# The JWST-SUSPENSE Ultradeep Spectroscopic Program:
Survey Overview and Star-Formation Histories of Quiescent Galaxies at
$\boldsymbol{1<z<3}$

Martje Slob Leiden Observatory, Leiden University, P.O. Box 9513, 2300 RA Leiden, The Netherlands  Mariska Kriek Leiden Observatory, Leiden University, P.O. Box 9513, 2300 RA Leiden, The Netherlands  Aliza G. Beverage Department of Astronomy, University of California, Berkeley, CA 94720, USA  Katherine A. Suess NHFP Hubble Fellow  Kavli Institute for Particle Astrophysics and Cosmology and Department of Physics, Stanford University, Stanford, CA 94305, USA  Guillermo Barro Department of Physics, University of the Pacific, Stockton, CA 90340 USA  Rachel Bezanson Department of Physics and Astronomy and PITT PACC, University of Pittsburgh, Pittsburgh, PA 15260, USA  Chloe M. Cheng Leiden Observatory, Leiden University, P.O. Box 9513, 2300 RA Leiden, The Netherlands  Charlie Conroy Center for Astrophysics | Harvard & Smithsonian, Cambridge, MA, 02138, USA  Anna de Graaff Max-Planck-Institut für Astronomie, Königstuhl 17, D-69117, Heidelberg, Germany  Natascha M. Förster Schreiber Max-Planck-Institut für extraterrestrische Physik, Giessenbachstrasse 1, D-85748 Garching, Germany  Marijn Franx Leiden Observatory, Leiden University, P.O. Box 9513, 2300 RA Leiden, The Netherlands  Brian Lorenz Department of Astronomy, University of California, Berkeley, CA 94720, USA  Pavel E. Mancera Piña Leiden Observatory, Leiden University, P.O. Box 9513, 2300 RA Leiden, The Netherlands  Danilo Marchesini Department of Physics & Astronomy, Tufts University, MA 02155, USA  Adam Muzzin Department of Physics and Astronomy, York University, 4700 Keele Street, Toronto, Ontario, ON MJ3 1P3, Canada  Andrew B. Newman Observatories of the Carnegie Institution for Science, 813 Santa Barbara St., Pasadena, CA 91101, USA  Sedona H. Price Department of Physics and Astronomy and PITT PACC, University of Pittsburgh, Pittsburgh, PA 15260, USA  Alice E. Shapley Department of Physics & Astronomy, University of California, Los Angeles, CA 90095, USA  Mauro Stefanon Departament d’Astronomia i Astrofìsica, Universitat de València, C. Dr. Moliner 50, E-46100 Burjassot, València, Spain  Unidad Asociada CSIC ”Grupo de Astrofísica Extragaláctica y Cosmología” (Instituto de Física de Cantabria - Universitat de València)  Pieter van Dokkum Astronomy Department, Yale University, 52 Hillhouse Ave, New Haven, CT 06511, USA  Daniel R. Weisz Department of Astronomy, University of California, Berkeley, CA 94720, USA Martje Slob<EMAIL_ADDRESS>
(Received April 19, 2024)

###### Abstract

We present an overview and first results from the Spectroscopic Ultradeep
Survey Probing Extragalactic Near-infrared Stellar Emission (SUSPENSE),
executed with NIRSpec on JWST. The primary goal of the SUSPENSE program is to
characterize the stellar, chemical, and kinematic properties of massive
quiescent galaxies at cosmic noon. In a single deep NIRSpec/MSA configuration,
we target 20 distant quiescent galaxy candidates ($z=1-3$, $H_{AB}<23$), as
well as 53 star-forming galaxies at $z=1-4$. With 16 hr of integration and the
G140M-F100LP dispersion-filter combination, we observe numerous Balmer and
metal absorption lines for all quiescent candidates. We derive stellar masses
(log$M_{*}/M_{\odot}\sim 10.3-11.5$) and detailed star-formation histories
(SFHs) and show that all 20 candidate quiescent galaxies indeed have quenched
stellar populations. These galaxies show a variety of mass-weighted ages
($0.8-3.0$ Gyr) and star formation timescales ($\sim 0.5-4$ Gyr), and four out
of 20 galaxies were already quiescent by $z=3$. On average, the $z>1.75$
$[z<1.75]$ galaxies formed 50% of their stellar mass before $z=4$ $[z=3]$.
Furthermore, the typical SFHs of galaxies in these two redshift bins
($z_{\text{mean}}=2.2$ and $z_{\text{mean}}=1.3$) indicate that galaxies at
higher redshift formed earlier and over shorter star-formation timescales
compared to lower redshifts. Although this evolution is naturally explained by
the growth of the quiescent galaxy population over cosmic time, we cannot rule
out that mergers and late-time star formation also contribute to the
evolution. In future work, we will further unravel the early formation,
quenching, and late-time evolution of these galaxies by extending this work
with studies on their chemical abundances, resolved stellar populations and
kinematics.

††journal: ApJ††software: Prospector (Leja et al., 2019a; Johnson et al.,
2021), MSAEXP (https://github.com/gbrammer/msaexp), EAZY (Brammer et al.,
2008), Source Extractor (Bertin & Arnouts, 1996), grizli (Brammer, 2023),
MSAFIT (de Graaff et al., 2023)††facilities: JWST (NIRSpec)

##  1 Introduction

One of the most remarkable discoveries in extra-galactic astronomy from the
past two decades is the existence of a population of quiescent galaxies at
$z\sim 2$ and beyond (e.g., Franx et al., 2003; Cimatti et al., 2004; Daddi et
al., 2005; Kriek et al., 2006; Straatman et al., 2014; Glazebrook et al.,
2017; Schreiber et al., 2018; Tanaka et al., 2019; Forrest et al., 2020a, b;
Esdaile et al., 2021; Valentino et al., 2023; Antwi-Danso et al., 2023;
Carnall et al., 2023a, b; Glazebrook et al., 2023; Nanayakkara et al., 2024).
The star-formation rates (SFRs) of these galaxies are strongly suppressed and
their inferred star formation histories (SFHs) indicate that they formed their
stars in vigorous bursts, followed by an efficient quenching process (e.g.,
Kriek et al., 2016; Carnall et al., 2019; Jafariyazani et al., 2020; Forrest
et al., 2020a, b; Valentino et al., 2020; Beverage et al., 2023). Galaxy
formation models struggle to create this galaxy population so early in the
universe’s history (e.g., Schreiber et al., 2018; Cecchi et al., 2019). Hence,
the early and fast formation of these galaxies, as well as the cessation of
star formation within them, has remained a problem.

Their subsequent evolution is also a puzzle. Distant quiescent galaxies are
much more compact and denser than massive early-type galaxies in the present-
day universe (e.g., Daddi et al., 2005; van Dokkum et al., 2008; van der Wel
et al., 2014; Suess et al., 2019, 2021). Furthermore, in contrast to the
majority of present-day quiescent galaxies, they appear to have disk-like
morphologies (van der Wel et al., 2011; Chang et al., 2013), and may be
rotationally supported (Toft et al., 2017; Newman et al., 2018; D’Eugenio et
al., 2023). Two popular scenarios emerged to explain the observed evolution.
In the first scenario, distant quiescent galaxies are the cores of present-day
massive elliptical galaxies (e.g., Naab et al., 2009; Bezanson et al., 2009;
van Dokkum et al., 2010), with their outer regions building up gradually over
cosmic time through a series of minor mergers. This scenario can also explain
how early quiescent disks evolved into the present-day massive elliptical
galaxies, as minor mergers can gradually perturb the ordered rotation
(Bournaud et al., 2007; Naab et al., 2014; Lagos et al., 2018). The minor
merger scenario is also supported by the finding that color gradients increase
with galaxy age (Suess et al., 2020) and that distant quiescent galaxies have
many small companions (Newman et al., 2012; Suess et al., 2023). In a second
popular scenario, the size evolution can be explained by the growth of the
quiescent galaxy population, as galaxies that quench at later times are
predicted to be larger (i.e., progenitor bias; e.g., Khochfar & Silk, 2006;
Poggianti et al., 2013; Carollo et al., 2013; Ji & Giavalisco, 2022). Both
scenarios are thought to play a role in the observed evolution (e.g., Belli et
al., 2017).

However, more recent observations show that this picture may be too
simplistic. First, neither progenitor bias nor minor mergers can explain the
difference in [Fe/H] of 0.2-0.3 dex between distant quiescent galaxies and the
cores of nearby early-type galaxies (Kriek et al., 2016, 2019; Carnall et al.,
2022; Gu et al., 2022; Zhuang et al., 2023; Beverage et al., 2023).
Furthermore, while the cores of nearby massive early-type galaxies are found
to have bottom-heavy initial mass functions (IMFs, Treu et al., 2010; Conroy &
van Dokkum, 2012), the IMF in distant quiescent galaxies appears more
consistent with a Milky Way (i.e., Kroupa, 2001; Chabrier, 2003) IMF (Mendel
et al., 2020; Forrest et al., 2022; Kriek et al., 2023). Thus, these
observations imply that the evolution may be more complicated than initially
proposed and major mergers and/or late-time star formation may play a role as
well.

In order to solve these puzzles and understand the early formation, quenching
mechanism, and late-time evolution of the distant quiescent galaxy population,
we need detailed stellar population, chemical, and kinematic properties of a
significant sample of distant quiescent galaxies. Obtaining such measurements
has been exceedingly challenging, as they rely on ultra-deep rest-frame
optical spectra. Consequently, the above spectroscopic studies are based on
only a few very massive and/or lensed galaxies. Furthermore, despite extreme
integration times with the most efficient near-infrared spectrographs on large
ground-based telescopes, the spectra still have significant uncertainties and
suffer from low spatial resolution. Thus, we have reached the limits of
current ground-based facilities.

With the advent of JWST we are finally capable of breaking this impasse. Early
results have demonstrated the power of JWST to observe distant quiescent
galaxies with the low-resolution mode (Glazebrook et al., 2023; Marchesini et
al., 2023; Nanayakkara et al., 2024; Setton et al., 2024; de Graaff et al.,
2024). Furthermore, using medium-resolution spectroscopy, Belli et al. (2023),
D’Eugenio et al. (2023), and Carnall et al. (2023a) demonstrate the detection
of – primarily Balmer – absorption lines in post-starburst galaxies at
$z=2.445$, $z=3.064$ and $z=4.658$, respectively. While these studies attest
to the unprecedented sensitivity of NIRSpec, they either lack the required
spectral resolution or do not have sufficiently old galaxies to observe the
metal absorption lines needed for detailed stellar population and chemical
abundance studies. Furthermore, the samples are still very small. Thus, the
next step is to obtain larger, medium-resolution samples and target older
quiescent galaxies, for which we can measure a range of metal absorption
lines. Taking full advantage of the multi-plexing capabilities of NIRSpec
(Ferruit et al., 2022) for studying quiescent galaxies, however, remains
challenging due to the low number densities of such galaxies; consequently,
they are rare in spectroscopic surveys executed in the deep legacy fields.

To successfully overcome these challenges and obtain ultradeep, medium-
resolution, rest-frame optical, spatially-resolved spectra of a significant
sample of distant quiescent galaxies, we are conducting the Cycle 1 SUSPENSE
(Spectroscopic Ultradeep Survey Probing Extragalactic Near-infrared Stellar
Emission) program. The SUSPENSE observations were collected with the JWST
NIRSpec/MSA in January 2024. SUSPENSE observes 20 quiescent galaxy candidates
at $1<z<3$ in a single NIRSpec/MSA configuration. The spectra are of
unprecedented depth, show a multitude of Balmer and metal absorption lines,
and extend over the full spatial range of the galaxies.

In this paper we present an overview of the survey, the observational
strategy, the data reduction and analysis, the sample characteristics, and the
SFHs of 20 quiescent galaxies in our sample. The paper is organized as
follows. In Section 2 we present the SUSPENSE survey design and observing
strategy. In Section 3 we discuss the two-dimensional (2D) data processing and
the extraction of the one-dimensional (1D) spectra. Section 4 describes the
stellar populations fitting, the sample characteristics, and the SFHs of the
quiescent galaxy sample. In Section 5 we discuss the implications of our
findings for the evolution of the quiescent galaxy population. Finally, in
Section 6 we present a summary.

Throughout this work we assume a $\Lambda$CDM cosmology with $\Omega_{\rm
m}=0.3$, $\Omega_{\Lambda}=0.7$, and $H_{0}=70\rm\,kms^{-1}\,Mpc^{-1}$. All
magnitudes are given in the AB-magnitude system (Oke & Gunn, 1983).

##  2 Observations

###  2.1 Target Selection and MSA Configuration

The JWST-Cycle 1 SUSPENSE program (ID 2110) aims to characterize the stellar,
chemical, and kinematic properties of the distant quiescent galaxy population.
To reach this goal, we require ultradeep rest-frame optical spectra of a
significant sample of distant quiescent galaxies. The NIRspec MSA’s
unparalleled sensitivity and multiplexing capabilities make it ideally suited
to this task. However, fully utilizing its multiplexing capabilities depends
on identifying a field with a high density of these galaxies.

To this end, we used the large overlapping area of the UltraVISTA (McCracken
et al., 2012; Muzzin et al., 2013a), COSMOS (F814W; Scoville et al., 2007) and
COSMOS-DASH (F160W Mowla et al., 2018; Momcheva et al., 2017; Cutler et al.,
2022) surveys to optimize our pointing. We first identified all
$z_{\text{phot}}>1.1$ quiescent galaxies in the UltraVISTA DR3 catalog (Muzzin
et al., 2013b) using the UVJ selection criteria by Muzzin et al. (2013a). The
redshift criterion ensured that we observe at least two Balmer lines for all
targets. We additionally required $H<22.6$, such that the galaxies are
sufficiently bright for resolved kinematic and elemental abundance studies. We
identified an extraordinary pointing for which we observe $\sim$20 such
galaxies in one NIRSpec/MSA configuration.

We re-derived and updated the coordinates of all galaxies and stars in our
pointing by running Source Extractor (Bertin & Arnouts, 1996) on the COSMOS-
DASH F160W image. For the galaxies we used the brightest-pixel positions,
while for the stars we used the barycenter positions. For the fainter galaxies
that were not detected in F160W (all 20 candidate quiescent galaxy targets are
detected), we used the original UltraVISTA coordinates.

We used the APT MSA planning tool to design two nearly identical MSA
configurations, offset in dispersion direction by 8 shutters. We assumed the
assigned PA of 71.6174°, three shutter slitlets, and a three nod pattern
(though we only use the two outer nod positions, as discussed in the next
section). As our quiescent galaxy candidates are extended, we used the
“unconstrained” (midbar) option for the source centering constraint, implying
that our galaxies may be centered behind both the horizontal and vertical MSA
bars. While this was the case for five of the quiescent targets, most galaxies
were centered in the open shutter area. We also allowed galaxies in areas of
the detector that are affected by failed open shutters.

Within the group of quiescent galaxies the highest priority was given to
galaxies at $z\geq 1.5$, followed by $1.3\leq z<1.5$ and $1.1\leq z<1.3$.
After we identified our optimal configuration, we extended the slitlets by one
microshutter, where possible. Next, we added filler targets by hand,
prioritizing bright star-forming galaxies at $z\sim 1.5$, for which we expect
to detect bright stellar continuum emission and all Balmer emission lines, as
well as fainter quiescent galaxies ($H>22.6$). Eventually we considered all
targets with $z>1.1$. In contrast to the primary targets, we did not require
the filler galaxies to have coverage in all nod and dither positions. Finally,
after no more targets could be added, we extended more slits and opened
microshutters on empty sky to construct a master background. Due to failed
closed shutters in the MSA we could not open the exact same shutters in the
two dithered configurations for all targets, which means that for some targets
the number of shutters in a slitlet differs between the two dithers.

In total we target 73 galaxies, of which 20 are quiescent galaxy candidates.
Two quiescent galaxies only have partial coverage, either because they were on
the edge of the detector (130183) or because they were not prioritized as they
are faint (129966). The remaining 18 quiescent galaxy candidates have full
coverage (see Table 2.2). In Figure 1 we show our pointing within the larger
COSMOS area, as well as the footprint of the NIRSpec/MSA and the targeted
galaxies.

Figure 1: Footprint of the SUSPENSE observations in the larger COSMOS field.
The left panel shows the weight map of all available public HST/F160W imaging,
constructed as part of the COSMOS-DASH program (Momcheva et al., 2017). The
darker blue central contiguous area represents the CANDELS survey (Koekemoer
et al., 2011; Grogin et al., 2011), and the three larger and lighter stripes
represent the shallower COSMOS-DASH survey, which overlaps with the deep
UltraVISTA stripes. In the right panel we zoom in on our pointing, now showing
the combined F160W image and the footprint of the NIRSpec/MSA. The red circles
and blue stars indicate the quiescent and star-forming targets, respectively.

###  2.2 Observing Strategy

Our program was executed on January 2 and 4, 2024, employing the NIRSpec/MSA
along with the G140M-F100LP dispersion-filter combination. The wavelength
coverage for this setting is 0.97–1.84 $\mu$m, with a spectral resolution of
$R\sim 1000$. This wavelength range corresponds to a rest-frame wavelength
coverage of $\sim 3700-7000$ Å for our median redshift ($z\sim 1.5$). Though
the exact rest-frame wavelength coverage depends on the redshift, for most
quiescent galaxies we observed 4600–5400 Å, which includes Mg I at 5178 Å as
well as several prominent Fe I lines. Furthermore, all spectra cover at least
one Balmer line.

We used a two-point nod pattern with a cross-dispersion offset of two
microshutters, corresponding to an angular offset of 1$\farcs$06\. This
angular offset translates to a distance of $8.6-9.0$ kpc at the targeted
redshift. This offset was strategically chosen based on the typical sizes of
quiescent targets at our targeted redshift range; with half-light radii of up
to $\sim$3 kpc (Cutler et al., 2022), a two-micro-shutter offset facilitates
the use of the adjacent offset frames as sky, as illustrated in Fig. 2. A
cross-dispersion offset of just one microshutter, as used in the majority of
NIRSpec/MSA studies of distant galaxies, would have resulted in a partial
self-subtraction of the signal during the standard reduction procedure.

We observed our galaxies in two different configurations, offset by 8 micro-
shutters in the dispersion direction. This dither, combined with the two-point
nod pattern, resulted in four offset positions, needed to adequately mitigate
detector and microshutter defects. Finally, our two offset MSA configurations
reduce the detector gap, as the two dithers resulted in spectra which were
shifted in the dispersion direction by $\sim$130 Å.

Because of microshutter defects, it was not possible to observe exactly the
same galaxies in all four positions. 34 galaxies have full coverage, including
18 quiescent targets. Four galaxies have $\sim 75$% coverage, 22 galaxies have
$\sim 50$% coverage, and 13 galaxies have $\sim 25$% coverage. We also
adjusted the slit lengths per galaxy based on the available shutters in the
detector. The quiescent targets have slit lengths varying from 3 to 7
microshutters, with an average of 5 shutters. The slit lengths of the others
targets varied between 1 and 7 shutters. Targets that were only observed in
one nodding position often only had 1 to 2 microshutter slitlets.

Our observations were split over two identical visits. For both target
acquisitions we used the MSATA method and six alignment stars. After the
acquisition, we took a confirmation image of 379 seconds of the first
configuration. During each visit, we observed the two dither configurations.
For both configurations we took 10 integrations of 1473 seconds each, with 20
groups per integration and the NRSIRS2 readout pattern. We nodded to a new
position after two exposures, and thus for each dither configuration per visit
we have 5 nod positions. The total integration time per configuration per
visit is 14,730 seconds. Combining both visits and configurations, we have a
total on-source integration time of 16.4 hrs.

Figure 2: Nod pattern shown for a massive $z\sim 2.1$ quiescent galaxy with an
$R_{e}$ of 2.67 kpc (Kriek et al., 2016). The image is taken with HST/F160W
from CANDELS. The red ellipsoid contains half of the light (for the NIRSpec
PSF), while the orange ellipsoid indicates the extent of two half-light radii
($R_{\rm e}$). The profiles on the left of the images show the light
distribution within the shutters. This figure illustrates that the galaxies
extend beyond a single shutter and thus a two-shutter nod is required to
minimize self-subtraction of the galaxy’s signal in the standard sky-
subtraction.  Table 1: Overview of quiescent galaxy candidate sample

| Coordinates |  |  | Rest-frame colors | Prospector fitting parameters |
---|---|---|---|---|---|---
ID | R.A. | Decl. | $H$ | $z_{\rm spec}$ | $U-V$ | $V-J$ | log $M_{*}^{\ a}$ | log SFR | Ageb | log $Z_{*}$ | $t_{\rm exp}$
| (hh:mm:ss) | (dd:mm:s) | (mag) |  |  |  | ($M_{\odot}$) | ($M_{\odot}$ yr-1) | (Gyr) | ($Z_{\odot}$) | (hr)
127345 | 10:02:02.85 | 2:26:03.4 | 21.3 | 1.168 | 1.85 | 0.97 | 10.65${}^{+0.01}_{-0.00}$ | $-4.32^{+1.73}_{-4.18}$ | 2.10${}^{+0.13}_{-0.06}$ | 0.14${}^{+0.03}_{-0.02}$ | 16.4
130040 | 10:02:06.58 | 2:28:36.2 | 20.4 | 1.170 | 2.03 | 1.33 | 11.14${}^{+0.01}_{-0.01}$ | $-1.22^{+0.48}_{-1.40}$ | 2.18${}^{+0.04}_{-0.05}$ | -0.20${}^{+0.02}_{-0.02}$ | 16.4
128452*† | 10:02:00.58 | 2:27:02.4 | 20.5 | 1.205 | 1.63 | 0.99 | 11.14${}^{+0.01}_{-0.01}$ | $0.72^{+0.05}_{-0.02}$ | 2.57${}^{+0.35}_{-0.06}$ | -0.59${}^{+0.02}_{-0.03}$ | 16.4
127154† | 10:01:57.32 | 2:25:54.5 | 21.2 | 1.205 | 1.92 | 1.01 | 10.86${}^{+0.01}_{-0.01}$ | $-5.94^{+3.00}_{-2.58}$ | 2.97${}^{+0.65}_{-0.66}$ | -0.30${}^{+0.02}_{-0.02}$ | 16.4
130208 | 10:02:05.08 | 2:28:48.5 | 20.8 | 1.231 | 1.71 | 0.80 | 10.86${}^{+0.01}_{-0.01}$ | $-3.37^{+0.82}_{-3.40}$ | 2.58${}^{+0.06}_{-0.10}$ | -0.59${}^{+0.03}_{-0.03}$ | 16.4
129982 | 10:01:55.14 | 2:28:20.7 | 20.3 | 1.249 | 1.73 | 1.18 | 11.19${}^{+0.00}_{-0.00}$ | $0.37^{+0.05}_{-0.05}$ | 2.63${}^{+0.02}_{-0.02}$ | -0.22${}^{+0.02}_{-0.03}$ | 16.4
127108 | 10:01:59.90 | 2:25:53.1 | 22.5 | 1.335 | 1.63 | 0.84 | 10.27${}^{+0.02}_{-0.02}$ | $-2.00^{+0.45}_{-0.83}$ | 2.25${}^{+0.11}_{-0.17}$ | -0.38${}^{+0.06}_{-0.05}$ | 16.4
129197* | 10:02:07.95 | 2:27:53.5 | 22.3 | 1.474 | 1.88 | 1.07 | 10.52${}^{+0.01}_{-0.02}$ | $-1.58^{+0.66}_{-2.53}$ | 1.74${}^{+0.44}_{-0.12}$ | 0.17${}^{+0.02}_{-0.49}$ | 16.4
130647 | 10:01:56.70 | 2:29:11.3 | 20.1 | 1.508 | 1.74 | 1.08 | 11.49${}^{+0.01}_{-0.01}$ | $0.53^{+0.03}_{-0.03}$ | 2.52${}^{+0.02}_{-0.02}$ | 0.17${}^{+0.01}_{-0.02}$ | 16.4
130934* | 10:02:02.36 | 2:29:34.6 | 21.9 | 1.565 | 1.57 | 1.08 | 10.65${}^{+0.01}_{-0.01}$ | $-7.01^{+3.42}_{-13.98}$ | 2.18${}^{+0.06}_{-0.06}$ | -0.48${}^{+0.03}_{-0.02}$ | 16.4
129149 | 10:01:58.27 | 2:27:49.2 | 21.2 | 1.579 | 1.78 | 0.93 | 11.00${}^{+0.01}_{-0.01}$ | $-2.65^{+0.69}_{-1.70}$ | 1.45${}^{+0.10}_{-0.10}$ | 0.19${}^{+0.00}_{-0.01}$ | 16.4
130183 | 10:02:01.77 | 2:28:49.9 | 22.2 | 1.757 | 1.62 | 1.06 | 10.69${}^{+0.03}_{-0.03}$ | $-0.71^{+0.31}_{-1.02}$ | 0.87${}^{+0.06}_{-0.09}$ | -0.42${}^{+0.08}_{-0.09}$ | 6.6
128041† | 10:01:56.61 | 2:26:44.1 | 21.9 | 1.760 | 1.42 | 0.97 | 10.76${}^{+0.01}_{-0.01}$ | $-0.16^{+0.06}_{-0.05}$ | 1.89${}^{+0.06}_{-0.11}$ | -0.83${}^{+0.02}_{-0.03}$ | 16.4
127700† | 10:02:06.66 | 2:26:26.8 | 22.6 | 2.013 | 1.62 | 1.11 | 10.83${}^{+0.04}_{-0.04}$ | $0.57^{+0.10}_{-0.11}$ | 0.94${}^{+0.17}_{-0.15}$ | -0.05${}^{+0.05}_{-0.09}$ | 16.4
129133 | 10:02:05.19 | 2:27:49.0 | 22.1 | 2.139 | 1.73 | 1.10 | 11.11${}^{+0.02}_{-0.01}$ | $-2.00^{+1.13}_{-5.23}$ | 2.03${}^{+0.00}_{-0.01}$ | -0.89${}^{+0.02}_{-0.02}$ | 16.4
127941 | 10:02:07.52 | 2:26:40.7 | 22.5 | 2.141 | 1.55 | 1.14 | 10.87${}^{+0.03}_{-0.04}$ | $-1.11^{+0.53}_{-6.12}$ | 1.84${}^{+0.04}_{-0.03}$ | -0.36${}^{+0.06}_{-0.02}$ | 16.4
128036 | 10:02:01.29 | 2:26:43.8 | 22.2 | 2.196 | 1.49 | 0.87 | 10.92${}^{+0.03}_{-0.03}$ | $-4.60^{+2.36}_{-8.91}$ | 1.11${}^{+0.08}_{-0.08}$ | 0.03${}^{+0.03}_{-0.02}$ | 16.4
128913 | 10:02:05.72 | 2:27:37.3 | 22.6 | 2.285 | 1.68 | 0.89 | 10.84${}^{+0.03}_{-0.03}$ | $-2.25^{+1.05}_{-3.58}$ | 1.14${}^{+0.09}_{-0.12}$ | 0.01${}^{+0.10}_{-0.10}$ | 16.4
130725* | 10:02:02.28 | 2:29:21.0 | 22.2 | 2.692 | 1.33 | 0.96 | 11.10${}^{+0.03}_{-0.03}$ | $0.09^{+0.06}_{-0.06}$ | 1.08${}^{+0.01}_{-0.01}$ | 0.12${}^{+0.02}_{-0.02}$ | 16.4
129966† | 10:01:57.65 | 2:28:38.7 | 23.2 | 2.923 | 1.45 | 0.81 | 10.89${}^{+0.03}_{-0.03}$ | $-0.90^{+0.93}_{-3.18}$ | 0.75${}^{+0.17}_{-0.20}$ | -0.31${}^{+0.09}_{-0.09}$ |

† Galaxies with a PSB best-fit SFH. * Galaxies observed in a failed open
shutter area a Surviving stellar mass. b Mass-weighted ages.

##  3 Reduction and data overview

###  3.1 Data Reduction

We reduced the data using a modified version of the JWST Science Calibration
Pipeline (Bushouse et al., 2022) v1.12.5, and version 1183 of the Calibration
Reference Data System (CRDS). Since the two dithers in our observations have
slightly different MSA configurations we reduce the dithers separately, and
combine the resulting 1D spectra. Below we describe the main data reduction
steps and our modifications to the standard JWST Calibration Pipeline.

In Stage 1 of the JWST Calibration Pipeline the master bias frame and dark
current were subtracted, and detector artifacts were removed. To remove large
cosmic-ray events (snowballs) we set expand_large_events = True, min_sat_area
= 15, and min_jump_area = 15 in the jump step. We then flag saturated pixels
and remove jumps due to cosmic rays, and obtain the count-rate frames by
fitting the slope of each pixel.

After Stage 1 of processing, the count-rate frames still contain $1/f$
correlated vertical read out noise (Schlawin et al., 2020). We remove this
noise using the correction algorithm from grizli (Brammer, 2023). In Stage 2
of the JWST Calibration Pipeline we perform the background-subtraction for the
entire detector of each count-rate frame. We construct the background for each
frame by taking the average of all frames that were observed during the same
visit and dither, but are in the opposite nodding position of our 2-nod
pattern. After background subtraction, the 2D spectra associated with our
targets were cut out from the full detector frame, and we applied a flat
field, pathloss and barshadow correction to each individual 2D spectrum. Since
our sources are extended with respect to the shutter size, we use the CRDS
pathloss calibration file corresponding to a uniformly illuminated slit. The
fluxes of the corrected 2D spectra were then calibrated to convert the data
from count rate units to surface brightness.

In Stage 3 of the data processing we combined the frames of all nods and
observing days for each target. In order to combine the individual frames, we
first rectified and resampled the 2D spectra to a common reference frame.
Next, we identified outlier pixels caused by additional cosmic ray impacts or
bad pixels in the individual frames of each target using the outlier detection
algorithm from MSAEXP111https://github.com/gbrammer/msaexp. While masking
outliers, we combined the rectified 2D frames for both nods and visits by
weighing each pixel by its inverse read noise to construct the final 2D
spectrum for each dither. We then used the Horne (1986) optimal extraction
algorithm to get the 1D spectra for each dither position. We re-scale the 1D
spectra to the overlapping photometry with a multiplicative factor calculated
from the median spectrum and photometry. This re-scaling accounts for the slit
loss caused by the fact that our extended targets partly fall outside of the
shutter, which is not properly corrected for in the pathloss correction step
in the reduction pipeline.

Finally, we combine the 1D spectra for both dither positions. For spectra that
are dispersed over both NIRSpec detectors there is a wavelength gap in the
spectrum caused by the separation of the two detectors. Our 8-shutter dither
in the dispersion direction shifts this detector gap by $\sim$130 Å between
the two dither positions, so that the detector gap is partly filled up in the
combined spectrum. The spectrum at the edges of the detector gap is thus
constructed from only one of the dithered spectra. We created a mask for each
spectrum, indicating the bad pixels and spectral coverage. Finally, we weighed
all unmasked pixels in the spectra by their on-source integration time to
combine them into the final 1D spectrum.

###  3.2 Data Overview

Figure 3: NIRSpec rest-frame spectra for our sample of 20 candidate quiescent
galaxies, sorted by redshift, and offset by a constant in the y-direction. We
median bin the spectra over 3 pixels. Dashed blue, red, and green lines mark
prominent emission, absorption, and Balmer features, respectively. For clarity
purposes, strong emission lines overlapping with other spectra are displayed
in gray. The dashed gray lines on the right indicate the zero-points of the
spectra. Figure 3: Continued.

Figure 5: Overview of UltraVISTA photometric SEDs (bottom-left), NIRSpec
spectra (right), and HST image (top-left) of two example quiescent galaxies in
our survey. The 2D spectra for the two observed dithers and the combined 1D
spectrum are shown in the top-right and bottom-right rows, respectively. Flux
densities ($F_{\lambda}$) are in $10^{-19}$ erg s-1 cm-2 Å-1. The coverage of
the NIRSpec spectra are indicated in the SED panels by the gray rectangles.
The best-fit Prospector models to the photometry and spectra are shown in red
(see Section 4.1). The images in the top-left panels show the HST-F160W
imaging, with an overlay of the NIRSpec MSA slit orientation for one nod
position. In Appendix A we show this figure for all targeted quiescent
galaxies.

We show an overview of the rest-frame normalized 1D spectra for all 20
quiescent targets in Figure 3, ordered by spectroscopic redshift (see Section
4.1). In Figure 5 and Appendix A we present the detailed observed-frame 1D
spectra (bottom-right panels), 2D spectra for both dither positions (top-right
panels), UltraVISTA photometry (bottom-left panels) and the F160W image from
COSMOS-DASH (Mowla et al., 2018). We also show the position of the NIRSpec MSA
shutters for the first nod on the image.

The rest-frame 1D spectra in Figure 3 show that we observe multiple Balmer
absorption lines (green dotted lines) for nearly all quiescent targets, and
the Mg I absorption line (red dotted line) is observed for 16 of our targets.
Two of our quiescent targets are at too low redshifts to capture the targeted
Mg I line, and for two galaxies the line falls in the detector gap. For the
twelve highest redshift targets we observe the 4000 Å break region, including
the two Ca II lines (red). Additionally, we detect Na I, Fe I, and Carbon
G-band absorption lines for the majority of targets. For some quiescent
targets we also detect emission lines; for seven sources with rest-frame
coverage below $3800$ Å we observe [O ii]$\lambda\lambda$3727,3730 emission.
Furthermore, for seven of the galaxies we also observe [N
ii]$\lambda\lambda$6550,6585, [O iii]$\lambda\lambda$4960,5000, and/or Balmer
emission lines. These emission lines in quiescent galaxies are thought to
originate either from AGN or hot evolved stars (e.g. Yan et al., 2006;
Belfiore et al., 2016; Maseda et al., 2021; Belli et al., 2021). Furthermore,
the emission line ratios ([N II]/H$\alpha$, [O iii]/H$\beta$) are also
indicative of a non-star-forming origin. Thus, these lines likely originate
from ionization by an AGN, shocks, or post-AGB stars (e.g. Yan & Blanton,
2012). A detailed investigation into the origin of these emission features
will be the subject of a future study.

Figure 6: Median spectral signal-to-noise ratio (SNR) between rest-frame
$4600-4800$ Å against rest-frame $V$-band magnitude (left panel), and
projected position on the shutter (right panel) for all quiescent targets. We
color the symbols in both panels by the ratio of the SNR to rest-frame
$V$-band flux, and the circles [squares] indicate sources with $z<[>]1.75$.
Sources that were observed in an area of the MSA that is affected by failed
open shutters are indicated with a plus, and crosses represent sources that
were not covered by all four nod/dither configurations. The gray area in the
right panel shows the area of the shutter that is covered by part of the bar
separating two shutters.

In Figure 6 we plot the median SNR per rest-frame Å between $4600-4800$ Å as a
function of rest-frame $V$-band magnitude. We use this spectral range to
compute the SNR as all quiescent targets cover this wavelength region, and it
captures no strong emission or absorption features that will bias the
calculated SNR for individual galaxies. Overall, the targets that are
brightest in the rest-frame $V$ band also have the highest SNRs, but for some
targets the signal is lower than expected based on their magnitude. For the
two sources that were not covered by all four nod/dither configurations
(indicated with cross symbols), the lower SNR can be explained from their
lower on-source integration time. Secondly, to include more quiescent targets
in our sample we allowed objects to fall in areas of the detector that are
affected by stray light from failed open shutters in the MSA. These targets
are indicated with a plus symbol, and indeed have lower SNRs than sources of a
comparable magnitude.

Additionally, to further maximize our sample size we allowed sources to fall
anywhere in a shutter when designing the mask, including behind the bars
separating neighboring shutters. In the right panel of Figure 6 we show an MSA
shutter with the projected positions of the central coordinates of our
quiescent targets, plotted using the same symbols as in the left panel. Here
we indicate the area of the shutter covered by the bars separating neighboring
shutters in gray. To compare the SNRs of the targets relative to their
brightness, we color the data-points by their SNR divided by rest-frame
$V$-band flux in both panels.

From this panel we conclude that most of the sources with relatively low SNR
lie at the edge of the shutters, or behind the shutter bars. For these less-
optimally centered sources a large fraction of their light will not fall into
the shutter. This means a smaller amount of the galaxy’s light is captured for
the spectrum compared to the $V$-band flux, which is calculated using the
galaxy’s entire profile. However, since the typical size of quiescent galaxies
in our redshift range at a rest-frame wavelength of $\sim$5000 Å is $\sim$2
kpc (Cutler et al., 2022), our targets are sufficiently extended to reach SNRs
$>10$ for all sources (with full coverage and not affected by the open shutter
areas) that are at the shutter edges. This is in part due to our custom
nodding pattern, where we do not use the shutters directly above or below the
shutter containing the target to do the background subtraction. This means
that we do not self-subtract the light from extended sources falling in
multiple shutters, so that sources centered at the top and bottom edges of a
shutter can also be captured by their neighboring shutters.

Lastly, we separate the quiescent galaxy candidates in a low and high redshift
bin ($z_{\text{lim}}=1.75$) to show how redshift influences the SNR. Overall,
we see that the low-redshift sources have SNRs that are slightly lower than
the sources in the high-redshift bin at similar $V$ band magnitudes. This is
likely due to the fact that at higher redshifts galaxies are more compact, so
that we capture a larger amount of their flux in the shutter.

On the other hand, for higher redshift sources the rest-frame $4600-4800$ Å
wavelength range falls in the observed-frame wavelength range where the
throughput of the disperser/filter combination is lower. Although this decline
in relative throughput as a function of redshift adds scatter to the SNRs of
our sources, the relatively high SNRs of high-redshift sources indicates that
this effect does not dominate the final SNRs we obtain for the spectra.

We also note that the difference in sizes, Sersic indices, and alignment of
our objects will increase the scatter in our SNRs, since the observed fraction
of each galaxy will differ. However, robust size measurements are not
available for a significant fraction of sources in our sample, so this effect
cannot be taken into account when comparing the SNRs.

In summary, the observed SNRs show that our choices for designing the MSA mask
affected the obtained flux levels of our spectra. Nonetheless, the final SNRs
($\gtrsim 10$) we obtain are sufficient for the goals of this survey. If we
had been more conservative in the mask design by not allowing sources to fall
behind the shutter bars and not using shutters affected by failed open
shutters, our sample would have decreased from 20 to eleven quiescent targets.
Overall, this shows that by allowing more flexibility in the mask-design we
have greatly increased the scientific potential of the survey, while still
achieving sufficiently high SNRs. However, we also note that for surveys that
have less extended sources or require very high SNRs, it is necessary to be
more conservative in the MSA design.

##  4 Analysis

###  4.1 Redshifts and Stellar Population Modeling

We derive redshifts and stellar population properties for all quiescent
targets by fitting the 1D spectra for the quiescent targets in addition to
their UltraVISTA DR3 photometry (Muzzin et al., 2013b) using the Prospector
code (Leja et al., 2019b; Johnson et al., 2021). Prospector uses the Flexible
Stellar Population Synthesis (FSPS; Conroy et al., 2009; Conroy & Gunn, 2010)
library, the MILES spectral library (Falcón-Barroso et al., 2011), and the
MIST isochrones (Choi et al., 2016; Dotter, 2016) to construct stellar
population models. We used the dynesty dynamic nested sampling package
(Speagle, 2020) to sample the posterior distributions. We do not include the
option in Prospector that fits for nebular emission lines in the spectrum, as
the models used to fit for these emission lines are often not flexible enough
to capture the observed line ratios due to the influence of, e.g., AGN,
abundance ratios, or radiative transfer through dust. Instead, we masked out
the spectrum within 50 Å of the [O ii]$\lambda\lambda$3727,3730 doublet, and
within 100 Å of the [O iii]$\lambda\lambda$4960,5000, [N
ii]$\lambda\lambda$6550,6585, and [S ii]$\lambda\lambda$6718, 6733 doublets.
For galaxies with strong emission lines (127700, 128451, 130040, 130647) we
also correct the photometry for the contribution of emission lines before
fitting. We describe the free parameters and priors that we used in our
modeling below, and summarize them in Table 2.

We fit two models with different non-parametric SFHs to our data. The first
SFH is the fixed-bin model described by Leja et al. (2019a). In this SFH there
are $N$ user-defined fixed time-bins, all of which have a constant SFR that is
varied in the fit. In our model we used 9 time-bins, where the first two bins
go from a lookback time of 0 – 30 Myr and 30 – 100 Myr, and the last bin
covers 0.85 $t_{\text{univ}}$ – $t_{\text{univ}}$ at the redshift of the
galaxy. The remaining 6 bins are spaced evenly in log lookback time. The
second SFH we fit is the post-starburst (PSB) model described by Suess et al.
(2022a). This SFH model allows for more flexibility in the SFH, enabling it to
capture short, recent bursts of star formation and rapid quenching. This is
achieved by fitting a SFH with 9 time-bins, where the 4 oldest
($t>0.6\,t_{\text{univ}}$) bins have fixed bin-edges, while the bin-edges of
the 5 youngest bins are varied in the fit. The 4 fixed bins have variable
SFRs, whereas the 5 bins with flexible edges all form an equal amount of
stellar mass. To capture low levels of star formation after the burst, the
last bin has both a variable SFR and a variable bin edge. After fitting both
SFH models we determined for each galaxy which model best fits the data based
on the lowest median residual of the best-fit model and the observed spectrum
and photometry.

Table 2: Parameters and priors used in the Prospector fits |  Parameter |  Description |  Prior
---|---|---|---
Global |  log($M_{*}/M_{\odot}$) |  Total stellar mass formed |  Uniform: min = 9.5, max = 12.5
. |  log($Z_{*}/Z_{\odot}$) |  Stellar metallicity |  Uniform: min = -1.4, max = 0.19
. |  $\sigma$ |  Stellar velocity dispersion |  Uniform: min = 5, max = 600 km s-1
. |  $z$ |  Redshift |  Gaussian: $\mu$ = estimated $z_{\text{spec}}$, $\sigma$ = 0.05
Dust |  $\hat{\tau}_{\lambda,2}$ |  Diffuse dust optical depth |  Uniform: min = 0.0, max = 2.5 mag
. |  $\hat{\tau}_{\lambda,1}$ |  Birth-cloud dust optical depth |  Fixed to $\hat{\tau}_{\lambda,2}$
. |  $n$ |  Slope of Kriek & Conroy dust law |  Uniform: min = $-1.0$, max = 0.4
SFH |  log SFR${}_{\text{ratio}}$ |  Ratio of SFR in adjacent bins |  Student $t$ (8-vector): $\tau$ = 0.3, $\nu$ = 1
SFH: PSB |  log SFR${}_{\text{ratio,young}}$ |  Ratio of SFR in youngest bin to the last flex bin |  Student $t$: $\tau$ = 0.3, $\nu$ = 1
. |  log SFR${}_{\text{ratio,old}}$ |  Ratio of SFR in old bins to the first flex bin |  Student $t$ (4-vector): $\tau$ = 0.3, $\nu$ = 1
. |  log SFR${}_{\text{ratio}}$ |  Ratio of SFR in adjacent flex bins |  Student $t$ (4-vector): $\tau$ = 0.3, $\nu$ = 1
. |  $t_{\text{last}}$ |  Width of youngest bin |  Uniform: min = 0.01 Gyr, max = 0.3 $t_{\text{univ}}$
Noise |  $j_{\text{spec}}$ |  Spectroscopic jitter term |  Uniform: min = 1, max = 5
. |  $f_{\text{out}}$ |  Fraction of pixels in spectrum that are outliers |  Uniform: min = 0.0, max = 0.5
. |  $s_{\text{out}}$ |  Inflation of the nominal noise for outlier pixels |  Fixed to 50
Note. The Student-$t$ priors for the SFHs are centered at the UniverseMachine
SFH predictions for a quiescent galaxy of similar mass and redshift, as
described in the text.

For both SFH models the SFR in each bin is determined by fitting the log of
the ratio of the SFR in adjacent bins (log SFR${}_{\text{ratio}}$) using a
Student-$t$ prior. Following Suess et al. (2022b), we used predicted SFHs for
quiescent galaxies from the UniverseMachine public data (Behroozi et al.,
2019) to set physically motivated priors. To this end, we first found the
UniverseMachine predicted SFH for quiescent galaxies at the redshift of the
galaxy to be fit, for a stellar mass of log $M_{*}/M_{\odot}=10.8$,
corresponding to the UniverseMachine mass-bin closest to the estimated stellar
masses of galaxies in our sample. We then calculated the log
SFR${}_{\text{ratio}}$ that would produce this SFH, and used these values to
set a Student-$t$ prior with a width of 0.3 dex and a degree of freedom of
one.

For the fixed-bin SFH model we fit the spectroscopic redshift of the galaxy
using a Gaussian prior. We estimated the redshift of each galaxy by hand
first, and set this as the mean of the prior, and set the width to 0.05. The
best-fit spectroscopic redshift of the fixed-bin model is then used as a fixed
parameter in the PSB model. All other parameters in the fit are set up
identically for the two models.

We assumed the Chabrier (2003) initial mass function, and added nebular
continuum and dust emission using the standard Prospector parameters. In
addition to the SFH shape, we also fit for the total stellar mass formed,
metallicity, velocity dispersion, and dust attenuation. We set a flat prior on
log $M_{*}/M_{\odot}$ between 9.5 and 12.5, and let log $Z/Z_{\odot}$ vary
between $-1.4$ and $0.19$, corresponding to the metallicity limits of the
MILES spectral library (Falcón-Barroso et al., 2011). The velocity dispersion
was varied between 5 and 600 km s-1. The prior range for the velocity
dispersion was chosen to be this extended in order to correct for
uncertainties in the effective spectral resolution of NIRSpec/MSA, as we
describe in more detail below. We use the Kriek & Conroy (2013) dust law, in
which the UV dust bump and dust slope are correlated. For the dust model we
set the optical depth and dust index as free parameters. We set a uniform
prior on the dust index between $-1$ and 0.4 (Noll et al., 2009). For the
diffuse dust optical depth we used a flat prior from 0.0 to 2.5 mag, and fixed
the birth-cloud dust optical depth such that young stars are attenuated twice
as much as old stars (Wild et al., 2020).

Furthermore, to ensure that bad pixels and calibration issues did not heavily
affect our fits, we fit for noise and calibration parameters. We included the
Prospector pixel outlier model that inflates the uncertainties of a fraction
$f_{\text{outlier}}$ of pixels by a factor $s_{\text{outlier}}$. We set a
uniform prior on $f_{\text{outlier}}$ between 0.0 and 0.5, and fix
$s_{\text{outlier}}$ to 50. Secondly, we fit for an increase of the noise of
the spectrum by including the noise model described in Johnson et al. (2021),
with a free spectroscopic jitter term that we varied between 1 and 5. Lastly,
to account for poor calibration of the spectra we include Prospector’s
polynomial SED model, which optimizes out a polynomial in the model spectrum.

To include the wavelength-dependent spectral resolution of NIRSpec in the fit,
we broadened the model spectra by the line-spread function (LSF) calculated
from the instrumental resolution curve provided in JWST User Documentation
(JDox)222https://jwst-docs.stsci.edu/jwst-near-infrared-
spectrograph/nirspecinstrumentation/nirspec-dispersers-and-filters. However,
these resolution curves are based on pre-launch estimates based on a uniformly
illuminated slit, and recent results from e.g. de Graaff et al. (2023),
Nidever et al. (2024), and Nanayakkara et al. (2024) indicate that the true
spectral resolution is likely significantly higher. However, these studies are
limited to point-like or compact sources and individual un-rectified 2D frames
or different disperser/filter combinations, so the difference between the
reported and true resolution is not known exactly for our observations. Using
the software from de Graaff et al. (2023) we find that, for an extended source
observed with our disperser and filter combination, the effective and reported
resolution in the un-rectified frames have the same wavelength dependence, and
vary only by a constant factor. We assumed this is also the case for the final
combined 1D spectra, and used emission line widths of our lowest-mass filler
targets, which have minimal Doppler broadening, to constrain the effective
resolution of our spectra. We found this to be a factor $\sim$1.2 higher than
the reported resolution (A. Beverage et al., in preparation), and multiplied
the JDox resolution curve by a factor 1.2. We then used this resolution curve
to calculate the LSF that we use to broaden the model spectra. We note that if
our estimate for the effective resolution is too conservative, we can still
obtain a good Prospector best-fit model, as the velocity dispersion also
accounts for broadening of the models. We thus adopted a wide range for the
prior on the velocity dispersion ($5-600$ km s-1), and do not use the best-fit
values for any science purposes in our analysis.

We show the resulting best-fit model to the spectra and photometry for two
representative quiescent galaxies in Figure 5. The best-fit models for the
other quiescent galaxies in our sample are shown in Appendix A, and we report
the best-fit stellar population parameters in Table 2.2. The stellar masses we
report are the surviving stellar masses, converted from the total stellar mass
formed using the surviving mass fraction obtained in the Prospector fit.

For the star-forming galaxy targets we derive spectroscopic redshifts by
fitting Gaussians to the observed emission lines (see Appendix C). Multiple
emission lines were detected for most filler galaxies, yielding robust
spectroscopic redshifts for 46 out of 53 star-forming filler galaxies. For a
few of the higher-redshift galaxies, only one emission feature was observed
([O ii]). Nonetheless, due to the consistency between the spectroscopic and
photometric redshift, we were confident about the line identification. For
seven star-forming filler galaxies no spectroscopic features were detected;
these galaxies were not included in Table C and omitted from the rest of this
study. Though the redshifts for all star-forming galaxies have been derived
from emission lines, we note that we detect continuum emission and absorption
lines for many star-forming galaxies as well.

###  4.2 Evaluation of Best-Fit SPS Models

For all galaxies the fit to the spectrum and photometry looks reasonable (see
Figure 5 and Appendix A). However, for some spectral regions the models do not
properly capture the spectrum due to the fact that the resolution of the MILES
stellar library varies with wavelength; the resolution is high between
$\sim$3750–7200 Å, but outside this range the resolution is lower than the
spectral resolution of our data. For targets with $z\gtrsim 2$ this affects
the fits to the spectra at the blue end, where the models follow the overall
shape of the observed spectra, but cannot capture details. At the upper
wavelength limit the low-resolution models affect galaxies in our sample with
redshifts below $z\sim 1.5$. The spectra of these sources cover the rest-frame
spectral region of $\lambda\gtrsim 6200$Å where TiO and CN absorption bands
are present. These absorption bands are characteristic of evolved, carbon and
oxygen-rich stars (e.g. Lu et al., 2024), and are generally only visible for
evolved stellar populations where young, blue stars do not dominate over this
population (e.g. Allard et al., 2000; Almeida et al., 2012). Because of the
low spectral resolution of the models, all galaxies in our sample with TiO
absorption bands in the spectra have best-fit models that fit this spectral
region poorly, both in overall spectral shape and in specific absorption
features.

Constraining spectral models of distant galaxies outside the $3750-7200$ Å
wavelength range was very challenging in the pre-JWST era, since high-
resolution ground-based observations have strong telluric absorption bands at
these wavelengths. The spectra we present in this work show that JWST
observations will provide the medium-resolution spectra needed to calibrate
models blue and red-ward of the current spectral coverage. Furthermore, these
spectra can also be used to improve models for the influence of evolved stars
in stellar populations with ages around $\sim 1$ Gyr (e.g., Maraston, 2005;
Kriek et al., 2010; Zibetti et al., 2013; Lu et al., 2024).

We also note that for some galaxies strong absorption lines (e.g. Mg I and Na
I in galaxy 129149) are not captured by the best-fit model. One possible
explanation for this discrepancy between the observed and model spectra is the
fact that we fit the spectrum and photometry simultaneously. Currently,
photometric data needs to be included in the modeling in order to fit for
inaccurate calibration of the spectra. However, by including photometry there
is less flexibility for the model to accurately fit the observed spectrum. A
second explanation for the inaccurate fit of specific absorption lines like Mg
I and Na I is the fact that Prospector only uses the Solar chemical abundance
pattern in its models. However, observations have shown that quiescent
galaxies at high redshifts have non-Solar abundances (e.g., Choi et al., 2014;
Maiolino & Mannucci, 2019; Beverage et al., 2023), leading to different
relative depths of absorption lines in a spectrum. To properly constrain all
absorption lines in the spectra, A. Beverage et al. (in preparation) will fit
the abundance of individual elements in the galaxies.

Another explanation for why Prospector fails to reproduce the very deep Na
id$\lambda\lambda$5891,5897Å absorption feature in some galaxies (e.g.
129133), is the fact that this line is also a tracer of neutral gas in the
interstellar medium (ISM) as well as AGN outflows. Although a detailed
analysis of the contributions of the ISM and outflows is beyond the scope of
this research, we note that the contribution of neutral gas could be used to
explain the excess Na id absorption in distant quiescent galaxies (e.g.,
Jafariyazani et al., 2020; Belli et al., 2023; Davies et al., 2024).

Lastly, it is possible that for some galaxies none of the stellar spectral
templates used by Prospector describe the observed spectrum well, for example
due to poor modeling of the influence of evolved stars, chemical abundances,
or AGN. Such a template mismatch will result in a best-fit spectrum that does
not fit the data well.

###  4.3 Sample Characteristics

Our quiescent galaxy candidate targets were selected to be at $z>1.1$, are
relatively bright, and fall in the quiescent box of the $UVJ$ diagram.
However, our selection criteria were only based on photometric information,
leading to uncertainties in the redshifts and resulting rest-frame $U-V$ and
$V-J$ colors. In this section we present the sample characteristics we find
when we include the spectroscopic information.

Figure 7: Top panel: $H$-band magnitude against redshift for all quiescent
targets (red circles) and the filler targets for which we measured a
spectroscopic redshift (blue stars). The symbol sizes of the quiescent targets
are scaled by their Prospector best-fit stellar mass. The dashed line at
$H=22.6$ shows the magnitude limit we initially used to select our quiescent
galaxy targets. One of our quiescent targets is fainter than this limit, and
was not prioritized when designing the MSA configuration. In the background we
show the parent UltraVISTA DR3 catalog from which our sample was drawn. Bottom
panel: Redshift distribution of all targets for which a spectroscopic redshift
was measured (white shading). The red shaded histogram shows the redshift
distribution of only the quiescent targets. Figure 8: Rest-frame $U-V$ against
$V-J$ colors for all galaxies observed in SUSPENSE, divided in three redshift
intervals. Prior to the observations, candidate quiescent galaxies (red
circles) were selected based on their rest-frame colors (obtained from
photometric redshifts) using the indicated selection box (Muzzin et al.,
2013b). However, when using spectroscopic redshifts several candidate
quiescent targets scattered just outside of this selection box. Nonetheless,
all of these galaxies have confirmed quiescent stellar populations (see
Section 4.4). We color the quiescent targets by their Prospector mass-weighted
stellar age. The star-forming filler galaxies for which a spectroscopic
redshift was measured are indicated by the blue stars. The size of each data-
point is scaled to the $H$-band magnitudes of the corresponding galaxy. In the
background, we show the colors of galaxies in the parent UltraVISTA catalog
for each redshift interval.

In Figure 7 we show the quiescent and star-forming targets in magnitude-
redshift space relative to the parent distribution from the full UltraVISTA
catalog. We only show the 46 out of 53 filler galaxies for which we could
measure a spectroscopic redshift. This figure shows that nearly all quiescent
targets are brighter than $H<22.6$; our criteria to ensure that our spectra
are sufficiently deep to achieve our science goals. Only one quiescent galaxy
in our sample is fainter; this galaxy was initially added as a filler target
when creating the MSA mask, but since the observed spectrum has a high enough
SNR we now include it in our quiescent galaxy sample (see Section 2.1). Since
we prioritized bright star-forming galaxies when selecting filler targets, the
majority of the star-forming targets are at the bright end of the parent
distribution, and only $\sim 1/5$ of the star-forming galaxies are relatively
faint compared to the complete population. We note that the magnitude-redshift
diagram for the parent catalog is based on photometric redshifts only, which
introduces (systematic) uncertainties on the background distribution.

The histogram in Figure 7 shows that the redshifts of the quiescent and star-
forming targets in our sample are not significantly clustered, except for a
slight peak at $z\sim 1.2$. This peak may suggest that there could be a
potential overdensity at this redshift, but, at face value, most of the
galaxies in our sample do not appear to be part of an overdensity. This
observation may be somewhat surprising, as we selected our pointing for the
extraordinarily large amount of quiescent galaxies we could observe within one
MSA configuration. A detailed investigation into potential overdensities in
our sample is beyond the scope of this paper.

In Figure 8 we show the rest-frame $U-V$ vs $V-J$ colors of all quiescent and
star-forming targets, separated into three redshift bins. The $UVJ$ colors
were calculated from the photometry using eazy (Brammer et al., 2008), with
the redshift set to the spectroscopic redshift. The top-left boxes in each
panel indicate the region we used to select the quiescent targets (red
symbols; Muzzin et al., 2013b), while star-forming targets lie outside of this
box. For the initial selection of our quiescent targets we used $UVJ$ colors
based on the photometric redshift. Three of our quiescent targets (IDs 127941,
128041, and 130725) scattered out of the box when using the spectroscopic
redshift. However, since these galaxies lie very close to the selection box
they may still be recently quenched or post-starburst galaxies with quiescent
stellar populations (e.g., Belli et al., 2019; Suess et al., 2021; Park et
al., 2023). We will investigate the star-formation properties of these
galaxies in more detail in the next section.

The quiescent galaxy sample spans across the full quiescent parent
distribution in $UVJ$ space, indicating that our sample is representative of
the full distant quiescent galaxy population. Specifically, in the low
redshift bin our sample contains old and red quiescent galaxies, while at
higher redshifts the quiescent galaxy sample shifts towards bluer colors. This
is consistent with the younger ages we find for the bluer galaxies, as
indicated by the colors of the symbols. This shift to younger ages and bluer
galaxies is expected, as the universe is much younger at this redshift.

###  4.4 Star-Formation Properties

We have selected our quiescent galaxy sample based on their location in $UVJ$
space. However, this selection was only based on photometric colors and
redshifts, and galaxies with incorrect photometric redshifts may have
erroneously scattered into the quiescent region, or the other way around. In
the previous section we indeed showed that after including spectroscopic
redshifts, the $UVJ$ colors of some galaxies shifted outside of the quiescent
galaxy selection box. In order to assess whether our galaxies are indeed
quiescent, we derived the star-formation properties of our quiescent sample,
using the detailed SFHs obtained from the Prospector fits described in Section
4.1.

Figure 9: Four example best-fit SFHs for galaxies in the quiescent galaxy
sample. The SFHs are ordered by decreasing redshift, as indicated in the top
right of each panel. The red shaded areas represent the 16th-84th percentile
confidence intervals of the SFHs. Targets 129966 and 128452 are best fit by a
PSB-SFH, while 128036 and 127345 have fixed-bin SFHs. The gray shaded area in
each panel corresponds to ages that are older than the age of the universe for
the observed redshift.

In Figure 9 we show the best-fit SFHs for four example galaxies in the
quiescent sample. These four SFHs illustrate the diversity of SFHs in our
sample. Galaxies 128036 and 127345 are best fit by the fixed-bin SFH model,
and have SFHs with long star-formation timescales and gradual quenching. In
contrast, 129966 and 128452 both have SFHs that are better fit by the PSB
model, with a gradually increasing amount of star-formation ending in a burst,
after which most of the star formation is suppressed. In total, five out of 20
primary targets (indicated in Table 2.2) have a SFH best described by the
bursty PSB model, while 15 galaxies have more gradual fixed-bin SFHs. From
this variety of SFHs we find that the star-formation timescales of galaxies in
our sample range between $\sim 0.5-4$ Gyr. We show the SFHs for all quiescent
targets in Appendix B.

Figure 10: Stellar mass against SFR of the primary targets in our sample. For
galaxies with log SFR$<-1$ we show a 3$\sigma$ upper limit to decrease the
dynamic range of the figure. The symbols are colored by the redshift at
observation, and we show the Leja et al. (2022) star-forming main sequence for
$z\sim 1$ (yellow shaded area), $z\sim 1.5$ (orange shaded area), and $z\sim
3$ (red shaded area). All candidate quiescent galaxies in our sample lie at
least $3.5\sigma$ below the star-forming main sequence at their redshift.

To assess whether our quiescent targets truly have quiescent stellar
populations, we obtain their current SFRs from the Prospector best-fit SFHs
(see Table 2.2). In Figure 10 we show the SFRs against stellar mass of our
quiescent sample. We also plot the Leja et al. (2022) star-forming main
sequence ridge model at $z\sim 1$, $z\sim 1.5$ and $z\sim 3$. To decrease the
dynamic range of this figure we show the $3\sigma$ upper limit for sources
with log SFR $<-1$. All data-points lie $>3.5\sigma$ below the star-forming
main sequence at their respective redshifts, which shows that all primary
targets indeed have quenched star formation.

In the previous section we found that three primary targets (IDs 127941,
128041, and 130725) had shifted outside the quiescent selection box of the
$UVJ$ diagram after including spectroscopic information. However, this
selection box was empirically selected (Muzzin et al., 2013b), and will not be
a hard limit for quiescence. Since all of the galaxies outside the selection
box lie $>3.5\sigma$ below the star-forming sequence, their SFRs are strongly
suppressed, and we include these galaxies in our quiescent sample.

Figure 11: Left panel: Mass-weighted age against stellar mass of all quiescent
galaxies in our sample. The symbols are colored by the redshift of the target.
The red line at 1.5 Gyr indicates the age we use to separate the sample in two
age bins. Right panel: median-stacked spectrum of all galaxies in each age
bin. The dashed vertical lines indicate Balmer, absorption and emission
features. The top-right panels zoom in on the spectral regions around
H$\delta$, H$\gamma$, Mg i, and Na i, to illustrate how these spectral
features differ between the two age bins.

###  4.5 Ages and Formation Times

We calculate the mass-weighted age of all primary galaxies from their SFHs
(see Table 2.2). We show these mean stellar ages as a function of stellar mass
in the left panel of Figure 11. The quiescent galaxies in our sample have ages
of $1-3$ Gyr, and show a trend with observed redshift; the youngest galaxies
in our sample were observed at the highest redshift. To illustrate how
different spectral features are affected by the stellar age of a galaxy, we
divide the sample into two age bins separated at a mass-weighted age of 1.5
Gyr. We create a median stack of the spectra of the galaxies in each age bin
at a rest-frame wavelength range of $3600-6000$ Å (right panel in Figure 11).
Before stacking we first normalized the spectra of each galaxy by its mean
flux at $4600-4800$ Å, and resampled the spectra to a common wavelength grid.

From the stacked spectra we can see clear differences in spectral features
between the two age bins. For example, the 4000 Å break in the $>1.5$ Gyr
stacked spectrum is more pronounced compared to the young age bin.
Furthermore, the Balmer absorption lines of the young age-bin are
significantly deeper compared to continuum levels. This is most clear for the
H$\delta$ and H$\gamma$ lines, which are sensitive to recently quenched star-
formation, while the difference in the depth of the H$\beta$ absorption line
is less distinct between the two bins. This is likely because the youngest,
most recently quenched galaxies still have some H$\beta$ emission, leading to
a less deep absorption feature. The Mg i and Na i metal absorption features,
on the other hand, are significantly deeper for the old age-bin due to the
fact that lower-mass, cooler stars with more prominent metal absorption
features start to dominate the spectrum at older ages. The distinct
differences in the stacked spectra of the two age bins illustrate that a range
of individual spectral features are strongly affected by the age of a galaxy,
and can be used to constrain detailed SFHs.

Figure 12: Age of the universe where 50% of the stellar mass in the galaxy has
formed ($t_{\text{form}}$) as a function of stellar mass for all quiescent
galaxies. The symbols are colored by the redshift at observation. We show the
Nanayakkara et al. (2024) $z\sim 3-4$ sample as gray diamonds, and the
Beverage et al. (2023) $z\sim 1.4$ and $z\sim 2.1$ samples as gray triangles
and pentagons, respectively. We also indicate four quiescent galaxies with
early-onset star formation identified by Carnall et al. (2023a) (gray plus),
Glazebrook et al. (2023) (gray square), Setton et al. (2024) (gray cross), and
Kriek et al. (2016) (gray star).

From the mass-weighted ages we also calculate the formation times
($t_{\text{form}}$) of the quiescent galaxies in our sample. Here
$t_{\text{form}}$ is defined as the age of the universe at which 50% of the
stellar mass in the galaxies has formed. In Figure 12 we show
$t_{\text{form}}$ as a function of stellar mass for our sample, with the
symbols colored by the observed redshift. The galaxies in our sample have an
average $t_{\text{form}}$ of $\sim 1-3$ Gyr, equivalent to a formation
redshift between $z_{\text{form}}\sim 5-2$. We see no strong trend between
average stellar formation time and stellar mass for our sample. For our
massive galaxies, this is consistent with the findings of Carnall et al.
(2019) and Gallazzi et al. (2014) at $z\sim 1.1$ and $z\sim 0.7$,
respectively, who found that while there is a trend between mass and formation
time at lower masses, this trend flattens out at log $M_{*}/M_{\odot}\gtrsim
11$.

In Figure 12, we also show the spectroscopic samples by Nanayakkara et al.
(2024) at ($z_{\text{obs}}\sim 3-4$) and Beverage et al. (2023) at
$z_{\text{obs}}\sim 1.4$ and $z_{\text{obs}}\sim 2.1$. The formation redshifts
for most galaxies in these samples are consistent with those found for our
sample. Additionally, we show four high-redshift quiescent galaxies for which
very early-onset star formation was identified from their spectra (Kriek et
al., 2016; Carnall et al., 2023a; Glazebrook et al., 2023; Setton et al.,
2024), with inferred formation redshifts of $z\sim 8$. Both the Nanayakkara et
al. (2024) and Beverage et al. (2023) samples contain galaxies with formation
redshifts $\gtrsim 6$ as well. These recent observations of early star
formation in high redshift quiescent targets suggest that the build up of
massive galaxies in the early universe was much more rapid compared to the
local universe. Interestingly, while four galaxies in our sample were already
quiescent by $z=3$, none of the quiescent galaxies in our sample have SFHs
that are consistent with very early formation times.

Figure 13: Top panel: Distribution of the observed redshifts, separated in two
redshift bins. The dashed lines indicate the mean redshift of each bin. Middle
panel: the average SFH of all quiescent targets, separated into two redshift
bins. Here the SFH corresponds to the total mass fraction formed as a function
of age of the universe. Bottom panel: the summed SFR of all quiescent galaxies
as a function of age of the universe, separated into two redshift bins. Before
summing the SFRs of the galaxies we first normalize them by their total
stellar mass. The shaded regions around the mean SFHs indicate the error on
the mean. In the middle and bottom panels, the dotted vertical lines indicate
the average $t_{\text{form}}$ of each redshift bin, with the vertical bands
corresponding to the standard deviation of $t_{\text{form}}$.

Figure 12 shows a clear trend with redshift and mean formation time for our
sample, with galaxies at lower redshift generally forming later than the
higher redshift galaxies. On average, the galaxies at $z_{\text{obs}}>1.75$
formed 50% of their stellar mass by $z\sim 4$, while for $z_{\text{obs}}<1.75$
galaxies the mean formation redshift is $z\sim 3$.

###  4.6 Average SFHs

In Figure 13 we show the average SFHs of our galaxy sample, split into two
redshift bins ($z_{\text{obs, lim}}=1.75$) with mean redshifts of
$z_{\text{obs}}=1.3$ and $z_{\text{obs}}=2.2$. The top panel of this figure
shows the average mass build up over time, calculated by taking the mean
cumulative mass fraction as a function of time for all galaxies in the bin.
The bottom panel of figure 13 shows the summed SFRs as a function of age of
the universe. Before summing the SFRs, all galaxies were normalized by their
total stellar mass. We then smooth the summed SFRs of the redshift bins with a
box kernel to reduce the noise from scatter in the individual galaxies’ SFHs.
Figure 13 shows that the star-formation timescale is much shorter for galaxies
at $z_{\text{obs}}\sim 2.2$ compared to the $z_{\text{obs}}\sim 1.3$ sample,
consistent with the higher mean formation redshift we find for the galaxies at
higher redshift. Furthermore, we also see that the onset of star-formation is
slightly later for the low redshift bin.

Only one galaxy in the low-$z$ sample (127154) already had a significantly
suppressed SFR at the redshifts at which the high-$z$ sample was observed (see
Figure 20); all the other galaxies quench at later times. This galaxy forms
its stars on a timescale that is comparable with galaxies in the high-$z$ bin,
while all other galaxies in the low-$z$ bin form their stars over a longer
period.

Figure 14: Illustrative “typical” SFH of quiescent galaxies at
$z_{\text{obs}}\sim 1.3$ and $z_{\text{obs}}\sim 2.2$, assuming a simplistic
Gaussian model. We take the mean of the Gaussian as the mean $t_{\text{form}}$
of each sample, and the FWHM of the Gaussian correspond to the median FWHM of
the SFHs of the individual galaxies in each redshift bin. The median age of
the universe for the two redshift bins are indicated by the dashed line. On
average, quiescent galaxies at high redshift formed earlier and more rapidly
than at low redshifts.

To account for broadening of the average SFHs due to the wide range of
observed redshifts, and directly compare the average star-formation timescales
of our two redshift bins and the Beverage et al. (2023) sample, we infer the
typical SFH of galaxies in each redshift bin. In Figure 14 we show an
illustrative model for the typical SFHs for the two redshift bins,
parameterized as a Gaussian. The FWHM of the Gaussian corresponds to the star-
formation timescale, and is estimated from the median FWHM of the star-
formation timescales of the individual galaxies in the redshift bin. The mean
of the typical SFH Gaussian is set to the median $t_{\text{form}}$ of the
redshift bin. It is important to note that this parameterization does not
reflect the true shape of the SFHs, but was chosen for its simplicity, and
should be taken as an illustration only. Nonetheless, there is a clear
difference between the typical SFHs at $z_{\text{obs}}\sim 1.3$ and
$z_{\text{obs}}\sim 2.2$, with higher-redshift quiescent galaxies forming more
rapidly than their low-redshift counterparts. Furthermore, the SFR of the
high-redshift bin is completely suppressed very shortly before observation
($z_{\text{obs}}$, indicated by the dashed lines) for the typical SFH. The
low-redshift sample on the other hand has typically quenched $\sim$0.5 Gyr
before observation. We also see that the typical SFH in both redshift ranges
is shorter compared to the summed SFHs from Figure 13, as the typical SFH is
not broadened due to redshift broadening effects.

Interestingly, when we compare the inferred typical SFHs for the SUSPENSE
sample with a sample of quiescent galaxies at similar redshifts
($z_{\text{obs}}\sim 1.4$ and $z_{\text{obs}}\sim 2.1$) from Beverage et al.
(2023), we see that the our star-formation timescales are a factor $\sim$2 and
$\sim$6 longer, for the low and high-redshift bins respectively. This
difference likely reflects the different methods to derive the star-formation
timescales; for the Beverage et al. (2023) sample these are inferred from
chemical abundances, interpreted using a simple chemical evolution model.
While the overall trend of high-redshift quiescent galaxies forming more
rapidly compared to the low-redshift population also holds for this study, the
large difference between the inferred star-formation timescales between the
two methods shows we need to further investigate these results. To this end,
A. Beverage et al. (in preparation) will measure detailed chemical abundances
of the galaxies in our sample and directly compare the inferred SFHs from the
two methods.

##  5 Discussion

###  5.1 Implications for the evolution of the massive quiescent galaxy
population

In the previous section, we showed that the star-formation timescales of
massive quiescent galaxies becomes significantly shorter from
$z_{\text{obs}}\sim 2.2$ to $z_{\text{obs}}\sim 1.3$, indicating that the mass
build-up of distant quiescent galaxies is much more rapid compared to later
times. This is consistent with the findings from Beverage et al. (2023), who
show that the star-formation timescale of quiescent galaxies differs
significantly between $z\sim 0$ and $z\sim 2.1$. Furthermore, observations of
quiescent galaxies out to $z\sim 5$ (e.g., Forrest et al., 2020b; Glazebrook
et al., 2023; Carnall et al., 2023b; Setton et al., 2024) suggest that the
evolution of star-formation timescale extends beyond the redshift range
covered by our sample. In this section we discuss the implications for the
evolution of the quiescent galaxy population from these observations.

One explanation for the observed evolution in the star-formation timescale
across redshift is progenitor bias (van Dokkum & Franx, 2001); between
$z_{\text{obs}}\sim 1.3$ and $z_{\text{obs}}\sim 2.2$ the quiescent galaxy
population grows in number over time as more star-forming galaxies are
quenched. Thus in our low redshift bin there are more galaxies that formed
more gradually (i.e. longer star formation timescales) and entered the
quiescent population at a later time.

A second explanation for the evolution of the SFHs are mergers, which change
the properties of individual galaxies. In this scenario, major and minor
mergers add new stellar populations to the galaxy, with SFHs that are
different to those from the stars that are formed in situ. If the SFH of the
accreted galaxy is more extended, or if the stars in that galaxy are younger,
we would find that the SFH is broadened. The importance of minor mergers in
the evolution of the quiescent galaxies has been supported by observations of
the evolution in size and color gradients (e.g., Bezanson et al., 2009; Naab
et al., 2009; van Dokkum et al., 2010; Suess et al., 2020, 2021; Miller et
al., 2023). Furthermore, observations have shown that distant quiescent
galaxies are surrounded by numerous small companions, which may merge into the
central galaxy at later times (Newman et al., 2012; Suess et al., 2023).

Furthermore, late-time star formation in rejuvenated galaxies could also
explain the more extended SFHs of individual galaxies in the low redshift bin.
In this scenario galaxies that were initially quenched experience a secondary
star-formation episode that contributes a small fraction of the stellar mass
but broadens the SFH of the galaxy. Observations have shown that such
rejuvenation is present in a significant fraction of the distant quiescent
galaxy population (e.g., Trager et al., 2000; Schawinski et al., 2007; Spilker
et al., 2018; Chauke et al., 2019). Although none of the SFHs of the quiescent
galaxies in our sample are consistent with such a rejuvenation episode (see
Appendix B), we note that our results could be biased to older star-formation
episodes as we do not include emission lines in the SFH modeling.

If progenitor bias, rather than mergers and late-time star formation, is the
main driver behind the observed evolution of the star-formation timescale,
this would imply that quiescent galaxies that formed rapidly in the early
universe should still exists as rare relics in the population at lower
redshifts. Indeed, in the $z_{\text{obs}}\sim 1.3$ bin we find one galaxy
(127154) with a formation time and star-formation timescale that is similar to
its high-redshift counterparts. This galaxy thus became part of the quiescent
galaxy population long before it was observed, and has not had significant
late-time star formation or mergers with younger galaxies. This finding shows
that for this individual galaxy its mass was built up fully during an initial
period of star formation.

Thus, though population growth can explain our observations, we cannot rule
out that mergers and late-time star formation also contribute to the observed
evolution of the star-formation timescale at $z>1$. A detailed analysis of the
(resolved) chemical, kinematic, and stellar population properties properties
of our sample will be subject of future studies, and will allow us to
investigate the influence of minor and major mergers on the observed evolution
in star-formation timescales in more detail.

###  5.2 Implications for “maximally old” distant quiescent galaxies

Several spectroscopic studies of distant quiescent galaxies have shown
indications of massive galaxies that have already formed nearly all their
stars by $z=5$ (e.g. Kriek et al., 2016; Forrest et al., 2020b; Glazebrook et
al., 2023; Carnall et al., 2023b; Setton et al., 2024; Beverage et al., 2023).
In our sample, the most extreme galaxy has an average formation redshift of
$z\sim 5$ and formed nearly all its stars by $z\sim 3$. Thus, our galaxies are
not as extreme as the ones found by the above studies.

The lack of maximally old galaxies in our sample could in part be explained by
the fact that the observation redshift of our sample is lower compared to most
of the studies mentioned above. We will thus, by construction, have fewer
maximally old galaxies in our sample, as the low-redshift population, on
average, quenched (and thus formed) at a later time compared to samples at
higher redshifts. Furthermore, while previously detected maximally old
galaxies are often extremely massive and/or bright, our sample is more
representative of the complete quiescent galaxy population (see Figure 8),
without a strong bias for extremely bright or massive galaxies. Compared to
previous studies of maximally old galaxies, our galaxy sample thus presents a
more complete overview of the quiescent galaxy population at its redshift,
making the detection of maximally old galaxies less likely.

We also note that the methods for obtaining the stellar age differ between our
study and other studies presenting maximally old galaxies, leading to
uncertainties in comparing the mean formation times of these samples. Firstly,
the ages of the galaxies presented by Forrest et al. (2020b); Glazebrook et
al. (2023); Nanayakkara et al. (2024) and Setton et al. (2024) were inferred
from low-resolution spectra for which individual metal absorption features
cannot be resolved. Without measurements of individual metal absorption
features it is difficult to break the age-metallicity degeneracy, leading to
large uncertainties in the reported stellar ages for these galaxies.
Furthermore, while the Beverage et al. (2023) and Kriek et al. (2016) samples
modeled the individual metal absorption features, the final age was modeled as
a simple single or two component age, rather than a full SFH. This
simplification of the SFHs of these samples compared to the non-parametric
SFHs used in this study makes comparison between the our study and these
studies more challenging.

We note that our current study also suffers from modeling uncertainties,
particularly as we model the photometric and spectroscopic data
simultaneously. For example, although we do not include emission lines in our
modeling, our inferred SFHs could be biased to late-time star formation, as
these younger stellar populations will outshine the oldest stars leading to
more extended and younger SFHs. Furthermore, as discussed in Section 4.1, we
do indeed find that the models do not always perfectly fit metal absorption
lines. Besides this, we find super-Solar metallicities for some galaxies in
our sample (see Table 2.2), which is higher compared to previous studies of
distant quiescent galaxies (e.g., Kriek et al., 2016, 2019; Morishita et al.,
2018; Saracco et al., 2020; Carnall et al., 2022; Zhuang et al., 2023;
Beverage et al., 2023). If we assume lower metallicities, the inferred ages
are older. However, we also note that Prospector cannot model the abundances
of individual chemical elements, leading to uncertainties in the inferred
stellar metallicities. In future work, we will fit our spectra with alf
(Conroy & van Dokkum, 2012; Conroy et al., 2018) to constrain abundances and
ages simultaneously and obtain more accurate ages and metallicities (A.
Beverage et al., in preparation).

##  6 Summary

In this paper we present an overview and first results of the Cycle 1 JWST-
SUSPENSE program. This ultradeep spectroscopic survey targeted 20 distant
quiescent galaxy candidates at $z=1-3$, as well as 53 star-forming galaxies at
similar and higher redshifts. The observations were executed with the NIRSpec
MSA, using the G140M-F100LP dispersion-filter combination resulting in a rest-
frame wavelength coverage of $\sim 3000-7000$ Å at the targeted redshift. We
use $\sim$5 shutter slitlets and a two-shutter nod, to observe the full extent
of our primary targets and avoid self-subtraction. With 16 hours of on-source
integration time, the SUSPENSE program has obtained the largest sample of
quiescent galaxies with ultra-deep (18 galaxies with continuum SNR$>10$ Å-1)
medium-resolution spectra at $z>1$ to date.

The spectra of all quiescent targets show clear Balmer and/or metal absorption
features. Our quiescent galaxy sample is representative of the full quiescent
galaxy population in our redshift range in $UVJ$ space. We fit spectroscopic
redshifts, stellar population parameters, and detailed SFHs for all quiescent
targets in our sample using spectrophotometric fitting with Prospector. All
galaxies in our sample are massive, with stellar masses ranging from
$10^{10.27}-10^{11.49}$ M⊙. We used the best-fit SFHs to determine the SFRs
and find that all 20 of our primary targets lie $>3.5\sigma$ below the star-
forming main sequence at their redshift, indicating that they are indeed
quiescent galaxies. Eleven of our targets show emission lines in their spectra
(primarily [O ii], and [N ii] emission), likely originating from ionization
due to an AGN or hot evolved stars. These emission lines will be the subject
of a future study.

The mass-weighted ages of the galaxies in our sample range from $0.8-3.0$ Gyr,
with star-formation timescales between $\sim 0.5-3$ Gyr. Four galaxies in our
sample were already quiescent by $z=3$. On average, the high redshift galaxies
formed earlier than their low-redshift counterparts; 50% of the stellar mass
of galaxies in the $z\sim 2.2$ bin formed at $z_{\text{form}}\sim 4$, while
for $z\sim 1.3$ bin the average formation redshift is $z_{\text{form}}\sim 3$.
This difference in formation redshift is consistent with the typically shorter
star formation timescales we find for galaxies at $z\sim 2.2$ compared to
$z\sim 1.3$.

The observed evolution in star-formation timescale can be explained by the
growth of the quiescent galaxy population as the universe ages; at lower
redshifts the addition of new galaxies with more extended SFHs alters the
average SFH properties of the quiescent population. Though, using our current
analysis we cannot rule out that for some galaxies mergers and/or late-time
star formation contribute to a more extended SFH.

In future studies we will extend upon this analysis by studying the (resolved)
stellar, chemical, and kinematic properties of our quiescent targets. Thus,
SUSPENSE presents a unique sample that will help us further unravel how these
massive quiescent galaxies formed, when and why their star formation quenched,
and how they evolved into the local quiescent population. Moreover, the
spectra from the SUSPENSE program illustrate the power of JWST to obtain the
deep, high-resolution spectroscopy needed to study distant, massive quiescent
galaxies in great detail. Thus, JWST has marked a new era in the study of
massive quiescent galaxies, where ultra-deep data will revolutionize our
understanding of the formation and evolution of quiescent galaxies over cosmic
time.

This work is based on observations made with the NASA/ESA/CSA James Webb Space
Telescope. The data were obtained from the Mikulski Archive for Space
Telescopes at the Space Telescope Science Institute, which is operated by the
Association of Universities for Research in Astronomy, Inc., under NASA
contract NAS 5-03127 for JWST. These observations are associated with program
JWST-GO-2110. Support for program JWST-GO-2110 was provided by NASA through a
grant from the Space Telescope Science Institute, which is operated by the
Association of Universities for Research in Astronomy, Inc., under NASA
contract NAS 5-03127. MK acknowledges funding from the Dutch Research Council
(NWO) through the award of the Vici grant VI.C.222.047 (project 2010007169).
PEMP acknowledges the support from the Dutch Research Council (NWO) through
the Veni grant VI.Veni.222.364.

## References

  * Allard et al. (2000) Allard, F., Hauschildt, P. H., & Schwenke, D. 2000, ApJ, 540, 1005, doi: 10.1086/309366
  * Almeida et al. (2012) Almeida, J. S., Terlevich, R., Terlevich, E., Fernandes, R. C., & Morales-Luis, A. B. 2012, The Astrophysical Journal, 756, 163, doi: 10.1088/0004-637X/756/2/163
  * Antwi-Danso et al. (2023) Antwi-Danso, J., Papovich, C., Esdaile, J., et al. 2023, arXiv e-prints, arXiv:2307.09590, doi: 10.48550/arXiv.2307.09590
  * Behroozi et al. (2019) Behroozi, P., Wechsler, R. H., Hearin, A. P., & Conroy, C. 2019, MNRAS, 488, 3143, doi: 10.1093/mnras/stz1182
  * Belfiore et al. (2016) Belfiore, F., Maiolino, R., Maraston, C., et al. 2016, MNRAS, 461, 3111, doi: 10.1093/mnras/stw1234
  * Belli et al. (2017) Belli, S., Newman, A. B., & Ellis, R. S. 2017, ApJ, 834, 18, doi: 10.3847/1538-4357/834/1/18
  * Belli et al. (2019) —. 2019, ApJ, 874, 17, doi: 10.3847/1538-4357/ab07af
  * Belli et al. (2021) Belli, S., Contursi, A., Genzel, R., et al. 2021, ApJ, 909, L11, doi: 10.3847/2041-8213/abe6a6
  * Belli et al. (2023) Belli, S., Park, M., Davies, R. L., et al. 2023, arXiv e-prints, arXiv:2308.05795, doi: 10.48550/arXiv.2308.05795
  * Bertin & Arnouts (1996) Bertin, E., & Arnouts, S. 1996, A&AS, 117, 393
  * Beverage et al. (2023) Beverage, A. G., Kriek, M., Suess, K. A., et al. 2023, arXiv e-prints, arXiv:2312.05307, doi: 10.48550/arXiv.2312.05307
  * Bezanson et al. (2009) Bezanson, R., van Dokkum, P. G., Tal, T., et al. 2009, ApJ, 697, 1290, doi: 10.1088/0004-637X/697/2/1290
  * Bournaud et al. (2007) Bournaud, F., Jog, C. J., & Combes, F. 2007, A&A, 476, 1179, doi: 10.1051/0004-6361:20078010
  * Brammer (2023) Brammer, G. 2023, grizli, 1.8.3, Zenodo, doi: 10.5281/zenodo.7767790
  * Brammer et al. (2008) Brammer, G. B., van Dokkum, P. G., & Coppi, P. 2008, ApJ, 686, 1503, doi: 10.1086/591786
  * Bushouse et al. (2022) Bushouse, H., Eisenhamer, J., Dencheva, N., et al. 2022, JWST Calibration Pipeline, 1.8.2, Zenodo, doi: 10.5281/zenodo.7229890
  * Carnall et al. (2019) Carnall, A. C., McLure, R. J., Dunlop, J. S., et al. 2019, MNRAS, 490, 417, doi: 10.1093/mnras/stz2544
  * Carnall et al. (2022) —. 2022, ApJ, 929, 131, doi: 10.3847/1538-4357/ac5b62
  * Carnall et al. (2023a) —. 2023a, Nature, 619, 716, doi: 10.1038/s41586-023-06158-6
  * Carnall et al. (2023b) Carnall, A. C., McLeod, D. J., McLure, R. J., et al. 2023b, MNRAS, 520, 3974, doi: 10.1093/mnras/stad369
  * Carollo et al. (2013) Carollo, C. M., Bschorr, T. J., Renzini, A., et al. 2013, ApJ, 773, 112, doi: 10.1088/0004-637X/773/2/112
  * Cecchi et al. (2019) Cecchi, R., Bolzonella, M., Cimatti, A., & Girelli, G. 2019, ApJ, 880, L14, doi: 10.3847/2041-8213/ab2c80
  * Chabrier (2003) Chabrier, G. 2003, PASP, 115, 763, doi: 10.1086/376392
  * Chang et al. (2013) Chang, Y.-Y., van der Wel, A., Rix, H.-W., et al. 2013, ApJ, 773, 149, doi: 10.1088/0004-637X/773/2/149
  * Chauke et al. (2019) Chauke, P., van der Wel, A., Pacifici, C., et al. 2019, ApJ, 877, 48, doi: 10.3847/1538-4357/ab164d
  * Choi et al. (2014) Choi, J., Conroy, C., Moustakas, J., et al. 2014, ApJ, 792, 95, doi: 10.1088/0004-637X/792/2/95
  * Choi et al. (2016) Choi, J., Dotter, A., Conroy, C., et al. 2016, ApJ, 823, 102, doi: 10.3847/0004-637X/823/2/102
  * Cimatti et al. (2004) Cimatti, A., Daddi, E., Renzini, A., et al. 2004, Nature, 430, 184, doi: 10.1038/nature02668
  * Conroy & Gunn (2010) Conroy, C., & Gunn, J. E. 2010, ApJ, 712, 833, doi: 10.1088/0004-637X/712/2/833
  * Conroy et al. (2009) Conroy, C., Gunn, J. E., & White, M. 2009, ApJ, 699, 486, doi: 10.1088/0004-637X/699/1/486
  * Conroy & van Dokkum (2012) Conroy, C., & van Dokkum, P. G. 2012, ApJ, 760, 71, doi: 10.1088/0004-637X/760/1/71
  * Conroy et al. (2018) Conroy, C., Villaume, A., van Dokkum, P. G., & Lind, K. 2018, ApJ, 854, 139, doi: 10.3847/1538-4357/aaab49
  * Cutler et al. (2022) Cutler, S. E., Whitaker, K. E., Mowla, L. A., et al. 2022, ApJ, 925, 34, doi: 10.3847/1538-4357/ac341c
  * Daddi et al. (2005) Daddi, E., Renzini, A., Pirzkal, N., et al. 2005, ApJ, 626, 680, doi: 10.1086/430104
  * Davies et al. (2024) Davies, R. L., Belli, S., Park, M., et al. 2024, MNRAS, 528, 4976, doi: 10.1093/mnras/stae327
  * de Graaff et al. (2023) de Graaff, A., Rix, H.-W., Carniani, S., et al. 2023, arXiv e-prints, arXiv:2308.09742, doi: 10.48550/arXiv.2308.09742
  * de Graaff et al. (2024) de Graaff, A., Setton, D. J., Brammer, G., et al. 2024, arXiv e-prints, arXiv:2404.05683, doi: 10.48550/arXiv.2404.05683
  * D’Eugenio et al. (2023) D’Eugenio, F., Perez-Gonzalez, P., Maiolino, R., et al. 2023, arXiv e-prints, arXiv:2308.06317, doi: 10.48550/arXiv.2308.06317
  * Dotter (2016) Dotter, A. 2016, ApJS, 222, 8, doi: 10.3847/0067-0049/222/1/8
  * Esdaile et al. (2021) Esdaile, J., Glazebrook, K., Labbé, I., et al. 2021, ApJ, 908, L35, doi: 10.3847/2041-8213/abe11e
  * Falcón-Barroso et al. (2011) Falcón-Barroso, J., Sánchez-Blázquez, P., Vazdekis, A., et al. 2011, A&A, 532, A95, doi: 10.1051/0004-6361/201116842
  * Ferruit et al. (2022) Ferruit, P., Jakobsen, P., Giardino, G., et al. 2022, A&A, 661, A81, doi: 10.1051/0004-6361/202142673
  * Forrest et al. (2020a) Forrest, B., Annunziatella, M., Wilson, G., et al. 2020a, ApJ, 890, L1, doi: 10.3847/2041-8213/ab5b9f
  * Forrest et al. (2020b) Forrest, B., Marsan, Z. C., Annunziatella, M., et al. 2020b, ApJ, 903, 47, doi: 10.3847/1538-4357/abb819
  * Forrest et al. (2022) Forrest, B., Wilson, G., Muzzin, A., et al. 2022, ApJ, 938, 109, doi: 10.3847/1538-4357/ac8747
  * Franx et al. (2003) Franx, M., Labbé, I., Rudnick, G., et al. 2003, ApJ, 587, L79, doi: 10.1086/375155
  * Gallazzi et al. (2014) Gallazzi, A., Bell, E. F., Zibetti, S., Brinchmann, J., & Kelson, D. D. 2014, ApJ, 788, 72, doi: 10.1088/0004-637X/788/1/72
  * Glazebrook et al. (2017) Glazebrook, K., Schreiber, C., Labbé, I., et al. 2017, Nature, 544, 71, doi: 10.1038/nature21680
  * Glazebrook et al. (2023) Glazebrook, K., Nanayakkara, T., Schreiber, C., et al. 2023, arXiv e-prints, arXiv:2308.05606, doi: 10.48550/arXiv.2308.05606
  * Grogin et al. (2011) Grogin, N. A., Kocevski, D. D., Faber, S. M., et al. 2011, ApJS, 197, 35, doi: 10.1088/0067-0049/197/2/35
  * Gu et al. (2022) Gu, M., Greene, J. E., Newman, A. B., et al. 2022, ApJ, 932, 103, doi: 10.3847/1538-4357/ac69ea
  * Horne (1986) Horne, K. 1986, PASP, 98, 609, doi: 10.1086/131801
  * Jafariyazani et al. (2020) Jafariyazani, M., Newman, A. B., Mobasher, B., et al. 2020, ApJ, 897, L42, doi: 10.3847/2041-8213/aba11c
  * Ji & Giavalisco (2022) Ji, Z., & Giavalisco, M. 2022, ApJ, 935, 120, doi: 10.3847/1538-4357/ac7f43
  * Johnson et al. (2021) Johnson, B. D., Leja, J., Conroy, C., & Speagle, J. S. 2021, ApJS, 254, 22, doi: 10.3847/1538-4365/abef67
  * Khochfar & Silk (2006) Khochfar, S., & Silk, J. 2006, ApJ, 648, L21, doi: 10.1086/507768
  * Koekemoer et al. (2011) Koekemoer, A. M., Faber, S. M., Ferguson, H. C., et al. 2011, ApJS, 197, 36, doi: 10.1088/0067-0049/197/2/36
  * Kriek & Conroy (2013) Kriek, M., & Conroy, C. 2013, ApJ, 775, L16, doi: 10.1088/2041-8205/775/1/L16
  * Kriek et al. (2006) Kriek, M., van Dokkum, P. G., Franx, M., et al. 2006, ApJ, 649, L71, doi: 10.1086/508371
  * Kriek et al. (2010) Kriek, M., Labbé, I., Conroy, C., et al. 2010, ApJ, 722, L64, doi: 10.1088/2041-8205/722/1/L64
  * Kriek et al. (2016) Kriek, M., Conroy, C., van Dokkum, P. G., et al. 2016, Nature, 540, 248, doi: 10.1038/nature20570
  * Kriek et al. (2019) Kriek, M., Price, S. H., Conroy, C., et al. 2019, ApJ, 880, L31, doi: 10.3847/2041-8213/ab2e75
  * Kriek et al. (2023) Kriek, M., Beverage, A. G., Price, S. H., et al. 2023, arXiv e-prints, arXiv:2311.16232, doi: 10.48550/arXiv.2311.16232
  * Kroupa (2001) Kroupa, P. 2001, MNRAS, 322, 231, doi: 10.1046/j.1365-8711.2001.04022.x
  * Lagos et al. (2018) Lagos, C. d. P., Stevens, A. R. H., Bower, R. G., et al. 2018, Monthly Notices of the Royal Astronomical Society, 473, 4956–4974, doi: 10.1093/mnras/stx2667
  * Leja et al. (2019a) Leja, J., Carnall, A. C., Johnson, B. D., Conroy, C., & Speagle, J. S. 2019a, ApJ, 876, 3, doi: 10.3847/1538-4357/ab133c
  * Leja et al. (2019b) Leja, J., Johnson, B. D., Conroy, C., et al. 2019b, ApJ, 877, 140, doi: 10.3847/1538-4357/ab1d5a
  * Leja et al. (2022) Leja, J., Speagle, J. S., Ting, Y.-S., et al. 2022, ApJ, 936, 165, doi: 10.3847/1538-4357/ac887d
  * Lu et al. (2024) Lu, S., Daddi, E., Maraston, C., et al. 2024, arXiv e-prints, arXiv:2403.07414.  https://arxiv.org/abs/2403.07414
  * Maiolino & Mannucci (2019) Maiolino, R., & Mannucci, F. 2019, A&A Rev., 27, 3, doi: 10.1007/s00159-018-0112-2
  * Maraston (2005) Maraston, C. 2005, MNRAS, 362, 799, doi: 10.1111/j.1365-2966.2005.09270.x
  * Marchesini et al. (2023) Marchesini, D., Brammer, G., Morishita, T., et al. 2023, ApJ, 942, L25, doi: 10.3847/2041-8213/acaaac
  * Maseda et al. (2021) Maseda, M. V., van der Wel, A., Franx, M., et al. 2021, ApJ, 923, 18, doi: 10.3847/1538-4357/ac2bfe
  * McCracken et al. (2012) McCracken, H. J., Milvang-Jensen, B., Dunlop, J., et al. 2012, A&A, 544, A156, doi: 10.1051/0004-6361/201219507
  * Mendel et al. (2020) Mendel, J. T., Beifiori, A., Saglia, R. P., et al. 2020, ApJ, 899, 87, doi: 10.3847/1538-4357/ab9ffc
  * Miller et al. (2023) Miller, T. B., van Dokkum, P., & Mowla, L. 2023, ApJ, 945, 155, doi: 10.3847/1538-4357/acbc74
  * Momcheva et al. (2017) Momcheva, I. G., van Dokkum, P. G., van der Wel, A., et al. 2017, PASP, 129, 015004, doi: 10.1088/1538-3873/129/971/015004
  * Morishita et al. (2018) Morishita, T., Abramson, L. E., Treu, T., et al. 2018, ApJ, 856, L4, doi: 10.3847/2041-8213/aab493
  * Mowla et al. (2018) Mowla, L., van Dokkum, P., Brammer, G., et al. 2018, ArXiv e-prints.  https://arxiv.org/abs/1808.04379
  * Muzzin et al. (2013a) Muzzin, A., Marchesini, D., Stefanon, M., et al. 2013a, ApJ, 777, 18, doi: 10.1088/0004-637X/777/1/18
  * Muzzin et al. (2013b) —. 2013b, ApJS, 206, 8, doi: 10.1088/0067-0049/206/1/8
  * Naab et al. (2009) Naab, T., Johansson, P. H., & Ostriker, J. P. 2009, ApJ, 699, L178, doi: 10.1088/0004-637X/699/2/L178
  * Naab et al. (2014) Naab, T., Oser, L., Emsellem, E., et al. 2014, MNRAS, 444, 3357, doi: 10.1093/mnras/stt1919
  * Nanayakkara et al. (2024) Nanayakkara, T., Glazebrook, K., Jacobs, C., et al. 2024, Scientific Reports, 14, 3724, doi: 10.1038/s41598-024-52585-4
  * Newman et al. (2018) Newman, A. B., Belli, S., Ellis, R. S., & Patel, S. G. 2018, ApJ, 862, 126, doi: 10.3847/1538-4357/aacd4f
  * Newman et al. (2012) Newman, A. B., Ellis, R. S., Bundy, K., & Treu, T. 2012, ApJ, 746, 162, doi: 10.1088/0004-637X/746/2/162
  * Nidever et al. (2024) Nidever, D. L., Gilbert, K., Tollerud, E., et al. 2024, in Early Disk-Galaxy Formation from JWST to the Milky Way, ed. F. Tabatabaei, B. Barbuy, & Y.-S. Ting, Vol. 377, 115–122, doi: 10.1017/S1743921323002016
  * Noll et al. (2009) Noll, S., Pierini, D., Cimatti, A., et al. 2009, A&A, 499, 69, doi: 10.1051/0004-6361/200811526
  * Oke & Gunn (1983) Oke, J. B., & Gunn, J. E. 1983, ApJ, 266, 713, doi: 10.1086/160817
  * Park et al. (2023) Park, M., Belli, S., Conroy, C., et al. 2023, ApJ, 953, 119, doi: 10.3847/1538-4357/acd54a
  * Poggianti et al. (2013) Poggianti, B. M., Calvi, R., Bindoni, D., et al. 2013, ApJ, 762, 77, doi: 10.1088/0004-637X/762/2/77
  * Saracco et al. (2020) Saracco, P., Marchesini, D., La Barbera, F., et al. 2020, ApJ, 905, 40, doi: 10.3847/1538-4357/abc7c4
  * Schawinski et al. (2007) Schawinski, K., Kaviraj, S., Khochfar, S., et al. 2007, ApJS, 173, 512, doi: 10.1086/516631
  * Schlawin et al. (2020) Schlawin, E., Leisenring, J., Misselt, K., et al. 2020, The Astronomical Journal, 160, 231, doi: 10.3847/1538-3881/abb811
  * Schreiber et al. (2018) Schreiber, C., Glazebrook, K., Nanayakkara, T., et al. 2018, A&A, 618, A85, doi: 10.1051/0004-6361/201833070
  * Scoville et al. (2007) Scoville, N., Aussel, H., Brusa, M., et al. 2007, ApJS, 172, 1, doi: 10.1086/516585
  * Setton et al. (2024) Setton, D. J., Khullar, G., Miller, T. B., et al. 2024, arXiv e-prints, arXiv:2402.05664, doi: 10.48550/arXiv.2402.05664
  * Speagle (2020) Speagle, J. S. 2020, MNRAS, 493, 3132, doi: 10.1093/mnras/staa278
  * Spilker et al. (2018) Spilker, J., Bezanson, R., Barišić, I., et al. 2018, ApJ, 860, 103, doi: 10.3847/1538-4357/aac438
  * Straatman et al. (2014) Straatman, C. M. S., Labbé, I., Spitler, L. R., et al. 2014, ApJ, 783, L14, doi: 10.1088/2041-8205/783/1/L14
  * Suess et al. (2019) Suess, K. A., Kriek, M., Price, S. H., & Barro, G. 2019, ApJ, 877, 103, doi: 10.3847/1538-4357/ab1bda
  * Suess et al. (2020) —. 2020, ApJ, 899, L26, doi: 10.3847/2041-8213/abacc9
  * Suess et al. (2021) —. 2021, ApJ, 915, 87, doi: 10.3847/1538-4357/abf1e4
  * Suess et al. (2022a) Suess, K. A., Kriek, M., Bezanson, R., et al. 2022a, ApJ, 926, 89, doi: 10.3847/1538-4357/ac404a
  * Suess et al. (2022b) Suess, K. A., Leja, J., Johnson, B. D., et al. 2022b, ApJ, 935, 146, doi: 10.3847/1538-4357/ac82b0
  * Suess et al. (2023) Suess, K. A., Williams, C. C., Robertson, B., et al. 2023, ApJ, 956, L42, doi: 10.3847/2041-8213/acf5e6
  * Tanaka et al. (2019) Tanaka, M., Valentino, F., Toft, S., et al. 2019, ApJ, 885, L34, doi: 10.3847/2041-8213/ab4ff3
  * Toft et al. (2017) Toft, S., Zabl, J., Richard, J., et al. 2017, Nature, 546, 510, doi: 10.1038/nature22388
  * Trager et al. (2000) Trager, S. C., Faber, S. M., Worthey, G., & González, J. J. 2000, AJ, 120, 165, doi: 10.1086/301442
  * Treu et al. (2010) Treu, T., Auger, M. W., Koopmans, L. V. E., et al. 2010, ApJ, 709, 1195, doi: 10.1088/0004-637X/709/2/1195
  * Valentino et al. (2020) Valentino, F., Tanaka, M., Davidzon, I., et al. 2020, ApJ, 889, 93, doi: 10.3847/1538-4357/ab64dc
  * Valentino et al. (2023) Valentino, F., Brammer, G., Gould, K. M. L., et al. 2023, ApJ, 947, 20, doi: 10.3847/1538-4357/acbefa
  * van der Wel et al. (2011) van der Wel, A., Rix, H.-W., Wuyts, S., et al. 2011, ApJ, 730, 38, doi: 10.1088/0004-637X/730/1/38
  * van der Wel et al. (2014) van der Wel, A., Franx, M., van Dokkum, P. G., et al. 2014, ApJ, 788, 28, doi: 10.1088/0004-637X/788/1/28
  * van Dokkum & Franx (2001) van Dokkum, P. G., & Franx, M. 2001, ApJ, 553, 90, doi: 10.1086/320645
  * van Dokkum et al. (2008) van Dokkum, P. G., Franx, M., Kriek, M., et al. 2008, ApJ, 677, L5, doi: 10.1086/587874
  * van Dokkum et al. (2010) van Dokkum, P. G., Whitaker, K. E., Brammer, G., et al. 2010, ApJ, 709, 1018, doi: 10.1088/0004-637X/709/2/1018
  * Wild et al. (2020) Wild, V., Taj Aldeen, L., Carnall, A., et al. 2020, MNRAS, 494, 529, doi: 10.1093/mnras/staa674
  * Yan & Blanton (2012) Yan, R., & Blanton, M. R. 2012, ApJ, 747, 61, doi: 10.1088/0004-637X/747/1/61
  * Yan et al. (2006) Yan, R., Newman, J. A., Faber, S. M., et al. 2006, ApJ, 648, 281, doi: 10.1086/505629
  * Zhuang et al. (2023) Zhuang, Z., Leethochawalit, N., Kirby, E. N., et al. 2023, ApJ, 948, 132, doi: 10.3847/1538-4357/acc79b
  * Zibetti et al. (2013) Zibetti, S., Gallazzi, A., Charlot, S., Pierini, D., & Pasquali, A. 2013, MNRAS, 428, 1479, doi: 10.1093/mnras/sts126

##  Appendix A Quiescent galaxy spectra

Figure 15: Same as Figure 5.

Figure 16: Continued.

Figure 17: Continued.

Figure 18: continued.

Figure 19: continued.

##  Appendix B SFHs

Figure 20: Best-fit SFHs for all galaxies in the quiescent sample. The SFHs
are ordered by decreasing redshift, as indicated in the top right of each
panel. The shaded areas represent the 16th-84th percentile confidence
intervals of the SFHs. The gray shaded area in each panel corresponds to ages
that are older than the age of the universe for the observed redshift.

##  Appendix C Filler galaxies

Table 3: Overview of confirmed star-forming filler galaxy sample