COSMIC EVOLUTION AND THE DARK SECTOR: A PFS SSP FOR THE SUBARU TELESCOPEThe Subaru Prime Focus Spectrograph (PFS) Collaboration
The full list of members can be found in the Appendix

ABSTRACT
We propose a large-scale survey with PFS to address fundamental and important questions in the dark sector(dark matter and dark energy) with significant implications for cosmology, galaxy evolution and the origin of
the Milky Way Galaxy. The unique wide-field and massively-multiplexed spectroscopic capability of PFS will
maintain and strengthen Subaru’s world-leading role in cosmology and astronomy for the next decade. Our
experienced team of Japanese and international astronomers has developed an ambitious 360 night survey to beundertaken over 5 years which fully exploits the unique capabilities of PFS to address outstanding questions
relating to the history and fate of the Universe as well as the physical processes and role of dark matter ingoverning the assembly of galaxies including our Milky Way. We commit to fully reducing the data from thislandmark survey and making it available to the global astronomical community in a timely manner.
1. INTRODUCTION

Multi-band imaging surveys in recent decades, such as those
conducted by the Sloan Digital Sky Survey (York et al. 2000),the Hubble Space Telescope (Scoville et al. 2007) and Subaru’s Hyper Suprime-Cam (HSC) imager (Aihara et al. 2018a),
have led to enormous progress in our understanding of the Universe. These data have enabled detailed studies of gravitational
lensing signals which trace the spatial distribution of dark matter (DM), and census studies of Galactic structures and highredshift galaxies. Together with other cosmological probes,
precise measures have been made of the amount of both nonbaryonic DM and the dark energy (DE) that propels the cosmic
acceleration. However, neither of these two dark constituentsis physically understood. Likewise, the standard picture ofgalaxy evolution based on the hierarchical assembly of DM
halos cannot fully explain observations of the evolving population of galaxies and the rich diversity of their present-day
morphologies. While the dark sector clearly governs cosmic
history, its role is not yet fully understood.
As the first massively-multiplexed spectrograph on a large
aperture telescope, Prime Focus Spectrograph (PFS) on the
8.2m Subaru Telescope offers an unique opportunity to addressthese fundamental questions. Exploiting wide field catalogs
produced by HSC, precise redshifts from PFS will chart largescale structures and the spectra will reveal detailed galaxy
properties over cosmic time. Radial velocities, metallicities
and physical properties of faint stars in both the Milky Way andits nearest neighbor, the Andromeda spiral, will complement
this story through more detailed measures of DM on smallerscales.
In the following we first describe scientific motivation, key
questions and competitiveness of the PFS-SSP program in
Sections 2–6 (referred to as “Part I”) and then describe thetechnical feasibility, survey design and management plan forcarrying out our program in Sections 7–10 (“Part II”).
2. SUMMARY OF PFS-SSP PROGRAMS
Here we propose an ambitious “Subaru Strategic Program”,
a 360-night survey to be undertaken over 5 years with PFS(hereafter PFS-SSP) that promises a revolution in our understanding of the Universe. The proposal comprises three in-terlocking science themes – Cosmology, Galaxy Evolution,
and Galactic Archaeology – which collectively address theover-arching theme of the role of the dark sector in cosmic
evolution (see Figure 1).
The PFS Cosmology program will deliver four million redshifts of [O ii] emitting galaxies over about 1100 deg2 to makeprecise measurements of angular diameter distances and thecosmic expansion rate over the redshift range 0.6 ≲ z ≲ 2.4,
thereby constraining the nature of DE and the curvature of the
Universe. The 3D clustering of galaxies and redshift-spacedistortions due to their peculiar velocities will
P determine thesum of neutrino masses to the precision of σ( mν ) ≃ 0.02 eV.
The PFS Galactic Archaeology program will provide unprecedented datasets of chemical abundances and radial velocities for stars in various environments in the Milky Way, its
satellite galaxies and the halo of Andromeda (M31) to determine the role of DM in the assembly history of these galaxies.
Velocity dispersion and chemical abundance profiles for a sample of nearby DM-dominated satellites will constrain whether
their puzzling cores arise from DM that is self-interacting,warm or wave-like (fuzzy), or are due to baryonic feedback.
Emission and absorption line data from the PFS Galaxy Evolution program will delineate the assembly history of galaxies
for a nearly stellar-mass limited sample, comparable in size tothat achieved locally by SDSS, in the formative era 1 ≲ z ≲ 2.
Spectra of fainter star-forming galaxies at 2 ≲ z ≲ 5 will
establish the connection to DM halos in the young universe
including reconstruction of the associated 3D large-scale distribution of HI gas in the intergalactic medium (IGM) at z ∼ 2.5.
Large samples of Lyman-α emitters over 5 ≲ z ≲ 7 selectedfrom existing HSC narrow-band imaging data will probe theIGM at the end of cosmic reionization. Collectively the PFS
Galaxy Evolution survey will reveal, for the first time, theinterplay between the IGM and galaxy assembly. In summary
the three themes of the PFS-SSP program will test the standard
ΛCDM model in a comprehensive fashion from kpc to Gpc
scales and cosmic epochs from the present to the end of cosmic
reionization at z ∼ 7.
Our PFS SSP survey strategy will select its spectroscopic
targets from deep multi-color imaging taken with HSC. The
unique instrumental combination of PFS+HSC will allow additional synergistic scientific opportunities. For example, weak2

PFS SSP team

Assembly historyof galaxies

GE

GA

CO

Testing ΛCDM

• PFS+HSC synergy• Nature & role of neutrinos• Expansion rate via BAO up to z=2.4 • Absorption probes with PFS/SDSSQSOs around PFS/HSC host galaxies• PFS+HSC tests of GR• Curvature of space: ⌦K• Stellar kinematics and chemical• Primordial power spectrum
abundances – MW & M31
assembly history• Nature of DM (dSphs)• Galaxy-halo connection: M⇤ /Mhalo• Structure of MW dark halo
• Outflows & inflows of gas• Small-scale tests of structure growth• Environment-dependent evolution<latexit sha1_base64="S4qifW+w6ltKNp/rHetswQIy1Pk=">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</latexit><latexit sha1_base64="nfBMxyfDuzR7UXTU6oDaUTuPQkQ=">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</latexit>

Importance of IGM• Search for emission from stacked
spectra• dSphs as relic probe of reionization
feedback• Past massive star IMF from element
abundances• Physics of cosmic reionization via
LAEs & 21cm studies• Tomography of gas & DM

Figure 1. An illustration summarizes the interlocking nature of the three themes – Cosmology (CO), Galactic Archaeology (GA) and Galaxy Evolution (GE) – inaddressing the scientific objectives of PFS-SSP program.

Spectrograph System (SpS)Prime Focus Instrument (PFI)+
Wide Field Corrector (WFC)

Fiber positioner "Cobra"

Fiber Optical Cable andConnector System (FOCCoS)

Metrology Camera System (MCS)

Figure 2. A schematic overview of PFS subsystems (see text for details).

lensing measures and photometric redshifts from HSC will
chart, respectively, the DM distribution and associated faintgalaxies with PFS survey galaxies of known redshift. Ourcarefully designed survey uses a sophisticated simulation tool
to optimize the fiber allocation strategy taking into accountthe different visibilities, target surface densities and exposure
times of the various targets, maximizing survey efficiency via
a single coherent strategy. Our team comprises an international consortium including experienced astronomers drawn
from the Japanese community, ASIAA in Taiwan, Caltech/JPL,
Princeton University, the Johns Hopkins University, Laboratoire d’Astrophysique de Marseille (LAM), the Brazilian PFS
Participant Consortium, MPA/MPE in Germany, the Chinese
PFS Participant Consortium, and the North-East Participation
Group in the USA.
3. THE INSTRUMENTS
The 8.2m Subaru Telescope has the largest prime focus fieldof any telescope of its class. Following the success of HSC,
a wide-field optical imager, PFS – a massively multiplexed,
optical and near-infrared (NIR) spectrometer – represents thenext logical scientific step forward. Its focal plane is equippedwith 2394 reconfigurable fibers distributed in the 1.38-degree
wide hexagonal field of view. The spectrograph system covers
a wide wavelength range, from 380nm to 1260nm in a single
exposure. PFS and HSC share the same wide field corrector
and collectively form the Subaru Measurement of Images andRedshifts (SuMIRe) project (PI: H. Murayama).
The PFS instrument is comprised of subsystems introduced
below. Schematically, light from celestial objects is fed tofibers configured at the prime focus and transmitted via fiber
cables to the spectrographs in the telescope enclosure building,and the spectra are recorded on optical and NIR detectors (seeFigure 2).
The Prime Focus Instrument (PFI) comprises the fiber
positioner system, science & fiducial fibers, Acquisition &Guide (AG) cameras, and a calibration system. The fiber positioner system consists of 42 modules each of which includes 57“Cobra” rotary actuators populated with science fibers. Eachscience fiber is tipped with a plano-concave microlens to increase the focal ratio of the input beam.
The Metrology Camera System (MCS) is installed at the
Cassegrain focus of the telescope. As the fiber positioners haveno encoders, an external system is required to ensure accurate
positioning. MCS takes images of both science and fiducial
fibers back-lit from the other side of prime focus, enabling
closed-loop operation of the positioners.
The Spectrograph System (SpS) includes a fiber feed, collimator, camera optics, dewars and detectors. The divergent
beams from the science fibers aligned along slits are collimatedand split into blue, red and NIR channels by two dichroic mirrors. The beams are then dispersed by VPH gratings and spectral images are formed on the detectors. A grating exchangemechanism allows a medium resolution option for stellar workin the red channel. There are four identical spectrograph modules each of which delivers ∼600 spectral images.
The Fiber System (FS) consists of two short-fiber systems
included in PFI and SpS respectively, and a long cable system
is routed via the telescope to connect PFI to SpS via two setsof fiber connectors. One of these is at the telescope top end toallow PFS to be removed from the telescope, and the other is
at SpS to ease its integration for operational and maintenancepurposes. Table 1 summarizes the key characteristics of PFS.
The Subaru Night Sky Spectrograph (SuNSS) (not shownin Figure 2) is a pair of small (36mm) telescopes, each with aSubaru PFS SSP Survey

3

Table 1
Instrumentation parameters
Field of view
Field of view area
Input f number to fiber
Fiber core diametera
Positioner pitch
Positioner patrol range
Minimum fiber separationb
Fiber configuration time
Number of fibers
Fiber density
Spectral arms
Spectral coverage
Dispersion
Spectral resolution
Detector type/read-out mode
Spectrograph throughputc

Prime Focus Instrument∼ 1.38 deg (hexagonal - diameter of circumscribed circle)∼ 1.25 deg2
2.8
127 µm (1.12 arcsec at the FoV center, 1.02 arcsec at the edge)
8 mm (90.4 arcsec at the FoV center, 82.4 arcsec at the edge)
9.5 mm (107.4 arcsec at the FoV center, 97.9 arcsec at the edge)∼ 30 arcsec∼ 60–120 sec
Science fibers
Fixed fiducial fibers
2394
96∼ 2000 deg−2 or ∼ 0.6 arcmin−2
Spectrograph
Blue
Red
NIR
Low Res.
Mid Res.
380 – 650 nm
630 – 970 nm
710 – 885 nm
940 – 1260 nm∼ 0.7Å/pix∼ 0.9Å/pix∼ 0.4Å/pix∼ 0.8Å/pix∼ 2300∼ 3000∼ 5000∼ 4300CCD
CCD
HgCdTe/SUTR∼ 53% (@500nm) ∼ 52% (@800nm) ∼ 47% (@800nm) ∼ 34% (@1100nm)

a This is a diameter of the sky projected onto the fiber core through the microlens with a magnification of 1.28.
b The minimum separation includes a physical limitation and a margin for collision avoidance.
c These values include detector QEs. The typical total throughput including primary mirror reflectivity, WFC, fiber

systems, spectrograph optics, detector QE, etc. in blue, red, NIR, and medium resolution arms are ∼22% (@500nm),∼26% (@800nm), ∼19% (@1100nm), and ∼23% (@800nm), respectively.

bundle of close-packed 128 fibers subtending 1.2 degrees on
the sky. The telescopes are permanently mounted on the top
ring of the Subaru Telescope and can feed one PFS spectrograph, allowing observations in parallel with any other Subaru
instrument. One SuNSS telescope studies spatial and temporalvariations in night sky emission, and the second uses a diffuser
to assess sky subtraction performance. As both SuNSS telescopes have the same focal ratio as the Subaru Telescope, thesky signal received through each SuNSS fiber is the same asthat through the PFI.
The PFS instrument will play an unique role amongst a
growing set of massively-multiplexed spectrographs (MMS) in
the next decade. We conclude this section with a brief overview
of its capabilities in the context of similar instruments underconstruction or being commissioned elsewhere.
The only other MMS exploiting an 8-meter aperture telescope on a premier site is the Multi-Object Optical and NearInfrared Spectrograph for the 8.2m VLT (MOONS). In comparison to PFS, MOONS has a more modest multiplex gain
(500/1000 depending on whether beam switching is employed),
accesses a smaller field of view (0.14 deg2 ), and with a wavelength range of 0.6–1.8 µm. Although its coverage extends
further into the near-infrared, its survey speed and limitedaccess to the full range of optical spectral features limits its
competitiveness in most of the science applications discussed
in this proposal.
The Dark Energy Spectroscopic Instrument (DESI) (DESI
Collaboration et al. 2022) is an optical instrument on the refurbished Mayall 4.0m telescope which is undertaking a numberof related galaxy and quasar surveys for studies of cosmology and large-scale structure (LSS). The largest component isa survey of 18M emission-line galaxies aimed at tracing thebaryonic acoustic oscillation feature over the redshift range
0.6 < z < 1.6. The multiplex gain of DESI (5000) and its fieldof view (8 deg2 ) are superior to those of PFS. However, theinfrared capability of PFS will enable similar galaxy surveys
to be conducted to redshifts beyond z = 1.6. While DESI
is a dedicated facility on the Mayall telescope, PFS has thebenefit of the significantly larger Subaru aperture and clearer
and darker Maunakea skies. We discuss the complementaryand competitive aspects of PFS and DESI further in Section 4.
The 4-meter Multi-Object Spectroscopic Telescope(4MOST) and the WHT Enhanced Area Velocity Explorer(WEAVE) are optical MMS facilities on the 4.1m VISTA
and 4.2m William Herschel telescopes at Cerro Paranal and
La Palma, respectively. 4MOST plans to undertake a wide
range of extragalactic, Galactic and transient object surveys
for the ESO community. It has a similar multiplex gain to
PFS but the telescope has a larger field of view (4.2 deg2 ). Its
main distinguishing feature is a high resolution (R ≃ 20, 000)
capability for stellar work. WEAVE is a more versatile facility
with multi-fiber, multi-IFU and a single large IFU capability.
The instrument exploits a new 3.1 deg2 field corrector. Its
maximum multiplex gain is 1000. 70% of WHT time will
be dedicated to a variety of surveys. Both 4MOST and
WEAVE plan to undertake a wider range of surveys suitable
for their 4m aperture telescopes and can be considered to becomplementary to the plans proposed herein for PFS.
4. PFS COSMOLOGY: DARK ENERGY, TESTS OFGRAVITY AND NEUTRINO MASS
4.1. Primary Goals
Cosmological studies are crucially important in astrophysics
given major unresolved questions relating to the nature of theworld model and the growth of structures. PFS has been designed to offer outstanding opportunities in this area. The
PFS cosmology program will map the three-dimensional positions of about 4 million emission-line galaxies (ELGs) over
1100 deg2 and a wide redshift range 0.6 < z < 2.4, with a highnumber density over a comoving volume of 7.1 (h−1 Gpc)3 .
With this dataset, we will study the nature of DE, test General Relativity (GR), and constrain cosmological parameters
including the sum of neutrino masses with exquisite precision.
The initial conditions of the Universe at the time of recombination (z ≃ 1090) are accurately known from cosmic mi-

PFS SSP team

1 Here f ≡ d ln D(a)/d ln a is the growth rate of the linear density fluctuations, D(a) is the growth factor, and σ8 is the present-day rms mass density
fluctuations within a sphere of radius 8h−1 Mpc.

PlanckPlanck+BOSS/eBOSS
Planck+BOSS/eBOSS+HSC
Planck+BOSS/eBOSS+HSC+PFS

Probability
0.00

0.05

0.10

0.15

m [eV]

0.20

0.25

1.0

12

0.8

10

0.6

8
6

0.4
1 Pinv /Pnorm
M / (M )

0.2
0.0
0.00

0.05

0.10

0.15

m [eV]

0.20

4
2
0
0.25

Detection significance for m

crowave background (CMB) data (Komatsu et al. 2014; Planck
Collaboration et al. 2020). We can use these to evolve theUniverse forward, making predictions for late-time observables, including the statistics of the galaxy distribution. The
PFS cosmology survey program will test these predictions in avariety of ways:• We will measure both the Hubble expansion rate H(z) andthe angular diameter distance DA (z) to 3% fractional accuracies
in each of 7 redshift bins over 0.6 < z < 2.4, using the baryon
acoustic oscillation (BAO) method and the Alcock-Paczyński(AP) effect.
• We will use the distance measurements to determine thecurvature parameter ΩK to 0.3% accuracy and the DE densityΩDE (z) to about 7% accuracy in each redshift bin, by combining with lower redshift BAO measurements.
• We will measure the redshift-space distortion (RSD) on
linear scales to determine the growth rate of LSS f σ8 (z)1 to
6% accuracy up to z = 2.4.
• We will also measure the broad-band shape and amplitudeof the galaxy power spectrum and bispectrum over 0.6 < z <2.4 to obtain tighter constraints on cosmological parameters.
With these measurements, we can make significant progressin our understanding of fundamental physics. We P
will constrain the total neutrino mass to a precision of σ( mν ) =0.02 eV. If the neutrino P
mass hierarchy is inverted, this will
enable a 5σ detection of mν , rather than an upper limit. We
will also constrain models of time-evolving DE and modifications to GR on cosmological scales out to z = 2.4. The
neutrino component described above represents “guaranteedscience”, whereas the DE campaign offers a “discovery potential” uniquely accessible to PFS. We describe these scientificgoals in further detail below.
Neutrino physics: Massive neutrinos retard the growth of cosmic structure because their large velocity dispersion makes
gravitational potentials shallower and the growth of density
fluctuations is suppressed on scales below the so-called “neutrino free-streaming length” (Takada et al. 2006; Lesgourgues& Pastor 2006). This leads to a scale- and redshift-dependent
suppression of the matter power spectrum amplitudes relative
to that with massless neutrinos. The PFS cosmology programis designed with a long lever arm in both spatial scale andredshift, allowing improved measures of the power spectrum
and thus the neutrino mass constraint. The top panel of Figure 3 shows the expected precision on the total neutrino massusing both the PFS power spectrum and bispectrum up to
kmax = 0.2 h/Mpc, as well as CMB data from Planck 2018
(Planck Collaboration et al. 2020), galaxy clustering from
BOSS/eBOSS (Alam et al. 2021), the expected weak lensing
power spectrum from the full HSC SSP data, and the crosspower spectrum of PFS galaxies and HSC weak lensing. With
PFS, these collected datasets willPmeasure the sum of the neutrino masses to a precision of σ( mν ) = 0.02 eV (68% CL).
Given existing neutrino oscillation data, the minimum
totalP
neutrino mass assuming the normal hierarchy is mnormal≈
P ν
0.06 eV, while that for the inverted hierarchy is minverted≈
ν
0.1 eV (Esteban et al. 2019; Choudhury & Hannestad 2020).
The bottom panel of Figure 3 shows the probability with which
we will be able to reject the inverted mass hierarchy as afunction of the actual total neutrino mass, as well as its de-Probability to reject the inverted hierarchy

4

Figure 3.
Top panel: Marginalised posterior distribution of thetotal neutrino mass from Planck 2018 data alone (black), Planck
2018+BOSS/eBOSS (red), Planck+BOSS/eBOSS+HSC
P forecast (blue), andPlanck+BOSS/eBOSS+HSC+PFS forecast (green). mν = 0.06 eV is assumed for the input neutrino mass, corresponding to the lower limit for thenormal mass hierarchy. The sum of neutrino masses will be constrained
to 0.02 eV and 0.04 eV (68% and 95% confidence, respectively, from
Planck+BOSS/eBOSS+HSC+PFS. The power spectrum and bispectrum areboth used for the PFS forecast. Bottom: The probability with which the inverted mass hierarchy can be rejected (blue solid, left axis) and the detection
significance number of sigma for the total neutrino mass (green dashed, rightaxis).

tection significance. The rejection probability is defined as
1−Pinv /Pnorm , where Pinv and Pnorm are obtained by integrating
theofPtotal neutrino mass at the
P posterior
Pmass range above
normalminvertedandm,respectively.
Ifmν = 0.06 eV, theνν
total mass can be detected with 3σ significance and the inverted
P mass hierarchy excluded at > 2σ (1-Pinv /Pnorm = 0.95).
If mν > 0.06 eV, the normal and inverted hierarchies become
indistinguishable
but the mass detection significance increases.
P
If mν > 0.1 eV, PFS will determine the total mass at > 5σ
significance. In any of these cases, the results of the PFS cosmology survey will have profound implications for cosmology
and particle physics.
P
The expected constraints on mν from PFS, DESI and Euclid are comparable because, for such small neutrino masses,
they are limited by the uncertainty in the optical depth of the
CMB, τ (Allison et al. 2015; Boyle & Komatsu 2018). Although DESI will have issued its first year (Y1) data releaseby the time the PFS survey commences (see below) and the
Euclid survey will follow with itsPcosmology results, each ofthese surveys will likely reach σ( mν ) = 0.02 eV on a similar
timeline. Indeed, any claimed detection of the total neutrinomass will need confirmation from multiple experiments. In
this sense, PFS, DESI and Euclid are complementary and thus
each is indispensable.
DE vs. modified gravity: DE affects the angular diameter dis-Subaru PFS SSP Survey
1.0

0.6

Dark energy density

GR+ CDMnDGP rc H0 = 1
f(R) 0.05 < k < 0.5 h/Mpc
PFS
DESI Y1 (ELGs)
6dF
2dF
VIPERSFastSound
SDSS/BOSS/eBOSS LRGeBOSS ELG

growth of structure f(z) 8 (z)DE (z)CDM
SDSS/BOSS
SDSS/BOSS+PFS

0.8

0.5

0.6

0.4

0.4
0.2
0.0
0.0

5

0.3

0.5

1.0

1.5

redshift z

2.0

2.5

0.0

0.5

1.0

1.5

redshift z

2.0

2.5

3.0

Figure 4. Left panel: Expected accuracy with which the PFS BAO-measured DA (z) and H(z) will determine the DE density parameter ΩDE (z), as a function ofredshift, assuming as input a flat ΛCDM model (solid curve). Right: Expected 1σ constraints on f σ8 as a function of redshift, from PFS combined with Planck
2018, as well as existing constraints. The solid line assumes standard cosmology (GR+ΛCDM). The PFS forecast is based on both the power spectrum andbispectrum.

tance DA (z) and the expansion rate H(z), which can be derived
from the BAO and AP measurements (Eisenstein et al. 2005;
Percival et al. 2010). While it is common to parameterize theproperties of DE with the equation of state parameter wDE , itis more useful to evaluate a direct reconstruction of the DE
density parameter ΩDE (z) as a function of redshift, as shown in
the left panel of Figure 4. PFS will extend this measurement
to z = 2.4, well beyond the DESI ELG sample. Because DA (z)depends on the integral of 1/H(z), it is sensitive to ΩDE (z) overa range of redshifts. Thus PFS will also improve the precisionof ΩDE (z) at low redshifts. Measuring ΩDE (z) over a wide
redshift range will constrain various DE models. For example,wDE (z) is predicted to oscillate around −1 in models in whicha continuous shift symmetry of the DE field is broken to adiscrete symmetry (Frieman et al. 1995; Dodelson et al. 2000;
D’Amico et al. 2016; Schmidt 2017). For a flat model with aconstant equation of state, the geometrical constraints expectedfrom PFS correspond to a precision σ(wDE ) ≃ 0.02.
In April 2024, the DESI collaboration reported first-year
cosmology results from their BAO measurements. These measurements alone are consistent with the standard flat ΛCDM
cosmological model with wDE = −0.99+0.15−0.13 , but hint at the possibility of a time-evolving dark energy when combined with
CMB and/or Type Ia supernovae constraints (DESI Collaboration et al. 2024). The significance of this tantalising claim
depends on the choice of the combined dataset. Nonetheless,given the importance of such a result, if correct, the PFS Cosmology program is uniquely placed to provide independent
BAO measurements that would verify or otherwise falsify thisclaim. Of particular importance is the fact that PFS will chart
the expansion history using a single tracer (ELGs) over an
extended redshift range 0.6 < z < 2.4, whereas DESI utilises a
mixture of luminous red galaxies (LRGs) over 0.6 < z < 1.1
and the Lyman-α forest over 1.8 < z < 4. BAO measurements from different tracers will robustly test whether DE is atime-dependent phenomenon.
Another major question is whether the cosmic acceleration
does not indicate the presence of DE but rather arises from a
modification of GR on large scales. Such a modified theoryof gravity would affect the growth rate of cosmic structures,
which can be tested by measuring the RSD effect (Peacocket al. 2001). General Relativity predicts the growth index
γ = 0.55 for f ≈ Ωm (z)γ . However, a recent study (Nguyen

et al. 2023) constrained the growth index γ = 0.633+0.025−0.024 using CMB data from Planck and LSS data from weak lensing,galaxy clustering, and RSD. All these data combined favor
growth suppression stronger than the growth predicted from
General Relativity. Currently this result is not based on any
measurements above z > 1.6. The right panel of Figure 4
shows the expected constraints from PFS on the linear growthrate of structure, f σ8 , as a function of redshift. The PFS cosmology program will measure f σ8 to ∼6% precision, which is
better than existing constraints from any other surveys, and will
be comparable to the constraint from the DESI Y1 analysis atz ≲ 1.2. However, we can measure f σ8 up to z ≃ 2.4, thereby
extending observations into a hitherto uncharted redshift range
and providing a powerful test of GR on growth of structure.
In summary, the PFS cosmology survey will chart the largescale distribution of galaxies out to z = 2.4 with unprecedented
fidelity over an enormous cosmic volume. This will enablestudies of the time evolution of structure over 80% of cosmic
history for the first time. In addition to measuring
the sumPof the neutrino masses to a precision of σ( mν ) = 0.02 eV,
PFS will provide a convincing test of the cosmological model
beyond standard ΛCDM.
4.2. Uniqueness of the PFS Cosmology Program
In comparison to other spectroscopic surveys, such as DESI
and Euclid, that will be underway at the same time, the PFS
cosmology program is distinctive in three respects: (i) its
extensive redshift coverage 0.6 < z < 2.4, (ii) its high number
density of galaxies (n̄g ≳ a few × 10−4 (h−1 Mpc)3 ) at z ≳ 1.5,and (iii) its synergy with the deep HSC imaging survey. Below
we describe the advantages of (i) and (ii), deferring discussionof (iii) until the following sub-section.
The PFS cosmology program samples its large redshift range
0.6 < z < 2.4 with a single LSS tracer, ELGs, which is key forminimizing systematic effects. As cosmological measurements
have become more precise, tensions have begun to appear between different probes. For example, the Hubble constant(H0 ) measured using local distance-ladder techniques differs
by 4.4σ from the Planck value (Bernal et al. 2016; Freedman
2017; Riess et al. 2019; Abdalla et al. 2022). PFS’s measurements of H(z) across a wide redshift range will address thistension by placing stringent constraints on the evolution of
the late-time universe. A further tension arises from the con-

6

PFS SSP team
1.0

PFS(0.6 < z < 1.2)+SDSS galaxy
PFS(1.2 < z < 2.4)
PFS(0.6 < z < 2.4)+SDSS galaxy
SDSS galaxy (z < 1.0)
SDSS Ly +QSO (z > 1.0)

0.86

CMB

0.8

70

PFS

ΛCDM

0.82
0.6

p

Ωm/0.30.84

S8 = σ 8

H0 [km/s/Mpc]

80

Weak
Lensing

CMB Lensing

0.80
0.4
0.78
0.2
0.76

60

0.2

0.3

0.4

0.5

0.74
0.0
0.0

0.5
0.2

1.00.4

1.50.6

2.0 0.8

1.0m
redshift (z)Figure 5. Left panel: Two-dimensional marginalised joint posterior distributions (68% and 95% CL) in the Ωm − H0 plane from BAO measures. All constraints
adopt a Big Bang nucleosynthesis (BBN) prior σ(Ωb h2 ) = 0.02. Dashed lines show constraints from current data (Alam et al. 2021): BAO at z < 1.0 (red) andline-of-sight BAO (quasar and Lyman-α) above z = 1.0 (blue). Filled contours show expected constraints from PFS BAO measurements in low-z (0.6 < z < 1.2;blue) and high-z (1.2 < z < 2.4; red) bins. The gray contour shows the combined constraint. Right panel: Expected 1σ constraints on S 8 as a function of redshift
from PFS (red shaded region) as well as the current constraints from different cosmological probes: weak lensing (green) and CMB (blue). The PFS forecasts
include both the power spectrum and bispectrum.

straints on Ωm and H0 within a flat ΛCDM model between
BAO measurements from the SDSS BOSS and eBOSS galaxies at z < 1.0, and those derived from the Lyman-α forest
seen in quasars at z > 1.0 (the left panel of Figure 5). Thistension between probes at low and high redshift could be anindication of new physics beyond the standard ΛCDM model.
Alternatively, it may simply represent systematic effects arising from the use of different tracers. Note that the degeneracies
in the Ωm − H0 plane (the left panel of Figure 5) are different
at low and high redshifts. Thus, by undertaking measurements
over a wide redshift range with a single tracer, PFS can breaksuch degeneracies and directly address such tensions free fromsystematic biases.
In addition to the use of a single tracer, PFS will make arobust measurement of f σ8 using the RSD effect whereas, athigh redshift, other surveys must use less precise methods.
For example, for z > 1.6 DESI aims to utilize the Lyman-αforest where extracting f σ8 is not straightforward because ofradiative transfer uncertainties.
Along
these lines, PFS will also robustly measure S 8 =√σ8 Ωm /0.3, the amplitude of the matter clustering, across
0.6 < z < 2.4. Recent weak lensing analyses (Troxel et al.
2018; Asgari et al. 2021; Amon et al. 2022; Li et al. 2023;
Dalal et al. 2023; Miyatake et al. 2023) and galaxy clustering
analyses (Kobayashi et al. 2022; Ivanov et al. 2023) that probe
LSS at z ∼ 0.5 have found S 8 values that are in ∼ 2σ “tension”
with the S 8 value expected from the best-fit ΛCDM cosmology from the Planck CMB (Planck Collaboration et al. 2020)and from the CMB lensing measurement (Madhavacheril et al.
2024). The right panel of Figure 5 shows the expected S 8 constraints from PFS as a function of redshift, as well as the existing constraints from weak lensing and CMB experiments. The
PFS cosmology program will measure S 8 beyond the redshift
ranges of DESI ELGs (z > 1.5 compared to zDESI ≲ 1.5) andEuclid Hα emitters (z > 1.7 to zEuclid ≲ 1.7). Measurementsfrom PFS will fully bridge the gap between S 8 constraints
from the late and early Universe and produce stringent tests ofthe ΛCDM model.
A further advantage of the PFS cosmology program is thehigh number density of galaxies at z ≳ 1.5 which permits
the study of under-dense regions (voids), providing comple-

mentary constraints and crucial internal consistency checks
on the cosmological parameters. A high number density will
be maintained throughout the redshift range, sufficient to robustly identify more than 103 voids per (h−1 Gpc)3 volume at
0.6 < z < 2.4 (whereas DESI expects a similar number only up
to z ∼ 1.3). The void size function constrains the DE equationof state and alternatives to GR (Pisani et al. 2015; Cai et al.
2015), while measuring the AP and RSD effects using voids
will provide complementary constraints on Ωm and the growthrate of structure (Sutter et al. 2014; Hamaus et al. 2017, 2020;
Pisani et al. 2019). PFS RSD and AP void statistics will measure f /b (where b is the linear bias parameter) to a precisionof 8%, and Ωm to 3%. In addition, the voidPsize function improves the neutrino mass constraints to σ( mν ) = 0.017 eV
when combined with galaxy clustering and weak lensing data
(Massara et al. 2015; Kreisch et al. 2019; Zhang et al. 2020;
Bayer et al. 2021; Kreisch et al. 2022).
PFS’s high galaxy number density will also enable the extraction of cosmological information on small scales, thereby
improving the measurement precision of both the rate of structure growth and the neutrino mass. Recent advances in emulators (Miyatake et al. 2022; Kobayashi et al. 2022) demonstrate that the power spectrum analyses can be extended to
non-linear scales. For example, by extending the power spectrum analysis to kmax = 0.5 h/Mpc from a fiducial value of
kmax = 0.2 h/Mpc, the neutrino mass constraint from the power
spectrum, bispectrum
and galaxy-lensing cross-spectrum imP
proves to σ( mν ) = 0.017 eV, and growth rate constraints
improve by ∼ 30% in all redshift bins. The neutrino mass constraints will further improve if measures of the bispectrum on
yet smaller scales are included (Hahn & Villaescusa-Navarro
2021; Chudaykin & Ivanov 2019). Information on higher-order
statistics can also be extracted by reconstructing the density
fields, the accuracy of which is ensured by the high number
density of PFS galaxies.
4.3. Synergy with the HSC and Other Datasets
Although the PFS cosmology program exploits statistics ofthe galaxy distribution, by surveying the same cosmologicalvolumes exploited by weak gravitational lensing campaigns,
PFS will uniquely provide joint (and thereby improved) cos-

Subaru PFS SSP Survey

1.4
1.2

EG /EG CDM

1.0
0.8
0.6
0.4

PFS+HSCPFS+ACT

0.2
0.0
0.0

0.5

1.0

redshift z

1.5

2.0

2.5

Figure 6. Expected 1-σ constraints on the EG (z) statistic (see text) relative
to that of the GR prediction (the fiducial ΛCDM model), from PFS in combination with lensing data from HSC and ACT. As EG does not depend on σ8 ,the size of the error bar shows how well PFS model-independently constrains
the growth rate, hence the theory of gravity. Gray points show existing EG
measurements at low redshifts (Reyes et al. 2010; Blake et al. 2016; Pullen
et al. 2016a; Alam et al. 2017; de la Torre et al. 2017; Amon et al. 2018; Singhet al. 2019; Jullo et al. 2019; Blake et al. 2020). To be conservative, only
large-scale PFS measurements are used in these predictions: ℓmax = 350 for
0.6 < z < 0.8 and 780 for 2.0 < z < 2.4. If smaller scale RSD and lensing
measurements are included, the precision of EG improves further.

mological constraints, test systematic effects via independent
approaches, and calibrate key lensing uncertainties such as
those arising from the use of photometric redshifts and intrinsic galaxy alignments. These synergies are particularly
relevant for the now-completed HSC imaging survey.
The HSC imaging data, which reaches i ≃ 26 (5σ for a point
source and 2′′ aperture) in 5 passbands (grizy) over 1100 deg2 ,is the deepest available over large areas and will not be surpassed until the era of LSST. These multi-color data are used
to select target galaxies for the PFS survey. The photometricaccuracy, uniformity and depth of this parent catalog provides
precise colors, ensuring the selection of an uniform ELG sample with a high redshift success rate. The expected throughputof the PFS instrument implies that ∼ 75% of the targeted galaxies will have [O ii] emission detected at S/N> 6 (see Section 7.2
for details), and thus have an accurate redshift. Simulations
suggest that less than 0.22% of the redshifts will be incorrectly
measured, thereby meeting the requirements necessary for accurately measuring the growth rate of structure (Pullen et al.
2016b). For comparison, Euclid’s low-resolution grism will
likely suffer from higher redshift misidentifications (Addison
et al. 2019).
PFS clustering measurements will be biased low by the limited patrol areas of the robotic positioners, as well as the mildsparse sampling (70%) of our targets. Both effects would leadto a systematic incompleteness of close pairs of galaxies. However, our simulations indicate that weighting galaxies by the
inverse of the probability that they are observed accurately mitigates this effect (Sunayama et al. 2020; Makiya & Sunayama
2022; Bianchi & Verde 2020). For this mitigation scheme
to work, the photometric error in the parent sample must be
less than a few percent and the HSC data readily satisfy thisstringent condition.
PFS measurements of the large-scale galaxy distribution canbe combined with complementary weak lensing informationfrom the HSC survey. Such joint measurements significantly

7

improve the cosmological and structure growth constraints and,crucially, reduce uncertainties arising from galaxy bias andnonlinear effects that are otherwise major sources of systematic error in all spectroscopic surveys (de la Torre et al. 2017).
By cross-correlating the HSC weak lensing data with the 3dimensional positions of PFS galaxies, we can measure thegalaxy-matter correlation as a function of redshift, i.e., tomography of weak lensing data, yielding signal-to-noise ratios of
20 and 5 at the lowest (z = 0.7) and highest (z = 2.2) redshiftbins, respectively (Makiya et al. 2021).
The synergy with HSC lensing data is particularly powerfulwhen testing GR: galaxy positions tell us how galaxies movein response to gravitational potentials, whereas lensing tells
us how light is bent by gravity in the same cosmic volumes.
GR makes specific predictions for how these two gravitational
effects are related. A joint analysis of redshift-space galaxy
clustering and lensing thereby provides a model-independent
test of gravity on cosmological scales much larger than the
Solar System. Specifically, we can use the statistic EG (z) =∇2 (ψ − ϕ)/ f δm (Zhang et al. 2007; Reyes et al. 2010), which is
directly observable given that lensing and RSD measurements
will both be available for the PFS galaxies. GR predicts a
specific form for EG (z), and its measurement is free fromuncertainties in the galaxy bias.
The PFS survey regions also overlap with those of ongoing and upcoming ground-based CMB experiments, including
the Atacama Cosmology Telescope (ACT) and the Simons
Observatory. These datasets will provide mass maps throughweak lensing of the CMB from z ≈ 0.5 to 3. Figure 6 showsthe expected constraints on EG (z) from the combination of
PFS, HSC, and ACT relative to that of the fiducial ΛCDM
model. Any significant deviation from unity in this plot would
represent a possible signature of modified gravity. Current
measurements have large uncertainties and a limited redshift
range, but hint at possible deviations. In combination with
HSC and ACT, PFS will extend these measurements to highredshifts (0.6 < z < 2.4) with unprecedented precision (∼ 5%
in each of 7 redshift bins) and provide decisive evidence for,or against, modified gravity.
Cross-correlating PFS spectroscopic data with photometric HSC galaxies will calibrate the photometric redshifts of
HSC galaxies and more accurately model the effect of intrinsicalignments, thereby improving cosmological analyses of the
HSC weak lensing measurements (Hikage et al. 2019; Oguri& Takada 2011). Furthermore, cross-correlation of the PFS
galaxies with HSC and CMB lensing data will permit calibration of various systematic effects inherent in each method anddataset (Schaan et al. 2017). These improvements will leadto more robust and accurate constraints on S 8 , where, as mentioned above, the Planck and the cosmic shear measurements
are currently in tension. In addition, the PFS ELG sample
can be used to calibrate the selection function of ELGs in theshallower DESI and Euclid surveys.
Thus, the PFS cosmology program promises to advance ourunderstanding of the nature of DE, to confirm robustly whether
the H0 and S 8 tensions are significant, and to obtain stringent
constraints on cosmological parameters including the neutrinomass.

5. PFS GALACTIC ARCHAEOLOGY: THE STRUCTURE

AND ASSEMBLY OF GALAXIES AND THEIR DARKMATTER HALOSPFS SSP teamCurrent
PFS forecast

FDM, SIDM (core)

0.0

db
fee

+
DM

C

Draco

ack

ck

ba

d
fee

+

M
CD

CDM (cusp, DM only)

−1.5

10−5

FornaxSextans−1.0

Ursa Minor

10−4

B
Dw righ
ar t
fs

−2.0

Sculptor

Boötes I

Cl
Dw assiar cal
fs

−0.5

Ul
t
Dw ra-faar intfs

5.1. Primary Goals of the PFS Galactic Archaeology
Program
Much of the DM physics is manifest on the scales probed
by individual galaxies (e.g. Ostriker & Steinhardt 2003). Thishas the consequence that different types of DM make different predictions for observationally accessible properties of thestellar populations within Milky Way mass (and below) DM
halos. However, the physics of baryonic matter also plays an
unavoidable major role in determining these same propertiesof the light sector. We have devised a program with PFS that
exploits its unprecedented field-of-view, depth and spectroscopic multiplexing capabilities to obtain the required large
samples of precise stellar line-of-sight velocities and chemical
abundances in galaxies of the Local Group, to disentangle theeffects of the dark and light sectors and hence constrain thenature of DM.
We propose to target stars in selected galaxies within the
Local Group and compare their derived properties with predictions from the concordance ΛCDM model of structure formation, plus alternative models with lower power on small scales.
The three primary goals are:• Determination of the DM density profile in dwarf
spheroidal galaxies (dSphs) with a range in stellar massand star-formation history. As is well-established, these
extremely DM-dominated systems are the best-suited targets
for testing the robust prediction of ΛCDM that the density
profile, in the absence of baryons, should be cusped (Navarro
et al. (1997), NFW). However, time-dependent gravitational
perturbations will modify this profile; e.g., bursts of star formation could erase cusps in DM profiles (Mashchenko et al.
2008). Our science goal requires spectroscopic samples of
thousands of stars, which PFS will provide. As discussed in
more detail below, we will apply several different analysis
techniques to our sample of dSphs, including an essentiallymodel-independent determination of the inner slope of the density law introduced by Walker & Peñarrubia (2011), and two
methods that break the mass-velocity dispersion anisotropy ofclassical spherical Jeans analyses.
• Comparison of the stellar populations in M31 with thoseof the Milky Way, through the first chemodynamic spectroscopic survey of individual stars in our companion large
disk galaxy. PFS’s unique ability to measure [α/Fe] for tensof thousands of stars in M31 will reveal both its major andminor merger histories. The major-merger history since z ∼ 2inferred for the Milky Way (MW) (e.g., Wyse 2001; Helmi
et al. 2018) is unusually quiescent for typical ΛCDM galaxies(e.g., Evans et al. 2020). The proposed PFS measurementsof kinematics and chemical abundances in M31 will further
contextualize the MW’s merger history and will provide themost detailed information for the assembly history of a galaxy
outside the MW to date. Key observables are the patterns of[α/Fe] against [Fe/H] for the stars in the disk(s) and stellar halo.
PFS will provide the chemical and kinematic data needed totest theories of disk formation that require a major merger (e.g.,
Renaud et al. 2021) or not (e.g., Vincenzo & Kobayashi 2020).
Similarly, the chemical and kinematic structure and substructure in M31’s halo will reflect the minor-merger history, as
seen the “low-alpha” halo stars in the Milky Way that are likely
debris from the Gaia-Enceladus Sausage (GES) merger (Helmi
et al. 2018). The copious evidence of more recent accretion
into M31, such as the Giant Southern Stream and the Northwest Stream, could potentially imprint a complex chemicalpattern into the halo, that would contrast with that of the MW

γDM: inner slope of DM profile

8

10−3

M∗/Mhalo
Figure 7. The inner DM density profile slope (γDM ) derived for our selected
sample of dSphs vs. their stellar-to-halo mass ratio [Hayashi et al. (2020)
with the addition of Boötes I]. ΛCDM predicts NFW cusps γDM ≲ −1 (grayshading). Hydrodynamical simulations (FIRE-2, orange: NIHAO, blue)
predict that strong, episodic baryonic feedback can modify cusps into cores.
However, most current estimates of γDM in MW dSphs (black points) areshallower than can be explained even with baryonic feedback, albeit withlarge error bars. PFS will give much more precise measurements, allowing
us to distinguish between CDM+feedback models and non-standard models(green shading). The open and filled magenta points illustrate how PFS
inferences of γDM would appear in these two respective cases. PFS will
also infer the burstiness of SFHs – an indicator of the strength of feedback –from measurements of the [α/Fe] ratios of individual stars. Thus, we can test
whether burstier SFHs result in a larger deviation from a cusp.

halo, which is dominated by a single, ancient merger (GES).
• Investigation of the physical mechanisms that determine
the ongoing build-up of the outer regions of the Milky Way.
We will use the unique combination of depth and areal coverage of PFS to target faint main-sequence turn-off stars in
the outermost regions of the disk(s) and stellar halo, where
dynamical times are longest and signatures of substructures
persist. Main sequence turn-off stars provide an unbiased sampling of the underlying population and crucially allow for thedetermination of ages from isochrone fitting and spectrophotometric distances together with line-of-sight kinematics andchemical abundances from the spectra. We will be able to investigate relationships between stellar age, chemical abundance
and kinematics, each as a function of Galactocentric distance.
These will constrain dynamical heating mechanisms (such as
those from fuzzy DM) and disk radial growth/rearrangement.
The data will also allow an improved understanding of theedge of the stellar halo and of the thick-disk/halo interface inkinematic/chemical phase space, providing new constraints on
their formation mechanism(s).
5.2. Dwarf galaxies: Cusps, Cores, and Starbursts
PFS will conduct an unprecedented survey of the density
profiles of six dSphs, thereby determining whether thedSphs’ density profiles support ΛCDM or alternative DM
models. The unique strengths that allow PFS to probe DM
on small scales are (i) 4000 – 15000 stars per dSph, (ii) widearea coverage well suited for the angular extent of dSphs,(iii) velocity precision (significantly less than 3 km/s for moststars) much smaller than the velocity dispersion of a dSph
(6 – 10 km/s), (iv) detailed abundance measurements, andSubaru PFS SSP Survey

9

Table 2
Dwarf Galaxies Targeted by PFS
GalaxyDistance [kpc]rtidal [′ ]M∗ [106 M⊙ ]⟨[Fe/H]⟩ [dex]ageBoötes I
Draco
Ursa Minor
Sextans
Sculptor
FornaxNGC 6822

66
76
76
86
86
147
460

33
42
51
83
77
71
···

0.034
0.32
0.54
0.70
3.9
24
83

−2.6
−1.9
−2.1
−1.7
−1.9
−1.0
−1.0

ancient
ancient
ancient
ancient
ancient
moderate
young

Nliterature

Npointings

NPFS

118
269
190
441
1497
2603
299

4
4
8
15
8
8
1

2000
7400
3000
6500
6900
14000
1000

Figure 8. For each target dSph, PFS will provide large samples with very wide-area coverage, precise velocities, elemental abundance measurements and excellent
membership probabilities, allowing us to undertake several new chemo-dynamical analyses. (a) MCMC posteriors on the inner profile slope (γ) and velocity
anisotropy (β) from the Jeans analysis using only the 2nd velocity moment (orange) and both the 2nd and 4th velocity moments, i.e., including non-Gaussianity(cyan). The non-Gaussian model breaks the degeneracy between γ and β and better reproduces the input values (γ = 1, β = 0) (Wardana et al. 2024). (b) DM
density profiles derived by non-spherical, 2nd-velocity-moment Jeans analysis. The underlying, Draco-like model is shown as a dashed line. The shaded curves
correspond to the recovered density profiles using “Current” (N = 500) and “PFS forecast” (N = 5, 000) samples, as shown in the inset. The ellipses correspond tothe half-light and tidal radii. The extensive PFS sample size and spatial coverage will uniquely and accurately recover the density profile. (c) [α/Fe] vs. [Fe/H] forcurrently available spectroscopic data in a dSph (red, Kirby et al. 2011) and those anticipated from PFS (black) based on a cosmological simulation (Hirai et al.
2022) with bursts that could affect the DM profile. The clumping of the black points illustrate the effect of repeated starbursts (inset). Such clumping will only be
revealed with PFS’s large sample.

(v) synergy with Subaru/HSC broad-band and narrow-band
pre-imaging.
The current, limited inferences for the inner density slopes,
γDM , in several dSphs are in tension even with the cored profiles predicted by hydrodynamical simulations that include
baryonic feedback (Figure 7). Extremely low-luminosity galaxies, such as Boötes I, seem to have shallow slopes despite thecomparative lack of baryons that could erode a cusp. Moreluminous gas-poor dSphs, such as Sculptor, seem to indicate
slopes even shallower than the hydrodynamical simulations.
This might hint that DM is not the usually adopted weaklyinteractive massive particle, but rather DM is fuzzy (FDM) or
self-interacting (SIDM), which would induce a central core
(Hui et al. 2017; Spergel & Steinhardt 2000; Chan et al. 2022).
Alternatively, these results could merely reflect the limitationsof the extant data or shortcomings in our understanding andmodeling of baryonic feedback.
We will quantify the density distributions of our sample ofdSphs using several independent techniques. First, Walker &Peñarrubia (2011, WP11) successfully used separate chemodynamical populations to quantify γDM in Fornax and Sculptor.

The WP11 technique infers the inner density slope without any
reliance or sensitivity to a choice of DM model, and it is notvery sensitive to the velocity anisotropy. It requires that thegalaxy has multiple stellar populations that have distinct sizes(half-light radii), kinematics (inner velocity dispersions), andchemistry (e.g., metallicity). The ability to identify multiple
chemodynamical populations and to measure their velocity
dispersion profiles requires thousands of stars with velocity
precision better than 3 km s−1 and abundance precision better
than 0.2 dex. PFS will meet these criteria in two crucial waysthat improve on the existing implementations of the WP11
technique: (i) PFS will observe not just the inner regions ofdSphs but also stars out to (and generally beyond) the nominal
tidal edge, giving the best opportunity to identify and quantify
multiple populations. (ii) PFS’s ability to measure detailed
elemental abundances will allow us to determine the existenceof multiple chemical populations not just in metallicity but
also in the space of [α/Fe] vs. [Fe/H].
The WP11 technique is powerful, but limited to estimations of enclosed mass at each sub-population’s half-light radius. Therefore, we will also infer density profiles by directly

10

PFS SSP teammodeling the velocity profile of each dSph via Jeans modeling. Typically, Jeans modeling assumes spherical symmetry,
which is not a good assumption for ΛCDM halos. Therefore,
we will employ non-spherical Jeans modeling (Hayashi et al.
2020), which breaks the degeneracy between mass and velocity
anisotropy. Jeans modeling is also typically only applied to
the velocity dispersion profile. By exploiting the large stellar
samples, we will instead model the full line-of-sight velocity distributions (LOSVDs). The LOSVD technique requires
samples of thousands of stars and secure membership determination. PFS uniquely satisfies both criteria for the five classicaldSphs in Table 2 (the ultra-faint galaxy Boötes I contains too
few stars). Coupled with the LOSVD analysis, the sample
sizes will be sufficient to break the mass–velocity anisotropy
degeneracy (see Figure 8 and Read et al. 2021). This technique
can distinguish cusps from cores even if nature did not provide
distinct chemodynamical stellar populations, and it gives thedensity profile over the full range of radii probed by the spectroscopic sample. Panels (a) and (b) of Figure 8 demonstrate
the various techniques for measuring density profile applied tomock PFS data for the Draco dSph.
Several complications can confound an accurate measurement of the density profile. For example, binary stars alter thevelocity distribution. Through detailed simulations of binary
orbits and of PFS observation strategies, we determined that
we can suppress the effect of binaries simply by observing
each dSph twice, separated by about one year. More complex
observational strategies have only marginal benefits. Rather
than discarding binary stars, we will construct a Bayesianmodel to infer their center-of-mass velocities for input into themass modeling. Another effect is dynamical disequilibrium,
e.g. tides. By observing dSphs beyond their nominal tidal
radii, derived from King-model fits to the light profiles, we candetermine the degree to which the galaxy is out of equilibrium
and adjust the models accordingly.
We have chosen a sample of six dSphs (Table 2) that span arange of M∗ /Mhalo and a range of SFHs. At the high-luminosity
end, Fornax is expected to be heavily affected by baryonicfeedback. At the low-luminosity end, feedback in Boötes I
is expected to be too weak to erode an NFW cusp. The starformation histories (as derived from Subaru and HST imaging;Okamoto et al. 2017; Weisz et al. 2014) span from being nearlycontinuous (Fornax) to being truncated 10 Gyr ago (Sculptor,
Boötes I). The sample includes three galaxies – Draco, Sextans, and Ursa Minor – with very similar M∗ /Mhalo but withdisparate chemical and orbital properties, suggesting that thegalaxies could have differing SFHs that could have influenced
the density profiles differently.
We have imaged each of these galaxies in multiple bands
with HSC, including the NB515 narrow-band filter which issensitive to stellar surface gravity (Komiyama et al. 2018).
This allows us to efficiently pre-select member red giant branchstars (RGB) and de-prioritize foreground MW dwarfs.
In addition to addressing the nature of DM, the PFS sample
will also allow us to study dwarf galaxy physics with unprecedented detail. We will measure [Fe/H] for 40,000 stars and[α/Fe] and individual elemental abundances (C, Mg, Ni) for
18,000 stars across all seven dwarf galaxies. These measurements add information about the SFH and nucleosynthesis
beyond what is available with [α/Fe]. Furthermore, we canestimate the relative ages of RGBs by combining their colors(measured by HSC) and elemental abundances (measured by
PFS) and comparing with isochrones, giving further independent measures of the SFHs (Hirai et al. 2024).

Our sample also includes NGC 6822, a dIrr that is not a
MW satellite and thus serves as a comparison for the dSphs,
whose dynamical and chemical properties have been shaped by
the MW. Previous spectroscopic samples for this galaxy havebeen limited to the center of the galaxy or to sparse single-slit
spectroscopy, sufficient only to establish that this system has acomplex dynamical structure (Valenzuela et al. 2007). PFS willmeasure the velocities and metallicities for 1,000 stars over theentire galaxy. This sample will hold the key to understanding
the unusual red giant population that is misaligned with theH i disk, as well as the apparent dynamical instability in theoutskirts of that disk (de Blok & Walter 2000).
5.3. M31: Assembly of Luminous and Dark Halos
PFS will conduct a massive large-scale spectroscopic survey of the internal kinematics and detailed chemistry of aspiral galaxy other than the MW. PFS will measure velocities (precise to 3 km s−1 ) and open a new chemical dimension
by providing [α/Fe] ratios (precise to 0.15 dex) of 30,000
member stars in M31. This sample size is orders of magnitudelarger than existing samples of stars with comparable spectroscopic measurements (e.g., fewer than 1000 stars analyzed by
Escala et al. 2022; Wojno et al. 2023). Figure 9 shows the wide
coverage of the halo and outer disk to be targeted.
Dey et al. (2023) recently demonstrated how massively multiplexed spectroscopy from DESI can illuminate the accretion
history of M31 by revealing the kinematic signature of a merger
event. PFS will further revolutionize the study of M31 not only
by measuring [α/Fe] but also by reaching up to one magnitude
deeper than DESI (for bluer RGB stars; we will reach 0.5 mag
deeper for the red RGB stars that were the primary targets of
Dey et al. 2023).
A galaxy’s accretion history imprints itself on the [α/Fe]
abundance pattern of its stars. For example, the quiet accretion
history of the MW means that the GES stands out clearly in
the [α/Fe] vs. [Fe/H] diagram of the stellar halo. The result isa bimodal distribution of [α/Fe] at fixed [Fe/H]. If M31 hada more active recent accretion history, it would have a lessordered halo [α/Fe] distribution, with multiple overlapping
tracks whose relative contributions would vary with locationand kinematic cuts, reflecting the individual stellar populations
and orbits of the accreted systems. The existing [α/Fe] measurements (e.g., Gilbert et al. 2019) are insufficient to indicate
whether M31’s halo has a component with GES-like chemistry.
PFS will definitively reveal chemical subpopulations (see rightpanel of Figure 9).
The accretion history affects the chemical composition of thedisk, in addition to the halo. More frequent or more massive
mergers cause more vertical heating (Hayashi & Chiba 2006),
which can displace older, α-enhanced stars. The existing resolved spectroscopy in M31 gives conflicting results about
whether the disk possesses a bimodality similar to the MW.
Although planetary nebulae (Arnaboldi et al. 2022, interpretedin Kobayashi et al. 2023) show evidence for a bimodal distribution of [O/Ar] at fixed [Ar/H] – a proxy for [α/Fe] vs [Fe/H]– JWST/NIRSpec spectroscopy of red giants (Nidever et al.
2024) does not confirm this bimodality. The measurement of[α/Fe] over the face of M31 sets PFS apart from these studies
and from other massively multiplexed spectrographic surveys
targeting M31, such as that with DESI (Dey et al. 2023).
PFS stellar velocities will allow us to distinguish between
high angular momentum (disk) and dispersion-supported (halo)components of M31, allowing us to untangle the disturbed
disk from disrupted satellites. In addition to quantifying theSubaru PFS SSP Survey

11

accretion history with [α/Fe], we will also model the DM
profile out to 60 kpc by measuring the velocity dispersion of
the halo as a function of projected radius. The sample will
inevitably contain a great deal of kinematic substructure in
the form of stellar streams. Published kinematics for stars
in the Giant Southern Stream (GSS) have only been derived
from pencil-beam spectroscopy at strategic locations (Gilbert
et al. 2019), while PFS will cover the entire structure. ThePFS footprint also includes the Northwest Stream, which hasvery limited spectroscopy at present. The measurements ofvelocities and elemental abundances will allow us to infer theorbits, stellar masses, and SFHs of the progenitor galaxies – orgalaxy (see, for example, Hammer et al. 2018).
As for the MW dSphs (Section 5.2), we have observed almost all M31 fields with HSC broadband and narrowband
filters to identify foreground dwarf stars (Ogami et al. 2024),
whose color–magnitude and color–color selection are indicatedin the middle panel of Figure 9 (note that the redder, fainter
RGB and dwarf stars will be classified using machine-learningtechniques; Ding, Filion & Wyse, in prep.).
M31 is distinct from the MW in possessing a bona fidespiral galaxy satellite, M33. We include pointings in M33 to
examine the structure of a galaxy intermediate in mass between
the MW/M31 and the dwarf galaxies of Section 5.2. The
outskirts of M33 are not well-observed, either photometrically(McMonigal et al. 2016) or spectroscopically (Gilbert et al.
2022). This is despite ΛCDM’s prediction that such a low
mass galaxy should have a stellar halo (Deason et al. 2022).
We propose to observe a few thousand candidate member stars
to test this definitively. Using kinematics we will quantify the
total mass of M33 and uncover its accretion history throughelemental abundances.

Figure 9. Top panel: Proposed PFS pointings (red hexagons) in M31 andM33 (inset). The coloring gives the stellar density of candidate member stars
selected with HSC broadband and narrow-band imaging. Middle: HSC color–magnitude and color–color diagrams for stars in M31. Candidate red giant
members (red points) are identified through color–color selection (note that theredder, fainter RGB and dwarf stars will be classified using machine-learningtechniques). Bottom: Abundances for a mock PFS observation of M31. PFS
can distinguish between an accreted and a monolithic halo, even if the accreted
component is only 5% of the total halo. PFS could also detect a MW-likechemodynamic bimodality in M31’s disk when the stars are differentiated byline-of-sight velocity.

5.4. To the Outer Limits of the Evolving Milky Way
PFS will provide precise chemodynamic data for unbiased tracers (main-sequence stars) in each of the stellar
components of the MW galaxy to unprecedented distances.
We propose a deep, multi-pencil-beam study of the disks andhalo targeting primarily distant main sequence FGK dwarf
stars, to obtain line-of-sight velocities, elemental abundances,
spectrophotometric distances and isochrone-based age estimates, out to distances of ∼ 30 kpc.
The main science goal is to determine the dominant mechanisms by which the MW evolved through the analysis of
the chemodynamics and age distributions of unbiased tracersof the stellar populations in the outer parts (main sequencestars). As discussed above, the growth of our Galaxy since
redshift > 2 was most likely dominated by smooth accretionfrom the circumgalactic medium and minor mergers – an ongoing process given the assimilation of the Sagittarius dwarf
and interactions with the Magellanic Clouds. Structures inchemo-dynamical (and coordinate) phase space persist in theouter Galaxy, reflecting the longer dynamical times there, andthus the outer Galaxy contains clues about the emergence ofthe present-day MW.
For example, the spatial distribution of bright tracers, suchas Blue Horizontal Branch stars, have indicated that there isa “pile-up” at ∼ 30 kpc (Deason et al. 2018), which could
be due to an apocenter turnaround of the now-disrupted GES
progenitor galaxy (see also Deason et al. 2020). PFS will
provide spectra for main-sequence stars at Galactocentric radiiout to, and including, this putative “pile-up”, allowing us tocharacterise it more fully. Further, the main-sequence stars thatare our primary targets reach out to the distance regime where12m12m simulation, l 180 , 15 < |b| < 30l 90 , bl 180 , 15 < |b| < 30

0.5

0.0

14

0.0

1.0
1.5

[Fe/H]

8

100
1.0

0.0
1.5

0.5

10

0

0.5

in-situ
2.0 accreted
15 < R < 17 kpc, mean age: 7.3 Gyr
2.5
m12f simulation, l 180 , 15 < |b| < 30
0.5

12

100

Vlos

[Fe/H]

0.5[Fe/H]

40

200

200

6

300

4

400

2

Age (Gyr)

0.5

PFS SSP team

1.0
in-situ
2.0 accreted
15 < R < 17 kpc, mean age: 3.8 Gyr
2.5
0
2
4
6
8
Age (Gyr)

9 < R < 11 kpc, m12f
15 < R < 17 kpc, m12f
9 < R < 11 kpc, m12m
15 < R < 17 kpc, m12m

2.0

1.5

10

12

14

2.5

0

2

4

500

6
8
Age (Gyr)

10

12

14

0

20

40 60 80 100 120
R (Spherical, Galactocentric)

140

0

Figure 10. PFS allows unprecedented analyses of the furthest regions of the Milky Way using main-sequence stars as tracers of the dominant population. Left andmiddle panels: The large samples of stars with estimated distances, ages and iron abundances in our proposed low-latitude fields will reveal the build-up of the outerdisk. The two leftmost panels show scatter plots of predicted age/iron abundance from two FIRE-2 simulations indicating how the merger history is discernable.
The middle panel shows predictions of the mean age at given iron abundance at two Galactocentric radii. The turnaround at the highest iron abundance is anindication of radial migration redistributing older more metal-rich stars outwards. Right: Predictions for higher-latitude halo stars based on FIRE-2 simulations.
The scatter plot shows the distinct ‘chevron’ pattern in line-of-sight velocity as a function of (spherical) Galactocentric distance resulting from radially biased
merger(s). The addition of age via main-sequence tracers will allow the merger to be delineated more clearly, as illustrated by the color-coding in the figure.

the dynamical perturbation from the Large Magellanic Cloud
should be evident in their kinematics (e.g., Erkal et al. 2021).
Much remains uncertain about the LMC-MW interaction, as
discussed in the recent review by Vasiliev (2023).
The debris from a disrupted satellite on a radial orbit will
create a “chevron” pattern in a plot of Vr , the componentof velocity towards/away from the Galactic center, against
Galactocentric distance, r, as seen in the FIRE simulation(Figure 10, right panel). This reflects wrapping of the debris’phase-space density and has recently been discovered for the
local Gaia sample (Belokurov et al. 2023). Our data will enable
us to quantify the phase-space structure of this debris out tothe probable furthest apocenter passage of the parent dwarf
galaxy and thus reveal the early evolution of the merger.
The disk-halo interface is of particular interest in terms of
separating the disturbed disk from substructure in the halo (seee.g., Li et al. 2017) and the strategy we have adopted includes
continuous coverage of the latitude range 15◦ to 40◦ in key
directions towards both that of Galactic rotation (ℓ = 90◦ ) andthe anti-center (ℓ = 180◦ ), where line-of-sight velocities are
dominated by contributions from Vϕ and VR respectively (seealso Part II of this proposal).
Similarly, we will use our derived ages, distances, chemical
abundances and line-of-sight velocities (combined with propermotions where available) in lower-latitude fields to trace thegrowth of the disk. The data will allow analyses of trends suchas the age-metallicity relation as a function of Galactocentric
distance (see left and middle panels of Figure 10), and as functions of α-abundance and kinematics, to distinguish thick andthin disks, for which different modes of formation and evolution have been proposed. The existence (or not) of distinct
α-sequences in the outermost disk can thus be quantified, significantly extending the influential analysis of Hayden et al.
(2015). We will compare the data with models for the thin diskthat combine radial migration – bringing older, more metalrich stars from the inner disk – with recent star-formation fromgas mixed with primordial inflow – adding younger, moremetal-poor stars. Our use of main-sequence stars means that
we can add the dimensions of age and chemical abundances tothe analysis of the kinematic phase-space structure of the outerdisk, significantly enhancing the information to be extracted(see Laporte et al. 2022 for how ages discriminate between
competing interpretations of the kinematic phase-space structure in the outer disk). PFS will also allow a first estimationof age-velocity dispersion relations for main-sequence stars
in the outer disk(s), an important test of fuzzy DM models(Chiang et al. 2023). As Chiang et al. (2023) discuss, it isparticularly important to obtain data at large Galactocentric
distances, R > 12 kpc, where the disk is expected to no longer
be self-gravitating and lacks internal perturbations such as giant molecular clouds, but beyond the reach of current analyses(cf. Mackereth et al. 2019).
Furthermore, the chemodynamic data and age estimates willallow us to evaluate and compare models in which (i) the early
MW formed as a thick, kinematically hot, stellar disk (e.g.
Bird et al. 2021) or (ii) one in which stars generally form inthin kinematically cold disks (e.g. Tamfal et al. 2022).
Fields at intermediate latitude probe the thick disk-halo interface and together with the high-latitude fields will provide thedata to test the suggestion, based primarily on the local Gaia
sample, that the in situ halo is largely the kinematic extreme of
the thick disk – the “Splash” of Belokurov et al. (2020). This
component of the halo should be more centrally concentrated
than the accreted debris of GES, as it should reflect the radial
profile of the pre-existing stellar disk that was heated kinematically by the merger event. Our data will test this throughanalysis of the chemodynamic phase-space structure of thein situ stellar halo, using main sequence stars rather than therarer, brighter tracers of previous analyses (see bottom panelof Figure 10).2
The high latitude fields along the lines of sight of distant(> 10 kpc) streams will be used to constrain the Galactic
acceleration field in the outer halo (Ibata et al. 2021). Those
streams provide a new constraint on the dynamical structure of
the MW, including its reflex motion in response to the approach
2 For example, the H3 survey, which similarly explores the structure of the
distant stellar halo, uses giant stars as tracers (e.g., Han et al. 2022, and inparticular their selection function in the Appendix.)Subaru PFS SSP Surveyof the LMC (Erkal et al. 2019).
Our multi-faceted approach to the characterization of theproperties of stars in Local Group galaxies should reveal theinterplay between stars and DM on critical scales for tests of
the nature of DM.
6. PFS GALAXY EVOLUTION STUDIESThe evolution of galaxies is inextricably linked to the cosmic
web (Somerville & Davé 2015). After inflation, the primordial
density fluctuations grow via gravitational instability, amplifying the DM density contrast, and eventually forming a cosmic
web of sheets, filaments, and nodes containing virialized DM
halos. Baryons flow with the DM on large scales, and someare incorporated into the halos. Unlike DM, baryons can lose
energy by radiation, and sink deeper into the potential well.
In the simplest picture, this inflow is halted by centrifugal
forces and the baryons form a disk (Mo et al. 1998; Burkert
et al. 2016). Galaxies continue to grow over billions of years,
primarily though continuing accretion of gas from the web,and secondarily through mergers with other DM halos andtheir baryonic contents (e.g., Vogelsberger et al. 2014). This
accretion fuels the formation of stars and supermassive black
holes, and the feedback from these play critical roles in theregulation of the galaxy properties over cosmic time, and in theionization and enrichment of the intergalactic medium. (e.g.,
Croton et al. 2006).
While this conceptual picture provides a good foundation,
there are many important questions that are not yet answered:• How and when was the intergalactic medium re-ionized?
• How does the interplay between dark and baryonic matter shape the evolution of galaxies?
• How do gas and metals flow into and out of galaxies?
• How do tight relations between fundamental galaxy
properties arise and evolve?
These questions about galaxy formation cannot be answered
without a large spectroscopic survey at z ≳ 0.7. Spectra provide a rich suite of diagnostics of the fundamental propertiesof galaxies and their environments. The sample must be sufficiently large to map out the distributions of, and causal connections between, these properties, and to chart their evolution inredshift. In order to optimally measure the galaxy properties,
the spectral resolution must be matched to the internal velocity
dispersions of galaxies and the signal-to-noise ratio must be
sufficient to measure the key spectral diagnostics. To connect
the properties of galaxies to the large-scale cosmic web, thespectroscopic survey must probe a large volume, and do sowith sufficient spatial sampling.
In this section we describe a program that is motivated bythese considerations through an optimal use of the capabilities
of PFS (see also Greene et al. 2022).
6.1. Setting the Stage
6.1.1. Survey Design
With ∼ 2000 science fibers deployable over 1.3 deg2 FoV,and wavelength coverage out to 1.26 µm, the PFS spectrographis uniquely positioned in the coming decade to probe the evolution of typical Milky Way-like galaxies from the epoch ofreionization at z ∼ 7 to the present, in the context of the cosmic
web. The PFS Galaxy Evolution (GE) survey will accomplish

13
Table 3
Galaxy samples and depths

Type

Redshiftrange

Selection

Exp. Time Expected # of(hrs) spectra (×103 )Continuum 0.7 − 2y, J < 22.8
2, 12
IGM
2.1 − 3.5
y < 24.3,g < 24.7
6, 12
LBG
3.5 − 7y < 24.5
6LAE
2.2, 5,7, 6.6 LLyα >3×1042 erg s−1 3, 6, 12
AGN
0.5 − 6.0
various (see text)
1−5261, 1430.3, 1422
7.4, 4.5, 2.8
4.2

this with complementary sub-samples, each designed to fully
leverage the instrumental capabilities and capture the physicalproperties of galaxies at critical moments in cosmic history(see Figure 11 and Table 3).
We will study the peak epoch of star formation using“continuum-selected galaxies” with JAB <22.8, which will capture ∼90% of the M∗ ≳ 3×1010 M⊙ population at redshifts ofz ≲ 2. This main sample of 360,000 galaxies will have a ∼70%
average completeness and thus include multiple galaxies ingroup-scale halos down to Mgroup ∼1013.5 M⊙ . Exposure timesof 2-hour integrations will provide a high spectroscopic redshift completeness. We will integrate for 8–12 hours on an
additional 10,000 galaxies to measure stellar ages, chemical
abundance ratios, stellar velocity dispersions, outflow properties, and faint emission lines such as [O iii] λ 4363.
The overarching connection of galaxies to LSS will be extended out to z∼6. In particular, the distribution of neutralhydrogen in the cosmic web at z=2.1−2.5 will be mapped ata co-moving scale of ∼4 Mpc through an “IGM tomographic
experiment” based on the detection of Lyman-α absorption
seen in the spectra of background galaxies at 2.5 ≲ z ≲ 3.5. At
the furthest distances, we will use a sample of ∼15k “Lymanα emitters” (LAEs) at 6<z<7 selected from the Subaru/HSC
narrow-band imaging to connect early galaxies to the cosmic
web and probe the epoch of reionization.
Together, these samples will allow us to jointly address two
main themes from z ∼ 0.7 to 7. On the largest scale, wewill map the cosmic web through the distribution of gas andgalaxies (Section 6.3). On smaller scales, we will measure
the evolution of the properties of the stars and gas in galaxies
to elucidate the underlying physical processes driving theirformation and growth (Sections 6.4 and 6.5). The detailed
design of our survey will be described in Part II of this SSP
proposal, guided by the above considerations.
6.1.2. The PFS GE Program in Context
This survey represents a dramatic improvement in sample
size, spectral resolution, and target density over previous spectroscopic surveys of the critical epoch of galaxy formation(z>1). Previous studies have been limited to lower redshifts,
e.g., zCOSMOS (Lilly et al. 2007) and VVDS (Le Fèvre et al.
2005), or at higher z to much smaller, biased samples, e.g.,
KBSS (Steidel et al. 2014), MOSDEF (Kriek et al. 2015), and
FMOS-COSMOS (Silverman et al. 2015), or low spectral resolution, e.g., 3D-HST (Momcheva et al. 2016), as illustrated
in Figure 12.
Our survey is also highly complementary to the other upcoming massively multiplexed spectroscopic survey studying
the properties of z = 1 − 6 galaxies, MOONRISE (Maiolino
et al. 2020). VLT/MOONS will cover the rest-frame opticallines to z ≈ 2, allowing for better characterization of ISM
physics at cosmic noon, but (unlike PFS) does not cover the

14

PFS SSP team

The PFS Galaxy Evolution Survey

Background galaxies!""#$%&$NB LAEs
14.2k

22k

7

43.3k

High-z continuum selected

NB LAEs
6

5

HI at <z>=6.6

Low-z main+deep

4
Density Field (<z>=2.4)261k/14k

IGM tomography map
Redshift32

1
Density Field (<z>=1.1)

TB

PFS
Reconstruction

PFS
Reconstruction

PFS
Reconstruction

Figure 11. Number of spectra in each subset of the PFS GE survey (top) and a representative light cone to demonstrate the key redshift regimes it will probe(middle). Pop-out panels depict the recovery of cosmic structures from the PFS SSP (top panels: truth, bottom panels: recovered) at ⟨z⟩ = 1.1, 2.4 and 6.6. The
top left panel shows reionization bubbles in the HI 21cm brightness temperature distribution from Kubota et al. (2020), while the bottom panel shows the LAE
distribution observed by PFS spectroscopy, which is anti-correlated with the ionized bubbles in this particular model. The top middle and right panels show thesimulated density fields from the Horizon AGN simulation at ⟨z⟩ = 2.4 and ⟨z⟩ = 1.1, while bottom middle and right show the reconstructed density from the PFS
galaxy redshift distribution and IGM absorption data using the TARDIS (Horowitz et al. 2019) and ARGO (Ata et al. 2015) algorithms.

Table 4Large-Scale Structure probed by the GE Survey
Component of the Web
Mhalo ≳ 1013 M⊙
Mhalo ≳ 1013.5 M⊙
Mhalo ≳ 1014 M⊙

Voids (z < 2, r > 7 cMpc)
Voids (z < 2, r > 20 cMpc)
Voids (z > 2, r > 7 cMpc)
Protoclusters

Expected Number
2200
450
35
132,000
3,000
1000
100

rest-frame far-UV at this epoch. PFS will have roughly twice
the number of fiber-hours (∼1.3 million hours versus 500,000
hours for the MOONRISE XSwitch strategy) and blue spectral coverage. Together these allow us to perform an IGM
tomography survey (characterizing the cosmic web and theIGM at cosmic noon), go deep on continuum-selected galaxies at z ≈ 1.5, map the Circum-Galactic Medium (CGM) atz ≈ 1 to 2, and probe Lyman-α emission in galaxies at z ≳ 2.
Relative to MOONRISE, we also benefit from the superb HSC
multi-band imaging data for our galaxies. Finally, our highspectral resolution and broad wavelength coverage will also
be complementary to the Euclid and Roman Space Telescopegrism surveys.
6.2. How and When was the Universe Re-ionized?
The re-ionization of the universe represents a crucial phasetransition in its baryonic content, and traces the formation of
the first stars, galaxies, and black holes at very early times.
This implies a close connection between these galaxies and thecosmic web from which they formed.
At 5.5 ≲ z ≲ 7, spanning the epoch of reionization, we

will map the galaxy-web connection by measuring the spatial
cross-power spectrum between the ionizing galaxies, detectedby PFS as LAEs, and the HI gas in the cosmic web detected
in redshifted 21 cm emission by the SKA1 array. Theoretical models predict that cross-power spectrum transits frompositive on small scales to negative on larger scales. Models
predict that on small scales (single ionized bubbles), one-haloclustering introduces a positive correlation. Beyond the bubble
radius, an anti-correlation will result if reionization proceedsfrom regions of high to low density. The amplitude of the signal, the spatial scale at which the correlations become negative,
and the overall shape of the cross-power spectrum, all constrain the reionization history of the Universe. The detectionof this signal will be a definitive confirmation of the 21-cm
signal from the EOR and an independent measurement of theionization history of the universe.
6.3. How does the interplay between dark and baryonicmatter shape the evolution of galaxies?
In this section we describe how the PFS data will reveal the
relationships between dark and baryonic matter over a wide
range of scales and redshifts. We begin with the largest scales(the cosmic web) and then move to smaller scales (DM halos).
The growth of LSS drives the evolution of DM halos andthe flow of gas between and into galaxies, and therefore is thefundamental process behind the formation and evolution ofgalaxies themselves. On the largest scales, we have strong
theoretical reasons to believe that galaxies evolving in voids
will have different star formation histories and angular momentum distributions from those in filaments or nodes. We
know that the orientation of filaments does impact the spinsof galaxies (e.g., Kereš et al. 2005; Pichon et al. 2011), andpossibly their star formation histories (e.g., Kraljic et al. 2018),

Subaru PFS SSP Survey

GLASS

CDFS

VUDS
WISPs

102

Roman HLSEUCLID Wide

VVDS
3D-HST/AGHAST

16

101

PFS
zFOURGENIR spectroscopy
HSC-UD
HSC-D
COSMOS
Optical spectroscopy CANDELS
AEGIS
KV450
Grism spectroscopy
HSC-W
Photometry
HFF

102

103

104

105

Number of objects

106

12
10

HETDEX-Ly

Roman HLS
VIPERS

2.25

PFS IGMPFS Low-z

8

2.75
2.50

SDSS

Euclid-Deep

6

MUSYC-ECDFS

101

Euclid-Wide14

Dinter (cMpc)

Spectral Resolution (R)

VANDELS

KBSS-UVBM/BX/LBG
zCOSMOS

3.00

DESI

Survey Median Redshift

18

MOONRISEDEEP2
MOSDEFTKRS2 zFIREKBSS-MOSFIRE
FMOS-COSMOS
Continuum Cosmology
DEEP3
FastSound IGM(z~2.3)(1<z<2)
GOODS-N

103

15

GAMA

2.00
1.75
1.50

4

1.25

2

107

0.0 0.1

0.3 0.5 0.7

1.0

Redshift

1.5

2.0 2.5 3.0

1.00

Figure 12. Left panel: The combined spectral resolution and statistical samples produced by the PFS GE survey components would lie in unprecedented parameter
space at z ≳ 1 (compilation adapted from Förster Schreiber & Wuyts 2020). Right: Dinter is the average distance between galaxies, highlighting that the dense
sampling and large volume probed by the PFS sample will extend maps of the cosmic web beyond the local Universe.

while mergers which take place as galaxies move along filaments will change the angular momentum direction and maylead to quenching (e.g., Dubois et al. 2014). Large galaxy
redshift surveys like VIPERS and zCOSMOS at z ≈ 0.7 makepreliminary detections of these effects (Malavasi et al. 2017;
Laigle et al. 2018).
Prior to PFS it was not possible for a single survey to test
this at earlier times with simultaneously (i) wide enough area
to probe the rarest overdensities and overcome cosmic varianceand (ii) dense enough sampling to trace the LSS on ∼1−3 Mpc
scales. Thanks to our unprecedented multiplexing and wavelength coverage, we will perform the redshift survey needed
to make a high-fidelity map of the LSS in the distant Universeand situate the galaxy properties within that context (Table 4).
We will be able to connect galaxies to the cosmic web at
two key epochs. We have already described in section 6.2
above how we will map the connection between galaxies andthe cosmic web during the EOR, using the cross-correlation
between PFS LAEs and HI gas.
At cosmic noon, the IGM tomography sample will not only
trace the cosmic web, but also provide new insight into the stateof the gas in and around galaxies. A critical component of ourprogram is the measurement of foreground galaxy redshifts,
i.e., 25k continuum-selected galaxies, 9.2k LAEs, and 1.3k
active galactic nuclei (AGNs), that lie within the HI web. With
this sample, we will use morphological features identified
through HSC (and later Roman) imaging to constrain possible
intrinsic alignments with respect to the cosmic web tracedby the HI tomography. We will also measure the 3D crosscorrelation between the galaxies and HI absorption to constrain
the underlying bias (and hence halo mass) of the galaxies
as a function of stellar mass, star-formation rate, metallicity,and other properties. Cross-correlations can also be extended
to metals near galaxies, allowing the study to extend down
to CGM scales in conjunction with detailed hydrodynamical
simulations (Fujita et al, in prep.).
On intermediate spatial scales, the DM distribution leads
to variations in galaxy overdensity, ranging from clusters togroups to isolated galaxies. With PFS we can robustly measure

the evolution in the stellar mass function and the distributionof specific star-formation rates as a function of the local mean
over-density, to extend known trends between quenching andenvironment at z < 1. There are tantalizing clues that thesign of the morphology-density relation may change at higher
redshift, with strong star formation occurring in proto-cluster
cores at z ≳ 2 (e.g., Wang et al. 2016). We can also test
whether the 3D location in the web (e.g. distance from thenearest filament) plays an additional role in affecting galaxy
properties at a range of epochs (Laigle et al. 2018).
Finally, on smaller spatial scales, the connection between thegalaxies and their host DM halos has provided a compelling
framework to understand the overall efficiency of galaxy formation (e.g., Wechsler & Tinker 2018). The most basic measure of this relation is the stellar mass-to-halo mass (SMHM)
relation, which captures the overall efficiency of star formation over the entire history of the Universe (Behroozi et al.
2013). This relation is often derived using two-point statistics to compare the biased clustering of galaxies at a given
stellar mass to compute the average masses of host DM halos (upper panel of Figure 13). The default analytic models
used to describe the “galaxy-halo connection” rely on deterministic mappings (including scatter) between Mhalo and M∗(e.g., Berlind & Weinberg 2002), but increasing evidence suggests that additional secondary factors, such as relative halo
assembly history, are important in driving the timing and efficiency of galaxy formation. Empirically, this “assembly bias”
manifests as stronger clustering of older (or less-star-forming)
galaxies at fixed stellar mass (lower panel of Figure 13) (e.g.,
Gao & White 2007). No other photometric or spectroscopic
survey at z > 1 would have the necessary statistics, accurate
3D positions, stellar masses, star formation rates, and halo
masses derived from clustering measurements and halo occupation distribution models (e.g., Durkalec et al. 2015; Kashino
et al. 2017) to measure the SMHM relation and test for theimportance of assembly bias in understanding the galaxy-haloconnection in the early Universe.
6.4. How do gas and metals flow into and out of galaxies?

Standard HOD Model(M§ | Mhalo)

16Standard HOD Model
P(M⋆ | Mhalo)(M⋆)

11 < log(M⋆ /M⊙) ≤ 11.5

−1
ww
Mpc]
p(r(r
p) [h
) [h°1 Mpc]

10.5 < log(M⋆ /M⊙) ≤ 11
2

p

p

10
100

101
10

FMOS

11 < log M§ < 11.5
log M⋆ /M⊙ > 9.6
10.5 < log M§ < 11

Model with
Assembly Bias
100
101

P(M⋆ | Mhalo)(MsSFR | Mhalo(t))
r [h⋆°1, Mpc]
p-9.5 ≤ log(sSFR)

wp(rp) [h −1Mpc]-11.5 ≤ log(sSFR) < -9.5log(sSFR) < -11.5

100

10

11 < log(M⋆ /M⊙) ≤ 11.5

10.5 < log(M⋆ /M⊙) ≤ 111

rp [h −1Mpc]

Model with Assembly Bias(M
| Mlines.
stellarabsorption
This will require stacking to achieve§, sSFRhalo(t))PFS SSP team

10

Figure 13. Two-point correlation functions of spectroscopic samples ofgalaxies provide a robust statistical measurement of host DM halo mass,
thus constraining the SMHM relation; PFS will constrain this relation from
0.7 ≲ z ≲ 4.5. Furthermore, we will test models of the overall efficiency ofgalaxy formation, testing the existence of assembly bias in the mapping fromgalaxy stellar mass to halo mass.

Galaxies grow primarily through the accretion of gas fromthe cosmic web. This inflow fuels new star formation andblack hole growth, which in turn can drive outflows that may
change the dynamical and thermal state of the inflowing gas.
This cycle likely plays a key role in the self-regulation (andquenching) of star formation and black hole growth. The PFS
galaxy evolution program is designed to document these flows
by acquiring spectra of about 360,000 galaxies from redshifts∼ 0.7 to 5.5. This will enable us to quantify the strength andprevalence of outflows on galactic and circum-galactic scales,and seek evidence for gas inflows with the CGM.
With PFS, feedback from both massive stars and AGN canbe characterized by measuring the incidence rate and outflow
velocities of warm ionized gas as traced by blue shifted inter-

the necessary S/N, but the PFS sample size is so large that thelog besSFR> °9.5stacking candone
in many bins in the 4D space of redshift,stellar mass,
SFR,
andsSFRAGN<luminosity.
This will enable direct
°11.5
< log
°9.5
tests of competing feedback models: momentum-driven (e.g.,°11.5
Murray etlog
al. sSFR
2005)<vs.
energy-driven winds (e.g., Chevalier &Clegg 1985), that predict how outflow velocities should dependon these quantities.
The interface where the inflow meets feedback is the CGM,
a region with a scale of roughly the virial radius, and whichcontains a baryonic mass comparable to the stellar component(Tumlinson et al. 2017). PFS will probe the CGM aroundthe z∼1 continuum-selected galaxy sample using the Lyman-α
tomography sample as backlights, reaching expected equivalent widths of 0.3 Å within 200 kpc impact parameters for
100-galaxy stacks. The Cosmology sample will also be probed
by SDSS quasars as backlights, reaching 0.10 Å equivalent
widthsstacked spectra. At intermediate redshifts, there
11< log inM§the< 11.5is tantalizing evidence for outflows in the region of the CGM
10.5< logalongM§ < the11 minor axis of the galaxy, and inflows along
locatedthe major axis (Lan & Mo 2018; Schroetter et al. 2019; Ho& Martin2020). Our data, combinedwith HSC and Roman
100
101
images, will°1allow us to map these anisotropic outflows andMpc] how they evolve between z ∼ 0.7 to 1.6.
p [h determine
inflowsrand
At the same time, as described in the next subsection, we
will have access to the detailed star formation and chemicalenrichment histories of the target galaxies to connect withthese gas flows.
6.5. How do tight relations between fundamental galaxy
properties arise and evolve?
Perhaps the most critical key to understanding the evolutionof galaxies is the existence of tight correlations between all
the most fundamental properties of the baryonic componentof galaxies: mass, star-formation rate (SFR), size, velocity
dispersion and/or rotation speed, and chemical composition.
The PFS GE program will tackle this by providing a robust
description of the nature of these relationships and their evolution over cosmic time. This is made possible by the size of thesample, its completeness, and the quality of the spectra. Herewe highlight those aspects where we expect PFS to have thelargest impact.
6.5.1. Star-Formation Histories
The bulk of star formation over cosmic time occurs in galaxies on the “Star Forming Main Sequence”, a correlation between SFR and stellar mass (M∗ ) that evolves strongly towards
higher SFR with increasing redshift (e.g., Speagle et al. 2014).
While this evolution can be largely understood as tracing theoverall cosmic rate of accretion and merging, the processes
that lead to a relatively small dispersion in the relation at agiven redshift are poorly understood. Additional populations
lie above and below this relation; starburst galaxies responsible for ∼10% of the global star formation and populations ofquenching and fully quenched galaxies dominate at the highest masses today. Understanding the mechanism(s) by which
star-formation is quenched is especially important.
With the PFS dataset, the distribution of galaxies along andacross the main sequence, and the fraction of star-forming, starburst, quenching, and quenched galaxies will be determined
as a function of redshift. For the subset with deep (12 hour)spectra, the continuum spectra will hold critical information todescribe higher order moments of the star formation histories

Subaru PFS SSP Survey
PFS 12 hour spectra of z=1.4 galaxies

17

Star Formation Histories (from spectral modeling)
Star forming SFH(Intrinsic)

Quiescent SFH(Intrinsic)
Recovered

Chemical Abundances

PFS Continuum
Main + Deep

Figure 14. Star formation histories inferred from local galaxies fundamentally fail to capture the full growth of galaxies, and particularly diverge at Cosmic Noon
(Adapted from Leja et al. 2019). PFS spectroscopy from the Deep sample (top left) will directly probe average and higher order moments of the instantaneous andrecent (≲2 Gyr) star formation histories at this crucial epoch. The red models represent non-parametric reconstructions (top right) that manage to recover the mainfeatures of the input star formation histories. [Fe/H] and [Mg/Fe] are recovered within the uncertainties as well.

of galaxies, where photometric measurements are fundamentally limited by the adopted priors (e.g., Carnall et al. 2019),
as illustrated in Figure 14. Thus, we will begin to quantify theextent of ongoing star formation, the importance of rejuvenation, and time the quenching for >10,000 galaxies between
0.7 ≲ z ≲ 2.
There is good evidence that the quenching process has
evolved significantly between z ∼ 1 and z ∼ 0 (Behroozi et al.
2019). As described in Section 6.3, PFS will provide environmental metrics on scales from individual DM halos to large
scales (e.g., filaments and voids) and galaxy star formation
histories. This will be used to test the role of the environment
in galaxy quenching.
We will also explore the role of AGN in halting star formation. The main sample of AGNs will be drawn directlyfrom the galaxy sample, making it straightforward to quantify
incidence rates in the 3D parameter space of stellar mass, SFR,
and redshift (Kauffmann et al. 2004; Silverman et al. 2009).
The measurements of star formation histories describe above
will allow us to test the idea that powerful AGN are preferentially associated with rapidly-quenching galaxies (Wild et al.
2010). This will provide complementary insights to thosegleaned from the direct observations of AGN-driven outflows
described in Section 6.4 above. This sample will be supplemented by AGNs selected by a diverse set of multi-wavelength
criteria, designed to fully probe the population of AGNs atthese redshifts.
6.5.2. Chemical evolution
The chemical abundances of galaxies provide powerfulconstraints on their prior star-formation histories and on theroles of inflows of low-metallicity gas and outflows of highmetallicity gas (Maiolino & Mannucci 2019). Historically, thestate-of-the-art for studying chemical abundances of galaxy

populations has been the measurement of the correlation between galaxy stellar mass and metallicity (Garnett 2002; Erb
2008; Finlator & Davé 2008). With PFS, the statistical characterization of the mass-metallicity relation at z ∼ 0.7 − 1.7 will
for the first time be comparable to the analysis that has beenpossible for almost two decades at z ∼ 0 using SDSS. This
represents a significant advance over what is currently possible with smaller and more heterogeneously-selected samples
at intermediate redshifts (e.g., Lamareille et al. 2009; Zahid
et al. 2011). Because of the dramatic changes in galaxies’
star-formation histories during this period, completing a detailed, robust census of enrichment as a function of galaxy
properties will serve as an important benchmark for theoreticalpredictions.
In addition to single element gas-phase abundances like
[O/H] or [Fe/H], PFS will also enable the determination ofabundance ratios such as [O/Fe], [N/O], and [C/O] with sufficient S/N. Iron abundances can be determined from stellar
continua in deep individual spectra or in spectral stacks. BothN and C are primarily measured using emission lines in individual galaxy spectra: the [N II]λλ6549, 83 doublet can only
be observed with PFS at z ≲ 0.9, but beyond z > 1.1 the
C III]λλ1907, 9 and O IIIλλ1661, 66 doublets can be used todetermine [C/O] (Berg et al. 2019). The ratio of α abundances
to Fe that we can recover from the Deep spectra (Figure 14)
probe the most recent episode of star formation, distinguishing
between contributions of Type Ia supernovas from low-massstars with that of Type II from high-mass stars. Elements likeC and N, which are thought to be formed in intermediate-massstars, trace timescales in between core-collapse and Type Ia
supernovae. With PFS, this analysis can be performed withstacked deep spectra in bins of stellar mass, star-formation rate,and, uniquely, with redshift.

18

PFS SSP team

6.6. Summary and Connections with Galactic Archaeology
and Cosmology Pillars
We propose an ambitious galaxy evolution survey designed
to tackle the most important questions about galaxy evolution.
It will study half a million galaxies with 0.7 ≲ z ≲ 7, in orderto (i) probe the physics of the reionization of the universe,(ii) chart the co-evolution of the cosmic web, DM, and galaxies, (iii) detect and characterize the flows of gas into and out ofgalaxies, and (iv) connect together fundamental galaxy properties, including star-formation histories, gas-phase metallicities,
outflows, and AGN content as functions of galaxy mass andredshift.
Our main science themes connect naturally with the goalsof the PFS Cosmology and Galactic Archeology surveys. Allthemes rely on galaxies as a tracer of the underlying DM structure at different epochs. Our survey of galaxy evolution will
chart the path of a galaxy like the Milky Way through time,
while the Galactic Archaeology survey will focus in detail on
the fossil record of the Milky Way and its massive neighbor
Andromeda. The Cosmology survey is interested in evolution
in the cosmic web on very large scales, using galaxies as baryonic signposts. The redshift range and galaxy masses probed
by the survey are mapped by the Galaxy Evolution survey as
well, especially in the Lyα tomography program. Moreover,the high-quality spectra from the GE survey will help us understand the properties of the cosmology tracer galaxies, ELGs.
At the same time, the PFS team will exploit the large areaof the cosmology survey to sample rare populations such as
luminous AGN or foreground/background galaxy pairs needed
to probe the CGM.
7. SURVEY DESIGN AND STRATEGY
We now provide a detailed description of the proposed Subaru Strategic Program (SSP) that will allow us to attain thescience goals described in Sections 4–6 in the most efficientmanner. We are proposing a 360 night survey to be undertakenover a 5 year period. The various science projects we discussed
above fully utilize the capabilities of the unique PFS instrument
we have constructed. They range from 15 minute exposuresof emission line galaxies as tracers of LSS and probes of cosmology, to 12 hour exposures of more distant galaxies seen
when the universe was still in its infancy. Likewise, our Galactic Archaeology program also exploits a purposely-designed
medium resolution grating to provide accurate measures ofkinematics and chemical abundances of stars in a range ofnearby stellar systems. At this writing, the PFS instrument isnearing the end of engineering/commissioning observations.
As we detail below, we have demonstrated that the instrument
throughput, fiber pointing accuracy, and fiber configuration
time are essentially at specifications, and that sky subtraction
accuracy is close to photon-limited. While we have obtained
data to test our target selection algorithms (Section 7.2.1), we
anticipate refining these algorithms during the first year of thesurvey.
To construct a coherent and efficient observing program
given the range of magnitude limits, target surface densities
and exposure times, we have developed a detailed and highlyflexible software system to design our SSP program. Incorporating the expected and measured performance of the PFS
instrument discussed below and the HSC-based photometriccatalogs of our targets, we have constructed an end-to-end
survey plan that takes full account of the necessary exposure
times for each of the various components of our scientific planand the visibility of the survey fields throughout each time
allocation period, thereby ensuring realistic estimates of theoverall completeness of the SSP during its survey lifetime.
In the following we describe and illustrate each of the above
points.
7.1. Rationale
Whereas it was organizationally helpful in Sections 4–6 todescribe our scientific goals in terms of the three topics of
cosmology, Galactic archaeology and galaxy evolution, as Figure 1 emphasizes, our program has an over-arching scientifictheme addressing cosmic evolution and the role of the dark
sector. This coherence is also reflected in our integrated survey
plan which has been designed to exploit the numerous uniquecharacteristics of the PFS instrument, the widely-distributed
survey fields ensuring synergy with exquisite HSC imaging,
and a range of target brightness essential for exploiting bothdark and grey periods of allocated lunations.
We have designed PFS to be a versatile instrument enabling
rapid on-sky re-configuration of the fiber positioner whichis particularly necessary for an efficient survey of 4 million
galaxies for the cosmology program. The high instrument
throughput and exceptional wavelength coverage from 380nm
through 1.26µm, together with Subaru’s 8.2m aperture, offers
unique opportunities for surveys of faint, high redshift galaxies
for the galaxy evolution program. The Galactic archaeology
program exploits both spectroscopic resolutions available andcan make valuable use of grey time. Moreover, noting theshared survey fields, we can fully utilize PFS’ multiplex gainby sharing fibers across different survey components. As with
our scientific vision for the SSP, we have successfully integrated each survey component in an optimum manner. To
downgrade any component would not only fail to fully exploit
PFS, but would significantly reduce the efficiency of the overall
program as well as weaken it scientifically.
Finally, our survey planning software also allows us to optimally prioritize components of our SSP survey for which
there is competition from other massively-multiplexed spectroscopic instruments (discussed in Section 3). Since PFS has
greater multiplex and wider FoV advantages and covers theblue spectral region, this will enable GE science that cannot
be done by MOONS (see Section 6). Likewise, Subaru’s 8.2m
aperture ensures our planned PFS Galactic Archaeology program is not threatened by 4m facilities such as 4MOST and
WEAVE. As discussed in Section 4, the primary challenge liesin PFS Cosmology where DESI is targeting similar science
goals. Although a 4m telescope facility, DESI has a larger
multiplex gain and a two year head-start on PFS. Our planning
tool enables us to demonstrate how we can remain competitive
with DESI whilst still achieving the required completeness forthe overall program.
7.2. Survey Parameters
We begin with a brief overview of the integrated SSP. ThePFS Cosmology program aims to observe four million emission line galaxies in the redshift range 0.8 < z < 2.4 drawn
from the 1100 deg2 HSC Wide Survey with relatively short 15
minute exposures. The PFS Galactic Archeology (GA) component mostly targets high Galactic latitude fields, selectedsatellite dwarf galaxies and M31 making effective use of themedium resolution mode. The PFS Galaxy Evolution (GE)component is multi-facetted and involves a range of targets
spanning the redshift range 0.7 < z < 7 drawn from the HSC
Deep Survey which, collectively, spans 15 deg2 .

Subaru PFS SSP Survey

75°

60°

PFS SSP Survey Fields

45°

Ursa Minor
Draco

30°
15°

Halo

M31
M33
Triangulum

Outer disk

Outer disk

Hermus
NGC5466

Hyllus0h DEEP23

XMM-LSS

12h

Halo

Halo

Field of streams

Bootes
HSC wide (spring)
Halo

18h

HSC wide (fall)

0°

19

COSMOS
Sextans

NGC6822-15°
Fornax-30°

PFS Cosmology
PFS Galactic Archaeology (dSph/dIrr)
PFS Galactic Archaeology (Halo)PFS Galactic Archaeology (Halo Stream)PFS Galactic Archaeology (Outer disk)
PFS Galactic Archaeology (M31/M33)
PFS Galaxy Evolution (Deep)

Sculptor

-45°
-60°

-75°

Figure 15. Sky distribution of the PFS survey fields. All targets are drawn from multi-color HSC data.

Table 5
Sample and Depths
Layer
Cosmology
ancillary targets
gals z ≲ 1gals 0.7 < z < 1gals 1 < z < 2gals 0.7 < z < 2gals 2.1 < z < 2.5
gals 2.5 < z < 3.5
gals 3.5 < z < 7z ∼ 2.2 LAEs fz ∼ 5.7, 6.6 LAEs
< z < 6
AGN 0.5 ∼∼MW-dSphs/dIrrg
g
M31/M33
MWg

Field
HSC-W
HSC-W
HSC-D
HSC-D
HSC-D
HSC-D
HSC-D
HSC-D
HSC-D
HSC-D
HSC-D
HSC-D
HSC-D
HSC-D
HSC-D
HSCh
HSCh
HSC

Selection
grizy
gri(zy)+ext. data (Gaia, etc.)
i < 23y < 22.5+zph
y < 22.5 + zph
y > 22.5, J < 22.8 + zph
J < 22.8 + zph
y < 24.3 + zph
y < 24.3, g < 24.2 + zph
y < 24.3, 24.2 < g < 24.7 + zph
y < 24.5 + zph
NB387, LLyα > 3 × 1042
NB816,921, LLyα > 5 × 1042
NB816,921, LLyα = 3 − 5 × 1042
i < 24(grizy)g < 23i < 23g < 22

exp. timea
15 min15–30min
2 hrs
2 hrs
2 hrs
2 hrs
12 hrs
6 hrs
6 hrs
12 hrs
6 hrs
3 hrs
6 hrs
12 hrs
1–4hrs
3 hrs
5 hrs
3 hrs

# of FoVsb∼ 1100∼ 1100
11
11
11
11
11
11
11
11
11
11
11
11
11
48i
52
89

nightsc
∼70
–
∼ 3.5
∼ 8.5
∼ 8.7
∼ 12
∼ 16
∼ 8.3
∼ 3.1
∼ 10.5
∼ 8.3
∼ 1.4
∼ 1.7
∼ 2.1
∼ 1.8
25.3
33.4
32.5

# of spectra∼ 4M∼ 100K
∼ 28K
∼ 68K
∼ 69K
∼ 96K
∼ 14K
∼ 22K
∼ 8.3K∼ 14K∼ 22K∼ 7.4K∼ 4.5K∼ 2.8K∼ 9.7K
∼ 60K
∼ 13K
∼ 26K

Requirement(s)dredshift ([O ii])–
spectral features
spectral features
spectral features
spectral features
spectral features
spectral features
spectral features
spectral features
spectral features
spectral features
redshift (Lyα)
redshift (Lyα)
spectral features
S/N|cont. > 10
S/N|cont. > 10
S/N|cont. > 10

Main sciencee
BAO, RSD, LSS
GA (stars, WDs), GE (e.g., QSOs)
GE (control sample, deep)
GE (0.7 < z < 1)
GE (1 < z < 2)
GE (1 < z < 2, main)
GE (0.7 < z < 2, deep)
GE (IGM/foreground)GE (IGM/background)GE (IGM/background)GE (high-z)
GE, cosmic reionization
cosmic reionization
cosmic reionization
GE, CGM, IGM
DM profiles, [Fe/H] and [α/Fe] dist.
assembly history, DM subhalos, M33
MW grav., macro DM (incl. PBH)

Notes – a) The total exposure time for each sample on source. b) The number of pointings (roughly corresponding to survey area). c) The primary requirementon spectroscopic observation for each sample. d) The primary science drivers. e) The main science. f ) The units of LLyα are [erg s−1 ]. g) Medium-resolution
mode spectroscopic observation is included. h) Including NB515 narrow-band imaging to discriminate member giants from foreground dwarf stars. i) Two visit
observation for 18 pointings within nominal tidal radii to identify binary stars.

Table 5 summarises each sub-component of the Cosmology,
GA and GE programs. This is the essential input into thesurvey planning software. The areal number density for each
sub-component is estimated using the actual HSC photometric catalog together, for the Cosmology component, with the
COSMOS mock catalog used to gauge the likely rate of occurrence of strong [O ii] emission for the proposed photometriccolor selection. The software delivers, as its output, a semesterby-semester timetable of observations (survey field, target
configuration, exposure time) and the running completenessfor each component of the science program. In this section,
we describe the necessary input data for this exercise and theassumptions made prior to defining a proposed plan over 5
years in the following subsection.
7.2.1. Target selection and survey fields

PFS observations for the Cosmology, GA and GE programs
have targets selected from pre-existing HSC images. A summary of the survey fields for each of the Cosmology, GA and
GE programs is provided in Figure 15. We now discuss thephotometric selection and depths of the various components
with reference to Table 5.
Cosmology program: We fully exploit the multicolor (grizy)
imaging data of the HSC-Wide area over 1100 deg2 with depths
gri ≃ 26 (5σ detection for a point source as shown in Aihara et al. 2018b) to optimally and securely select targets of[O ii] emission-line galaxy candidates over the redshift range
0.6 < z < 2.4. The wide wavelength coverage of the PFS
red and near-infrared arms allows us to detect [O ii] emittersover this large redshift range. In addition, the HSC depth
minimizes contamination from photometric errors and ensures

20

PFS SSP team

uniform sampling across different fields which is crucial forhigh-precision measurements of redshift-space galaxy clustering (Sunayama et al. 2020) (also see discussion in Section 4.3).
The survey will undertake pairs of 15-minute spectroscopic
exposures in each pointing, yielding a total of ∼ 4000 galaxy
redshifts. Given the absence of reliable [O ii] fluxes for the
faint high-redshift galaxies we wish to target, we have adopted
the improved COSMOS mock catalog, “EL-COSMOS” (Saito
et al. 2020; Gao & Jing 2021) to develop our target selection
strategy. From the mock catalog, we find that the following
magnitude and color cuts securely select [O ii] emitter candidates:
23 < g < 24.5 AND g − r > 1.33g − 31.95
AND NOT (g > 23.8 AND r − i > 0.25)
AND (i − z > 0.286(g − r) − 0.014 AND 0 < g − r < 0.4).
The first and third cuts select blue galaxies and remove thosewith weak emission lines and those outside the redshift range
0.6 < z < 2.4, while the second condition prioritises galaxies
at z > 1.6 (Takada et al. 2014). In this way 85% of the selectedgalaxies in the simulation have a predicted [O ii] line strength
consistent with a PFS S/N> 6, thereby yielding a precise
redshift. However, the predicted constraints on [O ii] emittersat z > 1.5 are less reliable and thus we propose to refine ourtarget selection criteria once the commissioning observation or
survey is underway. This can be efficiently done by analysing
the spectra of a representative sample selected from wider
magnitude-color cuts in the HSC catalog.
GA program: The Milky Way dwarf galaxies and M31 – The
extragalactic portion of the GA program involves observations of individual giant stars distinguished from foreground
Milky Way dwarfs using narrow-band HSC imaging data obtained via Keck/Subaru time exchange programs. The relevantnarrow-band filter NB515 measures the gravity-sensitive Mg b
triplet, weak in giants and strong in dwarfs, enabling a robust
probabilistic separation. A final membership criterion will bebased on a hierarchical Bayesian mixture model of the stellar
populations in each dwarf galaxy and for the Milky Way foreground (Dobos et al. 2024) which incorporates all sources of
uncertainty in a consistent statistical framework and includes
ancillary information as priors. The application of deep neural networks and other machine learning techniques makes it
possible to determine membership from quickly reduced, low
signal-to-noise spectra. This will enable an adaptive targetingstrategy whereby fibers originally assigned to stars that turnout to be non-members can be reassigned between exposures.
Note that non-members that are field halo stars will continue
to be observed. The PFS pointings for seven dwarf galaxies
are listed in Table 2.
The Milky Way outer disk – The outer disk lines of sight are
centered on Galactic longitudes of ℓ = 180◦ and ℓ = 90◦ ,with latitudes in the range 15◦ ≤ |b| ≤ 30◦ , in bands of elevencontiguous PFS fields above and below the plane. Uniform PanSTARRS photometry (PS1, Chambers et al. 2016) of sufficientdepth and quality to be used for the selection of target FG main
sequence stars to gHSC ∼ gPS1 = 21 is available for all suchlines of sight. The targeted magnitude range gPS1 ∼ 18 − 21corresponds to heliocentric distances of ∼ 6 − 25 kpc. At
these characteristic distances, a single PFS FoV corresponds to∼ 0.1 to ∼ 0.6 kpc, respectively, and the planned observations
probe vertical heights of z ∼ 2 kpc to ∼ 10 kpc. The estimatednumber of color-selected FG-MS stars brighter than gPS1 = 21

ranges from ∼ 2, 000 to ∼ 9, 000 per PFS FoV (increasing withdecreasing |b| and ℓ). The PS1 photometry will be corrected
for extinction as appropriate.
The Milky Way field halo – We select 8 halo fields: thefields reflecting Galactic rotation and radial motion [(ℓ, b) =(90◦ , ±60◦ ), (270◦ , 60◦ ) and (0◦ , 45◦ )] and the intermediate
directions including halo streams, NGC5466 (42◦ , 74◦ ), Hermus (70◦ , 44◦ ), Hyllus (55◦ , 43◦ ) and Triangulum (133◦ , −32◦ ).
Three contiguous PFS pointings are allocated to each field,with target stars selected from PS1 photometry to gPS1 = 22.
The majority of contaminating thin disk stars will be removed
using multi-band colors, parallaxes and proper motions fromPS1 photometry and Gaia astrometry. We expect ∼ 1000 halostars (main-sequence and red giants) beyond 10 kpc for mostof the lines of sight. A similar strategy will be adopted for the
pointings in the “Field of Streams”.
GE program: The GE program has several components but all
will be drawn from targets in three of the HSC-Deep fields;
these contain multi-band near-infrared photometry to JAB ≳23 and Spitzer 3.6 and 4.5 µm IRAC data to similar depths[SERVS/DeepDrill, Lacy et al. (2021), Annunziatella et al.
(2023), PI: M. Lacy, Mauduit et al. (2012), S-COSMOS, PI:
D. Sanders, SPLASH, PI: P. Capak, Steinhardt et al. (2014),
SpIES, Timlin et al. (2016), PI: G. Richards, Cycle 14 “Missing
Piece”, PI: A. Sajina]. U band data over 20 deg2 to UAB ∼27 mag (5σ) in sub-arcsec seeing has been taken from the
CFHT Large Area U-band Deep Survey (CLAUDS) (Sawicki
et al. 2019). For the Lyman α emitters surveys, Subaru narrowband data is available at central wavelengths of 3870, 8160,
and 9210Å.
Targeting for the continuum survey at z < 2 (“gals z < 1”
to “gals 0.7 < z < 2” in Table 5) will be based on HSC
photometric redshifts using the UgrizyJ + 3.6µm + 4.5µm
photometry. Magnitude limits are determined by the band that
contains the 4000Å break (e.g. y at z < 1, J at z > 1), tofacilitate a uniform selection of galaxies by their total stellar
mass. The depths are tuned such that we reach ∼ L∗ (∼ 3 ×1010 M⊙ ) at z ∼ 1.5. The sampling of 70% is set to ensurethat we will have more than one spectrum per group in Mh ∼1013 M⊙ groups, to give high-fidelity group catalogs.
Targeting for the IGM tomography survey (“gals 2.1 < z <2.5” and “gals 2.5 < z < 3.5” in Table 5) is determined by theneed to probe enough background sightlines (∼ 1000 deg−2 )to produce IGM maps that reconstruct the underlying matter
density field at a Pearson coefficient of r > 0.70 over spatial
scales of 3.9 cMpc. Galaxy targets at z > 3.5 (“gals 3.5 < z <7” in Table 5), primarily dropouts based on HSC photometry(y < 24.5), will be observed to reach a similar S/N of 2 perresolution element as the z ∼ 2.5 tomographic sample. We
plan to target all Lyman-α emitters at z = 2.2, 5.7.6.6. Thishigh sampling is needed to probe the correlations between
sources and derive the spatial scale of reionization.
For the collective PFS program, in terms of Right Ascension (RA), the survey fields are widely distributed; some areaccessible at any time during a given semester but, overall,there are fewer fields for observation during June and Julywhen the Galactic plane is most prominent on the sky. In
terms of scheduling, the survey requires slightly more nights
in the autumn season (semester ‘B’), largely as a result of the
focus on the M31 halo region and the need to simultaneously
maintain progress in the Cosmology and GE fields. Two dwarf
galaxies, Fornax and Sculptor, have Declinations δ ≃ −40◦

Subaru PFS SSP Survey

7.2.2. Completeness and fiber assignment
Each of the three programs has specific requirements inorder to achieve its respective scientific goals. The Cosmology
program requires an efficient and fast survey operation andboth a high completeness (fraction of photometrically-definedtargets observed with PFS) and a high success rate (fraction ofobserved galaxies yielding successful redshifts). As the survey
is magnitude limited, relative bright galaxies will be observedin grey time, drawing additional bright sources (quasars andextremely metal-poor stars) from other SSP components.
Much higher signal/noise spectra are required for stars in
the GA program where bright and faint targets can be similarly
assigned to grey and dark nights, respectively. However, in
this case, there is less scope for fillers from other programs
given the specific nature of the chosen survey fields. For the
GE program, a uniform completeness (or sampling rate) withrespect to the input photometric catalog is required for reliably
tracing LSS and conducting statistical analyses without significant selection biases. Accurate sky subtraction (discussed in
Section 8.1) will be critical in ensuring high-quality spectra
for both the faintest high redshift targets and those for which
reliable continuum measurements are needed. Most of the GE
observations require dark nights.
An Exposure Targeting Software (ETS) simulates the fiber
assignment to targets with a purposely-designed optimization
algorithm that takes into account user-supplied priorities, thegeometry of the focal plane, the configuration and patrol regionof science fibers and the characteristics of each fiber including
the risk of collisions. Where necessary, the schedule includes
repeat visits and new fiber configurations to attain the requiredcompleteness. In this way is it possible to track, semester-bysemester, how the SSP survey is progressing to a satisfactory
conclusion.
The GE component employs the same ETS, but optimizes
the source-by-source trade-off between scientific usefulness
and necessary exposure time. The latter is estimated from the
S/N achieved for the relevant spectral features measured frommultiple realizations of realistic noisy mock spectra. We are
exploring a machine-learning approach to map photometricmeasurements from HSC onto proposed fiber assignments. It
maximizes the overall scientific utility of the resulting sample
achievable with the PFS fiber layout over the total time allocation. As an additional benefit of the end-to-end optimizationof the fiber allocation process, the final sample selection canbe understood and reproduced by the wider community from agiven photometric catalog.

0.6
0.5
total throughput

reaching elevations of ≤ 30◦ . Careful scheduling is essential
to ensure adequate data quality. Our survey planning software
takes proper account of these and other constraints as indicatedbelow.

0.4

21
PFS(blue)
PFS(red)PFS(NIR)PFS(MidRes)

DEIMOS
GMOS-NFOCAS
X-SHOOTERMOSFIRE
SDSS/BOSS
DESI

800
1000
wavelength (nm)

1200

0.3
0.2
0.1
0.0 400

600

Figure 16. Total system throughputs for the PFS blue, red and NIR arms,
based on commissioning data up to May 2024 (Yabe et al. in prep.). For
comparison, we also show throughputs for selected spectrographs on other8-10m facilities and the SDSS/BOSS spectrograph.

Satisfactorily, the measured throughput is within the marginsof the original design specifications for each of the three spectrograph arms. As further commissioning data is taken andanalysed, more accurate estimates will be determined enabling
us to refine the survey strategy. However, we do not envisage
significant changes at this stage.
The required exposure time for each type of target in Table 5
is based on an Exposure Time Calculator (ETC) developedby our team using a throughput consistent with the most recent measurements. The ETC incorporates a night sky modelthat includes both continuum and airglow emission lines, theelevation-dependent effects of seeing and atmospheric attenuation, lunar phase and moon-target distance, and the overall
instrument throughput. A statistical error of 1% rms due toscattered light from the night sky is incorporated in calculations of the S/N (a detailed discussion of the sky subtraction
performance of PFS is given in Section 8). The left panel of
Figure 18 shows the limiting magnitude of continuum at asignal/noise S/N=5, after 3 pixel binning, in a 1 hour exposure
as a function of wavelength.
7.3.2. Demonstration of Fiber Positioning Performance

7.3. Current PFS Performance
The PFS team has been analysing the commissioning data
to verify the performance of the various instrument components. In this section, we discuss performance metrics thatvalidate the efficiency of PFS in the context of its targeted
specifications.

Using the commissioning data in May 2024 run, the PFS
team has verified, as shown in Figure 17, that each of the fiber
positioners can be accurately placed to an accuracy of 17µm or
better in the focal plane, corresponding to about 0.17 arcsec on
the sky. This positioning accuracy includes the precision arising from field acquisition, auto-guiding and coordinate transformations between the guiding camera system, the Metrology
system, and the focal plane of PFI. The overall accuracy isclose to that in the original PFS specifications, verifying thevalidity of detailed algorithms developed for the system control
software (see Section 3).
Furthermore, the commissioning data has also verified thatthe fiber configuration time for a new field is about 130 sec,
again very close to the original specification (2 min). This
reconfiguration time is sufficient to ensure a rapid survey speed
for the PFS Cosmology program that has shortest exposures(15 min) among our programs.

7.3.1. Demonstration of System Throughput

7.3.3. Spectral Performance of PFS

The PFS team has determined the overall throughput of theentire system (atmosphere+telescope+PFS) based on the latest commissioning data (May 2024), as shown in Figure 16.

Having demonstrated the actual performance of key PFS
components, (i) the system throughput, (ii) the fiber positioning
accuracy, and (iii) the survey speed (fiber reconfiguration time),

22

PFS SSP team

110444..110450 raster_field_ras14-15_pa+00_run16_fluxstd_v3.3
N(SCIENCE+FLUXSTD)=1065
X(PFI)
350
number

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250
200
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800
600
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Residual [um]500nocorr
corr
0

50

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Figure 17. Upper panel: Results of a fiber “raster”-dither test undertaken in
the May 2024 commissioning run: the observations were made at the fiducial
pointing and each of the hexagonal vertices around the pointing center witha 1 arcsec pitch separation. Each arrow shows the amplitude and directionof the offset of each fiber in focal plane coordinates, reflecting the difference
between the fiber position and the best-fit value estimated from the dither
data. Different colors denote the magnitudes of the stars estimated from theirmeasured spectrum, as denoted in the color bar. For reference, an offset of
50µm (0.5 arcsec) is indicated by an arrow in the upper-left corner. Notethat the fiber diameter is 127µm (1.12 arcsec, see Table 1) and some large
offsets likely arise from contamination by nearby bright stars. Lower panel:
The histograms of the offset in the x- and y-axis directions. Note that “corr”
denotes the residual after fitting the global trend incorporating shift, rotationand scale factors. The lower-right panel shows the cumulative distribution;
about 95% of fibers achieved about 14.7µm position accuracy which is close
to the original PFS specification.

we below discuss the required exposure time and show thesimulated and actual spectra for each type of target in Table 5,
using ETC. We discuss the quality of spectra and the data
analysis tools we will use in order to justify the scientificpracticality of our proposed PFS campaign.
In the case of the Cosmology program which targets[O ii] emission-line galaxies, we adopt a detection thresholdof S/N ≃ 6 for a 15 min exposure for each target. The rightpanel of Figure 18 demonstrates the detection limit or selection
function of [O ii] emitters as a function of wavelength or theredshift of [O ii] emitters based on the on-sky PFS performance(Figure 16).
For the GA targets, we will measure stellar parameters (vr ,T eff , [Fe/H], [α/Fe], etc.) using state-of-the art algorithms.
We have developed two pipelines for this purpose: one basedon physics-informed deep learning which delivers the radial

velocities and the stellar atmospheric parameters, then a second
pipeline, based on classical spectral synthesis, that delivers theelemental abundances.
The deep learning method builds a principal component analysis (PCA) over very high-resolution model spectra in eachregion. We feed the PCA coefficients to a Deep Learning Neural Net. This set of parameter values is then used to determine
the radial velocity (vr ) with an optimized maximum likelihood
code, based on point source detection (Kaiser 2004). We canpredict the uncertainty in vr from the Fisher matrix (Szalay
et al. in preparation). A PCA expansion of the log fluxes inthe vicinity of the best model spectrum, Doppler-shifted by
the now established velocity, can be used with a neural net
to infer the stellar atmospheric parameters. Finally, we estimate the log likelihood and the Fisher matrix around theseparameter values with high precision. This technique will use
the correlated patterns of different absorption lines, extracting
features commonly used, rather than just using the raw pixels,
and combines the speed and simplicity of Deep Learning withthe rigor of accurate confidence intervals from well-understood
statistical techniques.
From here, the second pipeline uses spectral synthesis (e.g.,
Kirby et al. 2010; Escala et al. 2019) to measure [Fe/H], [α/Fe],and individual abundance ratios. We can verify the measured
abundances using synthetic spectra of K-giants (for the dSph
and M31 projects) and G-dwarfs (for Milky Way studies) withmetallicities between [Fe/H] = −2.0 and −0.5 in 0.5 dex increments, by using the spectrum synthesis code Turbospectrum(Plez 2012). The synthetic spectra scaled to various g-band
magnitudes are then used to calculate continuum S/N ratios
and simulated PFS spectra with an appropriate exposure timewith the ETC under the assumption of a dark night and at thefield center.
The upper panel of Figure 19 demonstrates the quality ofspectra for a 3 hour exposure in medium-resolution mode fora K-giant. The lower panels show the expected statistical precision (excluding systematic errors expected to be of order
0.1 dex) of chemical abundances estimated from 100 realizations of the simulated spectra for each parameter combination.
These measurement algorithms are discussed in Section 8. The
PFS blue arm will provide carbon abundances (from the CH
G-band at 4300 Å) and iron in very metal-poor stars (from acalibration of the Calcium I K line).
In the GE case, mock spectra are based on the 30-band
COSMOS+UVISTA photometric catalog (Muzzin et al. 2013).
Adopting the relevant redshift and apparent magnitude limits,these are matched to the PFS instrumental parameters. Stellar
continua are based on the Bruzual & Charlot (2003) synthesis code using the MILES stellar library (Falcón-Barroso et al.
2011). Rest-frame optical emission-lines are included adopting
prescriptions from Valentino et al. (2017) for physical properties derived from the COSMOS+UVISTA photometry. In
the case of weaker lines, we adopt line ratios from Strom et al.
(2017). In the rest-frame near-UV (185–300 nm), we exploit
the SDSS-IV eBOSS survey (Dawson et al. 2016) using bothemission-line galaxies (Comparat et al. 2016; Raichoor et al.
2017) and luminous red galaxies (Prakash et al. 2016). In the
far-UV, to match the superior spectral resolution of PFS, weinterpolate continuum-normalized spectra of z = 1 − 3 galaxies
from Steidel et al. (2016). IGM absorption features are incorporated using the UVES Spectral Quasar Absorption Database(SQUAD; Murphy et al. 2019).
As shown in Figure 20, our mock spectra demonstrate theSubaru PFS SSP Survey
3600 sec. exposure, S/N=5, 3 pix binning

16

0.0

Limiting flux of a single emission line [erg/s/cm2]

Limiting magnitude for continuum [AB mag]

17
18
19
20
21
22
23
24

400

600

800
Wavelength [nm]

1000

23
1.0

Redshift

1.5

2.0

2.5

10 16

450 sec. × 2 exposure, S/N=6
10 17

1200

0.5

400

600

800
Wavelength [nm]

1000

1200

Figure 18. Left panel shows limiting magnitude for the continuum with 3 pixel binning in the wavelength direction for an on-source exposure time of 1 hour,while right panel shows the limiting flux of a single emission line corresponding to a S/N=6 for an on-source exposure time of 15 min (2 × 450 sec) as a function of
wavelength or redshifts of [O ii] emitters, which mimics the expected observation of the PFS Cosmology program. These results are based on the verified on-sky
throughput in Figure 16. The gray horizontal bar indicates the average value within the range while the open circle shows the representative value at the specificwavelength where the spectrum is not affected by the sky line. The simulation assumes a seeing of 0.8 arcsec FWHM and a point source observed at new moon ata zenith angle of 45 degrees in 0.675 degrees from the field center. The fraction of incoming flux into the fiber aperture is assumed to vary from ∼ 62% at thefield center to ∼ 54% at the field edge. Galactic extinction and fiber positioning error are ignored. The sky subtraction model assumes a systematic error of 1%
and scattered light corresponding to 2% of the incoming sky flux. Detector dark currents [e− /pix/s] are 0.0002 (blue and red) and 0.01 (NIR); read-out noise[e− RMS/pix] are 3.0 (blue and red) and 4.0 (NIR).

K-giant [Fe/H] = 0.5 rmag = 21

1.0
0.8

K-giant
G-dwarf[Fe/H] = 2.0[Fe/H] = 0.5

8650
8700
Wavelength [Å]
100

10 1
16

18

Fe I

Ca II

Fe I
Fe I

Fe I

8600

Error in [ /Fe]

Error in [Fe/H]

100

8550

20
rmag

22

24

Mg I
Fe I

8500

Ca II

0.4

HI

Observation
Model

0.6

Ca II
Fe I

Normalized flux

1.2

8750

8800

8850

10 1
16

18

20
rmag

22

24

Figure 19. Upper panel: Small portions of a simulated observation of an r-mag 21 K-giant (T eff = 4316 K, log(g) = 1.69) in a 3 hr exposure with PFS. The
best-fit model is overplotted and some key atomic features are highlighted and labeled. Lower left: Expected errors in the inferred metallicity as a function ofmagnitude. The random errors are taken as the standard deviation of the best-fit parameters over 100 noise realizations. A systematic contribution of 0.05 dex
derived from PFS commissioning observations of globular cluster member stars was added in quadrature. Right right: Corresponding results for α-enhancement.
All simulations were carried out in the medium resolution mode.

required precision for key spectral diagnostics such as the4000Å break, Hδ absorption equivalent width, emission flux,
outflow velocities from features such as Mg II, and both stellar
and gas-phase velocity dispersions. In combination with HSC
photometry, we predict stellar masses to < 0.2 dex for JAB < 23
and 4000Å break measures to 10% for most galaxies. Deeper
12 hour spectra will constrain Hδ equivalent widths to < 12%and enable stellar and gas-phase dispersions to < 25%. By

stacking ≈ 100 galaxies we will be able to measure average
outflow velocities in UV ISM absorption lines such as Mg II
to better than ≈ 50 km/s enabling studies of feedback as afunction of galaxy properties.
7.3.4. Calibrations
The ETC enables calculation of the required exposure time
for a given S/N for any target given our demonstrated throughput (Figure 16), and our efficacy of sky subtraction (Sec-24

PFS SSP team
5000

12 hr

z≈4

z ≈ 2.1 − 3.5
Lyα emittersvs gz ≈ 5.7 & 6.6 20000
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z ≈ 0.7 − 2

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vs z & Y

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0810 900
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vs i[O III]Hβ[O II]

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z = 1.389 galaxy
Y = 22.3 & 2 hr

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600

z = 6.6 Lyα
12 hr

Figure 20. Left panel: Expected signal-to-noise ratio for key components of the Galaxy Evolution survey as a function of magnitude. Note that these calculations
are based on an expected throughput
that is indeed quite
close to SFR
what hasbeen delivered. Achieving
this depth
is critical tointegrationdeliver our promised observables, as
redshiftlogM*[M_sun/yr)
magfeatureoutlined in Table 6. Right panels: Expected spectral quality for galaxy types that the GE survey will target with the simulated spectrum in black, the expected
1.389
10.15
38
22.28
Y
[O
II]
6e-17
2 hrs
1-sigma error array in blue, and the best fit model in red. From top to bottom, the panels show a star-forming galaxy at z = 1.389,
a quiescent galaxy at z = 1.422,
an IGM tomography background galaxy,andtwoexamplesof
Lyαemitters
atz=5.7(left)
and6.6(right).
The
top
three
panelsarebinned for display purposes by
1.422
10.91
1.9
22.5 Y[O II] 1.2e-17
12 hrsa factor of 5, 7, and 4 times the native PFS pixel scale, respectively. The panels are labeled with their exposure times, and the continuum selected galaxies (top3.145 magnitudes.10.3
24.2 gn/a
6 hrs
three) are also labeled with their apparent

5.7
6.6

n/a
n/a

n/a
n/a

n/a
n/a

logL(Lya)=42.6
logL(Lya)=42.7

6 hrs
12 hrsFigure 21. Representative examples of “on-sky” emission- and absorption-line spectra for i ∼ 22.5 galaxies with about 3 hour exposures, obtained from PFS
commissioning observations. These can be compared in quality with the simulated spectra in Figure 20. Here we used the PFS data reduction pipeline to obtain the
spectra, and used the 5 and 7 pixel binning scheme in the left and right panels, respectively, similarly to the right-top two panels in Figure 20. Some key features
are highlighted. The red curve in each panel denotes the statistical noise level.

tion 8.1). However, a full demonstration of the performanceof PFS requires on-sky observations, especially for the moredemanding SSP programs which include redshifts and emission line measures for the faintest, highest redshift galaxies,
and absorption line studies of brighter, intermediate redshiftsources. Due to the challenges of weather and technical issues
with the Subaru primary mirror, we have obtained only a limited number of exposures of up to three hours integration on
faint targets. Nonetheless, the resulting spectra demonstrateadequate calibrations with wavelengths accurate to 1-2 km/s
and relative fiber-to-fiber throughputs calibrated to better than
1%. This follows precise dark/bias corrections, wavelength
calibration and dome flat exposures taken during the daytime
before and after each observing night for every science fiber.
For dome flats, we have constructed a special screen and arc
lamp system which ensures a uniform illumination. Figure 21
shows representative examples of both emission and absorp-

tion line spectra of i ∼ 22.5 galaxies with 3 hour exposuresobtained from commissioning observations. These faint spectra represent the most challenging aspects of the PFS-SSP
survey, and can be readily compared in quality with the simulations shown in Figure 20. The comparison demonstrates ourdata reduction pipeline ensures adequate calibrations with asufficient signal-to-noise to measure the required spectroscopic
features.
7.4. Survey Strategy
With the above inputs, we are able to develop an optimalsurvey strategy for a SSP of 360 nights conducted over 5 years.
We assume a classical observing mode (i.e., SSP observations
are not shared with other programs on a given night) and up
to 10 non-contiguous nights every month spanning dark andgrey time. Exposure times allow for airmass, moon phase andseeing variations (the latter based upon Maunakea statistics),

Subaru PFS SSP Survey
run14 (2026/12/31 - 2027/1/14)
GE_HSCD1

GA_ODIS3

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Figure 22. Upper panel: An example of night allocation of the PFS survey
fields for one realization of the PFS survey assuming an allocation of 10 nights
within each two weeks PFS run in Jan 2026. Observing blocks assigned to eachsurvey component are shown by colored bars, where the color indicates theelevation angle of the observed component. The name of the observed survey
component is labelled above each block, where CO , GA , and GE means
the Cosmology, Galactic Archaeology, and Galaxy Evolution component,
respectively, LOST means that the observing block was not observed due
to lost to weather, and CAL means the blocs for taking calibration data. By
having three programs, we can keep a high elevation of each target field for
the PFS observation. Lower panel: A simulation of the survey completenesswith allocated survey nights. Here, LOST is the ratio of lost observing time
due to weather condition, NONE is the ratio of observing time which cannot
be assigned to any fields. We take a survey strategy to prioritize Cosmology
survey in early stage, but we secure ∼10 nights each for Galactic Archeology
and Galaxy Evolution survey, and we achieve the final completeness of ≳ 95%
for all survey components.

and include all observing overheads (telescope slewing, instrument rotator, field acquisition, fiber positioner configurationand detector readout time). Finally we assume 30% of thenights are lost due to weather averaged over the entire survey.
Figure 22 presents a highly realistic plan for our proposed
SSP program using the survey simulation techniques and input
parameters discussed above. A variety of survey realizations(differing in random seeds of weather conditions and priorities
of different target fields or science programs) demonstrate thatthe strategy robustly leads to >90% completeness over a 5
year 360 night survey for each of the Cosmology, GE and GA
programs (lower panel of Figure 22).
Early priority is given to the Cosmology targets and GA
dwarf galaxy targets, which have complementary RA distributions, to ensure that the PFS Cosmology is timely and remains
complementary to DESI. This enables us to meanwhile fully
evaluate and fine-tune the sky subtraction performance forthe more demanding components of the GE program. Within
the first two years we can still ensure at least ≃5 nights persemester to the GE component which would lead to 20 − 30 K
spectra; this is equivalent to the zCOSMOS catalog (∼ 30 K

25spectra) that was collected over several years with VLT (Lilly
et al. 2009). The early-year GE survey will spend these nights(i) obtaining 1-2 visits in each pointing, to secure LSS measurements and observe the brightest exemplars in all object classes,and (ii) probing the full color-space (i.e., without photometricredshift selection) of galaxies down to J = 22.8.
The major advantage of our survey planning software is its
ability to rapidly adjust the survey strategy and re-prioritize
targets to take account of (i) improved information on thetarget selection and the quality of PFS data obtained in earlycommissioning, (ii) survey progress as affected by weather
losses or competition with independent projects, and (iii) othertechnical or time allocation changes. Our team will continue tomaintain an optimal survey design to maximize the scientificreturns given our science goals.
8. SOFTWARE AND SURVEY DELIVERABLES
The PFS project will deliver fully wavelength-calibrated,flux-calibrated and sky-subtracted spectra for each object observed, together with a meaningful estimate of the uncertainty
per pixel. For galaxies, redshifts and their errors will be measured, in addition to emission-line strengths and properties. For
targets with higher S/N spectra, we will additionally measuredetailed spectral characteristics such as velocity dispersions
and outflow velocities. For stars, we will measure radial velocities, surface temperatures, surface gravities, and metallicities,
again with meaningful errors. These data, both calibrated spectra and tables of measured quantities, will be initially madeavailable to collaboration members and the Japanese astronomical community, via a sophisticated database unifying HSC
and PFS data, and then will be distributed to the world in public data releases. In what follows, we describe the software
pipelines that will bring us from raw data to these measured
quantities, the performance of the algorithms, and the databasemachinery we are building.

8.1. Calibrated, Wavelength-Corrected, Sky-Subtracted
SpectraGiven the faintness of the star and galaxy targets that PFS
will observe, the major challenge is to extract the spectra andaccurately subtract the sky. For the wavelength range of the
PFS, the sky spectrum is a smooth continuum with a large
number of superposed bright emission lines, in particular from
∼6000Å to the NIR end of the PFS coverage at 1.26µm, due
to non-thermal rotational/vibrational transitions of the OH
molecule (Rousselot et al. 2000; Hart 2019). Any given sky
emission line observed through a single fiber will appear as a
two-dimensional Point Spread Function (PSF) on the detector.
Our approach is to forward-model the detector map (the twodimensional position on the detector of a given wavelength
through a given fiber), the PSF, and the intrinsic sky spectrum. We will then subtract the strongest few hundred lines ofthe sky spectrum, extract the object spectra, and then modeland subtract the remaining sky continuum, weaker lines, andresiduals using a PCA approach. This follows the spirit of the“Spectro-Perfectionism” approach (Bolton & Schlegel 2010),
but with significantly smaller computational costs.
The spectral extraction code accurately maps the twodimensional position of a given wavelength through a given
fiber on a detector, and models the wings of the PSF to highaccuracy, recognizing the modest overlap of the wings of the
profiles of adjacent fibers on the detector (separated by only
∼ 6 pixels from one another). The PSF is modeled including

26

PFS SSP teamthe contributions of telescope pupil illumination, optical aberrations in the spectrographic cameras, and focal ratio degradation(FRD) in the fibers. The code also measures the relative transmission of each fiber by referencing all flux measurements tothe flux from a quartz flat-field exposure. This is then converted to physical units using flux standards observed in eachfield.
During commissioning of PFS, we assessed the accuracy ofthe sky subtraction algorithms through direct observations of
the night sky, in which all fibers except for a small fraction
reserved for engineering were pointed on blank patches ofthe sky (as selected from HSC imaging data). To test thesky subtraction routines, the background sky spectrum wasdetermined from 10% of the fibers; the remaining 90% of thefibers were treated as science targets. In Figure 23, we show
results from Spectrograph 1 of a single representative sky plate
observed in the July 2023 commissioning run for a 450-second
exposure. After fiber reassignment, there are 548 science fibers
and 49 sky fibers.
Neglecting pixel-pixel correlations, the sky-subtracted spectra should be consistent with zero, retaining only the residualsof sky-subtraction, read noise, and Poisson noise. As a loworder test of sky subtraction quality, we consider each fiber’s
pixel-level distribution of χ, the ratio of the residual flux tothe uncertainty in that pixel, estimated by the pipeline. The
upper panels of Figure 23 shows these distributions separately
for each arm for each of the 548 fibers. The distributions are
well-described by a Gaussian centered at 0 with a standard
deviation of 1. To simulate longer exposures, we averaged
the sky-subtracted residuals across all 548 fibers, creating aneffective exposure time of 2.5 × 105 seconds, almost 70 hours.
The result is shown in the lower panel, and is compared withthe average sky spectrum (gray line) scaled down by a factor of100. This demonstrates that the remaining systematic errors inthe sky subtraction are of order 1% of the sky flux, as we had
hoped. Detailed tests (not shown) of the sky residuals showthat the χ distributions remain close to Gaussian even in the
cores of strong sky lines, and that there is no significant structure to the residuals associated with the lines. We anticipate
that the results will continue to improve as we further refine
our sky subtraction algorithms.
8.2. Measurements of Redshifts and Spectral Properties
The pipeline described above will deliver fully flux- andwavelength-calibrated 1D spectra of each object. The redshiftsof galaxies will be measured with a code, “Algorithms for Massive Automatic Z Evaluation and Determination” (AMAZED;
Schmitt et al. 2019), which has been used extensively forground-based multi-object surveys such as those by VIMOS,
and will be used to analyze data from the Euclid survey.
AMAZED models galaxies as a superposition of continuum,
emission lines, stellar and interstellar absorption lines, andincluding a model for the Lyα forest as a function of redshift.
These models are fit to the data by minimizing χ2 , resulting
in an estimate of the redshift and its uncertainty, as well as
emission-line and absorption-line parameters. We have tested
this approach extensively on simulated PFS spectra, which
include realistic distributions of underlying galaxy populations
from both the Cosmology and GE components of the survey.
We have found that 90% of a simulated Galaxy Evolution sample (see Section 7.2) with z < 2.1 have measured redshifts with∆z/(1 + z) < 5 × 10−4 , and 98% have errors less than 10−3 asshown in Figure 24. Similarly, 97% of a simulated sample ofobjects targeted with the Cosmology algorithm have correctly

measured redshifts.
We have developed a separate dedicated pipeline to measurestellar atmospheric parameters (effective temperature, surface
gravity, and Fe and alpha abundances) for objects in the GA
survey, based on algorithms described in Escala et al. (2019)and Kirby et al. (2009). After shifting the spectrum to therest frame using the radial velocity determined by our own
code (Dobos et al. 2024) and normalizing by the continuum,
the code fits the spectrum to a grid of synthetic spectra calculated based on the ATLAS9 (Kurucz 1993; Castelli et al. 1997)
model stellar atmospheres. The code additionally makes useof photometric information for the stellar parameter determination by comparing them with stellar isochrone models. The
performance of this pipeline is described in Section 7.2.
Table 6 summarizes main deliverables we measure fromeach of the PFS Cosmology, GA and GE programs.
8.3. Database and Distribution
As described above, the principal outputs of the pipelines
will be fully calibrated 1D spectra of every object observed,
as well as redshifts/radial velocities, emission and absorption
line properties for galaxies, and effective temperatures, surface
gravity, and metallicities for stars. In addition, extensive metadata from each PFS exposure, and intermediate data products(such as the calibrated spectra from each arm of the spectrograph) will be useful for science analyses. Furthermore, as PFS
target selection is largely based on imaging data from HSC, thePFS data will need to be closely coupled to the HSC photometric catalogs. The database will also include extensive metadatafrom each PFS exposure, intermediate data products (such as
the calibrated spectra from each arm of the spectrograph), anddetailed targeting information.
We have developed an extensive data access framework built
on a modern science platform to serve these massive datasetsto the members of the collaboration and the Japanese astronomical community. The PFS science platform allows a user
to access the HSC and PFS data directly with no overheads;
the user can perform science analyses directly on the data andno massive data transfer is required to their local machine. Its
capabilities complement the tools that the HSC collaboration
has already built, such as HSCMap, a powerful visualizationinterface for the HSC images. The PFS science platform adds
a file sharing service, a Jupyter notebook analysis system thatallows notebooks to be executed in interactive mode, and aspectrum analysis tool called Spectrum Viewer that can be
invoked in standalone mode as well as from a Jupyter notebook. The third generation of the prototype system has beenreleased internally, and the system will be ready for extensive
data analyses by the time the PFS survey begins.
The HSC collaboration has distributed the data from their
330-night survey in a series of public data releases (Aihara
et al. 2018a, 2019). This has been highly successful; the data
are being used by astronomers all over the world, and the
public releases have significantly increased the visibility of the
HSC survey. At this writing the HSC collaboration and theworld-wide community has written over 500 refereed papers
based on the survey. Building on the HSC experience andsuccess, we plan to make fully public releases of PFS data to
the world every other year through the five years of the PFS
survey to further contribute to the world-wide community.
9. TEAM EXPERTISE & MANAGEMENT PLAN

Many team members have extensive experience in the management of large collaborative efforts including those of theSubaru PFS SSP Survey

27

Figure 23. Sky subtraction diagnostics from a representative commissioning sky plate. Top row: summary of the χ distributions for the sky-subtracted spectraby spectrograph arm; each of the 548 curves is the pixel level χ distribution for a single fiber. The columns correspond to the blue, red, and near-infrared armsof the spectrograph, respectively. A Gaussian centered at 0 with a standard deviation of 1 is plotted as a black line. Bottom row: the average over 548 spectra(corresponding to a total exposure time of almost 70 hours) of sky-subtracted residuals is shown as gray points; the running median and 1σ confidence interval are
shown as a dashed line and the shaded region, respectively. For reference, the gray line is a high-signal sky spectrum scaled down by a factor of 100.

Table 6
Deliverables of PFS SSP Survey
Sample[OII] ELGs
0.7 < z < 1.7 galsz ≳ 2 gals
LAEs
stars in MW/dSphs/M31

Deliverablez
f[OII]
0.7 < z < 1.7∼200K zM∗ , SFR, τ∗ , b, (O/H), voutflow[Fe/H], [α/Fe], T e
2.1 ≤ z ≤ 7∼ 60K zτLα , M∗ , SFR, b, (O/H), voutflow
z = 2.2, 5.7, 6.6 ∼ 15K z
f Lα
z=0∼ 75K radial vel.
[Fe/H], [α/Fe]z-range
0.8 ≤ z ≤ 2.4#∼ 4M

highly successful Subaru Hyper Suprime-Cam campaign, theSloan Digital Sky Survey, the AAT 2 degree Field Redshift Survey and the HST-based COSMOS survey. We have structured
our PFS management plan based upon lessons learned fromthe foregoing surveys and closely followed the arrangementsthat worked successfully for the HSC SSP.
The overall PFS SSP operation and issues related to instrument performance will be overseen jointly by the PFS
Project Manager, Naoyuki Tamura (NAOJ), the Commissioning Observation Lead, Yuki Moritani (NAOJ), and the Survey
Operation Lead, Kiyoto Yabe (NAOJ). The data pipeline andprocessing will be overseen by Robert Lupton (Princeton) andKiyoto Yabe, and data archiving managed by Masayuki Tanaka(NAOJ). The PFS science program and its priorities will beoverseen by co-science leads Masahiro Takada (Kavli IPMU)
and Richard Ellis (UCL). Any issues related to the interestsof the international PFS science team will be addressed by
PFS Steering Committee comprising a member from eachparticipating institution and NAOJ chaired by Hitoshi Murayama (Kavli IPMU). Issues related to the participation of
the Japanese community can be relayed through the Subaru
Advisory Committee (SAC) to both NAOJ and the Steering
Committee.
As discussed in Section 8, all data will be distributed simultaneously to the international PFS team members and, after

Precision∆z/(1 + z) ≲ 5 × 10−4
∆ f[OII] / f[OII] ≲ 0.2∆z/(1 + z) ≲ 5 × 10−4
S /N ≈ 2 Å−1 , for the main sample
S /N ≈ 6 Å−1 , for the deep sample∆z/(1 + z) ≲ 10−3
S /N ≈ 2 Å−1∆z/(1 + z) ≤ 10−3
S /N ≈ 10
3–10 km/s
0.2

Note≳ 70% success rate
∼90% sampling rate

∼ 75% sampling rate
∼ 100% sampling rate

an agreed proprietary period, to the global astronomical community via a sophisticated database at NAOJ, similar to thatdeveloped for the HSC collaboration (Aihara et al. 2019). The
proposed SSP plan (Figure 22) will enable important scientific progress from each component (Cosmology, GE and GA)
using the first year of data, thereby stimulating the PFS team
to work continuously as more data arrives. Our team is already organized in highly structured working groups aroundthe broad science themes described in this proposal as well asones responsible for technical aspects, publication and teammembership policies. All PFS team publications will be subject to a collaboration-wide data policy, with clear guidelines
on communication and authorship that encourage junior scientists to lead scientific projects and become first authors on the
resulting papers.
10. ANCILLARY SCIENCE

Clearly a powerful instrument such as PFS SSP will provide
unique opportunities for ancillary science beyond the main
focus of the SSP described in this proposal. As the Cosmology program will undertake a comprehensive campaign overabout 1100 deg2 , it offers the opportunity for spectroscopic
observations of additional rare bright objects thereby strengthening synergistic aspects of our scientific themes (see Figure 1).
Examples where the Cosmology survey can lead to such syn-28

PFS SSP teamdwarf galaxy pointings in the GA program from Open Use
programs. The selected dwarf galaxies (see Table 2) represent
a flagship science component of the PFS SSP which could bereadily challenged by even a single night’s observation. No
such protection from Open Use science programs is requested
for the GE and Cosmology fields. The team is likewise willingto accept Target-of-Opportunity (ToO) observations in all SSP
survey fields.
The combination of Subaru HSC and PFS data will deliver a
unique legacy value. The team has been developing a science
database whereby pipeline products of PFS data, together withthe associated HSC images are available3 . This development
is partly funded by a US National Science Foundation MSIP
(Mid-Scale Innovations Program in Astronomical Sciences)
led by team members at JHU, Princeton and Caltech. The
database will have an online image browser interface, extending the existing one hscMap, thereby providing a user-friendly
environment to browser the extensive combined HSC and PFS
dataset. This will bring considerable benefits to facilitate andmaximize the science return from both HSC and PFS surveys.

Figure 24. The median (points), interquartile range (smaller errorbars) andrange including 90% of the points (larger errorbars), of the difference between
true and inferred redshift for a sample of simulated GE galaxies (Section 7.2).
Results are shown as a function both of galaxy magnitude and [OII] emissionline strength. The vast majority of galaxies have redshifts determined to within|∆z| = 10−3 .

ergies include studies of (i) AGN/quasar candidates identified
from multi-color HSC datasets (Matsuoka et al. 2018) providing complementary information on the co-evolution of supermassive black holes and their host galaxies, (ii) low-surface
brightness galaxies (Greco et al. 2018) uniquely identified by
HSC datasets whose redshifts will aid in studies of this intriguing population, (iii) galaxies/quasars in strong lens systems(Sonnenfeld et al. 2019) whose characterization will lead to
improved constraints on the Hubble constant (H0 ), complementing those derived from baryonic acoustic oscillations derived from the primary cosmology program, and (iv) extremely
metal-poor halo stars and white dwarfs whose physical properties and radial velocities will complement studies in specificfields in the GA program.
Additional “ancillary science” targets are collated from PFS
team members and prioritised for each pointing in the Cosmology survey. Our fiber assignment algorithm has the capabilityof prioritising cosmology targets ([O ii] emitter candidates) andancillary targets in order to ensure the required completenessand success rate of the former whilst avoiding biases arisingfrom their ‘artificial clustering’.
The PFS team recognizes there are likely to be many OpenUse requests for PFS observations in the popular COSMOSand SXDS GE fields. By design however, it is unlikely that
our GE campaign will have any spare fibers for such observations to be added, even if the relevant exposure times were tomatch. However, if the Subaru Advisory Committee requests
some form of coordination of the SSP with such Open Use
programs, it may be possible to re-arrange the SSP GE strategy to accomplish such a trade, so long as the total SSP timeremains unchanged via subsequent allocations of time and
the GE survey is optimally completed within the SSP survey
period.
Finally, the team requests that the Subaru TAC protects the

11. CONCLUDING REMARKS
Following more than 15 years of instrument design andconstruction, scientific planning, software design and bothnational and international fund-raising, the PFS team is enthusiastically ready to embark on an ambitious, but demonstrably
practical, SSP survey that fully utilizes PFS’s unique widefield and massively-multiplex spectroscopic capabilities at theprime focus of the 8.2 m Subaru aperture on Maunakea. Our
proposed survey promises unrivalled and dramatic progressin three broad areas: the nature of dark energy and dark matter (including the neutrino mass), the assembly history of the
Milky Way and our nearest neighbouring galaxies M31, and acomprehensive census of galaxy properties over a wide range
in redshift (0.7 ≲ z ≲ 7). This program will be an outstanding
legacy of the Subaru telescope that will propel further scientific endeavors in the era of Extremely Large Telescopes andnext-generation panoramic surveys such as the Vera Rubin
Observatory (LSST), ESA’s Euclid mission and the NASA
Roman Space Telescope.

ACKNOWLEDGEMENTS
We would like to dedicate this proposal to the memory ofProfs. Naruhisa Takato and Olivier Le Fèvre, who were crucial
individuals in the PFS project. Olivier had a strong scientificvision especially in high-redshift science as well as expertisein instrumentation that made it possible for the dedicated teamof engineers at LAM to work on the PFS. Under his leadership, LAM has been making crucial contributions to thedevelopments of spectrograph system and 1D pipeline, aspects
at the heart of the PFS endeavor. The PFS project has taken
more than 15 years from the launch to completion. The PFS
team acknowledges various financial contributions: the Funding Program for World-Leading Innovative R&D on Scienceand Technology (FIRST program: PI: H. Murayama), contributions from the participating international institutions andgroups (ASIAA, Brazilian PFS Participation Consortium, Caltech/JPL, Chinese PFS Participation Consortium, JHU, Kavli
IPMU, LAM, MPA/MPE, NAOJ, USA North-East Participation Group, Princeton), the KAKENHI Grant-in-Aid for Scientific Research on Innovative Area (PI: H. Murayama), NSF’s
3 See https://hscdata.mtk.nao.ac.jp:4443/hsc_ssp/ for the
HSC database.

Subaru PFS SSP Survey
Mid-Scale Innovations Program in Astronomical Sciences (PI:
M. Strauss), and KAKENHI Transformative Research Area
(A) (PI: H. Murayama).
REFERENCES
Abdalla, E., et al. 2022, Journal of High Energy Astrophysics, 34, 49
Addison, G. E., Bennett, C. L., Jeong, D., Komatsu, E., & Weiland, J. L. 2019,
ApJ, 879, 15
Aihara, H., et al. 2018a, PASJ, 70, S8—. 2018b, PASJ, 70, S4—. 2019, PASJ, 71, 114
Alam, S., Miyatake, H., More, S., Ho, S., & Mandelbaum, R. 2017, MNRAS,
465, 4853
Alam, S., et al. 2021, Phys. Rev. D, 103, 083533
Allison, R., Caucal, P., Calabrese, E., Dunkley, J., & Louis, T. 2015,
Phys. Rev. D, 92, 123535
Amon, A., et al. 2018, Mon. Not. Roy. Astron. Soc., 479, 3422
Amon, A., et al. 2022, Phys. Rev. D, 105, 023514
Annunziatella, M., et al. 2023, AJ, 166, 25
Arnaboldi, M., et al. 2022, A&A, 666, A109
Asgari, M., et al. 2021, A&A, 645, A104
Ata, M., Kitaura, F.-S., & Müller, V. 2015, MNRAS, 446, 4250
Bayer, A. E., et al. 2021, ApJ, 919, 24
Behroozi, P., Wechsler, R. H., Hearin, A. P., & Conroy, C. 2019, MNRAS,
488, 3143
Behroozi, P. S., Wechsler, R. H., & Conroy, C. 2013, ApJ, 770, 57
Belokurov, V., Sanders, J. L., Fattahi, A., Smith, M. C., Deason, A. J., Evans,
N. W., & Grand, R. J. J. 2020, MNRAS, 494, 3880
Belokurov, V., Vasiliev, E., Deason, A. J., Koposov, S. E., Fattahi, A.,
Dillamore, A. M., Davies, E. Y., & Grand, R. J. J. 2023, MNRAS, 518,
6200
Berg, D. A., Erb, D. K., Henry, R. B. C., Skillman, E. D., & McQuinn, K.
B. W. 2019, ApJ, 874, 93
Berlind, A. A., & Weinberg, D. H. 2002, ApJ, 575, 587
Bernal, J. L., Verde, L., & Riess, A. G. 2016, J. Cosmology Astropart. Phys.,
10, 019
Bianchi, D., & Verde, L. 2020, MNRAS, 495, 1511
Bird, J. C., Loebman, S. R., Weinberg, D. H., Brooks, A. M., Quinn, T. R., &Christensen, C. R. 2021, MNRAS, 503, 1815
Blake, C., et al. 2016, MNRAS, 456, 2806
Blake, C., et al. 2020, Astron. Astrophys., 642, A158
Bolton, A. S., & Schlegel, D. J. 2010, Publications of the Astronomical
Society of the Pacific, 122, 248
Boyle, A., & Komatsu, E. 2018, J. Cosmology Astropart. Phys., 3, 035
Bruzual, G., & Charlot, S. 2003, MNRAS, 344, 1000
Burkert, A., et al. 2016, ApJ, 826, 214
Cai, Y.-C., Padilla, N., & Li, B. 2015, MNRAS, 451, 1036
Carnall, A. C., Leja, J., Johnson, B. D., McLure, R. J., Dunlop, J. S., &Conroy, C. 2019, ApJ, 873, 44
Castelli, F., Gratton, R. G., & Kurucz, R. L. 1997, A&A, 318, 841
Chambers, K. C., et al. 2016, arXiv e-prints, arXiv:1612.05560
Chan, H. Y. J., Ferreira, E. G. M., May, S., Hayashi, K., & Chiba, M. 2022,
MNRAS, 511, 943
Chevalier, R. A., & Clegg, A. W. 1985, Nature, 317, 44
Chiang, B. T., Ostriker, J. P., & Schive, H.-Y. 2023, MNRAS, 518, 4045
Choudhury, S. R., & Hannestad, S. 2020, J. Cosmology Astropart. Phys.,
2020, 037
Chudaykin, A., & Ivanov, M. M. 2019, J. Cosmology Astropart. Phys., 2019,034
Comparat, J., et al. 2016, A&A, 592, A121
Croton, D. J., et al. 2006, MNRAS, 365, 11
Dalal, R., et al. 2023, Phys. Rev. D, 108, 123519
D’Amico, G., Hamill, T., & Kaloper, N. 2016, Phys. Rev. D, 94, 103526
Dawson, K. S., et al. 2016, AJ, 151, 44
de Blok, W. J. G., & Walter, F. 2000, ApJL, 537, L95
de la Torre, S., et al. 2017, A&A, 608, A44
Deason, A. J., Belokurov, V., Koposov, S. E., & Lancaster, L. 2018, ApJL,
862, L1
Deason, A. J., Bose, S., Fattahi, A., Amorisco, N. C., Hellwing, W., & Frenk,
C. S. 2022, MNRAS, 511, 4044
Deason, A. J., Fattahi, A., Frenk, C. S., Grand, R. J. J., Oman, K. A.,
Garrison-Kimmel, S., Simpson, C. M., & Navarro, J. F. 2020, MNRAS,
496, 3929
DESI Collaboration et al. 2022, AJ, 164, 207—. 2024, arXiv e-prints, arXiv:2404.03002
Dey, A., et al. 2023, ApJ, 944, 1
Dobos, L., et al. 2024, AJ submitted
Dodelson, S., Kaplinghat, M., & Stewart, E. 2000, Phys. Rev. Lett., 85, 5276
Dubois, Y., et al. 2014, MNRAS, 444, 1453
Durkalec, A., et al. 2015, A&A, 576, L7
Eisenstein, D. J., et al. 2005, ApJ, 633, 560
Erb, D. K. 2008, ApJ, 674, 151
Erkal, D., et al. 2019, MNRAS, 487, 2685—. 2021, MNRAS, 506, 2677

29

Escala, I., Gilbert, K. M., Fardal, M., Guhathakurta, P., Sanderson, R. E.,
Kalirai, J. S., & Mobasher, B. 2022, AJ, 164, 20
Escala, I., Kirby, E. N., Gilbert, K. M., Cunningham, E. C., & Wojno, J. 2019,
ApJ, 878, 42
Esteban, I., Gonzalez-Garcia, M. C., Hernandez-Cabezudo, A., Maltoni, M.,& Schwetz, T. 2019, Journal of High Energy Physics, 2019, 106
Evans, T. A., Fattahi, A., Deason, A. J., & Frenk, C. S. 2020, MNRAS, 497,
4311
Falcón-Barroso, J., Sánchez-Blázquez, P., Vazdekis, A., Ricciardelli, E.,
Cardiel, N., Cenarro, A. J., Gorgas, J., & Peletier, R. F. 2011, A&A, 532,
A95
Finlator, K., & Davé, R. 2008, MNRAS, 385, 2181
Förster Schreiber, N. M., & Wuyts, S. 2020, ARA&A, 58, 661
Freedman, W. L. 2017, Nature Astronomy, 1, 0121
Frieman, J. A., Hill, C. T., Stebbins, A., & Waga, I. 1995, Phys. Rev. Lett., 75,
2077
Gao, H., & Jing, Y. P. 2021, ApJ, 908, 43
Gao, L., & White, S. D. M. 2007, MNRAS, 377, L5
Garnett, D. R. 2002, ApJ, 581, 1019
Gilbert, K. M., Kirby, E. N., Escala, I., Wojno, J., Kalirai, J. S., &Guhathakurta, P. 2019, ApJ, 883, 128
Gilbert, K. M., et al. 2022, ApJ, 924, 116
Greco, J. P., et al. 2018, ApJ, 857, 104
Greene, J., Bezanson, R., Ouchi, M., Silverman, J., & the PFS Galaxy
Evolution Working Group. 2022, arXiv e-prints, arXiv:2206.14908
Hahn, C., & Villaescusa-Navarro, F. 2021, J. Cosmology Astropart. Phys.,2021, 029
Hamaus, N., Cousinou, M.-C., Pisani, A., Aubert, M., Escoffier, S., & Weller,
J. 2017, J. Cosmology Astropart. Phys., 7, 014
Hamaus, N., Pisani, A., Choi, J.-A., Lavaux, G., Wandelt, B. D., & Weller, J.
2020, J. Cosmology Astropart. Phys., 2020, 023
Hammer, F., Yang, Y. B., Wang, J. L., Ibata, R., Flores, H., & Puech, M. 2018,
MNRAS, 475, 2754
Han, J. J., et al. 2022, AJ, 164, 249
Hart, M. 2019, PASP, 131, 015003
Hayashi, H., & Chiba, M. 2006, PASJ, 58, 835
Hayashi, K., Chiba, M., & Ishiyama, T. 2020, ApJ, 904, 45
Hayden, M. R., et al. 2015, ApJ, 808, 132
Helmi, A., Babusiaux, C., Koppelman, H. H., Massari, D., Veljanoski, J., &Brown, A. G. A. 2018, Nature, 563, 85
Hikage, C., et al. 2019, PASJ, 71, 43
Hirai, Y., Beers, T. C., Chiba, M., Aoki, W., Shank, D., Saitoh, T. R.,
Okamoto, T., & Makino, J. 2022, MNRAS, 517, 4856
Hirai, Y., Kirby, E. N., Chiba, M., Hayashi, K., Anguiano, B., Saitoh, T. R.,
Ishigaki, M. N., & Beers, T. C. 2024, arXiv e-prints, arXiv:2405.05330
Ho, S. H., & Martin, C. L. 2020, ApJ, 888, 14
Horowitz, B., Lee, K.-G., White, M., Krolewski, A., & Ata, M. 2019, ApJ,
887, 61
Hui, L., Ostriker, J. P., Tremaine, S., & Witten, E. 2017, Phys. Rev. D, 95,
043541
Ibata, R., et al. 2021, ApJ, 914, 123
Ivanov, M. M., Philcox, O. H. E., Cabass, G., Nishimichi, T., Simonović, M.,& Zaldarriaga, M. 2023, Phys. Rev. D, 107, 083515
Jullo, E., et al. 2019, Astron. Astrophys., 627, A137
Kaiser, N. 2004, Pan-STARRS Technical Report, PDSC-002-010-XX
Kashino, D., et al. 2017, ApJ, 843, 138
Kauffmann, G., White, S. D. M., Heckman, T. M., Ménard, B., Brinchmann,
J., Charlot, S., Tremonti, C., & Brinkmann, J. 2004, MNRAS, 353, 713
Kereš, D., Katz, N., Weinberg, D. H., & Davé, R. 2005, MNRAS, 363, 2
Kirby, E. N., Cohen, J. G., Smith, G. H., Majewski, S. R., Sohn, S. T., &Guhathakurta, P. 2011, ApJ, 727, 79
Kirby, E. N., Guhathakurta, P., Bolte, M., Sneden, C., & Geha, M. C. 2009,
ApJ, 705, 328
Kirby, E. N., et al. 2010, ApJS, 191, 352
Kobayashi, C., Bhattacharya, S., Arnaboldi, M., & Gerhard, O. 2023, ApJL,
956, L14
Kobayashi, Y., Nishimichi, T., Takada, M., & Miyatake, H. 2022,
Phys. Rev. D, 105, 083517
Komatsu, E., et al. 2014, Progr. Theor. Exp. Phys., 2014, 06B102
Komiyama, Y., et al. 2018, ApJ, 853, 29
Kraljic, K., et al. 2018, MNRAS, 474, 547
Kreisch, C. D., Pisani, A., Carbone, C., Liu, J., Hawken, A. J., Massara, E.,
Spergel, D. N., & Wandelt, B. D. 2019, MNRAS, 488, 4413
Kreisch, C. D., Pisani, A., Villaescusa-Navarro, F., Spergel, D. N., Wandelt,
B. D., Hamaus, N., & Bayer, A. E. 2022, ApJ, 935, 100
Kriek, M., et al. 2015, ApJS, 218, 15
Kubota, K., Inoue, A. K., Hasegawa, K., & Takahashi, K. 2020, MNRAS,
494, 3131
Kurucz, R. L. 1993, Physica Scripta Volume T, 47, 110
Lacy, M., et al. 2021, MNRAS, 501, 892
Laigle, C., et al. 2018, MNRAS, 474, 5437
Lamareille, F., et al. 2009, A&A, 495, 53
Lan, T.-W., & Mo, H. 2018, ApJ, 866, 36
Laporte, C. F. P., Koposov, S. E., & Belokurov, V. 2022, MNRAS, 510, L13
Le Fèvre, O., et al. 2005, A&A, 439, 845
Leja, J., Carnall, A. C., Johnson, B. D., Conroy, C., & Speagle, J. S. 2019,
ApJ, 876, 3

30

PFS SSP teamLesgourgues, J., & Pastor, S. 2006, Phys. Rep., 429, 307
Li, T. S., et al. 2017, ApJ, 844, 74
Li, X., et al. 2023, Phys. Rev. D, 108, 123518
Lilly, S. J., et al. 2007, ApJS, 172, 70—. 2009, ApJS, 184, 218
Mackereth, J. T., et al. 2019, MNRAS, 489, 176
Madhavacheril, M. S., et al. 2024, ApJ, 962, 113
Maiolino, R., & Mannucci, F. 2019, A&A Rev., 27, 3
Maiolino, R., et al. 2020, The Messenger, 180, 24
Makiya, R., Kayo, I., & Komatsu, E. 2021, J. Cosmology Astropart. Phys.,2021, 095
Makiya, R., & Sunayama, T. 2022, J. Cosmology Astropart. Phys., 2022, 008
Malavasi, N., et al. 2017, MNRAS, 465, 3817
Mashchenko, S., Wadsley, J., & Couchman, H. M. P. 2008, Science, 319, 174
Massara, E., Villaescusa-Navarro, F., Viel, M., & Sutter, P. M. 2015, J.
Cosmology Astropart. Phys., 2015, 018
Matsuoka, Y., et al. 2018, PASJ, 70, S35
Mauduit, J. C., et al. 2012, PASP, 124, 714
McMonigal, B., et al. 2016, MNRAS, 461, 4374
Miyatake, H., et al. 2022, Phys. Rev. D, 106, 083520—. 2023, Phys. Rev. D, 108, 123517
Mo, H. J., Mao, S., & White, S. D. M. 1998, MNRAS, 295, 319
Momcheva, I. G., et al. 2016, ApJS, 225, 27
Murphy, M. T., Kacprzak, G. G., Savorgnan, G. A. D., & Carswell, R. F.
2019, MNRAS, 482, 3458
Murray, N., Quataert, E., & Thompson, T. A. 2005, ApJ, 618, 569
Muzzin, A., et al. 2013, ApJ, 777, 18
Navarro, J. F., Frenk, C. S., & White, S. D. M. 1997, ApJ, 490, 493
Nguyen, N.-M., Huterer, D., & Wen, Y. 2023, Phys. Rev. Lett., 131, 111001
Nidever, D. L., et al. 2024, Early Disk-Galaxy Formation from JWST to the
Milky Way, 377, 115
Ogami, I., et al. 2024, arXiv e-prints, arXiv:2401.00668
Oguri, M., & Takada, M. 2011, Phys. Rev. D, 83, 023008
Okamoto, S., Arimoto, N., Tolstoy, E., Jablonka, P., Irwin, M. J., Komiyama,Y., Yamada, Y., & Onodera, M. 2017, MNRAS, 467, 208
Ostriker, J. P., & Steinhardt, P. 2003, Science, 300, 1909
Peacock, J. A., et al. 2001, Nature, 410, 169
Percival, W. J., et al. 2010, MNRAS, 401, 2148
Pichon, C., Pogosyan, D., Kimm, T., Slyz, A., Devriendt, J., & Dubois, Y.
2011, MNRAS, 418, 2493
Pisani, A., Sutter, P. M., Hamaus, N., Alizadeh, E., Biswas, R., Wandelt,
B. D., & Hirata, C. M. 2015, Phys. Rev. D, 92, 083531
Pisani, A., et al. 2019, BAAS, 51, 40
Planck Collaboration et al. 2020, A&A, 641, A6
Plez, B. 2012, Turbospectrum: Code for spectral synthesis, Astrophysics
Source Code Library, record ascl:1205.004
Prakash, A., et al. 2016, ApJS, 224, 34
Pullen, A. R., Alam, S., He, S., & Ho, S. 2016a, MNRAS, 460, 4098
Pullen, A. R., Hirata, C. M., Doré, O., & Raccanelli, A. 2016b, PASJ, 68, 12
Raichoor, A., et al. 2017, MNRAS, 471, 3955
Read, J. I., et al. 2021, MNRAS, 501, 978
Renaud, F., Agertz, O., Read, J. I., Ryde, N., Andersson, E. P., Bensby, T.,
Rey, M. P., & Feuillet, D. K. 2021, MNRAS, 503, 5846
Reyes, R., Mandelbaum, R., Seljak, U., Baldauf, T., Gunn, J. E., Lombriser,L., & Smith, R. E. 2010, Nature, 464, 256
Riess, A. G., Casertano, S., Yuan, W., Macri, L. M., & Scolnic, D. 2019, ApJ,
876, 85
Rousselot, P., Lidman, C., Cuby, J. G., Moreels, G., & Monnet, G. 2000,
A&A, 354, 1134
Saito, S., de la Torre, S., Ilbert, O., Dubois, C., Yabe, K., & Coupon, J. 2020,
MNRAS, 494, 199

PFS COLLABORATION
Principal Investigator (PI)
Hitoshi Murayama
Kavli IPMU
Co-Principal Investigator (Co-PI)
Naoyuki Tamura
NAOJ
Project Manager/Project System Engineer
Naoyuki Tamura
NAOJ
Survey Scientist
Kiyoto Yabe
NAOJ
Co-Chairs of the PFS Survey Committee
Richard S Ellis
UCL
Masahiro Takada
Kavli IPMU
Co-Chairs of the PFS Science Working Groups

Sawicki, M., et al. 2019, MNRAS, 489, 5202
Schaan, E., Krause, E., Eifler, T., Doré, O., Miyatake, H., Rhodes, J., &Spergel, D. N. 2017, Phys. Rev. D, 95, 123512
Schmidt, F. 2017, ArXiv e-prints, arXiv:1709.01544
Schmitt, A., et al. 2019, in Astronomical Society of the Pacific Conference
Series, Vol. 521, Astronomical Data Analysis Software and Systems XXVI,
ed. M. Molinaro, K. Shortridge, & F. Pasian, 398
Schroetter, I., et al. 2019, MNRAS, 490, 4368
Scoville, N., et al. 2007, ApJS, 172, 38
Silverman, J. D., et al. 2009, ApJ, 695, 171—. 2015, ApJS, 220, 12Singh, S., Alam, S., Mandelbaum, R., Seljak, U., Rodriguez-Torres, S., & Ho,
S. 2019, Mon. Not. Roy. Astron. Soc., 482, 785
Somerville, R. S., & Davé, R. 2015, ARA&A, 53, 51
Sonnenfeld, A., Jaelani, A. T., Chan, J., More, A., Suyu, S. H., Wong, K. C.,
Oguri, M., & Lee, C.-H. 2019, A&A, 630, A71
Speagle, J. S., Steinhardt, C. L., Capak, P. L., & Silverman, J. D. 2014, ApJS,
214, 15
Spergel, D. N., & Steinhardt, P. J. 2000, Phys. Rev. Lett., 84, 3760
Steidel, C. C., Strom, A. L., Pettini, M., Rudie, G. C., Reddy, N. A., &Trainor, R. F. 2016, ApJ, 826, 159
Steidel, C. C., et al. 2014, ApJ, 795, 165
Steinhardt, C. L., et al. 2014, ApJL, 791, L25
Strom, A. L., Steidel, C. C., Rudie, G. C., Trainor, R. F., Pettini, M., & Reddy,
N. A. 2017, ApJ, 836, 164
Sunayama, T., et al. 2020, J. Cosmology Astropart. Phys., 2020, 057
Sutter, P. M., Pisani, A., Wandelt, B. D., & Weinberg, D. H. 2014, MNRAS,
443, 2983
Takada, M., Komatsu, E., & Futamase, T. 2006, Phys. Rev. D, 73, 083520
Takada, M., et al. 2014, PASJ, 66, R1
Tamfal, T., Mayer, L., Quinn, T. R., Babul, A., Madau, P., Capelo, P. R., Shen,
S., & Staub, M. 2022, ApJ, 928, 106
Timlin, J. D., et al. 2016, ApJS, 225, 1
Troxel, M. A., et al. 2018, Phys. Rev. D, 98, 043528
Tumlinson, J., Peeples, M. S., & Werk, J. K. 2017, ARA&A, 55, 389
Valentino, F., et al. 2017, MNRAS, 472, 4878
Valenzuela, O., Rhee, G., Klypin, A., Governato, F., Stinson, G., Quinn, T., &Wadsley, J. 2007, ApJ, 657, 773
Vasiliev, E. 2023, Galaxies, 11, 59
Vincenzo, F., & Kobayashi, C. 2020, MNRAS, 496, 80
Vogelsberger, M., et al. 2014, MNRAS, 444, 1518
Walker, M. G., & Peñarrubia, J. 2011, ApJ, 742, 20
Wang, T., et al. 2016, ApJ, 816, 84
Wardana, M. D., Chiba, M., & Hayashi, K. 2024, arXiv e-prints,
arXiv:2404.12671
Wechsler, R. H., & Tinker, J. L. 2018, ARA&A, 56, 435
Weisz, D. R., Dolphin, A. E., Skillman, E. D., Holtzman, J., Gilbert, K. M.,
Dalcanton, J. J., & Williams, B. F. 2014, ApJ, 789, 147
Wild, V., Heckman, T., & Charlot, S. 2010, MNRAS, 405, 933
Wojno, J. L., et al. 2023, ApJ, 951, 12
Wyse, R. F. G. 2001, in Astronomical Society of the Pacific Conference
Series, Vol. 230, Galaxy Disks and Disk Galaxies, ed. J. G. Funes & E. M.
Corsini, 71–80
York, D. G., et al. 2000, AJ, 120, 1579
Zahid, H. J., Kewley, L. J., & Bresolin, F. 2011, ApJ, 730, 137
Zhang, G., Li, Z., Liu, J., Spergel, D. N., Kreisch, C. D., Pisani, A., &Wandelt, B. D. 2020, Phys. Rev. D, 102, 083537
Zhang, P., Liguori, M., Bean, R., & Dodelson, S. 2007, Phys. Rev. Lett., 99,
141302PFS Cosmology WG
Tomomi Sunayama
ChangHoon Hahn
PFS Galactic Archaeology WG
Masashi Chiba
Judith Cohen
Evan Kirby
Rosemary Wyse
PFS Galaxy Evolution WG
Rachel Bezanson
Jenny Greene
Chiaki Kobayashi
Khee-Gan Lee
Masami Ouchi
John Silverman

Nagoya U.
Princeton U.
Tohoku U.
CaltechU. of Notre Dame
JHU
U. of Pittsburgh
Princeton U.
U. Hertfordshire
Kavli IPMU
NAOJ/U. Tokyo
Kavli IPMUPFS Steering Committee
S. Arnouts (LAM), Y.-T. Lin (ASIAA), R. S. Ellis (UCL),

Subaru PFS SSP Survey
T. Heckman (JHU), Y.-.P. Jing (SJTU), E. Komatsu (MPA),
D. Marchesini (Tufts U.), H. Murayama (Kavli IPMU),
M. Seiffert (JPL/Caltech), L. Sodre Jr. (U. São Paulo),
M. Strauss (Princeton), M. Takada (Kavli IPMU), M. Oguri(Chiba U./Subaru Advisory Committee chair), S. Miyazaki(NAOJ/Director of the NAOJ Hawaii Observatory)
Members in the PFS Collaboration
K. Adachi1 , H. Aihara2,3 , M. Akiyama1 , H. Algera4 , S. S. Ali5 ,A. Allaoui6 , A. Amon7 , M. Ando8 , J. Arita3 , S. Arnouts6 ,G. M. Azevedo9 , C. L. Bennett10 , M. Bethermin6,11 ,D. F. Braun12 , V. L. Brun6 , D. Burgarella6 , J. Burnham13 ,N. Caplar14 , M. M. Carneiro71 , M. A. Carr7 , P.-Y. Chabaud6 ,T.-C. Chang12,13 , A. Chen2,3 , C.-C. Chen15 , Z. Chen31 , Y.K. Chiang15 , M. Chiba1 , A. L. Chies-Santos9 , R. C. Y. Chou15 ,S. Christian6 , L. Clotilde16 , A. P. Cooper17 , S. de la Torre6 ,A. C. de Oliveira18 , C. M. de Oliveira19 , L. S. de Oliveira18 ,O. Doré12,13 , R. S. Ellis20 , T. Enßlin21 , M. Fabricius22 ,R. C. Fernandes23 , D. Ferreira18 , E. G. M. Ferreira2,3 ,C. Filion10 , C. Fisher24 , K. Fukushima25 , A. L. Fur7 ,H. Furusawa8 , A. Genina21 , R. Gerasimov26 , S. Gezari10 ,T. Goto17 , C. Gouin16 , A. D. Goulding7 , J. Gracia-Carpio22 ,J. Gross12 , J. E. Gunn7 , R. Hada27 , C. Hahn7 , Y. Harikane28 ,N. P. Hathi29 , G. R. Hauschild-Roier9 , Kohei Hayashi30 ,Kota Hayashi2,3 , T. Heckman10 , C. Hernández-Aguayo21 ,Y. Hirai1 , W. Hong31 , B. Horowitz2,3 , H. Hoshi32 , B.C. Hsieh15 , K. Ichikawa25 , R. Ikeda33 , O. Ilbert6 , M. Imanishi8 ,A. K. Inoue25 , K. Ishida1 , R. Ishikawa1 , T. Ishiyama34 , K. Ito32 ,K. Iwasawa35 , C. K. Jespersen7 , H.-Y. Jian15 , L. Jiang36 ,Y.-P. Jing31 , S. D. Johnson37 , K. Kakiichi38 , J. Kang39 ,N. Kashikawa32 , G. Kauffmann21 , I. Kayo40 , S. Kikuta32 ,M. Kimura15 , M. King12 , E. N. Kirby26 , T. Kirihara41 ,T. Kiyota42 , C. Kobayashi43 , S. Kobayashi44 , T. Kodama1 ,M. Kokubo8 , E. Komatsu21 , Y. Komiyama45 , S. Koshida5 ,Y. Koyama5 , K. Kraljic46 , M. Kubo1 , H. Kusakabe8 ,P. B. Kuzma8 , R. Laishram1 , T.-W. Lan46 , N. Laporte6 ,H. A. N. Le39 , K. Lee1 , J. Leja47 , C. Li48 , Q. Li31 ,X. Li31 , Y. Liang28 , V. S. M. Lima18 , L. Lin15 , Y.T. Lin15 , Y. Lin31 , H.-H. Ling15 , J. Liu2,3 , X. Liu49 ,Y. Luo10 , L. A. MacArthur7 , P. H. Mao12 , D. Marchesini50 ,N. F. Martin46 , M. Matsubara44 , S. Matsui32 , A. Matsumoto28 ,N. Matsumoto1 , Y. Matsuoka44 , J. McCullough7 , P. Melchior7 ,T. Melia2,3 , H. Miyatake51,52 , R. Momose53 , R. MonteiroOliveira15 , J. Moon21 , C. Morantz12 , K. Morihana8 ,T. Morishima8 , Y. Moritani8 K. Motohara8 , H. Murayama2 ,J. Myles7 , K. Nagamine54 , T. Nagao44 , K. Nakajima8 ,S. Nakano2,3 , K. Nakashima52 , Y. Nan2,3 , T. Nishimichi55 ,A. J. Nishizawa56 , K. Nomoto2,3 , C. A. Norman10 , K. Nose2 ,I. Ogami42 , K. Ogura57 , N. Okabe4 , H. Okada58 , S. Okamoto5 ,Y. Oku59 , T. Okumura15 , Y. Ono28 , M. Onodera5 , M. Onoue2,3 ,K. Osato34 , M. Ouchi3,8 , R. Overzier60 , T. Patrice6 , U.L. Pen15 , M. M. Pieri6 , A. Pisani6 , P. A. Price7 , T.S. Pyo5 , C. Qin39 , M. Reinecke21 , W. Ren39 , L. Robbins50 ,J. A. Rosa18 , K. Rowlands10 , S. Saga51 , S. Saito61 ,A. Sajina50 , K. Sato42 , C. Schimd6 , K. C. Schlaufman10 ,F. Schmidt21 , A. Schmitt6 , B. R. Scott62 , M. Seiffert12,13 ,M. D. Seiffert12,13 , D. J. Settob7 , Z. Shao31 , J. Shi2,3 ,K. Shibata44 , R. Shimakawa25 , K. Shimasaku32 , S. Shimizu32 ,Y. Shirasaki8 , H. Siddiqui7 , J. C. Siegel7 , L. G. Silva19 ,L. Sodré Jr.19 , T. Storchi-Bergmann9 , M. A. Strauss7 ,A. L. Strom62 , Y. Sugahara25 , N. Sugiyama8 , T. Sunayama63 ,31

T. Suzuki2,3 , Y. Suzuki1 , K. Tadaki64 , M. Takada2,3 ,Y. Takeda32 , S.-I. Tam15 , N. Tamura5 , T. Tamura65 ,M. Tanaka8 , T. S. Tanaka2,3 , T. Taniguchi2,3 , L. A. M. Tasca6 ,R. Terasawa2,3 , T. Terawaki2,3 , Y. Toba8 , A. Tokiwa2,3 ,N. Tominaga8 , V. L. Torre50 , J. Toshikawa58 , L. Tresse6 ,J. R. Trump66 , H. Uchiyama8 , Y. Ueda67 , H. Umeda28 ,H. Umehata52 , K. Umetsu15 , D. Vibert6 , M. C. Vicentin19 ,B. Vijarnwannaluk15 , Jialai Wang39 , Jiaqi Wang31 , S.Y. Wang15 , W.-H. Wang15 , Yirong Wang31 , Y-J. Wang15 ,Yuting Wang68 , M. D. Wardana1 , K. E. Whitaker69 ,S. White21 , L. Wu39 , P.-F. Wu46 , R. Xia39 , X. Xu62 ,Y. Xu28 , Y. Xue39 , K. Yabe5 , S. Yamada70 , Y. Yamada32 ,M. Yamazaki2,3 , H. Yanagisawa28 , X. Yang31 , N. Yasuda2,3 ,J. Yokoyama2,3 , N. Yoshida2,3 , T. Yoshida44 , S. Yuan68 ,K. Zaidi50 , N. Zakamska10 , H. Zhang8 , J. Zhang31 , T. Zhang32 ,Y. Zhang8 , Z. Zhang54 , G.-B. Zhao68 , H.-M. Zhu68 , Y. Zu31
1 Tohoku University, Japan
2 Kavli IPMU (WPI), Japan
3 The University of Tokyo, Japan
4 Hiroshima University, Japan
5 Subaru Telescope, NAOJ, Japan
6 Laboratoire d’Astrophysique de Marseille, France
7 Princeton University, USA
8 National Astronomical Observatory of Japan (NAOJ), Japan
9 Universidade Federal do Rio Grande do Sul, Brazil
10 Johns Hopkins University, USA
11 Université de Strasbourg, France
12 Jet Propulsion Laboratory, USA
13 California Institute of Technology, USA
14 University of Washington, USA
15 Academic Sinica Institute of Astronomy and Astrophysics (ASIAA), Taiwan
16 Sorbonne Université, France
17 National Tsing Hua University, Taiwan
18 Laboratório Nacional de Astrofisica, Brazil
19 Universidade de Sao Paulo, Brazil
20 University College London, UK
21 Max Planck Institute for Astrophysics, Germany
22 Max Planck Institute for Extraterrestrial Physics, Germany
23 Universidade Federal de Santa Catarina, Brazil
24 University of Colorado Boulder, USA
25 Waseda University, Japan
26 University of Notre Dame, USA
27 The Ohio State University, USA
28 Institute for Cosmic Ray Research, The University of Tokyo, Japan
29 Subaru Telescope, Japan
30 National Institute of Technology, Sendai College, Japan
31 Shanghai Jiao Tong University, China
32 The University of Tokyo, Japan
33 The Graduate University for Advanced Studies (SOKENDAI), Japan
34 Chiba University, Japan
35 Universitat de Barcelona, Spain
36 Peking University, China
37 University of Michigan, USA
38 University of Copenhagen, Denmark
39 University of Science and Technology of China, China
40 Tokyo University of Technology, Japan
41 Kitami Institute of Technology, Japan
42 SOKENDAI, NAOJ, Japan
43 University of Hertfordshire, UK
44 Ehime University, Japan
45 Hosei University, Japan
46 National Taiwan University, Taiwan
47 The Pennsylvania State University, USA

32

PFS SSP team48 Tsinghua University, China
49 University of Illinois at Urbana Champaign, USA
50 Tufts University, USA
51 Kobayashi-Maskawa Institute for the Origin of Particles and the Universe,

Nagoya University, Japan
52 Nagoya University, Japan
53 The Observatories of the Carnegie Institution for Science, USA
54 Osaka University, Japan
55 Kyoto Sangyo University, Japan
56 Gifu Shotoku Gakuen University, Japan
57 Kobe City College of Technology, Japan
58 The University of Hyogo, Japan
59 Zhejiang University, China
60 Observatório Nacional/MCTI, Brazil
61 Missouri University of Science and Technology, USA
62 Northwestern University, USA
63 The University of Arizona, USA
64 Hokkai-Gakuen University, Japan
65 Institute of Space and Astronautical Science, Japan
66 University of Connecticut, USA
67 Kyoto University, Japan
68 National Astronomical Observatories, Chinese Academy of Sciences(NAOC), China
69 University of Massachusetts Amherst, USA
70 RIKEN, Japan
71 Instituto Nacional de Pesquisas Espaciais (INPE), Brazil