# JADES Data Release 3 – NIRSpec/MSA spectroscopy for 4,000 galaxies in the
GOODS fields

Francesco D’Eugenio These authors contributed equally to this work  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 Francesco D’Eugenio<EMAIL_ADDRESS>Alex J. Cameron These authors contributed equally to this work  Department of
Physics, University of Oxford, Denys Wilkinson Building, Keble Road, Oxford
OX1 3RH, UK  Jan Scholtz These authors contributed equally to this work  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  Stefano Carniani These authors contributed
equally to this work  Scuola Normale Superiore, Piazza dei Cavalieri 7,
I-56126 Pisa, Italy  Chris J. Willott These authors contributed equally to
this work  NRC Herzberg, 5071 West Saanich Rd, Victoria, BC V9E 2E7, Canada
Emma Curtis-Lake Centre for Astrophysics Research, Department of Physics,
Astronomy and Mathematics, University of Hertfordshire, Hatfield AL10 9AB, UK
Andrew J. Bunker Department of Physics, University of Oxford, Denys Wilkinson
Building, Keble Road, Oxford OX1 3RH, UK  Eleonora Parlanti Scuola Normale
Superiore, Piazza dei Cavalieri 7, I-56126 Pisa, Italy  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  Christopher N. A. Willmer
Steward Observatory, University of Arizona, 933 North Cherry Avenue, Tucson,
AZ 85721, USA  Peter Jakobsen Cosmic Dawn Center (DAWN), Copenhagen, Denmark
2. Niels Bohr Institute, University of Copenhagen, Jagtvej 128, DK-2200,
Copenhagen, Denmark  Brant E. Robertson Department of Astronomy and
Astrophysics, University of California, Santa Cruz, 1156 High Street, Santa
Cruz, CA 95064, USA  Benjamin D. Johnson Center for Astrophysics $|$ Harvard &
Smithsonian, 60 Garden St., Cambridge MA 02138 USA  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  Phillip A. Cargile Center for Astrophysics $|$
Harvard & Smithsonian, 60 Garden St., Cambridge MA 02138 USA  Tim Rawle
European Space Agency (ESA), European Space Astronomy Centre (ESAC), Camino
Bajo del Castillo s/n, 28692 Villafranca del Castillo, Madrid, Spain  Santiago
Arribas Centro de Astrobiología (CAB), CSIC–INTA, Cra. de Ajalvir Km. 4,
28850- Torrejón de Ardoz, Madrid, Spain  Jacopo Chevallard Department of
Physics, University of Oxford, Denys Wilkinson Building, Keble Road, Oxford
OX1 3RH, UK  Mirko Curti European Southern Observatory, Karl-Schwarzschild-
Strasse 2, 85748 Garching, Germany  Eiichi Egami Steward Observatory,
University of Arizona, 933 North Cherry Avenue, Tucson, AZ 85721, USA  Daniel
J. Eisenstein Center for Astrophysics $|$ Harvard & Smithsonian, 60 Garden
St., Cambridge MA 02138 USA  Nimisha Kumari AURA for European Space Agency,
Space Telescope Science Institute, 3700 San Martin Drive. Baltimore, MD, 21210
Tobias J. Looser 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  Marcia J. Rieke
Steward Observatory, University of Arizona, 933 North Cherry Avenue, Tucson,
AZ 85721, USA  Bruno Rodríguez Del Pino Centro de Astrobiología (CAB),
CSIC–INTA, Cra. de Ajalvir Km. 4, 28850- Torrejón de Ardoz, Madrid, Spain
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  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  Giacomo Venturi Scuola Normale
Superiore, Piazza dei Cavalieri 7, I-56126 Pisa, Italy  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  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  Rachana Bhatawdekar European Space Agency (ESA), European Space Astronomy
Centre (ESAC), Camino Bajo del Castillo s/n, 28692 Villanueva de la Cañada,
Madrid, Spain  Nina Bonaventura Steward Observatory University of Arizona 933
N. Cherry Avenue Tucson AZ 85721, USA  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
Stephane Charlot Sorbonne Université, CNRS, UMR 7095, Institut d’Astrophysique
de Paris, 98 bis bd Arago, 75014 Paris, France  A. Lola Danhaive 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  Kevin N. Hainline Steward Observatory,
University of Arizona, 933 North Cherry Avenue, Tucson, AZ 85721, USA  Ryan
Hausen Department of Physics and Astronomy, The Johns Hopkins University, 3400
N. Charles St., Baltimore, MD 21218  Jakob M. Helton Steward Observatory,
University of Arizona, 933 North Cherry Avenue, Tucson, AZ 85721, USA  Xihan
Ji 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  Zhiyuan Ji Steward Observatory,
University of Arizona, 933 North Cherry Avenue, Tucson, AZ 85721, USA  Gareth
C. Jones Department of Physics, University of Oxford, Denys Wilkinson
Building, Keble Road, Oxford OX1 3RH, UK  Ignas Joudžbalis 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  Michael V. Maseda Department of Astronomy, University of
Wisconsin-Madison, 475 N. Charter St., Madison, WI 53706 USA  Pablo G. Pérez-
González Centro de Astrobiología (CAB), CSIC–INTA, Cra. de Ajalvir Km. 4,
28850- Torrejón de Ardoz, Madrid, Spain  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  Irene Shivaei Centro
de Astrobiología (CAB), CSIC–INTA, Cra. de Ajalvir Km. 4, 28850- Torrejón de
Ardoz, Madrid, Spain  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  Renske Smit Astrophysics Research Institute, Liverpool John Moores
University, 146 Brownlow Hill, Liverpool L3 5RF, UK  Fengwu Sun Steward
Observatory, University of Arizona, 933 North Cherry Avenue, Tucson, AZ 85721,
USA  Natalia C. Villanueva 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  Steward
Observatory, University of Arizona, 933 North Cherry Avenue, Tucson, AZ 85721,
USA  Yongda Zhu Steward Observatory, University of Arizona, 933 North Cherry
Avenue, Tucson, AZ 85721, USA

###### Abstract

We present the third data release of JADES, the Advanced Deep Extragalactic
Survey, providing both imaging and spectroscopy in the two GOODS fields.
Spectroscopy consists of medium-depth and deep NIRSpec/MSA spectra of 4,000
targets, covering the spectral range $0.6\text{--}5.3$ and observed with both
the low-dispersion prism ($R=30\text{--}300$) and all three medium-resolution
gratings ($R=500\text{--}1,500$). We describe the observations, data
reduction, sample selection, and target allocation. We measured 2,375
redshifts (2,053 from multiple emission lines); our targets span the range
from $z=0.5$ up to $z=13$, including 404 at $z>5$. The data release includes
2-d and 1-d fully reduced spectra, with slit-loss corrections and background
subtraction optimized for point sources. We also provide redshifts and $S/N>5$
emission-line flux catalogs for the prism and grating spectra, and concise
guidelines on how to use these data products. Alongside spectroscopy, we are
also publishing fully calibrated NIRCam imaging, which enables studying the
JADES sample with the combined power of imaging and spectroscopy. Together,
these data provide the largest statistical sample to date to characterize the
properties of galaxy populations in the first billion years after the Big
Bang.

††journal: ApJS††journal: ApJS††facilities: (NIRCam), (NIRSpec/MSA), (ACS),
(WFC3)††software:  astropy (Astropy Collaboration et al., 2013), corner
(Foreman-Mackey, 2016), ds9 (Joye & Mandel, 2003), emcee (Foreman-Mackey et
al., 2013), fitsmap (Hausen & Robertson, 2022), fsps (Conroy et al., 2009;
Conroy & Gunn, 2010), ltsfit (Cappellari et al., 2013), matplotlib (Hunter,
2007), numpy (Harris et al., 2020), ppxf (Cappellari, 2017, 2023), scipy
(Jones et al., 2001), smplotlib (Li, 2023), and topcat(Taylor, 2005).

%=11

##  1 Introduction

The long-awaited launch of has revolutionized our ability to observe the early
universe. Already in the first two years of operations, enabled an amazing
number of discoveries and studies. Many of these breakthroughs have been made
possible by the unprecedented sensitivity and wavelength coverage of the
NIRSpec instrument (Jakobsen et al., 2022). These include the spectroscopic
confirmation of galaxies beyond redshift $z=10$ (Curtis-Lake et al., 2023;
Arrabal Haro et al., 2023; Wang et al., 2023) – including through emission
lines (Bunker et al., 2023a); the discovery of substantial neutral-gas
absorption in galaxies at $z=9\text{--}11$ (e.g., Heintz et al., 2023; Umeda
et al., 2023); the first studies of metallicity and chemical abundances using
well-known optical lines (e.g., Curti et al., 2023a; Nakajima et al., 2023);
the discovery of massive, quiescent and old galaxies at $z=3\text{--}5$ (e.g.,
Carnall et al., 2023; Glazebrook et al., 2023); the first ‘mini-quenched’
galaxies (Looser et al., 2023a; Strait et al., 2023); neutral-phase outflows
in massive quiescent galaxies (Belli et al., 2023; D’Eugenio et al., 2023a;
Davies et al., 2024); the discovery of bright, metal-poor active galactic
nuclei (e.g., Kocevski et al., 2023; Übler et al., 2023); the most distant
active galactic nuclei (AGN; Maiolino et al., 2023a; Goulding et al., 2023);
and even tentative evidence of the first generation of stars (Maiolino et al.,
2023b).

However, for spectroscopy, sample sizes are still small, of the order of tens
to one hundred objects (Looser et al., 2023b; Curti et al., 2023b; Nakajima et
al., 2023). The availability of large samples is key to characterizing the
properties of galaxy populations, studying their cosmic evolution, and
disentangling the intricate pattern of cause and effect between the observed
properties of galaxies. Studies of galaxies at redshifts $z<1$ rely on
thousands or even hundreds of thousands of spectroscopic targets (e.g.,
Abazajian et al., 2009; Driver et al., 2018; DESI Collaboration et al., 2016).
By studying several physical properties at a time (e.g., Kauffmann et al.,
2003a, b; Peng et al., 2010; Graves & Faber, 2010), or by using machine-
learning techniques (e.g., Bluck et al., 2022; Baker et al., 2022; Barsanti et
al., 2023; Walmsley et al., 2023), these studies have made tremendous progress
in understanding the links between many galaxy properties like morphology,
star-formation rate, age, gas fraction, star-formation efficiency,
supermassive black-hole mass, local and global environment, and metallicity.
In the last decades, large spectroscopic surveys in the near-infrared have
enabled the study of hundreds of galaxies at redshifts $z=1\text{--}4$ (e.g.,
Wisnioski et al., 2015; Stott et al., 2016; Kriek et al., 2015). In the near
future, new surveys will observe even more galaxies at redshifts $z\lesssim 4$
(Dalton et al., 2012; Tamura et al., 2016; de Jong et al., 2019; Maiolino et
al., 2020). However – at least for the next decade – nothing other than and,
in particular, NIRSpec will be able to obtain deep rest-frame optical
spectroscopy for large samples of galaxies at redshifts 3–10, the crucial
early phases of galaxy formation.

The NIRSpec Micro Shutter Assembly (MSA, Ferruit et al., 2022) was designed to
observe more than one hundred targets simultaneously, and is now the highest-
multiplicity slit-based spectrograph in the near-infrared. In the NIRSpec/MSA,
this high multiplicity marries an unprecedented combination of large
collecting area, low background, and long wavelength coverage, which all
together enable us, for the first time, to efficiently observe large samples
of galaxies at redshifts $z>4$, covering their strongest rest-frame optical
features e.g., Treu et al., 2022, Bezanson et al., 2022, Fujimoto et al.,
2023, Oesch et al., 2023a, Bunker et al., 2023b, hereafter: B23.

One of the goals of JADES, the Advanced Deep Extragalactic Survey (Eisenstein
et al., 2023a), is to observe a statistical sample of galaxies at redshifts
$z>3$, thus enabling spectroscopic studies to move from the ‘discovery’ stage
to a more quantitative understanding of galaxy populations. To enable this
progress, JADES – a collaboration between the NIRCam and NIRSpec GTO teams –
was designed to fully exploit the synergy between photometry and spectroscopy.
The JADES strategy divides the survey time between two tiers: medium-depth and
deep observations (for the least deep and widest tier of the NIRSpec GTO see
Maseda et al., 2024). All observations are in the two GOODS fields (Great
Observatories Origins Deep Survey; Giavalisco et al., 2004), but the medium
tier is divided between the GOODS South and North fields (hereafter, GOODS-S
and GOODS-N), whereas the deep tier is in GOODS-S only.

In this article, we present NIRSpec observations of the deep tier from the
JADES Origins Field (Eisenstein et al., 2023b), as well as medium-depth
spectra from both GOODS-S and GOODS-N. We provide fully reduced and calibrated
1-d and 2-d spectra, as well as measurements of redshift and emission-line
fluxes111Available on the JADES website https://jades-
survey.github.io/scientists/data.html.. The NIRSpec data presented here cover
all JADES observations up to October 2023; spectroscopic data collected from
November 2023 will be the subject of a future data release. In support of this
spectroscopy, we also present previously unpublished NIRCam imaging in
GOODS-N, including photometric catalogs and photometric redshifts. After
presenting the new NIRCam data (Section 2), we move to spectroscopy with a
summary of the NIRSpec observations and sample selection (Sections 3 and 4)
and of the data reduction (Section 5). We then outline the measurements of
spectroscopic redshifts and line fluxes (Sections 6–8). In Sections 9 and 10
we present an assessment of the data products and guidelines for their use,
and in Section 11 we showcase exciting highlights from the current data. We
conclude with a short summary and brief outlook (Section 12).

Note that the current data release employs the same data reduction as the
previous NIRSpec data release (DR1; B23, ), with the only difference being a
different algorithm for the measurement of emission-line fluxes. This is the
third JADES data release (hereafter: DR3), but only the second data release
for spectroscopy; JADES DR2 included only NIRCam imaging. Throughout this
work, we use the AB magnitude system (Oke & Gunn, 1983).

##  2 Release of NIRCam Imaging in GOODS-N

GOODS-N is a very important deep field, as it includes the iconic Hubble Deep
Field (HDF; Williams et al., 1996) and the many other programs that followed
in and around it. Of particular note are the substantial HST optical and near-
infrared imaging from GOODS (Giavalisco et al., 2004) and CANDELS (Grogin et
al., 2011; Koekemoer et al., 2011), very deep X-ray imaging from Chandra
(Alexander et al., 2003; Xue et al., 2016), sub/millimeter observations
(Chapin et al., 2009; Mullaney et al., 2012; Magnelli et al., 2013; Cowie et
al., 2017), and broad-band radio coverage (Morrison et al., 2010; Murphy et
al., 2017). This region also has extensive ground-based spectroscopy (e.g.,
Wirth et al., 2004; Treu et al., 2005; Newman et al., 2013; Kriek et al.,
2015), HST grism spectroscopy (Momcheva et al., 2016), and hosts other JWST
NIRSpec programs such as AURORA (Program ID, PID 1914; Shapley et al., 2021).

As a part of JADES DR3, we are including images and catalogs from the NIRCam
imaging in GOODS-N, observed as the Medium Prime part of JWST program 1181
(PI: Eisenstein). These data were observed in February 2023 and include 7
overlapping medium-deep pointings, each with 8-9 separate filters. Four of the
pointings are mildly deeper than the other three; details are in Eisenstein et
al. (2023a). The imaging data are supplemented with F182M, F210M, and shallow
F444W imaging from the FRESCO survey (Oesch et al., 2023b).

The reduction of these images follows closely the processing used for JADES
DR1 (Rieke et al., 2023a) and DR2 (Eisenstein et al., 2023b). For the
photometric catalog release, we follow the methods described in Rieke et al.
(2023a), Eisenstein et al. (2023b), and Robertson et al. (2023a). These
methods were engineered on the deeper JADES GOODS-S imaging. To remove some
spurious extended low surface brightness sources, we conservatively remove
objects with an average surface brightness of $SB<0.045$ nJy per pixel within
the detection segmentation. Beyond this revision, the techniques follow
exactly Robertson et al. (2023a) and will be described in more detail in
Robertson et al. (in prep.). As before, the JADES GOODS-N release includes
mosaic imaging in all bands, object detection and photometric catalogs from
the JADES reduction pipelines, and photometric redshifts determined using
(Brammer et al., 2008). An interactive (Hausen & Robertson, 2022) website
displaying the images, catalogs, and NIRSpec slit overlays and extracted
spectra is available via https://jades-survey.github.io/viewer/. Figure 1
shows the extent of the JADES NIRCam imaging in the release, visualized as a
red-green-blue false-color image using F444W, F200W, and F090W, respectively.
To provide a sense of the depth of the image, which reaches $\sim 30$ mag in
some JADES filters, we show in Figure 2 the JADES NIRCam F444W/F200W/F090W
image of the Hubble Deep Field footprint. This image highlights the advance of
JWST in sensitivity and resolution, providing a deep near-infrared view of the
iconic Hubble Deep Field.

In total, this JADES release covers 56 arcmin2 of NIRCam imaging in GOODS-N,
detecting 85709 distinct objects. A summary of the area, median depths, and
median exposure times in each filter are provided in Table 1. Maps of the
local 5-$\sigma$ point-source, aperture-corrected depths measured in circular
apertures of radius $r=0.15$ arcsec are shown for each filter in Figure 3,
along with the location of the HDF footprint for reference.

In releasing the JADES NIRSpec spectroscopic and NIRCam imaging data jointly,
we note the unique synergy between these JWST datasets. Beyond the scientific
synergies, the NIRCam data complement the NIRSpec data by providing targets,
enabling consistency checks on the flux calibration of NIRSpec spectral modes,
improved slit-loss corrections by providing information on source
morphologies, and providing important checks on possible slit contamination by
faint sources proximate to the primary NIRSpec targets. The design and
execution of the NIRSpec spectroscopic surveys benefit from deep NIRCam
imaging, and co-spatial NIRCam imaging data enhance the science return of
complex NIRSpec MSA campaigns.

Figure 1: Mosaic of JADES JWST/NIRCam data in the GOODS-N field acquired in
February 2023. The F444W, F200W, and F090W mosaics are shown as the red,
green, and blue channels in this multicolor image. The scale bar indicates 1
arcmin.  Figure 2: Mosaic of JADES JWST/NIRCam data covering the Hubble Deep
Field region. Shown are the F444W, F200W, and F090W mosaics as the red-green-
blue channels of this multicolor image. The iconic Hubble Deep Field (Williams
et al., 1996) footprint is shown as a silver line. We note the linear diffuse
green feature in the center of the image results from local noise in the F200W
image and is not an astrophysical object.  Figure 3: Depth maps of the
JWST/NIRCam imaging in the GOODS-N field. Shown are the local, aperture-
corrected 5-$\sigma$ point source depths measured in circular apertures with
radii $r=0.3$ arcsec. The F090W, F115W, F150W, F200W, F277W, F335M, F356W, and
F410M images use data from the JADES Program. The F182M and F210M data were
acquired by the FRESCO survey (Oesch et al., 2023b). The deep F444W data are
from the JADES Program, and the shallow/wide F444W tier is from FRESCO. The
aperture correction is performed using the model point spread functions from
Ji et al. (2023). The color bar indicates the local depth in each filter, as
measured in nJy. For reference, the outline of the Hubble Deep Field (Williams
et al., 1996) is shown in white on the F090W depth map.  Table 1: GOODS-N
JWST/NIRCam Depths

Band | Area | Median Exposure Time | Median Deptha | Median Depth
---|---|---|---|---
| [arcmin2] | [s] | [nJy] | [AB]
JADES F090W | 56.4 | 11338 | 9.8 | 29.92
JADES F115W | 56.4 | 22676 | 6.9 | 29.30
JADES F150W | 56.4 | 11338 | 7.6 | 29.19
FRESCO F182Mb | 63.0 | 12211 | 11.3 | 28.77
JADES F200W | 56.4 | 11338 | 6.4 | 29.38
FRESCO F210Mb | 60.6 | 10558 | 14.4 | 28.50
JADES F277W | 55.5 | 11338 | 3.8 | 29.95
JADES F335M | 47.1 | 8503 | 6.0 | 29.45
JADES F356W | 55.5 | 11338 | 3.7 | 29.97
JADES F410M | 55.5 | 8503 | 7.7 | 29.19
JADES+FRESCOb F444W | 83.0 | 10393 | 6.4 | 29.38

Note. — a Median $r=0.15$ arcsec aperture corrected $5\sigma$ point-source
depth. b This filter uses data from the FRESCO Program (Oesch et al., 2023b).

##  3 NIRSpec/MSA observations

Table 2: Summary of JADES NIRSpec/MSA observations. Under each disperser we
report the (minimum/mean/maximum) exposure times; the minimum exposure time
can be 0, due to disobedient shutters (for PRISM) and for protecting high-
priority targets from overlap (for the gratings).

PID | Field | Depth | Selection | Tier name | PRISM | G140M | G235M | G395M | Targets | Release
---|---|---|---|---|---|---|---|---|---|---
|  |  |  |  | [h] | [h] | [h] | [h] |  |
1210 | GOODS-S | Deep | HST/JWST | `goods-s-deephst` | ( 7.8/16.5/28.0) | ( 2.3/ 4.1/ 7.0) | ( 2.3/ 4.1/ 7.0) | ( 2.3/ 4.1/ 7.0) | 253 | B23
1180a | GOODS-S | Medium | HST | `goods-s-mediumhst` | ( 0.9/ 1.0/ 4.3) | ( 0.9/ 1.0/ 4.3) | ( 0.9/ 1.0/ 4.3) | ( 0.9/ 1.0/ 4.3) | 1342 | This work
1180 | GOODS-S | Medium | JWST | `goods-s-mediumjwst1180` | ( 0.3/ 2.1/ 5.2) | ( 0.9/ 1.8/ 4.3) | ( 0.9/ 1.8/ 4.3) | ( 0.9/ 1.8/ 4.3) | 533 | ”
1181 | GOODS-N | Medium | HST | `goods-n-mediumhst` | ( 0.6/ 2.0/ 6.9) | ( 0.9/ 1.0/ 3.5) | ( 0.9/ 1.0/ 3.5) | ( 0.9/ 1.0/ 3.5) | 853 | ”
1181 | GOODS-N | Medium | JWST | `goods-n-mediumjwst` | ( 0.3/ 1.6/ 5.2) | ( 0.9/ 1.7/ 5.2) | ( 0.9/ 1.7/ 5.2) | ( 0.9/ 1.7/ 5.2) | 709 | ”
1286 | GOODS-S | Medium | JWST | `goods-s-mediumjwst` | ( 0.5/ 2.1/ 2.2) | ( 0.7/ 2.1/ 2.2) | ( 0.9/ 2.4/ 2.6) | ( 0.9/ 2.4/ 2.6) | 169 | ”
3215 | GOODS-S | Deep | HST | `goods-s-ultradeep` | ( 2.8/32.4/61.6) | ( 2.8/ 7.7/11.2) | — | (11.2/23.0/33.6) | 228 | ”
1286b | GOODS-S | Medium | JWST | — |  |  |  |  |  | 2025
1287 | GOODS-S | Deep | JWST | — |  |  |  |  |  | ”

a Two-thirds of these observations were affected by ‘shorts’. See 3.2 and
Appendix A for more details. b This data release includes only 1 of 8
observations from PID 1286; the remaining 7 observations were obtained in
Decmeber 2023 and will be part of a future data release.

All observations used NIRSpec in Multi-Object Spectroscopy mode, with the
NIRSpec/MSA (Ferruit et al., 2022). The MSA configurations were planned as
described in Section 4 using the strategy detailed in Eisenstein et al.
(2023a). For each visit, a set of target acquisition (TA) objects (stars and
compact galaxies) were identified on the same images as those used for
measuring the positions of the science targets. All TA objects were visually
inspected to ensure they were compact, symmetric, and did not have color
gradients or nearby sources. All TAs used the NIRSpec CLEAR filter and longest
readout time (mode NRSRAPIDD6) because the GOODS fields only contain faint TA
objects. Further details on the TA setups are provided in Eisenstein et al.
(2023a). All 35 TAs performed for JADES so far have been successful.

There were some technical issues with JADES NIRSpec observations that resulted
in visits being skipped or having only partial data collection. The two
sources of these issues are guide-star acquisition or re-acquisition failures
from the Fine Guidance Sensor and ‘shorts’ (electrical short circuits with the
NIRSpec MSA; Rawle et al., 2022). For cases where observations of a MSA
configuration were partially successful, our strategy is to return with the
same MSA configuration one year later to complete the data acquisition on the
same targets. For cases where no data were obtained for a MSA configuration,
we have replanned at either the same or at a different orientation.

Table 2 gives a summary of all JADES NIRSpec observations, including the
typical integration time (we provide the minimum, mean and maximum integration
time for each spectral configuration). In the following subsections, we
provide an outline of the observations that are the subject of this data
release. The DR3 electronic files provide more details such as observation
date and actual integration times per target. We generally describe the
observations with a label structured as ‘depth/selection’, where depth is
either ‘Medium’, ‘Deep’, or ‘Ultradeep’, and selection is either ‘HST’ or
‘JWST’, depending on how the majority of targets was selected; these labels
are then ‘translated’ into the `TIER` column in the published tables, and are
part of the file names for the published spectra.

###  3.1 1210: GOODS-S Deep/HST (+JWST)

Observations from the first deep tier of JADES were already presented and
extensively described in B23. We refer the reader to that article for all
information concerning the observations in program 1210.

###  3.2 1180: GOODS-S Medium/HST (+JWST)

The intent of this program was to observe galaxies mainly selected from HST
imaging, before any JWST NIRCam images were available. Six observations were
planned, each consisting of two pointings offset by $\approx 7$ arcsec to
dither over the short-wave detector gap in the parallel NIRCam observations.
Unfortunately, these observations in October 2022 were heavily impacted by MSA
shorts see also Eisenstein et al., 2023a for the specific case of shorts in
program 1180. As a result, only four of the 12 target sets were successfully
observed in 2022, with three more completed in October 2023. Even though these
failed observations were compensated with replanned observations (as described
below), they can still be used to measure redshifts for sufficiently bright
galaxies. For this reason, we provide reduced data also for the shorts-
affected observations. Examples of shorts-contaminated data are provided in
Appendix A; a ‘data-reduction flag’ signals spectra affected by data-reduction
problems; this includes all spectra from shorts-affected observations,
including when the data quality was not severely affected (`DR_flag`). The
time for the five remaining pointings was replanned as two observations
targeted from NIRCam imaging, with target selection criteria closely matching
that of GOODS-S Medium/JWST program 1286. These are referred to as 1180
GOODS-S Medium/JWST.

The seven completed GOODS-S Medium/HST pointings were observed in a single
3-point nodding pattern with each of the PRISM, G140M, G235M and G395M
dispersers yielding total integration times of 3.8, 3.1, 3.1 and 3.1 ksec,
respectively. The highest priority targets were observed in more than one
pointing when possible, so some targets have longer integration times.

The two replanned observations in 1180 are known as 1180 GOODS-S Medium/JWST.
Due to the amount of time available, observation 134 has two sub-pointings
whereas observation 135 has three. Each sub-pointing has the same dispersers
and integration times as in Medium/HST above. The pointings were no longer
constrained to be offset by 7 arcsec, and we adopted a strategy to maximize
the exposure times of different objects. These observations were executed in
January 2023.

###  3.3 1181: GOODS-N Medium/HST

This program consists of four observations planned similarly to the six of
GOODS-S Medium/HST, with each observation comprising two pointings offset by
$\approx 7$ arcsec. However, for GOODS-N, the total time used with the PRISM
per pointing was increased to 6.3 ksec, using two sets of the 3-point nodding
pattern. All but one observations were successfully completed in February
2023.

###  3.4 1181: GOODS-N Medium/JWST

This program was planned using JWST NIRCam imaging for target selection. Four
observations were planned, each with three sub-pointings offset by $\approx 1$
arcsec. Three of the observations were completed between April and May 2023.
The fourth was delayed until late May by a guide-star acquisition failure and
then partially impacted by MSA shorts. It is scheduled to be completed in May
2024. Each sub-pointing uses the PRISM, G140M, G235M, G395M and G395H
dispersers with total integration times of 3.1 ksec each. The MSA
configurations are designed to maximize commonality of the targets in each of
the three sub-pointings to yield total integration times of 9.3 ksec per
disperser.

###  3.5 1286: GOODS-S Medium/JWST

Program ID 1286 is the main GOODS-S Medium/JWST program. These observations
were planned the same way as those of GOODS-N Medium/JWST above, each with
three sub-pointings separated by $\approx 1$ arcsec. Only one of the eight
observations was executed during Cycle 1 in January 2023. The remaining seven
were observed in October and December 2023. Only the visit from January 2023
is included in this data release. For this visit the integration times per
sub-pointing in some dispersers were reduced from 3.1 to 2.7 ksec to fit
within the available allocation.

###  3.6 3215: GOODS-S Ultra-Deep/JWST

Program ID 3215 is a Cycle 2 GO program that builds on the parallel NIRSpec
and NIRCam JADES observations in program 1210 (Eisenstein et al., 2023b). The
NIRSpec MSA observation consists of five sub-pointings within 2 arcsec, close
to the three sub-pointings of Deep/HST. For each sub-pointing the total
integration time is 33 ksec in each of PRISM and G395M and 8.3 ksec in G140M.
Four sub-pointings were successfully executed in October 2023. The fifth
suffered from a bright MSA short. All of the grating and 25% of the PRISM
exposures in visit 5 were unusable. These are scheduled to be re-observed in
October 2024.

Figure 4: A subset of the observed spectra with multiple emission lines,
sorted from bottom to top by increasing redshift. A number of continuum and
line features is apparent, illustrating the simultaneous coverage of the rest-
frame UV and optical ranges before cosmic noon.

A subset of the observed spectra is shown in Figure 4, where the targets are
in order of increasing redshift from the bottom to the top row. The varying
noise level reflects the combination of exposures of different depth.

##  4 Sample selection

As described in our first JADES NIRSpec data release paper (B23), we employ a
priority class system to most efficiently use the micro shutter array of
NIRSpec (see Ferruit et al. 2022). The highest priority classes are reserved
for objects that are few in number (i.e. having low sky density), typically
targeting very high redshift candidates. We use these galaxies to optimize
each pointing of NIRSpec (see Section 4.5). Lower-priority classes contain
many more galaxies, only a fraction of which actually get placed on shutters.
With this, we aim to achieve a statistical sample, and JADES aims to span
galaxy evolution from Cosmic Noon to within the Epoch of Reionization. As
outlined in Eisenstein et al. (2023c), the JADES spectroscopy has a tiered
‘wedding cake’ survey design, where a smaller number of deep pointings (with
long exposure times) are supplemented with medium-depth pointings covering a
larger area. As JADES spans a range of science cases, there is not a single
selection function for the spectroscopic sample. Instead, each tier has its
own prioritization scheme (Tables
LABEL:tab:priorities_Medium_HST–LABEL:tab:priorities_3215). However, each of
these schemes is structured in broadly the same way, with the exact criteria
changing to reflect the differing input catalogs and differing sensitivity of
the observations in each tier.

B23 presented deep spectroscopic observations around the Hubble Ultra Deep
Field, including details of the prioritization scheme used for target
selection. The data release presented in this paper predominantly introduces
the medium tiers of JADES, and the new deep pointing from Program ID 3215.

Some spectroscopic observations were obtained before having JWST/NIRCam
imaging. For these, we had to rely on targets selected from existing imaging,
predominantly HST, augmented by data from other facilities - hereafter, we
refer to observations planned in this way as ‘Medium/HST’ (Section 4.2).

Later observations benefited from JWST/NIRCam imaging, and targets were
selected from these new datasets where possible. This is called Medium/JWST
(Section 4.3). However, we note that in some Medium/JWST observations, the MSA
footprint extended beyond the NIRCam coverage. These areas of the MSA had to
be filled by HST-based catalogs with selection criteria detailed in Column 4
of Table LABEL:tab:priorities_Medium_JWST, which was designed to mimic the
Medium/JWST criteria as best as possible.

We note that Medium/JWST observations were typically deeper than Medium/HST,
so although the overall aims of the two tiers are similar, the exact magnitude
cuts are different. This is discussed in more detail below.

###  4.1 Deep/HST

The sample selection for program 1210 is described in B23.

###  4.2 Medium/HST

Our observations span the two well studied fields GOODS-S and GOODS-N, meaning
that our HST-based input target catalogs for our initial spectroscopic
observations already had a high target density.

GOODS-S: As described in B23, we assembled a HST-based catalog in GOODS-S by
compiling $z>5.7$ candidates from multiple literature sources that had used a
combination of Lyman break dropout criteria and/or photometric redshifts
(Bunker et al., 2004; Yan & Windhorst, 2004; Oesch et al., 2010, 2013;
Lorenzoni et al., 2011, 2013; Yan et al., 2010; Ellis et al., 2013; McLure et
al., 2013; Schenker et al., 2013; Bouwens et al., 2015, 2021; Finkelstein et
al., 2015; Harikane et al., 2016). These were supplemented by targets of any
redshift from large photometric catalog releases, including Skelton et al.
(2014), Whitaker et al. (2019), Rafelski et al. (2015), Guo et al. (2013),
Caldwell et al. (2008), and Coe et al. (2006). Critical to assembling our HST-
based catalog was ensuring the astrometry of these literature sources was
accurate relative to the GAIA DR2 astrometric frame used in target
acquisition. The details of how this was achieved, by relating catalogs to the
Complete Hubble Archive for Galaxy Evolution (CHArGE) re-reduction of the HST
imaging (Kokorev et al., 2022; Brammer,
2023)222https://s3.amazonaws.com/grizli-stsci/Mosaics/index.html, are given in
Appendix A of B23.

To establish photometry and a redshift for target prioritization, we cross-
matched targets across each of these catalogs. HST broad-band magnitudes were
adopted from the latest available catalog in which the target appeared out of
Whitaker et al. (2019), Skelton et al. (2014), Rafelski et al. (2015), and Guo
et al. (2013), or the discovery paper for targets not appearing in any of
these catalogs. If the target did not have a reported magnitude, we remeasured
the aperture photometry at the given coordinates. For $z>5.7$ candidates, we
adopt the photometric redshifts of the discovery paper. Other targets are
assigned a photometric redshift according to the most recent catalog that
reports one. We also cross-matched targets with literature spectroscopic
redshift catalogs in GOODS-S (Tasca et al., 2017; Garilli et al., 2008; Kurk
et al., 2013; Stark et al., 2013; Kriek et al., 2015; Morris et al., 2015;
Momcheva et al., 2016; Inami et al., 2017; Herenz et al., 2017; Pentericci et
al., 2018a, b; Maseda et al., 2018). Where we identify matches, these
redshifts supersede any photometric redshifts, provided the quality flagging
from those catalogs indicated that the redshift was based on either multiple
high S/N emission lines, or a high S/N detection of an asymmetric Ly emission
profile.

GOODS-N: The GOODS-N HST-based catalog was assembled in largely the same way.
Many of the $z>5.7$ selection papers listed above also extend their samples to
GOODS-N (Oesch et al., 2014; Bouwens et al., 2015, 2021; Finkelstein et al.,
2015; Harikane et al., 2016). Again, these were supplemented by targets from
large HST photometric releases. Skelton et al. (2014) also covers GOODS-N and
was included in our catalog, while we also add in targets from the Barro et
al. (2019) catalog. Astrometry was corrected following exactly the same
procedure as in GOODS-S.

Photometry was taken from the most recent catalogs of Barro et al. (2019) and
Skelton et al. (2014) if available, otherwise from the discovery paper, or,
failing that, from our own remeasured aperture magnitudes. Photometric
redshifts were established in the same way as above, and these were again
superseded by spectroscopic redshifts if a positional match was identified
with a target in a literature catalog, as described above (Reddy et al., 2006;
Barger et al., 2008; Stark et al., 2011, 2013; Kriek et al., 2015; U et al.,
2015; Momcheva et al., 2016; Maseda et al., 2018).

Additionally, for a small number of our Medium/HST observations in GOODS-N,
one of the MSA quadrants partially extended beyond the main GOODS-N CANDELS
footprint (see Figure 4 in Eisenstein et al. 2023c). CANDELS imaging only
extended into this area in one band (F814W) of HST/WFC3 imaging (Grogin et
al., 2011). Thus, this MSA real estate could not be populated with targets
with robust photometric redshifts in this region. We identified eight objects
in this area with X-ray counterparts from the Chandra Deep Field North X-ray
survey (Xue et al., 2016), and these were included in class 3.0 (see Table
LABEL:tab:priorities_Medium_HST). We then populated the input catalog in this
region with targets from more extended imaging, either from Spitzer/IRAC
(Ashby et al., 2013) or ground-based imaging from Subaru (Capak et al., 2004).
The astrometry of these catalogs is less reliable, and thus, all of these
targets were placed only in class 8, although $\sim$60 were observed since
there was very little competition for shutter real estate in this part of the
MSA, and these formed the vast majority of targets from this class that were
observed.

Table 3: Target prioritisation categories for Medium/HST

Priority | Redshift | Criteria | Targets observed
---|---|---|---
1 | $z>5.7$ |  $F160W<27.5$; V.I. Class1 0 | 68
2.0 | $z>5.7$ |  $F160W<27.5$; V.I. Class 1 |
|  |  $27.5<F160W<29$; V.I. Classes 0, 1 | 93
3.0 | $1.5<z<5.7$ | Rare target (e.g., Quiescent, AGN, ALMA…) | 27
3.5 | $1.5<z<5.7$ | $F160W<23.5$ | 43
4.1 | $4.5<z<5.7$ | $F160W<25.5$ | 14
5.1 | $4.5<z<5.7$ | $F160W<27$ | 87
6.1 | $4.5<z<5.7$ | $S/N(\text{H\textalpha})>15$ | 44
4.2 | $3.5<z<4.5$ | $F160W<25.5$ | 45
5.2 | $3.5<z<4.5$ | $F160W<27$ | 148
6.2 | $3.5<z<4.5$ | $S/N(\text{H\textalpha})>15$ | 63
4.3 | $2.5<z<3.5$ | $F160W<25.5$ | 122
5.3 | $2.5<z<3.5$ | $F160W<27$ | 171
6.3 | $2.5<z<3.5$ | $S/N(\text{H\textalpha})>15$ | 59
4.4 | $1.5<z<2.5$ | $F160W<25.5$ | 176
5.4 | $1.5<z<2.5$ | $F160W<27$ | 219
6.4 | $1.5<z<2.5$ | $S/N(\text{H\textalpha})>15$ | 54
7 | $z>1.5$ | Has GAIA2 coords and $F160W>23.5$ | 265
7.5 | $z<1.5$ | Has GAIA2 coords $23.5<F160W<27$ | 317
7.6 | $z<1.5$ | Has GAIA2 coords $F160W>27$ | 87
8 | any $z$ | Anything else with $F160W>23.5$ | 115

1Targets were assigned one of the following visual inspection (V.I.) classes:
(0) Most compelling, (1) Plausible $z>5.7$, but less compelling, (2) Real
object but likely $z<5.7$, (-1) Reject.

Medium/HST target prioritization: For each of GOODS-S and GOODS-N, these
catalogs were then divided into priority classes for Medium/HST observations
following the scheme outlined in Table LABEL:tab:priorities_Medium_HST. The
highest priority targets in Medium/HST comprised high-redshift galaxies with
photometric redshifts $z>5.7$, with the lower redshift cut corresponding to
the ‘$i$-band drop-out’ galaxies with the Lyman-break in the HST/ACS F775W
filter (see Bunker et al. 2004). These targets were subdivided based on an
F160W magnitude cut, prioritizing the brightest sources. Additionally, we
performed a visual inspection of the HST imaging, placing the most robust
candidates in our highest class. More marginal targets we retained in Class 2,
and unconvincing targets were removed.

The next priority classes comprised targets with low surface density,
including very bright ($m_{\rm F160W}<23.5$) $z>2$ objects (to obtain
exceptionally high S/N spectra of a small subset), and any targets considered
likely to be AGN, quiescent, or Lyman-continuum leakers at $1.5<z<5.7$.

To include more typical star-forming galaxies over this redshift range, we
sub-divide our input catalog into four photometric redshift slices ($4.5\leq
z<5.7$; $3.5\leq z<4.5$; $2.5\leq z<3.5$ and $1.5\leq z<2.5$), with the higher
redshift slices having higher priority given that the surface density of these
targets is typically lower.

Each redshift slice is then subdivided into three priority classes, the first
two based on the HST F160W magnitude (which is the reddest available band in
HST, and the best single-band approximation to a stellar-mass selection). This
is then supplemented by a star-formation-rate based selection (using SFRs from
the 3DHST catalog of Skelton et al. 2014). We converted the SFRs to H line
fluxes, and then to expected $S/N$ for the H line accounting for typical slit
losses of the MSA. Those predicted to have $S/N(\text{H\textalpha})>15$ in the
NIRSpec prism were included in this priority class.

Class 7 then comprised any other literature source with coordinates that could
be robustly tied to the GAIA-DR2 astrometric frame (as described in B23, )
with a photometric redshift $z>1.5$, and sources at lower redshift (also with
GAIA-DR2) subject to F160W magnitude cuts. Finally, we included anything else
that was not at risk of saturating exposures in Class 8.

###  4.3 Medium/JWST

For Medium/JWST, our primary target list came from the recent NIRCam images,
which extends to longer wavelengths (and in many cases greater depth) than the
HST images, and hence revealed more targets with more robust photometric
redshifts at high redshift. The target selection criteria are detailed in
Table LABEL:tab:priorities_Medium_JWST

The JADES team produced photometric catalogs (see e.g. Rieke et al. 2023b) and
we performed SED fitting with and (see e.g. Hainline et al. 2023) to determine
photometric redshift probability distributions for each target. In addition to
the SED-fitting-based photometric redshifts, we also used color cuts to
identify Lyman-break candidates at $z\gtrsim 5.7$. We note that the
photometric catalog was being frequently updated as imaging depth was added
and NIRCam data reduction was improved. Target selection was always performed
with the latest catalog available to us at the time of designing the
observations. For this reason, in general, the input catalogs differ from
catalogs associated with later public data releases of the imaging.

Throughout this section, where we refer to magnitudes, these are usually the
‘CIRC2’ apertures from the photometric catalogs, which was selected to
approximate the open area of a NIRSpec mirco-shutter. However, for Class $\geq
7.0$ where we move to $F444W$-based selections, we switch to using larger
apertures, to better approximate a stellar-mass-based selection. For GOODS-S,
we used the Kron apertures, while for GOODS-N we used the CIRC4 aperture
because the Kron aperture photometry was not available in GOODS-N at the time
of target selection.

Table 4: Target prioritization categories for Medium/JWST

Priority | Redshift | Criteria (if JWST-based ) | Criteria (if HST-based) | Targets
---|---|---|---|---
1 | $z>8$ |  $m_{\rm UV}<28.0$ (V.I. Class = 0)2 |  | 20
2 | $z>8$ | $m_{\rm UV}<28.0$ |  $z>8.5$, $F160W<28.0$ | 7
3 | $z>8$ | $28.0<m_{\rm UV}<28.5$ |  $z>8.5$, $28.5<F160W<28.0$ | 11
4 | $5.7<z<8$ |  $m_{\rm UV}<26.5$ or |  |
|  | L.E.1 ($F_{\rm line}\geq 10^{-17.3}$) |  | 19
5 | $z>2$ | $m_{\rm AB}<22$ |  $2<z<5.7$ $F160W<22$ | 5
6.0 & 6.1 | $5.7<z<9$ |  $26.5<m_{\rm UV}<28$ or | $F160W<28$ |
|  | L.E.1 $10^{-17.8}<F_{\rm line}<10^{-17.3}$, |  | 78
6.2 | $5.7<z<8.5$ | $28<m_{\rm UV}<28.5$ | $F160W>28$ | 10
7.1 | $4.5<z<5.7$ | UVJ & $F444W<27$; X-ray sources | $F160W<28$ | 3
7.2 | $3.5<z<4.5$ | UVJ & $F444W<27$; X-ray sources | $F160W<28$ | 6
7.3 | $2.5<z<3.5$ | UVJ & $F444W<27$; X-ray sources | $F160W<27.5$ | 14
7.4 | $1.5<z<2.5$ | UVJ & $F444W<27$; X-ray sources | $F160W<27.5$ | 19
7.5 | $4.5<z<5.7$ | $F444W<27$ | $F160W<28$ | 65
7.6 | $3.5<z<4.5$ | $F444W<27$ | $F160W<28$ | 108
7.7 | $2.5<z<3.5$ | $F444W<27$ | $F160W<27.5$ | 177
7.8 | $1.5<z<2.5$ | $F444W<26$ | $F160W<27.5$ | 155
7.9 | $1.5<z<2.5$ |  $26<F444W<27$; or |  |
| $z<5.7$ | L.E.1 $10^{-17.9}<F_{\rm line}<10^{-17.3}$ |  | 50
8.0 & 8.1 | $z>1.5$ |  $F444W<28$ mag or $S/N({\rm H}\alpha)>20$ |  $F160W>28.5$ & has GAIA2 coords | 352
8.2 | $z<1.5$ |  $F444W<28$ mag |  $24.5<F160W<29$ & has GAIA2 coords | 171
8.3 | no redshift cut | $F444W<29$ |  $F160W>29$ & has GAIA2 coords | 99
9 | — |  | $F160W>24.5$ | 46

1Strong line emitters (L.E., units erg cm-2 s-1)were selected based on
measurements from FRESCO or MUSE, or targets with a F410M excess. 1Visual
inspection (V.I.) Class=0 are the most robust candidates.

We placed galaxies with photometric redshifts higher than 8 in the top
priorities, with those brighter than $m_{AB}=28$ mag in the rest-frame UV
ranked top, followed by those with $28<m_{AB}<28.5$ mag.

Priority class 4 covers galaxies with $5.7<z<8$ with a magnitude cut on the
filter which best approximates to the rest-frame UV around 1500 Å. As with the
Deep/HST Class 4 in B23 we set a relatively bright magnitude cut such that we
would expect the rest-optical emission lines to be well detected to facilitate
line ratio diagnostics (with $S/N(\text{H\textalpha})>25$ in the prism). For
Medium/JWST, this rest-UV cut corresponds to $m_{AB}<26.5$ mag, following the
methodology in Section 2.1 of B23. We supplemented this class with some
emission-line selected objects with fluxes $>10^{-17.3}$ erg cm-2 s-1 drawn
from the FRESCO survey Oesch et al., 2023b, using a custom data reduction; Sun
et al., 2023, from MUSE (Inami et al., 2017; Herenz et al., 2017) and galaxies
exhibiting flux excesses in the F410M NIRCam filter consistent with strong
line emission.

Following this, the next class was a small number of very bright ($m_{AB}<22$
mag) $1.5<z<5.7$ targets to enable continuum science. We then targeted more
galaxies in the range $5.7<z<8$ but fainter than Class 4, split into two rest-
UV magnitude bins ($26.5<m_{AB}<28$ mag and $m_{AB}>28$ mag). We supplemented
this class with emission-line selected objects with fluxes below $<10^{-17.3}$
erg cm-2 s-1. In the very first epoch of Medium/JWST observations, taken in
January 2023, we had sub-prioritised these line emitters as Class 6.0,
slightly ahead of the $26.5<m_{AB}<28$ mag magnitude cut (Class 6.1). But in
all subsequent epochs, these were folded in together as Class 6.1.

For lower-redshift galaxies ($1.5<z<5.7$) we used the same 4 redshift slices
as for Medium/HST. We created a class of upweighted sources with low target
density comprising candidate passive galaxies (selected through the UVJ color
criterion, Williams et al., 2009; Leja et al., 2019, and with $F444W<27$ mag),
along with X-ray selected sources (Luo et al., 2017), and these were allocated
in descending order of redshift slice. We then turned to the remainder of the
galaxies in each redshift bin, where we used the reddest NIRCam wide filter
F444W as a proxy for a stellar-mass-selected sample (selecting on $F444W<27$
mag), placing each redshift slice in turn as before. We note that this differs
from our Medium/HST selection (which is F160W based), as we take advantage of
the availability of the redder filter to better approximate to a stellar-mass-
limited sample.

Any unused MSA real estate was filled with fainter targets as described in
Table LABEL:tab:priorities_Medium_JWST, Classes 8.0–8.3 & 9\. The exact
criteria defining Classes $\geq 8.0$ were not fixed across all epochs of the
Medium/JWST survey. We note that these lower classes were never constructed
with a view toward being able to conduct well-defined sample-based studies,
and were rather aimed at potentially capturing a few extra worthwhile spectra
with what is otherwise spare MSA real estate. For our first Medium/JWST
pointings in GOODS-S, Class 8.0 was made up of leftover targets for which,
based on the UV magnitude, we predicted $S/N(\text{H\textalpha})>20$ based on
the SFR, and Class 8.1 was remaining candidates with $F444W<28$ mag. However,
it turned out that many of the targets in this Class 8.0 were pushing below
the noise threshold such that this did not end up being a very successful
selection, and in later iterations we did not keep this delineation. As a
result, we advise caution when considering Class $\geq 8.0$ objects in the
context of sample-based analyses.

###  4.4 Ultra-deep 3215

Our primary input catalogs for the ultra-deep NIRSpec MSA observations came
from the work of Endsley et al. (2023) at $6\lesssim z<9$ and Hainline et al.
(2023) at $z>8$, both based on the JADES NIRCam imaging. These were
supplemented with photometric redshifts fits on all galaxies in NIRCam JADES
catalog (down to a flux limit) using the and codes. These photometric
redshifts were combined using a permissive consensus criterion to avoid
missing good high redshift candidates.

The ultra-deep spectroscopy in this tier has long exposure times, and, based
on our experience with the Deep/HST NIRSpec observation (B23), redshifts could
be obtained for galaxies as faint as $AB<30$ mag. Our highest priority was
$z>11$ galaxies with confident photometric redshifts and magnitudes $AB<30$
mag, followed by those with less confident redshift solutions. The next class
was galaxies at $10<z<11$ and $AB<30$ mag, followed by $8<z<10$ (again split
into those with convincing photometric redshifts followed by those which had
lower confidence).

Table 5: Target prioritization categories for 3215 ‘UltraDeep’

Priority | Redshift | Criteria | Targets
---|---|---|---
1.1 | $z>11$ | $m_{AB}<30$ | 4
1.2 | $z>11$ |  $m_{AB}<30$ and less reliable phot-$z$ | 0
2.1 | $10<z<11$ | $m_{AB}<30$ | 0
2.3 | $8<z<10$ | $m_{AB}<30$ | 6
2.4 | $8<z<10$ |  $m_{AB}<30$ and less reliable phot-$z$ | 2
3.1 |  | rare objects1 | 5
3.2 |  | rare objects2 | 4
4.1 | $7<z<8$ |  $m_{AB}<30$ from Endsley et al. |
| $5.7<z<8$ |  $m_{AB}<28.5$ from other phot-$z$ | 3
4.2 | $5.7<z<7$ |  $m_{AB}<30$ from Endsley et al. | 8
5.1 | $4<z<5.7$ | $m_{AB}<28$ | 15
5.2 | $4<z<5.7$ | $m_{AB}<29$ | 23
6.1 | $5.7<z<8$ | $28.5<m_{AB}<30$ | 14
6.2 | $4<z<5.7$ | $m_{AB}<30$ | 29
7.1 | $2.5<z<4$ | $25<m_{AB}<28$ | 15
7.2 | $2.5<z<4$ | $28<m_{AB}<29$ | 12
7.3 | $1.5<z<2.5$ | $25<m_{AB}<28$ | 14
7.4 | $1.5<z<2.5$ | $28<m_{AB}<29$ | 15
7.5 | $z>1.5$ | $29<m_{AB}<30$ | 17
8.1 | $z<1.5$ | $25<m_{AB}<28$ | 21
8.2 | $z<1.5$ | $28<m_{AB}<29$ | 7
8.3 | $z<1.5$ | $29<m_{AB}<30$ | 5
9 |  | class 9 objects in Deep/HST | 9

1Rare objects includes: blue UV slopes, AGN $7<z<12$, MIRI $z>7$, X-ray $z>4$,
medium-band $\log(line~{}flux/erg\,cm^{-2}\,s^{-1})>-18.3$
2Rare objects includes: ALMA, MIRI $z<7$, AGN $4<z<7$, medium-band
$\log(line~{}flux/erg\,cm^{-2}\,s^{-1})<-18.3$

The next class comprised galaxies with unusual properties, such as extremely
blue rest-UV spectral slopes (2 targets), evidence for quiescence (2 targets),
high-redshift AGN (2 targets), an object with a large flux excess in the F410M
medium-band filter consistent with strong [O iii]5007 emission at $z\approx
7$, and FRESCO strong line emitters (2 targets).

We then considered the standard sample of objects, moving down in the redshift
range. Class 4 includes targets in the range $5.7<z<8$ from the Endsley et al.
(2023) catalog, subdividing these into sub-classes 4.1 and 4.2, based on a
redshift cut of 7. These objects went down to our $m_{AB}<30$ mag cut. We
supplemented sub-class 4.1 with $5.7<z<8$ targets from the full JADES catalog,
for which we impose a brighter magnitude cut of $m_{AB}<28.5$ mag since
objects fainter than this had less reliable photometric redshifts.

We then descend in redshift to $4<z<5.7$ from our JADES photometric catalog,
prioritizing $m_{AB}<28$ mag (sub-class 5.1) and then $28<m_{AB}<29$ mag (sub-
class 5.2). Then in class 6, we place leftover objects down to $m_{AB}<30$ mag
in these $5.7<z<8$ and $4<z<5.7$ slices (as sub-classes 6.1 and 6.2).

We then move to Class 7 which places objects in redshift slices of $2.5<z<4$
and $1.5<z<2.5$, with each slice subdivided into two classes based on
$m_{AB}<28$ mag and $28<m_{AB}<29$ mag. Class 7.5 then places all remaining
$z>1.5$ objects with $m_{AB}<30$ mag.

Class 8 then places $z<1.5$ objects divided into three magnitude slices
($m_{AB}<28$ mag, $28<m_{AB}<29$ mag, $29<m_{AB}<30$ mag). Finally, Class 9
contains filler objects from the HST-based catalogs described above.

###  4.5 Target assignment with and visual inspection

Target placement was performed using the software (Bonaventura et al., 2023)
and proceeded using the same method as described in B23. Pointing centers are
driven by the highest priority class in each tier; Class 1 for Medium/HST and
Medium/JWST, and Class 1.1 for Ultra-deep 3215. For all candidates with
$z>5.7$, we visually inspected the individual images and quality of the
photometric fits before running the to ensure they had good redshift fidelity.
An inspection of the full input catalog of many tens of thousands of galaxies
was not practical, but we did inspect everything that the had allocated
shutters to when designing trial MSA configurations. Sources that were badly
contaminated by neighboring objects were removed, and the re-run at the same
location to assign new targets in place of those rejected (this was typically
less than 10 objects per MSA pointing). In allocating shutters, we require the
centroid of the object to fall within an ‘admittance zone’ as described in
B23. For the low-dispersion prism, we do not allow the spectra of any target
to overlap. As with our Deep/HST observations, we keep the same MSA
configuration for the grating spectra as for the prism, which means that the
higher-dispersion spectra (which are more extended on the array) do overlap,
and we use the prism spectra to avoid confusion in line identifications. The
grating spectra of a small number of sources (the highest priority sources,
and very bright objects) are protected against overlap by closing the shutters
of lower-priority targets in nearby rows. This means that a small number of
objects are observed in the prism alone.

##  5 NIRSpec/MSA Data Reduction

The data reduction pipeline for this release is the same as in B23. However,
the larger dataset enables us to discuss more in detail some of the
calibration issues highlighted in DR1.

The pipeline is developed by the ESA NIRSpec Science Operations Team (SOT) and
Guaranteed Time Observations (GTO) NIRSpec teams (Alves de Oliveira et al.,
2018; Ferruit et al., 2022). Most of the processing steps are similar to those
adopted by the STScI pipeline used to generate the MAST archive products, but
the background subtraction, rectification, 1D extraction, and spectra
combination steps have been optimized for the targets observed in JADES
programs (see details in B23). In particular, we apply a wavelength correction
to compensate for the wavelength bias of non-centered compact sources. This
bias arises for sources that are smaller than the slit width, when they are
spatially offset within the shutter along the dispersion axis. The issue is
discussed in Ferruit et al. (2022), and we apply the correction they propose
(cf. their Figure 9).

During the quality assessment of NIRSpec observations, we noted that some
shutters failed to open when the MSA was configured at each pointing. These
unexpected disobedient shutters might corrupt both the estimate of the
background emission and the science spectrum during the data processing
workflow. The impact of disobedient shutters is evident in the PRISM/CLEAR
observations where the background and target emission are prominent.
Therefore, we initially analyzed the presence of these failed shutters by
processing the data without background subtraction and identifying those
shutters in which the signal is consistent with no emission. We removed such
disobedient shutters from the MSA mask and re-processed the data following the
standard procedure. Only 3 per cent of the targets are affected by disobedient
shutters, reducing the total exposure time dedicated to the selected galaxy.
In most cases, we just removed only one of the 3 shutters forming the target
slitlet, but for a few targets, the number of disobedient shutters for the
slitlet was 2 and even 3.

In Figure 5 we illustrate our current flux and wavelength calibration issues
using 199773 (panel c), a massive quiescent galaxy at redshift $z=2.8$, where
we detect several stellar absorption lines in the medium-resolution gratings.
In panel (a) we show the prism spectrum (solid black) and the combined G140M
and G395M spectra (blue; no G235M observations are present in PID 3215). The
yellow line is the grating spectrum re-binned to the prism wavelength grid,
the red line shows the grating spectrum after matching the resolution of the
prism (using a Gaussian kernel) and then rebinning. In principle, the red and
black lines should overlap, but we can see substantial mismatches in both flux
level and wavelength; this is illustrated in panel (b), where we show the
ratio between the flux densities of the gratings and prism (after rebinning,
yellow, and after smoothing and rebinning, red). At wavelengths $\lambda<1.3$
the flux levels agree to within 5 per cent, but at $1.3-1.5~{}\mum$ the G140M
flux increases reaching a ratio of 10–15 per cent, before going down again
from 1.5 . Being part of PID 3215, this object has no G235M data, but the
G395M data show instead a higher flux mismatch of 15 per cent on average with
respect to the prism, though with tapering both at the blue and red ends of
the grating spectrum (see also Figure 16 in Section 11, illustrating another
source with continuum detected in the medium gratings).

At the same time, we confirm that overall the medium gratings require a lower
redshift solution than the prism Section 9 and B23.

Figure 5: Target ID 199773 from PID 3215, illustrating remaining calibration
problems. Panel (a) shows the prism and gratings data (black and blue, G140M
to the left and G395M to the right), and the gratings data after rebinning to
the prism grid (yellow) and after matching the nominal resolution of the prism
and then rebinning (red; vertical gray areas highlight spectral regions where
we interpolated over missing grating data, due to bad pixels or the detector
gap; the red line is clipped at the edges of the wavelength range due to the
size of the convolution kernel). Panel (b) shows the ratio between the
rebinned and smoothed-then-rebinned grating spectrum and the prism spectrum
(same line colors as panel a). The flux calibration mismatch between prism and
gratings is wavelength dependent, and is most severe in G395M. The galaxy
image is shown in panel (c), with the MSA shutters overlaid. From Z. Ji et al.
(in prep.)

##  6 preliminary redshift identification

Prior to running the flux measurement software, we measure an initial redshift
estimate using a two-step process. In the first step, we run the spectral
modeling software (Carnall et al., 2019) on the prism data. Our setup is
optimized for time-efficient redshift measurement, by using a parametric star-
formation history which may not fit well the stellar continuum. This step
returns both a redshift estimate and a fiducial model spectrum. A detailed
description of this procedure is available in B23; an example model fit is
reported in Figure 6 (orange line). Each galaxy is then visually inspected by
at least two team members, who use a rudimentary graphics interface to compare
the model to all the available data, including the grating spectra, if
available. To assist in the decision, the interface displays a set of strong
spectral features (Section 6.1). In this step, the astronomers can change the
redshift and assign a quality flag.

###  6.1 Visual redshift determination

The visual inspection is performed using a program which presents the user
simultaneously with all the available information in a compact interface. The
console is shown in Figure 6, open on target ID 5591 (GN-z11; Oesch et al.,
2016; Bunker et al., 2023a; Maiolino et al., 2023a) from . The top panel shows
the 1-d prism spectrum, with overlaid the model, and a set of reference
spectral features (vertical dashed lines). Various buttons enable the user to
display the 1-d S/N, the 1-d uncertainty, additional lines and, crucially,
data from other dispersers. The bottom panel shows the 2-d S/N map. Finally,
the console automatically opens (Hausen & Robertson, 2022) centered on the
current target; gives the user access to the panchromatic /NIRCam and
photometry (Rieke et al., 2023a), including the photometric redshifts based on
(Brammer et al., 2008; Hainline et al., 2023).

The user is able to change interactively the redshift and judge different
solutions. The outcome of this inspection is a user-validated redshift (or no
redshift) and a set of flags (Table 6). Users can optionally enter comments;
the only mandatory comment is to specify when there is a serendipitous source
in the shutter. Typical comments include prominent or peculiar morphologies
and doubt about alternative possibilities.

Figure 6: The interface used to visually inspect NIRSpec data, showing GN-z11
(ID 5591 in ; Oesch et al., 2016; Bunker et al., 2023a; Maiolino et al.,
2023a). The console displays simultaneously the 1-d spectrum and 2-d S/N map.
The user is able to move a set of bright spectral features to be used as
reference (vertical dashed lines). The bottom row of console is the set of
flags the user can assign. Table 6: Flag values and meanings used in the
visual inspection. These can be thought of as bit flags, hence in general a
target has multiple flags.

Value | Description
---|---
0a | Not inspected
1b | Impossible to determine
2 | Tentative
3 | Peculiarc
4 | From continuum
5 | Single prism line
6 | Multiple prism lines
7 | Multiple medium-grating lines
8 | Multiple high-resolution grating lines
9 | Prism data corrupted
10 | Medium-resolution data corrupted
11 | High-resolution data corrupted

a Cannot be combined with other flags. b Cannot be combined with other flags,
except 9, 10 and 11. c Usually a serendipitous source in the shutter. User
must enter a comment.

Each galaxy has been inspected by at least two people and by up to four. When
the sample is fully inspected, the user sends the resulting catalog of
redshifts and flags for merging. The catalogs are compared based on their
redshift value. Redshifts that agree to within a tolerance of a spectral pixel
are averaged; targets that have different redshifts or redshift flags are re-
inspected and a final decision is taken. The resulting redshifts are then used
as input in various analysis steps, ranging from emission-line measurements
(as described in this article), to Bayesian spectral modeling (e.g., , ).

Figure 7: Summary of the visual inspection; the left column shows targets for
which a redshift could not be determined (flag 1). Galaxies with uncertain
redshifts (center) and with secure redshifts (right) are split by the highest-
confidence redshift flag, colored as labeled. Overall, the current success
rate of JADES redshifts is 65 per cent.

The distribution of visual redshift flags from this procedure is illustrated
in Figure 7.

##  7 prism emission-line fluxes

We use the spectral fitting software (Cappellari, 2023), to model the data as
a linear combination of spectral templates. As input templates, we use a set
of simple stellar-population spectra from (SSP; Conroy et al., 2009). The
spectra were calculated using mist isochrones (Choi et al., 2016), the C3K
model atmospheres (Conroy et al., 2019), and a Salpeter initial mass function
(Salpeter, 1955). The spectral resolution is R=10,000 between $0.1<\lambda<3$
; these templates are available from C. Conroy upon reasonable request. We
consider a subset of the templates grid spanning logarithmically ages
$0.03\text{--}20$ Gyr and metallicities [Z/H] $-2.5\text{--}0$. We adjust the
age grid to each target, ensuring that the oldest available SSP is consistent
with the age of the universe at the redshift of the target (see e.g., Looser
et al., 2023b).

In addition to these stellar templates, we use a set of Gaussian templates to
represent nebular emission lines. The gas templates are of three kinds; single
Gaussians that represent individual emission lines that are spectrally
isolated at any redshift (e.g., He i5787, Pa), single Gaussians that represent
multiple, spectrally blended lines (e.g.,
$\text{H\textalpha}+\text{[{N}\,{\sc{ii}}]{\textlambda}{\textlambda}{6549,}{6584}}$,
$\text{H\textgamma}+\text{[{O}\,{\sc{iii}}]{\textlambda}{4363}{}{}}$), and
doublet Gaussians representing doublets with fixed ratios (e.g., [O
iii]4959,5007, [S iii]9069,9532). A summary of emission-line templates and
their redshift range is shown in Table 7. Note that the exact set of templates
used depends on the source initial redshift; this is because the spectral
resolution of the prism is a strong function of wavelength (Jakobsen et al.,
2022), causing emission-line groups to be spectrally resolved or unresolved at
different redshifts. Moreover, we include a step function that is meant to
capture very strong Balmer jumps (e.g., Cameron et al., 2023). All these
templates are bound to have non-negative coefficients in the linear
combination. Finally, we use a 10th-order multiplicative Legendre polynomial
to adapt the shape of the continuum to the data; this can be thought of as a
combination of physical effects (e.g., dust reddening) and flux calibration
(e.g., incorrect slit-loss corrections, for extended objects and for objects
with strongly wavelength-dependent morphology). Before running , each input
template is smoothed to twice the spectral resolution of the data, the
templates are truncated to match the approximate rest-frame wavelength range
of the data, and stellar flux blue-ward of Ly is set to 0. The templates are
additionally convolved with a velocity distribution, modeled as a Gaussian. We
run two times for each galaxy; in the first instance, we ‘tie’ the templates
in kinematic subsets, constrained to have the same velocity and velocity
dispersion. The kinematic groups are: Balmer lines and stellar templates,
rest-frame UV lines, rest-frame optical lines and rest-frame NIR lines. After
this first pass, all lines detected to at least 5are kept, whereas the others
are discarded. In the second run, we fix the kinematics of the stellar
continuum absorption, use only previously detected emission line templates,
and remove almost all kinematic groups. Exceptions to the latter rule are: the
blend group formed by [S ii]6717,6730 and the blend
$\text{H\textalpha}+\text{[{N}\,{\sc{ii}}]{\textlambda}{\textlambda}{6549,}{6584}}$;
the group formed by H and [O iii]4959,5007; the group formed by H and [O
iii]4363; and the group of He i10830 and Pa, whose kinematics are always tied
together. We note that He i10830 is resonant, therefore this emission-line
tends to be redshifted relative to the systemic velocity; however, leaving the
line kinematics free relative to Pa tended to produce bad fits due to low
spectral resolution. Therefore, we opted to keep these lines tied. These
conditions track the setup of B23, and are necessary due to the limited
spectral resolution of the prism, particularly in the range 1–2 . A difference
with respect to B23 is that we fix the flux ratio between the emission lines
of the [O iii]4959,5007 and [S iii]9069,9532 doublets. Other doublets with
fixed line ratios are not enforced due to being unresolved (e.g., O
iii]1661,1666) or blended with other lines (e.g., [Ne iii]3869,3968).

After each fit, we post-process the line fluxes as follows. Below redshift
$z<2$, we combine H and [O iii]4959,5007 and
$\text{H\textalpha}+\text{[{N}\,{\sc{ii}}]{\textlambda}{\textlambda}{6549,}{6584}}$
and [S ii]6717,6730; the line uncertainties are added in quadrature. Between
$2\leq z<5.3$, we combine the flux from the [O iii]4959,5007 doublet. Unlike
for DR1, the flux of Ly is never provided, due to the difficulty of modeling
the source continuum in the vicinity of this line. We refer the reader to G.
Jones (in prep.) for Ly emitters.

The resulting best-fit spectra were visually inspected for artifacts and bad
fits. The most common of these are low equivalent width emission lines near
the Balmer break, emission lines due to contaminants, and outliers, especially
in shorts-affected observations. The low equivalent width emission lines near
the Balmer break arise when the shape of the break is not fit correctly, and
the algorithm may use [O iii]4959,5007, [Ne iii]3869,3968 and H to add to the
continuum. Contaminants and artifacts may escape the sigma-clipping in when
they fall close to strong emission lines in the intended target. All these
instances were masked in the data table, and are flagged with a dedicated flag
`PRISM_flux_flag`.

Table 7: List of the emission lines fit in the prism spectra. All wavelengths
are in vacuum.

| Line(s) |  $\lambda$ [Å] |  $z$ range | Column name | Notes
---|---|---|---|---|---
| C iv1549,1551 | 1549.48 | — | `C4_1549` |
|  He ii1640 +O iii]1661,1666 | 1650.00 | — | `Blnd_He2_O3_1650` |
| C iii]1907,1909 | 1907.71 | — | `C3_1907` |
| Mg ii2796,2803 | 2799.94 | — | `Mg2_2796` |
| [O ii]3726,3729 | 3728.49 | — | `O2_3727` |
| [Ne iii]3869,3968 | 3869.86,3968.59 | $0<z<5.3$ |  `Ne3_3869`,`Ne3_3968` |
| [Ne iii]3869 | 3869.86 | $z\geq 5.3$ | `Ne3_3869` |
|  [Ne iii]3968 +H | 3968.59 | $z\geq 5.3$ | `Ne3_3968` |
| H | 3728.49 | — | `HD_4102` |
|  H +[O iii]4363 | 4341.65 | $0<z<5.3$ | `Blnd_HG_O3` |
{2*[] | H | 4341.65 | $z\geq 5.3$ | `HG_4341` |
| [O iii]4363 | 4363.44 | $z\geq 5.3$ | `O3_4363` |
{2*[] | H | 4862.64 | $0<z<2$ | Blnd_HB_O35007d |
| [O iii]4959,5007 | 4960.30,5008.24 | $0<z<2$ |
{2*[] | H | 4862.64 | $2\leq z<5.3$ | `HB_4861` |
| [O iii]4959,5007 | 4960.30,5008.24 | $2\leq z<5.3$ | `O3_5007d` |
{2*[] | H | 4862.64 | $z\geq 5.3$ | `HB_4861` |
| [O iii]4959,5007 | 4960.30,5008.24 | $z\geq 5.3$ |  `O3_4959`,`O3_5007` |
| He i5875 | 5877.25 | — | `He1_5875` |
| [O i]6300,6363 | 6302.05,6363.67 | — | `O1_6300` |
{2*[] |  H +[N ii]6549,6584 | 6564.52 | $0<z<2$ | Blnd_HA_N2_S2 |
| [S ii]6717,6730 | 6725.00 | $0<z<2$ |
{2*[] |  H +[N ii]6549,6584 | 6564.52 | $z\geq 2$ | `HA_6563` |
| [S ii]6717,6730 | 6725.00 | $z\geq 2$ | `S2_6725` |
| He i7065 | 7067.14 | — | `He1_7065` |
| [S iii]9069,9532 | 9071.10,9533.20 | — |  `S3_9069`,`S3_9532` |
| Pa | 10052.12 | — | `PaD_10049` |
{2*[] | He i10829 | 10832.06 | — | `He1_10829` |
| Pa | 10940.98 | — | `PaG_10938` |
| Pa | 12821.43 | — | `PaB_12818` |
| Pa | 18755.80 | — | `PaA_18751` |

The set of templates used to fit any given galaxy depends on its initial
redshift guess; this is because the spectral resolution of the prism is a
strong function of wavelength (Jakobsen et al., 2022), causing emission-line
groups to be spectrally resolved or unresolved at different redshifts. Empty
redshift ranges indicate the template is used at all redshifts. Rows connected
by curly braces indicate emission-line pairs/groups that have tied velocity
and velocity dispersion.

Table 8: Structure of the prism flux table. The full list of emission lines is
reported in Table 7; all fluxes are in units of [-18].

Column name | Description
---|---
NIRSpec_ID | ID of the target in eMPT‡
TIER | Name of subset‡
PID | Program ID
RA_TARG | Target right ascension [degrees]
Dec_TARG | Target declination [degrees]
Field | Name of field (GS or GN)
NIRCam_ID | ID of matched NIRCam source†
RA_NIRCam | NIRCam right ascension [degree]
Dec_NIRCam | NIRCam declination [degree]
x_offset | Intra-shutter target offset [arcsec]
y_offset | Intra-shutter target offset [arcsec]
ObsDate | Date of observations
Priority | Target Priority
assigned_Prism |  True if has prism observations
… | …
assigned_G395H |  True if has G395H observations
nDither_Pr | Number of dithers for prism
nDither_Gr | Number of dithers for gratings
nInt_Prism | Number of integrations for prism
… | …
nInt_G395H | Number of integrations for G395H
tExp_PRISM | Exposure time for prism [s]
… | …
tExp_G395H | Exposure time for F290LP/G395H [s]
DR_flag |  True if problem in reduction or shorts
PRISM_flux_flag |  True if at least one line flagged
z_Spec | Redshift (both prism and gratings)
z_Spec_flag | Redshift flag (both prism and gratings)
z_PRISM | Prism-based redshift
C4_1549_flux |  C iv1549,1551 flux
C4_1549_err |  C iv1549,1551 flux uncertainty
… | …
PaA_18751_flux |  Pa flux
PaA_18751_err |  Pa flux uncertainty

‡ NIRSpec_ID are not unique in the table, but the combination of NIRSpec_ID
and TIER is unique. † NIRCam_IDs are unique, but whether they match the
NIRSpec_IDs depends on target selection (vs selection), as well as on whether
the NIRCam catalog was revised after the NIRSpec observation (which may result
in sources being lost to blending and to crossing the non-detection
threshold).

##  8 medium-resolution gratings emission-line fluxes

We fitted the medium-resolution spectra using
QubeSpec’s333https://github.com/honzascholtz/Qubespec fitting module. Each
emission line was fitted using a single Gaussian component and the continuum
was fitted as a power law. This simplistic approach is sufficient for
describing a narrow range of the continuum around an emission line of interest
($\pm 100~{}\AA$), because usually the continuum is poorly detected. The
majority of the emission lines are fitted in isolation except for a group of
emission lines that are close to each other. We show the full list of emission
lines fitted in this work and the groups fitted together in Table 9.

Table 9: List of the emission lines fit in the medium-resolution grating spectra. All wavelengths are in vacuum. Rows connected by curly braces indicate emission lines that were fitted using the same redshift and FWHM during the same fit because they are sufficiently close in wavelength that the continuum can be modeled simultaneously.  | Line(s) |  $\lambda$ [Å] | Column name
---|---|---|---
{3*[] | C iv1549,1551 | 1549.48 | `C4_1549`
| He ii1640 | 1640.00 | `He2_1640`
| O iii]1661,1666 | 1663.00 | `O3_1663`
| C iii]1907,1909 | 1907.71 | `C3_1907`
{2*[] | [O ii]3726,3729 | 3728.49 | `O2_3727`
| [Ne iii]3869 | 3869.86 | `Ne3_3869`
| H | 3728.49 | `HD_4102`
{2*[] | H | 4341.65 | `HG_4341`
| [O iii]4363 | 4363.44 | `O3_4363`
{2*[] | H | 4862.64 | `HB_4861`
| [O iii]4959,5007 | 4960.30,5008.24 | `O3_5007`
| He i5875 | 5877.25 | `He1_5875`
| [O i]6300,6363 | 6302.05 | `O1_6300`
{3*[] | H | 6564.52 | `HA_6563`
| [N ii]6549,6584 | 6585.27, 6549.86 | `N2_6584`
| [S ii]6717,6730 | 6718.29, 6732.67 |  `S2_6718`, `S2_6732`
| He i7065 | 7067.14 | `He1_7065`
| [S iii]9069,9532 | 9071.10,9533.20 |  `S3_9069`,`S3_9532`
| Pa | 10052.12 | `PaD_10049`
| He i10829 | 10832.06 | `He1_10829`
| Pa | 10940.98 | `PaG_10938`
| Pa | 12821.43 | `PaB_12818`
| Pa | 18755.80 | `PaA_18751`

To estimate the model parameters we use QubeSpec, a Bayesian modeling code
implemented with the Markov-Chain Monte-Carlo (MCMC) integrator emcee
(Foreman-Mackey et al., 2013). To measure the emission-line fluxes, we need to
set prior probabilities for each of the variables. The peaks of the Gaussian
profiles and the continuum normalization are given a log-uniform prior, while
the FWHMs are set to a uniform distribution spanning from the minimum spectral
resolution of the NIRSpec/MSA ($\sim$200 ) up to a maximum of 800 . The prior
on the redshift was a truncated normal distribution centered on the redshift
from the visual inspection and with a standard deviation of 300 and with a
maximum allowed deviation of 1,000 .

For emission lines that lie in the overlap of the gratings, we fit both sets
of the data and report the properties of the fit with the highest SNR. We do
not attempt to stack these spectral overlaps due to different line spread
function and potential flux calibration offsets between the gratings. This
will be further investigated in a future data release.

We fit only a single Gaussian per emission line in the medium-resolution
grating. We note that there are some objects with detected outflows or broad
line regions. These fits will be further investigate in I. Juodžbalis et al.
(in prep.) and S. Carniani et al. (in prep.).

After the initial fitting run, we visually inspect every model for any
incorrect fits or spurious line detection that are caused by unflagged
outliers. These flagged fits are then refitted and re-inspected. The fluxes
are calculated using the MCMC chains (after discarding the burn-in chains) and
the final reported values and their uncertainties are the median value and
standard deviation from the chains. We only report detections at SNR$>5$; for
non-detections we report the 1-uncertainties for the user to define their own
upper limits. The final redshift from the medium-resolution spectra is the
redshift inferred from the best detected emission line.

The structure of the gratings emission line catalog is presented in Table 10
in units of $\times 10^{-18}$ erg s-1 cm-2. The names of the individual
emission lines column are the same as reported in Table 9. For the emission
line doublets with fixed line ratios (such as [N ii]6549,6584 and [O
iii]4959,5007) we only report the flux of the stronger emission line.
Alongside the fluxes and their uncertainties, the initial rows are the same as
for the prism table.

Table 10: Structure of the gratings flux table. The initial rows are the same
as for the prism (between NIRSpec_ID and z_PRISM; cf. Table 8); all fluxes are
in units of [-18].

Column name | Description
---|---
NIRSpec_ID | ID of the target in eMPT‡
TIER | Name of subset‡
… | …
tExp_G395H | Exposure time for F290LP/G395H [s]
z_Spec | Redshift (both prism and gratings)
z_Spec_flag | Redshift flag (both prism and gratings)
z_PRISM | Prism-based redshift
C4_1549_flux |  C iv1549,1551 flux
C4_1549_err |  C iv1549,1551 flux uncertainty
… | …
PaA_18751_flux |  Pa flux
PaA_18751_err |  Pa flux uncertainty

‡ NIRSpec_ID are not unique in the table, but the combination of NIRSpec_ID
and TIER is unique.

##  9 Quality assessment

###  9.1 Redshift combination and comparison: prism vs medium gratings

In Figure 8 we compare the redshift measurements from the prism and from the
medium gratings, where both are available. Having defined $\Delta z\equiv
z_{\mathrm{prism}}-z_{\mathrm{gratings}}$, we find a mean of $0.0042\pm
0.0002$ and a standard deviation of 0.079, consistent with the findings of
B23. This statistically significant offset points to a residual wavelength
calibration problem in the prism or medium gratings. We find the offset to be
redshift independent; a line fit with the robust least trimmed squares
algorithm (using the python implementation ltsfit; Rousseeuw & Driessen, 2006;
Cappellari et al., 2013) is consistent with a flat slope of $0.0002\pm
0.0001$. The increased scatter at low redshift is a consequence of the rapid
increase of the prism spectral resolution with wavelength; multiplying $\Delta
z$ by $R/(1+z)$ the scatter becomes approximately uniform with $z$ (where $R$
is the prism spectral resolution of the [O iii]5007 line at redshift $z$, and
$z$ is the redshift of each source).

Figure 8: Comparison of redshifts between the prism and the medium-resolution
gratings. We find a redshift-independent offset $\Delta\,z=0.0042$ (blue
line), consistent with B23; the shaded region is the standard deviation. The
increased dispersion at low redshifts is expected from the strong dependence
of the prism spectral resolution with wavelength. The black diamonds are the
moving median; there is some evidence for a reduced bias around $z<1$.

Whenever we have a strong line detection (5 ) in the medium gratings, we adopt
the redshift of this line as the object redshift (flag A). Using a single
emission line is warranted because medium-resolution fits are made only for
galaxies with a visual-inspection flag of 7 (Section 6). We checked that there
are no cases where the grating and prism spectra disagree by more than
$\Delta\,z=0.05$, so we can rule out any mis-identified lines. Large offsets
($|\Delta\,z-\langle\Delta\,z\rangle|>0.015$ (Figure 8) were visually
inspected, and are mostly due to uncertainties in the H-[O iii]4959,5007 blend
and to low signal-to-noise data. If no lines have been detected in the medium
gratings, we use the prism redshift, requiring at least two emission lines for
a secure redshift (flag B), or the combination of a line and/or a strong
continuum break (for a less secure or less precise redshift, flag C). An even
lower class is reserved for redshifts identified as tentative in the visual
inspection; in this case, we report the visual-inspection redshift (flag D).
All other redshifts are assigned -1 (flag E). To summarize, the final redshift
flags are:

  * A

Redshift from at least one emission line in the medium-resolution grating.

  * B

Redshift from two or more prism emission lines.

  * C

Redshift from the continuum, or from the continuum and a single prism emission
line.

  * D

Tentative, from visual inspection.

  * E

No redshift.

Note that the first three flags are the same as in B23.

The combined redshift distribution of the sample is shown in Figure 9, color-
coded by flag. There is a drop in the distribution at $z_{\mathrm{Spec}}\sim
7.5$, which reflects at least in part a similar dearth of targets in the
distribution of photometric redshifts of the targets selected for observation.
The overall distribution of the spectroscopic sample vs magnitude is displayed
in Figure 10 (we show only targets with flag A–C, and with a secure match in
NIRCam). The effect of the cosmic evolution of the luminosity function is
clearly visible. There is a lack of galaxies fainter than 29 mag at redshifts
lower than $z_{\mathrm{Spec}}\lesssim 2$ and higher than
$z_{\mathrm{Spec}}\gtrsim 9.5$; this is caused by both sample selection and
sensitivity as follows. At low redshifts, NIRCam photometry becomes less able
to clearly distinguish line excesses, because the spacing of strong emission
lines reduces as $1+z$; the lower sensitivity of NIRSpec at wavelengths
$\lambda<1~{}\mum$ compounds the problem. At redshifts higher than
$z_{\mathrm{Spec}}\approx 9.5$, instead, the strongest emission lines ([O
iii]4959,5007) are redshifted out of the NIRSpec coverage, so redshifts
measurements rest solely on the Ly drop and on less prominent lines – both of
which are harder to detect in faint targets.

Figure 9: Redshift distribution of the sample, color-coded by the final
redshift flag (Section 9). We note a drop in redshift distribution at
$z_{\mathrm{Spec}}\sim 7.5$; this reflects a similar drop in the distribution
of photometric redshifts of the targets selected for observation.

In Figure 11 we show $M_{\mathrm{UV}}$ vs redshift, for the sample where
magnitudes could be measured directly from the NIRSpec data; to this end, we
used a nominal top-hat filter between rest-frame 1,400 and 1,600 Å. The
resulting magnitudes were corrected for aperture effects upscaling by the
ratio between the observed and synthetic magnitude in the NIRCam band nearest
in wavelength. The color coding is the equivalent width of H +[N ii]6549,6584,
measured directly on the prism data. There are clear trends of EW with both
$M_{\mathrm{UV}}$ at fixed redshift and with redshift at fixed
$M_{\mathrm{UV}}$; the first trend arises from the sub-linear slope of the
star-forming main sequence, where galaxies have lower specific star-formation
rate with increasing stellar mass. The second trend follows the decreasing
normalization of the star-forming sequence with increasing cosmic time.

Figure 10: Redshift vs magnitude distribution of the sample; NIRSpec deep
spectroscopy can measure redshifts for targets fainter than 30 mag.  Figure
11: Redshift vs UV magnitude distribution of the sample, color-coded by the
equivalent width of [O iii]5007. Magnitudes were calculated directly from the
prism spectra, using aperture corrections estimated by comparing the prism
magnitude to the 0.35-arcsec NIRCam magnitude in the filter nearest to rest-
frame 1,500 Å. [O iii]5007 falls outside of the NIRSpec wavelength range at
$z\gtrsim 9.5$.

###  9.2 Flux comparison: prism vs medium gratings

In Figure 12 we compare the flux measurements from the medium gratings to the
corresponding measurements from the prism; the top, middle and bottom rows
show respectively [O ii]3726,3729, [O iii]4959,5007 and H +[N ii]6549,6584.
For [O iii]4959,5007, we consider only galaxies at $z>2$, wherein the prism
catalog the doublet is clearly separated from H; for H +[N ii]6549,6584, we
take the grating measurements of H and [N ii]6584 and add them, upscaling [N
ii]6584 by 1.34 to take into account [N ii]6548. For each set of emission
lines, we consider the ratio $f_{\mathrm{rat}}\equiv
F_{\mathrm{gratings}}/F_{\mathrm{prism}}$, and study this value as a function
of $F_{\mathrm{prism}}$ (left column) and redshift; due to the requirement to
have both prism and medium-grating measurements, redshift is always
$z_{\mathrm{gratings}}$.

For [O ii]3726,3729, we find that the grating fluxes are 17 per cent higher
than the prism values, with large scatter (25 per cent) and a significant
decreasing trend with redshift. We interpret this discrepancy as due to how
the continuum is modeled in the prism; in particular, using an incorrect value
of the spectral resolution can significantly affect the recovered [O
ii]3726,3729 flux in the low-resolution regimes found at low redshift.

For [O iii]4959,5007, we find good agreement (median ratio 1.01) but a large
scatter (albeit smaller than for [O ii]3726,3729, 14 per cent). We believe the
better agreement is due to the fact that the galaxy continuum around 5,000 Åis
relatively featureless, compared to the region of [O ii]3726,3729. We find a
decreasing trend with $F_{\mathrm{prism}}$ (panel b) and an increasing trend
with $z_{\mathrm{grating}}$ (panel d). A partial-correlation analysis confirms
that the redshift correlation is the main one, and that the flux correlation
arises from the anti-correlation between flux and redshift. The fact that the
ratio increases with redshift is in agreement with the findings of Figure 5,
where it seems that the flux discrepancy between the prism and medium-
resolution gratings is smallest in G140M and highest in G395M.

Finally, panels c and f show H +[N ii]6549,6584; the results here are
consistent with what found for [O iii]4959,5007. The significant correlation
between $f_{\mathrm{rat}}$ and flux is driven by the outliers at
$F_{\mathrm{prism}}>60$ [-18]; removing these points also removes the
correlation.

Figure 12 shows jumps in $f_{\mathrm{rat}}$ at certain values of
$z_{\mathrm{grating}}$; for example, in Figure 12e these jumps happen at
$z_{\mathrm{grating}}\approx 2.5$ and 4.7, which correspond to when the
observed wavelength of [O iii]4959,5007 moves respectively from G140M into
G235M, and from G235M into G395M. To estimate the average flux calibration
offset in our data, we divide the sample in three redshift bins, determined by
when [O iii]4959,5007 (our brightest line on average) is observed with G140M,
G235M or G395M (respectively, $0<z_{\mathrm{grating}}<2.5$,
$2.5<z_{\mathrm{grating}}<4.7$ and $z_{\mathrm{grating}}>4.7$). In these bins,
we find an average value of $f_{\mathrm{rat}}$ of $0.90\pm 0.03$, $1.00\pm
0.01$, and $1.10\pm 0.02$. However, the scatter is large, and galaxies with a
clearly detected continuum (e.g., Figs. 5 and 16) show that the flux-
calibration mismatch is wavelength dependent. We did not find statistical
evidence for a dependence of $f_{\mathrm{rat}}$ on the target location on the
MSA.

To further investigate this discrepancy between the medium gratings and prism
we compared the fluxes of emission lines that are observed in two different
gratings. This occurs for lines in the region of the spectrum probed by two
configurations simultaneously; i.e. $1.6\lesssim\lambda\lesssim 1.8~{}\mum$
for G140M/F070LP and G235M/F170LP, and $2.9\lesssim\lambda\lesssim 3.1~{}\mum$
for G235M/F170LP and G395M/F290LP. We find that the average flux ratios are
$f_{\mathrm{G140M/G235M}}=1.07\pm 0.01$ and $f_{\mathrm{G395M/G235M}}=1.01\pm
0.01$. These results seem to contradict the findings from comparing the
emission-line fluxes from the gratings to the prism (where G140M is lower than
the prism, G235M is consistent, and G395M is higher than the prism; Figure
12). However, this analysis focuses on a specific region of the wavelength
range, where two gratings overlap, whereas the emission-line comparison spans
the entire wavelength range of NIRSpec. As shown in Figure 5, the flux-
calibration bias between the prism and G395M seems to be wavelength-dependent.

Figure 12:  Comparison of the emission-line fluxes measured from the prism and
from the medium-resolution gratings, as a function of prism flux (left column)
and redshift (right column). The central inset between the columns are the
median and standard deviation for each emission-line complex (also displayed
as green horizontal lines and shaded regions), while the bottom right corner
of each panel reports the Spearman rank correlation coefficient and associated
p-value. [O ii]3726,3729 (panels a and d) shows the regime where the gratings-
to-prism flux ratio $f_{\mathrm{rat}}$ is dominated by systematics in the
continuum model; [O iii]4959,5007 (panels b and e) illustrates with high
significance the strong correlation between $f_{\mathrm{rat}}$ and redshift,
which indicates the flux-calibration mismatch between prism and gratings is
most severe in G395M. The dotted vertical lines in panels (e) and (f) show the
redshift where [O iii]5007 and H are observed at 2.9 – the bluest wavelength
captured by the G395M/F290LP disperser/filter combination. Finally, H +[N
ii]6549,6584 shows that the total fluxes measured in the prism (where we do
not separate H from [N ii]6549,6584) match well the fluxes measured in the
gratings (where the emission-line complex is well resolved); the trends with
flux and redshift are consistent with what we see for [O iii]4959,5007.

###  9.3 Accuracy of the wavelength calibration

To assess the accuracy of the wavelength calibration, we use the metric
$\Delta v$; for each target, $\Delta v\equiv
v(1<\lambda<2~{}\mum)-v(\lambda>3~{}\mum)$, where $v(1<\lambda<2~{}\mum)$ is
the mean velocity of emission lines with observed wavelengths between 1 and 2
, and $v(\lambda>3~{}\mum)$ is the mean velocity of emission lines with
observed wavelengths between 3 and the maximum wavelength. For this test, we
consider only galaxies with more than three independent emission lines having
$S/N>7$. In Figure 13 we show $\Delta~{}v$ as a function of the intra-shutter
source positions, $\delta x$ (closely aligned along the dispersion direction)
and $\delta y$; the gray dots are individual galaxies, the green line with
bands is a robust least-squares fit (Cappellari et al., 2013). For an unbiased
solution, both the zero-point and slope of the best-fit line should be zero.
In contrast, we find an average zero-point offset of 300 (for the prism) and
30 (for the gratings); these values correspond to 0.1–1 pixels (prism) to
0.1–0.3 pixels (medium gratings). These offsets indicate an overall bias of
the wavelength solution. In addition to this zero-point offset, there is also
a clear negative correlation between $\Delta v$ and $\delta x$ for both prism
and gratings. The correlation for the prism is both stronger and more
statistically significant, reaching an excursion of 0.5–3 pixels. Before
interpreting these correlations, we remark that the coordinates of the MSA are
opposite to the pixel coordinates of the detector on the focal plane assembly
see Ferruit et al., 2022, their Figure 4; and Jakobsen et al., 2022, their
Figure 4; this means that positive $\delta x$ are offset toward bluer
wavelength values. With this in mind, the anti-correlation we find means that
the correction to the wavelength solution due to intra-shutter offsets of
compact sources (Section 5 and Ferruit et al., 2022) is insufficient. A
solution to these remaining calibration issues is beyond the time constraints
of this data release, and will be presented in a future work. In Appendix C we
further show that on average the wavelength bias of the prism depends on the
global position of the source in the MSA.

Figure 13: Wavelength calibration bias as a function of intra-shutter source
position; for both the prism (top) and medium gratings (bottom). $\delta x$ is
the spatial offset of the target with respect to the center of the micro
shutter, measured along the dispersion direction. For an unbiased wavelength
solution, we would expect $\Delta v=0$ at $\delta x=0$ and $\delta y=0$, and
no correlation. The observed correlation with $\delta x$ means that the
correction we adopted for the intra-shutter source position is insufficient
see Section 5 and Ferruit et al., 2022; $\delta x$ increases toward bluer
wavelengths. Neglecting intra-shutter source position entirely would result in
an even larger bias than what reported here.

##  10 Using the NIRSpec data products

In this section, we provide a concise summary on how to use the data products,
and a list of the limitations of the current data release. The prospective
user of the data provided in this release is encouraged to consider these
limitations carefully.

  * •

Data reduction problems and short circuits. Spectra with data reduction
problems are flagged with `DR_flag`=`True`; if a redshift was given, it is
guaranteed to be accurate from visual inspection. However, for any purpose
other than redshift, these data should be visually inspected to assess whether
they are suitable. Under this flag we also collect observations affected by
MSA short circuits, which may present abnormally bright background, including
steep background gradients across the detector. In all cases, this results in
lower signal-to-noise spectra than one would expect given the source
magnitudes and integration time. In the most severe cases, shorts cause
incorrect background subtraction and no useful observations (Section 3,
Appendix A).

  * •

Aperture correction. Aperture corrections assume the target has point-source
geometry; for extended sources, this implies both the total flux and the color
of the spectrum are inaccurate. For calculating emission-line ratios over long
wavelength separations (e.g., H/H, [O iii]5007/[O ii]3726,3729, Pa/H), we
recommend using aperture corrections derived from the photometry. Sources more
extended than one shutter should be considered with particular care, or even
excluded (see Background subtraction).

  * •

Background subtraction. The background subtraction strategy is optimized for
compact sources; while shutters affected by contaminants are pre-identified
and not considered in the subtraction, shutters affected by the same source
cause self subtraction. Depending on the source size and spatial gradients,
this may bias the shape of the spectrum and the total flux.

  * •

Noise spectrum. The noise spectrum is based on variance-conserving resampling,
to mitigate the effect of correlated noise (Dorner, 2012). A full analysis of
correlated noise in NIRSpec will be presented in a future work (P. Jakobsen,
in prep.)

  * •

Wavelength calibration. There is a discrepancy between the wavelength
calibration of the prism and gratings (causing a typical $\Delta z=0.0042$;
Figure 8). In addition, we find an overall offset in the wavelength
calibration of both the prism (mean value 300 ) and for the gratings (mean
value 30 ). We apply a correction for the wavelength offset due to the intra-
shutter position of each source, but there is still a residual bias. After
correcting empirically for this bias, we show that the prism wavelength offset
depends on the spatial location in the MSA (Section 9.3 and Appendix C); for
the medium gratings the residual wavelength dependence on the intra-shutter
position is milder, and we find no detectable trend with spatial location on
the MSA.

  * •

Flux calibration. The relative flux calibration between the prism and gratings
is accurate to within 15 per cent, and depends on the wavelength (e.g.,
figures 5, 16, 12). The user is encouraged to consider this problem when
measuring flux ratios, particularly when comparing between different
dispersers and wavelengths.

##  11 highlights

The diversity of the JADES DR3 spectroscopic sample is illustrated by
comparing some highlights (Figs. 14–20). In Figure 14 we show 200733, an
example of a low-redshift quiescent galaxy at $z=2.86$. NIRCam photometry
(panel a) indicates a smooth, peaked light profile with an extended halo,
suggesting a high ($n\gtrsim 2$) Sérsic index and, therefore, a dynamically
evolved system. The 1-d spectrum (panel c) displays the signatures of an
evolved stellar population, with a distinct 4000-Åbreak – typical of old
($>1.5$ Gyr), metal-rich stellar populations, accompanied by a possible,
fainter Balmer break ($\approx 3750~{}\AA$), indicating a younger (0.5–1 Gyr
old) stellar population. The galaxy displays a number of stellar absorption
features with high equivalent width; the Balmer series, Mg i5167–5184, and the
‘calcium triplet’, Ca ii8498–8662.

Figure 14: A low-redshift quiescent galaxy, displaying a smooth, peaked light
distribution and an evolved stellar population. Panel (a) shows a false-color
NIRCam image, with the adopted filters in the bottom right corner. Panel (b)
is the NIRSpec/MSA 2-d signal-to-noise map of the prism spectrum; three
shutters are indicated. Here and in all other 2-d maps (Figs. 15b–20b),
negative signal-to-noise is caused by the nod-and-subtract strategy for
removing the background. Panel (c) is the 1-d, 5-pixel box-car extracted
spectrum. Besides the strong 4000-Åbreak, several stellar and ISM absorption
features are detected. Notice that the aperture correction applied in this
data reduction is optimized for point-like sources; extended galaxies like
200733 would require both a different aperture correction and a different
background-subtraction strategy, to avoid self subtraction. From Z. Ji et al.
(in prep.).

At slightly higher redshift ($z=2.95$) we find 1000721 (Figure 15), an AGN-
host galaxy with type-1 AGN and high-velocity outflows, a secure signature of
an active supermassive black hole. The host galaxy is clearly seen in NIRCam;
the lack of flux in F090W (cf. Figure 14a) indicates high dust reddening,
which is indeed seen in the NIRSpec data (panel c). Emission from the [O
iii]4959,5007 and H +[N ii]6549,6584 complexes is spatially extended (panel
b), indicating a resolved disc or narrow-line region. In the medium-resolution
grating spectra (blue line in panel c) the presence of an outflow can be
clearly seen in [O iii]4959,5007 and [S ii]6717,6730, while H shows evidence
of both ionized gas outflows and a broad line region. The prism spectrum
(black line) reveals a Balmer break, indicating that the continuum emission is
dominated by stars; a number of auroral lines is readily detected in rest-
frame $r-$ and $Y-$bands.

Figure 15: Example of a dust-reddened AGN host, with clear outflows (as seen
in the broad component of forbidden [O iii]4959,5007, [S ii]6717,6730 and [S
iii]9069,9532), stellar continuum (there is evidence of a Balmer break) and
high temperature (from the detection of several auroral lines, [O ii]7319–7332
and [S ii]10290–10373). The blue line in panel c is the (spliced) medium-
resolution grating spectra (gaps are due to the gaps between the NIRSpec
detectors). The black rectangles show details of the H–[O iii]4959,5007 and
H–[N ii]6549,6584–[S ii]6717,6730 emission-line regions. All other symbols are
the same as Figure 14.

‘Pablo’s Galaxy’ (197911, GS-10578; Fig 16) is a marvelous massive galaxy at
$z=3.06$, identified as quiescent via the UVJ color-color diagram (Williams et
al., 2009). This extraordinary galaxy displays stellar rotation (D’Eugenio et
al., 2023a), an X-ray and MIR-detected type-2 AGN (Circosta et al., 2019),
fast ionized-gas outflows in [O iii]5007, and neutral-gas outflows with high
mass loading (Na i5890,5896 absorption in panel c; D’Eugenio et al., 2023a).
Medium-resolution observations spanning the entire NIRSpec wavelength enable
the study of Mg ii2796,2803 emission and absorption, high-ionization species
([Ne v]3346,3426), electron densities ([O ii]3726,3729 and [S ii]6717,6730),
and stellar -elements abundance (Mg i5167–5184, Ca ii8498–8662). Stringent
upper limits on Pa disfavor a dust-obscured starburst.

Figure 16: ‘Pablo’s Galaxy’, a UVJ-quiescent galaxy at $z=3$ hosting an X-ray
and MIR AGN and multi-phase outflows. This system displays a rich set of
stellar and ISM absorption lines (Mg ii2796,2803 and Na i5890,5896, the latter
tracing a fast neutral-gas outflow; D’Eugenio et al., 2023a). Emission lines
trace both low-ionization gas [O ii]3726,3729, [O i]6300,6363, [N
ii]6549,6584, [S ii]6717,6730 – possibly due to shocked or stripped gas, as
well as higher-ionization species ([Ne v]3346,3426). Note the flux-calibration
offset between G140M (blue) and G235M (light blue: 10 per cent) and G235M and
G395M (blue; 7 per cent). Like 200733 (Figure 14), Pablo’s galaxy is fairly
extended, meaning the standard aperture correction and background subtraction
are not optimal. From J. Scholtz et al. (in prep). All symbols are the same as
Figure 14.

1080660 is an example of a higher-redshift quiescent galaxy at $z=4.4$ (Figure
17), among the highest-redshift quiescent galaxies known (cf. Carnall et al.,
2023; Nanayakkara et al., 2022). NIRCam (panel a) shows two interlopers (north
west; photometric redshift 2.5) and a possible dusty companion to the east
(1080661, with photometric redshift 3.8, but this value is highly uncertain
due to the dusty nature of this target). 1080660 itself has an evolved
morphology, consisting of a bright central core and more extended emission
along the north-east–south-west direction; the extended emission appears
significantly redder than the core (green vs white in the false-colour image
of panel a), suggesting a possible central starburst, as seen in some local
post-starburst galaxies (D’Eugenio et al., 2020) and, recently, in NIRCam
imaging (Wright et al., 2023). The spectrum (panel c) exhibits a clear Balmer
break, H, H and H absorption, and [O iii]4959,5007 and H +[N ii]6549,6584
emission of relatively low equivalent width. There is no evidence for
spatially extended nebular emission in the 2-d (panel c).

Figure 17: Example of a quiescent galaxy at $z=4.4$, ID 1080660. This object
displays a complex morphology with both a blue core (white in panel a) and a
redder extended component (in green). The prominent Balmer break and flat
rest-UV spectrum indicate this galaxy is an early quiescent system. We see
clear emission from the
$\text{H\textalpha}+\text{[{N}\,{\sc{ii}}]{\textlambda}{\textlambda}{6549,}{6584}}$
blended complex, from [O iii]4959,5007, and from Mg ii2796,2803, indicating
AGN activity and, possibly, ongoing outflows, as seen in quiescent galaxies at
lower redshifts. All symbols and panels are the same as Figure 14.

Figure 18 shows 1028761, a merger between a relatively un-obscured galaxy and
a dusty galaxy at $z=6.76$. This system displays high values of the emission-
line ratios [O ii]3726,3729/[O iii]5007, [N ii]6584/H and [S ii]6717,6730/H,
characteristic of high-metallicity gas or shock-dominated emission.

Figure 18: Example of a high-redshift galaxy with high metallicity and/or
signatures of shocked gas, ID 1028761. The NIRSpec 2-d signal-to-noise map
(panel b) clearly reveals spatially extended emission; a different background
subtraction strategy is clearly required for this class of targets; in cases
like this, using the provided emission-line fluxes may result in unphysical
line ratios, or harder-to-identify bias. From A. Cameron et al. (in prep.).
All symbols and panels are the same as Figure 14.

In Figure 19 we show 99915, which displays a strong single emission line at
4.8 . The line is also seen in the G395M grating spectrum, and in the NIRCam
grism spectrum (F. Sun et al., in prep.), which rules out an artifact. The
galaxy also shows a photometric drop between the NIRCam F090W and F150W
filters, which at face value rules out a solution where the emission line is
H. Identifying the line as either H or [O iii]5007 would match the Ly drop
seen in NIRCam, but is not without problems. If the line was H, we would
expect to observe H at about half the H flux; similarly, the tentative
solution at $z=8.69$, which identifies the observed line with [O iii]5007,
would require observing [O iii]4959 at about one third of the line flux. None
of these accompanying lines (the putative H nor [O iii]4959) are seen either
in prism, grating, or NIRCam grism, leaving this object as a tentative
redshift determination.

Figure 19: Example of a high-redshift galaxy with uncertain redshift, ID
36424. The clear emission line at 4.8 could be identified as H, but this would
be inconsistent with the photometric break between F090W and F115W. A
tentative identification of the line as [O iii]5007 is presented here; while
consistent with the photometric break, this solution is itself problematic due
to the missing [O iii]4959. We can rule out an artifact, because the line is
also detected in our G395M spectrum and in the NIRCam grism from FRESCO; Oesch
et al., 2023a; see also F. Sun et al., in prep.. All symbols and panels are
the same as Figure 14.

Finally, in Figure 20 we show JADES-GS-z12-0 (Curtis-Lake et al., 2023;
Robertson et al., 2023b), which at the time of this data release is the
highest-redshift detection of a metal emission line (D’Eugenio et al., 2023b).
The data included in this release consists separately of observations from PID
3215 and PID 1210. In this data release, we use the redshift from D’Eugenio et
al. (2023b), based off clearly detected C iii]1907,1909 emission. This value
is lower than the redshift reported in our previous articles Curtis-Lake et
al., 2023, Robertson et al., 2023b, B23; the latter was measured from the
wavelength of the Ly drop, assuming only IGM absorption, and the discrepancy
with the C iii]1907,1909 redshift is explained by DLA (Damped Ly absorption;
e.g., Wolfe et al., 2005). Increasing evidence is building up that a
substantial fraction of $z>10$ galaxies may have DLA absorption (e.g., Heintz
et al., 2023), which may bias photometric and Ly-drop redshifts to higher
values e.g., D’Eugenio et al., 2023b; see especially Hainline et al., 2024.

Figure 20: One of the highest-redshift sources in this data release, 96216
JADES-GS-z12-0, Curtis-Lake et al., 2023; Robertson et al., 2023b; 2773, B23.
This system shows the highest-redshift detection of metals to date (D’Eugenio
et al., 2023b). All symbols and panels are the same as Figure 14.

##  12 Conclusions

In this work, we presented new and updated JADES NIRCam and NIRSpec
observations obtained up until October 2023 in the two GOODS fields. The
spectra include both medium-depth and deep observations up to redshift $z\sim
13$, reaching the deepest unlensed spectroscopic observations to date (up to
45 hours on source). The sample size and data quality of the spectra are a
testament to the success of the NIRSpec/MSA instrument. The high success rate
of the redshift identification validates the selection criteria, including the
quality of NIRCam data, the accuracy of the photometric analysis, and the
precision of the photometric redshift determination with and .

We release fully reduced and calibrated images and spectra, and present
catalogs of photometry, photometric redshifts, spectroscopic redshifts, and
emission-line fluxes. Insight from this large sample enabled us to pin down
some remaining challenges in the data reduction: a mismatch in the redshift
and flux-calibration between different dispersers, background subtraction and
slit-loss corrections appropriate for extended sources, and residual
wavelength calibration issues. Future calibration programs will certainly
address these problems. An additional challenge is deriving an accurate
selection function for correctly weighting each galaxy, which we will provide
in the next data release.

In the future, significantly larger samples of the general galaxy population
in the redshift and mass range probed by JADES would require a substantial
investment of time, or a revised observing strategy. Larger samples of
specific classes of objects will still be crucial for understanding rare
types, where JADES has only hinted at the potential (e.g., high-redshift
quiescent galaxies, extremely reddened galaxies, little red dots, $z>8$ Ly
emitters).

In the meantime, the current sample is the largest extragalactic sample with
low- and medium-resolution spectroscopy spanning 0.6–5.3 ; the depth of the
medium and deep spectra, and the synergy with medium- and wide-band imaging,
enables for the first time a statistical study combining morphology and rest-
frame optical spectroscopy of galaxies between the peak of the star-formation
rate density and the first few hundred Myr after the Big Bang.

FDE, JS, RM, TJL, JW, WMB, XJ, IJ and CS acknowledge support by the Science
and Technology Facilities Council (STFC), by the ERC through Advanced Grant
695671 “QUENCH”, and by the UKRI Frontier Research grant RISEandFALL. RM also
acknowledges funding from a research professorship from the Royal Society.
AJC, AJB, JC, AS and GCJ acknowledge funding from the “FirstGalaxies” Advanced
Grant from the European Research Council (ERC) under the European Union’s
Horizon 2020 research and innovation programme (Grant agreement No. 789056)
SC, EP and GV acknowledge support by European Union’s HE ERC Starting Grant
No. 101040227 - WINGS. ECL acknowledges support of an STFC Webb Fellowship
(ST/W001438/1). SA, BRP and MP acknowledge support from Grant
PID2021-127718NB-I00 funded by the Spanish Ministry of Science and
Innovation/State Agency of Research (MICIN/AEI/ 10.13039/501100011033). MP
also acknowledges support from the Programa Atracción de Talento de la
Comunidad de Madrid via grant 2018-T2/TIC-11715. CNAW, BER, BDJ, DJE, PAC, EE,
MJR and FS acknowledge JWST/NIRCam contract to the University of Arizona
NAS5-02015. BER also acknowledges support from the JWST Program 3215. DJE is
supported as a Simons Investigator. The Cosmic Dawn Center (DAWN) is funded by
the Danish National Research Foundation under grant DNRF140. ST acknowledges
support by the Royal Society Research Grant G125142. HÜ gratefully
acknowledges support by the Isaac Newton Trust and by the Kavli Foundation
through a Newton-Kavli Junior Fellowship. This research is supported in part
by the Australian Research Council Centre of Excellence for All Sky
Astrophysics in 3 Dimensions (ASTRO 3D), through project number CE170100013.
ALD thanks the University of Cambridge Harding Distinguished Postgraduate
Scholars Programme and Technology Facilities Council (STFC) Center for
Doctoral Training (CDT) in Data intensive science at the University of
Cambridge (STFC grant number 2742605) for a PhD studentship. Funding for this
research was provided by the Johns Hopkins University, Institute for Data
Intensive Engineering and Science (IDIES). PGP-G acknowledges support from
grant PID2022-139567NB-I00 funded by Spanish Ministerio de Ciencia e
Innovación MCIN/AEI/10.13039/501100011033, FEDER, UE. DP acknowledges support
by the Huo Family Foundation through a P.C. Ho PhD Studentship. MSS
acknowledges support by the Science and Technology Facilities Council (STFC)
grant ST/V506709/1. RS acknowledges support from a STFC Ernest Rutherford
Fellowship (ST/S004831/1). NCV acknowledges support from the Charles and Julia
Henry Fund through the Henry Fellowship. The research of CCW is supported by
NOIRLab, which is managed by the Association of Universities for Research in
Astronomy (AURA) under a cooperative agreement with the National Science
Foundation. This work was performed using resources provided by the Cambridge
Service for Data Driven Discovery (CSD3) operated by the University of
Cambridge Research Computing Service (www.csd3.cam.ac.uk), provided by Dell
EMC and Intel using Tier-2 funding from the Engineering and Physical Sciences
Research Council (capital grant EP/T022159/1), and DiRAC funding from the
Science and Technology Facilities Council (www.dirac.ac.uk) The authors
acknowledge use of the lux supercomputer at UC Santa Cruz, funded by NSF MRI
grant AST 1828315. This study made use of the Prospero high performance
computing facility at Liverpool John Moores University.

http://dx.doi.org/10.17909/8tdj-8n28 (catalog 10.17909/8tdj-8n28).

##  Appendix A Observations affected by MSA short circuits

Some observations in this data release were affected by MSA short circuits, or
‘shorts’. For most programs, these were only a minority, and were excluded
from the data reduction (e.g., PID 3215, Section 3.6). However, for PID 1180
shorts affected two thirds of the initial observations. The brightness of the
shorts emission can vary drastically between occurrences: the brightest can
render the entire integration unusable (Figure 21), while the faintest may
contaminate only a few sources near the affected region of the field of view.

Figure 21: Imaging count-rate maps from NIRSpec detector NRS1 carried out
during one of the visit of program PID 1180. Left and right panels illustrate
the effect of the short circuits on the count-rate maps before (left) and
after (right) background subtraction.

As we argued in Section 3.2, these shorts-contaminated data are still useful
to measure redshifts, and are included in this data release (Figure 22).

There are three major ways in which shorts affect the quality of the data.
First, shorts increase the background level, reducing the signal-to-noise of
the observations. Second, the shorts’ background is tied to a given exposure,
and, therefore can vary between different sky and nod positions; this causes
our background subtraction to fail, because our strategy assumes the same
background between different nods. Third, the combination of increased and
varying background affects subsequent steps of the pipeline, which can cause
excessive or insufficient outlier removal. Users are encouraged to treat these
observations with caution, particularly for measurements using the spectral
continuum. The flag `DR_flag` in the published tables identifies all spectra
where there is a problem in the data reduction, or where the observations were
affected by shorts (including when the actual contamination is low).

Figure 22: A selection of targets from program 1180, comparing a successful
observation (panels a–c) and an observation affected by ‘shorts’ (panels d–l).
The successful observation shows the expected signal-to-noise both in the 2-d
map (panel b) and in the extracted 1-d spectrum (panel c). In contrast, panels
(e), (h) and (k) show clear problems. Panel (e) shows different background
levels between different integrations (captured at different nod positions,
resulting in horizontal striping). Panels (h) and (k) have similar background
to panel (b), but the shorts affect the data reduction pipeline in other ways.
For 148429, the shorts are likely responsible for the excess of outliers in
the region 1–2 ; nevertheless, the data can still be used to measure the
galaxy redshift. For 212327 (whose redshift is known from the medium-
resolution grating), the shorts caused a mis-alignment of the already extended
source; the resulting data is unusable.

Figure 22: (continued).

##  Appendix B Comparison with DR1

In this section, we compare the measurements obtained by applying the
algorithms used in this data release (DR3), to the data previously released in
DR1 (B23). We remark that NIRSpec DR1 and DR3 use the same data reduction,
hence the spectra are exactly the same as in B23. In Figure 23 we compare the
flux (top rows) and uncertainties (bottom rows) between DR3 and DR1. We find
excellent agreement for the overall flux measurements, even though some bright
lines ($F>$[-18][], panel a) display highly significant differences.

Unlike for flux, the measurement uncertainties from DR3 and DR1 are different,
with the present values 3 per cent smaller. We find a statistically
significant trend with flux, suggesting the mismatch in the uncertainties is
due to systematics at the bright end of the sample; adding 1 per cent
systematic uncertainty to the flux measurements removes this correlation (gray
points in panel d).

The agreement presented in Figure 23 varies from line to line; in Figure 24,
as an example, we show H. Here the DR3 fluxes are 4 per cent higher, with the
discrepancy correlating with flux and decreasing with redshift, as expected
from the continuum correction applied by .

Figure 23: Comparison of the prism emission-line fluxes and their
uncertainties between DR1 and DR3; the top row compares the fluxes, the bottom
row compares the uncertainties. We find a statistically significant
correlation between the ratio of uncertainties and flux (panel d), indicating
that our implementation tends to underestimate the line noise in the high-S/N
regime, relative to DR1. The gray points in the bottom panel illustrate the
effect of adding 1 per cent relative uncertainties to the DR3 flux
measurements.  Figure 24: Comparison of the H flux measured from the prism
between DR1 and DR3; the symbols are the same as in Figure 23. Below redshift
$z=2$, H is blended with [O iii]4959,5007 and is not reported in this figure.
The H flux ratio between DR3 and DR1 is 1.04, while for all emission lines the
ratio is 1.003.

A similar result is found for the medium-resolution gratings, where again we
find excellent agreement in the overall flux (within 1 per cent), but smaller
uncertainties (by 16 per cent).

Figure 25: Comparison of the medium-resolution emission-line fluxes and their
uncertainties between DR1 and DR3; the top row compares the fluxes, the bottom
row compares the uncertainties. Fluxes are in excellent agreement, while we
find the uncertainties to be smaller by 20 per cent, on average.

##  Appendix C Field dependence of the prism wavelength calibration bias

In this appendix, we show that the bias in the wavelength calibration of the
prism spectra has a residual dependence on the source position within the MSA.
We calculate $\Delta v$ for each galaxy (with the same definitions and cuts as
in Section 9), then average $\Delta v$ inside each of the four MSA quadrants.
This reveals a bias only in quadrant 2 (hereafter: Q2), with a 4-detection. We
then use the best-fit linear relation of Figure 13 to remove the wavelength
bias trend with $\delta x$, and repeat the test. The results are shown in
Figure 26; there is a clearly detected zero-point offset in $\langle\Delta
v\rangle$, as expected from Figure 13. The offset is smallest in Q3
(2.5-significance) and highest in Q2 (8 ), with intermediate values in Q1 and
Q4 (3- to 4-significance). The diagonal direction from Q3 to Q2 is also the
direction where the field-dependent spectral resolution increases, suggesting
that the observed bias could be due to insufficient correction of the
wavelength bias due to intra-shutter source position.

Figure 26: Wavelength solution bias $\Delta v$ as a function of location on
the MSA; the four rectangles are the four MSA quadrants Q1–Q4, with the
location of the sample galaxies marked by small gray dots. The large dots
represent galaxies with more than four emission lines detected at $S/N>5$, for
which we could calculate the velocity offset between lines observed at
wavelengths $1-2$ and lines observed at $3-5.3~{}\mum$. $\Delta v$ has been
corrected empirically for residual intra-shutter wavelength bias (Section 9.
$\langle\Delta v\rangle$ is the average of $\Delta v$ inside each of the
quadrants, illustrating a trend of increasing bias diagonally from Q3 to Q2.

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