# DESI Survey Validation Spectra Reveal an Increasing Fraction of Recently
Quenched Galaxies at $z\sim 1$

David J. Setton Department of Physics and Astronomy and PITT PACC, University
of Pittsburgh, Pittsburgh, PA 15260, USA  Biprateep Dey Department of Physics
and Astronomy and PITT PACC, University of Pittsburgh, Pittsburgh, PA 15260,
USA  Gourav Khullar Department of Physics and Astronomy and PITT PACC,
University of Pittsburgh, Pittsburgh, PA 15260, USA  Rachel Bezanson
Department of Physics and Astronomy and PITT PACC, University of Pittsburgh,
Pittsburgh, PA 15260, USA  Jeffrey A. Newman Department of Physics and
Astronomy and PITT PACC, University of Pittsburgh, Pittsburgh, PA 15260, USA
Jessica N. Aguilar Lawrence Berkeley National Laboratory, 1 Cyclotron Road,
Berkeley, CA 94720, USA  Steven Ahlen Boston University, 590 Commonwealth
Avenue, Boston, MA 02215, USA  Brett H. Andrews Department of Physics and
Astronomy and PITT PACC, University of Pittsburgh, Pittsburgh, PA 15260, USA
David Brooks  Department of Physics & Astronomy, University College London,
Gower Street, London, WC1E 6BT, UK  Axel de la Macorra  Instituto de Física,
Universidad Nacional Autónoma de México, Cd. de México C.P. 04510, México
Arjun Dey NSF’s NOIRLab, 950 N. Cherry Avenue, Tucson, AZ 85719, USA  Sarah
Eftekharzadeh SOFIA Science Center, NASA Ames Research Center, Moffett Field,
CA 94035, USA  Andreu Font-Ribera Institut de Física d’Altes Energies (IFAE),
The Barcelona Institute of Science and Technology, Campus UAB, 08193
Bellaterra Barcelona, Spain  Satya Gontcho A Gontcho Lawrence Berkeley
National Laboratory, 1 Cyclotron Road, Berkeley, CA 94720, USA  Anthony Kremin
Lawrence Berkeley National Laboratory, 1 Cyclotron Road, Berkeley, CA 94720,
USA  Stephanie Juneau NSF’s NOIRLab, 950 N. Cherry Avenue, Tucson, AZ 85719,
USA  Martin Landriau Lawrence Berkeley National Laboratory, 1 Cyclotron Road,
Berkeley, CA 94720, USA  Aaron Meisner NSF’s NOIRLab, 950 N. Cherry Ave.,
Tucson, AZ 85719, USA  Ramon Miquel  Institució Catalana de Recerca i Estudis
Avançats, Passeig de Lluís Companys, 23, 08010 Barcelona, Spain  Institut de
Física d’Altes Energies (IFAE), The Barcelona Institute of Science and
Technology, Campus UAB, 08193 Bellaterra Barcelona, Spain  John Moustakas
Department of Physics and Astronomy, Siena College, 515 Loudon Road,
Loudonville, NY 12211  Alan Pearl Department of Physics and Astronomy and PITT
PACC, University of Pittsburgh, Pittsburgh, PA 15260, USA  Francisco Prada
Instituto de Astrofísica de Andalucía (CSIC), Glorieta de la Astronomía, s/n,
E-18008 Granada, Spain  Gregory Tarlé University of Michigan, Ann Arbor, MI
48109  Małgorzata Siudek Institute of Space Sciences (ICE, CSIC), Campus UAB,
Carrer de Magrans, 08193 Barcelona, Spain  Institut de Física d’Altes Energies
(IFAE), The Barcelona Institute of Science and Technology, 08193 Bellaterra
(Barcelona), Spain  Benjamin Alan Weaver  NSF’s NOIRLab, 950 N. Cherry Ave.,
Tucson, AZ 85719, USA  Zhimin Zhou National Astronomical Observatories,
Chinese Academy of Sciences, A20 Datun Rd., Chaoyang District, Beijing,
100012, P.R. China  Hu Zou National Astronomical Observatories, Chinese
Academy of Sciences, A20 Datun Rd., Chaoyang District, Beijing, 100012, P.R.
China David J. Setton<EMAIL_ADDRESS>
###### Abstract

We utilize $\sim 17000$ bright Luminous Red Galaxies (LRGs) from the novel
Dark Energy Spectroscopic Instrument Survey Validation spectroscopic sample,
leveraging its deep ($\sim 2.5$ hour/galaxy exposure time) spectra to
characterize the contribution of recently quenched galaxies to the massive
galaxy population at $0.4<z<1.3$. We use Prospector to infer non-parametric
star formation histories and identify a significant population of recently
quenched galaxies that have joined the quiescent population within the past
$\sim 1$ Gyr. The highest redshift subset (277 at $z>1$) of our sample of
recently quenched galaxies represents the largest spectroscopic sample of
post-starburst galaxies at that epoch. At $0.4<z<0.8$, we measure the number
density of quiescent LRGs, finding that recently quenched galaxies constitute
a growing fraction of the massive galaxy population with increasing lookback
time. Finally, we quantify the importance of this population amongst massive
(log($M_{\star}/M_{\odot}$) $>11.2$) LRGs by measuring the fraction of stellar
mass each galaxy formed in the Gyr before observation, $f_{\mathrm{1Gyr}}$.
Although galaxies with $f_{\mathrm{1Gyr}}$ $>0.1$ are rare at $z\sim 0.4$
($\lesssim 0.5\%$ of the population), by $z\sim 0.8$ they constitute $\sim
3\%$ of massive galaxies. Relaxing this threshold, we find that galaxies with
$f_{\mathrm{1Gyr}}>5\%$ constitute $\sim 10\%$ of the massive galaxy
population at $z\sim 0.8$. We also identify a small but significant sample of
galaxies at $z=1.1-1.3$ that formed with $f_{\mathrm{1Gyr}}$ $>50\%$, implying
that they may be analogues to high-redshift quiescent galaxies that formed on
similar timescales. Future analysis of this unprecedented sample promises to
illuminate the physical mechanisms that drive the quenching of massive
galaxies after cosmic noon.

Post-starburst galaxies (2176), Galaxy quenching (2040), Galaxy evolution
(594), Quenched galaxies (2016), Galaxies (573)

††journal: ApJ Letters

citnum

##  1 Introduction

In the local Universe, the vast majority of massive
(log$(M_{\star}/M_{\odot})\gtrsim 11$) galaxies are completely quiescent and
have been so for $5-10$ Gyr (e.g., Muzzin et al., 2013; Donnari et al., 2019;
Leja et al., 2021; Weaver et al., 2022). There is a growing consensus that two
distinct pathways to quiescence are at play, with a rapid path dominating the
buildup of quiescent galaxies at high redshifts and a slower channel that
populates the “green valley” at low redshift (e.g., Schawinski et al., 2014;
Wu et al., 2018; Maltby et al., 2018; Belli et al., 2019; Suess et al., 2021).
While the observational evidence for more rapid early star-formation in the
most massive systems at early times is strong (e.g., “downsizing” trends
observed in Juneau et al., 2005), the precise details of how the quiescent
population grows from the rapid quenching pathway as a function of cosmic time
remain very uncertain. Some studies have characterized the rates of rapid
quenching as a function of cosmic time using either photometric (Whitaker et
al., 2012; Wild et al., 2016; Belli et al., 2019; Park et al., 2022) or
shallow spectroscopic (Rowlands et al., 2018) samples and have found that
recently quenched galaxies, sometimes known as post-starburst galaxies,
stopped contributing significantly to the quiescent population by $z\gtrsim
0.5$. However, photometric studies yield weak constraints on timescales and
star formation histories. Thus, our picture of precisely when galaxies shut
off their star formation and the contribution of late-time star formation
remains poorly constrained.

Ideally, one would study the assembly of the red sequence by modeling the
star-formation histories of complete samples of massive galaxies and studying
how the incidence and characteristics of the population vary as a function of
cosmic time. An immense amount of work has been done over the past decades to
study that star formation histories of quiescent systems across cosmic time
using photometric and spectroscopic data (e.g., Tinsley & Gunn, 1976; Dressler
et al., 2004, 2016; Gallazzi et al., 2005, 2014; Daddi et al., 2005; Pacifici
et al., 2016; Carnall et al., 2019; Belli et al., 2019; Tacchella et al.,
2022). However, measuring the high-order moments of a star-formation history,
such as timescales and burst fractions, requires high signal-to-noise
continuum spectroscopy (Suess et al., 2022a). The limiting factor in
performing such modeling has been the availability of sufficiently deep
spectra beyond $z\gtrsim 0.5$. The largest existing spectroscopic samples have
not prioritized observing the gamut of quiescent galaxies; the SDSS LRG
(Eisenstein et al., 2001) and BOSS (Dawson et al., 2013) surveys targeted the
reddest quiescent galaxies, prioritizing pure, uniform samples at the expense
of younger, bluer galaxies, with targeting that steeply drops off at $z\sim
0.5$ where the post-starburst population beings to emerge (Wild et al., 2016;
Belli et al., 2019). In contrast, the EBOSS (Dawson et al., 2016) survey
poorly sampled the quiescent population in favor of more accessible emission
line sources. Deeper, more targeted surveys such as LEGA-C (van der Wel et
al., 2021; Wu et al., 2018), Carnegie-Spitzer-IMACS (Dressler et al., 2016),
and VANDELS (McLure et al., 2018; Carnall et al., 2019) have identified
samples of $\sim$1000s of massive quiescent galaxies at $z\gtrsim 0.5$,
requiring significant investments on deep fields to reveal spectroscopic
information for small samples.

The next generation of large spectroscopic surveys will revolutionize the
availability of continuum spectroscopy of massive galaxies. Here, we utilize
the Dark Energy Spectroscopic Instrument (DESI), a robotic, fiber-fed, highly
multiplexed spectroscopic surveyor that operates on the Mayall 4-meter
telescope at Kitt Peak National Observatory (DESI Collaboration et al.,
2016a). DESI, which can obtain simultaneous spectra of almost 5000 objects
over a $\sim 3^{\circ}$ field (DESI Collaboration et al., 2016b; Silber et
al., 2022, Miller et al. in preparation), is currently over a year into a
five-year survey of approximately one-third of the sky (DESI Collaboration et
al., 2016a), and has already observed more galaxies than the entire Sloan
Digital Sky Survey. The DESI Luminous Red Galaxy (LRG) target selection is
both broader in color and faintness relative to surveys like BOSS, and as a
result is complete to higher redshift ($z\sim 0.8$) and observes the Balmer
break out to $z\sim 1.3$ (Zhou et al., 2022). Here we show that even the
relatively small ($\sim 20000$ galaxies) but deep Survey Validation (SV)
sample of LRGs within the DESI Survey can be leveraged to identify new and
exciting samples of recently quenched galaxies that push well beyond what
previous surveys have been capable of.

In this letter, we infer non-parametric star formation histories of LRGs in
the DESI SV sample (DESI Collaboration et al. in preparation) and use them to
study the growth of the red sequence from recently quenched galaxies. In
Section 2, we describe the parent sample and demonstrate the use of non-
parametric star formation histories to fit the spectrophotometric data with
Prospector (Johnson & Leja, 2017; Leja et al., 2017; Johnson et al., 2021). In
Section 3, we use the results of this fitting to identify recently quenched
galaxies and characterize their evolving number densities as a function of
cosmic time. Finally, in Section 4, we discuss the implications of these
findings on our understanding of the physical mechanisms that are driving the
production of massive, quiescent galaxies through the rapid quenching channel.

Throughout this letter, we compare our own selection of “recently quenched
galaxies” to literature samples and selection criteria for post-starburst
galaxies. We note that many of these post-starburst selections do not
explicitly require a burst of star formation, as any dramatic truncation in
star formation can produce an A-star dominated SED.. We assume a concordance
$\Lambda$CDM cosmology with $\Omega_{\Lambda}=0.7$, $\Omega_{m}=0.3$ and
$H_{0}=70$ $\mathrm{km\,s^{-1}\,Mpc^{-1}}$, and quote AB magnitudes.

##  2 Data

###  2.1 The DESI LRG SV Sample

In order to characterize the growth of the population of quiescent galaxies at
intermediate redshifts, this work relies on the large program of deep spectra
that were taken as a part of the DESI SV LRG sample (Zhou et al., 2020, 2022,
DESI Collaboration et al. in preparation). The primary objective of DESI is to
determine the nature of dark energy with precise cosmological measurements
(Levi et al., 2013), but the wealth of spectroscopy provides an excellent
sample for studies of galaxy evolution. The data volume of the DESI requires
multiple supporting software pipelines and products used in this work. Target
selection and photometry, which included forward modeling of the differential
effect of the PSF across bands, was performed on imaging from the DESI Legacy
Surveys (Zou et al., 2017; Dey et al., 2019, Schlegel et al. in preparation).
Fiber assignments, tiling, and target selection were performed with the
algorithms outlined in Raichoor et al., Schlafly et al., and Myers et al. (in
preparation) respectively. All redshifts were determined with the Redrock
pipeline (Bailey et al. in preparation). All spectroscopy used was reduced
using the “Fuji” internal spectroscopic data release which will be identical
to the DESI Early Data Release (Guy et al., 2022, EDR, tentatively expected in
early 2023).

There are two primary reasons for the choice to utilize the SV sample. First,
the SV selection is more inclusive than subsequent Survey Validation samples
and the main DESI sample (see Appendix A in Zhou et al., 2022). While this was
intended as a test of the redshift recovery so that targeting could be refined
from the main survey, these expanded color cuts mitigate potential bias
against observing young, recently quenched LRGs. Second, the SV observations
were an order of magnitude deeper than the observations for the main survey,
with $\sim 2.5$ hours of integration per spectrum, resulting in the high
signal-to-noise measurements of the continuum. While the SV sample included
fainter targets, we restrict this study to the brightest SV LRGs with an
observed fiber z magnitude $z_{\mathrm{fiber}}<21.6$ cut similar to the one
that is used in the full LRG sample.

We select all tiles that were observed under the dark time observing
conditions in SV. We then select all galaxies which meet the LRG SV cuts
outlined in Zhou et al. (2022) with an additional $z_{\mathrm{fiber}}<21.6$
magnitude constraint, a cut at $z>0.4$ (above which the SV LRG sample begins
to be mass complete), and a cut at $z<1.3$ (at which point the age-sensitive
$H_{\delta}$ absorption feature is no longer covered by DESI spectroscopy). We
remove galaxies with poor redshift measurements by applying a cut of ZWARN
$==0$ to the DESI catalog. We then remove the 580/17797 galaxies that did not
reach target depth (exposure time $t_{\mathrm{exp}}>1$ hour). The median
exposure time of this final sample is 2.4 hours, with 16th and 84th percentile
exposure times of 1.5 and 4.1 hours respectively. This selection results in a
total sample of 17217 galaxies.

Figure 1: Example old (top, TARGETID=3963332462851262) and recently quenched
(bottom, TARGETID=39627817440253139) galaxies from the DESI SV LRG Sample with
Prospector fits using the star-formation history model from Suess et al.
(2022a). For each galaxy, we show the median and 68% confidence interval star-
formation history (top left) with selected galaxy properties. We also show the
best fitting models (color) to the observed photometry ($g/r/z/W1/W2$, black)
(top right). Finally, we show the DESI spectrum (observed, grey; 5 pixel
boxcar smoothed, black) along with the best fitting model (color) (bottom).
From this modeling, we identify quiescent LRGs and infer the dominance of
recent star formation and the timescale of quenching.

###  2.2 Inferring Star Formation Histories with Prospector

We model the DESI spectra and photometry using non-parametric star formation
histories with the SED fitting code Prospector (Johnson & Leja, 2017; Leja et
al., 2017; Johnson et al., 2021) to infer the detailed stellar populations of
the sample. Non-parametric star-formation histories (SFHs) are particularly
useful for fitting post-starburst galaxies because they do not impose an
analytic form on the shape of the SFH, which allows for multiple rises and
falls over the course of a galaxy’s lifetime. We adopt a flexible bin model
that is optimized to model recently quenching galaxies (Suess et al., 2022a).
The model utilizes 3 fixed time bins at early times ($t_{\mathrm{lookback}}>2$
Gyr), 5 flexible bins that each form the same amount of total stellar mass
(allowing for greater resolution near periods of intense star-formation), and
a final flexible bin that allows a galaxy to remain quenched after star
formation is finished. This scheme was extensively tested and is well designed
to recover quenching timescales and burst mass fractions (Suess et al., 2022a,
b).

We use the dynesty dynamic nested sampling package (Speagle, 2020), the
Flexible Stellar Population Synthesis (FSPS) stellar population synthesis
models (Conroy et al., 2009; Conroy & Gunn, 2010), the MILES spectral library
(Sánchez-Blázquez et al., 2006; Falcón-Barroso et al., 2011), and the MIST
isochrones (Choi et al., 2016; Dotter, 2016). We assume a Chabrier (2003)
Initial Mass Function and fix the model redshift to the spectroscopic
redshift. In contrast with the Suess et al. (2022b) prescription for fitting
post-starburst galaxies, we elect to fit nebular emission non-physically by
marginalizing over Gaussian lines at the locations of emission features in the
spectrum. The massive LRG sample likely hosts many active galactic nuclei
(AGN) which can contribute strongly to a galaxy’s emission line strength
(especially the recently quenched galaxies, e.g., Greene et al., 2020).
Additionally, the LRG selection allows for the targeting of a small fraction
of dusty star-forming galaxies with strong emission lines; we want to be
completely agnostic to the source of emission when fitting star formation
histories to these galaxies. This procedure subtracts out the emission from
the spectrum at each step in the fitting before calculating the likelihood,
which allows the fits to utilize continuum information (e.g. $H\beta$
absorption) despite the existence of emission that our models do not generate
using information about the current SFR.

We use the mass–metallicity prior described in Leja et al. (2019). We utilize
the PolySpecModel procedure which accounts for deviations between the shape of
the photometry and the spectrum by dividing out a polynomial from the observed
and model spectra during fitting, using a Prospecter-default 12th order
Chebyshev polynomial. We assume the Kriek & Conroy (2013) dust law with a free
$A_{v}$ and dust index. Additionally, following Wild et al. (2020), we assume
that the attenuation is doubled around young ($<10^{7}$ yr) stars. We fix the
shape of the IR SED following the Draine et al. (2007) dust emission
templates, with $U_{\mathrm{min}}$ = 1.0, $\gamma_{e}$ = 0.01, and
$q_{\mathrm{PAH}}$ = 2.0. Finally, we include both a spectroscopic jitter term
to account for the possibility of underestimated noise and the Prospector
pixel outlier model. We center priors on the SFH such that they follow the
predicted SFH of a massive quiescent galaxy from the UNIVERSEMACHINE catalog
(Behroozi et al., 2019); this weakly prefers solutions with early-time star
formation in the star formation histories we fit to ensure that outshining of
a young stellar population is treated conservatively. The fidelity of this
star formation history at recovering mock parameters is illustrated in Suess
et al. (2022a). Of principle importance to this work, the burst fraction is
well recovered when $<50\%$ of a galaxy’s stellar mass is formed in a burst.
For greater burst fractions, outshining by the young stellar population
becomes so dominant that the relative strength of the oldest stellar
population cannot be constrained by the existing data, and as such, our
conservative prior drives the fits to a higher burst fraction solution than
the inputs. Thus, burst fractions measured in this work to be $\gtrsim$50% can
be thought of as strong lower limits.

We fit all $17217$ galaxies in the DESI SV LRG sample
($z_{\mathrm{fiber}}<21.6$) with this procedure, providing the Milky Way
extinction corrected $g/r/z/W1/W2$ photometry (using the extinction maps from
Schlegel et al. 1998) and the galaxy spectrum. The scaling of the SED being
fit is set by the photometry that captures all galaxy light rather than just
the light in the fiber. We expect the total fraction of galaxy light contained
in the fiber to vary as a function of redshift but to always be $\gtrsim 50\%$
of the total light, as the fiber size is 0.75” in radius ( 4 kpc at z=0.4, 6.5
kpc at z=1.3). Our fits are constrained by the SED shape of the photometry ,
and the polynomial correction to the spectrum will account for any color
gradients, though we expect those to be minimal given that both the spectrum
should be representative of the majority of the galaxy light for most of the
sample. Because the signal in the redshift range of interest is concentrated
at the red end of the spectrograph, we elect to only fit the spectra from the
R and Z arms of the spectrograph
($5800\mathrm{\AA}<\lambda_{\mathrm{obs}}<9824\mathrm{\AA}$) to save on
computation time and to avoid any issues with the flux calibration at the
fainter end of the spectra. In this wavelength range, the resolution R
($\lambda/\Delta\lambda$) ranges from $\sim 3200-5100$. While the 1.5”
($\sim$8 kpc at $z=0.4$, $\sim$13 kpc at $z=1.3$) diameter aperture of the
DESI fiber is large enough to capture the majority of galaxy light at highest
redshift end of our sample, we do note that our modeling approach assumes a
lack of color gradients in the galaxies and that the light represented in the
spectrum is identical to that of the photometry, which models all galaxy
light. Fits failed to converge for 52/17217 galaxies (0.3% of the total
sample). Visual inspection of the spectra of these failed fits suggests that
they broadly fall into four categories: extremely low signal-to-noise
galaxies, spectra with large masked regions, galaxies with incorrect redshift
assignment, and broad-line AGN/QSOs (which our models are not equipped to
characterize). As such, we omit the unmodeled galaxies and perform all
analysis on the 17703 successfully fit galaxies.

Table 1: Selected Fit Quantities and Errors

z | log($M_{\star}/M_{\odot}$) | SFR [$M_{\odot}$/yr] | $\Delta\mathrm{SFR}$ | $f_{\mathrm{1Gyr}}$
---|---|---|---|---
0.5568 | 11.23${}_{-0.01}^{+0.01}$ | 2.31${}_{-0.35}^{+0.47}$ | -1.16${}_{-0.07}^{+0.08}$ | 0.11${}_{-0.01}^{+0.01}$
0.6701 | 11.22${}_{-0.01}^{+0.01}$ | 1.17${}_{-0.14}^{+0.14}$ | -1.52${}_{-0.06}^{+0.05}$ | 0.0${}_{-0.0}^{+0.0}$
0.8976 | 11.23${}_{-0.1}^{+0.02}$ | 0.92${}_{-0.31}^{+0.84}$ | -1.76${}_{-0.18}^{+0.39}$ | 0.0${}_{-0.0}^{+0.07}$
0.5396 | 11.36${}_{-0.01}^{+0.01}$ | 2.98${}_{-0.34}^{+0.4}$ | -1.16${}_{-0.06}^{+0.06}$ | 0.05${}_{-0.02}^{+0.01}$
0.4364 | 11.12${}_{-0.01}^{+0.01}$ | 2.85${}_{-0.24}^{+0.27}$ | -0.9${}_{-0.04}^{+0.05}$ | 0.01${}_{-0.0}^{+0.0}$
0.8807 | 11.34${}_{-0.03}^{+0.03}$ | 0.08${}_{-0.08}^{+0.28}$ | -2.86${}_{-1.51}^{+0.62}$ | 0.0${}_{-0.0}^{+0.0}$
0.6999 | 10.8${}_{-0.04}^{+0.03}$ | 19.39${}_{-2.4}^{+2.56}$ | 0.08${}_{-0.09}^{+0.07}$ | 0.22${}_{-0.04}^{+0.05}$
0.5415 | 11.11${}_{-0.03}^{+0.04}$ | 0.09${}_{-0.04}^{+0.05}$ | -2.45${}_{-0.28}^{+0.19}$ | 0.01${}_{-0.0}^{+0.01}$
0.5166 | 11.2${}_{-0.03}^{+0.02}$ | 21.25${}_{-3.02}^{+2.17}$ | -0.15${}_{-0.08}^{+0.06}$ | 0.1${}_{-0.02}^{+0.02}$
1.0623 | 11.04${}_{-0.03}^{+0.03}$ | 4.67${}_{-1.63}^{+3.76}$ | -0.94${}_{-0.18}^{+0.26}$ | 0.45${}_{-0.06}^{+0.07}$

Note. — Selected median and 68% confidence values of relevant parameters
derived from the posteriors of the Prospector fits to DESI SV LRGs for a
random sample of galaxies.

Example fits to quiescent (top, red) and recently quenched (bottom, green)
galaxies are shown in Figure 1. The quiescent galaxy that is representative of
the majority of the DESI LRG sample is fit entirely with early star formation,
consistent with a very old stellar population, and as such, all the mass was
formed in the three fixed-width early-time bins. In contrast, the recently
quenched galaxy is clearly fit with a post-starburst SED shape with strong
Balmer absorption features and a characteristic lack of emission line infill.
This indicates that the post-starburst galaxy has quenched after a period of
intense star formation, and the star formation history reflects this. We infer
that the galaxy began rapidly forming stars $\sim 500$ Myr before observation,
and quenched $\sim 150$ Myr ago.

From the posteriors on the star formation histories, we derive a number of
model parameters, many of which we directly use to select and characterize the
properties of recently quenched galaxies. Stellar masses are calculated
accounting for mass loss and have typical $1\sigma$ uncertainties of 0.025
dex, and rest absolute magnitudes are calculated directly from the spectra
generated from the posterior. We measure the star-formation rate in all
galaxies as the star-formation rate in the closest bin to the epoch of
observation in the non-parametric star formation history. Above $\sim
1M_{\odot}$/yr, these star-formation rates have been shown to reliably recover
the instantaneous star-formation rate of mock galaxies, and our measurements
have typical uncertainties of $\sim 15\%$. Below this, they are effectively
upper limits (Suess et al., 2022a). Additionally, we quantify the offset from
the star-forming sequence, $\Delta\mathrm{SFR}$, as:

Figure 2: Properties of the full LRG sample (red contours) and a subset of
galaxies that recently quenched a significant episode of star formation using
our fiducial selection ($f_{\mathrm{1Gyr}}>0.1$, $\Delta$SFR$<-0.6$, green
points). All plotted points are the median values from the posterior of the
Prospector fits. In panel (a), we show the stellar mass versus the absolute
magnitude ($M_{z}$) along with the magnitude limited threshold ($M_{z}<-23.2$)
and the mass complete threshold (log($M_{\star}/M_{\odot}>11.2$) discussed in
Section 2.3. In panel (b), we show the star-formation rate versus stellar
mass, with the star-forming sequence at $z=0.7$, the median redshift of our
sample, shown as a black line with characteristic $\sim 0.3$ dex $1\sigma$
scatter (Leja et al., 2021). In panel (c), we show the recently quenched
selection plane, $f_{\mathrm{1Gyr}}$ versus $\Delta\mathrm{SFR}$, with the
fiducial selection cuts illustrated as dashed lines. The sample is
significantly brighter than the parent sample at fixed stellar mass and
occupies a unique part of parameter space by having formed a significant
amount of recent stellar mass despite being fully quenched.

$\mathrm{\Delta SFR=log(SFR)-log(SFR_{SFS}(z)})$ (1)

where $\mathrm{SFR_{SFS}(z)}$ is the inferred star formation rate from the
star-forming sequence at the observed redshift of the galaxy defined in Leja
et al. (2021), which is also measured using Prospector SED fits. We set a
fiducial threshold for quiescence at $\Delta\mathrm{SFR}=-0.6$, $\sim 2\sigma$
below the main sequence at a given redshift. Near the fiducial value, the
typical uncertainty in $\Delta\mathrm{SFR}$ is $\sim 0.1$ dex. As with the
star formation rate, this value is significantly more uncertain for measured
values. Finally, we measure the fraction of the total stellar mass formed in
the Gyr before observation, $f_{\mathrm{1Gyr}}$. Galaxies with small
$f_{\mathrm{1Gyr}}$ are very well constrained to be small, and for galaxies
which formed $10-70\%$ of their stellar mass in the past Gyr, typical
uncertainties are $15-30\%$. A sample of constraints on parameters is shown in
Table 1.

We show some of the observed and derived characteristics of the full LRG
sample as red contours in Figure 2. In the first panel, we show the stellar
mass versus the rest frame absolute magnitude, $M_{z}$, illustrating the tight
correlation between the two parameters. We additionally show lines which
correspond to the cuts we make in the two parameters to construct the volume
limited samples described in Section 2.3. In the next panel, we show the star
formation rate versus the stellar mass along with the “star forming sequence”
at $z=0.7$ with 0.3 dex scatter from Leja et al. (2021) to illustrate that the
sample is largely quiescent. Finally, we show the sample in the selection
plane of $f_{\mathrm{1Gyr}}$ versus $\Delta\mathrm{SFR}$ discussed in Section
3.1 with our fiducial cuts to select recently quenched galaxies. In all 3
planes, we show the fiducial sample of galaxies as green points.

###  2.3 Selecting Volume Limited Samples

Because the choices made in spectroscopic targeting significantly impact the
observed sample, it is necessary to select a volume limited sample to fairly
compare galaxies across redshift bins. This is especially true because the
$z_{\mathrm{fiber}}<21.6$ cut in observed magnitude would observe a faint
galaxy at low-redshift but not high-redshift. We use the fits to the
spectrophometric data to select samples which we can use to infer number
densities. Throughout this letter, we utilize three relevant samples: the full
LRG sample, the rest absolute Z-magnitude selected “magnitude limited” sample,
and the “mass complete” sample to select recently quenched galaxies.

####  2.3.1 The Magnitude Limited Sample

By virtue of being the youngest and brightest galaxies in any given quiescent
sample, galaxies have the lowest $M_{\star}/L$ ratios at fixed stellar mass
and therefore are relatively bright compared to the majority of LRGs. As such,
in order to get large, complete samples of galaxies to study as a function of
redshift, a luminosity cut will maximize the sample size. We define a
magnitude limited sample with rest-frame $M_{z}<-23.2$, at which the entirety
of the reddest (in rest $g-z$ color, which should map to the highest
$M_{\star}/L$ ratios) 2.5% of the LRG sample is selected at $z=0.8$. This
selection results in the largest volume limited sample we can obtain where we
expect to have observed all bright galaxies in DESI out to $z\sim 0.8$,
yielding a total of 8683 galaxies.

Figure 3: Redshift distributions of spectroscopic samples of recently quenched
galaxies from $0.4<z<2.0$, with an inset focusing on $z>1$ where the
improvement in sample size from this work is most significant. Our fiducial
sample ($f_{\mathrm{1Gyr}}$ $>0.1$, $\Delta\mathrm{SFR}$ $<-0.6$, selected
from the full LRG sample) is shown as a filled green histogram. Other samples
shown include PCA-identified post-starburst galaxies from Rowlands et al.
(2018) and Wild et al. (2020), galaxies with $t_{50}<1.5$ Gyr from Belli et
al. (2019), galaxies selected with K+A template fitting from the SDSS
Pattarakijwanich et al. (2016), and galaxies selected using rest UBV filters
from the SQuIGG$\vec{L}$E sample also selected from the SDSS (Suess et al.,
2022b).

####  2.3.2 The Mass Complete Sample

While a magnitude limited sample selects the bulk of the galaxies in the SV
sample, in order to characterize the growth of the population relative to the
fainter (at fixed stellar mass) old quiescent population, we instead require a
mass complete sample. In the redshift range $0.4<z<0.8$, the DESI LRG
targeting only selects a sample which is $\gtrsim 80\%$ mass complete for very
massive galaxies (log($M_{\star}/M_{\odot}$) $\gtrsim 11.2$–accounting for
systematic differences between the stellar masses we measure and those in Zhou
et al., 2022). As such, in situations where we wish to compare to the
quiescent population as a whole, we elect to use only galaxies above this
stellar mass, regardless of their rest-frame $M_{z}$. This sample is
significantly smaller than the magnitude limited sample, with only 5375
galaxies above the stellar mass cut at $z<0.8$.

We show the cuts in rest-frame $M_{z}$ and stellar mass that result in the two
subsamples in Figure 2a, illustrating that the stellar mass cut is
significantly more restrictive than the magnitude cut, which lets through
galaxies at masses as low as $10^{10.8}M_{\odot}$. At fixed stellar mass, the
fiducial sample (see Section 3.1) is significantly brighter than a typical LRG
(red contours), and we therefore maximize our ability to constrain the number
density of recently quenched galaxies as a population by instituting a cut on
the absolute magnitude.

##  3 Analysis

###  3.1 Selecting Recently Quenched Galaxies

There are a number of ways of selecting recently quenched/post-starburst
galaxies, all of which share the common goal of selecting galaxies that
recently quenched after a period of significant star formation (French, 2021).
Historically, these galaxies have been selected using a combination of
emission line cuts (to select against current star formation) and Balmer
absorption depth (to select for a stellar population dominated by A type
stars) (Dressler & Gunn, 1983; Zabludoff et al., 1996; Balogh et al., 1999).
Here, we leverage the tightly constrained star formation histories to select a
physically motivated sample of recently quenched galaxies. First, we focus on
selecting a pure quiescent sample. In Figure 2b+c, it is clear that some
galaxies that are dusty and star-forming have been selected due to their red
colors and exist in the LRG parent sample. To remove these, we perform a
conservative cut in $\Delta\mathrm{SFR}$, classifying galaxies as quiescent
only if their median $\Delta\mathrm{SFR}$ is $\sim 2\sigma$ (0.6 dex) below
the star-forming sequence at their redshift from Leja et al. (2021). This
selection, which is highlighted in Figure 2c, removes 2622 galaxies ($\sim
15\%$ of the total sample). All qualitative results in this work are
insensitive to the exact definition of quiescence that we adopt, though exact
sample sizes and number densities will by definition differ slightly.

Secondly, we are interested in separating the quiescent galaxy population
physically into recent additions to the red sequence and older galaxies. In
this work, our definition of recently quenched does not require a burst, as we
are interested in classifying all galaxies which rapidly formed a significant
amount of stellar mass before quenching as galaxies. To select such a sample,
we leverage the inferred star formation histories to measure the fraction of
the stellar mass formed within the last Gyr ($f_{\mathrm{1Gyr}}$) for all
galaxies (see also Webb et al., 2020). In combination with the cut for
quiescence, selecting galaxies with high $f_{\mathrm{1Gyr}}$ identifies a
sample that must have rapidly truncated its star formation in order to have
formed a large amount of its stellar mass while also reaching quiescence
within 1 Gyr. We adopt $f_{\mathrm{1Gyr}}$ $>0.1$ (also shown in Figure 2c)
for our fiducial selection and explore the impact of different thresholds in
Section 4. The fiducial selection identifies 1089 galaxies from the 15012
quiescent LRGs using the fiducial $f_{\mathrm{1Gyr}}$ $>0.1$ selection.

Figure 4: (Left): Number densities within the DESI SV LRG sample (full sample,
gray; luminosity-complete and quiescent, red) and a variety of selections from
the magnitude limited (see Section 2.3.1) quiescent sample ($H_{\delta,a}>4$,
light blue; $H_{\delta,a}>5$, dark blue; SQuIGG$\vec{L}$E SED selection, light
green; and $f_{\mathrm{1Gyr}}$, green). Beyond $z\sim 0.8$, we indicate that
the measured number densities are lower limits by plotting as upward facing
arrows. All selections show an increasing number density over the redshift
range in which we are complete, with varying normalization resulting from the
relative restrictiveness of the post-starburst criteria. (Right): The same
magnitude limited LRG and $f_{\mathrm{1Gyr}}>0.1$ samples as the previous
panel in addition to literature measurements (photometric: open symbols;
spectroscopic: filled symbols). All three of the Wild et al. (2016)
($M_{\star}>10^{10.8}M_{\odot}$), Rowlands et al. (2018)
($M_{\star}>10^{11}M_{\odot}$), and Belli et al. (2019)
($M_{\star}>10^{10.8}M_{\odot}$) samples show a trend of increasing number
density with redshift, but the normalization differs between the different
samples as a result of differing stellar mass limits and selection techniques.

This sample of galaxies is unparelled in size beyond $z\gtrsim 1$. In Figure
3, we show the redshift distributions of this sample compared to other large
spectroscopic samples of post-starburst galaxies at intermediate redshift. Our
sample of 100s of galaxies at $z<0.8$ is smaller than other samples which
select galaxies from the full Sloan Digital Sky Survey (Pattarakijwanich et
al., 2016; Suess et al., 2022b) or VIPERS Survey (Rowlands et al., 2018).
However, at $z>1$ (shown in the inset), we find that this sample dramatically
increases the number of spectroscopically confirmed galaxies at the tail end
of cosmic noon.

While our selection of galaxies relies on our inferred star formation
histories, there are many other selections that use empirical measures of
spectroscopic features to select post-starburst galaxies (French, 2021). We
choose a few common post-starburst identification methods and compare the
resulting number densities with our fiducial model (see Section 3.2). For all
literature comparisons, we use the same $\Delta\mathrm{SFR}$ $\leq-0.6$
criterion for quiescence rather than relying on common empirical metrics like
EW $H_{\alpha}$, which falls out of our spectral window for most of the
sample, or EW [OII], which is an uncertain tracer of SFR due to potential
contribution from AGN/LINERs. We note that while exact definitions of
$H_{\delta}$ spectral indices vary in the literature (e.g., Alatalo et al.
(2016) uses $H_{\delta}$, French et al. (2015) uses $H_{\delta,A}$, and Baron
et al. (2022) $H_{\delta,F}$), these differences are subtle. We adopt
$H_{\delta,A}$ as our preferred definition, as it is optimized for features
from A-type stars (Worthey & Ottaviani, 1997). The three selections we compare
to our fiducial selection ($f_{\mathrm{1Gyr}}$ $>0.1$ and $\Delta\mathrm{SFR}$
$<-0.6$) are as follows (all numbers quoted are the raw number of galaxies in
the full sample, not in a volume limited sample):

  1. 1.

$H_{\delta,A}$ $>4$ Å: After applying the quiescence criteria, we select
$1727$ galaxies with $H_{\delta,A}$ $>$ 4Å following e.g., French et al.
(2015, 2018); Wu et al. (2018); Yesuf (2022).

  2. 2.

$H_{\delta,A}$ $>5$ Å: We impose a more stringent cut, $H_{\delta,A}$ $>$5 Å,
following e.g., Alatalo et al. (2016); Baron et al. (2022), selecting $1035$
post-starburst galaxies.

  3. 3.

SQuIGG$\vec{L}$E Selection: Finally, after applying the quiescence criteria,
we use medium band synthetic rest-frame $UBV$ filters to identify post-
starburst galaxies with $U-B>0.975$ and $-0.25<B-V<0.45$ following the
procedure for selecting galaxies with SEDs dominated by A-type stellar
populations (Suess et al., 2022b). We apply these cuts to the median best-fit
models because the spectral coverage is not red enough to consistently overlap
with the synthetic $V$ filter. This selection finds only $324$ post-starburst
galaxies.

###  3.2 The number density of recently quenched galaxies

The DESI SV LRG selection is designed to have a uniform comoving number
density of galaxies at $0.4<z<0.8$, which enables robust determination of
number densities of subsets of the spectroscopic sample (Zhou et al., 2022).
For this selection, we use the target density of 1439 $\mathrm{deg^{-2}}$ to
calculate the number density in bins of $\Delta z=0.1$ in redshift from
$z=0.4$ to $z=1.3$ by measuring the density of targets for a given selection
criterion and dividing by the volume of the bin. We measure the number
densities only for the magnitude limited or mass complete samples. We utilize
jackknife resampling of the 31 SV pointings to calculate the errors on the
measured number densities. The errors do not account for catastrophic redshift
errors, but those should be very rare ($\leq 0.5\%$, see Zhou et al. 2022) and
subdominant relative to cosmic variance and Poisson errors.

Figure 5: The number densities (left) and fractions (right) of recently
quenched galaxies in the mass complete sample (log($M_{\star}/M_{\odot}$)
$>11.2$, see Section 2.3.2). The dashed line and grey band (left) represent
the stellar mass function of similarly mass galaxies (Leja et al., 2020), and
the red points show the number density of all LRGs above the mass limit. The
light green, green, dark green, and black points represent the number
densities and fractions of recently quenched galaxies with $f_{\mathrm{1Gyr}}$
greater than 0.05, 0.1, 0.2, and 0.5 respectively, as compared to the stellar
mass function from Leja et al. (2020). On the right panel, the open points of
the same colors show the fraction of galaxies as compared to our own massive
galaxy number density measurements. Above $z=0.8$, measurements are indicated
as lower limits with errors inflated to encapsulate the possibility that all
galaxies which were not targeted by DESI meet the selection criterion.

The comoving number density of each recently quenched subsample of the
magnitude limited sample as a function of redshift is shown in Figure 4. The
raw number density of the DESI LRG SV sample ($z_{\mathrm{fiber}}<21.6$) is
shown in grey. We show the number density of the rest-frame magnitude limited
($M_{z}<-23.2$) sample with the fiducial quiescence cut ($\Delta\mathrm{SFR}$
$<-0.6$) in red. We then apply the post-starburst selections outlined in
Section 3.1 to the magnitude limited sample. The number densities are shown
for $H_{\delta,A}$ $>4$ Å (light blue), for $H_{\delta,A}$ $>5$ Å (dark blue),
SQuIGG$\vec{L}$E-like (light green), and our fiducial $f_{\mathrm{1Gyr}}>0.1$
selection (dark green). In all cases, the number density of post-starburst
galaxies rises as a function of redshift in the range of redshifts where the
parent LRG sample is complete ($z<0.8$). Above this, we illustrate that our
measurements are lower limits.

In the second panel of Figure 4, we compare our fiducial sample of galaxies to
several measurements from the literature. We find qualitative agreement with
previous studies that observe the number density of recently quenched galaxies
increasing with redshift (Wild et al., 2016; Rowlands et al., 2018; Belli et
al., 2019). Additionally, the number density of the galaxies that we measure
is very similar to that of compact star forming galaxies at z=0.5, adding
credence to the argument that such galaxies may be progenitors to local post-
starburst galaxies (Tremonti et al., 2007; Diamond-Stanic et al., 2021; Whalen
et al., 2022). However, in detail, this comparison is limited by systematic
effects; our sample is systematically more massive than other post-starburst
samples, and is selected using a magnitude (not mass) limit. Additionally, as
shown in the first panel of Figure 4, differing identification techniques can
significantly impact the measured number density of post-starburst galaxies.
Still, a clear consensus emerges from this comparison that recently quenched
galaxies were increasingly common at greater lookback time.

###  3.3 Exploring the Growth of the Red Sequence by Rapidly Quenched Galaxies

In the previous section, we studied the number density of a magnitude limited
sample of galaxies to maximize our sample size. Here, we attempt to explicitly
quantify the fraction of massive galaxies that have recently quenched and
joined the red sequence as a function of cosmic time. To do so, we utilize the
mass complete (log($M_{\star}/M_{\odot}$) $>11.2$, see Section 2.3.2) subset
of the LRG sample, which we show in the first panel of Figure 5 (red) along
with the corresponding stellar mass function from Leja et al. (2020). This
measurement over-predicts the stellar mass function by $\sim 0.2$ dex at
$z\sim 0.4$ while matching well at $z\sim 0.7$. This may be due to systematic
differences in the stellar mass estimates (e.g., differences in modeled star
formation histories, unmodeled contributions from AGN, or spectrophotometric
modeling in our fits versus broadband multiwavelength SEDs), and the mismatch
in redshift evolution may be a result of the targeting incompleteness. As
such, we adopt the number densities from the stellar mass function as the
total abundance of massive (log($M_{\star}/M_{\odot}$) $<11.2$) galaxies and
note that the fractions we measure may be systematically lower than reported
by $\sim 0.2$ dex. Above $z=0.8$ where LRG targeting is known to be
incomplete, we inflate the upper error bar on the measured lower limits by
assuming that every galaxy we have not targeted meets the selection criteria
(quantified by the deviation between the measured number density and the
stellar mass function) to capture the possibility that every galaxy we did not
measure is a recently quenched galaxy. Since this is unlikely due to the lower
$M_{\star}/L$ ratio of galaxies, this conservative estimates captures the full
range of possibility in the number density of galaxies in a given selection at
$z>0.8$.

We show the number densities of four different selections of recently quenched
galaxies: $f_{\mathrm{1Gyr}}$ $>0.05$ (light green), $f_{\mathrm{1Gyr}}$
$>0.1$ (green), $f_{\mathrm{1Gyr}}$ $>0.2$ (dark green), and
$f_{\mathrm{1Gyr}}$ $>0.5$ (black). Points that do not appear indicate that
the redshift bin contained zero galaxies that met the selection criteria. All
four sets of recently quenched galaxies show increasing number densities with
redshift. However, even at $z\sim 0.8$, galaxies which formed a large fraction
of their stellar mass in the past Gyr are very rare. For example, at $z=0.8$,
galaxies that formed $>20\%$ of their stellar mass in the past Gyr were
significantly ($>1$ dex) rarer than those which formed 5% of their stellar
mass. We find that the number density of the $f_{\mathrm{1Gyr}}>20\%$
population cannot be decreasing with lookback time, and in fact, at $z\sim
1.2$ the lower limit number density of this population is higher than the
number density at z=0.8. The rarity of such objects at intermediate-z is
extremely consistent with the rarity of “late bloomers,” galaxies that formed
the majority of their stellar mass in the 2 Gyr before quenching (Dressler et
al., 2018). Additionally, we identify a very small population of galaxies
which rapidly formed $\geq 50\%$ of their stellar mass in the Gyr before
observation at $z=1.1-1.3$, with lower limits that indicate a number density
of at least $\mathrm{log_{10}(n)>}-6.5\ \mathrm{Mpc^{-3}}$. Similar extreme
post-starburst galaxies have been found in photometric samples with comparably
low number densities and could represent analogs to the formation of massive
quiescent galaxies at high-z (Park et al., 2022).

In the right panel of Figure 5, we show the same samples as fractions of the
total massive galaxy population (shown with solid points using the stellar
mass function from Leja et al. (2020) as the denominator and empty points
using our own measurements of the LRG number density). We find that galaxies
which formed $>20\%$ of their stellar mass represent $\sim 0.5\%$ of the total
galaxy population at z=0.8, but by $z\sim 1.2$ must be at least 1%, with an
upper limit that extends to them being $\sim 50\%$ of the quiescent
population. Similarly, the most extreme burst-dominated systems
($f_{\mathrm{1Gyr}}$ $>50\%$) must be at least $\sim 0.5\%$ of the total
galaxy population at z=1.2, but this fraction could be as high as 20%. In
contrast, galaxies with $f_{\mathrm{1Gyr}}$ $>5\%$ and $>10\%$ are significant
even at z=0.4, representing $\sim 1.5\%$ and $0.5\%$ of the massive galaxy
population, and by $z=0.8$ they are $\sim 10\%$ and $3\%$ of the total
population. Studies of massive quiescent and post-starburst galaxies at
similar redshifts have measured similar burst fractions of $\sim 5\%$ in the
bulk of their samples, indicating that at the massive end, the vast majority
of “post-starburst” galaxies are the evolutionary products of a recent dusting
of star formation rather than the truncation of their primary epoch of star
formation (Patel et al., 2011; French et al., 2018).

The general rarity of extreme massive post-starburst galaxies in this sample
is consistent with findings that the formation redshift of
log($M_{\star}/M_{\odot}$) $=11.2$ galaxies is $z_{\mathrm{form}}\sim 2-3$
(Gallazzi et al., 2014; Pacifici et al., 2016; Fumagalli et al., 2016; Carnall
et al., 2019; Estrada-Carpenter et al., 2019; Díaz-García et al., 2019; Webb
et al., 2020; Khullar et al., 2022); at the epochs we are probing, the average
massive quiescent galaxy quenched long in the past. However, we find that a
significant number of massive galaxies are still quenching with very high
$f_{\mathrm{1Gyr}}$ well after cosmic noon ($z\sim 2$), and expect that with a
more complete sample, at higher redshift the population dominated by recent
star formation would become the norm.The sharp observed decline in rapid
quenching after cosmic noon suggests a fundamental shift in the evolutionary
histories of massive galaxies. By combining this preliminary analysis with
similar stellar population synthesis modeling of larger, mass-complete samples
and ancillary datasets (e.g., by analyzing galaxy structural evolution and
morphological transformation), we hope to illuminate the physical mechanism(s)
that are responsible for halting star formation and sustaining quiescence of
massive galaxies since $z\sim 1$.

##  4 Discussion and Conclusions

Using the DESI SV sample, we measure non-parametric star formation histories
for a novel sample of Luminous Red Galaxies. We select physically motivated
samples of galaxies and leverage the well characterized parent sample to
characterize the increasing number density of recently quenched galaxies with
lookback time. We find the following:

  1. 1.

The sample of quiescent galaxies which formed $>10\%$ of its stellar mass in
the past Gyr represents a novel spectroscopic sample. The sample of 277
galaxies we identify at $z>1$ is an order of magnitude larger than previous
samples (see Figure 3).

  2. 2.

The number density of galaxies rises steadily with redshift from $z=0.4-0.8$
based on our model selection and empirical identification methods; post-
starburst galaxies were more common at earlier cosmic time (see Figure 4).

  3. 3.

The fraction of massive (log($M_{\star}/M_{\odot}$) $>11.2$) galaxies which
have recently quenched their star formation and which formed $>10\%$ of their
stellar mass in the past Gyr rises in this redshift range from $\lesssim
0.5\%$ at z=0.4 to $\sim 3\%$ in at z=0.8 (see Figure 5). Furthermore, at
$z>1$, we find a significant emerging population that formed $>20\%$ and
$>50\%$ of its stellar mass in the past Gyr.

As our criteria for selecting recently quenched galaxies simply required a
rapid truncation in a galaxy’s star formation rate that drove a galaxy into
quiescence in $<\lesssim$1 Gyr, there is substantial variety in the star
formation histories of galaxies which fall into a given selection for
$f_{\mathrm{1Gyr}}$. The simplicity of this selection allows for simple
determination of the rate at which galaxies have entered into quiescence, but
it does not distinguish between, for example, a secondary starburst in an
already quiescent galaxy versus a rapid truncation of the primary epoch of
star formation at fixed $f_{\mathrm{1Gyr}}$. Future work will endeavor to
combine these star formation histories with ancillary data to paint a holistic
picture of the quenching of these galaxies. For now, we use constraints on the
fraction of galaxies which recently entered into quiescence to discuss
possible physical mechanisms that could be driving the rapid cessation of star
formation in this sample of galaxies.

One of the most compelling fast processes that could induce, then shut off,
star formation and produce post-starburst galaxies is major mergers (e.g.,
Hopkins et al., 2008). After cosmic noon, simulations have found that many
massive galaxies that quench do so via major mergers (e.g., Wellons et al.,
2015), which funnel gas inward and induce a burst of star formation that
rapidly shuts off. Indeed, many studies of post-starburst galaxies have found
that merger features are more common in post-starburst systems (e.g., Pawlik
et al., 2016; Sazonova et al., 2021; Ellison et al., 2022; Verrico et al.,
2022).

We estimate the relative frequency of major mergers using the UNIVERSEMACHINE
(Behroozi et al., 2019) and find that 15% and 20% of
log($M_{\star}/M_{\odot}$) $>11.2$ galaxies, at $z=0.4$ and $z=0.8$
respectively, experienced a major merger ($M_{\star,2}/M_{\star,1}>25\%$ in
the progenitor galaxies in the merger tree) in the past Gyr. This rate is
significantly higher than the the 0.5% and 3% fractions we find for our
fiducial sample of recently quenched galaxies, and the merger fraction
increases more slowly than the fraction. Some of this difference may be driven
by gas-poor major mergers between already quiescent systems or gas rich
mergers that do not quench, and we conclude that it is plausible that every
very massive galaxy that rapidly quenches between $0.4<z<0.8$ does so as a
result of a major merger, and that not every major merger results in a post-
starburst galaxy. This is in line with predictions from the Illustris TNG
simulation that only $\sim 5\%$ of massive galaxies will quench within
$\sim$500 Myr of coalescence after a major merger (Quai et al., 2021), and
indicates that even if major mergers are an essential part of the quenching
process, they do not universally produce post-starburst galaxies. However, at
high-z, our measured lower limits fall short of placing strong constraints.

Still, the high-z tail of our distribution promises to be a very powerful tool
for studying rapid quenching. Prior to DESI, only a small number of
spectroscopic continuum observations from surveys could be mined for post-
starburst galaxies above $z\gtrsim 1$ (Wild et al., 2020), and often samples
can only be obtained through targeted followup of photometrically identified
sources (e.g., Belli et al., 2019). Even in the smallest (but highest signal-
to-noise) subset of DESI LRG spectra, we have identified an order of magnitude
more spectroscopically confirmed galaxies than had been measured previously.
Future work will leverage these star formation histories further to study
trends using parameters such as the time since quenching (Suess et al.,
2022b), which has been used in post-starburst populations to constrain the
evolution of AGN incidence (Greene et al., 2020), sizes (Setton et al., 2022),
molecular gas contents (Bezanson et al., 2022; Spilker et al., 2022), and
merger fractions (Verrico et al., 2022). Using the combination of the unique
spectroscopically derived moments of the star formation history and ancillary
data, we hope to place strong constraints on the mechanisms that drive the
quenching of massive galaxies as close to cosmic noon as is currently
possible. Future surveys, such as PFS (Greene et al., 2022) and MOONRISE
(Maiolino et al., 2020) will extend wavelength coverage into the NIR, pushing
farther in redshift to cosmic noon. In conjunction with this sample,
comprehensive studies of the properties of galaxies from $z=0$ to $z=2$ will
paint a cohesive picture of the rapid quenching process and its role in
producing the present-day quiescent population.

This research is supported by the Director, Office of Science, Office of High
Energy Physics of the U.S. Department of Energy under Contract No.
DE–AC02–05CH11231, and by the National Energy Research Scientific Computing
Center, a DOE Office of Science User Facility under the same contract;
additional support for DESI is provided by the U.S. National Science
Foundation, Division of Astronomical Sciences under Contract No. AST-0950945
to the NSF’s National Optical-Infrared Astronomy Research Laboratory; the
Science and Technologies Facilities Council of the United Kingdom; the Gordon
and Betty Moore Foundation; the Heising-Simons Foundation; the French
Alternative Energies and Atomic Energy Commission (CEA); the National Council
of Science and Technology of Mexico (CONACYT); the Ministry of Science and
Innovation of Spain (MICINN), and by the DESI Member Institutions:
https://www.desi.lbl.gov/collaborating-institutions. DS gratefully
acknowledges support from NSF-AAG#1907697 and the PITT PACC graduate
fellowship. The results published were also funded by the Polish National
Agency for Academic Exchange (Bekker grant BPN/BEK/2021/1/00298/DEC/1), the
European Union’s Horizon 2020 research and innovation programme under the
Maria Skłodowska-Curie (grant agreement No 754510) and by the Spanish Ministry
of Science and Innovation through Juan de la Cierva-formacion program
(reference FJC2018-038792-I). DS acknowledges helpful conversations with Joel
Leja, Wren Suess, Sirio Belli, and Sandro Tacchella that improved this work.
We also thank the reviewer for their deep and thorough reading of the paper,
resulting in a significantly improved final product. The authors are honored
to be permitted to conduct scientific research on Iolkam Du’ag (Kitt Peak), a
mountain with particular significance to the Tohono O’odham Nation. We direct
readers to Astrobite’s coverage of the Tohono O’odham People and Kitt Peak
National Observatory, as part of their series on the interactions between
predominantly western astrophysics observatories and Indigenous communities:
https://astrobites.org/2019/08/16/a-tale-of-two-observatories.

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