# Overview of the JWST Advanced Deep Extragalactic Survey (JADES)

Daniel J. Eisenstein Center for Astrophysics $|$ Harvard & Smithsonian, 60
Garden St., Cambridge MA 02138 USA  Chris Willott NRC Herzberg, 5071 West
Saanich Rd, Victoria, BC V9E 2E7, Canada  Stacey Alberts Steward Observatory,
University of Arizona, 933 N. Cherry Avenue, Tucson AZ 85721 USA  Santiago
Arribas  Centro de Astrobiología (CAB), CSIC–INTA, Cra. de Ajalvir Km. 4,
28850- Torrejón de Ardoz, Madrid, Spain  Nina Bonaventura Cosmic Dawn Center
(DAWN), Copenhagen, Denmark  Niels Bohr Institute, University of Copenhagen,
Jagtvej 128, DK-2200, Copenhagen, Denmark  Steward Observatory, University of
Arizona, 933 N. Cherry Avenue, Tucson AZ 85721 USA  Andrew J. Bunker
Department of Physics, University of Oxford, Denys Wilkinson Building, Keble
Road, Oxford OX1 3RH, UK  Alex J. Cameron Department of Physics, University of
Oxford, Denys Wilkinson Building, Keble Road, Oxford OX1 3RH, UK  Stefano
Carniani Scuola Normale Superiore, Piazza dei Cavalieri 7, I-56126 Pisa, Italy
Stephane Charlot Sorbonne Université, CNRS, UMR 7095, Institut d’Astrophysique
de Paris, 98 bis bd Arago, 75014 Paris, France  Emma Curtis-Lake Centre for
Astrophysics Research, Department of Physics, Astronomy and Mathematics,
University of Hertfordshire, Hatfield AL10 9AB, UK  Francesco D’Eugenio Kavli
Institute for Cosmology, University of Cambridge, Madingley Road, Cambridge
CB3 0HA, UK  Cavendish Laboratory, University of Cambridge, 19 JJ Thomson
Avenue, Cambridge CB3 0HE, UK  Pierre Ferruit European Space Agency, European
Space Astronomy Centre, Camino Bajo del Castillo s/n, 28692 Villafranca del
Castillo, Madrid, Spain  Giovanna Giardino ATG Europe for the European Space
Agency, ESTEC, Noordwijk, The Netherlands  Kevin Hainline Steward Observatory,
University of Arizona, 933 N. Cherry Avenue, Tucson AZ 85721 USA  Ryan Hausen
Department of Physics and Astronomy, The Johns Hopkins University, 3400 N.
Charles St., Baltimore, MD 21218  Peter Jakobsen Cosmic Dawn Center (DAWN),
Copenhagen, Denmark  Niels Bohr Institute, University of Copenhagen, Jagtvej
128, DK-2200, Copenhagen, Denmark  Benjamin D. Johnson Center for Astrophysics
$|$ Harvard & Smithsonian, 60 Garden St., Cambridge MA 02138 USA  Roberto
Maiolino Kavli Institute for Cosmology, University of Cambridge, Madingley
Road, Cambridge CB3 0HA, UK  Cavendish Laboratory, University of Cambridge, 19
JJ Thomson Avenue, Cambridge CB3 0HE, UK  Department of Physics and Astronomy,
University College London, Gower Street, London WC1E 6BT, UK  Marcia Rieke
Steward Observatory, University of Arizona, 933 N. Cherry Avenue, Tucson AZ
85721 USA  George Rieke Steward Observatory and Dept of Planetary Sciences,
University of Arizona 933 N. Cherry Avenue Tucson AZ 85721 USA  Hans-Walter
Rix Max-Planck-Institut für Astronomie, Königstuhl 17, D-69117, Heidelberg,
Germany  Brant Robertson Department of Astronomy and Astrophysics University
of California, Santa Cruz, 1156 High Street, Santa Cruz CA 96054 USA  Daniel
P. Stark Steward Observatory, University of Arizona, 933 N. Cherry Avenue,
Tucson AZ 85721 USA  Sandro Tacchella Kavli Institute for Cosmology,
University of Cambridge, Madingley Road, Cambridge CB3 0HA, UK  Cavendish
Laboratory, University of Cambridge, 19 JJ Thomson Avenue, Cambridge CB3 0HE,
UK  Christina C. Williams NSF’s National Optical-Infrared Astronomy Research
Laboratory, 950 North Cherry Avenue, Tucson, AZ 85719 USA  Christopher N. A.
Willmer Steward Observatory, University of Arizona, 933 N. Cherry Avenue,
Tucson AZ 85721 USA  William M. Baker Kavli Institute for Cosmology,
University of Cambridge, Madingley Road, Cambridge CB3 0HA, UK  Cavendish
Laboratory, University of Cambridge, 19 JJ Thomson Avenue, Cambridge CB3 0HE,
UK  Stefi Baum Department of Physics and Astronomy, University of Manitoba,
Winnipeg, MB R3T 2N2, Canada  Rachana Bhatawdekar European Space Agency (ESA),
European Space Astronomy Centre (ESAC), Camino Bajo del Castillo s/n, 28692
Villanueva de la Cañada, Madrid, Spain  European Space Agency, ESA/ESTEC,
Keplerlaan 1, 2201 AZ Noordwijk, NL  Kristan Boyett School of Physics,
University of Melbourne, Parkville 3010, VIC, Australia  ARC Centre of
Excellence for All Sky Astrophysics in 3 Dimensions (ASTRO 3D), Australia
Zuyi Chen Steward Observatory, University of Arizona, 933 N. Cherry Avenue,
Tucson AZ 85721 USA  Jacopo Chevallard Department of Physics, University of
Oxford, Denys Wilkinson Building, Keble Road, Oxford OX1 3RH, UK  Chiara
Circosta European Space Agency (ESA), European Space Astronomy Centre (ESAC),
Camino Bajo del Castillo s/n, 28692 Villanueva de la Cañada, Madrid, Spain
Mirko Curti European Southern Observatory, Karl-Schwarzschild-Strasse 2, 85748
Garching, Germany  Kavli Institute for Cosmology, University of Cambridge,
Madingley Road, Cambridge CB3 0HA, UK  Cavendish Laboratory, University of
Cambridge, 19 JJ Thomson Avenue, Cambridge CB3 0HE, UK  A. Lola Danhaive Kavli
Institute for Cosmology, University of Cambridge, Madingley Road, Cambridge
CB3 0HA, UK  Christa DeCoursey Steward Observatory, University of Arizona, 933
N. Cherry Avenue, Tucson AZ 85721 USA  Ryan Endsley Department of Astronomy,
University of Texas, Austin, TX 78712 USA  Anna de Graaff Max-Planck-Institut
für Astronomie, Königstuhl 17, D-69117, Heidelberg, Germany  Alan Dressler The
Observatories of the Carnegie Institution for Science, 813 Santa Barbara St.,
Pasadena, CA 91101  Eiichi Egami Steward Observatory, University of Arizona,
933 N. Cherry Avenue, Tucson AZ 85721 USA  Jakob M. Helton Steward
Observatory, University of Arizona, 933 N. Cherry Avenue, Tucson AZ 85721 USA
Raphael E. Hviding Steward Observatory, University of Arizona, 933 N. Cherry
Avenue, Tucson AZ 85721 USA  Zhiyuan Ji Steward Observatory, University of
Arizona, 933 N. Cherry Avenue, Tucson AZ 85721 USA  Gareth C. Jones Department
of Physics, University of Oxford, Denys Wilkinson Building, Keble Road, Oxford
OX1 3RH, UK  Nimisha Kumari AURA for European Space Agency, Space Telescope
Science Institute, 3700 San Martin Drive. Baltimore, MD, 21210  Nora
Lützgendorf European Space Agency, Space Telescope Science Institute,
Baltimore, Maryland, US  Isaac Laseter Department of Astronomy, University of
Wisconsin-Madison, 475 N. Charter St., Madison, WI 53706 USA  Tobias J. Looser
Kavli Institute for Cosmology, University of Cambridge, Madingley Road,
Cambridge CB3 0HA, UK  Jianwei Lyu Steward Observatory, University of Arizona,
933 N. Cherry Avenue, Tucson AZ 85721 USA  Michael V. Maseda Department of
Astronomy, University of Wisconsin-Madison, 475 N. Charter St., Madison, WI
53706 USA  Erica Nelson Department for Astrophysical and Planetary Science,
University of Colorado, Boulder, CO 80309 USA  Eleonora Parlanti Scuola
Normale Superiore, Piazza dei Cavalieri 7, I-56126 Pisa, Italy  Michele Perna
Centro de Astrobiología (CAB), CSIC–INTA, Cra. de Ajalvir Km. 4, 28850-
Torrejón de Ardoz, Madrid, Spain  Dávid Puskás Kavli Institute for Cosmology,
University of Cambridge, Madingley Road, Cambridge CB3 0HA, UK  Cavendish
Laboratory, University of Cambridge, 19 JJ Thomson Avenue, Cambridge CB3 0HE,
UK  Tim Rawle European Space Agency (ESA), European Space Astronomy Centre
(ESAC), Camino Bajo del Castillo s/n, 28692 Villafranca del Castillo, Madrid,
Spain  Bruno Rodríguez Del Pino Centro de Astrobiología (CAB), CSIC–INTA, Cra.
de Ajalvir Km. 4, 28850- Torrejón de Ardoz, Madrid, Spain  Lester Sandles
Kavli Institute for Cosmology, University of Cambridge, Madingley Road,
Cambridge CB3 0HA, UK  Cavendish Laboratory, University of Cambridge, 19 JJ
Thomson Avenue, Cambridge CB3 0HE, UK  Aayush Saxena Department of Physics,
University of Oxford, Denys Wilkinson Building, Keble Road, Oxford OX1 3RH, UK
Department of Physics and Astronomy, University College London, Gower Street,
London WC1E 6BT, UK  Jan Scholtz  Kavli Institute for Cosmology, University of
Cambridge, Madingley Road, Cambridge CB3 0HA, UK  Cavendish Laboratory,
University of Cambridge, 19 JJ Thomson Avenue, Cambridge CB3 0HE, UK
Katherine Sharpe Center for Astrophysics $|$ Harvard & Smithsonian, 60 Garden
St., Cambridge MA 02138 USA  Irene Shivaei Steward Observatory, University of
Arizona, 933 N. Cherry Avenue, Tucson AZ 85721 USA  Maddie S. Silcock  Centre
for Astrophysics Research, Department of Physics, Astronomy and Mathematics,
University of Hertfordshire, Hatfield AL10 9AB, UK  Charlotte Simmonds Kavli
Institute for Cosmology, University of Cambridge, Madingley Road, Cambridge
CB3 0HA, UK  Cavendish Laboratory, University of Cambridge, 19 JJ Thomson
Avenue, Cambridge CB3 0HE, UK  Maya Skarbinski Center for Astrophysics $|$
Harvard & Smithsonian, 60 Garden St., Cambridge MA 02138 USA  Renske Smit
Astrophysics Research Institute, Liverpool John Moores University, 146
Brownlow Hill, Liverpool L3 5RF, UK  Meredith Stone Steward Observatory,
University of Arizona, 933 N. Cherry Avenue, Tucson AZ 85721 USA  Katherine A.
Suess Department of Astronomy and Astrophysics University of California, Santa
Cruz, 1156 High Street, Santa Cruz CA 96054 USA  Kavli Institute for Particle
Astrophysics and Cosmology and Department of Physics, Stanford University,
Stanford, CA 94305 USA  Fengwu Sun Steward Observatory, University of Arizona,
933 N. Cherry Avenue, Tucson AZ 85721 USA  Mengtao Tang Steward Observatory,
University of Arizona, 933 N. Cherry Avenue, Tucson AZ 85721 USA  Michael W.
Topping Steward Observatory, University of Arizona, 933 N. Cherry Avenue,
Tucson AZ 85721 USA  Hannah Übler Kavli Institute for Cosmology, University of
Cambridge, Madingley Road, Cambridge CB3 0HA, UK  Cavendish Laboratory,
University of Cambridge, 19 JJ Thomson Avenue, Cambridge CB3 0HE, UK  Natalia
C. Villanueva Center for Astrophysics $|$ Harvard & Smithsonian, 60 Garden
St., Cambridge MA 02138 USA  Imaan E. B. Wallace Department of Physics,
University of Oxford, Denys Wilkinson Building, Keble Road, Oxford OX1 3RH, UK
Lily Whitler Steward Observatory, University of Arizona, 933 N. Cherry Avenue,
Tucson AZ 85721 USA  Joris Witstok Kavli Institute for Cosmology, University
of Cambridge, Madingley Road, Cambridge CB3 0HA, UK  Cavendish Laboratory,
University of Cambridge, 19 JJ Thomson Avenue, Cambridge CB3 0HE, UK  Charity
Woodrum Steward Observatory, University of Arizona, 933 N. Cherry Avenue,
Tucson AZ 85721 USA

###### Abstract

We present an overview of the James Webb Space Telescope (JWST) Advanced Deep
Extragalactic Survey (JADES), an ambitious program of infrared imaging and
spectroscopy in the GOODS-S and GOODS-N deep fields, designed to study galaxy
evolution from high redshift to cosmic noon. JADES uses about 770 hours of
Cycle 1 guaranteed time largely from the Near-Infrared Camera (NIRCam) and
Near-Infrared Spectrograph (NIRSpec) instrument teams. In GOODS-S, in and
around the Hubble Ultra Deep Field and Chandra Deep Field South, JADES
produces a deep imaging region of $\sim$45 arcmin2 with an average of 130 hrs
of exposure time spread over 9 NIRCam filters. This is extended at medium
depth in GOODS-S and GOODS-N with NIRCam imaging of $\sim$175 arcmin2 with an
average exposure time of 20 hrs spread over 8–10 filters. In both fields, we
conduct extensive NIRSpec multi-object spectroscopy, including 2 deep
pointings of 55 hrs exposure time, 14 medium pointings of $\sim$12 hrs, and 15
shallower pointings of $\sim$4 hrs, targeting over 5000 HST and JWST-detected
faint sources with 5 low, medium, and high-resolution dispersers covering
0.6–5.3 $\mu$m. Finally, JADES extends redward via coordinated parallels with
the JWST Mid-Infrared Instrument (MIRI), featuring $\sim$9 arcmin2 with 43
hours of exposure at 7.7 $\mu\rm m$ and twice that area with 2–6.5 hours of
exposure at 12.8 $\mu\rm m$. For nearly 30 years, the GOODS-S and GOODS-N
fields have been developed as the premier deep fields on the sky; JADES is now
providing a compelling start on the JWST legacy in these fields.

early universe — galaxies: evolution — galaxies: high-redshift

##  1 Introduction

The James Webb Space Telescope (JWST) is revolutionizing the study of galaxy
evolution by giving us unprecedented access to deep, sharp, and nuanced
infrared imaging and spectroscopy. Designed to push the redshift frontier and
bring the early growth of galaxies into clear focus, the telescope is
performing at, or even better than, expectations (Rigby et al., 2023). JWST
takes marvelous advantage of the faintness of the zodiacal foregrounds at 2–10
$\mu$m and state-of-the-art infrared detectors to unlock the rest-frame
optical at redshifts $z>4$, combining large collecting area and diffraction-
limited imaging. Exploiting this telescope are ambitious multi-purpose
instruments (Gardner et al., 2023) that give us dozens of selectable filters,
exquisite slitless, integral-field or multi-object spectroscopy (MOS) and
multiple coronagraphs.

Unraveling the physics of high-redshift galaxies will require the combination
of many different kinds of observations. While there are important
observations of rare, extreme phenomena, many goals in studying the general
population are well served by deep-sky general surveys, as each image contains
an unbiased superposition of all epochs of galaxy evolution. Of course, low
redshifts are best served by wider, shallower data, but great depth is
required to identify and characterize high-redshift galaxies.

The Hubble Deep Field was a dramatic advance in this regard. It boldly
unveiled the high-redshift Universe in a single multi-color blank-field image
(Williams et al., 1996; Ferguson et al., 2000). Since then, such surveys have
been vigorously pursued, utilizing virtually every high-sensitivity narrow-
field telescope. This field and that of the Chandra Deep Field South (Giacconi
et al., 2002) were broadened out to form the Great Observatories Origins Deep
Survey (GOODS Giavalisco et al., 2004), utilizing the new opportunities of the
HST Advanced Camera for Surveys (ACS) and Spitzer infrared telescope to
partner with deep Chandra X-ray imaging (Luo et al., 2008). Soon after, the
Hubble Ultra Deep Field (HUDF) was sited in the heart of GOODS-S (Beckwith et
al., 2006). It has since become the standard bearer of deep fields, pushing
into the epoch of reionization by leveraging exceptional optical and infrared
Hubble Space Telescope imaging, e.g., the UDF09 (Bouwens et al., 2010) and
UDF12 programs (Ellis et al., 2013), with tremendous investments across the
electromagnetic spectrum from many other imaging and spectroscopic facilities.

JWST is designed to pursue such surveys; the telescope provides exquisite
image quality and depth, but it moves slowly enough that deep fields are
operationally favored. Every pointing of practical depth reveals tens of
thousands of galaxies, including many at $z>6$ where the intergalactic medium
(IGM) completely blocks optical light. Further, JWST provides a novel
opportunity to conduct detailed faint multi-object spectroscopy beyond 2
$\mu$m, where critical rest-optical lines are shifted at high redshift.
Numerous projects have already been approved to start this work, which we
expect will be one of the enduring legacies of the telescope (Robertson,
2022).

Here, we describe the JWST Advanced Deep Extragalactic Survey (JADES), a
collaboration of the Near-Infrared Camera (NIRCam) and Near-Infrared
Spectrograph (NIRSpec) Instrument Development Teams. The plans to conduct
deep-field imaging and spectroscopy were featured in the original instrument
proposals, with the intent to devote a substantial amount of guaranteed time
to this topic. In 2015, the teams joined to form a larger and more coordinated
project, now called JADES, to focus on the exceptional opportunities of JWST
onto the GOODS-S and GOODS-N fields. In doing so, it became possible to carry
out a project with fewer compromises: deep and wide enough to support the
geometry of the instrument footprints and utilize efficient parallel
observations, with robust well-dithered imaging and spectroscopy in many
filters and several dispersion modes.

At about 770 hours of observing time plus coordinated parallels, JADES is the
largest program operating in JWST Cycle 1 and is a very large investment of
instrument team guaranteed time. The time was roughly evenly split between the
NIRCam and NIRSpec GTO budgets, with a supplementary contribution from the
MIRI-US team. By applying the experience of the teams that designed and
commissioned the instruments, we aim to provide an exquisite legacy data set
for these deep fields.

In this overview paper, we describe the scientific motivations and resulting
survey design of JADES, providing an overview of how we developed our strategy
to maximize performance in both imaging and spectroscopy in the targeted
fields. Subsequent papers will describe the imaging data reduction and
spectroscopic target selection, as well as the first data release centered on
the HUDF (Bunker et al., 2023a; Rieke et al., 2023a).

##  2 JADES Science Goals

###  2.1 Motivations

The sensitivity and instrumentation of JWST provide a singular opportunity to
study the evolution of galaxies from the earliest epochs $\lesssim 300$ Myr
after the Big Bang, through the Epoch of Reionization during the first billion
years of cosmic history, and on to Cosmic High Noon where the stellar mass and
black hole mass densities of the universe were well-established. Unlike all
other previous studies of high-redshift (i.e., $z>3$) galaxy populations where
only rest-frame ultraviolet spectral properties have been accessible, JWST
enables for the first time, via its instruments NIRCam (Rieke et al., 2023b),
NIRSpec (Jakobsen et al., 2022; Ferruit et al., 2022), NIRISS (Doyon et al.,
2023) and MIRI (Wright et al., 2023), photometry and spectroscopy extending
from blueward of the Lyman break to redward of the Balmer/$4000$Å break
region. In this section we describe how our view of the Universe before the
launch of JWST shaped the design of JADES and how early JWST observations
support our decisions.

At the earliest times in cosmic history (e.g., $<500$ Myr), the first abundant
population of star-forming galaxies developed. Galaxy formation is a self-
regulated process, and the ways in which early galaxies respond to the rapid
accretion and cooling of gas greatly affects their bulk properties like
luminosity and size. Through perseverance in HST imaging surveys, a handful of
galaxies at $z\sim 10-11$ were discovered (Coe et al., 2013; Oesch et al.,
2016), enabling a first glance at the primitive galaxy formation process.

JWST has the sensitivity and the required array of infrared filters to
identify galaxies selected in rest-frame ultraviolet (UV) at $z>12$, farther
than any tentative HST detections. Hundreds of hours of NIRCam multi-filter
imaging will yield sufficient source counts to measure the UV luminosity
function evolution out to $z\sim 10$ and beyond. JADES includes medium-band
filters in the NIRCam long wave channel that can help identify and distinguish
high-z candidates from dusty, strong emission line sources at lower redshifts
e.g. Zavala et al. 2023; Fujimoto et al. 2022; Arrabal Haro et al. 2023). The
most distant galaxies will inevitably be very faint and these NIRCam
discoveries will require very long integrations with the NIRSpec low-
resolution prism for redshift confirmation, especially at redshifts $z>10$
where the strongest optical lines are redshifted beyond the NIRSpec range
(Robertson et al., 2023a; Curtis-Lake et al., 2023).

Extending redder than Hubble and reaching many magnitudes deeper than ever
achieved with Spitzer, JWST greatly improves our census of the early Universe,
tracing the growth of galaxies at this early epoch. Combining the SFR and
stellar masses derived from spectral-energy distributions (SED) spanning the
rest-UV and optical, NIRCam and MIRI imaging allow estimates of the stellar
birthrate of galaxies out to $z\sim 10$ and can deliver our earliest
constraints on the efficiency of galaxy formation (e.g. Labbe et al., 2022;
Endsley et al., 2022; Tacchella et al., 2023). Stellar masses measured at
$z\sim 10$ allow us to infer a bulk star formation rate to $z\gtrsim 12$
(given the minimum $\sim 100$ Myr timescale typically required for the
development of strong rest-frame optical breaks; Whitler et al. 2023a;
Dressler et al. 2023). Rest-UV emission lines, such as [CIV](1549Å),
HeII(1640Å), OIII](1663Å) and CIII](1909Å), are accessible to NIRSpec to the
highest redshifts to measure the physical properties of nebular gas and infer
their sources of ionization (Bunker et al., 2023b; Hsiao et al., 2023; Tang et
al., 2023). Our window into early galaxies with JWST becomes dramatically
richer at just slightly later times ($z\sim 8-9$) where NIRSpec can measure
the rest-frame optical strong lines (e.g., [OII], [NeIII], H$\beta$, [OIII],
and even H$\alpha$ at $z<7$) and both the Lyman and Balmer/$4000$Å breaks
(Cameron et al., 2023; Sanders et al., 2023; Reddy et al., 2023a; Tang et al.,
2023). NIRCam imaging in medium and wide filters can measure these breaks and
strong lines, and given enough filters can differentiate between the two
(Endsley et al., 2022; Williams et al., 2023; Withers et al., 2023).

Figure 1: A grand design spiral at redshift 2 revealed in JADES imaging in
GOODS-S. (left) The JWST NIRCam image combining F090W, F200W, and F444W
filters in the Deep Prime region of program 1180. (right) The HST ACS F850LP
image, where this star-forming galaxy is all but invisible.

From previous surveys of the cosmic microwave background, quasars and
galaxies, we know that these epochs experience the first important
contributions of galaxies to the cosmic reionization process (Planck
Collaboration et al., 2020; Fan et al., 2022; Robertson et al., 2022). Here,
JWST simultaneously constrains the evolving rest-frame UV galaxy luminosity
density and provides information on the hardness of the ionizing continuum and
the escape fraction of Lyman continuum photons (Simmonds et al., 2023; Mascia
et al., 2023; Endsley et al., 2022; Donnan et al., 2023; Bouwens et al.,
2023). These measurements will result in a more accurate “balancing of the
budget” for cosmic reionization, where we weigh the cosmic ionization rate
against the recombination of the intergalactic hydrogen in determining the
evolving bulk IGM neutrality. Mapping with spectra the “Lyman-$\alpha$
disappearance”, measuring the evolving fraction of UV-dropout selected
galaxies that show (or not) Lyman-$\alpha$ emission, can track how the
increased IGM neutrality at earlier times extinguishes observed line emission
in progressively more of the sources (Stark et al., 2010; Fontana et al.,
2010; Pentericci et al., 2014; Mason et al., 2018; Ouchi et al., 2020; Jones
et al., 2023). Correlating the transmission of Lyman-$\alpha$ with photometric
or spectroscopic measurements of environment can provide further insight into
the topology of ionized bubbles during this epoch (Tang et al., 2023; Witstok
et al., 2023a; Endsley et al., 2023; Jung et al., 2023; Lu et al., 2023;
Whitler et al., 2023b).

Rest-frame optical line spectroscopy at $z\sim 6$–9 dramatically extends our
knowledge of the chemical enrichment of galaxies and reveals the physical
conditions in the warm interstellar medium of early star-forming galaxies.
Line excitation diagrams provide insights into the ionization state of the
star-forming ISM in these systems; NIRSpec allows to apply these diagnostics
in an entirely new redshift regime (e.g., Cameron et al., 2023; Sanders et
al., 2023; Reddy et al., 2023a), connecting them with the properties of the
exciting stellar populations. Via the combined measures of star formation rate
in the rest-UV, stellar mass in the rest-optical, and metallicity from nebular
lines we can explore whether the fundamental metallicity and mass-metallicity
relations are already in place after only $\sim 1$ billion years of cosmic
history (Curti et al., 2023; Nakajima et al., 2023).

JWST is revealing the emergence of morphological structures at $z>2$ through
superb infrared imaging (Robertson et al., 2023b; Kartaltepe et al., 2023;
Ferreira et al., 2022a, b; Jacobs et al., 2023; Huertas-Company et al., 2023;
Magnelli et al., 2023; Baker et al., 2023). JWST can resolve these galaxies
from the rest-UV to the rest-optical, providing spatially-resolved measures of
color gradients and stellar population properties, vastly outperforming HST
(e.g. Figure 1). We can distinguish the clumpy UV-bright morphology from the
rest-optical light on a galaxy-by-galaxy basis, and thereby constrain the role
of large-scale gravitational instability in setting galaxy structures at
$z\sim 2-3$. NIRSpec spectroscopy with the medium- or high-resolution gratings
will connect these morphological measures to the dynamics of the galaxies, and
through measuring outflows further constrain the role of feedback in shaping
these maturing galaxies.

HST has found compact red galaxies at $z=2$, but JWST’s angular resolution,
sensitivity and redder bands are proving revolutionary to explore old stellar
populations and their morphology at z$>$3 (Carnall et al., 2023; Ji et al.,
2023, Suess et al. in prep). With NIRCam imaging, including medium filters,
Balmer and D4000 breaks can be cleanly picked out. Spectroscopy at $R=100$
with the NIRSpec prism provides precise redshifts and break strengths, but
higher resolution spectroscopy enables more detailed constraints on SFH and
abundances at $z>3-4$, an era that prior to JWST was prohibitive or impossible
to study, but critical to our understanding of why and how galaxies stop
forming stars (Carnall et al., 2023; Nanayakkara et al., 2022). The importance
of burstiness in the evolution of galaxies is now becoming clear with several
examples of $z>5$ galaxies undergoing ‘mini-quenching’ episodes (Looser et
al., 2023b; Strait et al., 2023). Additional evidence for burstiness is now
becoming apparent in statistical samples of NIRCam SEDs (Endsley et al., 2023;
Dressler et al., 2023).

Figure 2: Example spectra for the $z=4.65$ galaxy JADES-GS+53.13284-27.80185
(ID=00008083) from the JADES Deep/HST observations. The upper panel shows the
low-resolution prism spectrum (total integration time $10^{5}$ seconds). This
spectrum reveals many emission lines and high S/N continuum. However, some
emission lines are blended or have low equivalent width, motivating the
acquisition of grating spectra for most of the galaxies with prism spectra in
JADES. The rms uncertainty is shown in the gray shaded spectrum at the bottom
of the plot. The lower-left panel shows the JADES Deep imaging data of this
galaxy with an overlay of the position of the microshutters during the three
nods. The green color of the galaxy indicates [OIII] and H$\beta$ line
emission dominating in the F277W filter. For the angular scale, we remind that
the individual microshutters are 0.2′′ by 0.46′′. The lower-right panel shows
the H$\alpha$ spectra obtained with the medium G395M and high G395H resolution
gratings. Both these spectra reveal a broad ($\sigma=800$ km s-1) emission
line from a low-luminosity AGN broad line region (Maiolino et al., in prep.).
However, the G395H grating is required to spectrally resolve the narrow line
emission from the galaxy itself (observed $\sigma=65$ km s-1, compared to
instrumental line spread function $\sigma=30$ km s-1; de Graaff et al. (in
prep.)).

Dust attenuation and reddening in the rest-frame UV and optical spectra and
SEDs of galaxies strongly affect the inferred physical quantities, so it is
important to study the underlying dust properties. JWST opens a new window to
identify and characterize obscured populations that were completely missed by
even the deepest Spitzer and HST surveys. ALMA has revealed that such hidden
galaxies likely contribute significantly to the cosmic star formation rate
density at $3<z<8$ (e.g. Williams et al., 2019; Fudamoto et al., 2021; Algera
et al., 2023), and early JWST data is supporting this finding (Barrufet et
al., 2023). The stellar SEDs and morphologies of dust obscured galaxies at
$z>3$ can now be characterized in detail for the first time (Gómez-Guijarro et
al., 2023; Nelson et al., 2022; Pérez-González et al., 2023). JWST is
indicating that massive galaxies have non-negligible dust content at high-
redshift, pointing to efficient production mechanisms even out to $z\sim 8$
(McKinney et al., 2023; Akins et al., 2023). The combination of multi-band
NIRCam photometry and multi-line NIRSpec spectroscopy offers the opportunity
to tackle these issues through SED-fitting and through line diagnostics such
as the Paschen and Balmer decrements and 2200Å bump (e.g., Witstok et al.,
2023b; Shapley et al., 2023; Reddy et al., 2023b; Sandles et al., 2023).

The intimate connection between the growth of galaxies and their supermassive
black holes can be traced back to the earliest epochs with deep JWST imaging
and spectroscopy. Active galactic nuclei (AGN) are being discovered via their
pointlike morphology (particularly in the redder bands, e.g. Labbe et al.,
2022; Furtak et al., 2022), broad wings of Balmer emission lines (e.g. Figure
2) and highly-ionized narrow lines (Kocevski et al., 2023; Harikane et al.,
2023; Maiolino et al., 2023; Scholtz et al., 2023; Larson et al., 2023). In
most cases these AGN are not detectable even by very deep Chandra or JVLA
imaging, putting JWST at the forefront of the quest for the earliest
supermassive black holes. With JADES we obtain deep NIRCam and MIRI imaging
plus deep NIRSpec spectrocopy to discover these previously hidden AGN and
investigate the evolving relationship between supermassive black holes and
their host galaxies.

Throughout these epochs, the development of the galaxy populations remains
tightly connected with the structure formation process in our $\Lambda$CDM
cosmology universe. The rates of star formation, stellar population aging,
merging, and dynamical and morphological transformation are ultimately
manifestations of the growth of dark matter halos. JWST is providing a new
context for understanding the connection between galaxy and dark matter
structure formation by aiming to discover the earliest galaxies that form in
rare peaks of the density field, establishing both the SFR-halo mass and
stellar mass-halo mass relations out to $z\sim 10$, watching the emergence of
dynamically cold galactic structures, and by observing the assembly of the
first massive galaxies that form primarily through dissipationless mergers.
The new spectroscopic capabilities allow us to identify physically-associated
galaxies in the early universe rather than just projected overdensities
(Kashino et al., 2022; Helton et al., 2023; Morishita et al., 2023) and enable
us to distinguish between how central and satellite galaxies evolve further
back in time than has previously been possible. The combination of area and
depth allow for clustering analyses down to very faint magnitudes on
spectroscopically-informed samples with well-constrained redshift selection
functions. This combination will also address critical gaps in our knowledge
of environmentally-driven galaxy evolution. The key epochs of stellar growth
and the subsequent quenching in groups and (proto-)clusters likely often occur
in a dust-obscured phase (see Alberts & Noble, 2022, for a review),
necessitating rest-frame near- and mid-infrared observations that are robust
against extinction and directly probe obscured activity. In all, JWST allows a
more physically complete view of galaxy formation that builds directly from
the underlying $\Lambda$CDM framework.

We stress that most if not all of these science drivers require a substantial
survey volume, not just depth. We aim to slice the galaxy samples in a variety
of parameters for inter-comparison. Going deep may (slowly) reveal the less
luminous galaxies, but we need to gather sufficient samples of the $L^{*}$ and
brighter ones as well. Rare phases, such as AGN and extreme starbursts, can be
important for the evolutionary story. Large-scale structure is prominent even
at high redshifts because galaxies are extremely biased tracers of the
underlying density field. On the scale of one NIRCam or NIRSpec MOS pointing,
this can cause the fluctuations in the number of objects, particularly those
from the most massive halos, to vary substantially (Steinhardt et al., 2021).
Larger surveys allow one to measure more accurate luminosity functions, but
also to potentially measure the clustering amplitude itself, which bears on
the mass of the host halos as well as on possible Mpc-scale environmental
drivers in galaxy evolution.

The above science cases can all be addressed efficiently through a deep
extragalactic survey. Typical high-redshift galaxies are common on the sky but
very faint. The scientific exploitation of images is necessarily broad simply
because of projection of the line of sight, but this is also true for
efficient use of multi-object spectroscopy. Since the advent of the Hubble
Deep Field, the community has been focusing its resources onto a small number
of deep fields, so that the synergies between different types of data can be
best exploited. Our survey follows this same logic.

###  2.2 Opportunities of a Combined Imaging & Spectroscopy Program

As the combination of imaging and spectroscopy is a key driver of our
coordinated parallel strategy, we want to stress that JWST imaging and
spectroscopy reinforce each other in numerous critical ways.

First, one has the obvious aspect that imaging and spectroscopy constrain
different physical properties of the galaxies, which we seek to combine.

Second, having accurate redshifts is important for the interpretation of
imaging in terms of luminosities, rest-frame colors, and proper sizes. For
example, the conversion of SEDs to stellar masses and star formation histories
can easily be degenerate with redshift uncertainty. Spectroscopy is the gold
standard for redshifts, and JWST has sufficient sensitivity and multiplex to
provide spectroscopic redshifts for thousands of galaxies all the way to
z$>$10\. Moreover, our program is providing large training samples for the
photometric redshift methods that will supply redshifts for the rest of the
imaging sample.

Third, there are technical synergies. NIRCam broad-band filters in the rest-
frame optical can have substantial contribution from very strong emission
lines, as illustrated by the recent JWST results (Cameron et al., 2023;
Matthee et al., 2022). NIRSpec spectroscopy is providing the location and
fluxes of these lines, enabling us to subtract them to accurately measure the
continuum SED. This is important for the estimation of stellar population age
distributions. We will do this subtraction directly in thousands of objects,
but also measure the trends and variations needed to model the purely
photometric samples.

NIRCam, in turn, is important for NIRSpec MOS to understand its slit losses,
background subtraction, and to aid in the interpretation of emission line
kinematics. Unlike ground-based slit masks, the NIRSpec micro-shutter array
(MSA) provides a fixed grid of slits. Galaxies will fall at various
registrations relative to those slits and with a range of sizes. Achieving
accurate line flux calibration requires the imaging to provide a model of
this. Further, NIRSpec MOS background subtraction requires the subtraction of
neighboring shutters; only NIRCam can provide a deep 2–5 $\mu$m probe of
contaminating objects in these shutters. Eventually, we expect that the sharp
NIRCam images will provide morphological templates for more ambitious
extraction of undersampled NIRSpec spectra, going beyond just summing along
the spatial direction of a slit.

Fourth, JWST imaging can allow more efficient target selection for NIRSpec MOS
spectroscopy of rare populations. While HST can provide Lyman-dropout
selection for UV bright targets, the longer wavelength coverage of JWST yields
much improved photometric redshifts for redder objects. NIRCam medium-band
imaging can isolate objects with strong rest-frame optical line emission that
can then be targeted for line profile studies with the NIRSpec gratings.

Finally, there are more subtle astrophysical synergies. For strong line
emitters, the high S/N and the excellent angular resolution of NIRCam
provides, via the comparison of different filters, measurements of size and
morphology of the line emission relative to the stellar light. NIRCam imaging
can reveal color gradients to be correlated with spectral properties. For
spatially extended galaxies, one can even connect these to resolved spectral
variations along the slits.

We note that while the combination of imaging and spectroscopy is critically
important, it is not the case that one requires spectroscopy for every imaging
object. Rather one intends to use the spectroscopy to build models of the
trends, so that one can perform statistical work on the non-spectroscopic
sample.

##  3 JADES Survey Design

###  3.1 Field Selection

JADES seeks to combine deep multi-band imaging and spectroscopy in pursuit of
the science goals described in § 2. It is designed to bring NIRCam and NIRSpec
MOS together on a common region of the sky, while covering substantial areas
in two different fields in order to increase the statistical reach for rare
objects and sample large-scale structure.

We observe two fields in JADES, to avoid concern that the large-scale
structure of highly biased tracers and the radiation transport of reionization
could make any one field peculiar and limit confidence in any unusual results.
Further, we wanted to spread the observing around the year to ease the
constraints on scheduling such a large program. Of course, even more fields
would better mitigate the concerns about cosmic variance, but this would limit
the depth and area of each.

The choice of location was driven by the availability of deep pan-chromatic
imaging and spectroscopy, as the study of galaxy evolution draws on a wide
range of such input. This led us clearly to the GOODS-South and GOODS-North
fields, which have received huge investments of telescope time over the past
25 years from essentially every facility that bears on the high-redshift
Universe.

GOODS-South, home of the Chandra Deep Field South and the Hubble Ultra Deep
Field as well as very deep ALMA (Walter et al., 2016; Dunlop et al., 2017;
Franco et al., 2018; Hatsukade et al., 2018) and JVLA data (Rujopakarn et al.,
2016; Alberts et al., 2020), is the preeminent deep field on the sky. We chose
this as the primary field for JADES and focused the majority of the observing
time there. GOODS-North, home of the Hubble Deep Field and exceptionally deep
Chandra data, was chosen as the second field.

By placing JADES in the legacy GOODS fields, we seek to augment the rich HST
community data products with a comprehensive set of JWST imaging and
spectroscopy. The GOODS (Giavalisco et al., 2004), CANDELS (Grogin et al.,
2011; Koekemoer et al., 2011), and UDF (Beckwith et al., 2006; Ellis et al.,
2013; Illingworth et al., 2013) data have been reduced and released as
components of the Hubble Legacy Fields (HLF Illingworth et al., 2016; Whitaker
et al., 2019a). The HLF reductions provide an excellent matched set of HST
images in multiple ACS and WFC3 bands for use with the JADES JWST NIRCam
imaging. We also utilize astrometric registration to Gaia performed by G.
Brammer (private communication) using the methods of Kokorev et al. (2022) and
grizli111https://doi.org/10.5281/zenodo.7963066.

###  3.2 Tiers and Geometrical Constraints

JADES is built as a two-layer wedding cake, with Deep portions of both imaging
and spectroscopy, flanked by larger Medium depth regions. Bringing imaging and
spectroscopy to bear on the same targets, while making efficient use of
coordinated parallel observations, are driving goals of the survey design.
Here we begin to describe these considerations.

The differing on-sky geometries of the NIRCam and NIRSpec instruments mean
that these two cannot be efficiently overlapped with a single pointing of
NIRCam. It takes at least a 2x2 mosaic of NIRCam to produce a filled area
large enough to cover one NIRSpec MOS pointing. Further, the ability to use
two instruments at once is an important opportunity to increase the science
return, but the angular separation between the instantaneous fields of the
instruments drives one to a large field. We note that, as a consequence of the
visibility constraints, neither of the JADES fields allows JWST to return at a
180∘ position angle, so as to swap the instrument locations. Instead, we have
to construct an adequately sized mosaic, and choose the parallels to maximize
the science return. Most of the MIRI parallel data and the NIRSpec MOS
parallel data falls on NIRCam imaging, and nearly all of the NIRSpec parallel
data falls on the GOODS/CANDELS HST imaging.

We placed the Deep portion of JADES in GOODS-S, while the Medium data are in
both fields. We considered placing Deep pointings in GOODS-N, but as full
support of the NIRSpec MOS footprint requires 4 NIRCam pointings, this would
have become overly expensive.

The NIRCam data in JADES fall into 4 categories. There are contiguous portions
in regular mosaics, of both deep and medium variety; we call these “Prime”.
Other portions occur as parallel exposures to NIRSpec MOS pointings, whose
positions are therefore dictated by the location and position angle of the
spectroscopy; we call these “Parallel”. Again, these come in deep and medium
variety.

In detail, ten of the Medium-depth Prime NIRCam exposures were taken with
NIRSpec in parallel, but the structure of JWST coordinated parallel
observations is that NIRSpec is formally prime in the planning tool. We refer
to NIRCam as prime (and NIRSpec as parallel) despite this, because the exact
pointing and exposure times were being dictated by the NIRCam science goals.

Other NIRCam pointings were taken with MIRI in parallel; these yield Deep and
Medium MIRI imaging.

The JADES NIRSpec MOS data fall into 3 tiers. One tier comprises two deep
pointings, one scheduled early in the program and targeted without JWST
imaging, the other scheduled at the end and targeted from the JADES imaging.
These are called Deep/HST and Deep/JWST, respectively. Then there are two
tiers of medium depth. One named Medium/JWST is targeted from JADES imaging,
the other named Medium/HST has target selection prior to JWST imaging and is
somewhat shallower. All of the NIRSpec MOS data is taken with NIRCam in
parallel.

The Medium-depth designs with both NIRCam and NIRSpec MOS are shaped heavily
by a desire to take well-dithered data, with at least 6 pixel locations to
provide robustness to bad pixels and the undersampled point-spread function,
and to use long enough exposures to keep observatory overheads low (these
concerns are easily satisfied with the Deep data). Hence, even this flanking
data is quite deep in comparison to pre-JWST opportunities. Because of the
full use of coordinated parallel observations, we paid particular attention to
minimizing the data rate for telemetry, typically utilizing the DEEP8 readout
pattern for NIRCam and SLOW readouts for MIRI. This kept the program to about
2 GB/hr of data volume.

Although JADES was designed to be observed in a single year, over-scheduling
in Cycle 1 resulted in it being scheduled over 18 months. In particular, the
large investment in GOODS-S was spread over two observing seasons. This led to
some reoptimization relative to the original design, induced by exact position
angles, instrument problems for some observations, and further on-orbit
appreciation of science opportunities. We will focus in this paper on the
observed program, making only passing mention of the original layout.

###  3.3 JADES Footprint

Figure 3: Layout of the JADES observations in the GOODS-South field. JADES
observations with NIRCam, NIRSpec MOS, and MIRI are shown as colored shaded
regions. Higher opacity indicates higher exposure time for NIRCam and MIRI or
overlapping MSA pointings for NIRSpec. Dithers and nods smaller than 2
arcseconds are not plotted. For NIRCam, only the SW quadrants are shown for
clarity. For NIRSpec, only the active area of the MSA that was used for target
placement, excluding regions that lead to truncated prism spectra, is shown.
Some of the observations yet to be made in Cycle 2 (NIRSpec Deep/JWST and most
Medium/JWST and their associated NIRCam parallels) have positions and
orientations that are still to be defined based on scheduling. Outlines of
other surveys, including the HST/ACS UDF and CANDELS, are shown with black and
olive green curves. The smaller sub-panels show the same information split by
instrument for clarity because it can be difficult to see the details when all
observations are plotted together. The NIRCam sub-plot in upper-right
additionally includes field outlines for the public JWST Cycle 1 NIRCam
imaging from the JEMS, FRESCO and NGDEEEP programs.

Figure 4: Layout of the JADES observations in the GOODS-North field. Details
as in Figure 3. GOODS-North data collection is nearly complete, so this figure
is nearly final. Only the southern-most of the Medium/JWST pointings remains,
due to a guide-star acquisition failure; this pointing will be repeated at an
unknown location and position angle. The NIRCam sub-plot in the upper-right
additionally includes the outlines of the public JWST Cycle 1 NIRCam imaging
from the FRESCO program.

The footprint of the JADES survey is shown in Figures 3 for GOODS-S and 4 for
GOODS-N. As the geometry is complicated with the various overlapping
footprints, we include separate images of each major portion of the survey. We
stress that portions of the GOODS-S footprint are provisional: the position
angle and exact location of most of the Medium/JWST NIRSpec and parallel
NIRCam data have not been confirmed yet, and observational hiccups may yet
occur.

In GOODS-S, one can see how the primary Deep portion of the survey, centered
on the UDF, and the Medium portion to the west support each other with
coordinated parallel observations. For instance, the MIRI parallels are
largely partnered with NIRCam imaging, including the first NIRCam Deep
Parallel. We also generate NIRSpec Medium/HST parallels that fall back on the
UDF, providing high-multiplex spectroscopy for our Deep imaging. Finally, the
NIRSpec Medium/JWST and Deep/JWST pointing create NIRCam parallels that fan
out to create more imaging area.

In GOODS-N, the area of the prime survey is a little smaller, which makes it
harder to fully utilize the parallels. We do utilize the NIRCam Medium Prime
fields to cover the HDF and provide imaging for the initial NIRSpec Medium/HST
spectroscopy as well as targets for the Medium/JWST followup. The parallels
from that later spectroscopy produce NIRCam images that cover additional
GOODS/CANDELS imaging. Two of the NIRCam prime pointings did not have MIRI or
NIRSpec parallels that would fall on NIRCam imaging or GOODS; we opted to use
MIRI, to be partnered with HST CANDELS imaging and perhaps future NIRCam data.

A major constraint on the layout of the survey comes from the intention to be
able to conduct NIRSpec follow-up of NIRCam-selected targets within a single
observing window. We judged that 60 days would be the minimum separation of
these visits, and therefore that it is needed to perform the NIRCam imaging
early in the window and the NIRSpec follow-up late in the window. In GOODS-N,
this plan is what has been scheduled. Unfortunately, our GOODS-S observations
ended up split over two years, and we are seeking to use more optimal position
angles (PA) and observing windows in the second year.

In GOODS-S, the year 1 NIRCam Deep Prime imaging was taken at V3 PA = 298.56∘,
in early October 2022. The year 1 Medium Prime imaging was taken a week later
at V3 PA = 308∘. The first NIRSpec Deep/HST pointing followed at V3 PA = 321∘.
When we return in year 2, we will seek to match the Deep Prime and Medium
Prime position angles to those of year 1. The position angles of the year 2
follow-up spectroscopy are assigned but might change, and the exact locations
will depend on the location of high-priority targets.

In GOODS-N, we observed the NIRCam Medium Prime data at PA = 241∘ in early
February 2023. The NIRSpec Medium/JWST data then followed in early May, at PA
= 150.48∘. Due to a fault with acquiring guide stars, one observation was
skipped and had to be redesigned and observed at PA = $132.93^{\circ}$.

###  3.4 Other Overlapping JWST Data

The GOODS fields, UDF, and HDF have of course been observed by other programs
in JWST Cycle 1. Here we briefly describe programs whose data overlap JADES.
Figures 3 and 4 show the JADES NIRCam footprint with overlays with some of
these and other programs. We surely expect this list to grow in future cycles!

The First Reionization Epoch Spectroscopic Complete program (FRESCO, PI:
Oesch, Program 1895, Oesch et al., 2023) conducted NIRCam F444W slitless
spectroscopy (2 hr depth), paired with F182M and F210M medium-band imaging (1
hr each). These 8-pointing mosaics in GOODS-S and GOODS-N overlap heavily with
JADES. FRESCO has proven to be highly complementary to JADES, as the strong
emission lines are imaged as excesses in JADES photometry and then redshifts
are obtained in FRESCO. The additional medium-band imaging, while shallower
than JADES, provides more spectral resolution on mid-redshift galaxies.

The JWST Extragalactic Medium-band Survey (JEMS; PIs: Williams, Tacchella, &
Maseda; Program 1963, Williams et al., 2023) observed one pointing on the UDF
in F182M, F210M, F430M, F460M, and F480M, with 4–8 hrs/filter. This dataset
heavily overlaps with JADES, providing additional filters with compelling
depth. In addition, JEMS conducted a NIRISS parallel in F430M and F480M, about
half of which overlaps JADES NIRCam imaging.

Between these two, we note that F182M and F210M will be available for a
notable portion of JADES. We have co-reduced JEMS and FRESCO NIRCam imaging
with JADES, and include these data in JADES photometric catalogs.

In addition to FRESCO’s wider medium-depth slitless spectroscopy, the Next
Generation Deep Extragalactic Exploratory Public survey (NGDEEP, PI:
Finkelstein, Program 2079) is producing very deep NIRISS slitless spectroscopy
on the UDF (Bagley et al., 2023). We expect that these programs will
complement the deeper but targeted NIRSpec MOS spectroscopy. The NGDEEP NIRCam
imaging parallel falls off the JADES footprint, to the south-east.

There is also other deep MIRI GTO imaging in GOODS-S. Program 1283 (PI:
Oestlin) is observing one extremely deep pointing in the UDF, reaching 49 hrs
in F560W. The NIRISS parallel from this pointing falls at the southern edge of
JADES NIRCam imaging. For wider MIRI coverage, program 1207 (PI: G. Rieke) is
observing 15 pointings in 8 filters, all overlapping the JADES NIRCam imaging.

The push for deep imaging and spectroscopy in GOODS-S will continue in Cycle 2
with an approved GO program 3215 (PI: Eisenstein) that will return to the
footprint of the JADES 1210 program to add very deep NIRCam imaging in six
medium bands—F162M, F182M, F210M, F250M, F300M, and F335M—to refine the search
for $z>15$ Lyman dropouts. At the same time, ultra-deep NIRSpec MOS
spectroscopy will be obtained on top of the JADES Deep Prime imaging.

Finally, there are several other NIRSpec programs in the fields. Two examples
are program 2674 (PI: Arrabal Haro), which conducted NIRSpec MOS and
coordinated NIRCam imaging in GOODS-N that will complement the JADES
footprint, and program 2198 (PI: Barrufet), which conducted NIRSpec MOS and
NIRCam pre-imaging on two fields in GOODS-S. Other spectroscopic programs are
the GTO NIRSpec WIDE MOS survey from programs 1211 and 1212 (PI: Luetzgendorf)
and IFU program 1216 (PI: Luetzgendorf). Additional NIRCam pure-parallel
imaging overlapping both fields is being obtained by the PANORAMIC program
(PI: Williams, Program 2514).

##  4 NIRCam Observations

In this section, we detail the JADES NIRCam imaging. We begin by describing
the details of the four categories of NIRCam imaging: Deep Prime, Medium
Prime, Deep Parallel, and Medium Parallel. We then discuss cross-cutting
aspects: filter selection, data quality, and a summary of depths.

| Deep Prime | Deep Parallel
---|---|---
Filter | $N_{\rm exp}$ | $t_{\rm exp}$ | $N_{\rm exp}$ | $t_{\rm exp}$
F090W | 26 | 35.7 | 36 | 49.5
F115W | 44 | 60.5 | 48 | 66.0
F150W | 26 | 35.7 | 36 | 49.5
F200W | 18 | 24.7 | 24 | 33.0
F277W | 26 | 35.7 | 30 | 41.2
F335M | 18 | 24.7 | 18 | 24.7
F356W | 18 | 24.7 | 24 | 33.0
F410M | 26 | 35.7 | 36 | 49.5
F444W | 26 | 35.7 | 36 | 49.5
Total | 114 | 156.7 | 144 | 198.0
Table 1: Overview of the NIRCam Deep imaging in GOODS-S in different filters,
listing the number of separate exposures and the total exposure time per
pointing, in ksec. We note that some pointings overlap, doubling the depth,
and that many Deep pointings also overlap with Medium, further increasing the
depth.  Figure 5: A small portion of the Hubble Ultra Deep Field, comparing
HST and JWST imaging. (left) The F160W image from HST WFC3, from the Hubble
Legacy Field reduction of Whitaker et al. (2019b), with an exposure time of
about 65 hrs. This is the single deepest H-band image taken with HST. (right)
The F150W JWST NIRCam image from the first year of JADES with an exposure time
of about 10 hours. The superior depth and image quality of JWST is clear.

###  4.1 NIRCam Deep Prime

In the heart of GOODS-S centered on the UDF, we observe 4 NIRCam fields to
image a $4.4^{\prime}$ by $6.1^{\prime}$ rectangular field. A pair of NIRCam
pointings is offset by $\sim$$61.5^{\prime\prime}$ in V2 so as to cover the
NIRCam inter-module gap, and each pointing contains many exposures including
offsets in V3 to cover the shortwave chip gap. This pattern is then repeated
in the second pair of pointings, offset in V3 leaving only a small overlap
with the first pair. We size this offset to the height of the long-wave (LW)
footprint, as this is slightly smaller than the short-wave (SW) footprint. We
note that the mosaic was laid out for V3 PA of 300∘ but observed at 298.56∘,
causing a small cosmetic deviation from a rectangular layout.

We use the DEEP8 readout pattern with 7 groups, yielding individual exposure
times of 1375 seconds. DEEP8 was used to reduce the data volume, but will
yield somewhat worse recovery from cosmic rays than a MEDIUM choice would
have. In total, each pointing utilizes 114 such exposures, each with a SW and
LW filter choice. Because each pointing includes more than 2 days of exposure
time, we must split the observations into 3 visits. The first two use 9-point
subpixel dithers (and no primary dither), each with 5 filter pairs, yielding
45 exposures. These two visits are offset in V3 by $\sim$5′′ to step over the
SW chip gap. The third visit uses a mosaic of two pointings, each with 4-point
subpixel dithers and 3 filter pairs, so 24 total exposures. The mosaic is
designed with a row overlap chosen to result in the same V3 step and with the
pointing chosen to result in the same footprint as the first two visits. Due
to a mistake in correcting for a small PA shift, the third visit of pointing 2
is offset mildly, $3^{\prime\prime}$, from the exact overlap. All filters are
included in both 9-point subpixel dither visits and therefore most points in
the image are observed by at least 18 different NIRCam pixels (9 if the
location falls in the SW chip gap in one of the visits).

These observations are all taken with MIRI in coordinated parallel, the
results of which will be described in §6. Because of this, in the first
season, we selected the subpixel dither based on F770W stepsizes. Having
inspected these results, we concluded that a mildly larger stepsize would be
better for background subtraction; we therefore changed to the F1500W dither
pattern in year 2.

We utilize 9 filters in the Deep Prime pointings: F090W, F115W, F150W, and
F200W on the SW arm, and F277W, F335M, F356W, F410M, and F444W on the LW arm.
The exposure times per filter are listed in Table 1. It is important to note
that about over a third of the total field is covered by two of the pointings,
doubling the total exposure times. Because of the importance of high-redshift
dropouts, we slant the SW exposure times toward the F115W and F150W filters,
while on the LW side we favor the longer filters where the zodiacal background
is reducing the sensitivity. A comparison of the imaging we obtained in the
NIRCam Deep Prime with that from the HUDF HST imaging is shown in Fig. 5.

Although JADES was designed to be observed in one year, Cycle 1 scheduling
constraints caused the program to be split. We opted to observe all 4
pointings in each year. In year 1, all pointings were observed with one of the
9-point dither visits, and two were observed with the 4-point dithers. This
will be repeated in year 2, completing the observing. Unfortunately, this
segmentation also caused a delay in the spectroscopic followup of the Deep
imaging. However, it does create an opportunity to consider year-scale time
variation in this nearly 25 arcmin2 deep field.

In total, this part of the JADES program was an investment of 229 hours and
resulted in 174 open-shutter hours of data, a utilization of 76%.

###  4.2 NIRCam Medium Prime

| w/NS-HST | w/MIRI | w/MIRI P4 | w/NS-JWST
---|---|---|---|---
Filter | $N_{\rm exp}$ | $t_{\rm exp}$ | $N_{\rm exp}$ | $t_{\rm exp}$ | $N_{\rm exp}$ | $t_{\rm exp}$ | $N_{\rm exp}$ | $t_{\rm exp}$
F070W |  $\cdots$ |  $\cdots$ |  $\cdots$ |  $\cdots$ |  $\cdots$ |  $\cdots$ | 6 | 5.03
F090W | 6 | 5.67 | 6 | 6.96 | 6 | 5.67 | 9 | 8.50
F115W | 6 | 6.96 | 12 | 11.34 | 12 | 11.34 | 12 | 10.05
F150W | 6 | 5.67 | 6 | 6.96 | 6 | 5.67 | 9 | 8.50
F200W | 6 | 5.67 | 6 | 5.67 | 6 | 5.03 | 9 | 7.54
F277W | 6 | 5.67 | 6 | 5.67 | 6 | 5.03 | 9 | 8.50
F335M |  $\cdots$ |  $\cdots$ | 6 | 5.67 | 6 | 5.67 | 6 | 5.03
F356W | 6 | 5.67 | 6 | 5.67 | 6 | 5.67 | 9 | 7.54
F410M | 6 | 5.67 | 6 | 6.96 | 6 | 5.67 | 9 | 8.50
F444W | 6 | 6.96 | 6 | 6.96 | 6 | 5.67 | 12 | 10.05
Total | 24 | 23.96 | 30 | 30.92 | 30 | 27.70 | 45 | 39.62
Table 2: Overview of the NIRCam GOODS-S Medium imaging in different filters, listing the number of separate exposures and the total exposure time per pointing, in ksec. We note that some pointings overlap, increasing depth. Pointing 4 (observation 22) with MIRI parallels is shorter than the others to balance the time within the allocation; this pointing is substantially overlapped by the Deep Prime mosaic. Pointing 25 of the NS-HST set lost half of the LW exposure time to illumination from a short circuit in NIRSpec.  | w/NS-HST | w/MIRI P1&2 | w/MIRI P3 | w/NS-JWST
---|---|---|---|---
Filter | $N_{\rm exp}$ | $t_{\rm exp}$ | $N_{\rm exp}$ | $t_{\rm exp}$ | $N_{\rm exp}$ | $t_{\rm exp}$ | $N_{\rm exp}$ | $t_{\rm exp}$
F070W |  $\cdots$ |  $\cdots$ |  $\cdots$ |  $\cdots$ |  $\cdots$ |  $\cdots$ | 6 | 5.67
F090W | 6 | 5.67 | 6 | 3.09 | 6 | 3.09 | 12 | 11.34
F115W | 12 | 11.34 | 12 | 6.18 | 6 | 3.74 | 12 | 11.34
F150W | 6 | 5.67 | 6 | 3.09 | 6 | 3.09 | 9 | 8.50
F200W | 6 | 5.67 | 6 | 3.09 | 6 | 3.09 | 6 | 5.67
F277W | 6 | 5.67 | 6 | 3.09 | 6 | 3.09 | 9 | 8.50
F335M | 6 | 5.67 | 6 | 3.09 |  $\cdots$ |  $\cdots$ | 6 | 5.67
F356W | 6 | 5.67 | 6 | 3.09 | 6 | 3.09 | 6 | 5.67
F410M | 6 | 5.67 | 6 | 3.09 | 6 | 3.74 | 12 | 11.34
F444W | 6 | 5.67 | 6 | 3.09 | 6 | 3.09 | 12 | 11.34
Total | 30 | 28.34 | 30 | 15.46 | 24 | 13.00 | 45 | 42.52
Table 3: Overview of the NIRCam GOODS-N Medium imaging in different filters,
listing the number of separate exposures and the total exposure time per
pointing, in ksec. We note that some pointings overlap, increasing depth.
Pointing 3 (observation 3) with MIRI parallels is shorter than the other two
to balance the time within the allocation. Pointings 4-7 are with NS-HST,
while pointings 8-11 are with NS-JWST.

To provide shallower flanking coverage and increase the area available for
NIRSpec MOS targeting, JADES includes 18 pointings in (mostly) regular mosaics
yielding contiguous coverage to medium depth. 7 of these are in GOODS-N and 11
in GOODS-S. Because of the footprint, 8 of the pointings have MIRI in
parallel, while 10 have NIRSpec MOS in parallel. In most cases, the pointings
are paired with a $62^{\prime\prime}$ step in V2 so as to fill a long
rectangle covering the inter-module gap, with some double coverage. In detail,
the step is chosen to match the width of one SW chip, so that the pointings
cover the V3-parallel chip gap of their partner.

Cycle 1 scheduling constraints caused half of the Medium Prime time in GOODS-S
to be delayed until fall 2023. We opted to observe in 2022 the six pointings
of the mosaic that did not overlap the NIRCam Deep Prime field, so that most
of the prime area could be observed in fall 2022, including the footprint of
the deep MIRI parallels. These pointings also provided NIRSpec MOS parallels
on and around the UDF. The GOODS-S mosaic was designed for and observed at
v3PA of 308∘. The small PA difference from the Deep Prime mosaic was included
to make the program more easily schedulable.

For these six pointings, the filters are as in Deep Prime save for omission of
F335M. Each of the 8 filters received 6 exposures, falling on 6 different
pixels. The filter pair of F115W and F444W received 1159 second exposures
using DEEP8 and 6 groups. The other three filter pairings receive 945 second
exposures; these use DEEP8 with 5 groups to reduce data volume. Unfortunately,
one pointing failed due to shorts in the NIRSpec MSA; this one will be
repeated in fall 2023. Another pointing had half of the LW imaging impacted by
the shorts; we opted to accept this one and adjust a future pointing to
compensate.

After this start, and in view of the ongoing scheduling of the NIRCam parallel
observations, we opted to make some minor adjustments. We had originally
planned for 19 Medium pointings, but we decided to remove one pointing in
GOODS-S and compress certain exposure times in GOODS-N in order to add F335M
back into filter set for 11 of the 12 remaining pointings, pairing with a
second exposure of F115W. We also had to adjust times to balance the program
within its allocation, due to small on-orbit alterations in the parallel
observation timing model. The detailed exposure times are presented in Table 2
for GOODS-S and Table 3 for GOODS-N. Most exposures use the DEEP8 readout
mode, but the shorter ones use MEDIUM8. In GOODS-S, relative to the original
grid, we removed one pointing that overlapped the Deep Prime imaging and
mildly compressed the time on a second that filled only a small gap between
Deep and the rest of Medium. We then took the northern most row and displaced
one pointing to compensate for the short-impacted year 1 pointing, and the
other pointing to fill more of the gap between Deep Prime and the expected
location of future Parallel imaging. In GOODS-N, we decreased the time in the
3 pointings with MIRI parallels; in one case we were unable to include F335M.

For the 7 pointings in GOODS-N, we pack four of them tightly in V2 to cover
the intermodule gap. The other three have no intermodule coverage, and we
further have chosen these to have less exposure time, as the MIRI parallels
are more flexible than the NIRSpec MOS parallels. The northern portion of the
mosaic is therefore somewhat shallower, but able to cover more of our early
NIRSpec Medium/HST data. We decided that it was more important to maintain a
6-fold dither than to insist on a filled footprint, expecting that this field
will likely attract larger coverage in future Cycles. The mosaic was designed
and observed at v3PA of 241∘.

Regarding the dither strategy, when operated with MIRI parallels, we use 3
dithers with the INTRAMODULEX pattern and 2-point subdithers. These were based
on the MIRI F1800W PSF in GOODS-N and F2100W in GOODS-S, chosen to increase
the dither step size for background subtraction. This dither uses small steps
at 45-degrees in V2 and V3, stepping over both SW chip gaps in each. Hence
most points in the chip gaps receive 4 SW exposures; only small overlaps from
the cross in the middle generate only 2. We remind that in most cases, the
center of each arm is covered by the other pointing in the pair.

When operated with NIRSpec parallels, the strategy is mildly different. Here,
we split the 6 exposures into two sets of 3. Each triplet is a different MSA
design with largely independent targets, to be described further in §5. In
each triplet, NIRSpec will execute its 3-step nod along the slits of the MSA.
We note that these steps are roughly at $45^{\circ}$ on the NIRCam pixel grid.
For the next triplet, we step the central pointing purely in the V3 direction
by an amount to cover the V2-parallel chip gap. The V3-parallel gap is not
covered within this pointing, but usually will be by the partner in the
mosaic.

In summary, this part of JADES was an investment of 195 hours, 123 in GOODS-S
and 72 in GOODS-N, resulting in 131 open-shutter hours (with the formally
prime instrument), a utilization of 67%.

Figure 6: A small portion of JADES NIRCam GOODS-S imaging, combining F090W,
F200W, and F444W filters in the Deep Parallel region of program 1210. This
image shows the great diversity of galaxies revealed in every JWST image, with
a wide variety of colors and morphologies. North is up.

###  4.3 NIRCam Deep Parallel

In GOODS-S, JADES executes two long NIRSpec MOS pointings (§5), each of 200 ks
open-shutter spectroscopy. Each of these is used to make a long NIRCam
parallel exposure, the location of which depends on the position angle and
observing window, which in turn was subject to the programmatic constraints of
the NIRSpec targeting.

It turned out, fortuitously, that the first of these pointings was at a
position angle (V3PA of 321∘) that caused the NIRCam parallel to fall on top
of the deep MIRI parallels produced by the NIRCam Deep Prime program.

The second pointing needs to be later in the observing window so that the
targets resulting from the analysis of the full NIRCam data set can be used.
We are designing this for v3PA of 53∘, which will place the NIRCam parallel
north of the Deep Prime field, near the location of the first Medium/JWST
parallel. The exact placement will depend on the location of the most
interesting high-redshift candidates in the Deep Prime field.

Each of these two deep fields use the same nine filters as the Deep Prime
program. We also use the same DEEP8 readout mode with 7 groups, yielding 1375
second integrations. Each field uses 144 such integrations for a total of 55
hours of open-shutter imaging, indeed mildly deeper than a single Deep Prime
pointing (but without the overlaps of the mosaic). The exposure time per
filter is shown in Table 1.

One limitation of these data is that NIRSpec only employs 9 dither locations,
3 nod locations in each of 3 different slit configurations. We were careful to
arrange that each filter is observed at least twice at every location. In
detail, the NIRSpec exposures are just over twice as long as the NIRCam
integration, so each nod position results in a pair of back-to-back otherwise
identical NIRCam integrations, 72 in all. We note that the dither pattern is
set by the geometry of the NIRSpec MSA and therefore not tuned to the pixel
scale of NIRCam; that said, the steps are not commensurate with the NIRCam
pixel scale and so the intra-pixel behavior is sampled.

Another limitation is that the 3 MSA configurations in NIRSpec are stepped
only a short distance $\sim$0.8′′, to limit the effects of distortions across
the MSA. This means that the data set does not fill the SW chip gaps. On the
flip side, the data maximize depth in the region that is covered. An example
of the superb quality obtained in this program is shown in Figure 6.

###  4.4 NIRCam Medium Parallel

JADES executes twelve medium-depth NIRSpec MOS pointings, 4 in GOODS-N and 8
in GOODS-S, to be described in §5. Each of these produces a NIRCam coordinated
parallel observation, with a total of 45 exposures. The exposure times are
given in Tables 2 and 3.

As these parallel fields were at risk to fall off of the HST GOODS and CANDELS
imaging, depending on the final position angles, we opted to include the F070W
filter in addition to the nine filters used in the Deep imaging. This improves
isolation of $z\sim 5.5$ Lyman $\alpha$ dropouts.

As with the NIRSpec Deep program, these observations also use 9 closely spaced
dither pointings, via 3 nod locations in each of 3 slit configurations. The SW
chip gaps are not covered. We ensure that each NIRCam filter is observed in at
least 2 of the 3 slits and hence at 6 dither locations. 2 of the 3 slits have
all ten filters observed; the remaining one is missing F070W, F200W, F335M,
and F356W. Therefore, the area covered by all ten filters is reduced by a tiny
amount.

As the prime spectroscopy will be placed on the NIRCam Prime mosaic, these
Medium parallels in both GOODS-S and GOODS-N fall almost entirely outside of
the NIRCam Prime footprint, thereby providing a substantial amount of
additional area at medium depth. In GOODS-N, we observed at v3PA of 150.48∘
and 132.92∘, placing the new imaging on the northeastern portion of the HST
GOODS-N field. Because of the location of high-priority spectroscopic targets,
these do not form a regular grid, but they are close enough to map a sizable
near-contiguous region. By coincidence, the orientation of NIRCam in this
Parallel imaging is almost 90∘ rotated from the Prime imaging, leaving an
obvious pattern for future observations to fill in the gap between the two.
Unfortunately, a guide-star acquisition failure caused Observation 8 to be
skipped, requiring a replan (Obs 98) that was observed a few weeks later with
18∘ of rotation relative to the other three pointings.

In GOODS-S, we will spread the spectroscopic pointings over the full Medium
mosaic. In the original plan, these would all have been late in the first
observing window, but given the scheduling delay, we are opting to spread them
out. The first field (observation 1) was observed on January 12–13, 2023, at a
V3PA of 56.17∘. The other 7 will be observed in late 2023, likely at a range
of position angles. Here, we assume V3 PA of 30∘ for display and summary
purposes. The exact parallel footprints cannot be specified until the targets
are in hand, and may move substantially.

###  4.5 Comments on Filter Selection

Early on in the design of JADES, we recognized that the strong increase in
zodiacal emission longward of 4.5 $\mu\rm m$ would increase the background in
F444W, such that F410M could be competitive despite its narrower bandpass. We
therefore opted to include both filters to increase the resolution of the
spectral energy distribution. This is particularly important because of the
strong rest-optical emission lines expected in some high-redshift galaxies, as
indicated by Spitzer imaging and now confirmed with early JWST spectroscopy.
The spacing of the H$\alpha$+[NII] and H$\beta$+[OIII] complexes is such that
only one can fall into F444W at any given redshift, and including F410M means
that the comparison will separate the line from continuum emission at most
redshifts (with ambiguity if the lines fall on the shoulder of the filter
curves). This separation is important for stellar population modeling, as we
want to measure the rest-optical continuum color relative to the ultraviolet.

As we studied this, we concluded that F335M and F356W offered a similar
opportunity and that the likelihood of strong H$\alpha$ or H$\beta$+[OIII]
emission recommended splitting this exposure time as well. This has been borne
out in practice: we have found the abilty to isolate strong emission lines at
3–5 $\mu$m to be very useful and interesting. In addition to the lines
themselves, the extra spectral resolution helps to isolate the Balmer jump at
high redshift and to measure the rest-optical continuum.

Where possible, we observe F277W somewhat longer than F356W because of the
extra coverage from F335M. We also chose to slant the exposure time toward
making F115W deeper, emphasizing the selection of $z>9$ candidates.

In summary, we have found the NIRCam coverage in the nine base bands to be
highly effective for photometric redshifts. For example, at $z\approx 7$, one
observes the Lyman $\alpha$ drop in F090W and H$\beta$+[OIII] in the longest
bands.

###  4.6 Data Quality Caveats

While a detailed description of the data reduction and performance will be
left for later papers (Robertson et al., in prep.; Tacchella et al., in
prep.), we here describe some issues that we have already seen and that might
be of interest to other users.

Our observing was split into many separate visits rather than long campaigns,
and we encountered substantial persistence at the start of some visits, left
over from the immediately preceding program. These signals last for several
hours and will require detailed modeling. The effect is more severe on the SW
chips A3, B3, and B4; the other SW and both LW chips are much milder.

Most dramatically, in the Deep program, observations 7 and 10, were observed
immediately after observations of the bright Trapezium nebula, leaving
substantial diffuse emission over portions of SW chips A3, B3, and A4. This is
most severe in the F090W filter (the first used), but there are faint traces
in the next filter (F115W), 3.5 hours later.

We stress that the persistence is not simply coming from bright stars in the
previous images, but the change in the diffuse background illumination level
from the previous program. This greatly increases the affected area,
particularly in A3 and B4. The decay time for A3 is particularly long.
Observation 4 in 1181 shows a similar morphology of persistence incurred by a
change in the background level relative to the previous program.

We also see persistence in A3 in visits following wavefront sensing
operations, creating a moderate-size ($\sim$100 pixel across) hexagonal image.
While such sensing is common, the reference star is planned to fall on a
consistent part of the chip. We hope that the frequent re-occurence of this
signal will allow that area of the chip to be particularly well characterized.
1180 observations 15 and 27 are affected by this.

We have also seen a case (1180 observation 18) in which a bright star happened
to fall on the NIRCam field during the preceding MIRI observation, even though
NIRCam was not being used in parallel. NIRCam is typically left open to the
sky when not used. One can see the imprint of the whole MIRI dither pattern,
as well as the trail when the telescope slewed away from the field.

Of course, these regions do also incur persistence from brighter sources in
our own observations. For instance, in 1181 observation 2, there is a bright
star in the bad portion of A3 that creates a recurrent glow in all 6 dither
locations.

Mitigating these persistent signals is particularly vexing when one is using a
small-angle dither, as a pixel may never find a blank-sky location away from a
larger galaxy. We are therefore increasing our sub-pixel dither selection in
the remainder of our observations. However, this is not always possible when
observing jointly with NIRSpec. We caution that we have not yet considered the
effect of persistence on our photometry, but in the worst regions of these
chips, we believe this should be studied. By construction, the dither pattern
is repeated between filters, so the persistence from the first filter will
affect the next.

Like many NIRCam observations, we occasionally have noticeable illumination of
the SW detector through an off-axis stray-light path, producing the so-called
“wisps”. These affect F200W and F150W most strongly. While these signals are
known to modulate in amplitude due to the brightness of stars in the source
region on the sky, we have found that the exact morphology of the pattern also
varies within our program. We are still analyzing this, but hope that a low-
dimensional set of templates will suffice to remove them.

We have found it very helpful to visualize the calibrated exposures of a
dither sequence in animations, fixed in pixel coordinates so that the true
objects move and the detector artifacts stay still.

We have found it easier to disentagle persistence from wisps when F090W or
F115W are observed first in a visit, so that the persistence has decayed away
before the wisp-affected F150W and F200W bands are observed.

We now turn to rarer problems. The second half of one pointing (observation
30) of the Medium Prime mosaic in GOODS-S had to be skipped due to an on-board
issue unrelated to our program. There was not enough time in the observing
window (constrained by the spectroscopic coordinated parallel) to try again.
Fortunately, this pointing is at the edge of the mosaic. The first half of
this same pointing had its LW data badly contaminated by a glowing short
circuit in the NIRSpec MSA. We will repeat this entire pointing in October
2023 to complete the 6-dither coverage.

One half of another pointing (observation 25, first 4 dither sequences) was
also affected by the NIRSpec glowing short (Rawle et al., 2022). In this case,
because the location of the field was favorable to access in a different way
in the year 2 observing, we opted not to repeat this imaging location but
instead combine the NIRSpec re-do with another pointing.

In both of these cases, we found that the LW data were badly affected. The
background was roughly doubled, but further there are many patterns of
concentric rings, with spacing depending on wavelength, likely due to some
diffractive pattern from where the light from NIRSpec has bounced off of the
tertiary mirror. As the short circuit is apparently not particularly hot, the
effect on SW is much less and we think this data is usable. There are a
handful of faintly detected rings in F200W, chips A1, A2, A4, and B4.

Next, in the 3 deep visits of 1210, we find an enigmatic set of arcsecond-
scale blobs near an edge of B3. These are bright in the second visit, but
detectable in the other two. They are therefore not due to persistence, and we
are confident they are not astrophysical as they do not appear in 1180 images
of the same region. The morphology is very different from a wisp.

Some of our exposures have a plume of what appears to be scattered light in a
corner of B4. We hypothesize that this may be due to a bright star striking
the chip mask, as the signal changes slightly between the three visits of
1210, which move only at the arcsecond level, suggesting a well-focused
source. However, we also see it in observations 27, 28, 29, and 30 of 1180, so
it seems that the cause is not particularly rare.

###  4.7 NIRCam Imaging Depth

| Deepest (9 arcmin2) | Deep (36 arcmin2) | Medium (175 arcmin2)
---|---|---|---
Filter | Time | PS Depth | Time | PS Depth | Time | PS Depth
| (ks) | AB mag | (ks) | AB mag | (ks) | AB mag
F070Wa |  $\cdots$ |  $\cdots$ |  $\cdots$ |  $\cdots$ | 7.1 | 28.59
F090W | 79.3 | 30.02 | 48.6 | 29.75 | 9.2 | 28.85
F115W | 134.5 | 30.29 | 71.9 | 29.95 | 12.1 | 28.98
F150W | 79.3 | 30.13 | 48.6 | 29.87 | 8.6 | 28.93
F200W | 56.0 | 30.11 | 34.3 | 29.84 | 7.6 | 29.02
F277W | 78.0 | 30.52 | 44.0 | 30.21 | 8.5 | 29.32
F335Mb | 56.3 | 30.05 | 27.7 | 29.66 | 6.7 | 28.89
F356W | 56.3 | 30.41 | 34.4 | 30.14 | 7.6 | 29.32
F410M | 79.3 | 30.04 | 48.6 | 29.77 | 9.2 | 28.87
F444W | 79.3 | 30.15 | 49.3 | 29.89 | 10.1 | 29.03
Table 4: A Summary of Average Exposure Times and Depths in the NIRCam Deep and
Medium Surveys. The 6 Deep pointings cover a total footprint of $\sim$45
arcmin2, with $\sim$9 of these being double-covered and marked as Deepest. The
31 Medium pointings are anticipated to cover a total additional footprint of
$\sim$175 arcmin2 exclusive of the Deep footprint. Within each, we compute the
average exposure time per filter, including the contribution of Medium to the
deeper regions. We then present the 10-$\sigma$ $0.2^{\prime\prime}$ diameter
aperture depth, including point source aperture correction, for these average
exposure times, scaling from observed errors in the first observed pointing of
1286 as described in the text. aThe F070W filter is used in only a subset of
Medium pointings, covering an area of $\sim$76 arcmin2. We quote the average
exposure time and depth in this smaller area. bThe F335M filter is not used in
some Medium pointings, so that this filter covers a Medium footprint of
$\sim$134 arcmin2. We quote the average exposure time and depth in that
smaller region.

As shown in Figures 3 and 4, the NIRCam pointings often overlap, so that the
survey is deeper than what appears in Table 1–3. Further, the depth varies
because of the geometry of these overlaps. To provide a useful summary of the
NIRCam program, we divide the footprint into 3 disjoint regions: Deepest,
Deep, and Medium. Deepest refers to the area where two or more of the Deep
Prime pointings overlap. Deep refers to the remainder of the area of the Deep
Prime and Parallel fields. Medium refers to the rest of the area, including
both GOODS-S and GOODS-N. In each case, we add up all of the exposure time in
each filter, including the contribution of Medium pointings to the Deep
regions, along with a corresponding point source depth from the Exposure Time
Calculator. These areas and averages are approximate, as we do not yet have
the final exposure locations and have not used an exact weight map, which
would include the detailed impact of the dithers on the boundaries and the
impact of bad pixels. We also remind that the exposure times do vary within
the regions; for example, the northern portion of GOODS-N is shallower by a
factor of 2-3 in exposure time than the bulk of the Medium region.
Nevertheless, these summaries are reasonable averages for forecasts and
contextual comparisons.

We then convert the representative exposure times to anticipated 10-$\sigma$
depths for background-limited $0.2^{\prime\prime}$ diameter apertures with
point source aperture corrections. We do this by using the first pointing of
1286, as a representative and currently non-overlapped Medium pointing. The
aperture error is derived from measuring many such apertures in blank regions
of the mosaic and computing the rms in bands of exposure time. We note that
the Deep survey was observed with longer exposure times and usually lower
zodiacal background level, which will make it slightly deeper than the scaling
from this 1286 pointing. However, the final mosaic will have mildly lower
effective exposure time due to bad pixels and cosmic rays.

We remind that the Deep area is all in GOODS-S, while the medium area is split
approximately evenly between GOODS-S and GOODS-N. The average depths in
GOODS-N is mildly shallower than in GOODS-S, but the two fields are
sufficiently similar that we do not separate them for this summary.

In Figure 7, we show a visualization of the estimated depth of the JADES,
JEMS, and FRESCO NIRCam and JADES MIRI imaging, along with the estimates for
HST ACS and WFC3 imaging (Whitaker et al., 2019b) in the HUDF and CANDELS
fields that JADES overlaps. We have measured the JEMS and FRESCO depths in the
same manner as that of JADES, using our own reductions of these data. One sees
that the JADES imaging is comparable in depth in the deepest HUDF data, which
covered only a single HST pointing. One also sees that the ratio of optical
ACS to infrared JADES depth is less favorable in the broader CANDELS region
compared to that of the HUDF.

Figure 7: Depth versus Wavelength for JADES and other data sets. Black, blue,
and red solid squares show the 10-$\sigma$ point source depth for JADES
Deepest, Deep, and Medium NIRCam data, using the variation in 0.2′′ blank-sky
apertures. Purple and magenta circles show the depth of the JEMS and FRESCO
medium-band imaging. Horizontal ranges show the filter widths. The JADES Deep
and Medium MIRI depths are shown with grey and pink points on the right. The
0.2′′ aperture is mildly too small for the HST WFC3 image quality, causing
these estimates of depth to be too optimistic by 0.1–0.2 mag compared to
larger apertures. Comparison is shown (dark and light green) to the depths
measured in the same method from the Hubble Legacy Field (Whitaker et al.,
2019b) mosaics, separating the single HUDF pointing from the broader CANDELS
region. The HUDF ACS (WFC3) footprint is 11 (4.7) square arcminutes,
comparable to the area of the Deepest JADES data. The CANDELS area exceeds the
JADES Medium area. JADES improves over even the HUDF in area and spatial
resolution, and at wavelengths longward of 1.6 $\mu$m, the gains in depth and
resolution are immense.

##  5 NIRSpec Observations

As introduced in §3, the JADES NIRSpec MOS data fall into 3 tiers: Deep,
Medium/JWST, and Medium/HST. All 3 tiers use the low-resolution prism as well
as several gratings, with grating spectra available for most of the prism
targets. Table 5 provides a summary of disperser configurations and exposure
times for each tier. This section presents some of the features common to all
tiers before describing each tier separately.

Each of the NIRSpec gratings are used with a matching long-pass filter to
prevent overlap of first order spectra by higher orders. For the band 1 G140M
grating there are two available filters; F070LP and F100LP. F100LP blocks all
light below 1 $\mu{\rm m}$, thereby ensuring no second order overlap within
the nominal spectral range up to 1.8 $\mu{\rm m}$. F070LP allows through light
at $>0.7\,\mu{\rm m}$, so it has the advantage of enabling observation in the
range 0.7 to 1.0 $\mu{\rm m}$. This corresponds to the wavelength of the
Lyman-$\alpha$ transition at redshifts of 4.8 to 7.2, bridging the epoch of
the end of cosmic reionization (Robertson, 2022). Therefore we chose to use
the F070LP filter for all the JADES G140M spectroscopy. We accept that there
will be second order overlap from the sky, increasing the sky background at
$>1.4\,\mu{\rm m}$, and from galaxies at redshifts below 7 that have flux in
this wavelength region. However, much like the overlapping grating spectra
allowed by our MSA configurations described in the following subsection, this
increased continuum flux will have little impact on our emission line
measurements from the G140M spectra.

###  5.1 MSA Configuration Design

JADES designed its NIRSpec multi-object observations using the tool eMPT
(Bonaventura et al., 2023) that provided key features beyond what was found in
the baseline tools for MSA design. In particular, eMPT allowed us to:

  1. 1.

Constrain the NIRSpec pointings to a rigid mosaic of NIRCam fields, once the
position angle is specified.

  2. 2.

Impose a detailed prioritization system for our targets and have complete
control over the order in which each class of targets is attempted placed on
the MSA at a given pointing.

  3. 3.

Optimize repeated observations across multiple overlapping MSA designs to
maximize exposure time on the highest priority targets.

  4. 4.

Identify and eliminate beforehand targets having contaminating objects falling
within their (nodded) slitlets.

  5. 5.

Avoid the use of shutters leading to prism spectra truncated by the NIRSpec
detector gap or contaminated by the spectra of failed open shutters.

  6. 6.

Enable overlap of the grating spectra (except for some high priority targets
whose grating spectra are protected from overlap) to maximize the gratings
multiplexing, while keeping those of the prism distinct.

  7. 7.

Open additional blank-sky shutters that disperse onto unused detector real
estate to support master background subtraction.

To do this, eMPT contains the full NIRSpec model of the astrometric
distortions and multi-shutter geometry and constraints, from which it can
accurately predict how given astrometric positions will fall onto shutters,
whether those shutters are available to use, and where the resulting spectra
will fall on the detectors (and thereby whether they will overlap). The code
thereby revealed the detailed outcome of each target, with which one can
proceed to accept targets in complex priority orders. After this process
determines which shutters were to be opened, the final optimal pointings and
matching MSA masks were imported into the standard APT/MPT workflow for
further execution.

The MSA shutters are on a rigid grid, and the opaque regions between the
shutters block enough light from compact sources that one typically chooses to
retain only the fraction of targets that are sufficiently well centered in
their shutters. Not all shutters function properly with 22 failed open that
always disperse light onto the detector and 17.5% of the unvignetted shutters
that are permanently closed (Böker et al., 2023). Our MSA masks require a
3-shutter-high slitlet for nodding and background subtraction. Locations where
one can open a 3-shutter-high slitlet are therefore limited by this MSA
operability leading to the concept of a ‘viable slitlet’ map. In all the JADES
tiers we perform MSA reconfigurations with small (always $<10$ and mostly $<1$
arcsec) offsets where we attempt to obtain spectra of at least the highest
priority targets in multiple configurations. Simulations have shown that these
offsets need to be kept small to maximize the overlap of the viable slitlet
maps and to avoid the astrometric distortion at the NIRSpec MSA plane that
cause some objects to become insufficiently centered. For most tiers of JADES,
the main constraint on these offsets is to ensure maximal coverage of the
highest priority targets in multiple MSA configurations.

Because of these constraints, one needs a very high target density in order to
achieve a high multiplex of assigned targets. JADES typically supplies at
least 200 targets per square arcminute, yielding about 150 assigned targets on
the prism designs, corresponding to an average assignment rate of only 9%. An
obvious consequence of this low average rate is that one does not want to
serve the rarer higher-value targets in this limited way. We therefore
developed a detailed prioritization of the targets, largely by redshift and
flux (see § 5.2). The eMPT allows us to assign slits in a greedy order,
assigning higher priorities first. While this slightly decreases the total
multiplex, it yields much higher assignment rates on high value targets.

NIRSpec prism spectra are relatively short, allowing multiple columns of non-
overlapping spectra. For a given prism MSA design, there is a matching grating
MSA design that is nearly identical. Importantly, we keep nearly all of the
same shutters open for the grating configuration so that most of our galaxies
will have information at multiple spectral resolutions. The longer traces of
the gratings may overlap, as may the zero-order emission, but the emission
lines are sparse in these spectra. Emission lines can be associated to their
parent object in multiple ways: the location along the three shutter tall
$1.5^{\prime\prime}$ slit, the prism spectrum, where the lines appear
unoverlapped, and the wavelength ratio of multiple detections. The dispersed
continua of the typical faint targets is below the detector noise in the
grating spectra and hence the continua of the overlapping spectra do not
substantially increase the noise. For the highest priority targets and for the
infrequent brighter targets, we do close some shutters to avoid overlap; so a
small fraction of objects are observed only with the prism.

|  |  |  |  | Exposure Times (ksec)
---|---|---|---|---|---
Subsurvey | Program | # Fields | Subpointings | # Targets | Prism | G140M | G235M | G395M | G395H
GOODS-S Deep/HST | 1210 | 1 | 3 | 250 | 100 | 25 | 25 | 25 | 25
GOODS-S Deep/JWST | 1287 | 1 | 3 | 250 | 100 | 25 | 25 | 25 | 25
GOODS-S Medium/HST | 1180 | 7ab | 1 | 580 | 3.8 | 3.1 | 3.1 | 3.1 | —
GOODS-S Redo Obs 134 | 1180 | 1c | 2 | 180 | 7.5 | 6.2 | 6.2 | 6.2 | —
GOODS-S Redo Obs 135 | 1180 | 1c | 3 | 180 | 11.3 | 9.3 | 9.3 | 9.3 | —
GOODS-S Medium/JWST | 1286 | 8 | 3 | 2000 | 8.0 | 8.0 | 9.3 | 9.3 | 8.0
GOODS-N Medium/HST | 1181 | 8a | 1 | 860 | 6.2 | 3.1 | 3.1 | 3.1 | —
GOODS-N Medium/JWST | 1181 | 4 | 3 | 1010 | 9.3 | 9.3 | 9.3 | 9.3 | 9.3
Table 5: Summary of the NIRSpec MOS Observations. For each program, we list
the number of separate MSA fields, as well as the exposure time per disperser
in kiloseconds. Each field consists of 1 to 3 sub-pointings, each with two
nearly identical MSA designs: one for the prism and a second for the grating;
the latter closes a few shutters to protect certain high-priority spectra from
overlap. The quoted times are summed over the sub-pointings, but not all
targets can be placed on all sub-pointings. The number of unique targets in
each subsurvey is listed; bold values indicate completed observations, whereas
other values are estimates at this time. The long-pass filter choices for the
gratings are F070LP, F170LP, F290LP, and F290LP, respectively. aEach
Medium/HST pointing is split into two distinct MSA locations, separated by the
primary dither step across the NIRCam SW chip gaps. As the larger dither
causes us to typically observe distinct galaxies, we account each pointing as
two fields with one sub-pointing. When the slit registrations are favorable,
we do reobserve high-priority targets to double the exposure times on these.
bTwelve MSA locations were planned, but only 4 were completed in cycle 1 due
to instrument problems. 3 will be redone in cycle 2. cThese 5 re-dos were
organized to be similar to two Medium/JWST locations, one with 3 sub-positions
and one with 2.

###  5.2 Target Prioritization

Here we summarize the design of the JADES spectroscopic target selection
process. The main criteria for placing galaxies into priority classes are
their redshifts and fluxes. Redshifts are estimated via photometric redshift
algorithms and/or Lyman-break color selection. The highest redshifts are
prioritized both because they are rare and because one of the main scientific
goals of JADES is to understand the earliest phase of galaxy evolution.
Galaxies with higher fluxes (in continuum or predicted emission lines,
depending on the category) are prioritized since higher S/N spectra allow a
wider range of science investigations.

The details of the priority classes depend on the tier (Deep, Medium/JWST or
Medium/HST) and whether the targeting is based only on HST imaging or on the
JADES imaging. Deeper spectroscopic observations have lower flux limits for
similar classes so that the achieved S/N will be similar for the different
tiers.

The highest priority class contains relatively bright galaxies at the highest
redshifts; $z>8.5$ for Deep and Medium/JWST and $z>5.7$ for Medium/HST
program. After these, we prioritize fainter galaxies at the same redshift and
then progressively lower redshift bins, favoring the brighter galaxies.
Through this, we aim to build up a statistical sample between redshift
$1.5<z<5.7$ in the lower priority classes over the tiers, with the shallower
tiers contributing to the bright end and deeper tiers contributing to the
faint end. In addition to these classes we also include a small fraction of
galaxies identified as special in other data, e.g. with ALMA, Chandra,
Lyman-$\alpha$ emitters selected with the ESO VLT/MUSE instrument.

We also prioritize a few bright ($H_{AB}<23.5$) moderate-redshift ($z>1.5$)
galaxies, enabling the collection of exquisite infrared spectra from objects
around cosmic noon. After these, we prioritize in photometric redshift bins,
favoring the rarer brighter examples. Galaxies with photometric redshifts
below $z<1.5$ are used as a low priority filler sample; nevertheless, the
large number of these targets yields a substantial observed set. Further
details on the target prioritization, including variations per tier, are
provided in the JADES Deep/HST data release paper (Bunker et al., 2023a).

The distribution of spectroscopic redshift and F444W magnitudes are shown in
Figure 8. One sees that NIRSpec is recovering redshifts to extremely faint
flux levels, particularly in the Deep pointing.

Figure 8: The observed distribution of F444W AB magnitude versus spectroscopic
redshift for the Deep/HST pointing (top) and the first Medium/JWST pointing
(bottom). The target selection has successfully weighted toward higher
redshift galaxies, resulting in a more even redshift distribution.

###  5.3 MSA Target Acquisition

Successful use of NIRSpec MOS depends critically on high-quality astrometry.
Astrometric distortions in the target coordinates will tend to perturb targets
away from the centers of their shutters, lowering performance. More
insidiously, NIRSpec MSA Target Acquisition relies on a few bright compact
sources to align the MSA to the desired location on the sky, so astrometric
errors on those few objects can cause the entire MSA to be misaligned.

JADES was fortunate to be able to utilize recent reductions of HST imaging in
the GOODS fields that had been aligned to the Gaia DR2 reference frame (G.
Brammer priv. comm., Gaia Collaboration et al. 2016, 2018). This alleviated
concerns of distortions or mismatches between faint targets and bright
acquisition sources.

However, the GOODS-S and GOODS-N fields, being intentionally placed in regions
of very low stellar density, do present a severe deficit of stars suitable for
target acquisition. Instead, we had to use the HST imaging to identify compact
galaxies, using the longest exposure time for the acquisition image to reach
down to 24-27 mag. All the JADES target acquisitions so far were successful,
although the increased centroiding error for such extended targets may
somewhat have reduced the alignment accuracy of the NIRSpec observations. In
later spectroscopy, when NIRCam data was available, we used its imaging to
select roughly circular compact galaxies and stars, using the multi-band near-
IR photometry to more confidently estimate the fluxes in the NIRSpec CLEAR and
F140X target acquisition filters and to enforce isolation criteria.

###  5.4 NIRSpec Deep Spectroscopy

JADES features two long NIRSpec multi-object observations, each of 200 ksec
total exposure time and both located in GOODS-S on the UDF and mostly inside
the NIRCam Deep Prime footprint. The first pointing was designed to be
observed early and be targeted using HST GOODS and CANDELS imaging,
supplemented with other pre-JADES data. The second deep pointing will be
observed at the end of the program and will use targets from the full JADES
imaging. We refer to these two pointings as Deep/HST and Deep/JWST,
respectively.

The NIRSpec Deep/HST observations occurred over three visits between 21 and 25
October 2022 at a V3PA of 321∘. As described above, these observations were
intended to be targeted solely on pre-JWST data. Shortly before the scheduled
visits, NIRSpec suffered some shorts on the MSA that required us to replan the
MSA configurations for these observations. The NIRCam Deep Prime data were
taken at the start of October, and an early reduction of the multi-band images
and photometry catalogs were available. This enabled us to include some JWST-
selected targets, at the expense of lower priority HST-selected targets, in
the NIRSpec observations. We also re-prioritised the catalog using the NIRCam
data to improve high-redshift photometric redshift accuracy, and to homogenize
the selection in the lower priority classes with respect to the Deep/JWST to
be based on the F444W filter. We note that the pointings of the observations
were not changed, only the choice of which shutters to open. Two of the
additional target galaxies with photometric redshifts from the NIRCam imaging
(Robertson et al., 2023a) were spectroscopically confirmed in Deep/HST to lie
at the highest redshifts known of 12.6 and 13.2 (Curtis-Lake et al., 2023).

The NIRSpec Deep spectroscopy utilizes five dispersers: the prism and four
gratings: G140M/F070LP, G235M/F170LP, G395M/F290LP, and G395H/F290LP. The
prism is observed for 100 ksec, and the gratings for 25 ksec each.

Each pointing uses slitlets of 3 shutters, with the 3-point nod. To provide
additional pixel diversity and some dithering in the spectral direction, we
design 3 sub-pointings for each pointing, typically separated by 3-5 shutters
in the dispersion direction and 1-2 shutters in the spatial direction such
that the optimal common coverage of the highest priority targets in all three
dithered pointings is achieved. This results in the target light from a given
wavelength for the majority of sources appearing in up to 9 pixel locations.
As described earlier, there are actually 2 MSA designs for each sub-pointing
(6 in total) because we use a separate configuration for the prism relative to
that of the gratings. Each integration uses NRSIRS2 readout with 19 groups,
yielding 1400 second apiece. We conduct two integrations per exposure. For the
gratings, this means that each nod location is visited only once for 2
consecutive integrations. For the prism, the telescope repeats the nodding 4
times, with 2 integrations per time.

As discussed in § 5.1, it is inevitable that not all targets can be placed on
all three sub-pointings, even though this is our preference. We accept this
and fill in some targets with only 1 or 2 sub-pointings. Using a smaller
dither step increases the ability to repeat targets.

This part of JADES is an investment of 145 hours and results in 111 prime
open-shutter hours, a utilization of 76%. The exceptional line-flux
sensitivity achieved as a function of wavelength is shown in Figure 9.

###  5.5 NIRSpec Medium/JWST Spectroscopy

JADES includes 12 medium-depth pointings that are targeted from JWST imaging,
but are otherwise scaled down versions of the Deep pointings. Relative to
Deep, the prism is scaled down more than the gratings, reflecting the goal of
studying the galaxy spectra in more detail. Exposure times are listed in Table
5. The original plan was 8665 sec in each of the 5 dispersion modes. However,
small on-orbit changes in the timing model for parallel observations caused us
to make small adjustments in the exposure times to be more efficient with the
NIRCam parallels and to fit the program into the allocation.

As with Deep, the observing uses 3-point nods with 3-shutter slits at each of
3 sub-pointing locations, for a total of 9 pixel dither locations. Each of
these exposures is a single integration, using NRSIRS2 readout with 12 or 14
groups. As for the Deep pointings, some targets can only be placed on one or
two sub-pointings, resulting in proportionally lower exposure time.

Four of the twelve Medium/JWST pointings are placed in GOODS-N and eight are
in GOODS-S that has wider JADES NIRCam imaging. Three of the GOODS-N pointings
were observed between April 30 and May 5, 2023 at V3 PA 150.48∘, covering a
large fraction of the NIRCam Medium Prime mosaic. The fourth was delayed by an
observatory failure to acquire guide stars and was observed at V3 PA 132.93∘
on May 27, 2023.

Unfortunately, this final JWST/Medium observation in GOODS-N was affected by a
short circuit in the NIRSpec MSA for some configurations, which when triggered
produced a glow of light that flooded the detectors, ruining the NIRSpec data
(Rawle et al., 2022). Half of the configurations were affected by these shorts
(two of three prism and one of three grating). The other three configurations
did not address the susceptible column of the MSA and therefore did not have
the glow. The possible effects on the NIRCam coordinated parallel imaging data
are still being investigated at this time.

For GOODS-S, four pointings are in the NIRCam Deep Prime mosaic, with the
other four on the Medium Prime mosaic. Most of the GOODS-S Medium/JWST was
delayed until Cycle 2, save one pointing observed on January 12 and 13, 2023,
at V3 PA 56.17∘. The exact pointing positions and orientations of the
remaining seven observations are still to be determined. The pointings will be
based on the highest priority targets from the NIRCam Deep Prime, NIRCam
Medium Prime and first NIRCam Deep parallel observations.

This part of JADES is an investment of 222 hours, 77 in GOODS-N and 145 in
GOODS-S, resulting in 146 prime open-shutter hours, a utilization of 66%. As
before, the line detection sensitivity as a function of wavelength is shown in
Figure 9.

###  5.6 NIRSpec Medium/HST Spectroscopy

The final tier is the shallowest and results from “parallel” observations
during ten of the NIRCam Medium Prime fields, four in GOODS-N and six in
GOODS-S. We remind that our naming convention is following the instrument that
is driving our science design; in all coordinated parallels including NIRSpec
MOS, NIRSpec is formally the prime instrument. The HST name refers to the fact
that in the survey design, the JWST imaging was not yet available and hence
the targeting was from HST data. We will describe explicitly the few cases we
could use JWST-based targets, due to interruptions in the program.

As the imaging program requires a large $7^{\prime\prime}$ offset to step over
the V2-parallel SW chip gap, we opt to split each parallel opportunity into
two largely distinct sets of targets. Offsets of this size when combined with
the NIRSpec astrometric distortion at the MSA plane would otherwise cause many
targets to become poorly registered within their slits. Each pointing in the
mosaic was given a small $1^{\prime\prime}$ freedom of motion to optimize the
slit centration of a few highest priority targets.

For each of these 20 target sets, we use 4 dispersers, omitting the G395H
higher resolution grating. Exposure times are listed in Table 5. GOODS-S was
observed first, and here we used NRSIRS2 readout with 17 groups for the prism
and 14 groups for the gratings, each observed once at each of 3 nod locations.
Based on the first tranche of data in GOODS-S, we replanned GOODS-N so that
the prism was observed twice with 14 groups at each pointing, to increase the
S/N.

As this program results from the parallels of a reasonably tightly packed
imaging mosaic, these NIRSpec MOS fields overlap substantially. However, any
given object in the footprint of a single MSA pointing will often be poorly
centered in its possible shutter, leading to many targets being rejected due
to the low expected throughput. Collisions of prism spectral traces block many
other targets. Therefore, several returns to a given area can be supported
without much duplication. We do allow our targets to be observed twice (and
our highest priority targets up to four times), if the slit registration is
favorable.

Our observations of the GOODS-S portion of this mosaic (observations 25–30 of
program 1180) were affected by two short circuits in the NIRSpec MSA, similar
to those described in the previous section. Some configurations did not use
these columns of the MSA and therefore did not have the glow. The NIRCam
coordinated parallel imaging data was unaffected in most cases, but the first
half of observations 25 and 30 suffer from a short so bright that the
illumination even reached NIRCam, as described in § 4.6.

Only 4 of the 12 target sets were successfully observed without a bright short
glow, and even in these cases there is some persistence that affects the prism
exposure in a small portion of the image. These are MSA configurations 2, 4,
6, and 10, which are the second half of observations 25, 26, 27, and 29,
respectively. For a 5th target set (configuration 5, the first half of
observation 26), the grating exposures are unaffected, but the prism exposures
were flooded by the short. We plan to complete this 5th set with a return in
October 2023.

One of the target sets was not observed due to an unrelated telescope issue
(configuration 12, second half of observation 30). As the first half of
observation 30 was badly affected by shorts, we will perform a complete repeat
of this pointing in October 2023 at the original position angle, albeit with a
different set of targets based on NIRCam selection.

The other 5 target sets were re-observed on January 27 & 28, 2023, without new
NIRCam parallels. Because the new position angle was already going to require
a complete re-plan, we opted to collect the 5 single-nod designs into two
pointings with smaller dithers, akin to the Medium/JWST program. Observation
134 has two sub-pointings; observation 135 has three. For these, we use the
same NIRCam-based target selection that was used for the first Medium/JWST
observation in GOODS-S.

Figure 9: The unresolved line detection limit as a function of wavelength for
two JADES NIRSpec tiers. We assume a well-centered point source and use a
$10\sigma$ detection threshold. The blue long lines are the prism, while the
orange, green, and cyan lines are the three $R=1000$ gratings. The solid lines
show the depth in the Deep/HST pointing, while the dashed lines are a
representative Medium/JWST pointing. For an unresolved line, the G395H grating
has a similar line detection limit to G395M plotted here.

###  5.7 Data Quality Caveats

The NIRSpec MOS observations have been processed by using a pipeline developed
by the ESA NIRSpec Science Operations Team and the NIRSpec GTO Team. The major
steps of the data processing are described in (Bunker et al., 2023a), while a
detailed description of the pipeline and its performance will be reported in a
forthcoming paper (Carniani, in prep.). Here we discuss the main issues
encountered during the data processing and analysis phase.

As mentioned in the previous sections, program 1180 was affected by short
circuits in the NIRSpec MSA that produced bright glows of artificial light,
ruining most of the exposures. In particular, 76 out of 132 exposures are
ruined by such a bright glow. The other exposures did not suffer from this
problem, but we found some persistence for the prism exposures that
contaminate the spectra of $\sim 10\%$ targets in the MSA design. In some
cases, the signal of the persistence is as high as the sky background
emission.

By inspecting the count rate maps before background subtraction, we noticed
that some shutters dedicated to the targets did not open (Rawle et al., 2022).
We excluded these temporarily failed shutters from the data processing
workflow. In each pointing, we found, on average, that $\sim$1% of the targets
in the MSA masks are affected by this issue. In most cases, only one of the
3-slitlet shutters was unexpectedly closed, but there are the same targets in
which two or even all three shutters did not open during the observations. In
these cases, the noise of the final products increases as the total exposure
time is reduced.

Although the target selection was optimized to adopt the 3-point nod strategy
for the background subtraction process, some background shutters were
contaminated by either background or foreground source. We have thus exploited
either HST or NIRCam images to identify automatically contaminated shutters
and excluded them in the background subtraction steps of the pipeline. This
increases the noise of the background subtraction image but avoids a possible
over-subtraction of background emission that could alter the final spectra of
the targets.

##  6 MIRI Observations

###  6.1 MIRI Deep Parallel

Figure 10: RGB (F770W, F4442W, F335M) image of a trio of $z\sim 6$ photometric redshift candidates detected in the GOODS-S JADES Deep/MIRI parallel (24-25.6 AB with $\mathrm{SNR}\sim 20-80$ in F770W). All three are likely emission line galaxies, with H$\alpha$ in F444W. The leftmost candidate additionally has significant [OIII]5007 emission in the F335M medium band filter, resulting in a more purple color. The F770W probes the rest-frame $1\mu$m emission, a powerful constraint on the properties of older stellar populations. Subsurvey | Number | Area | Exposure Times (Ksec) | 10$\sigma$ AB Mag Limit
---|---|---|---|---
| Pointings | $\square^{\prime}$ | F770W | F1280W | F770W | F1280W
GOODS-S Deep/MIRI | 4 | 9 | 155.2c | — | 27.1 | —
GOODS-S Medium/MIRI | 5a | 11.5 | 6.9 | 23.5 | 25.5 | 25.2
GOODS-N Medium/MIRI | 3b | 6.7 | 6.0 | 9.0 | 25.5 | 24.5
Table 6:  Summary of MIRI Observations. The number of pointings, area in
arcmin2, exposure times per filter, and the final AB magnitude detection
threshold per filter for a 10-$\sigma$ point source. For GOODS-N Medium/MIRI
and GOODS-S Deep/MIRI, these thresholds are based on or extrapolated from
already obtained observations assuming a 0.4′′ aperture and aperture-corrected
for both filters. The GOODS-S Medium/MIRI values are projected from GOODS-N
Medium/MIRI. Note that the Deep/MIRI pointings partially overlap those of
Medium/MIRI, but we do not coadd these exposure times when computing the
depth. aOne of the GOODS-S pointings is mildly shallower, with 5.6 and 21.5 ks
of exposure in F770W and F1280W, respectively. bOne of the GOODS-N pointings
is mildly shallower, with 6.6 ks of exposure in F1280W. cTo date, 61-94 ks
have been obtained per pointing of GOODS-S Deep/MIRI.

The NIRCam Deep Prime program creates very deep MIRI parallels in GOODS-S,
totaling 43.1 hrs of open-shutter time in each of the four fields. The fields
overlap only slightly, so that the deep area is about 9 arcmin2.

The fields were designed to overlap the NIRCam Medium Parallel mosaic, but it
turned out that one of the NIRCam Deep Parallel fields substantially overlaps
as well. This allows these MIRI images to provide a very deep look at the
high-redshift universe. We note that while the MIRI fields are not on the UDF
itself, they do fall near the center of the Chandra Deep Field South.

We decided to focus this time to the study of rest-frame 1–2 $\mu\rm m$
imaging of galaxies at redshifts above 3. For this, we selected the F770W
filter as the most promising compromise between the rising background to the
red and the lever arm relative to the deeper F410M and F444W data. An example
of robust F770W detections of a trio of $z\sim 6$ photometric redshift
candidates in the GOODS-S Deep/MIRI footprint can be seen in Figure 10. F770W
is somewhat less sensitive in AB magnitude than F560W, but it more than
doubles the logarithmic wavelength gap relative to 4.4 $\mu\rm m$, allowing it
to better detect power-law deviations in the slope of the near-IR SED. The
longer band is also less likely to suffer from rest-optical emission line
contamination of the continuum light measurement; H$\alpha$ will enter F560W
at $6.6<z<8.4$, which might be at the frontier of detectability with these
long exposures.

The MIRI data must of course follow the NIRCam exposure times and dither
pattern. To reduce data volume, we use SLOWR1 readout mode with 57 groups to
yield a single integration of 1361 seconds per exposure. Each pointing then
has 114 of these exposures, taken at 22 different dither points, for a total
of 155.2 ksec. However, on short time scales, the MIRI data is taken cycling
through 9 or 4 subpixel dither locations. The resulting 10$\sigma$ point
source sensitivity is 27.1 AB (Table 6).

###  6.2 MIRI Medium Parallel

In addition to the deep data, we conduct MIRI parallels with eight of the
NIRCam Medium Prime pointings. Five of these are in GOODS-S and three in
GOODS-N.

As these data are considerably shallower and yet only mildly more area, we opt
to focus on the science of intermediate-redshift galaxies ($z\sim 3-5$), where
we can place strong constraints on the stellar emission SED, such as the
regime of the contribution from TP-AGB stars, robustly identify the rising
continuum associated with AGN, and look for unusual SEDs. To accomplish these
goals, we take moderately deep exposures in F770W and then use most of the
time in the F1280W, which gains in sensitivity over the WISE W3 band by a
factor of $\sim 1000$. We note that some but not all of the medium area in
GOODS-S is also covered by the far deeper F770W imaging from the Deep
parallels. In GOODS-N, 2/3 of the MIRI pointings fall off of the planned
NIRCam coverage, but are covered by CANDELS.

The exposure times are listed in Table 6. In all pointings, the data set uses
6 dither locations. The dither locations have 3 close pairs with relatively
long strides between them, and hence there is a relatively large boundary
region that has only 2 or 4 exposures. However, since MIRI is Nyquist sampled,
this was considered acceptable. We always use the SLOWR1 readout, so as to
reduce the data rate. F770W uses 1 integration per readout; F1280W uses 2 or 3
to avoid saturation on the background. In GOODS-S, where the available
exposure times are longer, we observe F770W once per dither location and
return to F1280W four times. In GOODS-N, our exposures are shorter and we do
not have any coverage from the Deep MIRI parallels; we therefore opt to
include two exposures each of F770W and F1280W per dither location.

###  6.3 Data Quality Caveats

So far, we have not encountered any substantial concerns with the MIRI data
acquired. We did find that the subpixel dither pattern used in the first Deep
data, based on the F770W PSF size, was smaller than we would have preferred to
use for the generation of sky flats. We find this can be mitigated by
generating sky flats from roughly contemporaneous exposures over multiple
pointings. Nevertheless, we have increased the dither steps in later
observations. Moreover, the return to the same Deep field in year 2 will
improve the pixel sampling, so we expect this will not be a lasting concern.

##  7 Preparing for JADES

Like many JWST observing programs, the JADES team engaged in substantial
preparations for the data set. We were particularly driven by the tight time
scale, likely at most 6 weeks, to provide targets for multi-object
spectroscopic follow-up from the NIRCam and MIRI imaging. This central goal of
the program requires image reduction, mosaicing, source detection, source
photometry, photometric redshift generation, target selection, and MSA design
to be ready to run in quick order. We also sought to prepare for analysis of
the spectroscopy, most obviously for the data reduction, extraction, and
spectral analysis, as clearly the spectroscopic results would be desired to
feed into the processing of the second field to be observed.

Part of this preparation was inherent in the needs of the instrument teams to
support a wider range of commissioning and early science observations. Most
obviously, we rely on the effort to develop exposure-level reduction software
such as NCDhas written by K. Misselt and the pre-processing pipeline developed
by the ESA NIRSpec Science Operations Team (Birkmann et al., 2022), which
subsequently became the basis for the STScI stage 1 and 2 pipelines. To build
and validate these tools pre-launch required creation of detailed codes to
simulate instrument data: Guitarra222https://github.com/cnaw/guitarra for
NIRCam and the NIRSpec Instrument Performance Simulator (IPS; Dorner et al.
2016).

For NIRCam, we used Montage333http://www.ascl.net/1010.036 to combine the
individual exposures into a mosaic. In order to identify outlier pixels (such
as cosmic rays that have not been picked up by NCDhas), we first construct a
medium-based mosaic, which we then projected this medium-based mosaic back to
the individual exposures. We identified and masked outlier pixels that are
3$\sigma$ outliers. In a final step, we constructed the mean-based mosaic and
fully propagated the errors.

An important additional aspect of JADES preparation included optimizing our
instrument configurations, integration times, and area to maximize our key
science goals (e.g. bottom panel of Figure 4). To this end, we developed
JAGUAR, a novel phenomenological model of galaxy evolution out to $z\sim 15$
(JAdes extraGalactic Ultradeep Artificial Realizations; Williams et al.,
2018)444https://fenrir.as.arizona.edu/jwstmock, incorporating known galaxy
abundances, flux, color, and morphology relations across redshift. A key
utility of JAGUAR includes both mock SEDs and full-resolution spectra, which
we generated using BEAGLE (Chevallard & Charlot, 2016) based on self-
consistent models of stellar radiation and its transfer through the
interstellar and intergalactic medium. Beyond survey design, JAGUAR also
enabled simulation of realistic galaxy fields using mock imaging tools like
Guitarra, mock NIRSpec spectra and optimizing our MSA design procedures.

With these tools, JADES then performed data challenges, simulating mock galaxy
fields down to individual ramps and then reducing the data to make high-level
products. For NIRCam, this included mosaicing, object detection, photometry,
and photometric redshifting. For NIRSpec, mock target lists were used to build
MSA designs with the eMPT code (Bonaventura et al., 2023), and simulated
spectra (Chevallard et al., 2018) were run through reduction and extraction to
develop tools for redshift and line flux estimation (Giardino et al., 2019). A
key advantage of these data challenges was to define data models for the
interfaces between segments of the analysis. They also allowed us to generate
test suites to validate each segment.

JADES conducted Data Challenge 1 (DC1) to initiate this process. The NIRCam
component of DC1 concentrated on a single visit of Proposal 1180 (Observation
7, Visit 2), which is part of the NIRCam Deep Prime pointings. The DC1
simulations used 9 ramps with 7 DEEP8 groups for all filters used in the
original JADES deep survey design (F090W, F115W, F150W, F200W, F277W, F356W,
F410M and F444W). The source catalog used for DC1 was derived solely from the
JAGUAR mock catalog, where we assigned random positions in R.A and declination
for the selected galaxies but in such a way to maintain the same surface
density of galaxies as the JAGUAR parent sample.

The NIRSpec component of DC1 was based on the NIRSpec Deep/JWST program. 200
point source galaxies with realistic JAGUAR spectra were simulated in the
prism and medium resolution grating dispersers in each of the three Deep/JWST
pointings. The data were processed with the NIRSpec IPS Pipeline Software
(NIPS; Dorner et al. 2016). The resulting output was simulated, flux-
calibrated, combined spectra for 370 galaxies, used to validate our choice of
integration times for these faint targets (Giardino et al., 2019).

Following improvements in the code base, we then conducted Data Challenge 2
(DC2) as a set of more comprehensive exercises. DC2 covered about 2/3 of the
area of the JADES survey in GOODS-S ($\sim$80 arcmin2) and included Deep and
Medium NIRCam pointings, using the same setup as the planned observations
(e.g., exposure time, read out mode, dither positions) as recovered from the
APT file. In contrast to DC1, the DC2 simulations included the field-of-view
distortions, particularly important to test the ability to make
astrometrically correct mosaics in sky coordinates and accounting for pixel
sub-sampling when constructing these mosaics. Cosmic-ray hits were also added
to the individual ramps. The DC2 sample used a combination of the CANDELS
(Grogin et al., 2011; Koekemoer et al., 2011) catalog with Sérsic parameters
estimated by van der Wel et al. (2012) and objects from JAGUAR, which also
provides Sérsic parameters. The latter enabled including objects beyond the
apparent magnitude limit and redshift cutoff of the HST data. In this process,
we used the positions and shapes of all observed galaxies and supplemented
these with mock galaxies where a fraction of the latter were included until
the counts in apparent magnitudes and redshifts were as close as possible to
the JAGUAR magnitude-redshift distribution. In addition to the galaxy catalog,
we also created a separate set of images with stellar sources, that were used
to verify the photometric calibration procedure. In both Data Challenges a few
objects with abnormal colors and Population III galaxies (Zackrisson et al.,
2011) were added to test the efficacy of algorithms being tailored to detect
outliers. For DC2, HST fluxes were also calculated (though no HST images
created) which were used to estimate the number of low-redshift contaminants
in the photometric redshift calculation. For the JAGUAR galaxies in DC2, noise
was added to the mock HST fluxes and uncertainties were estimated according to
the depth of the available ancillary HST imaging.

For NIRSpec, these data challenges not only served to verify and practice
ingesting NIRCam-generated and -formated target catalogs and images into the
NIRSpec target selection and MSA mask design work flow, but also provided a
critical opportunity to augment the eMPT with needed features. In particular,
we modified eMPT to be able to point the ‘prime’ NIRSpec instrument such that
the ‘secondary’ NIRCam instrument achieved its intended elaborate mosaicking
of the NIRCam Medium Prime fields described in Sections 4.2 and 5.6, while
simultaneously exploiting the $\simeq 1$ arcsec level permissible deviations
from the nominal NIRCam pointing pattern to optimize the parallel NIRSpec
exposures such that the largest possible number of the highest priority HST
targets were captured by the MSA. This task was further complicated by the
peculiar manner in which the roll orientation of the NIRSpec MSA assigned to
an observation by STScI does not refer to the center of the MSA, but rather to
a reference point defined by median location of all targets contained in the
NIRSpec input catalog entered into the APT (Bonaventura et al., 2023).
Limiting the impact of this complication over the $6.4^{\prime}$ lever arm
between the field centers of the two instruments required an iterative
approach in which the NIRSpec input catalog was gradually trimmed down to
match the outer envelop of the final NIRSpec footprint.

The NIRSpec component of DC2 simulated an approximation of the GOODS-South
Medium/HST tier. The NIRCam source scene described above was used to assign
spectra and morphologies to the known HST prioritized target catalog. The eMPT
was exercised to determine the optimum set of six pairs of pointing locations,
within the small tolerance allowed given that in the real Medium/HST a NIRCam
mosaic would be made in parallel, that maximized the number of highest
priority targets assigned shutters. The eMPT was then run to assign targets to
shutters in order of priority class. Spectra were simulated and processed in a
similar manner to DC1. One difference is that contaminants that would fall
within the target or background shutter were included in the simulation to
assess the effects of contamination.

To manipulate these Data Challenges and to prepare for the real data, JADES
also built visualization tools. To browse the sky, we developed FitsMap
(Hausen & Robertson, 2022), inspired in part by the Legacy Survey viewer led
by D. Lang (Dey et al., 2019). FitsMap allows us to zoom and pan the sky,
easily changing between image layers, with overlays from various catalogs that
provide pop-up access to the database information. To study the SEDs and
photometric redshift outputs, we developed
JADESview555https://github.com/kevinhainline/JADESView, which shows image
thumbnails, photometric SEDs with best-fit template overlays, and photometric
redshift likelihoods versus redshift.

We have found these preparations to be invaluable in handling the in-flight
data. That said, unsurprisingly the real data have presented additional
challenges to which the team (and the community more broadly) must adjust. Our