelt, MD 20771, USA  Mario Nonino INAF-Osservatorio Astronomico di Trieste, Via Bazzoni 2, 34124 Trieste, Italy  Massimo Pascale Department of Astronomy, University of California, 501 Campbell Hall #3411, Berkeley, CA 94720, USA  Justin D. R. Pierel Space Telescope Science Institute, 3700 San Martin Drive, Baltimore, MD 21210, USA  Andreea Petric Space Telescope Science Institute, 3700 San Martin Drive, Baltimore, MD 21210, USA  Maria del Carmen Polletta INAF, Istituto di Astrofisica Spaziale e Fisica cosmica (IASF) Milano, Via A. Corti 12, 20133 Milan, Italy  Huub J. A. Röttgering Leiden Observatory, PO Box 9513, 2300 RA Leiden, The Netherlands  Michael J. Rutkowski Minnesota State University-Mankato, Trafton North Science Center, Mankato, MN, 56001, USA  Ian Smail Centre for Extragalactic Astronomy, Department of Physics, Durham University, South Road, Durham DH1 3LE, UK  Amber N. Straughn Astrophysics Science Division, NASA Goddard Space Flight Center, 8800 Greenbelt Rd, Greenbelt, MD 20771, USA  Louis-Gregory Strolger Space Telescope Science Institute, 3700 San Martin Drive, Baltimore, MD 21210, USA  James A. A. Trussler Jodrell Bank Centre for Astrophysics, Alan Turing Building, University of Manchester, Oxford Road, Manchester M13 9PL, UK  Lifan Wang Texas A&M University/Physics and Astronomy, College Station, TX 77842-4242, USA  Brian Welch Center for Astrophysical Sciences, Department of Physics and Astronomy, The Johns Hopkins University, 3400 N Charles St., Baltimore, MD 21218, USA  J. Stuart B. Wyithe School of Physics, University of Melbourne, Parkville, VIC 3010, Australia  ARC Centre of Excellence for All Sky Astrophysics in 3 Dimensions (ASTRO 3D), Australia  Min Yun Department of Astronomy, University of Massachusetts, Amherst, MA 01003, USA  Erik Zackrisson Observational Astrophysics, Department of Physics and Astronomy, Uppsala University, Box 516, SE-751 20 Uppsala, Sweden  Jiashuo Zhang Department of Physics, The Hong Kong University of Science and Technology, Clear Water Bay, Kowloon, Hong Kong  Xiurui Zhao Center for Astrophysics | Harvard & Smithsonian, 60 Garden St., Cambridge, MA 02138, USA<EMAIL_ADDRESS>
###### Abstract

We give an overview and describe the rationale, methods, and first results
from NIRCam images of the JWST “Prime Extragalactic Areas for Reionization and
Lensing Science” (“PEARLS”) project. PEARLS uses up to eight NIRCam filters to
survey several prime extragalactic survey areas: two fields at the North
Ecliptic Pole (NEP); seven gravitationally lensing clusters; two high redshift
proto-clusters; and the iconic backlit VV 191 galaxy system to map its dust
attenuation. PEARLS also includes NIRISS spectra for one of the NEP fields and
NIRSpec spectra of two high-redshift quasars. The main goal of PEARLS is to
study the epoch of galaxy assembly, AGN growth, and First Light. Five fields,
the JWST NEP Time-Domain Field (TDF), IRAC Dark Field (IDF), and three lensing
clusters, will be observed in up to four epochs over a year. The cadence and
sensitivity of the imaging data are ideally suited to find faint variable
objects such as weak AGN, high-redshift supernovae, and cluster caustic
transits. Both NEP fields have sightlines through our Galaxy, providing
significant numbers of very faint brown dwarfs whose proper motions can be
studied. Observations from the first spoke in the NEP TDF are public. This
paper presents our first PEARLS observations, their NIRCam data reduction and
analysis, our first object catalogs, the 0.9–4.5 µm galaxy counts and
Integrated Galaxy Light. We assess the JWST sky brightness in 13 NIRCam
filters, yielding our first constraints to diffuse light at 0.9–4.5 µm. PEARLS
is designed to be of lasting benefit to the community.

Instruments: James Webb Space Telescope—Solar System: Zodiacal Light—Stars:
Galactic Star Counts—Galaxies: Galaxy Counts— Cosmology: Extragalactic
Background Light

††software:  Astropy: http://www.astropy.org (Astropy Collaboration et al.,
2013, 2018); IDL Astronomy Library: https://idlastro.gsfc.nasa.gov (Landsman,
1993); Photutils: https://photutils.readthedocs.io/en/stable/ (Bradley et al.,
2020); ProFound: https://github.com/asgr/ProFound (Robotham et al., 2017,
2018); ProFit: https://github.com/ICRAR/ProFit (Robotham et al., 2018);
SourceExtractor: SourceExtractor:
https://www.astromatic.net/software/sextractor/ or
https://sextractor.readthedocs.io/en/latest/ (Bertin & Arnouts, 1996).
††facilities:  Hubble and James Webb Space Telescope Mikulski Archive
https://archive.stsci.edu

##  1 Introduction

The James Webb Space Telescope (“JWST”) was designed in the 1990s and 2000s to
observe very faint objects at near- and mid-infrared wavelengths from the
Sun–Earth L2 Lagrange point (e.g., Rieke et al., 2005; Gardner et al., 2006;
Beichman et al., 2012; Windhorst et al., 2008). With its 6.5-meter aperture
and state-of-the-art scientific instruments,111https://www.stsci.edu/jwst/ and
https://www.stsci.edu/jwst/instrumentation/ JWST builds on the scientific
results from two of NASA’s previous flagship missions: the Hubble Space
Telescope (HST; for a review of 27 years of HST imaging data, see, e.g.,
Windhorst et al., 2022) and the Spitzer Space Telescope (e.g., Werner et al.,
2004; Soifer et al., 2008; Werner et al., 2022). The NASA/ESA/CSA JWST was
successfully launched on 2021 December 25 on an Ariane V launch vehicle into a
direct-insertion trajectory to L2. JWST was subsequently deployed, cooled to
its intended cryogenic temperatures behind its giant sunshield,222e.g.,
https://webb.nasa.gov and its instruments were successfully commissioned and
calibrated (e.g., Rigby et al.,
2022).333https://www.stsci.edu/contents/news/jwst/2022/learn-about-jwsts-
known-scientific-performance.html In its 96-minute low earth orbit (LEO), the
Hubble Space Telescope (HST) has experienced over 175,000 sunrises and sunsets
since its launch on 1990 April 24\. This, for instance, leads to HST’s
“orbital breathing” and time-dependent point-spread functions (PSFs; e.g.,
Mechtley et al., 2012, 2016; Marshall et al., 2020, 2021), as well as its
significant orbital-phase-dependent sky surface-brightness (sky-SB) levels
(e.g., Carleton et al., 2022; Windhorst et al., 2022). In contrast, JWST was
designed to have exactly one sunrise and one sunset during its planned
10+-year mission: its one and only sunrise occurred when the Ariane launch
fairing opened on Christmas Day 2021, and its one-and-only sunset came when
its sunshield fully deployed in early January 2022\. Compared to HST, JWST
will have more stable PSFs and foreground sky-SB levels, which depend
primarily on its component temperatures and its pointing direction (pitch
angle), respectively. The resulting very dark and stable L2 environment makes
JWST particularly suited for faint-object detection in the observatory’s
0.6–29 µm wavelength range, as well as assessing its sky-SB which PEARLS will
pursue at 0.9–4.5 µm.

From the start of observatory design in the early 2000s, JWST had four main
science themes that drove its performance requirements: First Light &
Reionization, Assembly of Galaxies, Birth of Stars and Protoplanetary Systems,
and Planetary Systems and Origins of Life (e.g., Gardner et al., 2006). Now
almost twenty years later, these remain key research areas with major
unknowns, and these themes are reflected in the Cycle 1 proposals from the
astronomical community and in the observing time granted.444e.g.,
https://www.stsci.edu/jwst/science-execution/approved-programs/ As part of the
science planning of JWST, R. Windhorst was chosen as a JWST Interdisciplinary
Scientist in 2002 June. His 20+-year effort and commitment comes with 110
hours of Guaranteed Time Observations (GTO). This paper gives an overview and
describes the rationale, methods, and first scientific results of our project
“Prime Extragalactic Areas for Reionization and Lensing Science”, or “PEARLS.”

PEARLS’ main science goals address JWST’s first two themes: First Light &
Reionization and Assembly of Galaxies, including supermassive black-hole
(SMBH) growth. Specifically, PEARLS will observe three “blank” fields, seven
galaxy clusters that show strong gravitational lensing, two high-redshift
proto-clusters, two high-redshift quasars, and one nearby spiral galaxy
backlit by a neighboring elliptical galaxy. Two of the blank fields are
especially suited for time-domain science (e.g., Jansen & Windhorst, 2018; Yan
et al., 2018). These reside in program PID 2738 (PIs R. Windhorst & H.
Hammel). All other PEARLS observations reside in PID 1176 (PI Windhorst). In
collaboration with GTO programs by C. Willott (PID 1208) and M. Stiavelli (PID
1199), two of the lensing clusters (MACS0416 and MACS1149) will have a
significant additional time baseline to search for caustic transits of stars
at redshifts $z\gtrsim 1$ (e.g., Kelly et al., 2018; Diego et al., 2018; Chen
et al., 2022) or even individual highly-magnified stars or stellar-mass black-
hole accretion disks at $z\gtrsim 6$ (e.g., Windhorst et al., 2018; Meena et
al., 2022; Welch et al., 2022a, b).

Section 2 of this paper describes the PEARLS rationale and target selection
along with the planning and scheduling of the JWST observations. Section 3
describes the first PEARLS JWST/NIRCam data and their initial calibration.
Section 4 presents the NIRCam catalogs of the first PEARLS blank-field survey
including their completeness, the star–galaxy classification procedure, and
the object counts in broad-band filters covering 0.9–4.5 µm. Section 5
describes the detected and extrapolated integrated galaxy light (IGL) as
derived from the 0.9–4.5 µm galaxy counts, and analyses the JWST sky-SB in 13
NIRCam filters to assess what is required to set limits to diffuse light,
including any diffuse Extragalactic Background Light (EBL). Section 6
discusses the significance of our early PEARLS results, and Section 7
summarizes our results and future prospects. PEARLS is designed to be of
lasting benefit to the community, and we hope that it will catalyze a variety
of multi-wavelength studies during the lifetime of JWST.

This paper uses Planck cosmology (Planck Collaboration et al., 2020):
$H_{0}=67.4\pm 0.5$ km s-1 Mpc-1, matter density parameter
$\Omega_{M}=0.315\pm 0.007$ and vacuum energy density
$\Omega_{\Lambda}=0.685$. These give the Universe an age of 13.8 Gyr. To
compare our NIRCam results to decades of previous work, our object fluxes are
in AB units555Defined as AB-mag = –2.5 log($F_{\nu}$) + 8.90, where the flux
density $F_{\nu}$ is in Jy units. (Oke & Gunn, 1983). Surface brightness (SB)
values are in units of AB mag arcsec-2 or in MJy/sr.666All JWST pixel values
are in units of MJy/sr, which can be converted to units of nW m-2 sr-1 by
multiplying by 10-11(c/$\lambda_{c})$, where $\lambda_{c}$ is the filter pivot
wavelength in microns (e.g., Equation A15 of Bessell & Murphy, 2012).

##  2 PEARLS Rationale

###  2.1 PEARLS Science Objectives

PEARLS targets for First Light and Reionization studies include high-redshift
Lyman-$\alpha$ galaxies and protoclusters. In light of observations with HST
WFC3 over the past 13 years, PEARLS will also image several rich galaxy
clusters that boost the signal of faint, high-redshift objects via strong
gravitational lensing. Blank-field surveys will contribute to the First Light
theme via number counts. To study the Assembly of Galaxies, we will observe
galaxies up to the highest redshifts, and lowest masses and luminosities, in
different environments. We will also investigate SMBH growth by observing
high-redshift galaxies having an active nucleus: quasars and radio galaxies.
The blank fields at high Ecliptic latitude will contribute time-domain
information. PEARLS will also study VV 191, a nearby, overlapping galaxy pair,
to provide a benchmark dust-attenuation profile for studying high-redshift,
dusty environments. Table 1 summarizes the PEARLS fields observed thus far (as
of 2022 July 31), and Table 2 the PEARLS field to be observed subsequently.
The 112.3 calendar hours allocated to PEARLS include 2.3 hours from H. Hammel.
In all, PEARLS will image 16 NIRCam and four NIRISS fields in up to eight
filters to $\rm AB\lesssim 28.5$–29 mag and will cover $\sim$165.66 arcmin2 or
0.046 deg2, equivalent to $\sim$34 HUDF/XDF fields (e.g., Beckwith et al.,
2006; Koekemoer et al., 2013).

PEARLS will obtain data over at least 13 independent lines of sight more than
three degrees apart from each other and is therefore more robust against
Cosmic Variance (CV) at $\rm AB\lesssim 28.5$ mag than programs that image
only a few areas (e.g., Somerville et al., 2004; Driver & Robotham, 2010;
Windhorst et al., 2022). Figure 1 compares the area and depth covered by
PEARLS to other JWST Cycle 1 surveys. While not as deep or wide as other
contiguous JWST Cycle 1 surveys, PEARLS covers more fields across the sky to
decrease the effects of CV. The expected CV for PEARLS fields can be found
with the calculator777https://cosmocalc.icrar.org/ of Driver & Robotham (2010)
based on the areas covered and sensitivity limits Tables 1 and 2. To $\rm
AB\lesssim 28.5$ mag, the PEARLS fields sample a typical redshift range of
$z\simeq 0.3$–8 with a median redshift of $z\simeq 1$–2 (see Section 4.4 and
Appendix B.2). The NIRCam FOV covers $\sim$0.0026 deg2 (Section 3.1 or
$\sim$1.1$\times$2.2 Mpc), over which its CV is then predicted to be $\sim$3%.
For the two PEARLS fields with galaxy counts presented here in Section 4.4, CV
is expected to be $\lesssim$9%. At the end of JWST Cycle 1, large JWST NIRCam
parallel programs like PANORAMIC (PID 2514; C. Williams PI) may push CV of the
sampled objects to $\sim$1–2%.

In four of our NIRCam pointings, coordinated NIRISS grism and imaging
parallels will cover a significant portion of our NIRCam images (Table 2),
while UV-optical images are available from HST WFC3+ACS. The coordinated
NIRISS parallels will be used for both object characterization and redshifts,
and to expand the area and time-baseline of time-domain studies. The
coordinated parallel observations are critical to obtain imaging and grism
data that is as homogeneous as possible, over as large an area as possible,
and in the least amount of time feasible with JWST.

Two of the PEARLS blank fields and two galaxy cluster fields will be observed
more than once. This time-domain component will allow us to find and study
Galactic brown dwarfs via high proper motion or atmospheric variability,
variable AGN, high-redshift supernovae, and any time-varying objects seen
behind lensing clusters, including possible cluster caustic transits. The
PEARLS time-domain data at the North Ecliptic Pole (NEP) may also reveal some
faint moving objects at high Ecliptic latitude in our outer solar system.

To encourage immediate use of JWST data by the community and follow-up
proposals by JWST Cycle 2 GO proposers, we will make the first epoch of our
JWST NEP Time-Domain Field (TDF) public immediately (#112.* in Table 2). The
other three JWST NEP TDF epochs will be released together with the v1 data
products as soon as we have these. Also public right away are the Cycle 25
(Jansen et al. 2022, in preparation), 28, and 29 HST WFC3/UVIS F275W and
ACS/WFC F435W+F606W observations of the NEP TDF and other ancillary data
across the electromagnetic spectrum, as these become available and their data
reduction is completed. These include 600 ks NuSTAR 3–24 keV images (Zhao et
al., 2021, Civano et al. in preparation), 900 ks of Chandra ACIS 0.2–10 keV
images (Maksym et al. 2022, in preparation), 31 hours of JCMT/SCUBA-2 plus 66
hours of SMA data at 0.85 mm, as well as 70 hours of VLA 3 GHz A+B-array
images (Hyun et al. 2022, in preparation), 147 hrs of VLBA 4.5 GHz data at
milliarcsec (m.a.s.) resolution to sub-microJy levels, and 75 hours of LOFAR
150 MHz images including LOFAR VLBI. The presence of a $S_{3GHz}\simeq$239 mJy
quasar at $z=1.4429$ in the JWST NEP TDF that is unresolved at VLBI m.a.s.
resolution is used as phase calibrator to provide high resolution VLA/VLBA and
LOFAR/VLBI images of very high dynamic range in the NEP TDF. The NEP TDF
database also includes multi-epoch Large Binocular Telescope/LBC + Subaru/HSC
$Ugiz$ images to AB$\lesssim$26.0 mag, Gran Telescopio Canarias/HiPERCAM
$ugriz$ images to $\rm AB\lesssim 27$ mag, Multiple Mirror Telescope/MMIRS
images to $YJHK\lesssim 24$–23 mag, and MMT/Binospec and MMIRS spectra to
22–24 mag (Willmer et al. 2022, in preparation), plus JPAS 56-narrow-band
spectrophotometry to provide confirmation of the astrometric, photometric, and
spectroscopic calibration of our JWST NIRCam+NIRISS observations.

###  2.2 PEARLS Target Selection

PEARLS target selection began in the early 2010’s, when it became clear that
JWST had a viable path towards launch and that it could perform as designed.
The largest blank field is in the JWST continuous viewing zone (CVZ) near the
North Ecliptic Pole (NEP). This NEP Time Domain Field has the best combination
of low foreground extinction and absence of AB$\lesssim$16 mag stars (Jansen &
Windhorst, 2018). A second blank field is within the IRAC Dark Field, which is
a Spitzer/IRAC calibration field near the NEP observed repeatedly for over 15
years. These historical light curves offer several examples of what might be
high-redshift, dusty supernovae (SNe) in ultra luminous infrared galaxies
selected by Herschel (Yan et al., 2018). Figure 1a & 2a of Jansen & Windhorst
(2018) give a layout of the JWST CVZ in the NEP, where the IRAC Dark Field
(IDF) is $\sim$3.56∘ NE of the TDF. Our Figure 2 shows the first-epoch NIRCam
observation of the JWST IDF (hereafter the JWIDF). The final blank field is in
the WFC3 ERS area (Windhorst et al., 2011), which is in the northern part of
the GOODS-South area.

PEARLS gravitational-lensing clusters were selected to have high mass and
central compactness or to have apparent double-cluster nature. The latter
could result in higher transverse motions and therefore makes caustic transits
more likely. Possible transiting sources include distant, luminous single
stars, double stars, and possibly stellar-mass black-hole accretion disks
(e.g., Miralda-Escude, 1991). All of our selected clusters show
gravitationally lensed arcs, and all have lensing magnification maps produced
with multiple independent lensing models, which will be refined with the JWST
data. Other lensing clusters were similarly selected because of their high
mass and high central compactness, and their lower IntraCluster Light (ICL)
content, which could make it easier to detect caustic transits with less
microlensing by foreground stars in the cluster ICL (e.g., Windhorst et al.,
2018). The PEARLS lensing clusters are:

  * •

The HFF (Lotz et al., 2017) cluster MACS J0416.1$-$2403 at $z\simeq 0.42$.
This field will be covered by three JWST epochs about six months apart to
maximize the chance of seeing caustic transits at $z\geq 6$ (e.g., Windhorst
et al., 2018; Welch et al., 2022a, b). A number of plausible caustic transits
at $z\simeq 0.9$–1.5 have already been observed for this cluster by HST (Dai
et al., 2018; Kelly et al., 2018; Dai, 2021).

  * •

Abell 2744 at $z\simeq 0.31$ and MACS J1149.5+2223 at $z\simeq 0.54$. These
are likewise HFF (Lotz et al., 2017) clusters. They will have additional GTO
observations by C. Willott and the NIRISS GTO team, and by M. Stiavelli and
his team to look for variable objects in or behind these clusters, and by the
GLASS team (PID 1324; PI: Treu). This allows us to monitor potential high-
redshift caustic transits on timescales longer than a year.

  * •

The cluster known as El Gordo at $z\simeq 0.87$ (Menanteau et al., 2012;
Zitrin et al., 2013; Cerny et al., 2018; Diego et al., 2020; Caputi et al.,
2021). This cluster was selected because of its enormous mass (Menanteau et
al., 2012, $\sim$2$\times$1015M⊙), its elongation to maximize the probability
of caustic transits (Windhorst et al., 2018), and its rich collection of
distant lensed source candidates. The field includes a background galaxy
grouping at z$\simeq$4.3 (Caputi et al., 2021) that is lensed by the cluster.
Figure 3 shows the module around El Gordo that did not cover the central part
of the cluster (hereafter referred to as the “non-cluster” module; see Section
4.4).

  * •

PLCK G165.7+67.0 (G165) is a double cluster at $z\simeq 0.35$ selected by its
FIR colors, and not by the Sunyaev-Zeldovich effect (e.g., Cañameras et al.,
2015; Harrington et al., 2016; Planck Collaboration et al., 2016). Many
caustic-crossing arcs are detected, which are well-suited to transient
science. One example is a strongly-lensed red and dusty Sub-Millimeter Galaxy
(SMG) detected in the HST imaging, whose counter image appears in the
LBT/LUCI+ARGOS laser-guided AO K-band images (e.g., Frye et al., 2019; Rabien
et al., 2019). Spectral Energy Distributions (SEDs) fit to the optical–near-IR
images yield photometric redshift estimates for some image families, which
constrain the lens model (Pascale et al., 2022). The model confirms the bi-
modal mass distribution of this ongoing merger that is only a low-luminosity
X-ray source. The JWST observations aim to constrain the dynamical state of
this cluster and detect a significant number of lensed background sources.

  * •

The Clio cluster at $z\simeq 0.42$ from the GAMA survey (Driver & Robotham,
2010; Alpaslan et al., 2012) is a massive, compact cluster selected to have
significant potential for lensing background sources. A ground-based VLT image
(Griffiths et al., 2018) already showed strongly lensed arcs and a lower-than-
average amount of IntraCluster Light. This is attractive because low-mass
IntraCluster Medium (ICM) stars can significantly lengthen caustic-transit
times (e.g., Diego et al., 2018; Windhorst et al., 2018) and complicate their
lensing analysis.

  * •

The cluster RXC J1212+27=A1489 at $z\simeq 0.35$. This cluster was chosen
because of strong gravitational lensing, using the automated implementation
(Zitrin et al., 2012) of the Light Traces Mass (LTM) method (e.g., Zitrin &
Broadhurst, 2016; Zitrin, 2017). HST images showed a significant number of
lensed sources that resulted in a good lensing model (Zitrin et al., 2020).

The first public JWST images (Pontoppidan et al., 2022) released starting 2022
July 12 have already inspired a number of further studies. Relevant for
PEARLS, a possible caustic transit candidate has been suggested at z$\simeq$3
in some of the public JWST images of the cluster SMACS0723 (e.g., Chen et al.,
2022), for which mass models were made by Pascale et al. (e.g., 2022), and in
which also a significant number of red spirals were identified (e.g., Ferreira
et al., 2022; Fudamoto et al., 2022). Indeed, some very high redshift
candidates were already suggested in some of the very first JWST ERS images
(Adams et al., 2022; Finkelstein et al., 2022). The PEARLS high redshift
protoclusters are:

  * •

PHz G191.24+62.04 (G191) is a protocluster candidate at $z=2.55$ with one of
the highest star formation rates (SFR$\simeq$23000 M⊙/yr) in the parent sample
of Planck high-z sources (PHz; Planck Collaboration et al., 2016). G191 hosts
an overdensity of red Spitzer sources (Planck Collaboration et al., 2015;
Martinache et al., 2018), containing $\sim$14 objects/arcmin2 with IRAC
3.6–4.5 µm colors $\gtrsim$–0.1 mag. Two of the Herschel sources have
spectroscopic redshifts and a large estimated SFRs$\simeq$1000–1500 M⊙/yr
(Polletta et al., 2022), i.e., high enough that they present challenges for
theoretical models (Granato et al., 2015; Lim et al., 2021; Gouin et al.,
2022). The JWST observations will constrain the stellar mass assembly and
fueling mechanism (e.g., major mergers, cold accretion) occurring in this
highly star-forming high-z structure.

  * •

TNJ1338$-$1942 is a protocluster at $z=4.1$ that was discovered with the VLT
as 60 Ly$\alpha$-emitters near a luminous, steep-spectrum radio source (e.g.,
De Breuck et al., 1999, 2000; Venemans et al., 2002; Miley et al., 2004;
Intema et al., 2006; Saito et al., 2015). The radio source’s AGN activity and
outflow will be studied by a JWST Cycle 1 GO program (PID 1964, PI R.
Overzier). PEARLS has imaged the field in the six NIRCam medium-band filters
that best straddle the Balmer/4000Å break at $z=4.1$ to help delineate the
ages of $\sim$30 of the Ly$\alpha$-emitters. Figure 4 shows part of the NIRCam
image around TNJ1338$-$1942\. To maximize the scientific return on
TNJ1338$-$1942, the analysis of the PEARLS and GO data will be coordinated.

In addition to our above two protocluster targets, the $z\simeq 4.3$ group of
galaxies behind El Gordo (Caputi et al., 2021) may also turn out to be a
protocluster candidate. Additional PEARLS targets are:

  * •

Two QSOs, QSO 1425+3254 (or NDWFS J142516.3+325409 at $z=5.85$; e.g., Mechtley
et al., 2012), and QSO J0005$-$0006 (or SDSS J000552.35$-$000655.6 at
$z=5.86$). The first has a number of possible $z\simeq 6$ companions (e.g.,
Marshall et al., 2020, 2021). PEARLS IFU observations will address whether
these form a group around the QSO. QSO J0005$-$0006 was selected because it
lacks both hot and cold dust (Wang et al., 2008; Jiang et al., 2010). It
therefore represents a rare sub-population of dust-free high-$z$ quasars.

  * •

The VV 191 system (Figure 5) consists of a foreground spiral galaxy with an
unassociated elliptical galaxy behind it (e.g., Keel et al., 2013). Light from
the elliptical suffers extinction from dust in the spiral. PEARLS NIRCam
imaging maps the extinction and determine its wavelength dependence (Keel et
al., 2022).

###  2.3 PEARLS’ Observation Planning

####  2.3.1 JWST Observation Planning of PEARLS Targets

Most PEARLS targets will be imaged with NIRCam in a set of eight broad-band
filters, as shown in Table 1. In a few fields, fewer filters are needed to
accomplish the intended science. The NIRISS Grism mode and NIRSpec Prism mode
are used in a few fields. One field (TNJ1338$-$1942) will be observed in five
NIRCam medium-band filters and one broad-band filter, as summarized in Table
2.

Four PEARLS fields have a time-domain component on time-scales of hours to a
year. This could reveal objects with high parallax in our solar system,
Galactic brown dwarfs with high proper motion and/or atmospheric variability,
variable AGN, high redshift supernovae, and caustic transits behind galaxy
clusters. The two PEARLS fields at high ecliptic latitude and with multiple
visits also enable searches for solar-system objects in high-inclination
orbits. To increase the search effectiveness, H. Hammel allocated a portion of
her GTO time to the NEP observations. The combined observations make up PID
2738, which is an efficient combination of three epochs of observation in the
JWIDF and four epochs in the TDF. Where possible, visits with similar
orientations were combined to save JWST overhead time. The PEARLS programs
1176+2738 require $62.0+53.7$ spacecraft hours and give 68.9 hours of net
exposure time (Tables 1 and 2). The observing efficiency is therefore
$\sim$59.5%. This is less than the maximum JWST spacecraft efficiency of
$\sim$70% achievable for very long integrations on deep fields, but it is in
line with the efficiency of JWST observations of average duration. Accepting
somewhat lower efficiencies was a deliberate choice to address CV.

The PEARLS time-domain fields are:

  1. 1.

The TDF. The field layout is four “spokes” with with orientations differing by
90∘. This is accomplished by observing at three-month intervals. Each spoke is
a 2$\times$1 mosaic of pointings with 57% overlap to fill the NIRCam inter-
chip gaps of each module. At each pointing in the mosaic, four dithers fill in
the gaps in the NIRCam SW detector module. All eight broad-band filters are
used. The TDF observations include coordinated parallel observations with
NIRISS/WFSS in the orthogonal low-resolution grisms GR150C and GR150R. A
broad-band filter must be used simultaneously to define the sampled spectral
wavelength range and so limit spectral overlap. The F200W888Note all JWST NIR
filter and grism names are numbered in units of 10 nm, i.e., GR150C or F200W
indicate a central wavelength of 1.5 or 2.0 µm, respectively. broad-band
filter was used for this purpose to explore a new wavelength range not sampled
by the HST WFC/IR G102 or G140 grisms. The field dimensions were chosen to
make the NIRISS footprints maximally overlap each NIRCam mosaic that was taken
$\sim$183 days earlier or later, i.e., 180∘ different position angle (Figure
7b of Jansen & Windhorst, 2018). The grism spectra will allow object
characterization and yield redshifts, and the direct NIRISS F200W images —
needed to identify which grism spectrum is which object — will give an
additional 2.0 µm epoch image for time-domain studies. In order to match the
number of primary and parallel exposures, the exposure time in several of the
NIRCam filters is split over two observing sequences.

  2. 2.

The JWIDF. The field cover a single rectangle of
$\sim$5$\farcm$9$\times$2$\farcm$4\. It will be observed in three epochs six
months apart, giving position angles that differ by 180∘, i.e., covering the
same area at each epoch. A 6-point, full-box dither pattern fills both intra-
module and intra-chip gaps. Four broad-band filters (SW: F150W and F200W; LW:
F356W and F444W) are observed. Our hope is that many future epochs will be
observed in GO time to provide long-duration monitoring, including dusty high
redshift supernovae in Herschel selected galaxies.

  3. 3.

MACS 0416$-$24, MACS J1149.5+2223, and Abell 2744 (see also Section 2.2). We
will observe MACS0416 in three different epochs to search for caustic
transits. Time intervals between epochs on the JWST Long Range Plan (LRP) are
scheduled $\sim$3 and $\sim$12 months after the first epoch, as listed in
Table 1.

JWST scheduling is a complex, ongoing and constantly changing process.
Information about when PEARLS observations have been, or will be, carried out
is available on https://www.stsci.edu/cgi-bin/get-visit-
status?id=1176&markupFormat=html&observatory=JWST for most PEARLS targets, and
on https://www.stsci.edu/cgi-bin/get-visit-
status?id=2738&markupFormat=html&observatory=JWST for the JWIDF and TDF. Full
details of observations are in the JWST “APT files” also available at these
websites999For the APT tool, see https://www.stsci.edu/scientific-
community/software/astronomers-proposal-tool-apt.

####  2.3.2 PEARLS’ Primary NIRCam Observations and Areas

NIRCam consists of two modules A and B, each imaging two sky regions
2$\farcm$15$\times$2$\farcm$15 in size separated by
$\sim$0$\farcm$7.101010https://jwst-docs.stsci.edu/jwst-near-infrared-
camera/nircam-instrumentation/nircam-filters In each module, dichroics direct
short-wavelength (SW, 0.6–2.3 µm) and long-wavelength (LW, 2.3–5.2 µm) light
from each sky region onto corresponding detectors. The LW modules have a
single detector with 2040$\times$2040 illuminated pixels at a scale of
0$\buildrel\prime\prime\over{.}$0629 per pixel. Each SW module has four
detectors to cover the same sky area at 0$\buildrel\prime\prime\over{.}$0312
per pixel. There are 4$\buildrel\prime\prime\over{.}$5 gaps between the four
SW detectors in each module. With 4-step dithering across the
4$\buildrel\prime\prime\over{.}$5 gaps, the total area covered by each
exposure is about 2$\farcm$2$\times$2$\farcm$2 in each of the two modules or
about 9.6 arcmin2 for a full all-detector exposure.

Our PEARLS NIRCam imaging uses both modules (A and B) and both detector units
(SW and LW) for a total of 10 detector readouts per integration. A 4-point
INTRAMODULEBOX dither pattern is used to filter out the cosmic ray (CR) flux
in JWST’s L2 orbit. The on-the-ramp readout patterns are typically either
MEDIUM8 with seven groups per integration or SHALLOW4 with 8–10 groups per
integration, whichever produced the required sensitivity according to the
NIRCam exposure time calculator (ETC)111111https://jwst.etc.stsci.edu. For
some shallower targets, a FULLBOX dither pattern was used with six primary
dithers (6TIGHT) and STANDARD dither-type to cover the SW inter-chip gaps in
order to make them schedulable. The resulting total net exposure times in each
NIRCam filter and their ETC sensitivities are listed in Tables 1 or 2. These
sensitivities will be verified with the object counts of Section 4.3–4.4.
Dither steps were made large enough to cover the small SW intra-module gaps.
Most PEARLS targets are small enough to fit in the field of view (FOV) of a
single NIRCam module. The exceptions are the NEP TDF and the JWIDF. For those
targets, dithers need to cover the 43″ inter-module gap and for the TDF also
the FOV covered by the NIRISS parallels (Jansen & Windhorst, 2018). This
results in four NIRCam spokes 2$\farcm$15$\times$6$\farcm$36 with a total area
of 13.67 arcmin2 per spoke, and a total area for the four TDF epochs of 54.79
arcmin2 at the nominal 4-dither point depth.

####  2.3.3 PEARLS’ Coordinated NIRISS Parallel Observations and Areas

NIRISS covers a single 133″$\times$133″ FOV with 2040$\times$2040 light
sensitive pixels121212https://jwst-docs.stsci.edu/jwst-near-infrared-imager-
and-slitless-spectrograph/niriss-instrumentation/niriss-detector-overview at a
scale of 0$\buildrel\prime\prime\over{.}$065 per pixel to cover an area of 4.9
arcmin2. Its wavelength coverage is 0.8–5.2 µm. The NIRISS parallels in the
TDF will consist of 2$\times$1 mosaics with orthogonal grisms GR150C and
GR150R. Each position will include finder images in the F200W filter that are
expected to reach AB$\lesssim$29.5 mag. Each of the dispersed NIRISS images
must be bracketed by F200W images to enable source identification and
wavelength calibration, so there are a total of four such direct images per
pointing. The NIRISS F200W images thus 6456 s total integration time, more
than the non-overlapping outskirts of the NIRCam F200W images, and therefore
may reach $\sim$0.4 mag deeper (Table 2).

The NIRISS grism exposures will use the readout pattern “NIS” and have
typically 13 groups per integration and two integrations per exposure. By
necessity, the NIRISS coordinated parallels have the same dither pattern as
the NIRCam primary images, and thus NIRISS covers about
2$\farcm$22$\times$4$\farcm$2 or 9.32 arcmin2 in each spoke. This will give
37.35 arcmin2 at the nominal 4-dither depth for the four TDF epochs combined.
Because of the larger NIRISS pixels, the optimal NIRCam dither pattern is not
optimal for NIRISS, so the NIRCam F200W primary images will provide a better-
sampled F200W image, but the very faintest objects in F200W will be detected
only by NIRISS. Net exposure times in F200W and the two grisms and their ETC
sensitivities are listed in Table 2.

When all four NEP TDF epochs are taken as planned, the NIRISS area of each
spoke will nearly perfectly overlap with the NIRCam spoke observed $\sim$183
days earlier or later. Details are given in Jansen & Windhorst (2018). The
resulting NIRCam spokes with total area 54.79 arcmin2 will have a depth of
$\rm AB\lesssim 28.5$ in most filters (5$\sigma$ for point sources, Table 2).
NIRISS will provide $R\sim 150$ spectra ranging from 1.75 to 2.22 reaching
$\rm AB\lesssim 25.9$ mag for objects in the coverage area, which is a total
of 37.35 arcmin2 when combining all four NIRISS spokes. This is the most
efficient way of getting both JWST NIRCam images and NIRISS spectra of the
same area.

According to the JWST ETC, typical 5-sigma sensitivities obtained for point
sources from our shallowest ($\sim$2 hours) to our deepest ($\sim$6 hours)
mosaics are $\sim$28–28.5 mag to $\sim$28.5–29 mag per target. According to
the ETC the reddest (3–5 µm) filters may be less sensitive than the bluer
(0.9–3 µm) ones in both NIRCam and NIRISS. However, as we will see in Section
4, the wider PSF of the redder filters more than compensates for any lower
sensitivity in detection of very faint and slightly extended galaxies. Modest
variations in sensitivity occur from field-to-field, depending on exactly how
much time could be fit into the scheduled APTs for each field within our total
GTO allocations, and on the actual Zodiacal-light brightness and the
straylight contributions in each field (Section 5).

##  3 PEARLS Calibration, Mosaicing and Data Quality

###  3.1 Initial Calibration

Calibration of PEARLS data obtained as of 2022 July used the calibration files
on the STSCI JWST website as of 2022 July 12. All data were processed with the
standard STScI pipeline CALWEBB131313https://jwst-docs.stsci.edu/getting-
started-with-jwst-data which comes in three stages: 1) detector-level
corrections to the raw individual exposures to produce count-rate images from
the non-destructive readouts (“ramps”); 2) photometric and astrometric
calibration of the individual exposures; and 3) drizzling the calibrated and
distortion-corrected images into mosaics. The NIRCam pipeline CALWEBB was used
to process all our images. The Calibration Reference Data System (CRDS)
provides the latest reference files that we used to calibrate our
data141414https://jwst-crds.stsci.edu/static/users_guide/web_site_use.html.
CRDS version 11.13.1 was used for all images.

Our NIRCam TNJ1338, VV191, and JWIDF images taken in early July were reduced
with Pipeline version v1.6.1.dev2+g408c711 and context file
jwst_0916.pmap_filters, which still contained the pre-launch ZP values. These
are corrected as described in Appendix B.1 and Table 6. Our more recent El
Gordo images of 2022 July 29 were reduced with Pipeline version 1.6.2 in early
August 2022 using context file jwst_0942.pmap_filters, which implements the
most recent Rigby et al. (2022) in-flight ZPs to all NIRCam filter images. We
refer to these calibrations and their resulting mosaics as version v0.5. Once
the on-orbit NIRCam flat-field and ZP-calibrations have been significantly
improved, we will reprocess all our PEARLS images into a version v1. We will
make v1 of the NEP TDF available to the community as soon as it is completed.

Performance of the NIRCam detectors is relevant to depth, calibration quality,
and accuracy of the sky-SB values discussed in Section 4–5. SW and LW module
characteristics relevant for PEARLS are151515https://jwst-docs.stsci.edu/jwst-
near-infrared-camera/nircam-instrumentation/nircam-detector-overview/nircam-
detector-performance see also http://ircamera.as.arizona.edu/nircam:

  * •

Average NIRCam read-noise values are $\sim$16.2 and $\sim$13.5 $\rm e^{-}$
pixel-1 using correlated double-sampling.

  * •

Average dark current values in typical exposures are very low:
$\sim$0.0019$\pm$0.002 and $\sim$0.027$\pm$0.005 $\rm e^{-}$/s.

  * •

Average detector gains are $\sim$2.05 and $\sim$1.82 $\rm e^{-}$ ADU-1,
respectively.

  * •

Persistence of charge from a previous equal-length exposure is $\lesssim$0.01%
of the original charge detected in the previous image.

###  3.2 Mosaicing of the PEARLS Images

The first step in mosaicing was to anchor all individual frames into the Gaia
DR3 reference frame (Gaia Collaboration et al., 2022). This step used catalogs
based on recent, deep ground-based or HST images already referenced to Gaia
DR3. Both stars and galaxies were used for the correction with star positions
corrected for proper motion from the epoch of the reference image to the JWST
observing epoch (Table 1). Coordinate differences between the catalog and JWST
frame were measured, and each frame’s center position and position angle were
adjusted to minimize the differences. Typical adjustments were $\lesssim$10
m.a.s., and the final uncertainty in each frame’s position is about 2–3 m.a.s.
rms. We used AstroDrizzle161616https://hst-
docs.stsci.edu/drizzpac/chapter-5-drizzlepac-software-
package/5-2-astrodrizzle-the-new-drizzle-workhorse (Koekemoer et al., 2013;
Avila et al., 2015) to drizzle the NIRCam images as calibrated in the CALWEBB
pipeline Stages 1 and 2 into two mosaics for each field. Pixel sizes used were
0$\buildrel\prime\prime\over{.}$0300 and 0$\buildrel\prime\prime\over{.}$0600
for SW and LW, respectively. For the LW module, we also provide
0$\buildrel\prime\prime\over{.}$0300/pixel mosaics to facilitate aligned
analysis. The higher resolution of the former samples the NIRCam PSFs better
in the SW channel, while the bigger pixels of the latter have better SB-
sensitivity for the generally short PEARLS exposures. All NIRcam images were
drizzled after we removed wisps as well as possible, applied a 1/f correction,
flagged the snowballs before object detection, and subtracted a surface-fit to
the sky-SB between all the detected objects. Details on these aspects are
given below.

###  3.3 First Assessment of Calibration Quality

$\bullet$ NIRCam $1/f$ Noise Pattern Removal: NIRCam images are read out
simultaneously in four vertical 510$\times$2040-pixel strips. Differing gains
or zero points in the four amplifiers causes banding or striping across the
images as they are read out. Rest (2014) presented a mathematical method to
separate this $1/f$ noise from the random read
noise171717http://www.stsci.edu/files/live/sites/www/files/home/jwst/documentation/technical-
documents/_documents/JWST-STScI-004118.pdf, and derives the time dependence of
the 1/f-noise, and finds that the 1/f-noise strongly correlates between the
amplifiers of a given detector, because it is caused by a common reference
voltage. Rest (2014) also finds that 1/f-noise has reproducible spatial
structure at the 10–20% level down to spatial scales of tens of pixels, which
does not seem to change on timescales of months. Schlawin et al. (2020)
describe methods to reduce 1/f-noise patterns in the highly demanding NIRCam
grism time-series observations of exoplanets, and find that spatial background
subtraction also efficiently removes many random detector defects, including
pre-amplifier-offsets, amplifier discontinuities, and even-odd column offsets.
The NIRCam 1/f-noise can be reduced by subtracting the values from background
pixels or reference pixels that are read closely in time. E.g., Schlawin et
al. (2020) remove the 1/f noise as a step in the pipeline after the superbias
correction181818https://tshirt.readthedocs.io/en/latest/specific_modules/ROEBA.html.
Hilbert et al. (2016) presented a method to subtract 1/f-noise from NIRCam
integrations before averaging the data to produce superbias maps, and are able
to produce superbias images with significantly lower noise levels than those
produced using the more traditional approach to superbias generation. C.
Willott provides a more recent code to remove 1/f noise that runs on the
calibrated files191919https://github.com/chriswillott/jwst.git. M. Bagley
presented a code to remove both the detector level offsets in the SW modules
and the is 1/f noise patterns for the CEERS project, which is part of their
SDR1 release202020https://ceers.github.io/releases.html#sdr1.

We used both the Willott code and the ProFound code (Robotham et al., 2017,
2018) to remove the 1/f noise patterns. Together with the low-level pedestal
removal between the SW detectors below, the ProFound-package resulted in
images that are mostly visually flat without major row-based artifacts. We
visually verified that the 1/f noise removal parameter settings did not
introduce other new artifacts in the final images. Further details are given
in Appendix A, where we check the results from both 1/f-noise removal methods
against each other, and verified that these did not noticeably affect object
photometry at S/N-levels $\gtrsim$5$\sigma$.

$\bullet$ SW Detector-Level Offsets: The eight detectors in the SW modules A
and B have detector-level offsets that are a combination of additive and
multiplicative corrections, although most of the effect seems to be additive.
Except in very crowded fields, these detector-level offsets are relatively
easy to remove early in our data reduction workflow with the ProFound-based
code (Robotham et al., 2018), as implemented in our previous HST WFC3/IR work
(e.g., Windhorst et al., 2022). In short, with ProFound we created a new
$fits$ extension $SKY$ that removed bad pixels and real objects detected to
AB$\lesssim$28.5 mag (Section 4), and that interpolated the local sky-SB plus
its local rms noise underneath each object. With a number of images in the
NIRCam broad-band filters now available, we created a low-frequency super-sky
image “SKY_SUPER” as a clipped mean over these $SKY$ images. The $SKY$ images
are then subtracted from each science $SCI$ image using:

${\mathrm{(}SCI\ -\ SKY)\ =\ SCI\ -\ (M\ \times\ SKY\\_SUPER\ +\ P)}$ (1)

where $M$ and $P$ describe the linear model and pedestal, respectively, of the
$SKY$ image pixels made for each detector and filter combination from these
SKY_SUPER frames. In this process, we also determine the lowest object-free
sky-SB in each detector following Section 4.2 of Windhorst et al. (2022),
which is used for sky-SB estimates in our 13 PEARLS filters in Section 5. By
design, our medium-deep PEARLS images come from relatively short integrations
(Tables 1–2), and so we adjust our signal-to-noise ratio criteria for object
detection (Section 4) to achieve uniform detection over and above any residual
1/f-noise patterns. Further details of the 1/f and pedestal removal procedures
are given in Appendix A.

$\bullet$ NIRCam SW Detector Wisps: The so-called “wisps” are caused by
straylight hitting a secondary mirror support bar and then being reflected
into the main light path. Only four NIRCam detectors are affected (SW A3, A4,
B3 and B4) and mainly in the F090W, F115W, F150W, F182M, 200W and F210M
filters. Wisps are fixed on the telescope/detector, so that a sky-flat made in
a given filter from available images is able to subtract much of the wisp
patten locally. We use our available NIRCam images to improve available wisp
templates to be subtracted from the images that show visible wisps. We then
subtract the wisp template from each image that matches the wisp amplitude.
This amplitude needs to be high enough that no significant positive wisp
signal is left, but not so high that a negative hole is created in the local
sky-SB. Some wisp residuals are left in some of the images, and we mask these
areas in the faint object detection phase (Section 4.1) and in the sky-SB
analysis (Section 5). Steady collection of NIRCam images over time is expected
to improve the wisp templates sufficiently that they can be more fully
subtracted in future image reductions.

$\bullet$ NIRCam Snowballs: NIRCam detectors can show large artifacts that
resemble “snowballs” when an energetic cosmic ray impact
occurs.212121https://jwst-docs.stsci.edu/data-artifacts-and-
features/snowballs-artifact By far most of the CR-hits only impact a few
detector pixels, but snowballs can affect several hundred pixels. They occur
approximately at the rate of 50 snowballs per 1000 sec of exposure time in
each NIRCam detector. Our four-point dithers in general enable the pipeline to
remove many of the snowball artifacts, but this process is not perfect. E.g.,
Figure 2 shows several dim green rings left over from snowballs that were not
fully removed in the drizzling process of the JWIDF F200W images. Because the
drizzle weight maps give these pixels a lower weight, we can still run
catalogs on this image. We therefore vet these catalogs carefully and mask out
all areas that are visibly affected by residual snowballs when doing faint
object counts in § 4. That is, any large remaining defects that generate a
local excess in the object catalogs are subsequently masked before running our
final catalogs.

$\bullet$ NIRCam PSFs: JWST NIRCam Point Spread Functions are detailed on the
STSCI website.222222https://jwst-docs.stsci.edu/jwst-near-infrared-
camera/nircam-predicted-performance/nircam-point-spread-functions We generated
JWST PSFs with the WebbPSF tool.232323https://www.stsci.edu/jwst/science-
planning/proposal-planning-toolbox/psf-simulation-tool Brighter star images
(Section 4.1) have PSF FWHM values consistent with a diffraction limited
telescope at 1.1 µm wavelength (Rigby et al., 2022), i.e., much better than
the JWST Observatory requirement of a diffraction limit that was designed and
kept during development at 2.00 µm (Section 4.3).

$\bullet$ NIRCam Flat Fields: The accuracy of the NIRCam flat-fields is better
than 7% rms (B. Sunnquist; private communication), and has improved to
$\sim$2% with the most recent release of the flat fields captured in
jwst_0952.pmap_filters. We process our images with this context file to
estimate the sky-SB values in each exposure and assess their quality in § 5.
Table 3 summarizes the parameters that characterize the 0.9–4.5 µm galaxy
counts and integrated galaxy light, which are needed in Tables 4–5 (see
Section 5). Table 4 gives the predicted and observed sky-SB for the three
PEARLS targets observed in 4–8 broad-band NIRCam filters, and Table 5 for
TNJ1338 and its five medium-band filters.

$\bullet$ NIRCam Zeropoints: Measured zeropoints (ZPs) for all
JWST+NIRCam+Filter are on the STScI
website242424https://www.stsci.edu/contents/news/jwst/2022/learn-about-jwsts-
known-scientific. The JWST Mission Requirement is that the absolute ZPs of the
imaging filters are known to better than 5%252525https://jwst-
docs.stsci.edu/jwst-data-calibration-considerations/, and they are stated to
be good to $\sim$4% for Cycle 1, or better (Rigby et al., 2022). The
“Throughput” column in their Table 4 lists the in-flight zeropoints in units
of DN/sec/nJy.

For context, it should be noted that all HST ZPs were defined in units of AB-
mag for a flux of 1.000 $\rm e^{-}$/pixel/s, which permitted monitoring of
their wavelength and time dependence over many decades (e.g., Calamida et al.,
2021; Windhorst et al., 2011, 2022, for a summary, see their Section 4 and
references therein). However, all JWST ZPs instead have been defined in units
of MJy/sr. Conversion between the two sets of units can be made using the
footnotes of Section 1, but also requires knowledge of the drizzled pixels
scale in the case of JWST. For completeness, we will therefore list both the
drizzled pixel scale and the resulting equivalent ZP in AB-mag for our PEARLS
NIRCam images in the footnotes of Table 1 and Appendix B.1.

Note that given this different JWST ZP definition, the equivalent JWST ZPs in
AB-mag are no longer wavelength dependent — unlike the case of HST — but only
depend on the adopted image pixel scale, which therefore should always be
stated. To leave no further ambiguity, for our basic drizzled pixel scale of
0$\buildrel\prime\prime\over{.}$0300/pixel, the JWST ZP for 1.000 MJy/sr
converted to AB-mag would thus be the following same value for every
wavelength:

${\mathrm{8}.900-2.5\ log\ [10^{6}/((360\times\ 3600)/(2\times\pi\times
0.0300))^{2}]\ =\ 28.0865\ AB-mag\ \ per\ \ pixel}$ (2)

The constant 28.0865 in Equation 2 would thus be valid at all wavelengths if
the flux calibration was final and fully implemented. However, the new ZPs in
Table 4 of Rigby et al. (2022) have not yet been implemented into the FITS
headers (in units of MJy/sr) from the STScI CALWEBB NIRCam pipeline versions
1.5.2–1.6.2 before 2022 July 29. One reason for this is that there are modest
variations between the ZPs of each of the 10 NIRCam detectors, and further
observations of on-orbit standard stars are currently being processed to
refine these ZP calibrations accurately for each detector. For the sake of
transparency in our own data products — many of which will be used by the
community very quickly — we decided not to apply the Rigby et al. (2022) ZPs
to the actual data in our FITS files, as the ZPs are still somewhat in flux.
Instead, we list in Table 6 of Appendix B1 the ZP-corrections that must be
applied to our first PEARLS images and mosaics at the catalog level until
STScI releases official ZP updates of the NIRCam pipeline. That is, to each
AB-magnitude derived from our current FITS images the user needs to add the
current ZPs listed on the third line of Table 6 to arrive at the current best
available flux density scale (in AB-mag). Once more definitive ZPs have been
released by STScI, in principle Equation 2 should become valid for all NIRCam
images. The ZP corrections in Table 6 will be verified with our 5-band NIRCam
PEARLS galaxy counts in Section 4.4, and through a comparison with the Spitzer
IRAC1 3.6 µm and IRAC2 4.5 µm fluxes of bright unsaturated objects in Appendix
B1.

$\bullet$ NIRCam Straylight Levels: JWST has an open-architecture Optical
Telescope Element (OTE), and it will have more straylight (SL) than a closed-
tube design such as HST or Spitzer. The JWST Project designed the JWST
sunshield and baffles to minimize SL with expected levels $\lesssim$20–40%
(worst case) of the Zodiacal SB in a given direction. Under non-optimal
angles, the Galactic Center — the brightest near-IR source in the sky other
than the Sun, Earth, and Moon — can add to the SL that reflects off the
sunshield and secondary mirror into the telescope path. Estimates of the SL
levels have been made by Lightsey (e.g., 2016), and are incorporated in
extensions of the JWST ETC predictions. Tables 4–5 show the predicted SL
levels for our PEARLS targets. The predicted SL levels are modest at 0.9–3.5
µm, but they increase at wavelengths longer than 4.5 µm due to the increased
thermal foreground from the telescope and Zodiacal belt. Further details on
the adopted SL levels are given in Section 5 and Appendix C.

Once we have verified that the astrometry and zeropoints of the images are
robust, our v1 mosaics and catalogs will be made available via our PEARLS
websites.262626http://www.webb-site.org or https://sites.google.com/view/webb-
windhorst

##  4 NIRCam Catalogs

###  4.1 PEARLS NIRCam Catalog Construction

We used both the SourceExtractor (Bertin & Arnouts, 1996) and ProFound
(Robotham et al., 2018) packages to generate object catalogs from our
processed NIRCam images. Both packages were designed to deblend close objects
and find the object total fluxes. Details of these procedures are given by
Windhorst et al. (2011) and Windhorst et al. (2022), and we applied similar
procedures to the PEARLS NIRCam images. The current paper focuses on single-
filter PEARLS object catalogs, and so we use the single-filter mode of object
detection with SourceExtractor. That is, we defer the SourceExtractor steps
necessary to produce accurate object colors, such as dual image-mode
extraction, the production of band-merged catalogs, and the application of
PSF-matching and aperture corrections to future papers, which will study the
colors of faint stars and galaxies (Ryan et al. 2022, in preparation), as well
as high-redshift dropout candidates (e.g., Yan et al. in preparation).

The SourceExtractor input parameters for the NIRCam images used a minimum
detection threshold above sky of $1.5\sigma$ and nine connected pixels above
this threshold for inclusion in the catalog. We found that using fewer
connected pixels resulted in too many small spurious sources, particularly in
the LW images where the original pixel size is larger. While the more
stringent nine-pixel requirement may result in missing a few real sources at
the faint end, we found this to be a good compromise between reliability and
completeness at all wavelengths. To detect sources, we used a 5$\times$5 pixel
convolution filter with Gaussian FWHM of 3.0 pixels
(0$\buildrel\prime\prime\over{.}$090). This value is close to the median size
of the faintest galaxies in Figure 6–8 (i.e., with object $\rm
FWHM\sim$0$\buildrel\prime\prime\over{.}$1 or half-light radii
$r_{e}$$\simeq$0$\buildrel\prime\prime\over{.}$05), which enables us to better
detect very faint, low-SB or clumpy galaxies. The SourceExtractor parameter
DEBLEND_MINCONT was set to 0.06 to assure that real objects were not over-
deblended. These parameters were chosen as a balance between extracting
objects deep enough to achieve sample completeness to approximately the
5$\sigma$ detection level (Section 4.2), and not so deep that a visible number
of bogus objects were detected around remaining low-level image artifacts
(Section 3.3).

For all images and filters, the corresponding weight maps were used to account
for image borders and properly characterize the photometric uncertainties and
the effective areas for each drizzled mosaic. We compared our catalogs to the
actual images and weight maps to look for any visible excess objects near
residual image structures and snowballs, and where needed applied additional
masking to the images and their weight maps. Most masked regions are due to
edge-effects, partially exposed regions or strips due to the dither pattern
adapted for our shallow PEARLS exposures, or in some cases due to bright
stellar diffraction spikes (e.g., Figure 3), or due to a residual wisp pattern
that was not completely subtracted. All of these areas were masked in the
object counts.

The first line of Table 1 lists the J2000 tangent point to which the images in
all filters of each target were drizzled, the observing date, the APT visit
number, the area covered, the net exposure time per filter and total net
hours, and the spacecraft efficiency of that visit. The second line for each
filter lists the PSF FWHM, and the third line the 5-sigma point source
sensitivity in AB-mag predicted by the pre-launch ETC for the net integration
time on the first line of each target. The fourth line in each filter
indicates the $\sim$5-sigma detection limit derived from the AB-level in
Figure 6–8, where the median SourceExtractor flux error is 0.20 mag. The fifth
line indicates the AB-level in Figure 6–8 where the galaxy counts are
$\sim$80% complete compared to a power law extrapolation, as derived from the
Figures in Section 4.4.

###  4.2 PEARLS Star–Galaxy Classification Procedure

Carefully separating stars from galaxies is important, especially for the
PEARLS NEP fields, which are located at Galactic latitudes +31$\fdg$6 (JWIDF)
and +33$\fdg$6 (TDF). Fields at these latitudes are expected to have more
faint brown dwarfs than fields at higher Galactic latitudes (e.g., Ryan et
al., 2011, 2017; Jansen & Windhorst, 2018; Ryan et al., 2022). The PEARLS
star–galaxy classification procedure is based on the method described by
Windhorst et al. (2011) for the 10-band WFC3 ERS images and by Windhorst et
al. (2022) for the HST Archival Legacy project SKYSURF sample of $\sim$249,000
HST images. Figure 6–8 show the object detection, classification, and count
diagnostics used for the JWIDF, as well as for the El Gordo non-cluster
module, respectively.

For each detected object, the left panels show the SourceExtractor magnitude
error vs. MAG_AUTO AB-mag. The horizontal dashed line indicates the adopted
detection limit where the MAG_AUTO error is $\geq$0.20 mag, which
approximately corresponds to a $\sim$5$\sigma$ detection for point sources.
Table 1 lists the average AB-mag values where the MAG_AUTO error reaches
$\geq$0.20 mag, as derived from the left panels Figure 6–8.

The middle panels shows the star-galaxy classification diagram using
SourceExtractor MAG_AUTO AB-magnitudes versus image FWHM. The NIRCam
diffraction limit is indicated in by the full-drawn left-most vertical line,
and the FWHM of the PSF is listed in the legend of each middle panel, and in
Table 1. Objects with FWHM $<$ FWHM(PSF) have been flagged and removed from
this plot as spurious detections or border imperfections. In short, objects
detected by SourceExtractor with sizes straddling the NIRCam diffraction limit
to a certain magnitude limit are classified as stars (red dots), following
Windhorst et al. (2011). Stars are generally located in a thin nearly vertical
column bordered by the right-most vertical line. The remainder objects are
classified as galaxies (blue dots). The green dot-dashed lines indicates the
$5\sigma$ sensitivity limits of each image, with the horizontal part showing
the $\sim$5$\sigma$ point-source limit, and the slanted part showing the SB
limit. For further details, see Windhorst et al. (2022).

The right panels of Figure 6–8 shows the resulting star counts (red filled
circles) and galaxy counts (blue filled circles). At most wavelengths
$\gtrsim$0.9 µm and at intermediate to high Galactic latitudes, the star-
counts generally have a very flat slope ($\gamma$$\simeq$0.04 dex/mag), while
the galaxy counts have relatively steep slopes $\gamma$$\simeq$0.21–0.25
dex/mag, continuing the trend seen at 0.2–1.6 µm wavelengths by Windhorst et
al. (2011). As a consequence, galaxies generally dominate the object counts
for AB$\gtrsim$18 mag and far outnumber Galactic stars at fainter magnitudes.
Hence, reliable identification of faint objects as stars becomes difficult for
AB$\gtrsim$26–27 mag, so we will not identify stars for AB$\gtrsim$26 mag.

Future work may be able to expand the identification of somewhat fainter stars
through color-color diagrams and comparison with theoretical stellar loci. In
a prior HST study (Windhorst et al., 2011), comparison of the 10-band WFC3 ERS
star counts to Galactic-structure model predictions verified the star–galaxy
classification procedure a posteriori. A similar check for the JWIDF data is
described in Ryan et al. (2022, in preparation). Windhorst et al. (2011) also
checked their star counts against spectroscopic ones from the HST ACS $R\sim
100$ grism survey “PEARS” (Probing Evolution And Reionization
Spectroscopically; Pirzkal et al., 2009). Such a posteriori verification of
our star–galaxy classification procedure will be possible for the PEARLS TDF
data when we receive the NIRISS grism spectra in all four NEP TDF epochs later
in Cycle 1.

Star-galaxy classification in JWST NIRcam images is — ironically — the hardest
at the bright magnitude levels of AB$\simeq$18–20 mag, as can be seen from
Figure 6–8. These bright objects are generally unsaturated in the NIRCam
images, but due to their significant diffraction spikes are too bright to be
automatically classified by SourceExtractor, and to derive accurate total
magnitudes and colors for accurate color-color diagrams to help identify
Galactic stars, as was done for the WFC3/IR images of Windhorst et al. (2011).
We therefore used the Gaia DR3 (e.g., Gaia Collaboration et al., 2022) catalog
to identify stars with AB$\lesssim$19 mag through their non-zero proper
motions, and the SDSS DR7 spectroscopic catalog to identify stars with
available spectra at AB$\lesssim$17.5 mag. Finally, all objects with
AB$\simeq$17–20 mag were visually inspected by two independent observers in
all NIRCam images and filters to arrive at a consensus on which bright objects
are stars and which are galaxies.

###  4.3 The Wavelength-Dependent Completeness of the PEARLS 0.9–4.5 µm Object
Counts

Figure 6–8 show that the stellar locus (vertical regions with red dots) moves
steadily towards smaller FWHM values at shorter wavelengths from 4.5–0.9 µm.
This is a consequence of the JWST diffraction limit being much better than its
requirement at 2.0 µm. That is, the stellar locus keeps moving to smaller
object sizes in Figure 6–8 in the 1.5 µm filter, and even more so in the
0.9–1.15 µm filters, although the stellar region itself does become somewhat
wider in the F090W and F115W filters. Achieving a diffraction limit well below
2.0 µm wavelength is a major accomplishment for the JWST Project team, as this
had to be planned in between 2003–2005 without further driving up the Project
cost. This was achieved by keeping the diffraction limit requirement at 2.0
µm, but polishing the JWST mirrors well enough that the high frequency Wave
Front Error error would have a sufficiently low rms that a diffraction limit
would be possible at shorter wavelengths than 2.0 µm, as long as the actuators
below the mirrors could remove the main low and mid-frequency errors well. The
telescope in L2 is indeed able to do this (Rigby et al., 2022).

The consequences of this achievement by the JWST Project are far-reaching,
also for the current paper: the median size of the faintest galaxies seen in
the PEARLS images is about FWHM$\simeq$0$\farcs$1, as shown in all the SW
panels of Figure 6–8. Hence, at JWST’s diffraction limited SW resolution at
$\sim$1.1–2.0 µm, essentially all faint galaxies in Figure 6–8 are resolved by
NIRCam, while in the LW 2.7–4.5 µm panels of Figure 6–8 a significant fraction
of faint galaxies remains unresolved, and therefore bunches up right against
the diffraction limits. Hence, faint galaxies with flat SEDs are therefore
more easily detectable with the wider LW PSF of $\sim$0$\farcs$17 FWHM
compared to the SW PSF which has 0$\farcs$06–0$\farcs$08 FWHM. This is
especially visible in Figure 6–8 in the filters F277W and longwards.

We quantified this in Table 1, where we use the 80% galaxy count completeness
limits as a fiducial, which were determined from power-law fits to the counts
and their extrapolations in Section 4.4. In all NIRCam SW filters, the 80%
galaxy count completeness limits typically appear to be about –0.5 to –1.2 mag
brighter than the 5$\sigma$ point source detection limit predicted by the pre-
launch ETC. This is indicated by the $\Delta AB_{lim}$(80%–ETC) values on the
sixth line for each target in Table 1. A small part of this difference is due
to the fact that some SW filters can be $\sim$10% less sensitive than the pre-
launch predictions (Rigby et al., 2022), but for the most part this “apparent
loss” in SW point source sensitivity occurs because the large majority of
faint galaxies observed in NIRCam SW are no longer point sources. The simple
reverse is true for the faintest galaxies in most NIRCam LW filters: our
PEARLS LW galaxy counts appear to be between +0.4 to +1.0 mag more sensitive
than the pre-launch ETC prediction for point sources. Part of this difference
occurs because most LW filters are 20–30% more sensitive than the pre-launch
predictions of Rigby et al. (2022). But in addition, the detection of the
faintest galaxies in the LW images is surely aided by the fact that a
significant fraction of the faintest galaxy sizes no longer exceeds the size
of the PSF FWHM in the LW filters. The tendency of faint galaxies to bunch up
against the HST diffraction limit at brighter flux levels was first suggested
based on the Hubble Deep Field images by Odewahn et al. (e.g., 1996) and
Windhorst et al. (1998), and based on more recent HST images also by Welch et
al. (e.g., 2022c), Windhorst et al. (2021), and references therein.

In conclusion, at the FWHM of the NIRCam PSFs delivered by the JWST Project,
faint galaxies with flat SEDs are thus noticeably easier to detect in the LW
filters compared to the SW filters. Late-type stars are bluer than galaxies
and are point sources in all NIRCam filters, so this PSF-advantage at longer
wavelengths for galaxies does not apply to stars. Current and future JWST
surveys that search for high redshift dropouts and other red objects will need
to keep this rather strongly wavelength dependent sensitivity to the typical
faint galaxy sizes into account.

###  4.4 The Combined 0.9–4.5 µm Galaxy Counts

$\bullet$ The PEARLS 0.9–4.5 µm Galaxy Counts: Our first PEARLS galaxy counts
are based on the data in two of our four fields fields observed so far with
the best available data in Table 1. We used all 10 NIRCam detectors in the
JWIDF plus the four SW and one LW detector in the El Gordo non-cluster module.
A comparison of the two sets of galaxy counts in the four filters where they
overlap then enables us to quantify if the El Gordo non-cluster module was
biased in a measurable way by the presence of the z=0.870 cluster in the
adjacent NIRCam module. We defer reporting galaxy counts in the VV 191 field
to a later paper, due to the presence of the two large targeted galaxies and
bright objects in the same nearby galaxy group that cover some of the other
detectors. We also do not present object counts in the five medium-band
filters of the TNJ-1338$-$1942 protocluster at z=4.1, because these medium-
band images are shallower than the broad-band images in Table 1, and because
there are no reference filters available from the ground for comparison to
brighter object counts. Nonetheless, we did carry out their star-galaxy
classification and object counts, as was done for the JWIDF and El Gordo in
Figure 6–8, to check on the reliability of our procedures, and to report their
5$\sigma$ point source sensitivities in Table 1 as compared to the ETC
predictions.

Our PEARLS galaxy counts in the JWIDF and El Gordo non-cluster module are
shown in Figure 9–10 as the solid brown and orange points, respectively. These
are based on our object catalogs of Section 4.1 with objects identified as
stars removed (Section 4.2). Error bars reflect the statistical uncertainties
in the remaining galaxy counts. At the bright end (AB$\simeq$18-21 mag),
larger discrepancies are seen in the counts between the two fields due to the
uncertainties in the star-galaxy classification procedure (Section 4.2) and
due to Cosmic Variance. Because we use two NIRCam fields far apart in the sky,
their Cosmic Variance is expected to be $\lesssim$9% (e.g., Driver & Robotham,
2010, see Section 2.1 here). This estimate uses the JWIDF+El Gordo survey area
of Table 1 and the redshift distribution expected for NIRCam objects with
18$\lesssim$AB$\lesssim$28 mag, assuming most objects are in the redshift
range 0.3$\lesssim$z$\lesssim$8\. Further details are given in Appendix B.2.
For the brighter fluxes of 18$\lesssim$AB$\lesssim$21 mag, CV can be as high
as 20% for redshifts in the range 0.1$\lesssim$z$\lesssim$1, explaining some
of the statistical variations seen at the bright end of the counts in Figures
9–10.

We will use a CV error of $\sim$9% in our discrete IGL error budget. The
bright end of the 0.9–4.5 µm galaxy counts (18$\lesssim$AB$\lesssim$21 mag)
plotted in Figure 9–10 has many other independent wider-area surveys
contributing (see below), so that the larger NIRCam statistical errors in this
flux range do not weigh significantly into our spline fits to the IGL in
Section 4.5. The bright-end of the 0.9–4.5 µm JWIDF and El Gordo counts in
Figure 9–10 are consistent with each other to within their error bars. Hence,
we see no evidence that the galaxy counts in the El Gordo non-cluster module
are higher than those in the JWIDF, which is a random survey field. We will
thus proceed with the JWIDF and El Gordo non-cluster module counts as
representative for the 0.9-4.5 µm galaxy counts.

A few objects may reside in the large-scale structure associated with the El
Gordo cluster at z=0.870, and may be removed from the galaxy counts in the El
Gordo non-cluster module when more spectra of the cluster and its surroundings
become available. Future work — when the JWST calibration and ZPs improve —
will improve the accuracy of galaxy counts when done over more fields. Given
the high surface density of background galaxies detected by NIRCam, and the
negative magnification bias predicted by the shallow NIR count slopes in
Figure 9–10, more accurate background galaxy counts combined with a weak shear
analysis of the same images may improve mass profile measurements of the
galaxy clusters (e.g., Umetsu et al., 2011).

$\bullet$ Comparison to Previous Galaxy Counts at 0.9–4.5 µm: The PEARLS
0.9–4.5 µm galaxy counts are compared to those at brighter levels from the
combined GAMA (Driver & Robotham, 2010; Driver et al., 2011, 2022) and DEVILS
(Davies et al., 2021) surveys at similar wavelengths in Figure 9–10, and at
the shorter wavelength (0.9–1.6 µm) also with the deepest available HST
counts. A possible check of catalog completeness and reliability is to compare
our relatively shallow NIRCam PEARLS object counts to the deepest available
HST counts in the same or similar filters, and see whether the agreement is
good to within the known ZP-errors and rms counting and Cosmic-Variance
errors, and verify at which flux level catalog incompleteness sets in.

The GAMA and COSMOS/DEVILS survey data were compiled over a wide range of
wavelengths by Driver et al. (2016b, and references therein), building on the
GAMA panchromatic data release of Driver et al. (2016a), and the COSMOS data
as reanalyzed by Andrews et al. (2017). This compendium was later extended by
Davies et al. (2018) and Davies et al. (2021) as part of the DEVILS survey,
while Bellstedt et al. (2020) extended the GAMA data to also include the ESO
VST KiDS data. These updates were reported in Koushan et al. (2021), which
forms the basis of the galaxy counting data used here. We note that these
compendia also include the ESO K-bands counts of Fontana et al. (2014), deep
Spitzer 3.6 and 4.5 µm counts (e.g., Ashby et al., 2009; Mauduit et al., 2012;
Ashby et al., 2015), and the WISE counts of Jarrett et al. (2017). The typical
combined ZP uncertainties in the combined GAMA+DEVILS surveys in the z-band to
K-band are 2–3%, after bringing the flux scale in every filter onto the flux
scale of the VISTA survey filters, which incorporated the GAMA and DEVILS
surveys (Table 4 of Koushan et al., 2021).

The deepest panchromatic HST ACS+WFC3 galaxy counts come from the combined
HUDF images, whose database has grown considerably over time since the launch
of WFC3 in May 2009 (see e.g., Windhorst et al., 2011; Koekemoer et al., 2013;
Rafelski et al., 2015, and references therein). Following the rich HUDF
database summarized in these papers, the panchromatic galaxy counts were once
more repeated on the deepest available HUDF images in 2015 with the same
procedures as in § 4.1–4.2, and were included in Driver et al. (2016b) and
Koushan et al. (2021). [This deepest 2015 realization of the HUDF counts is
listed in the legend of Figure 9–10 as “Windhorst et al. (2011, +)”, and
follows the same methods.] The ZP errors in the HST ACS, WFC/UVIS and WFC3/IR
images over the decades resulted in flux scales accurate to 1–3%, as
summarized in Section 4.1.5 and Table 5 of Windhorst et al. (2022), i.e.,
comparable to or slightly better than the 2–3% ZP accuracy of the VISTA
filters of Koushan et al. (2021).

To within these ZP errors, our 0.9–1.5 µm PEARLS galaxy counts that come from
shallow NIRCam exposures are consistent with the deeper HUDF counts in the HST
ACS and WFC3/IR filters F850LP, F105W/F125W, and F160W (green open circles in
Figures 9–10). It is important to realize that our PEARLS galaxy galaxy counts
come from 1890–3157 sec NIRCam exposures in the F090W, F115W, and F150W
filters and reach $\sim$28.5 mag (Table 1), while the HUDF galaxy counts reach
$\sim$29.5–28.5 mag in $\sim$156–87 HST orbits ($\simeq$117–65 hours of net
exposure time; Beckwith et al., 2006; Koekemoer et al., 2013) in the F850LP,
F105W/F125W, and F160W filters, respectively. Compared to the total HUDF ACS
exposure time of 421.6 ksec in F850LP, JWST NIRCam thus reaches approximately
$\sim$5$\times$ deeper per unit time in its F090W filter, while compared to
the total HUDF WFC3/IR exposure time of 236.1 ksec in F160W, NIRCam reaches
about $\sim$9–11$\times$ deeper per unit time in its F150W filter,
respectively. In the light of the discussion in § 4.3, this is an impressive
performance improvement, especially because NIRCam was optimized for
performance longwards of 2.0 µm.

Because our PEARLS 0.9–4.5 µm galaxy counts are done in NIRCam filters whose
central wavelengths and bandpasses can be somewhat different from the VISTA,
GAMA and DEVILS surveys, we investigate in Section B.2 if additional
corrections to the NIRCam flux scale are needed to compare the galaxy counts
in similar filters. For this, we use the fiducial flux scale of the VISTA
filters into which the GAMA and DEVILS surveys were anchored (Koushan et al.,
2021). Section B.2 shows that the corrections needed to transform the NIRCam
AB-mag scale onto the fiducial VISTA/IRAC filters are $\lesssim$3–4% with
combined uncertainties of $\lesssim$3–6%, so that no corrections to the NIRCam
AB-mag scale needed to be applied when plotting the results in Figure 9–10.
However, we will fold this $\lesssim$3–6% uncertainty into our error budget of
the 0.9–4.5 µm IGL of Section 4.5. We now have all the ingredients in place to
compare our 0.9–4.5 µm NIRCam galaxy counts to previous work at brighter
levels, and can do so without further wavelength dependent ZP corrections.

$\bullet$ The Faint-End Slope of the Combined 0.9–4.5 µm Galaxy Counts: Figure
9–10 compare our PEARLS 0.9-4.5 µm galaxy counts to previous work summarized
above, and extend it to AB$\lesssim$29 mag over this wavelength range. The
green lines in the left panels represent the hierarchical model predictions
for the 0.9–4.5 µm galaxy counts of Yung et al. (2022). Over the
AB$\simeq$16–29 mag range for which they are available at 0.9–4.5 µm, the Yung
et al. (2022) models are consistent with the combined GAMA/DEVILS ground-
based, Spitzer/WISE and our PEARLS NIRcam galaxy counts. The faint-end slopes
of our observed PEARLS galaxy counts have an average value of
$\gamma$$\simeq$0.23 $\pm$ 0.04 dex/mag over the magnitude range of
AB$\simeq$22–29 mag where the counts are a nearly straight power-law in Figure
9–10 (see Table 3). The middle panels of Figure 9–10 show the 0.9–4.5 µm IGL
energy counts, and are derived from the left panels after dividing by the 0.4
slope. The spline extrapolations (grey lines and error fans) in the middle
panels show that energy counts are clearly converging at all these
wavelengths. The resulting IGL integral is shown in the right panels of Figure
9–10. Under the assumption that the faint galaxy counts at AB$\gtrsim$29 mag
continue as a power-law with the same slope as observed for AB$\simeq$22–29
mag, these spline extrapolations will form the basis of our IGL values used in
Section 4.5 & 5. Further details on the faint-end slope of the galaxy counts
and its wavelength dependence are given by Tompkins et al. (2022, in
preparation).

A magnitude slope value of $\gamma$$\simeq$0.23 mag/dex corresponds to a faint
end slope of $\alpha$$\simeq$–1.58$\pm$0.1 in flux units, where $\alpha$ = –1
–2.5$\gamma$. Ground-based spectroscopic surveys with VLT/MUSE and the
spectro-photometric survey 3DHST with Hubble suggests that faint galaxies with
AB$\simeq$23–29 mag have a median redshift in the range $z_{\rm
med}$$\simeq$1–2 (e.g., Skelton et al., 2014; Inami et al., 2017), with the
caveat that the completeness of these surveys becomes more difficult to
quantify at faint magnitudes. Around this median redshift, the faint end of
the galaxy counts sample the power-law part of the Schechter luminosity
function (LF), which also has a faint-end flux slope of $\alpha$$\simeq$–1.4
to –1.5 at z$\simeq$1.5 (e.g., Hathi et al., 2010; Finkelstein, 2016). In
conclusion, over the AB$\simeq$22–29 magnitude range sampled by our 0.9–4.5 µm
NIRCam images, the PEARLS galaxy counts have a slope consistent with the
faint-end slope of the Schechter LF at the median redshift sampled by these
objects. Fainter JWST galaxy counts would then be expected to continue with
the same slope, if the LF over this redshift range were to continue with the
same slope towards fainter luminosities. Upcoming ultradeep JWST NIRCam GTO
surveys (M. Rieke PI) are designed to cast light on this issue.

###  4.5 Characteristics of the Integrated Galaxy Light at 0.9–4.5 µm

The parameters that best characterize the 0.9–4.5 µm galaxy counts and
integrated galaxy light are summarized Figure 11. The errors on these
parameters are summarized in Table 3 for each filter as the quadratic sum of
the NIRCam ZP uncertainties from Section B.1, and the uncertainty in bringing
the flux scale of the NIRCam filters onto the central wavelengths of the
VISTA/IRAC filters used as fiducial for the galaxy counts (Section 4.4). Some
interesting trends can be seen in the galaxy counts over the wavelength range
0.90–4.5 µm from Figure 9–10. (These trends are best seen if all PNG files of
Figures 9–10 are shown on a computer screen at high magnification in rapid
succession — all panels are plotted at exactly the same scale for this
purpose). The smooth behavior of the data in Figure 11 suggests that these
trends in the IGL are real and meaningful:

  1. 1.

The left panels of Figure 9–10 all show a clear change in slope from a steep
non-converging slope ($\rm\geq 0.4$ dex/mag) to shallow and converging slope
($\rm<0.4$ dex/mag). This change sets in around AB$\sim$19.3–20.3 mag at
0.9–4.5 µm (top panel of Figure 11). This peak AB-mag is the flux level where
most of the IGL is generated, and is a clear function of wavelength.

  2. 2.

In more detail, the normalized differential counts reaches the highest SB-
level around 2–3 µm wavelength, and declines to both longer and shorter
wavelengths (first and second panel of Figure 11, where the second panel shows
the peak SB-value of the IGL at the AB-magnitude peak of the top panel).

  3. 3.

The magnitude range over which most of the IGL is generated (here called the
“IGL FWHM” and measured as the interquartile 25%–75% range of the middle
panels in Figures 9–10) decreases from $\sim$4.5 mag at wavelengths
$\lesssim$1.25 µm to $\sim$3 mag at 4.5 µm (third panel of Figure 11). This
reflects the luminosity function and redshift distribution of the older,
earlier-type galaxies that dominate the 3.5–4.5 µm galaxy counts. These
filters sample the rest-frame $\sim$1.5 µm peak in the stellar emission of
early-type galaxies as caused by their stellar mass distribution at
z$\simeq$1–2. The JWST images at these wavelengths are visibly dominated by
earlier-type galaxies. At shorter wavelengths, we sample a larger fraction of
galaxies of later-types, which have a wider range of ages and extinction, and
extend to lower luminosities and redshifts, causing their larger contribution
to the IGL at bluer wavelengths (e.g., Driver et al., 1995, 2016b; Andrews et
al., 2018; Koushan et al., 2021). This can be seen in the wider distribution
in the width of the IGL at blue wavelengths in the third panel of Figure 11.

  4. 4.

The discrete IGL with the highest energy (in units of nW m-2 sr-1) comes from
wavelengths between 1 and 2 µm, as shown in the bottom panel of Figure 11.
These are derived from the converging integrals from the right panels of
Figure 9–10, and are the values we plot in the last Figure of Section 5, which
provides further discussion of the total IGL and diffuse light.

The new JWST results are most noticeable at the faint end of the AB-magnitude
scale plotted in Figures 9–10. The bright end of the galaxy counts can be —
and has been — done from the ground (i.e., the $z$, $Y$-, $J$-, $H$-, and
$K$-band filters) or from space with WISE and Spitzer at $L$\- (3.5 µm) and
$M$-band (4.5 µm). Nevertheless, JWST NIRCam and also HST WFC3/IR below 1.6 µm
have unique filters that are valuable for object counts. These include WFC3
F140W, and NIRCam F277W and F410M, as well as the other medium-band filters in
Table 1. These have no ground-based counterparts because telluric water vapor
blocks these wavelengths. Therefore bright-end galaxy counts to make a full
energy integral as in Figure 9–10 are absent for the F277W and F410M as well
as the medium-band filters.

Because of this, Carleton et al. (2022) had to interpolate the IGL integral in
the WFC3/IR F140W filter from the adjacent WFC3/IR F140W F125W and F160W
filters, which have extensive ground-based coverage of the bright end counts.
Fortunately, this is straightforward because the IGL SB is flat between the
F125W and F160W filters (Figure 11d). We therefore give low-order spline
functions to the IGL parameters vs. wavelength in Table 3 and plot these in
Figure 11. We use these splines to interpolate the IGL SB-values for the JWST
NIRCam F277W and F410M filters, which are tabulated as the PEARLS-IGL values
in Tables 4–5. This includes the values beyond the PEARLS NIRCam detection
limits of AB$>$28.5 mag, which were derived from the integrals in the right-
hand panels in Figures 9–10. This allows us to estimate the IGL as a function
of wavelength for the JWST filters at 0.9–4.5 µm wavelength, including the
$\lesssim$2.5% of the IGL that is not included in our faint object counts to
AB$\lesssim$28.5 mag (see Section 5.2 here, and Section 3.4.3 of Carleton et
al., 2022 for its procedure). Wider-area JWST surveys such as e.g., COSMOS-
Webb (PI J. Kartaltepe) will become available during JWST’s lifetime to
improve the bright-end of the galaxy counts, as they have for HST ACS and WFC3
during the last two decades (e.g., Windhorst et al., 2022).

##  5 NIRCam 13-band sky-SB Estimates and Limits on Diffuse Light

In this section we give our estimates of the sky-SB as measured in between the
detected discrete objects in the 13-band NIRCam filters observed with PEARLS,
and assess if we can set meaningful limits to diffuse light in excess of the
Integrated Galaxy Light from Section 4.4. In this process, we account for the
NIRCam systematics summarized in Section 3 and Appendix B.1–B.3 & C, and
include these in our error budget.

###  5.1 JWST sky-SB in the Context of Previous Diffuse Light Limits

JWST’s ability to work continuously in a dark-sky environment makes it
especially suitable for measurements of sky-SB. This is in contrast with HST,
which at best gets complete dark time for at most $\sim$30 minutes of its
96-minute orbit (e.g., Caddy & Spitler, 2021; Caddy et al., 2022; Windhorst et
al., 2022). Figures 12–13 summarize the astrophysical foreground and
background energy relevant to PEARLS compared to recent data (as summarized by
e.g., Driver et al., 2016b; Koushan et al., 2021; Carleton et al., 2022) and
IGL models (e.g., Andrews et al., 2018). Figure 13 shows the PEARLS discrete
IGL measurements of Section 4.5 compared to those of D16 and Koushan et al.
(2021). The IGL is sum of the integrated (observed) galaxy counts (iEBL) and
extrapolated galaxy counts (eEBL) derived in Section 4.4. The black line shown
in Figure 13 13 is a modification of the Andrews et al. (2018) IGL model for
accumulated star-formation in spheroids (red dashed), disks (green dashed),
and unobscured AGN (purple dashed lines). Here we have adjusted these
contributing elements from the published Andrews et al. (2018) model, as
described in the caption, to better fit the IGL data including the PEARLS
points.

JWST was meticulously designed and built to have the darkest possible sky as
seen from L2. Here we explore explore its capability to constrain potential
levels of diffuse light. Windhorst et al. (2022) stated that over 95% of the
0.6–1.25 µm photons in the HST archive come from the Zodiacal light in the
interplanetary dust (IPD) cloud, i.e., from distances $<$5 AU. This can also
be seen by comparing the typical Zodiacal light levels (green line) to the IGL
counts in Figure 13. The Zodiacal/IGL ratio decreases significantly towards
longer wavelengths in the 1.5–3.5 µm wavelength range. This is because the Sun
is a zero redshift 5770 K G-star, and the IGL is the summation over multiple
stellar populations, including hotter and cool stars, spanning a wide redshift
range (e.g., Madau & Dickinson, 2014). Longwards of 3.5 µm, thermal radiation
from the Zodiacal belt and also from the JWST telescope and instruments make
increasing contributions to the SB levels. The wavelength dependence of the
diffuse light is precisely what JWST can explore from its first images,
bearing in mind the significant JWST and NIRCam calibration uncertainties that
we expect (Section 3.3 and Appendix B.1–B.3).

Obtaining Diffuse Light (DL) estimates requires accurate modeling of the
Zodiacal Light (ZL) and Diffuse Galactic Light (DGL). ZL can be
$\sim$10–70$\times$ higher than the discrete iEBL+eEBL, dependent on the
direction and time of observation. (Figure 12–Figure 13). Constraints from
previous work shown in Figure 13 suggest that there may exist some level of
diffuse light at 0.6–1.6 µm, at a level of $\sim$8–30 nW m-2 sr-1. (Note that
all diffuse light estimates plotted in color in Figure 13 have the full IGL
already subtracted, and so truly represent the diffuse light levels or limits
reported by various groups). At this stage, it is not clear whether this
diffuse light is due to residual instrumental systematics that have not been
accounted for, a dim Zodiacal component (perhaps spherical or spheroidal) seen
from 1 AU that is not accounted for in the Zodiacal models, a truly diffuse
EBL component, or some combination of these possibilities. For details on this
topic, please see the discussions by, e.g., Conselice et al. (2016), Matsuura
et al. (2017), Sano et al. (2020), Carleton et al. (2022), Korngut et al.
(2022), Kramer et al. (2022), Lauer et al. (2022), and Windhorst et al.
(2022), and references therein.

###  5.2 JWST sky-SB Estimates and Possible Limits to Diffuse Light

Following Equation (2) of Windhorst et al. (2022), the sky-SB level between
the detected objects is a sum of Zodiacal Light, Diffuse Galactic Light, and
residual instrumental systematics including thermal and straylight
contributions. For JWST, that equation is:

${\rm SB}(\lambda,l^{\rm Ecl},b^{\rm Ecl},l^{\rm II},b^{\rm II},t,{\rm
SA},T)={\rm Th}(\lambda,T)+{\rm SL}(\lambda,l^{\rm Ecl},b^{\rm Ecl},t)+{\rm
ZL}(\lambda,l^{\rm Ecl},b^{\rm Ecl},t,{\rm SA})+{\rm DGL}(\lambda,l^{\rm
II},b^{\rm II})+{\rm dEBL}(\lambda)$ (3)

The left term in Equation 3 is the total sky-SB that JWST observes as a
function of wavelength $\lambda$, Ecliptic coordinates ($l^{\rm Ecl}$, $b^{\rm
Ecl}$), Galactic coordinates ($l^{\rm II}$, $b^{\rm II}$), time of the year
($t$ or Modified Julian Date MJD), solar elongation angle (SA), and telescope
and instrument temperatures, symbolized by $T$. The terms on the right side
include: thermal (Th) signal from blackbody photons in the instruments and
telescope that depends on wavelength and temperature; straylight (SL) that
depends on wavelength, pointing direction, and observing date (Section 3.3);
Zodiacal light (ZL) as seen from L2 that depends on wavelength, Ecliptic
coordinates, and observing date via its effect on the SA and on the path
through the Zodiacal dust cloud (especially JWST’s position above or below the
Ecliptic plane; see Appendix C); Diffuse Galactic Light that depends on
wavelength and Galactic coordinates; and any diffuse EBL (dEBL) that is not
already included in the discrete object catalogs to AB$\lesssim$28.5 mag in
Section 4, and therefore not yet masked out from our NIRCam images. This last
term includes the part of the discrete IGL extrapolated for AB$\gtrsim$28.5
mag (i.e., the eEBL), which is generally small and subtracted below.

At the start of JWST Cycle 1, we only have a limited number of JWST images and
so can only explore limits for the sky-SB values observed from L2 thus far by
PEARLS. When more JWST images become available, its full database can be
studied following the SKYSURF methods of Carleton et al. (2022), who presented
the HST sky-SB between discrete objects in 34,000 WFC3/IR images, and set
constraints on diffuse light from the subset of HST 1.25–1.6 µm images with
the darkest sky-SB values. At this stage, we will use our darkest available
JWST images to explore what constraints can be made currently and how these
may be improved in the future during JWST’s lifetime.

We measured sky-SB in the NIRCam images following the SKYSURF procedures of
Windhorst et al. (2022) and Carleton et al. (2022). In short, SKYSURF removes
the light from all detected objects from the WFC3/IR images even in short HST
exposures ($t_{\rm exp}$$\simeq$500 s) to a total-object flux limit of
$AB\lesssim 26.5$ mag at 1.25–1.6 µm wavelengths. For JWST NIRCam, we used the
same codes to remove the light from all detected objects (Section 3.3 and
Appendix B.3) to $\rm AB\lesssim 27.5$–28.5 mag at 0.9–4.5 µm given the
detection limits in Table 1, at which flux levels $\gtrsim$97.5% of the
discrete IGL is already detected in the JWST images, as the right panels of
Figure 9–10 show. Details on the uncertainty in the estimated sky-SB are given
in Appendix B.3.

Tables 4–5 summarize the JWST NIRCam instrumental and astronomical background
levels as predicted from, or actually observed from L2 for each of the four
PEARLS targets observed as of 2022 July 31. Background components are assumed
to be uniform across the field-of-view but depend on wavelength. Tables 4–5
list the ETC predictions for the L2 Zodi, thermal and straylight, as well as
the straylight level of Rigby et al. (2022, using their Figure 5). Details on
these predicted sky-SB component values and their uncertainties are given in
Appendix C, and we summarize aspects relevant to current discussion here. The
sky-SB predictions for all these components and their uncertainties (where
relevant) are given in Tables 4–5, and include:

  * •

(1) The ETC-predicted JWST thermal radiation, which is more than 100$\times$
lower than the predicted total sky-SB even at 4.5 µm (see Figure 12) and is
the dimmest component in Equation 3 for $\lambda$$\lesssim$4µm.

  * •

(2) The L2 model prediction for the Zodiacal sky-SB for each target. This was
based on the position and orientation of JWST at the actual time of the
observation. These are based on the (Kelsall et al., 1998) model, but for the
Zodiacal cloud geometry for L2 as seen by JWST at the time of the observation.
These predictions are uncertain by at least $\sim$9%–4% of the dimmest
Zodiacal sky-SB observed over the range 1.25–4.5µm, respectively.

  * •

(3) The IPAC IRSA prediction for the DGL value at the Galactic coordinates of
the target. The DGL is generally a factor of 20–100$\times$ lower than the
total predicted JWST sky-SB, and uncertain by up $\sim$0.3 dex.

  * •

(4) The actual straylight, which Rigby et al. (2022) noted is likely lower
than the pre-flight predictions. Indeed, using the full Rigby et al. (2022) SL
values would make the total sky-SB predictions from Equation 3 exceed the
observed values from our 13 PEARLS filters in Tables 4–5. As discussed above,
the 0.9–3.5 µm Zodiacal component is caused by Sunlight scattered off the
Zodiacal dust cloud components, and may have been underestimated in some of
the models. The 3.5–4.5µm sky-SB is dominated by the thermal contribution from
the Zodiacal dust cloud components, while the telescope+instrument thermal
components are still negligible in these filters. The thermal Zodical Light at
$\lambda$$\gtrsim$3.5 µm was the key component to be modeled by Kelsall et al.
(1998) for their COBE/DIRBE analysis. The minimum sky-SB is predicted to occur
around 3.5 µm in wavelength (Figures 12–13), so we will assume that: (a) the
thermal Zodiacal components at $\lambda$$\gtrsim$3.5 µm are more accurately
predicted than the scattered Sunlight components at $\lambda$$\lesssim$3.5 µm;
and (b) the predicted thermal Zodiacal components should match the values
observed at 4.5 µm without exceeding those observed in the minimum at 3.5µm.
Any truly diffuse astrophysical source is expected to be much dimmer than
this, so we do not expect it to significantly affect our fitting. In order to
not exceed the observed PEARLS sky-SB values, we then find that the implied SL
values are generally $f$$\sim$60–90% of the (Rigby et al., 2022) SL-values,
with an uncertainty in $f$ of at least $\sim$20% in Equation 4 (see Appendix
C). With these adopted SL values in Tables 4–5, our total predicted JWST sky-
SB matches the observations in all 13 PEARLS filters in Figure 12 to within
the uncertainties summarized above, using Equations 4–5 below.

  * •

(5) We use the IGL integral for the seven fiducial filter wavelengths in
Section 4.4 from Figure 9–10. For the NIRCam wavelengths for which a full IGL
integral is not yet available (F277W, F410M, and the medium-band filters), we
used the spline predictions at those wavelengths from Figure 11. The IGL is
assumed to be constant across the sky, and therefore to be the same for each
PEARLS target. The extrapolated discrete eEBL (eEBL) of objects currently
undetected in the JWST images for AB$\gtrsim$28.5 mag is derived from Figure
9–10 (Section 4.5). The extrapolation for AB$\gtrsim$28.5 mag typically
amounts to $\sim$2.5% of the total IGL. This is the only part of the IGL that
still need to be subtracted from the sky-SB, as our method already
automatically removes $\sim$97.5% of the light from all brighter objects
detected by NIRCam to AB$\lesssim$28.5 mag (see Carleton et al., 2022, for
details). (Section 4.7 and Equation 3 of Windhorst et al. (2022) also
corrected this fraction for SB-incompleteness, which can amount to $\sim$40%
at AB$\sim$28 mag, but the IGL correction for known objects at AB$\gtrsim$28.5
mag remains very small).

Our best prediction of the observed JWST Zodiacal sky-SB is then:

${\rm JWST(Pred)=ETC(Thermal)\ +\ f\times SL(Rigby2022)\ +\ Zodi(L2)\ +\ DGL\
+\ eEBL,}$ (4)

Tables 4–5 give this sum as predicted from the above models in all filters at
the actual time of PEARLS observations. Finally, these tables list our upper
limits to any diffuse light (DL) as the difference between the observed JWST
sky-SB “JWST(Obs)” and the total prediction of Equation 4 for L2:

${\rm Diffuse\ Light\ limit\ \lesssim\ JWST(Obs)\ -\ JWST(Pred)\ \pm\
ErrorBudget}$ (5)

The results from Equation 4–5 are listed on the bottom lines in each tier of
Tables 4–5. This includes the full error budget of $\sim$7–11% for JWST(Obs)
in the LW–SW modules, respectively, from Appendix B.3, and the combined
uncertainty of $\sim$10% in JWST(Pred) from Appendix C. The total uncertainty
in the difference of Equation 5 is thus $\sim$12–15% of the total sky-SB in
the LW–SW modules, respectively, assuming that the Observed and Predicted sky-
SB values are independent.

With our assumption that the total sky-SB model should fully predict the
observed sky-SB values in the four PEARLS filters at 3.5–4.5 µm, the model
predictions generally also match the observed sky-SB in the seven PEARLS
filters at 0.9–3.5 µm, including the medium-band filters in TNJ1338, within
the combined uncertainties. Therefore, to within the error budget of the
current assessment, we have no firm detection of remaining DL by JWST NIRCam.

Accordingly, all our diffuse light constraints are plotted as upper limits
using the combined uncertainties of Appendix B–C in Figure 13. Brown downward
open triangles indicate upper limits from our deepest filter-exposures in the
JWIDF and the El Gordo non-cluster field. Brown downward asterisk- and tripod-
shape indicate the upper limits from the shallower TNJ and VV191 exposures. We
excluded in this process the detectors that contained the overlapping nearby
galaxy pair of Figure 5 and other large objects. Our PEARLS constraints in
Figure 13 indicate upper limits to Diffuse Light captured by the grey hashed
area, and generally amount to 10–15% of the total sky-SB observed by NIRCam.
Within the current uncertainties in the JWST NIRCam calibration and in the
total JWST sky-SB model, we cannot make firmer statements about the diffuse
light as seen by JWST. In particular, if the JWST SL were even lower than we
adopted here, firmer constraints on DL may be made. For this purpose, future
work will require a more accurate assessment of the NIRCam calibration
uncertainties, and more accurate models for the JWST straylight, the Zodiacal
Light as seen from L2, and for the DGL.

The two JWIDF diffuse light points at 1.5 and 2.0 µm are marginally above the
total model predictions in Figure 12d. Our F150W and F200W NIRCam diffuse
light limits in Figure 13 are in line with the 1.1–1.6 µm CIBER detections of
Matsuura et al. (2017) and Sano et al. (2020) (purple triangles in Figure 13),
and with the SKYSURF upper limits in the HST/WFC3 F140W and F160M filters of
Carleton et al. (2022). These papers, as well as Tsumura (2018) and Korngut et
al. (2022), suggested that some very dim spherical — or nearly spherical —
Zodiacal component could be missing from the Kelsall et al. (1998) model.
Kelsall et al. (1998) also noted that a dim spherical Zodiacal component could
have been missed in their model of the COBE/DIRBE data.

At the longer NIRCam wavelengths of 2.7–4.5 µm, the PEARLS DL limits in Figure
13 are lower in value, reaching as low as 8–12 nW m-2 sr-1, and consistently
so between our four PEARLS fields within the current error budget. This is
because the total sky-SB is significantly darker (Figure 12), and the total
error budget of the LW modules (Appendix B) and the uncertainties in the sky-
SB models are correspondingly smaller at 2.7–4.5 µm (Appendix C).

##  6 Discussion

It is remarkable how even the first JWST images of our PEARLS fields — with
relatively short NIRCam exposures in a total of 13 filters — give us a fresh
look on the distant Universe. The fact that JWST achieved its diffraction
limit at wavelengths well below 2.0 µm is a tremendous achievement for the
JWST Project and of great value to the community. The JWST NIRCam PSF is so
sharp and stable that star–galaxy classification is straightforward with
existing methods, even in short exposures. The same is true for making object
catalogs and deriving galaxy counts. At 0.9, 1.1, 1.5, 2.0, 3.5, and 4.5 µm
wavelengths, comparison to existing ground-based, WISE, and Spitzer galaxy
counts is also straightforward.

Our JWST galaxy counts of Section 4.4 agree well with previous work, but go
$\gtrsim$2 mag deeper even in our short NIRCam exposures. Combining two fields
that are separated widely in the sky decreases the Cosmic Variance component
of the uncertainty in the counts, which can be $\lesssim$9%, or more at
brighter levels (e.g., Driver & Robotham, 2010, see Section 2). The combined
error in the counts from ZPs ($\lesssim$4%), transforming to the VISTA/IRAC
filters system ($\lesssim$3–6%), and CV ($\lesssim$9%) is $\lesssim$10–12%
(Section Appendix C), which is our uncertainty in the IGL. The galaxy counts
at 0.9, 1.1, 1.5, 2.0, 3.5, and 4.5 µm show some interesting trends. The
energy-normalized differential galaxy counts reach a maximum in the range
$\sim$19.3–20.3 AB mag. Objects in this range produce most of the IGL per
magnitude bin. The actual flux level in AB-mag and the width of the peak are
both functions of wavelength. This reflects the luminosity function and
redshift distribution of the galaxy population that dominates each of these
wavelengths.

The galaxy population slowly changes from later-type galaxies at lower
redshifts dominating in the blue (including the HST-unique wavelengths) to
earlier-type galaxies at higher redshifts that dominate at 3.56 and 4.44 µm.
Yet, not all of these galaxies are ellipticals, as discussed below. As the
beautiful first NIRCam images already attest, JWST images will thus see a
greater dominance of, and emphasis on, earlier-type galaxies, which will stand
out the most in JWST images (e.g., Ferreira et al., 2022). This is in contrast
to the “Faint Blue Galaxy” population of actively star-forming galaxies that
have dominated HST’s UV–optimally images for the past decades (e.g., Abraham
et al., 1996; Driver et al., 1995; Windhorst et al., 2011). The morphology of
nearby galaxies can be strongly wavelength dependent (e.g., Taylor-Mager et
al., 2007; Mager et al., 2018; Windhorst et al., 2002, and references
therein), especially for the earlier-type galaxies. We should therefore expect
that JWST will put our studies of “old galaxies” in a new light, and provide
the first glimpse of the first galaxies.

Our 3.5–4.5 µm IGL values in Figure 13 are somewhat below the Driver et al.
(2016b) points, but not significantly so given the current error budget. As a
consequence, to provide a best fit to the total PEARLS IGL in Figure 13, we
needed to reduce the spheroid contribution in the Andrews et al. (2018) model
to 95% of its value, and increase their disk component by 30% to match the
3.5–4.5 µm PEARLS points, while decreasing the unobscured AGN sky-SB to 75% of
Andrews et al. (2018) model in order to not over predict the UV-optical IGL
values of Driver et al. (2016b) and Koushan et al. (2021). To within the
current uncertainties, these conclusions are not unique, but they may point to
the need for a more significant fraction of red spiral galaxies at 3.4–4.5 µm,
as other recent JWST work has suggested (e.g., Ferreira et al., 2022; Fudamoto
et al., 2022). With the new NIRCam images now at hand, future IGL models may
need to include a larger fraction of (dusty) spirals.

Our 3.5–4.5 µm PEARLS IGL values are $\lesssim$40–50% below the direct EBL
constraints in Figures 13 from MAGIC (e.g., Dwek & Krennrich, 2013; Ahnen et
al., 2015, 2016), which are estimated from how intervening EBL photons distort
the $\gamma$-ray spectra of blazars over a range of redshifts. More recent
$\gamma$-ray blazar results are converging closer towards the total IGL values
(e.g., Fermi-LAT Collaboration et al., 2018). Various sources of diffuse light
may cause a discrepancy between the IGL and the $\gamma$-ray constraints, as
discussed by, e.g., Driver et al. (2016b), Windhorst et al. (2018), Carleton
et al. (2022), and Windhorst et al. (2022), and references therein. A possible
source of Diffuse Light are tidal tails of long-lived stars pulled out in
galaxy interactions over the entire redshift range where galaxy assembly
happens. For instance, Ashcraft et al. (2018) and Ashcraft et al. (2022)
analyzed ultradeep (32 hr) ground-based LBT U-band and r-band images at
various stacked seeing-FWHM values, and find r-band tidal tails in galaxy
pairs up to z$\lesssim$0.5–0.9. They suggest that $\sim$10–20% of the galaxy
light from brighter galaxies (AB$\sim$20–23 mag, which cause most of the IGL
in Figures 9–11) may be at large radii to SB-limits of AB$\lesssim$31–32 mag
arcsec-2. It is unlikely then that tidal tails between galaxies produce well
over 20% of the IGL. Remarkably, though, JWST indeed sees tidal tails between
galaxy pairs and groups in the CEERS images of Finkelstein et al. (2022), some
of which can be also seen in our JWIDF image of Figure 2 here. If tidal tails
consisting of older stars pulled out during galaxy interactions are common
place, future JWST imaging should find many more such examples, and be able to
better quantify the amount of DL present in dim tidal tails of faint galaxies.
At the median redshift of these galaxies, NIRCam 0.9–4.5 µm images are ideal
for such a study.

Our 3.5–4.5 µm PEARLS IGL values are a factor of $\sim$2–3 below our current
PEARLS DL constraints, as shown in Figure 13. At our reddest wavelengths of
2.7–4.5 µm, our PEARLS diffuse light limits are about $\sim$8–12 nW m-2 sr-1,
i.e., about the same level as the diffuse light seen by Lauer et al. (2022) at
$\sim$51 AU, who found a signal of 8$\pm$2 nW m-2 sr-1 at 0.6 µm. If such
diffuse light were caused by tidal tails or other stellar populations during
the history of cosmic star-formation, one may expect it to have a similar
wavelength dependence as the IGL, or be redder, i.e., the diffuse light seen
by Lauer et al. (2022) would amount to $\sim$5–7 nW m-2 sr-1 at 3.5 µm. This
is below our current diffuse light limits, but higher than the PEARLS IGL
values in Figure 13. When the JWST calibrations improve over time, and models
for its total sky-SB predictions are improved, future work should be able to
better assess how much truly diffuse light can be present in the infrared, and
what its nature may be.

##  7 Summary and Conclusions

In this paper, we present an overview and describe the rationale, methods, and
first results from the JWST GTO project “PEARLS.” The following are our main
highlights and results:

$\bullet$ (1) The first PEARLS NIRCam observations are those of the
overlapping galaxy pair VV 191, the radio-selected protocluster at $z=4.1$
around TNJ 1338$-$1942, the massive galaxy cluster known as El Gordo, and the
IRAC Dark Field.

$\bullet$ (2) Star–galaxy classification, object-catalog construction, and
galaxy counting are straightforward in the four fields observed so far
(excluding the areas affected by VV 191, TNJ 1338-1942, and the El Gordo
cluster itself).

$\bullet$ (3) The JWST galaxy counts at 0.9, 1.2, 1.5, 2.0, 3.5, and 4.5 µm
wavelengths are consistent with previous ground-based, HST and Spitzer/WISE
galaxy counts to within $\lesssim$10–20%, given the combined error budget from
ZPs, filter flux-scale transformations, and Cosmic Variance. Our PEARLS galaxy
counts extend the previous work by $\gtrsim$2 mag to AB$\lesssim$28.5–29 mag
at 3.5–4.5 µm wavelengths.

$\bullet$ (4) The normalized differential galaxy counts, to first order and
when normalized by the converging count slope of 0.4 dex/mag, reach a maximum
around AB$\simeq$20 mag at wavelengths of 0.9–4.5 µm. This peak corresponds to
the objects that produce most of the IGL. The PEARLS IGL converges to values
within $\lesssim$10% accuracy at 0.9–4.5 µm.

$\bullet$ (5) Both the AB-magnitude at which most IGL is produced and the
width over which the middle 50% of the IGL is produced depend on wavelength.
This reflects the luminosity function and redshift distribution of the galaxy
populations that dominate each wavelengths.

$\bullet$ (6) Our early JWST images, after removing discrete objects brighter
than $\rm AB\simeq 29$ mag, yield 0.9–4.5 µm diffuse light limits in good
agreement with model predictions from Zodiacal light, JWST thermal- and
straylight, and Diffuse Galactic Light. After removing available model
predictions for these components, and the small extrapolated contribution for
galaxies fainter than 28.5 mag (eEBL), our images provide upper limits to the
amount of diffuse light that may be present. Our best DL limits are in line
with previous work at 1–2 µm and are lower in value at 2.7–4.5 µm wavelengths,
because of the much lower total sky-SB and the correspondingly smaller
uncertainties in both the NIRCam sky-SB data and the models at 2.7–4.5 µm. The
search for diffuse light as part of the cosmic infrared background will become
more accurate as JWST gathers many more images across the sky during its
lifetime, and when its calibration and models of the L2 Zodiacal light and
JWST’s straylight levels improve.

$\bullet$ (7) During Cycle 1, PEARLS will provide NIRCam images, and some
NIRISS grism or NIRSpec spectra, for another 12 targets, which will be done
through 22 more pointings or epochs, as summarized in Table 2. v1 data
products on the NEP TDF and other targets will become available as soon as we
have them.

With the enormous new range in both flux and wavelength that the JWST images
provide, the community will now have the resources to expand and deepen the
study of the morphology, SED, star-formation rates, masses, dust content, and
extinction at redshifts extending to the epoch of First Light, as well as
better constrain how much diffuse light may be present in the infrared.

Acknowledgements: We dedicate this paper to Karin Valentine, who during her
life as Media Relations Manager at the ASU School of Earth & Space Exploration
was a true champion of outreach for NASA missions and was so eager to see the
first JWST images. We thank the JWST Project at NASA GSFC and JWST Program at
NASA HQ for their many-decades long dedication to make the JWST mission a
success. We especially thank Tony Roman, the JWST scheduling group and Mission
Operations Center staff at STScI for their continued dedicated support to get
the JWST observations scheduled. We thank Scott Kenyon and John Mackenty for
helpful discussions. 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 JWST programs 1176 and 2738. RAW, SHC, and
RAJ acknowledge support from NASA JWST Interdisciplinary Scientist grants
NAG5-12460, NNX14AN10G and 80NSSC18K0200 from GSFC. Work by RGA was supported
by NASA under award number 80GSFC21M0002. JFB was supported by grant
PHY-2012955 issued by the National Science Foundation. RAB gratefully
acknowledges support from the European Space Agency (ESA) Research Fellowship.
CC is supported by the National Natural Science Foundation of China, No.
11803044, 11933003, 12173045, (in part) by the Chinese Academy of Sciences
(CAS) through a grant to the CAS South America Center for Astronomy (CASSACA),
and science research grants from the China Manned Space Project with NO. CMS-
CSST-2021-A05. CJC acknowledges support from the European Research Council
(ERC) Advanced Investigator Grant EPOCHS (788113). LD acknowledges the
research grant support from the Alfred P. Sloan Foundation (award number
FG-2021-16495). KJD acknowledges funding from the European Union’s Horizon
2020 research and innovation programme under the Marie Skłodowska-Curie grant
agreement No. 892117 (HIZRAD). LF acknowledges funding from the Coordenaçao de
Aperfeiçoamento de Pessoal de Nível Superior in Brazil (CAPES). LF
acknowledges support by Grant No. 2020750 from the United States-Israel
Binational Science Foundation (BSF) and Grant No. 2109066 from the United
States National Science Foundation (NSF). BLF thanks the Berkeley Center for
Theoretical Physics for their hospitality during the writing of this paper. MH
acknowledges the support from the Korea Astronomy and Space Science Institute
grant funded by the Korean government (MSIT; Nr. 2022183005) MI acknowledges
support from the National Research Foundation of Korea through grants
2020R1A2C3011091 and 2021M3f7A1084525. PK is supported by NSF grant
AST-1908823. RLL is supported by the National Science Foundation Graduate
Research Fellowship under Grant No. DGE-1610403. WPM acknowledges that support
for this work was provided by the National Aeronautics and Space
Administration through Chandra Award Numbers GO8-19119X, GO9-20123X,
GO0-21126X and GO1-22134X issued by the Chandra X-ray Center, which is
operated by the Smithsonian Astrophysical Observatory for and on behalf of the
National Aeronautics Space Administration under contract NAS8-03060. MAM
acknowledges the support of a National Research Council of Canada Plaskett
Fellowship, and the Australian Research Council Centre of Excellence for All
Sky Astrophysics in 3 Dimensions (ASTRO 3D), through project number
CE17010001. AKM acknowledges support by the Ministry of Science & Technology,
Israel. IRS acknowledges support from STFC (ST/T000244/1). LW was supported by
grant AST 1817099 issued by the NSF and grant 80NSSC20K0538 issued by NASA.
CNAW acknowledges funding from the JWST/NIRCam contract NASS-0215 to the
University of Arizona. JSBW was supported by the Australian Research Council
Centre of Excellence for All Sky Astrophysics in 3 Dimensions (ASTRO 3D),
through project #CE170100013. EZ acknowledges funding from the Swedish
National Space Agency. AZ acknowledges support by the Ministry of Science &
Technology, Israel, and by Grant No. 2020750 from the United States-Israel
Binational Science Foundation (BSF) and Grant No. 2109066 from the United
States National Science Foundation (NSF). We also acknowledge the indigenous
peoples of Arizona, including the Akimel O’odham (Pima) and Pee Posh
(Maricopa) Indian Communities, whose care and keeping of the land has enabled
us to be at ASU’s Tempe campus in the Salt River Valley, where much of our
work was conducted.

    Table 1. PEARLS Targets with NIRCam Images Taken as of 2022 July: Depth from ETC, SourceExtractor and Galaxy Counts
    =============================================================================================================================
    Instr.+Filters  R.A.  (J2000)  Decl.      Obs. Date  Visit   Area   SCeff Net ------------- Net t_exp (sec) -----------------
    Target           h  m  s.sss   o  ’   "   YYYY-MM-DD  Nr    (’)x(’) (%)   Hrs ------- 5-sigma point-source AB-limit ---------
    -----------------------------------------------------------------------------------------------------------------------------

    NIRCam Broad-band:                                                            F090W F115W F150W F200W F277W F356W F410M F444W
    -----------------------------------------------------------------------------------------------------------------------------
    VV191-Backlit  13 48 22.0990 +25 40 40.01 2022-07-02 341.1 2.15x4.30 32.0 0.52 0934  ----  0934  ----  ----  0934  ----  0934
    PSF-FWHM (")                                                                  0.066  ---- 0.068  ----  ---- 0.164  ---- 0.163
    ETC 5sig AB-lim                                                               27.62  ---- 28.01  ----  ---- 28.00  ---- 27.59
    Cat 5sig AB-lim                                                               27.69  ---- 28.05  ----  ---- 29.12  ---- 29.13
    Counts 80% compl                                                              27.1   ---- 27.4   ----  ---- 28.6   ---- 28.6
    DelABlim(80%-ETC)                                                             -0.5   ---- -0.6   ----  ---- +0.6   ---- +1.0

    IRAC-Dark-ep1  17 40 08.5352 +68 58 27.00 2022-07-08 121.1 2.15x4.30 61.2 1.76 ----  ----  3157  3157  ----  3157  ----  3157
    PSF-FWHM (")                                                                   ----  ---- 0.063 0.075  ---- 0.166  ---- 0.164
    ETC 5sig AB-lim                                                                ----  ---- 28.96 29.13  ---- 28.81  ---- 28.41
    Cat 5sig AB-lim                                                                ----  ---- 28.59 28.52  ---- 29.74  ---- 29.77
    Counts 80% compl                                                               ----  ---- 27.9  27.9   ---- 29.2   ---- 29.3
    DelABlim(80%-ETC)                                                              ----  ---- -1.1  -1.2   ---- +0.4   ---- +0.9

    El-Gordo       01 02 55.4000 -49 15 38.00 2022-07-29 241.1 2.15x4.30 57.3 2.50 2491  2491  1890  2104  2104  1890  2491  2491
    PSF-FWHM (")                                                                  0.079 0.088 0.063 0.074 0.121 0.169 0.155 0.162
    ETC 5sig AB-lim                                                               28.43 28.61 28.60 28.88 28.58 28.55 28.03 28.32
    Cat 5sig AB-lim                                                               28.42 28.57 28.52 28.62 29.59 29.70 29.13 29.59
    Counts 80% compl                                                              27.6  27.7  27.4  27.7  29.2  29.2  28.5  29.2
    DelABlim(80%-ETC)                                                             -0.8  -0.9  -1.2  -1.2  +0.6  +0.7  +0.5  +0.9

    NIRCam Medium-band:                                                           F115W F150W F182M F210M F300M F335M F360M F444W
    -----------------------------------------------------------------------------------------------------------------------------
    TNJ1338-1942   13 38 26.1000 -19 42 28.00 2022-07-01 361.1 2.15x4.30 37.9 0.86 ----  1031  1031  1031  1031  1031  1031  ----
    PSF-FWHM (")                                                                   ---- 0.064 0.071 0.079 0.125 0.169 0.160  ----
    ETC 5sig AB-lim                                                                ---- 27.86 27.51 27.30 27.15 27.25 27.28  ----