Wide-field imaging with Hyper Suprime-Cam:
Cosmology and Galaxy Evolution
A Strategic Survey Proposal for the Subaru Telescope
PI: Satoshi Miyazaki (NAOJ)
Co-PI: Ikuru Iwata (NAOJ)
The HSC collaboration team1 : S. Abe(1) , H. Aihara?(2),(3) , M. Akiyama(4) , K. Aoki(5) , N. Arimoto?(5) , N. A. Bahcall(6) ,S. J. Bickerton(3) , J. Bosch(6) , K. Bundy†(3) , C. W. Chen(7) , M. Chiba†(4) , T. Chiba(8) , N. E. Chisari(6) , J. Coupon(7) , M.
Doi(2) , M. Enoki(9) S. Foucaud(10) , M. Fukugita(3) , H. Furusawa†(5) , T. Futamase(4) , R. Goto(2) , T. Goto(11) , J. E. Greene(6) ,J. E. Gunn†(6) , T. Hamana†(5) , T. Hashimoto(2) , M. Hayashi(5) , Y. Higuchi(2),(5) , C. Hikage(12) , J. C. Hill(6) , P. T. P. Ho?(7) ,B. C. Hsieh(7) , K. Y. Huang†(7) , H. Ikeda(13) , M. Imanishi(5) , N. Inada(14) , A. K. Inoue(15) , W.-H. Ip(1) , T. Ito(5) , K.
Iwasawa(16) , M. Iye(5) , H. Y. Jian(17) , Y. Kakazu(18) , H. Karoji(3) , N. Kashikawa(5) , N. Katayama(3) , T. Kawaguchi(19) , S.
Kawanomoto(5) , I. Kayo(20) , T. Kitayama(20) , G. R. Knapp(6) , T. Kodama(5) , K. Kohno(2) , M. Koike(5) , E. Kokubo(5) , M.
Kokubo(2) , Y. Komiyama(5) , A. Konno(2) , Y. Koyama(5) , C. N. Lackner(3) , D. Lang(6) , A. Leauthaud†(3) , M. J. Lehner(7) , K.Y. Lin(7) , L. Lin(7) , Y.-T. Lin†(7) , C. P. Loomis(6) , R. H. Lupton†(6) , P. S. Lykawka(21) , K. Maeda(3) , R. Mandelbaum†(22) , Y.
Matsuda(5) , K. Matsuoka(13),(23) , Y. Matsuoka(12) , S. Mineo(2) , T. Minezaki(2) , H. Miyatake(6) , R. Momose(2) , A. More(3) , S.
More(3) , T. J. Moriya(3) , T. Morokuma†(2) , H. Murayama?(3) , K. Nagamine(24) , T. Nagao†(23) , S. Nagataki(23) , Y. Naito(2) , K.
Nakajima(2) , F. Nakata(5) , H. Nakaya(5) , T. Namikawa(2) , C.-C. Ngeow(1) , T. Nishimichi(3) , H. Nishioka(7) , A. J. Nishizawa†(3) ,K. Nomoto(3) , M. Oguri†(3) , A. Oka(2) , N. Okabe(7) , S. Okamoto(25) , S. Okamura(26) , J. Okumura(23) , S. Okumura(27) , Y.
Okura(5) , Y. Ono(2) M. Onodera(28) , K. Ota(23) , M. Ouchi†(2) , S. Oyabu(12) , P. A. Price(6) , R. Quimby(3) , C. E. Rusu(2),(5) , S.
Saito(29) , T. Saito(3) , Y. Saitou(30) , M. Sato(12) , T. Shibuya(5) , K. Shimasaku†(2) , A. Shimono(3) , S. Shinogi(2) , M. Shirasaki(2) ,J. D. Silverman(3) , D. N. Spergel?(6),(3) , M. A. Strauss†(6) , H. Sugai(3) , N. Sugiyama(12),(3) , D. Suto(2) , Y. Suto?(2) , K. Tadaki(2) ,M. Takada†(3) , R. Takahashi(31) , S. Takahashi(5) , T. Takata(5) , T. T. Takeuchi(12) , N. Tamura(3) , M. Tanaka(5) , M. Tanaka†(3) ,M. Tanaka(4) , Y. Taniguchi(13) , A. Taruya(2) , T. Terai(5) , Y. Terashima(13) , N. Tominaga(32) , J. Toshikawa(30) , T. Totani(23) ,M. Tsai(1) , E. L. Turner?(6) , Y. Ueda(23) , K. Umetsu(7) , Y. Urata†(1) , Y. Utsumi(5) , B. Vulcani(3) , K. Wada(33) , S.-Y. Wang(7) ,W.-H. Wang(7) , T. Yamada(4) , Y. Yamada(5) , K. Yamamoto(34) , H. Yamanoi(5) , C.-H. Yan(7) , N. Yasuda†(3) , A. Yonehara(35) ,F. Yoshida†(5) , N. Yoshida(2) , M. Yoshikawa(36) , S. Yuma(2) (1) NCU, Taiwan (2) Tokyo (3) Kavli IPMU (4) Tohoku (5) NAOJ
(6) Princeton (7) ASIAA (8) Nihon (9) Tokyo Keizai (10) NTNU, Taiwan (11) DARK, Copenhagen (12) Nagoya (13) Ehime(14) NNCT (15) Osaka Sangyo (16) Barcelona (17) NTU, Taiwan (18) Chicago (19) Tsukuba (20) Toho (21) Kinki (22) CMU(23) Kyoto (24) Las Vegas (25) KIAA, China (26) Hosei (27) JSGA (28) ETH (29) Berkeley (30) GUAS (31) Hirosaki (32)
Konan (33) Kagoshima (34) Hiroshima (35) Kyoto Sangyo (36) JAXA

Executive Summary
We propose to carry out a three-layered, multi-band (grizy plus narrow-band ﬁlters) imaging survey with theHyper Suprime-Cam (HSC) on the 8.2m Subaru Telescope. By combining data from the three layers (Wide: 1400
deg2 , r ' 26; Deep: 27 deg2 , r ' 27; Ultradeep: 3.5 deg2 , r ' 28), we will address some of the most pressing problems
in modern cosmology and astrophysics: the origin of the acceleration of the Universe’s expansion, the properties andevolution of galaxies from z ' 7 to today, and the nature of cosmic reionization. The survey is uniquely designed
to enable all these science cases, with particular attention to controlling systematic errors, and the data will beanalyzed with a state-of-the-art software pipeline. We will use the excellent-quality (0.700 seeing), multi-broadband
images of distant galaxies from the Wide layer to statistically reconstruct the dark matter distribution in the Universe
up to z ' 1.5 via measurements of weak lensing (WL), coupled with photometric redshifts for every galaxy. The
Deep layer goes one magnitude deeper, with repeated observations, allowing us to verify our PSF and galaxy shapemeasurements as a function of seeing, depth and galaxy properties. Measurements of cosmic shear and other HSC WL
observables, in combination with geometrical constraints from lightcurves of ∼ 120 Type Ia supernovae up to z ' 1.4
from the Ultradeep layer, will enable us to constrain the dark energy parameters to precisions of σ(wDE ) ' 0.04
(constant dark energy equation of state) and the dark energy ﬁgure-of-merit FoM ≡ 1/[σ(wpivot )σ(wa )] ' 50 (for w(z)a two-parameter function of redshift), about a factor of 2 improvement over current constraints. Cross-correlating
the HSC WL observables with data from the arcminute-resolution, high-sensitivity ACT CMB experiment, Planck,and the SDSS/BOSS spectroscopic galaxy survey will improve the FoM to 100. We will also perform a stringent
test of gravity on cosmological distance scales by comparing dark matter clustering from HSC-WL observables withthe redshift-space distortion eﬀect measured in the BOSS galaxy clustering. In the ﬁeld of galaxy evolution, the
HSC survey will include over 20 million galaxies up to z ' 1 from the Wide layer, and a half-million galaxies over
<
<
1∼
z∼
2 from the Deep and Ultradeep layers. These galaxy catalogs, of unprecedented sizes and cosmologicalvolumes, will allow high-precision measurements of the properties of evolving galaxy populations and their relation
to the WL-reconstructed dark matter distribution. With samples constructed from the Wide layer, we will measureabsolute stellar growth rates over 2 orders of magnitude in stellar mass since z ∼ 1, and establish evolutionary links
between galaxy populations by tracking how the growth of some key sub-populations is related to the decline ofothers. A growth rate of 3% per Gyr will be measured with 10σ or greater precision across all mass bins probed.
The Deep and Ultradeep layers will also include broad- and narrow-band imaging surveys of Lyman-break galaxies
1
Those people with the “?” superscript are the HSC Executive Board members. Those people with the “†” superscript are co-chairs ofthe HSC working groups (Weak Lensing, AGN, Galactic Structure, Solar System, Variables/Transients, Low-z Galaxies, High-z Galaxies,
Clusters, Photometric Redshift, Photometric Calibration, Survey Strategy, Hardware, and Software & Data Distribution).

2

Wide-field imaging with Hyper Suprime-Cam(LBGs), Lyman-α emitters (LAEs) and quasars to an unprecedented depth and solid angle. The clustering of the
LBG samples will allow us to determine the dependence of the stellar mass and star formation rate on the host dark
halo mass over Mhalo ∼ 1011 – 1013 M in the era of galaxy formation, z ∼ 2 – 7. We will measure the clusteringand luminosity functions of LAEs at z = 2.2, 5.7, 6.6, and 7.3 with samples extending down to ∼ 0.3L∗ . At highredshift, these will allow us to constrain the neutral hydrogen fraction of the intergalactic medium, xHI , at z ∼ 7with a precision of σ(xHI ) ∼ 0.1, and to constrain the topology of spatially-inhomogeneous reionization.

1 Introduction
We live in a golden age for extragalactic astronomy and cosmology. We now have a quantitative and highly
predictive model for the overall composition and expansion history of the Universe that is in accord witha large array of independent and complementary observations. Observations of galaxies over most of the
13.7 billion year history of the Universe have led to a broad-brush understanding of the basics of galaxy
evolution. However, there are fundamental and inter-related questions that remain:• What is the physical nature of dark matter and dark energy? Is dark energy truly necessary, or could
the accelerated expansion of the Universe be explained by modiﬁcations of the law of gravity?
• How did galaxies assemble, and how did their properties change, over cosmic time?
• What is the topology and timing of reionization at high redshift? What were the ionizing sources?
These questions, and many more, can be addressed with a comprehensive deep and wide-angle imaging
survey of the sky, using the Hyper Suprime-Cam (HSC) on the 8.2m Subaru Telescope. The combinationof the large aperture of the Subaru Telescope, the large ﬁeld of view (1.77 deg2 ) of HSC, and the excellent
image quality of the site and the telescope make this the ideal instrument for addressing these fundamental
questions in modern cosmology and astronomy. We propose a 300-night strategic survey program, involving
astronomers from Japan, Taiwan, and Princeton University in the United States. The survey will consistof three layers, which together will explore galaxy evolution over the full range of cosmic history from thepresent to redshift 7, probing both starlight (from the photometry) and dark matter (using gravitational
lensing). The weak lensing (WL) allows us to measure the large-scale distribution of dark matter andits evolution with cosmic time. Cross-correlations of HSC WL observables with the spectroscopic galaxy
distribution in the SDSS/Baryon Oscillation Spectroscopic Survey (BOSS) and the observed temperatureand polarization ﬂuctuations in the Cosmic Microwave Background (CMB) will constrain the parametersof the standard model of cosmology, and test for exotic variations such as deviations from the predictions ofGeneral Relativity on cosmological scales. Studies of the highest-redshift galaxies and quasars discoveredin this survey will lead to a deeper understanding of reionization, a key event in the thermal history of the
Universe.
Table 1: Summary of HSC-Wide, Deep and Ultradeep layers
Layer
Wide
Deep
UltradeepArea[deg2 ]
1400
27
3.5

# of
HSC ﬁelds916
152

Filters & Depth
grizy (r ' 26)
grizy+3NBs (r ' 27)
grizy+3NBs (r ' 28)

Comoving volume[h−3 Gpc3 ]∼ 4.4 (z < 2)
∼ 0.5 (1 < z < 5)
∼ 0.07 (2 < z < 7)

Key Science
WL cosmology, z ∼ 1 gals, clusters< 2 gals, reionization, WL calib.
z∼>z ∼ 2 gals, reionization, SNeIa

The experience of the Sloan Digital Sky Survey (SDSS; York et al. 2000), and the tremendous success ofthe current prime-focus camera on Subaru, Suprime-Cam (Miyazaki et al. 2002a), have demonstrated thepower of wide-ﬁeld imaging to make science breakthroughs in a broad range of topics. The SDSS imaged
in ﬁve broad-bands (u, g, r, i, and z), to a depth of r ≈ 22.5 (5σ point source). It has produced more
highly cited papers in recent years than any other observational facility, including the Keck Telescopes
and the Hubble Space Telescope (Madrid & Macchetto 2009). The SDSS characterized the nature anddistribution of galaxies in the local present-day Universe. Observations with Suprime-Cam have led theworld in studies of the distant Universe, and have shown that an imager on the Subaru telescope has thepotential to extend SDSS low-redshift discoveries in the ﬁeld of cosmology and galaxy formation/evolution
to the intermediate- and high-redshift Universe. The HSC survey we propose will cover SDSS-like volumes
at high redshift, making it the ﬁrst truly large-scale survey of the distant Universe.
The top-level scientific goals for the HSC survey are:
• To derive stringent dark energy constraints from the combination of the HSC WL observables and thegalaxy clustering information from the BOSS survey to precisions of σ(wpivot ) ' 0.03 and the dark energy

Wide-field imaging with Hyper Suprime-Cam

3

HSC-UD
g
r
i
z
y

1.0

HSC-D

Transmission

0.8

HSC-Wide

NB387
NB816
NB921
NB101

0.6

0.4
0.2

Fλ

0.0

4000

5000

6000

7000

8000

Wavelength [A]
◦

9000

10000

11000

Figure 1: Left: The limiting magnitudes (in r) and solid angles of the HSC-Wide, Deep and Ultradeep (UD) layers,
compared with other existing, on-going, and planned surveys. The three layers are complementary to each other, andeach of the three layers covers a signiﬁcantly wider area than do other on-going surveys of comparable depth. The
narrow-band components of the Deep and Ultradeep layers are unique; no other project is planning a major survey
to comparable depth. Right: The HSC bandpasses, including the reﬂectivity of all mirrors, transmission of all optics
and ﬁlters, and response of the CCDs, assuming an airmass of 1.1. Both the broad-band and narrow-band ﬁlters areshown. The lower panel shows the spectrum of sky emission lines, demonstrating that the red narrow-band ﬁlters lie
in relatively dark regions of the sky spectrum.

ﬁgure-of-merit FoM ≡ 1/[σ(wpivot )σ(wa )] ' 100.
• To use WL to constrain deviations from General Relativity to a higher precision than the current SDSS
constraint (Reyes, Mandelbaum et al. 2010) by a factor of 4.
• To study SDSS-like volumes of galaxies in a series of redshift slices observed through broad- and narrowband ﬁlters to carefully-tuned depths, in order to understand the properties and evolution of galaxies fromz ∼ 7 to today, as well as to constrain the physics of cosmic reionization at high redshift, z ' 5 − 7.
To achieve these scientiﬁc goals, we propose a ‘wedding-cake’ survey with three layers:• The Wide layer will cover 1400 deg2 and will be done in ﬁve broad-bands, g, r, i, z, and y, to a depthof r ' 26, and to similar depths in the other bands. This is designed to characterize the z < 2 galaxy
population, and to measure WL shear as a function of redshift and spatial scale.
• The Deep layer will cover 27 deg2 in four carefully selected ﬁelds distributed over a range of rightascensions (RA). It will go a magnitude deeper than the Wide layer in the broad-bands, and will also
use three narrow-band ﬁlters to look for Lyman-α emitters (LAEs) at z = 2.2, 5.7, and 6.6 to study theirevolution and the topology of cosmic reionization. Its multiple repeat exposures will enable powerful testing
and mitigation of systematic lensing errors.
• The Ultradeep (UD) layer will image two ﬁelds (3.5 deg2 ) in both the ﬁve broad-band ﬁlters and three
narrow-band ﬁlters, going a magnitude fainter still, to discover ∼ 6000 LAEs at z = 5.7 and 6.6, several
tens of LAEs at z = 7.3, and about 120 Type Ia supernovae to z ∼ 1.4.
The left panel of Figure 1 shows that these three layers are complementary to each other and are signiﬁcantly
more powerful than are the previous, competitive on-going, and upcoming surveys. Combining the threelayers allows us to cover a broad range of science topics spanning a wide range of length scales and redshifts.
We need about 200 nights in total (including overheads and assuming that 30% of nights will have poor
weather) to carry out the Wide layer, and 100 nights for the Deep and Ultradeep layers. Table 1 summarizes
the survey parameters and main science drivers for each layer.
Our two scientiﬁc themes, cosmology and galaxy evolution, are intimately tied together, which is why
we tackle both under a single survey program. Using WL for cosmology requires detailed knowledge of thephotometric properties of galaxies, including their intrinsic shapes and spectral energy distributions in order4

Wide-field imaging with Hyper Suprime-Cam

to measure photometric redshifts. Conversely, one needs to understand galaxy evolution in a cosmologicalcontext, and tighter constraints on cosmological models allow a better theoretical understanding of how
galaxy formation takes place. In order to use galaxies and their spatial distribution as a cosmological probe,
we need to understand the dark matter halos in which they live, which we can probe with galaxy-galaxy
and cluster-galaxy weak lensing. Each of the Wide, Deep, and Ultradeep layers contributes to both ourcosmology and galaxy evolution studies in a fundamental way.
Our proposed survey will be the largest program ever carried out with Subaru, both in terms of thenumber of nights and the volume of data. This is a community-driven survey, with broad support in theJapanese community. Following the example of the SDSS collaboration, we have developed a sophisticatedimage processing pipeline and a clear management structure to operate the survey and promote the international collaboration. Based on the expertise we will establish with this HSC survey program, we, led by
the HSC project oﬃce at NAOJ, will support observations and data analysis with HSC for the community,
including open-use programs.
The next ﬁve years is the optimal time to carry out this survey. While there are several other major
imaging surveys being undertaken around the world, with related science goals (Pan-STARRS, Dark Energy
Survey (DES) on the CTIO 4m telescope, the KIlo-Degree Survey (KIDS) on the 2.6m VST, and SkyMapper
are the most prominent; see Figure 1 for details), none is being done on a telescope as large, and as highquality, as Subaru. The product of primary mirror collecting area and ﬁeld of view of HSC (the étendue)is the highest of any instrument in the world, and will continue to be so until the next decade (2020 era),
when even larger imaging surveys such as the Large Synoptic Survey Telescope (LSST) on the groundand the satellite missions Euclid (ESA) and WFIRST (NASA) see ﬁrst light. The survey we propose will
be a precursor survey to those projects, with related science goals and similar depth, and thus will place
Japanese astronomy in a leadership role in the ﬁeld into the next decade. It will also provide discoveries
and object lists that will be used for a wide-ﬁeld multi-object Prime Focus Spectrograph (PFS) planned
for Subaru, as well as the Thirty Meter Telescope.
After a brief description of the HSC instrument itself (Section 2), we describe our core science goals in
cosmology (Section 3), and galaxy evolution and cosmic reionization (Sections 4 and 5) and describe some
auxiliary science opportunities in Section 6. We describe our survey strategy in Section 7 and our softwareand calibration in Section 8, and conclude in Section 9. This proposal is limited to 30 pages. We havewritten a much more detailed White Paper describing the project feasibility and technical details for various
science cases; it is available at http://hscsurvey.pbworks.com/w/page/60427271/HSCWhitePaper.
2 The HSC Instrument
While there are many 8-meter class telescopes around the world, Subaru is the one with by far the largest
ﬁeld of view. Suprime-Cam (Miyazaki et al. 2002a), with its ∼ 0.25 deg2 ﬁeld of view and superb delivered
image quality (routinely 0.700 FWHM), has been a world leader in wide-ﬁeld studies of the distant andfaint Universe. Hyper Suprime-Cam, its successor, takes advantage of the full accessible ﬁeld of view of
the Subaru telescope (1.5◦ diameter), and thus has a survey power about 7 times larger than that ofSuprime-Cam.
The instrument has been designed and built by a team at NAOJ, led by Satoshi Miyazaki, the PI ofthis proposal, and was funded by the Grant-in-Aid for Scientiﬁc Research in Priority Areas “Exploring
Dark Energy with Wide-ﬁeld Imaging” and the FIRST program “Subaru Measurements of Images andRedshifts” (SuMIRe), CSTP, and WPI, MEXT, Japan, as well as Princeton University and ASIAA in
Taiwan. The basic components of the instrument are described in Table 2.
The instrument has a large and optically very sophisticated seven-element Wide-Field Corrector (WFC),
designed and built by Canon, which incorporates an Atmospheric Dispersion Corrector (ADC) and delivers
a instrumental Point-Spread Function (PSF) with a diameter enclosing 80% of the light (D80 ) of 0.200 or
better over the entire ﬁeld in all ﬁlters. A Prime Focus Unit (PFU) built by Mitsubishi, which incorporates aprecise hexapod for attitude and focus adjustment, holds the WFC and the camera in place at the telescope
prime focus. The entire structure is 3 meters tall, and weighs 3 tons. The corrector gives an unvignettedﬁeld of view to a diameter of 10 arcmin; vignetting is a roughly linear function of ﬁeld radius, reaching
26% at the edge of the ﬁeld at 0.75◦ . The Subaru top-end structure has been modiﬁed to accommodate
the PFU and WFC. The WFC can be used by other wide-ﬁeld instruments as well, and is incorporated
into the design of the planned Prime Focus Spectrograph.
The focal plane is paved with a total of 116 Hamamatsu Deep Depletion CCDs, each 2 K × 4K. Four ofWide-field imaging with Hyper Suprime-Cam

5

Table 2: Hyper Suprime-Cam Characteristics
Instrument weight
Field of View
Vignetting
Pixel scale
Delivered Image Quality
CCDs
CCD QE
CTE
Readnoise
Data Rate
Focal ratio at Focal Plane
Overhead between Exposures
Wide-Field Corrector
Shutter
Filters
Filter Exchanger
Filter Exchange Time

3.2 tons (estimate)1.5◦ diameter, 1.77 deg2
0 at 0.15◦ ; 26% at edge
15µm = 0.1600
D80 < 0.200 in all ﬁlters
116 2K × 4K Hamamatsu Fully-Depleted
40% at 4000Å, 10,000Å; 95% at peak (at -100◦ C)
0.999999
4.5 e−
2.31 GBytes/exposure (16-bit A-to-D)
2.25
29 sec7 optical elements, ADC
Roll-Type
grizy + 4 NB; Table 8
6 ﬁlters installed at a time
10 minutes

these are used for guiding and eight for automatically monitoring focus, leaving 104 science detectors with a
total area of 1.77 deg2 . These chips, which are three-side buttable and each have four independent readout
ampliﬁers, are currently installed in Suprime-Cam, which has demonstrated their excellent characteristics:
low read noise, excellent charge transfer eﬃciency, few cosmetic defects, and most importantly, high quantum eﬃciency from 4000Å to 10,000Å (blueward of 4000Å, the response is limited both by the CCDs andthe optical elements in the WFC). The model system response, including reﬂectivity and transmission of all
optics, is shown in the right panel of Figure 1. The CCD pixels are 15µm on a side, corresponding to 0.1600
at the focal plane. At this resolution, the images will be well-sampled in even the best seeing. Ray-tracingof the optics has shown that ghosting is minimal, with the worst ghost at an illuminance (fractional lightin a PSF aperture) of ∼ 5 × 10−8 .
The instrument is installed at prime focus using the existing Top Unit Exchanger (TUE) instrument
handler, though modiﬁcations were necessary to ensure that the delicate ceramic housing of the corrector
lens is not damaged during installation, as clearances are very small.
The camera has a roll-type shutter, with excellent timing accuracy, allowing uniform exposure timeover the ﬁeld of view. Including readout and all overheads, the minimum time between exposures is 29
seconds, allowing for eﬃcient surveying of the sky. The ﬁlter exchange mechanism can hold six ﬁlters at
one time, and requires about 10 minutes to exchange ﬁlters, with the telescope at zenith. The complementof ﬁlters can be changed during the daytime. As described in Section 7, our survey will use ﬁve broad-band
ﬁlters (grizy) modeled on the SDSS ﬁlter set (the right panel of Figure 1), as well as four narrow-band
ﬁlters to observe Lyα at a wide variety of redshifts. All the broad-band ﬁlters and one of the narrow-band
ﬁlters are in hand; the other three will be delivered in March 2013. HSC was put on the telescope for theﬁrst time in August 2012, which allowed us to conﬁrm that the optics of the WFC give good images.
3
3.1

Cosmology Science
HSC cosmology objectives

Characterizing the properties of dark energy (DE) is one of the primary goals of the HSC survey. DE causes
the expansion of the Universe to accelerate at late times, so it can naturally be probed by measuring the
expansion rate as a function of redshift, H(z) (the rate today H(z = 0) ≡ H0 , the Hubble constant). DE
also aﬀects the growth rate of large-scale structure G(k, z), leading to additional observables to constrain
its properties. The expansion rate has been constrained from observations of CMB, type-Ia supernovae(hereafter SNeIa), and Baryon Acoustic Oscillations (BAO), and the HSC survey will improve the constraints with observations of high-redshift SNeIa at z > 1. The growth rate of large-scale structure will
be measured to unprecedented precision from weak lensing. Combining these probes can not only measure
the parameters of DE, but also test alternative models in which modiﬁcations to General Relativity cause
the accelerated expansion of the Universe. We can also use HSC observables to probe the primordial power
spectrum Pζ (k) and primordial non-Gaussianity, both of which contain a wealth of information about early6Wide-field imaging with Hyper Suprime-Cam

Dark Energy
Equation of State w(z)Gravity Theory

Early Universe Physics

Simulations/Mock Catalogs
Perturbation Theory

Primary Manifestations

Expansion History
H(z)Luminosity
Distance

Linear Growth Rate
G(k, z)Galaxy/Cluster Clustering:
BAO + Amplitudes

Primordial Power Spectrum
P (k)Redshift-space
Distortion(RSD)

Weak Lensing (WL):
Dark matter clustering

PriorsCMB & Type-Ia SNe
Galaxy Surveys

Likelihood
Assignment

Data

BOSS/GAMA/HectoMap
CMB (ACT, Planck)
COSMOS
NIR data, etc.

Cross-correlations
between
WL shear andExternal catalogs

Photo-z’s calibrated withsecure-photo-z/spectroscopic-z

HSC-Deep

WL shear power
spectrum &Photometric galaxy
clustering

Luminosity
distances with
HSC Type Ia SNe

WL shear calibrated withdeeper/high-sampling imaging data

HSC-Wide

time-domain
photometry survey

Measurements

Cosmological WL-calibrated observables:
Observables 3D Galaxy Clustering + RSD,
Number counts/clustering ofgalaxy clusters

External datasets:

TheoryScience Goals

HSC-UD

Figure 2: Work ﬂow for HSC cosmology. Boxes are data or models; ovals hold tasks necessary to process them.
Top-level science goals (top of the chart) set theory tasks required for developing accurate models of the observables.
The imaging data and external data sets are at the bottom, and observables are indicated along the analysis path(arrows) we are planning to follow. The heavy arrow in the middle indicates that we will compare theory predictions
to measurements to yield likelihood assignments for possible dark energy/modiﬁed gravity models.

Universe physics.
To probe these functions, we will use the following observables: (1) We will measure the coherent
distortion of galaxy shapes produced by WL, in order to reconstruct the distribution of all matter (including
dark matter) in foreground structures, and combine the weak lensing results with photometric redshift
information to reconstruct the matter ﬁeld as a function of angular position and redshift. (2) We willrobustly constrain cosmology by measuring the auto- and cross-correlations of the galaxy distribution andshear field2 available from HSC and other data sets, including the SDSS-III BOSS spectroscopic galaxies
and secondary CMB eﬀects (CMB lensing and the Sunyaev-Zel’dovich, or SZ eﬀect) measured by the
Atacama Cosmology Telescope (ACT) and Planck experiments. (3) We will identify ∼120 high-redshift
SNeIa with well-sampled lightcurves from the Ultradeep layer to measure the distance-redshift relation,
which we will combine with CMB and the BAO measurements to tighten constraints on the expansion
history.
Converting the HSC observables into cosmological constraints requires us to account for complications
such as the non-linear evolution of the matter distribution and galaxy formation. We will use N -bodysimulations and mock catalogs to construct suﬃciently accurate model predictions to compare with thehigh-precision measurements of HSC observables (Sato et al. 2009; Takahashi et al 2009, 2011; Shirasaki
et al. 2012).
Figure 2 illustrates our working ﬂow chart. The top half shows how we will develop predictions for theobservables in terms of H(z), G(k, z) and Pζ (k). The lower half shows how we will construct our primaryobservables with HSC data alone and in combination with other data sets. The ﬁgure makes clear that
we need all three survey layers to construct our observables. By comparing the measurements with the2Higher-order correlations carry additional cosmological information. Measuring them is therefore an enhanced goal for
the HSC cosmology program (Takada & Jain 2004; Kayo et al. 2012).

Wide-field imaging with Hyper Suprime-Cam

7

theories, we will make likelihood assessments of possible DE/modiﬁed gravity theories. Our goal is toderive cosmological constraints from HSC at a precision comparable to a Dark Energy Task Force (DETF;
Albrecht et al. 2006) Stage-III experiment. Then we will achieve constraints comparable to a Stage-IV
experiment from a joint analysis of HSC, Planck, ACT, and BOSS, and eventually with the PFS survey.
The areas and depths of the Wide, Deep, and Ultradeep layers are designed to meet these science goals.
3.2 Technical approach and methodology
HSC weak lensing cosmology: Weak lensing measurements are a powerful probe of cosmology because
they directly trace the projected distribution of matter in the Universe. Our survey will ﬁnd of orderneﬀ ∼ 20 galaxy/arcmin2 (estimated from Suprime-Cam data with similar depths) for shape measurements,
the highest-quality data available over the next decade before LSST and Euclid see ﬁrst light. The highquality measurements of shapes and photometric redshift (photo-z) from the grizy photometry will enable
three-dimensional measurements of the shear-shear power spectrum and cross-correlations between theshear measurements and the large-scale distribution of galaxies and galaxy clusters.
Cosmic shear: Cosmic shear, the auto-correlation of galaxy shapes due to lensing by intervening largescale structure, has long been considered one of the most promising ways to constrain structure growthand, therefore, DE (Takada & Jain 2004). The left panel of Figure 3 shows the expected cosmic shear
power spectrum measurement for HSC-Wide. The total signal-to-noise ratio is S/N ' 143 when combining
three redshift bins and angular scales up to maximum multipole lmax = 2000, a factor of 5 improvement
over the current best measurements (CFHTLenS; Heymans et al. 2012). Our cosmic shear measurements
will have higher S/N than those for DES3 (S/N = 111), and will cover a broader range of redshift. The
right panel of Figure 3 shows how accurately the DE equation of state w(z) can be constrained by cosmic
shear tomography with three redshift bins, combined with the Planck CMB information. The expected DE
constraints are σ(wpivot ) = 0.045 and DE FoM= 24 for HSC4 , compared to σ(wpivot ) = 0.046 and FoM= 20
for DES. Thus HSC and DES have a comparable cosmological power, and are complementary to eachother in the sense that they probe diﬀerent redshift ranges. These forecasts assume that systematic errors
are well under control; we have a comprehensive program for constraining systematic errors in these weaklensing measurements, including a suite of consistency checks to be carried out on the data itself (including
measurements using HSC-Wide and Deep layers) and external data (spectroscopic surveys, space-based
imaging, and CMB data), as well as a planned set of tests using realistic image simulations – see below
and Section 3.4 for more details.
Galaxy-galaxy lensing and galaxy clustering: Our WL cosmology program will also employ lensing
cross-correlations with tracers of large-scale structure (galaxy-galaxy or cluster-galaxy lensing), a useful
method to restore the redshift information of the WL ﬁeld. Cross-correlating galaxy shapes with thepositions of foreground objects that have spectroscopic or secure photo-z redshift estimates (e.g., BOSS
galaxies or clusters) yields a measurement of the mean tangential shear γt as a function of separation,hγt i(r⊥ ; zl ) ∝ h∆Σhm (r⊥ ; zl )i|r⊥ =dA (zl )θ , where the factor of proportionality is related to the distances tothe lens and source along the line-of-sight. Here, dA (z) is the angular diameter distance to redshift z;r⊥ = dA (zl )θ is the projected radius from the foreground tracers; and h∆Σhm i(r⊥ ; zl ) is the mean excess
mass proﬁle around the tracers at zl . While the total lensing eﬀect on distant galaxies comes from all
foreground structures projected over Gpc scales in the radial direction, the cross-correlation is sensitive tothe lensing contribution by lenses at a particular redshift zl , thereby enabling a tomographic reconstructionof the lensing structures as a function of redshift and of physical separation. It has been shown (Baldauf
et al. 2010; Mandelbaum et al. 2012) that combining this measurement of the galaxy-mass correlation withthe galaxy clustering enables a relatively model-independent measurement of the growth of structure in
the matter density ﬁeld, while simultaneously constraining the relationship between the galaxy and matter
density ﬁelds on large scales. Because cosmic shear is a shear auto-correlation and galaxy-galaxy lensing isa shear cross-correlation, the two measurements are aﬀected diﬀerently by systematic errors (Mandelbaum
et al. 2005), thus comparing the two can give us conﬁdence that these systematics are under control.
The galaxy-galaxy lensing and cluster-galaxy lensing signature can be measured over relatively small solid
angles, making this approach particularly valuable for constraining cosmological parameters with the ﬁrst
year or two of HSC survey data.
For DES, we assume a mean source redshift hzs i = 0.7, eﬀective number density n̄eﬀ = 10 arcmin−2 , and area of 5000 deg2 .
The DE ﬁgure-of-merit (DE FoM) is deﬁned as DE FoM ≡ [σ(wpivot )σ(wa )]−1 , where σ(wpivot ) is the best-constraint error
on w0 at the pivot redshift and the errors include marginalization over other parameters.
3

4

8

Wide-field imaging with Hyper Suprime-Cam

Figure 3: Left: Expected weak lensing cosmic shear power spectra for three redshift bin tomography, where boxes
around each curve show the expected 1σ measurement accuracies. Thin curves are computed using a model in which
the DE equation of state is changed to wDE = −0.9 from wDE = −1, demonstrating that we will be able to cleanly
distinguish between these two models. Right: The marginalized error on w(z) as a function of z, expected fromcosmic shear tomography assuming three redshift bins and using the power spectrum information up to lmax = 2000
for the HSC and DES surveys combined with data from Planck. Here we employ the standard parametrizationw(z) = w0 + wa [z/(1 + z)] to model the DE equation of state.

Combining weak lensing with the halo mass function: We will also use the method proposed in Oguri &Takada (2011) to constrain cosmology with minimal systematic errors, by measuring cluster-galaxy lensing< 1.4.
using a single population of background source galaxies, for distinct lens redshift slices up to zl ∼Since the source redshift (zs ) dependence of the cross-correlations appears only via a geometrical factor
hdA (zl , zs )/dA (zs )izs , the relative strength of cross-correlation signals for diﬀerent zl allows us to calibrateout source redshift uncertainty (see Oguri & Takada 2011 for details), thereby relaxing the photo-z error
requirements for the cluster-galaxy lensing. Then we can combine the cluster-shear cross-correlation withthe cluster auto-correlation function and the number counts of clusters, which are highly sensitive tothe amplitude of matter ﬂuctuations and the DE equation of state. While the traditional cluster countapproach is subject to uncertainty in the mass-observable relation, Oguri & Takada (2011) showed thatcombining measurements of the stacked WL and the cluster auto-correlation function directly constrains
the mass-observable relation, and thus breaks its degeneracy with cosmological parameters. This approachis attractive because the rich data sets (especially BOSS and ACT) in the HSC footprint allow us toconstruct a robust, complete sample of clusters (see below). We will demonstrate in Section 3.3 thatthe cluster-shear cross-correlations can constrain cosmological parameters to a precision similar to that of
the more standard cosmic shear tomography with optimistic assumptions on systematic errors, even after
accounting for and fully marginalizing over these systematic errors.
The left panel of Figure 4 shows the expected, cumulative S/N for the WL cross-correlation, i.e., stacked
WL signals due to clusters with masses Mhalo > 1014 h−1 M in redshift slices of ∆z = 0.1 as a functionof the lens redshift, computed using the method described in Oguri & Takada (2011). The two curves are
the results expected for the proposed HSC Wide-layer and for DES. The ﬁgure shows that HSC measures
the stacked WL signals for halos at higher redshift than DES, given its greater depth. HSC WL allows asigniﬁcant detection of lensing by large-scale structure around clusters out to z ∼ 1; this signal arises fromthe average mass distribution surrounding the lens halos and is easier to model theoretically, as mentioned
above.
Overall approach: We will maximize the cosmological information in our analyses by combining multiple
observations, including cosmic shear, the galaxy- and cluster-galaxy lensing, and the galaxy/cluster autocorrelation. The ability to constrain structure growth and therefore cosmological parameters (Figure 2) inseveral ways is especially valuable given the systematic uncertainties due to shape measurement, photo-z
error, and intrinsic alignments of galaxy shapes due to tidal ﬁelds (which mimic WL signals). We ﬁndthat combining these diﬀerent measurements, while marginalizing over systematics, restores the loss of
information in cosmic shear alone from those systematics (Joachimi & Bridle 2010). We also plan toWide-field imaging with Hyper Suprime-Cam

9measure lensing magniﬁcation (e.g., Huﬀ & Graves 2011) of galaxy sizes and ﬂuxes, which probes theline-of-sight matter density with diﬀerent observational uncertainties, providing an additional cross-checkand further improving on shear-based cosmological constraints.
Synergy with other cosmological data sets: The HSC survey footprint overlaps other data sets, suchas the SDSS and the ACT survey. While the HSC survey alone can constrain the DE equation of stateparameter to a precision comparable to a DETF Stage-III experiment, σ(wpivot ) ' 0.04 (see Section 3.3),
and this constraining power is the primary motivation for our survey, we also note several synergisticscience cases with overlapping data sets.
The SDSS-III BOSS survey (Dawson et al. 2012) is a spectroscopic survey of massive galaxies with< z < 0.7 targeted from the SDSS imaging. These luminous red galaxies (LRGs) preferentially reside0.3 ∼∼> 1013 M (White et al. 2011). However, the WL eﬀect due to thesein massive dark matter halos, Mhalo ∼halos is measured only at low S/N due to the shallowness of the SDSS imaging. The HSC survey goesroughly 4 magnitudes deeper, increasing the total lensing S/N (for 0.1–60 h−1 Mpc) from ∼ 15 to ∼ 200.
Figure 4 (middle panel) shows the expected total WL S/N around the BOSS galaxies using HSC sources,
as compared with existing SDSS WL measurements.
A major uncertainty for the BOSS experiment is the unknown relationship between the galaxy anddark matter distributions, including the nonlinear galaxy bias, redshift-space distortions (RSD), and theFinger-of-God eﬀect due to virial motions of galaxies within their dark matter halos (Hikage et al. 2012).
Since the HSC survey region covers a representative subset of the BOSS survey region (about 15%), the
HSC WL can calibrate these systematic uncertainties. For instance, simulations suggest that we can
combine the BOSS galaxy power spectrum with BOSS galaxy-galaxy lensing in HSC to directly measure
the scale-dependent bias of BOSS galaxies, b(k) ∝ (PBOSS (k)/PBOSS−γ (k))1/2 , to a few percent accuracy
at each k-bin up to k ' 0.2 hMpc−1 , for 0.1 ≤ z ≤ 0.7 with bin width ∆z = 0.1 (Nishizawa et al. in prep.).
This bias reconstruction will enable us to use the amplitude and shape of the BOSS galaxy redshift-space
auto-correlations, both to constrain cosmology and to extract the RSD from anisotropic modulations of
the redshift-space power spectrum. By combining the RSD with dark matter clustering from WL, we canperform a model-independent test of gravity on cosmological scales, because the peculiar velocity ﬁeld isrelated to the dark matter distribution via gravity theory (Reyes et al. 2010; Guzik et al. 2010: Tang etal. 2011).
In Reyes et al. (2010), spectroscopic and imaging data from the SDSS were used to measure a parameter
called EG , which is insensitive to the amplitude of matter clustering and galaxy bias, and constrains a
combination of the growth rate and the ratio of the metric potentials (each of which could in principle
be modiﬁed in alternative gravity theories). This measurement requires overlapping spectroscopic andimaging data, and we estimate that the combination of HSC imaging with BOSS spectroscopy will allow
EG to be constrained to the 4% level, with roughly equal error budgets coming from the RSD and thelensing measurements. Unlike the 16% measurement with SDSS alone, this constraint will be enough tomeaningfully distinguish between General Relativity and interesting models for modiﬁed gravity.
The cluster-shear cross-correlation technique relies on a robust catalog of galaxy clusters. We can construct a unique catalog of clusters by ﬁnding, around each SDSS or BOSS LRG5 , red member galaxies fromthe deep HSC images up to z ' 0.7 and further by adding higher-redshift clusters from the ACT SZ survey(see below). In addition, one of our Wide target ﬁelds overlaps the HectoMAP survey, a magnitude-limited
spectroscopic survey for galaxies at r < 21.3 covering 55 deg2 with the 300-ﬁber Hectospec instrument on
the 6.5m MMT telescope, which provides a sample of about 300 optically-selected clusters with masses> 1014 h−1 M and at z < 0.6, each with more than a few tens of member galaxies with spectroscopic
Mhalo ∼∼
redshifts (Kurtz et al. 2012). We will combine dynamical mass estimates from the line-of-sight velocity
dispersion of member galaxies with HSC WL mass estimates to establish a well-calibrated mass-observable
relation for the HectoMAP clusters, and to understand projection eﬀects for our optically selected cluster
sample. We can also ﬁnd clusters as high peaks in the weak lensing mass map (Miyazaki et al. 2002b,
2007), which we will compare with the optically-selected clusters to test their robustness.
Combining the HSC survey and the arcminute-resolution, high-sensitivity ACT CMB experiment also
oﬀers unique, synergistic science opportunities. We are involved in the ACT experiment and its successor,
ACTPol, and our Wide survey ﬁelds overlap almost completely with the ACTPol region. The ACT
5>
Massive halos with ∼1014 h−1 M
et al. 2011).

at z ≤ 0.6 typically host one or more SDSS/BOSS spectroscopic red galaxies (White10

Wide-field imaging with Hyper Suprime-Cam

Figure 4: Left: The predicted cumulative signal-to-noise (S/N ) ratios for the stacked WL signals due to halos with
masses Mhalo ≥ 1014 h−1 M in redshift slices of ∆z = 0.1, as a function of the lens redshift. The red and blue curves
are the results for the HSC and DES surveys, respectively. Middle: The measured WL shear (solid black line) around
SDSS LRGs covering 7131 deg2 at hzi = 0.27, using SDSS galaxies as sources (Mandelbaum et al. 2012), and thepredicted shear and errors around BOSS galaxies at hzi = 0.5 (dashed red line), from 1400 deg2 of mock catalogs
populated by galaxies to match the observed BOSS clustering signal (White et al. 2011), using WL measurementsof background sources in the HSC survey. The bottom panel shows the inverse S/N per bin; the total S/N is ∼ 30
for SDSS and ∼ 200 for HSC. Right: The 68% C.L. constraint region for ΩDE and wDE for several experiments,
assuming ﬂat wCDM (constant wDE ). The outer black contour shows current constraints from WMAP7 and SDSSWL measurements (Mandelbaum et al. 2012). The smaller contours show the expected constraints for HSC, in
combination with the expected Planck CMB constraints and/or the BOSS galaxy clustering.

experiment will provide a unique, redshift-independent catalog of SZ-selected clusters with nearly 100%
> 0.6 (Niemack
completeness for very massive clusters (> 8 × 1014 M ) at all redshifts, especially those z ∼et al. 2010). The upgraded ACTPol survey will identify clusters down to a smaller mass threshold, by afactor of a few, providing an opportunity to construct a large, clean sample of high-z clusters in the HSC
footprint. As implied by the left panel of Figure 4, the HSC survey for WL studies of high-redshift clusters
is quite synergistic with the ACT SZ survey.
Lensing of the CMB by large-scale structure can be cross-correlated directly with our cosmic shear
maps. The CMB lensing and HSC-galaxy lensing signals at any given point on the sky arise from the samelarge-scale structure in the overlapping redshift range (which is large, given the depth of the HSC survey),
and thus this cross-correlation measures the same signal as the cosmic shear power spectrum, with diﬀerent
systematics. We estimate that the statistical errors on the HSC shear-CMB lensing cross-power spectrum
will be only 1.5 times as large as those on the cosmic shear power spectrum from HSC alone (and evenbetter than that on the largest scales), suggesting that the cross-correlation with CMB lensing will add
signiﬁcant power to HSC.
Geometrical test with HSC SNeIa survey: The Ultradeep layer, with its carefully-chosen cadence
for broad-band ﬁlter imaging, allows us to identify ∼ 120 SNeIa with well-sampled lightcurves (Figure 12),< z < 1.4. The eﬃcient detection of such high-redshift SNeIa is possible due
40 of which will be at 1 ∼∼to the unique capabilities of Subaru/HSC, its large aperture, ﬁeld of view, and red-sensitive CCDs (yband ﬁlter). The HSC SNeIa sample will be complementary to the current SNeIa sample (Suzuki et al.
< 0.7, and to samples that will be delivered from multi-color imaging2012), the majority of which are at z ∼surveys with 4-m telescopes such as the DES SNeIa survey (Bernstein et al. 2012). In general, using these
SNeIa for cosmology requires spectroscopic follow-up observations with 8-m class telescopes. However, typeclassiﬁcations and redshifts for many SNeIa will be available from multicolor lightcurve ﬁtting and fromphotometric redshifts and spectroscopy of their host galaxies. Real-time spectroscopy of the supernova
themselves is required for only ∼ 20 SNeIa per year for which, e.g., no host galaxy is evident, and will bereadily accessible given the breadth of our collaboration network.
3.3

Target accuracies of parameter constraints

We will derive stringent constraints on cosmological parameters following the methodology in Figure 2. Table 3 shows the expected accuracies of cosmological parameters based on the cluster-shear cross-correlationWide-field imaging with Hyper Suprime-Cam
Data
BOSS-BAO
HSC(WL)-B (baseline)
HSC(WL)-O (optimistic)
HSC(WL+SN)-B
HSC(WL+SN)-O
HSC-O+[BOSS-P (k)]HSC-O+[BOSS+PFS]

wpivot
0.064
0.080
0.068
0.043
0.041
0.028
0.027

wa
1.04
0.86
0.66
0.60
0.45
0.36
0.19

11
FoM
15
15
22
39
54
99
196

γg
–
0.15
0.083
0.15
0.081
0.038
0.035

mν,tot [eV]
–
0.16
0.082
0.16
0.081
0.076
0.07

fNL
–
30
18
30
18
17
17

ns
0.018
0.014
0.013
0.014
0.013
0.011
0.009

αs
0.00570.00410.00400.00410.00400.00290.0022

Table 3: Expected parameter accuracies for HSC cosmology using the Oguri & Takada (2011) shear method: The“Baseline” case (“HSC(WL)-B”) uses clusters with z < 1 and masses Mhalo > 1014 h−1 M , and without priors on
nuisance parameters, whereas the “Optimistic” case (“HSC(WL)-O”), uses clusters to z = 1.4, with some conservative
priors on nuisance parameters. The DE constraints listed in this table are also conservative in the sense that the errors
include marginalization over non-standard cosmological parameters such as γg , mν,tot , and fNL . The rows denoted“WL+SN” include the above HSC-WL and SNeIa measurements. The last two rows show the expected constraints
when we combine the HSC observables with spectroscopic surveys, BOSS and PFS (see Ellis et al. 2012 regarding
the planned PFS survey). The joint constraints assume that the HSC-WL observables can remove the spectroscopic
galaxy bias uncertainty, by comparing the galaxy clustering with the dark matter distributions reconstructed fromthe HSC-WL observables. This analysis does not include constraints from cosmic shear, which is largely independent,with diﬀerent systematics, and serves as a valuable cross-check.

approach, estimated using the Fisher information matrix formalism. For the parameter forecasts, we employed a conservative approach. In particular, we included both a broad range of cosmological parameters
and a number of nuisance parameters to model systematic errors in the observables (such as photo-z errors and shear multiplicative error; see Oguri & Takada 2011). For SNeIa, we forecast constraints from
120 SNeIa discovered by the Ultradeep layer plus 150 local SNeIa at z ∼ 0.1. We did not explicitly
include systematic errors for the SNeIa Fisher matrix, but instead assumed a conservative error on the
distance modulus of σ(µ) = 0.3 per object. To quantify the power of the HSC survey for constraining
the structure ∫growth rate, we use the parameter γg , which is deﬁned so that the linear growth rate isG(z) ∝ a exp[ 0a d ln a0 {Ωm (a0 )γg − 1}]. Given suﬃciently high accuracies for measuring w0 , wa and γg ,the HSC survey can constrain both the expansion history and the growth of large-scale structure. We also
included other interesting parameters: the sum of neutrino masses mν,tot , the primordial non-Gaussianity
parameter fNL , and the primordial power spectrum shape (the tilt ns and the running index αs ).
Table 3 suggests that HSC WL observables alone can constrain DE parameters to high precision,
similar to the Stage-III BOSS BAO experiment. The HSC DE constraints can be further improved whencombined with the geometric constraints derived from the high-redshift SNeIa of the Ultradeep layer.
Most importantly, because WL directly probes the clustering amplitude of dark matter as a function ofredshift, the HSC WL observables allow us to make a stringent test of gravity on cosmology scales (viathe parameter γg ), when combined with the geometrical probes (BOSS BAO and the HSC- and external
SNeIa). Furthermore, because the HSC survey ﬁelds lie completely within the BOSS survey footprints,
we will combine the HSC WL measurement with the BOSS clustering measurement to directly calibrate
the galaxy bias. The combination will allow us to signiﬁcantly improve the cosmological constraints,
achieving σ(wpivot ) ' 0.03 and FoM' 100. Figure 4 shows the improvement on cosmological parameter
constraints that this analysis will yield. The constraints we will get from cosmic shear (Figure 3) arelargely independent (and with diﬀerent systematics), and will act as a valuable cross-check of our results,
and allow even tighter constraints on parameters.
3.4 Observational challenges
To achieve the cosmological constraints above, we must control various systematic errors in these cosmological observations. We discuss here the two most important such eﬀects: the measurements of galaxy
shapes for WL, and photometric redshifts.
Shape measurements: The HSC data processing pipeline (Section 8.1) will make accurate measurementsof PSF and galaxy shapes needed for WL cosmology. Several methods use second moments of the surface
brightness to estimate the galaxy ellipticity: KSB (Kaiser, Squires & Broadhurst 1995; Miyazaki et al.
2002b; Okabe et al. 2010; Oguri et al. 2012), an extended KSB method using elliptical weight functions,
E-HOLICs (Okura & Futamase 2012), the re-Gaussianization method (Hirata & Seljak 2003; Mandelbaum

12

Wide-field imaging with Hyper Suprime-Cam

et al. 2005) and a Fourier-domain method (Zhang 2008; Katayama et al. in prep.). Others are modeldependent methods, where the galaxy ellipticity is estimated by ﬁtting the measured image to the model
galaxy proﬁle convolved with the PSF: a shapelet method (Bernstein & Jarvis 2002; Miyatake et al. 2012),
multi-scale shapelets (Bosch 2010), and the Spergel method (Spergel 2010; Hikage et al. in prep). We are
testing all these methods using simulated images generated from COSMOS Hubble Space Telescope (HST)data (Mandelbaum et al. 2011) and Suprime-Cam data on galaxy clusters to quantify the performanceof each method (Hikage et al. in prep; Miyatake et al. 2012; Reyes et al. 2012). We will use these
diﬀerent methods to make various cross-checks of accuracies and systematic errors in the estimated galaxy
ellipticities, and will also use simulation software from the upcoming lensing community data challenge(co-led by R. Mandelbaum), modiﬁed to include real HSC optics and detector eﬀects, to test for errors due
to all stages of the data analysis. From the simulation studies thus far, we have found that several of these< 1% for the best method, even for S/N ∼ 20
methods achieve an accuracy better than a few percent ( ∼galaxies with realistic galaxy morphologies) for the shear multiplicative error, which shows we are on our
way to achieving the goals in Section 8.1.
We have designed the HSC survey strategy (Section 7.3)
to better control WL systematic errors. Individual exposures are ∼ 3 minutes long, and thus we will have 6-7 exposures for each i-band pointing. First, we will take thei-band imaging data when the seeing is suﬃciently good
(0.4000 < FWHM < 0.700 , where the lower threshold helps
avoid the undersampling of PSF images). Second, we will
employ a large-angle dithering strategy so that objects appear in diﬀerent positions of the focal plane in each exposure, thus (at least partially) canceling out the diﬀerent optical and detector eﬀects over the multiple exposures. Third,
we spread the exposures of a given ﬁeld over diﬀerent nights,
giving independent sampling of the atmospheric PSF.
We use the Deep layer data to calibrate various aspectsof our WL measurements. The higher S/N of the Deep exposures allows us to carry out a statistical study of galaxy
properties (disk/bulge components, and the intrinsic ellipticity distribution) over a cosmologically fair volume. A
better understanding of the intrinsic galaxy population is Figure 5: The scatter between true and photo-z
necessary in order to interpret lensing shape measurements, estimated redshifts for simulations of the HSCIn addition, the many exposures in the Deep layer will pro- Wide layer. This simulation extends to i = 25,
vide a full database of diﬀerent observing conditions for each and uses redshifts from the zCOSMOS and 30band photo-z COSMOS catalogs (Lilly et al.
star and galaxy: position in the focal plane, seeing, eleva- 2009; Ilbert et al. 2009). We used the phototion, and so on. Thus, we can use the Deep data to study z posterior distribution of each galaxy to clip
how observing conditions propagate into errors in galaxy photo-z outliers.
shape measurements. We will also use the Deep data tobetter understand noise rectiﬁcation bias (the bias in lensing shear due to noise in the galaxy images, which occurs because shape measurement is a non-linear
operation; Mandelbaum et al. 2011) by exploring shape measurements as a function of S/N . Thus, while
the Deep layer does not cover enough solid angle for a cosmological lensing analysis on its own, it is criticalfor calibrating systematic errors in the Wide layer.
Photometric redshifts: The uncertainty in photo-z estimates is another potential major source of systematic error in WL analyses. To quantify this, we have constructed a mock HSC galaxy catalog basedon COSMOS photometry (Capak et al. 2007), with simulated magnitudes and errors as they would beobserved given the HSC depths and ﬁlters. By running various commonly used photo-z codes and our own
custom code, we have estimated that our expected scatter and outlier fractions in the Wide layer will be
σdz/(1+z) = 0.082, foutlier = 13% for galaxies down to i = 25.
Our HSC ﬁelds overlap a signiﬁcant number of public spectroscopic surveys, including SDSS/BOSS,zCOSMOS, GAMA, HectoMAP, DEEP2/3, PRIMUS, VIPERS, and VVDS (see Section 7.2), which wewill use to calibrate our photo-z templates and techniques at the bright end (to i ∼ 23). We will extend

Wide-field imaging with Hyper Suprime-Cam

13

the calibration ladder to fainter magnitudes using the deep, multi-wavelength photometry available in the
Deep and Ultradeep layers. The photo-z’s based on these exquisite data are much more accurate, witha scatter of order 0.04, than those for the Wide layer and can be used to calibrate and understand thephoto-z’s for Wide.
Another strength of HSC for photo-z’s is its superb photometric calibration (1%), as detailed in Section 8.2. Our galaxy model-ﬁtting code will allow us to estimate galaxy colors using only the bulge
component, which has an older stellar population and a more reliable photo-z estimate. We will use themultiple repeat exposures in the Deep and Ultradeep layers to demonstrate the robustness of the galaxycolors, and thus photo-z’s, as a function of observing conditions.
Among the various WL techniques that we will use for HSC, shear tomography has the most stringent
constraints on photo-z’s (Hearin et al. 2010). We carried out simulations using the mock HSC photo-z
catalog described above, together with mock HSC lensing data based on COSMOS WL catalogs (Leauthaud
et al. 2007). If we use information from the Deep layer on the redshift distribution, or remove likely
photo-z outliers based on the photo-z posterior probability distribution (Nishizawa et al. 2010), thesystematic errors on wpivot and wa from photo-z errors become comparable to our statistical errors. Figure 5
demonstrates accurate photo-z’s with the outlier clipping applied; here the rms scatter in z/(1+z) is 0.042,
with an outlier fraction of 6%. We will reduce these systematic errors further by using the cross-correlation
method (Newman 2008) with spectroscopic data from BOSS.
4 The Evolution of Galaxies at Intermediate Redshift
4.1 Overview: The unique power of the HSC Survey
The HSC survey will enable signiﬁcant advances in the study of galaxy evolution at z < 2 because of its
unprecedented sample size and statistical precision, high quality WL measurements, and superior spatial
resolution.
Table 4 summarizes the HSC galaxy sample in photometric redshift bins6 from the Wide and Deeplayers, incorporating 20 million and half a million galaxies, respectively. These enormous samples allow us
to study the properties in ﬁne bins of redshift, stellar mass, morphology, color, and star formation rate.
The volumes covered in each of many redshift slices are comparable to that of the SDSS Main Galaxy
sample (Strauss et al. 2002) which has been used to characterize the nature of galaxies at the present day.
Thus cosmic variance, which has plagued galaxy evolution studies at high redshift, will be negligible inmost contexts.
The Wide and Deep imaging depths are well-suited to galaxy studies at z < 2. Simulations of Widelayer stellar mass estimates normalized to the y-band show uncertainties of ∼0.2 dex and no systematic
biases to z = 1.1 as compared to COSMOS masses normalized in the K-band. Extant K-band imaging in
the Deep ﬁelds will provide reliable mass estimates to z ∼ 2. Star formation rate estimates in HSC will
be derived from spectral energy distribution (SED) ﬁtting (e.g., Salim et al. 2009). Mostek et al. (2012)
developed a star formation rate (SFR) estimator based on MB , (U − B), and (B − V ), which shows ascatter relative to that derived from L[O II] of 0.3 dex with no systematic biases, using imaging data morethan a magnitude shallower than HSC-Wide.
As described in Section 3 and shown in Figure 4, galaxy-galaxy lensing allows us to measure halo masses
for binned ensembles of the galaxy population. In this way, evolving subpopulations can be tied directly
to their dark matter halos, providing immediate comparisons to models based on N -body simulations andtesting proposed drivers of evolution, many of which ultimately depend on total mass.
The median expected seeing of 0.6500 corresponds to a PSF whose area is 50% the size of a typical galaxy
at z ∼ 1 (see Table 4). This allows for size, concentration, and inclination measurements. In addition,
10–15% of HSC data will have a seeing FWHM of 0.3500 or better, vastly increasing the volume and sample
size of studies tracking detailed morphological evolution (e.g., bulge growth, bars, spiral arms).
4.2 The physics of growth, star formation quenching, and mass assembly at z < 2
Numerous studies have now conﬁrmed that the SFR density in the Universe peaks roughly at z ∼ 1–2 andthen falls by a factor of ∼30 to the present day (e.g., Hopkins et al. 2006a). The average amount of starformation depends on the stellar mass, M∗ ; low-mass galaxies typically form stars at a greater relative
rate, and their star formation is quenched later, than in high-mass galaxies. The processes responsible forthese phenomena are poorly understood, but operate against the backdrop of continuing mass assembly
6

These are accurate enough to resolve timescales of ∼ 1 Gyr, typical for a variety of evolutionary processes.

14

Wide-field imaging with Hyper Suprime-Cam

z
0.1
0.3
0.5
0.7
0.9
1.2
1.5
1.8

0.6500 (kpc)
1.2
2.9
4.0
4.6
5.1
5.4
5.5
5.5

HSC-Wide
Vol (Gpc3 ) log M∗lim
0.1
8.2
0.5
8.9
1.0
9.4
1.6
9.8
2.6
10.0
3.9
11.5
4.5
11.6
4.8
11.8

Ngal
0.4M
2.2M
4.4M
6.0M
8.8M
0.1M
23k
1k

HSC-DeepVol (Gpc3 ) log M∗lim
0.001
8.7
0.008
9.3
0.019
9.8
0.029
10.1
0.049
10.2
0.073
10.4
0.083
10.6
0.090
10.8

Ngal
7.2k
38.3k
71.8k
94.4k
137.3k166.2k145.7k
108.9kTable 4: Galaxy sample characteristics in a series of redshift slices for both Wide and Deep. The ﬁrst column provides
the mean redshift of each slice. The second column gives the physical scale subtended by 0.6500 , the median i-band
seeing in both layers. The remaining columns indicate the volumes, stellar mass completeness limits, and numberof galaxies expected above these limits for both layers. For Wide at z < 1, log M∗lim is deﬁned by the faintest red
galaxy that is detectable with a y-band ﬂux 1 mag shallower than our 5σ depth limit. At z > 1, we switch to a
UKIDSS/LAS K-band 5σ depth limit of K = 20.2 AB. For Deep, mass limits correspond to 1 mag shallower than a
5σ limit of K = 22.8 AB. We assume a standard cosmology with H0 = 70 km s−1 Mpc−1 .

driven by galaxy interactions and mergers, as well as gas infall from larger scales. By precisely charting thestar formation rates and stellar masses—and tying these to the halo masses (Mhalo )—for an unprecedented
sample of galaxies, the HSC survey will constrain the physical mechanisms that regulate star formation anddevelop the first complete picture of how galaxy mass is formed and assembled since z ∼ 1.
1) How do star formation and mergers drive mass assembly? Most previous studies of the crucial
1 < z < 2 regime, such as GOODS, have surveyed tiny regions of sky, ∼0.1 deg2 . The Deep layer will be
more than 200 times larger, enabling a comparison between star formation and stellar mass growth at theepoch when the global SFR begins to decline.
At z < 1, the Wide layer will provide the statistical power necessary to compare mass growth fromnew stars to that assembled via mergers. The two panels in Figure 6 show how HSC will be able tomeasure evolution in the star formation rate and the stellar mass function, respectively. Meanwhile,
merger rates in HSC will be derived both from morphological irregularities (calibrated with comparisons
to high-resolution merger simulations; Lotz et al. 2011) and pair counting with photo-z contamination
corrections (e.g., Kartaltepe et al. 2007, but see also Lin et al. 2010). Because the HSC imaging is so
deep, statistical studies of the merger rate down to very low mass ratios (e.g., 1:30), especially in the
low-z regime, will be possible. These quantities are related via the M∗ continuity equation: Ṅ (M∗ , z) =SFR(M∗ , z) + Mergers(M∗ , z) − Ṁloss (M∗ , z), where Ṅ (M∗ , z) represents the evolving stellar mass function,
SFR(M∗ , z) includes fresh gas infall, and Ṁloss accounts for mass loss due to stellar evolution as well as
tidal stripping. By testing the validity of this equation, we can constrain Ṁloss and identify inconsistencies
that may reveal an evolving initial mass function (IMF) or changes in the merger timescale.
2) How is star formation fueled? Diﬀerent fueling mechanisms such as cold ﬂows, re-accretion of winds,cooling from a hot gaseous halo, and cold gas carried in by mergers lead to diﬀerent timescales and degrees
of stochasticity in the star formation history of galaxies. Mergers in particular should drive bursty modesof star formation (e.g., Cowie & Barger 2008) while gradual accretion or cooling produce smoothly varying
modes (e.g., Noeske et al. 2007). HSC will provide deﬁnitive answers by delivering the statistical precisionnecessary to measure the SFR distribution in diﬀerent M∗ and redshift bins (left panel of Figure 6). In agiven bin, the presence and strength of a tail towards high rates of star formation reveals the frequency ofgalaxies in a “burst” mode, e.g., those having a SFR greater than their M∗ divided by the Hubble time.
3) What causes quenching? In addition to a global decline in the SFR, star formation in high massgalaxies at z ∼ 1 is observed to shut down entirely; the mechanism driving this “quenching” remains
unclear. At later times, quenching occurs in lower-mass galaxies (e.g., Bundy et al. 2006). Comparing the
evolving SFR and mass functions in Figure 6 will determine the quenching rate, a key step in understanding
whether quenching is violent (e.g., AGN feedback) or gradual (e.g., starvation).
Further insight will come from measuring “ﬂow diagrams” of various galaxy populations (see Figure 7).
For the ﬁrst time, HSC will determine deﬁnitive evolutionary links by precisely equating the diminishingnumbers of one population (e.g., star-forming galaxies) with the rising occurrence of another (e.g., quencheddisks or ellipticals). The SFR versus bulge-to-total ﬂow diagram in Figure 7 would reveal the relativeWide-field imaging with Hyper Suprime-Cam

−1.5

15

−1.5

Quenched
Galaxiesz = 1.0
z = 0.8z = 0.6z = 0.4

−2.0

−2.0
Time
Log Number Density

Log Number Density

Time
−2.5
−3.0
−3.5
−4.0

z = 1.0
z = 0.8z = 0.6z = 0.4

−2.5
−3.0
−3.5
−4.0
Time

−4.5

−4.5

Log M* ~ 10.2
Log M* ~ 11.0

−5.0
−1.0

−0.5

0.0
0.5
Log SFR

1.0

1.5

2.0

−5.0
9.5

Passive
Green Valley
Star−forming

10.0

10.5
Log Mstar

11.0

11.5

Figure 6: Expected HSC constraints on the galaxy number density evolution in the SFR-stellar mass plane, basedon the Millennium Simulation (de Lucia et al. 2006). The left panel shows the number density as a function of
SFR in two bins of stellar mass, while the right panel plots the same quantity as a function of mass in bins of SFR.
In both cases, the line thickness increases with time. The dashed portions of the mass functions in the right-handpanel show the deeper mass completeness limits of the Deep layer. The plots demonstrate the statistical power of
HSC observations to reveal modes of star formation at diﬀerent masses and their contribution to the galaxy massassembly history.

importance of two diﬀerent quenching channels. In the present example drawn from the Millennium
Simulation, a non-merger channel that preserves disk components in quenched galaxies (such as starvationor the creation of a halo shock) is favored over a major-merger scenario that builds bulge mass ﬁrst while the
SFR declines. This ﬁgure shows just one slice of the multivariate space to be explored in HSC. In addition,
the superior image quality at Subaru allows for a range of structural and morphological parameters to be
included, vastly increasing our ability to ﬁnd and test physical scenarios.
4) How do galaxy sizes and morphologies evolve? One of the most exciting discoveries over the
past few years is that elliptical galaxies at ﬁxed mass were smaller than they are today by factors of two
to four (van Dokkum et al. 2008), as recently as z ≈ 1. With HSC, we can ask whether size evolution
proceeded diﬀerently for ellipticals with diﬀerent star formation histories or stellar mass, and whether size
growth proceeds diﬀerently in clusters, where galaxies have been found to be as compact at lower-z as
those at z ∼ 2 (Valentinuzzi et al. 2010). It is thought that galaxies grew since z ∼ 1 via minor merging,with accreted material from small “ﬂuﬀy” galaxies remaining mostly at the outskirts of their massive hosts(Naab et al. 2009). We will test this by directly tracking the color evolution in the outskirts of massive
elliptical galaxies out to z ∼ 1, both individually and through stacking (e.g. Tal & van Dokkum 2011). If
the outskirts are built via the accretion of small galaxies, the color gradients should grow bluer at latertimes. We can also compare the observed growth to the inferred minor merger rates (see above) for galaxies
with the same mass and morphology.
4.3

How does the relationship between galaxies and dark matter halos evolve?

The processes regulating galaxy growth and assembly as described above are intimately tied to and driven
by the growth of dark matter halos, which we can probe with gravitational lensing up to z ' 1.4 (Figure 4).
We will measure the stellar-to-halo mass relation (SHMR, Figure 8) which tracks the average halo mass ofcentral galaxies in each halo as a function of their observed stellar mass. When combined with measurementsof clustering on small scales, the stellar-to-halo relation for satellites can simultaneously be constrained aswell. Current studies (e.g., Mandelbaum et al. 2006, Moster et al. 2010, Leauthaud et al. 2012) suggest thatthe eﬃciency of stellar mass growth at z ∼ 0 depends strongly on halo mass, peaking at Mhalo ∼ 1012 M(the “pivot” mass scale, corresponding to a stellar mass of ∼ 1011 M ). HSC will measure the SHMR fromz = 0.2 (M∗ > 108.8 M ) to z = 1 (M∗ > 1010.4 M ) using a combination of galaxy-galaxy lensing andthe angular clustering of galaxies. HSC is expected to improve the current precision of the constraints on
the SHMR by a factor of 10 with a near-vanishing sample variance. This will allow us to determine the16

Wide-field imaging with Hyper Suprime-Cam
2.0

2.0

1.5

1.0

Star-forming Disks
Log SFR

Log SFR

z=0.3

Major-merger
Quenching

1.5

1.0

0.5

0.0

Ellipticals

Passive Disks

0.5

−1.0

−0.5

0.4

0.6
B/T

0.8

1.0

0.0

Increasing numbers
determines rateof growth

0.5

0.0

Disk
Quenching

Rejuvenation

−1.0
0.2

1.5

1.0

?

0.0

−0.5

0.0

2.0

z=0.6

Log SFR

z=1.0

−0.5

−1.0
0.2

0.4

0.6
B/T

0.8

1.0

0.0

0.2

0.4

0.6
B/T

0.8

1.0

Figure 7: Mock HSC ﬂow diagrams. The correlation between star formation rate and bulge-to-disk (B/T) ratioof galaxies from the Millennium Simulation selected in a narrow stellar mass bin centered at M∗ = 1011 M , withincreasing time from left panel to right. B/T will be well measured at low redshift and can be related to concentration(which can be measured at higher redshifts). The redshift evolution of this diagram demonstrates how HSC data
will allow us to track how galaxies “ﬂow” within this parameter space. Annotations in the left panel identify
speciﬁc populations. The middle panel illustrates how various physical processes drive ﬂows in this space. The
lack of objects in the upper-right portion shows that ellipticals are not formed by major mergers of disks. Much
more common is “disk quenching” of satellites as they fall into larger halos, with some evidence for rejuvenated starformation in ellipticals. We see in the right-hand panel that the relative fractions of diﬀerent populations have evolved
signiﬁcantly. Measuring the evolving numbers of galaxies in diﬀerent populations in such diagrams constructed formany redshift slices will constrain their rate of growth along various pathways.

precise redshift evolution of the pivot mass which, in combination with dark matter halo growth derived
from numerical simulations, provides fundamental insight on how the regulation of star formation is tied
to the growing dark matter halo.
4.4 Galaxy evolution in extreme environments< 1.4 and masses
The Wide layer will provide a unique sample of ∼20,000 massive clusters with redshifts z ∼14
above 10 M . We will study the cluster galaxy populations as a function of cluster mass using statisticalbackground subtraction to distinguish members from the ﬁeld. Our large sample will not only elucidate
signatures of quenching and morphological changes, but determine whether the SFR-mass relation forstar-forming galaxies is altered in dense environments. This will reveal whether environmental mechanisms
include gradual processes (e.g., strangulation or interruption of gas streams) or are primarily fast-acting(e.g., ram-pressure stripping). We can place the results in context by using halo mass estimates (viaweak lensing, and in some cases from ACTPol SZ and eROSITA X-ray measurements) to statistically link
clusters at diﬀerent epochs by comparing to expected growth in cosmological simulations.
Most galaxy formation models (e.g., de Lucia et al. 2007) predict that the brightest cluster galaxies(BCGs) acquired nearly half of their ﬁnal mass below z ∼ 0.5. However, it is far from clear that there areenough mergers at late times to drive this growth. In addition, there is evidence for BCGs in massive clusters
at z ∼ 1 (e.g., Collins et al. 2009) which appear to be fully formed. The HSC sample of a statistically linked
evolutionary cluster sequence will help determine when BCGs ﬁnish evolving by tracking BCG growth atlate epochs.
5 Galaxy and Quasar Studies at High Redshift
Our HSC survey will also probe the epoch of galaxy formation at z > 2, when cosmic SFR rose with cosmic
time. Suprime-Cam has led the world in wide-ﬁeld studies of high-redshift galaxies, and has unveiled theevolution, clustering, environments, and physical properties of Lyman-break galaxies (LBG), Lyman-αemitters (LAE), and Lyman-α blobs (LAB) from z ∼ 2 to z ∼ 7. With the much larger ﬁeld of viewof HSC, we will address the next generation of key questions in the ﬁeld, which require going deep oversubstantially larger solid angles with both broad-band and narrow-band (NB) ﬁlters. In particular, theNB component of our survey is unique: no other existing or planned wide-ﬁeld survey is doing or will do
NB imaging.
The detailed design of our Deep and Ultradeep layers is described below in Section 7; see especially
Table 8. The broad-band depths are designed to select LBGs over the full redshift range 2 < z < 7, as wellas to detect the continuum in LAEs at all redshifts probed by the NB data. The depths and solid angles

1014

17

WL, Leauthaud et al. 2011 z=0.37

z=1.0 mass limit

M200b [ MO• ]1015

z=0.6 mass limit

z=0.2 mass limit

Wide-field imaging with Hyper Suprime-Cam

1015

WL, Leauthaud et al. 2010, z=0.3
1014

13

Halo Mass

10

12

10

10

10

SK, More et al. 2010, z~0.05
TF, Geha et al. 2006, z=0

1011

10

AM, Behroozi et al. 2010, z=0.1
SK, Conroy et al. 2007, z~0.06

1012

10

WL, Hoekstra et al. 2007, z~0.2
AM, Moster et al. 2010, z=0.1

1013

11

WL, Mandelbaum et al. 2006, z=0.1

TF, Pizagno et al. 2006, z=0
TF, Springob et al. 2005, z=0

7

10

8

9

10
Stellar Mass

1010
M* [ MO• ]

10

11

10

12

1010

Figure 8: The low redshift stellar-to-halo mass relation (SHMR) as probed by WL, abundance matching (AM),satellite kinematics (SK), and Tully-Fisher (TF) estimates. A key feature in the SHMR is the “pivot mass” scale atM∗ ∼ 1011 M where the quenching of star formation in massive galaxies causes the SHMR to bend sharply upwards.
The dashed blue vertical lines show the stellar mass completeness limits of the HSC survey at z = 0.2, z = 0.6,
and z = 1.0, demonstrating that HSC will be able to probe the redshift evolution of the pivot mass scale out toz = 1. Measurements of this pivot mass scale out to z = 1 should yield insights into the mechanisms that cause thequenching of star formation.

will allow us to accurately measure the clustering and luminosity functions of these populations. In the
Ultradeep layer, the z and y ﬁlters will reach to L∗ at z ∼ 7, allowing the selection of dropout galaxies.
With the NB ﬁlters, we will study the evolution of the properties of LAEs to z = 7.3, the nature of LABs,
and the neutral hydrogen fraction and the topology of reionization at z ∼ 6 − 7.
The Lyman-break and narrowband-excess techniques are well established, and we will need spectro> 7), meaning
scopic conﬁrmation for only a small fraction of our objects (except for the rare objects at z ∼that followup spectroscopy will not be a bottleneck in our study. PFS is designed to carry out more detailed
studies of well-deﬁned populations identiﬁed in this survey. Tables 5 and 6 show the expected numbers of
LBGs and LAEs for the three layers calculated from the observed luminosity functions (LFs), mostly takenfrom our own studies with Suprime-Cam, with an extrapolation toward lower luminosities where needed.
Each sample at each redshift is designed to address at least one key science goal.

Table 5: LBG samples

sampleredshift
c
NUD
NDc
c
NW
d
VUD
VDd
d
VW
e
MUD
e
MD
e
MW
sciencef

BX/BMau-dropa

2.3 ± 0.5
0.9M
0.8M
–
16
129
–
−18.0/ − 17.0
−19.5
–
GE

3.0 ± 0.5
0.22M
98k
–
16
129
–
−18.3
−20.8
–
GE

g-drop3.8 ± 0.5
0.24M
1.1M
17M
15
122
6100
−18.2
−18.8
−19.8
GE

r-drop5.0 ± 0.5
50k
0.2M
1.9M
14
108
5400
−19.0
−19.6
−20.6
GE

i-drop5.9 ± 0.5
11k
34k
38k
12
98
4900
−19.9
−20.4
−21.6
GE

z-drop6.8 ± 0.5
700
99
4
11
89
4450
−20.6
−21.6
−22.5
GE, CRy-dropb
7.8 ± 0.3
2
0
–
2.6
24
–
−21.6
−24.1
–
CR

Notes – a) Using CFHT u∗ images for the Deep layer (u∗ = 25 − 25.5 mag) from the archive and the Ultradeep layer (u∗ =27−27.5) from the on-going Foucaud et al. program. b) y-dropouts identiﬁed by the combination of HSC, VISTA, and UKIDSS
data in the Ultradeep ﬁelds. c) Expected number of LBGs in each redshift slice. d) The comoving volume of each redshift slice
in units of 106 (h−1 Mpc)3 . e) Limiting absolute AB magnitude of the sample at rest frame 1300–1500Å. f ) Key science cases.
GE: galaxy evolution in dark halos (Section 5.1), CR: cosmic reionization (Section 5.4).

18

Wide-field imaging with Hyper Suprime-Cam

Table 6: LAE and LAB Samples

narrow-band
redshift
b
NUD
b
ND
c
VUD
VDc
L(Lyα)dUD
L(Lyα)dD
sciencee

NB387
2.18 ± 0.02–
9.0k (730)
–
6.0
–
2.7
LA

NB816a5.71 ± 0.05
3.9k (60)14k (360)
1.2
9.6
1.5
2.9
LA, CR

NB921a6.57 ± 0.05
1.7k (30)5.5k (100)
1.2
9.8
2.5
4.1
LA, CR

NB101a7.30 ± 0.04
39 (0)
–
0.79
–
6.8
–
LA, CR

Notes – a) We will use these narrow-band data down to the 4σ limits, following the convention in the literature. b) Expected
number of LAEs, with numbers of LABs in parentheses, in each redshift slice. c) The comoving volume of each redshift slice inunits of 106 (h−1 Mpc)3 . d) Limiting Lyα luminosity in units of 1042 erg s−1 . e) Key science cases. LA: evolution of LAEs and
LABs (Section 5.2), CR: cosmic reionization (Section 5.4).

5.1

Galaxy evolution in dark matter halos

As described in Section 4.2, understanding the physics of galaxy evolution must be done in the context ofthe dark matter halos in which galaxies lie. We will measure dark-halo masses for galaxy populations byﬁtting Halo Occupation Distribution (HOD) models to the angular correlation function (ACF), constraining
the relationship between galaxies and dark halos as a function of cosmic time from the reionization era
to z ∼ 2, which links smoothly with the weak lensing analysis of intermediate-z galaxies described in
Section 4.
We will use the LBG samples over 2 < z < 7 from the three layers to determine the dependence of SFR
and stellar mass (M∗ ) on halo mass (Mhalo ) over Mhalo ∼ 1011 M – 1013 M . The Wide data are essential
to identify the rare, highest-mass halos that existing studies (e.g., Foucaud et al. 2003; Ouchi et al. 2004;
Lee et al. 2009; McLure et al. 2009) do not have the solid angle to discover, while the Deep and Ultradeep
layers will probe to low masses and to the initial stages of mass assembly at 5 < z < 7. SFRs will bederived from the observed far-UV luminosities for all galaxies. For galaxies in the Ultradeep layer and over
60% of the Deep layer (COSMOS, XMM-LSS and ELAIS-N1), we will use deep NIR data (Table 10) to
derive their M∗ and ages by stacking, if necessary, down to mass scales of dwarf galaxies, ∼ 107 M . For
the remaining two Deep ﬁelds, we will estimate M∗ from SFRs using the SFR–M∗ relation to increase thestatistics.
Figure 9 compares the observed M∗ /Mhalo –Mhalo relation with those predicted for z < 2 (Foucaud etal. 2010; cf., Figure 8). With the increased dynamic mass range of our sample (1011 − 1013 M ), we will beable to detect the predicted peak at Mhalo ≈ 1012 M (de Lucia & Blaizot 2007), testing models of fuelingand quenching mechanisms as a function of redshift. The dependence of M∗ and SFR on Mhalo revealed
by our study will allow us to understand the observed SFR - M∗ sequence (Daddi et al. 2007) in termsof physical models of structure formation. At least 1 × 104 galaxies are needed to constrain HOD models;
our sample will be large enough to do so in multiple bins of redshift, luminosity and/or stellar mass. We
will constrain the average number of galaxies hosted in dark halos and the star formation (SF) duty cycle,
both of which are important parameters related to galaxy merging and stochastic SF. At z ∼ 7, we will
not be able to measure the 1-halo term, but we will measure the ACF on larger scales for the ﬁrst time,
constraining SF in dark halos in the reionization era.
5.2

Evolution of LAEs and LABs

LAEs are on average less massive and less obscured than LBGs (e.g., Ono et al. 2010), and they evolve
very diﬀerently from LBGs, becoming more common at higher redshift (e.g, Ouchi et al. 2008). However,clustering studies to date are not large enough to measure the 1-halo term (Ouchi et al. 2010, Guaita et al.
2010). Our LAE samples will have the sensitivity to determine dark halo masses and galaxy distributionsin them for the ﬁrst time at z = 2.2, 5.7 and 6.6, comparing the results directly with those for the LBGs
in the context of the analysis of Figure 9.
Stacked narrow-band images of LAEs in our sample may reveal diﬀuse Lyα (and UV) halos extendingout to the virial radii of dark halos (∼ 100 kpc: Steidel et al. 2011, Matsuda et al. 2012), allowing us to
explore the interaction (both infall and outﬂow) of galaxies with ambient baryonic gas, as a function of darkWide-field imaging with Hyper Suprime-Cam

Ultra
Deep

Deep

19

Wide

Figure 9: Left: Relation between M∗ /Mhalo and Mhalo at z < 2. Compare with the low-redshift version shown in
Figure 8. Combining data from the three layers will accurately determine this relation at z > 2 by bracketing thepivot mass at Mhalo ≈ 1012 M . Right: Expected quasar LF over z ∼ 4 – 7 with our HSC data. The ﬁlled and open
circles correspond to the Wide and Deep layers, respectively.

halo mass and environment from our clustering analyses (Matsuda et al. 2011, 2012). For individual objects
in which extended gas is detected, we will determine whether inﬂows occur along large-scale ﬁlaments bycorrelating their position angles to the background ﬁlaments traced by LAEs.
> 20 kpc), LABs, are very rare objects, possibly tracing very massive
Individually detected Lyα halos ( ∼systems (e.g., Uchimoto et al. 2008) or short-term aspects of galaxy-formation processes (e.g., Saito etal. 2008). Even the HETDEX survey (Hill et al. 2010) will not be able to discover such objects, given its
1/7 ﬁll factor. We predict that there will be of order 1000 LAB in our narrow-band imaging (Table 6). Atz = 2.2 alone, we expect to discover 700 such objects, enough to measure their clustering, which will allow
us to place them in the framework of structure formation.
5.3 Quasars and active galactic nuclei
The quasar phenomenon is a key aspect of galaxy formation: it marks the growth of the central supermassive
black hole (SMBH), and must be related to galaxy evolution overall given the observed correlation between
host galaxy and SMBH properties in the present-day Universe. The SDSS quasar sample is the largest
survey of quasars to date, but it samples only a narrow range of luminosity at each redshift, does not havethe depth to probe the galaxy population corresponding to quasar hosts at any but the lowest redshifts,
and cannot probe above z = 6.4 because of the red limit of its ﬁlter set. HSC will be eﬀective in pushing
beyond these limitations. With the HSC data, we will select quasars via their colors (Richards et al. 2002),
matching to infrared and X-ray data (Table 10) and via variability (Morokuma et al. 2008). Table 7
summarizes the number of objects that we expect to ﬁnd. In the Wide layer, we will detect 280 z ∼ 6quasars (10 times more than SDSS) and 50 z ∼ 7 quasars, thanks to the deep z- and y-band data.
Variability selection is sensitive to even quite low-luminosity AGNs that are missed by other techniques;
the cadence in the Deep broad-band data is well-matched to quasar variability timescales. We predict oforder 200 (50) low-luminosity z ∼ 4 (z ∼ 5) quasars in the Deep layer, and 2000 at z ∼ 1. In addition,
we will detect a few z ∼ 7 low-luminosity quasars, which are important to measure the faint end of theluminosity function, and will allow us to distinguish between models for quasar evolution and reionization.
The colors lead to excellent photometric redshifts, especially at redshifts above 3, where the Lyα forest
gives a distinctive signature in the colors as a function of redshift.
Table 7: Quasar Samples

redshiftmag. range
number

3.7–4.6r < 23.0
6000

Wide (1400 deg2 )4.6–5.7
5.9–6.4
i < 24.0 z < 24.0
3500
280

6.6–7.2y < 23.4
50<1
i < 25.0
2000

Deep (27 deg2 )3.7–4.6 4.6–5.7
i < 25.0 i < 25.0
200
50

6.6–7.2y < 25.33

20

Wide-field imaging with Hyper Suprime-Cam

Evolution of supermassive black holes: The bright end of the quasar LF (MUV < −26) is well-ﬁt
with a power law whose normalization peaks at z ∼ 3 (Richards et al. 2006). At lower luminosities, theoptical LF shows a break from a pure power-law (e.g., Croom et al. 2009), but this break is very poorly
measured at z > 3, where SMBH are actively growing, and where diﬀerent feedback models make quite
diﬀerent predictions for the LF shape (Ikeda et al. 2011). With HSC, we will measure the overall shapeof the quasar LF up to z ∼ 5 for the ﬁrst time, probing well below the break. Speciﬁcally at z = 4 − 6,
we will detect quasars down to MUV ∼ −22.5 in the Wide ﬁelds through the color selection, and down to
MUV ∼ −21.5 in the Deep ﬁelds via their time variability (right panel of Figure 9). While high-luminosity
quasars may be accreting at close to the Eddington rate, the lower-luminosity objects are likely to beundergoing a diﬀerent mode of accretion, which can be probed with detailed measurement of the LF
shape.
Clustering properties and environments of quasars: With precisely measured spatial clustering of
quasars (and the galaxies around them) as a function of luminosity, one can infer the masses of hostinghalos, the locations of quasars in the halos, and (with the LF measurements above) quasar duty-cycles,
all of which are crucial to constrain models of the growth mechanisms of quasars, especially at z > 3 andat low luminosities. We will examine the properties of galaxies in the vicinity of quasars to search forlarge-scale feedback eﬀects from the quasars. Combining our quasar sample with galaxy samples from thesame imaging data, we will evaluate large-scale eﬀects with much improved statistics than has been done
to date (e.g., Kashikawa et al. 2007) to constrain galaxy evolution models.
Relationship of AGN to their host galaxies: The superb imaging aﬀorded by HSC allows us to
study the host galaxy properties of the z < 1 AGN, including their stellar masses, star formation rates
and morphologies. The number density of moderate-luminosity AGNs peaks at z ∼ 1; it is unknown
whether these sources are dominated by massive elliptical galaxies whose nuclear activity is shutting down,
or smaller black holes undergoing major growth episodes (i.e., dust-obscured starbursts). If AGN activity
is responsible for star-formation quenching, we might expect AGN to appear in transitioning galaxies, suchas those undergoing merging (e.g., Hopkins et al. 2006b). We will explore the morphologies and colorsof hosts using our variability-selected low-luminosity AGNs and X-ray-selected type-2 AGNs in the HSC
Deep layer.
5.4 Cosmic reionization
As we probe further back in cosmic time, we reach the epoch of cosmic reionization, when the formation of
the ﬁrst galaxies described above reionized the intergalactic medium. Our HSC observations will address
three of the current biggest questions in the study of cosmic reionization: (i) when did it take place? (ii)what was the topology of reionization? (iii) what are the ionizing sources responsible for the transition?
While the Gunn-Peterson test (Gunn & Peterson 1965) from the spectra of distant quasars suggests anincrease in the neutral fraction of the intergalactic medium (IGM), xHI , beyond z ∼ 6 (Fan et al. 2006),
the CMB polarization data from WMAP rule out instantaneous reionization below z = 8.2 (6.7) at the2σ (3σ) level (Dunkley et al. 2009). Thus reionization probably occurred over a range of epochs, and ispredicted to have been spatially quite inhomogeneous (e.g., McQuinn et al. 2008).
Reionization epoch and topology: LAEs are a powerful probe of the IGM neutral fraction xHI during
reionization. Neutral hydrogen in the IGM scatters Lyα photons from LAEs, leading to evolution of theobserved Lyα LF (e.g., Malhotra & Rhoads 2004; Kashikawa et al. 2006; Iye et al. 2006; Ota et al. 2008;
Ouchi et al. 2010; see the left panel of Figure 10). Measurements of the LF as a function of position
can thus constrain the reionization history, and measure the reionization topology due to the high surface
density of LAEs. Studies to date are limited by the small solid angles of existing samples, and the paucity> 7.
of LAEs at z ∼With our large sample of LAEs from the Deep and Ultradeep layers (Table 6), we will construct the
Lyα LFs at z = 5.7, 6.6, and 7.3, and determine the evolution of the Lyα LF at the > 3σ level up toz = 7.3 (Figure 10). At z = 5.7 and 6.6, the Ultradeep data are deep enough to detect LAEs signiﬁcantly
fainter than L∗ (the two separate ﬁelds mitigate cosmic variance), while the Deep layer is sensitive to thehigh-luminosity end of the LF. With the Ultradeep data, we will increase the number of known LAEs at
z = 7.3 by an order of magnitude; the resulting LF will give the ﬁrst meaningful constraint on xHI beyondz = 7. The SEDs of these objects, obtained by stacking the multi-band data from optical to mid-infrared(Table 10), will allow us to determine whether they have primordial features such as top-heavy IMFs or
extremely low metallicities, which would aﬀect the production rate of ionizing photons.

Wide-field imaging with Hyper Suprime-Cam

21

Figure 10: Left: Expected measurements of the Lyα LFs at z = 5.7 (blue), 6.6 (green), and 7.3 (red) with the
Ultradeep and Deep layer data. The existing Suprime-Cam measurements at z = 7.3 go very deep, although overa very small area. The open circles are the current best measurements given by Suprime-Cam observations. The
decrease in the LF with increasing redshift is interpreted as due to the onset of reionization. Right: Expected
ACF ω(θ) of z = 6.6 LAEs from the Deep layer in the case of full ionization, assuming that they are hosted by
Mhalo = 3 × 1010 M halos (red dots). The solid curves indicate the ACFs of LAEs with Mhalo = 3 × 1010 M
for xHI = 0, 0.3, 0.5, 0.8 taken from McQuinn et al. (2007) simulations of inhomogeneous reionization. The blacksquares are the best estimates of the z = 6.6 LAE ACF available to date (Ouchi et al. 2010). Our accurate ACF
measurements over a range of a factor of 30 in θ will allow us to detect the diﬀerence in the ACF shape between thefull and partial ionization cases and to constrain xHI with an uncertainty of ∼ 0.2.

The sample of z = 5.7 and z = 6.6 LAEs from the Deep layer will cover unprecedented solid angles,
allowing us to detect the eﬀect of ionized bubbles on the angular clustering. The solid angle of the Deeplayer corresponds to 0.6 Gpc2 and should include tens of ionizing bubbles, whose signature imprints a
distinctive pattern in the ACF. This will allow us to infer xHI with an accuracy of ∼ 0.2 at z = 6.6(Figure 10), and constrain models for the topology of reionization. Combining the LF and ACF results,
we will obtain xHI at z = 6.6 with a predicted precision of ±0.1, and will constrain the physical natureof the objects causing the reionization. Spectroscopic follow-up of the high-redshift quasars we discover(Section 5.3) will allow us to explore the structure of the Gunn-Peterson absorption in three dimensions,further constraining the topology of reionization.
Finally, the Low Frequency Array (LOFAR) will probe the neutral hydrogen distribution at z ∼ 6 − 7(Zaroubi et al. 2012) over the HSC ELAIS-N1 Deep layer ﬁeld. The cross-correlation function of the
LOFAR data and HSC LAEs will reveal the signature of reionization and the evolution of ionized bubbles
at the ∼ 5σ level (Lidz et al. 2009). We are in close communication with the LOFAR team for thiscross-correlation analysis.
Reionizing sources: Star-forming galaxies are thought to be the principal sources of the ultraviolet
photons that reionized the IGM at high redshift, but recent studies indicate a shortfall in UV photons fromgalaxies at z ∼ 6 − 7 (e.g. Robertson et al. 2010). Ouchi et al. (2009) claim that galaxies are adequate
to ionize the Universe at z ∼ 7 only if α < −1.9, where α is the faint-end slope of the LF. However, thecurrent constraint on α is not strong enough to draw any ﬁrm conclusions.
Even the deepest HST imaging programs, such as CANDELS and UDF, do not probe far enoughdown the luminosity function of z ∼ 7 galaxies to observe the bulk of the galaxies that are thought to be
responsible for reionization. However, if we assume the LF has a Schechter-like power-law form at the faintend, we need to constrain the LF shape around the knee (L∗ ) to measure α from the HST data. Our HSC
survey will contain 800 bright z-dropout galaxies with MUV < −20.6, providing just that constraint.
6 Ancillary Science
The science goals of the proposed survey are focused on cosmology and galaxy evolution. However, thelesson of all major surveys is that the data one gathers to carry out the main science goals can be used to22Wide-field imaging with Hyper Suprime-Cam

enable breakthroughs in quite diﬀerent areas of astronomy. The SDSS, for example, was designed originallyto map the large-scale distribution of galaxies, but among its high-proﬁle unanticipated discoveries werethe coolest brown dwarfs (e.g., Strauss et al. 1999), streams in the Galactic halo (Belokurov et al. 2006),
dark matter halo masses from the weak lensing signal from galaxies (Mandelbaum et al. 2006), and theproperties of tens of thousands of small Solar System objects from detection of moving objects (Ivezić et al.
2001). We similarly anticipate that the HSC survey will enable signiﬁcant discoveries in areas far removed
from our core science goals. Among the opportunities are:• Studies of transient and variable objects of all sorts. While Type Ia supernovae will be used asa cosmological probe, our survey will discover signiﬁcant numbers of core-collapse supernovae as well,including signiﬁcant numbers of superluminous supernova up to z ∼ 4, and rare optical transients such asorphan gamma-ray burst afterglows. These data can be used to constrain the rate of supernova explosions
with cosmic time, the distribution of the time between star formation and supernova explosions (whichconstrains models for the progenitors) and correlations with host galaxy properties. We are also particularlyinterested in discovering the shock-breakout event (which lasts a few hours) in supernova explosions; wepredict that we may ﬁnd several tens of shock-breakout events in the HSC data.
• Discovery of rare populations of asteroids in our Solar System. Perhaps the greatest discovery potential
is in the Ultradeep layer, where the repeated cadence and great imaging depth will make us sensitive tofaint trans-Neptunian objects, allowing us to measure orbits and measure light curves in multiple bands.
In addition, discoveries of contact binaries and main belt comets are expected from the analysis of the PSF
and moving properties of objects.
• The depth of the imaging allows main sequence stars to be seen to the outer Milky Way halo. The
halo is thought to have been built up from the cannibalized debris of neighboring galaxies, and we predict
3-4 new stellar streams and of order 20 new ultra-faint dwarf galaxies in the Wide layer.
7 HSC Survey Design and Strategy
Having described the science drivers for the HSC survey, we now turn to a detailed description of the survey
design that will allow us to reach our science goals. We describe our survey areas and depths of each ﬁlter
in Section 7.1, our choices of ﬁelds in Section 7.2, and our detailed survey strategy in Section 7.3.
7.1 Filters and depths
The nature, depth, and solid angle of each ﬁlter for the HSC survey were carefully determined to meetthe main science requirements. Tables 8 and 9 summarize the ﬁlters, depths and total requested exposure
time for each survey layer. Here we describe the survey parameters for each layer, relating to the scientiﬁcgoals described in detail in Sections 3, 4 and 5.
HSC-Wide layer: the primary science driver for HSC-Wide is WL cosmology, as detailed in Section 3.
To meet the WL science goals, the top-level survey requirements are:
< 0.700 )• To carry out i-band imaging (20 min in total per ﬁeld) in nights of good seeing conditions (FWHM ∼in order to carry out high-precision measurements of the shapes of faint, distant galaxies. This depth gives
us a weighted mean number density of galaxies for which shapes can be measured of n̄g ' 20 arcmin−2 ,with hzi ' 1.
• To combine the i-band data with other ﬁlter data (grzy) for the Wide layer to estimate photo-z’s forevery galaxy used in the WL analysis.
• To cover a solid angle of 1400 deg2 , which leads to our FoM requirement on WL observables.
With these data, we will recover the dark matter distribution with unprecedented statistical precision
to higher redshifts than previously reached or can be reached with 4m-class telescopes. The statisticalaccuracies of our WL measurement will depend on the number density of galaxies usable for WL analysis
and the total solid angle (which, given the redshift range of our sample, determines the total comoving
volume covered by the survey). The i-band is ideal for measuring faint galaxy shapes, given the red colorsof high-redshift galaxies, the high throughput and relatively low sky of the ﬁlter, and the good seeing.
< 0.700 seeing yield a weighted number density ofTwenty-minute i-band exposures with Suprime-Cam in ∼galaxies with measurable shapes for weak lensing analyses of about 20 galaxies/arcmin2 . With this exposure
time, we go deep enough to probe WL to z > 1, allowing us in 200 nights to cover the cosmological volumenecessary to attain our desired constraints on w and the DE FoM.
The total exposure time is split into 6 exposures for each ﬁeld, and includes a single 30-second exposure
to allow the photometry of each ﬁeld to be tied to SDSS with bright stars (see Section 8.1). We will

Wide-field imaging with Hyper Suprime-Cam

23

Table 8: Filters and Depths
Lim. mag.b(5σ, 200 )26.5, 26.1
25.9
25.1, 24.4

Moonc
phase
d
d
g

Requirement(s)dMain scientiﬁc driver(s)e

g, r
i
z, y

Exp.a
(# of epochs)
10 min (3)20 min (6)20 min (6)photo<
FWHM ∼
0.700
photo

Deep
Deep

g, r
i

1.4 hrs (10)2.1 hrs (10)27.5, 27.1
26.8

d
d

Deep

z

3.5 hrs (10)

26.3

g

cadence<
FWHM ∼
0.700 ,
cadencecadence<photo-z, z ∼2 gals, QSO<
WL, z ∼ 2 gals, QSO
photo-z, clusters,z ∼ 1 gals, z ∼ 6–7 QSO
SNeIa
WL calibration, SNeIa

Deep
Deep
Deep
Deep
UD
UD
UD
UD
UD
UD

y
N 387
N 816
N 921
g, r
i
z, y
N 816
N 921
N 101

2.1 hrs (10)
1.4 hrs ('10)
2.8 hrs (' 10)
4.2 hrs (' 10)
7 hrs (20)
14 hrs (20)18.9 hrs (20)
10.5 hrs (' 10)
14 hrs (' 10)
17.5 hrs (' 10)

25.3
24.5
25.8
25.6
28.1, 27.7
27.4
26.8, 26.3
26.5
26.2
24.8

g
d
g/d
g/d
d
d
g
g/d
g/d
g/d

cadence
photo
photo
photo
cadencecadencecadence
photo
photo
photo

Layer

Filter

Wide
Wide
Wide

<
z∼
2 gals,ionization topology, SNeIa, QSO<z∼
2 gals, SNeIa, QSO
z ' 2.2 LAEs & LABs
ionization topology, z ' 5.7 LAEs & LABs
ionization topology, z ' 6.6 LAEs & LABs>z∼
2 gals, SNeIa>z∼
2 gals, SNeIa, QSO>z∼
2 gals, SNeIa, QSO
xHI (5.7), z ' 5.7 LAEs & LABsxHI (6.6), z ' 6.6 LAEs & LABsxHI (7.3), z ' 7.3 LAEs

Notes – a) The total exposure time for each ﬁlter and the number of epochs over which the exposure will be split. The exposure
times listed are the eﬀective exposure time without accounting for weather losses. b) The expected 5σ limiting magnitude fora point source (200 diameter aperture) under optimal conditions. PSF magnitude limits for point sources are 0.3 mag deeper
than the numbers quoted in the table, but about 0.3 mag is lost when taking into account airmass eﬀects. c) The moonphase for each ﬁlter imaging; gray (g) or dark (d) nights. d) The primary requirement on each ﬁlter imaging: photometricconditions (photo), good seeing conditions or cadence (for SN light curves and quasar variability). e) The primary sciencedrivers for imaging in each ﬁlter. LBGs, LAEs, and LABs refer to Lyman-break galaxies, Lyman-α emitters, and Lyman-α
blobs, respectively, and xHI (z) is the neutral hydrogen fraction.

dither the exposures by ∼ 0.6 degrees to make the exposure coverage more uniform. The exposures will
be separated by at least a day, so that every star and galaxy are observed at diﬀerent positions on the
focal plane and under diﬀerent atmospheric conditions. This strategy allows us to quantify and ameliorate
systematic eﬀects inherent in the shape measurement (Section 3.4).
The photo-z information, obtained from the combined data (grizy), is crucial for studying the evolution< 2 compared to the benchmark results of SDSS galaxies
and nature of intermediate-redshift galaxies at z ∼at z ∼ 0. The photo-z’s allow us to study the WL signals as a function of redshift, and to carry out
WL tomography, which signiﬁcantly improves constraints on cosmological parameters (see Section 3). The
depths in each ﬁlter are chosen to optimize the measurements of photo-z’s for galaxies up to z ∼ 1.5. As in
the i-band, we split the exposure of each ﬁlter into 3-6 visits, and dither, in order to maintain uniformity.
HSC-Deep/Ultradeep layers: The primary science drivers for the Deep and Ultradeep (UD) layers are
ﬁvefold: (1) to constrain the physics of growth, star formation quenching, and mass assembly of galaxies
at z < 2; (2) to study the halo mass dependence of star formation and stellar mass assembly up to z < 7from clustering and WL analyses; (3) to understand the physical processes of cosmic reionization frommeasurements of the IGM neutral fraction and its spatial inhomogeneity up to z = 7.3; (4) to study theevolution of faint quasars at all redshifts; (5) to probe the cosmic expansion history via the luminositydistance relation using lightcurves of ∼ 120 SNeIa up to z ' 1.4. The knowledge on galaxy properties
obtained from the Deep/Ultradeep data will be used to calibrate photo-z’s and shape measurements for the
HSC-Wide WL cosmology. In addition, the repeated observations will allow us to diagnose systematic errors
in the PSF/galaxy shape measurements. To meet the scientiﬁc goals, the top-level survey requirements
for the HSC Deep/Ultradeep layers are:
• To use the broad- and narrow-band ﬁlter images in the proposed depths, as given in Table 8, in order
to identify a suﬃciently large number of LBGs, LAEs and LABs at the target redshifts, allowing us tomeasure the luminosity function and the two-point correlation functions to the desired precision at eachredshift (see Section 5 for details).
• To observe in the broad-band ﬁlters in the Ultradeep ﬁelds with a cadence optimized to identify SNeIa
at high redshift up to z ' 1.4 as well as other intriguing transients such as superluminous supernovae andgamma-ray burst afterglows.
• To go deep enough in the broad-band ﬁlters (grizy) for Deep to obtain good photo-z estimates of every

24

Wide-field imaging with Hyper Suprime-Cam

Table 9: Total exposure time and overhead
Unit. exp.
[min]
3.3
3.3
3.3
3.3
3.3

# of ﬁeldsa

g
r
i
z
y

Exp./ﬁeld
[min]
10
10
20
20
20

Deep

g
r
i
z
y
N387
N816
N921

84
84
126
210
126
84
168
252

4
4
6
6
6
21
21
21

15
15
15
15
15
15
15
15

Ultradeep

g
r
i
z
y
NB816
NB921
NB101

420
420
840
1134
1134
630
840
1050

4
4
6
6
6
21
21
21

2
2
2
2
2
2
2
2

Filter exch.d
Subtotale

0.25 [hrs] × 2 [times/night] × 227 [nights] ' 114 hrs

Calib. exp.f

grizy

Layer/Comp.

ﬁlter

Wide

0.5×5

0.5

916
916
916
916
916

920

Total exp.
[hrs]
152.7
152.7
305.3
305.3
305.3
1221.2
21
21
31.5
52.5
31.5
21
42
63
283.5
14
14
28
37.8
37.8
21
28
35
215.6

Total readoutb
[hrs]
22.1
22.1
44.3
44.3
44.3
177.1
2.54
2.54
2.54
4.23
2.54
0.48
0.97
1.45
17.3
1.69
1.69
2.25
3.04
3.04
0.48
0.64
0.8
13.6

Subtotalc[hrs] (nights)

38.3

37.1

75.4 hrs1398.3 hrs (155.4 nights)

300.8 hrs (33.4 nights)

229.2 hrs (25.5 nights)
114 hrs2042 hrs (227 nights)a)Notes – The net exposure time and the overhead for each layer. The total number of ﬁelds to cover the area of each layer.
We will dither around each ﬁeld in each ﬁlter to improve photometric calibration. b) We assume 29 second readout overhead
between exposures in a given ﬁeld (see Table 2); the numbers shown are the total readout overhead in each band and eachlayer. c) To estimate the total number of requested nights, we assume that about 9 hours per night on average are available.
d)
For the ﬁlter exchange overhead, we assume that each ﬁlter exchange takes 15 minutes including the time for the telescope
to slew to the zenith, each observing night has two ﬁlter exchanges, and we have a total of 227 clear observing nights. e) The
total of 227 nights is estimated from the sum of the nights needed for the Wide, Deep and Ultradeep layers, including theﬁlter-exchange overhead. f ) “Calib. exp.” refers to the 30-second calibration exposures we will take for each ﬁeld in each ﬁlter
to tie the HSC photometry to that of the much shallower SDSS (see Section 8.2). We assume that the calibration exposures
can be taken under twilight or bad-seeing conditions.

galaxy down to i ' 25 (σdz/(1+z) ' 0.04 and less than 10% photo-z outlier rate) when combined with the
deep NIR data available for two of the four target ﬁelds (Section 7.2).
As we emphasized in Section 5, the deep wide-ﬁeld narrow-band imaging, in combination with deep broadband imaging, is unique; no other 8m-class telescope is capable of doing this survey at all.
The proposed cadence of observations can be done within the SSP observation framework by mixing
observations from the Wide, Deep and Ultradeep layers in the schedule (see Section 7.3 for details). The
observations in each broad-band ﬁlter for the Ultradeep layer are split over 20 epochs. In the i- andz-bands, each epoch is 60 or 80 min long, needed to carry out > 5σ photometry of z = 1.4 SNeIa one
week before, and two weeks after, the peak of the lightcurve. The exposure of each broad-band ﬁlter for
the Deep layer is split over 10 epochs. The single-epoch exposure time for the r-, g-, and i-bands allows
us to do > 5σ photometry of z = 1 − 1.2 SNeIa one week before, and two weeks after, the peak of thelightcurve. The cadence covers timescales ranging from a day to a few months, and the single-epoch depths
match requirements to identify transients at high redshift beyond the reach of 4m-class telescopes (see theright-lower panel of Figure 12).
7.2

Survey fields

Table 10 and Figure 11 summarize the target ﬁelds for the HSC-Wide, Deep and Ultradeep layers. We
impose the following requirements for the survey ﬁeld selection:• The HSC footprints should overlap the SDSS/BOSS footprint, because we use the SDSS for baseline
photometric and astrometric calibration of the HSC data (Section 8.1). The BOSS data will provide ahuge spectroscopic sample of galaxies up to z ∼ 0.7, which will be used to calibrate photometric redshifts
and the cluster-ﬁnding algorithm, and will be used for cosmological analyses.

Wide-field imaging with Hyper Suprime-Cam

25

HSC-D

R.A.

HSC-W

HSC-D/UDDEC
Galactic Extinction E(B-V)

Figure 11: The location of the HSC-Wide, Deep (D) and Ultradeep (UD) ﬁelds on the sky in equatorial coordinates.
A variety of external data sets and the Galactic dust extinction are also shown. The shaded region is the region
accessible from the CMB polarization experiment, ACTPol, in Chile.

Table 10: Survey Fields

a

Layer

Name

RA, DecArea[deg2 ]
'640

Wide

Fall equatorial
Spring equatorial

22:00≤RA≤02:40 & -1◦ ≤Dec≤ +7◦
01:50≤RA≤02:40 & -7◦ ≤Dec≤-1◦
08:30≤RA≤15:00 & -2◦ ≤Dec≤+5◦

Northern sky

13:20≤RA≤16:40 & +42.5◦ ≤Dec≤+44◦

55

Deep

XMM-LSS

02:25:00 -04◦ 300 0000

5.3

UD

E-COSMOSELAIS-N1
DEEP2-3
SXDS/UKIDSS

10:00:29 +02◦ 120 2100
16:10:00 +54◦ 000 0000
23:30:00 +00◦ 000 0000
02:18:00 -05◦ 000 0000

7.2
7.2
7.2
1.8

COSMOS

10:00:29 +02◦ 120 2100

1.8

'680

Key Other data
ACT(ACTPol), VIPERS, DEEP2,
XMM, UKIDSS, WiggleZ
ACT(ACTPol), VIKING/KIDS, UKIDSS,
GAMA, Herschelspec-z (HectoMAP: r < 21.3)
UKIDSS-DXS(NIR), VIDEO-XMM-LSS(NIR),
VVDS(spec-z), PRIMUS(spec-z)
UKIRT/CFHT(NIR), VVDS(spec-z)
UKIDSS-DXS(NIR), LOFAR-Deep(radio)
DEEP2(spec-z), PRIMUS(spec-z)
UKIDSS-UDS(NIR), SpUDS(MIR), VVDS(spec-z),
CANDELS(HST), PRIMUS(spec-z), UDSz(spec-z)
UltraVISTA(NIR), CANDELS(HST) VVDS(spec-z),zCOSMOS(spec-z), PRIMUS(spec-z), Spitzera

We were recently approved for 1250 hours of warm Spitzer time for deep observations of the COSMOS ﬁeld.

• The ﬁelds should be well distributed over a wide range of RA, such that ﬁelds are reachable at all
times of the year.
• The ﬁelds should overlap other multi-wavelength data sets to maximize scientiﬁc potential when combined with the HSC data. The major data sets which oﬀer unique synergy with HSC data are the arcminuteresolution, high-sensitivity CMB survey by ACT in Chile, and its polarization extension ACTPol, for which
Princeton is playing a major role; X-ray surveys from XMM and eROSITA; near-/mid-infrared imaging
surveys (e.g., VIKING/VIDEO and UKIDSS); and deep spectroscopic surveys (e.g., VIPERS, GAMA,COSMOS, HectoMAP).
• The Ultradeep regions should be included in the Deep ﬁelds, and (with one exception, see below) the
Deep ﬁelds should be included in the Wide ﬁelds.
• The ﬁelds should be low in Galactic dust extinction.
One of our wide ﬁelds matches a unique 55 deg2 region, the HectoMAP ﬁeld, where Kurtz et al. (2012)
are carrying out a spectroscopic survey for galaxies with r < 21.3 with Hectospec, a wide-ﬁeld multi-object
optical spectrograph, on the 6.5m MMT telescope. We will use the spatially-dense spectroscopic galaxy
catalog to calibrate cluster ﬁnding methods for the Wide data.
Although it is not listed in Table 10, we will also obtain broad-band images in grizy for the Allwavelength Extended Groth Strip (AEGIS) ﬁeld (RA = 14h 17m , Dec = +52◦ 300 ) to the depths of the
Deep layer. The AEGIS data is the largest ﬁeld with publicly available densely sampled spectroscopic
redshifts down to R < 24.1, including the DEEP2 and DEEP3 spectroscopic samples; this sample is key
for calibrating photometric redshifts. The ﬁeld can be observed with HSC with a single pointing, thus the

26

Wide-field imaging with Hyper Suprime-Cam

23

SN3693 @ z = 1.207

1st year∆ DM = 0.31

r

24

SN4765 @ z = 1.404

23∆ DM = 0.16
24

z
y

26
27
28
− 20

3rd year

Peak magnitude

5th year

26
27

0

20

40 60 80 100 120
Date (days)

Shock breakout
GRB orphana erglow-24

4th year

25

-22
-20

28
− 20

0

20

40 60 80 100 120
Date (days)

Superluminous SN

10Type Ia SN

10

-18
-16

Core-collapse SN

-14
-12

10

10
10

1

10

45
44
43
42
41

Peak luminosity [erg/s]

2nd yearMag (AB)

Mag (AB)

i

25

100

Characteris!c !mescale [day]
25.0

25.5

26.0

Figure 12: Left: An example of the results of survey simulations, showing how the i-band imaging for the Wide layer is built
up over the ﬁve years of the survey. We consider six dithering pointings in each ﬁeld. The color scales display the expected
limiting magnitude for 200 aperture photometry for a point source in each ﬁeld, taking into account the seeing, airmass, andsky brightness of each ﬁeld. Right-upper: The expected sampling rate and photometric accuracy of lightcurves of SNeIa atvarious redshifts seen in the Ultradeep survey simulation. Each curve corresponds to a diﬀerent ﬁlter. t = 0 corresponds tothe beginning of the 4 month period that either one of the Ultradeep ﬁelds is observed. The quantity “∆DM” shown in eachpanel is the expected 1σ error of the distance modulus for the simulated SNeIa, demonstrating that the sampling is adequate
to measure distances. Right-lower: Typical bolometric absolute magnitude of various transients that will be found in HSC
survey as a function of the characteristic timescale of variability.

broad-band imaging in all ﬁve bands requires less than a night of observing. We will observe this ﬁeld
early in the survey, or during commissioning if possible.
While it does not lie inside the Wide footprint, the ELAIS-N1 ﬁeld of the Deep layer is also unique: it
has deep NIR data (UKIDSS-DXS), and is one of the deep LOFAR survey ﬁelds.
7.3

Survey strategy

The unique opportunity of the Subaru SSP program allows us to carry out a very eﬃcient survey interleaving
the Wide, Deep, and Ultradeep layers. We use survey simulations to develop an optimal survey strategy
within the SSP framework. We assume up to 6 nights every month, 60 nights per year and 300 nights
in total for a 5 year survey. Our simulations include eﬀects such as observing overheads (ﬁlter exchange,
slewing time of the telescope, and the readout time of the camera), the elevation and location of each target
ﬁeld on a given night, the fact that the camera can hold up to six ﬁlters on a given run, the statistics ofseeing and weather conditions at Mauna Kea (30% of the nights are assumed lost to weather), the airmass,and the moon phase and its location on the sky. We ran simulations using diﬀerent cadences for the Deepand Ultradeep layers, and diﬀerent priorities for covering the full footprint of the Wide layer uniformly
from the beginning. Based on these simulations (Figure 12), we have settled on the following strategy:• We request allocations of 5 non-contiguous nights for each month, with phases relative to new moon
as follows: (−6, −3, 0, +3, +6 days) (14 runs) (−5, −4, 0, +4, +5) (18 runs) and (+2, +3, +4, +5, +6) (28runs)7 .
• Our observations include a mixture of exposures for each of the three layers. The i-band ﬁlter is always
in the instrument. There are up to three NB ﬁlters in at any given time.
• We carry out i-band imaging for the Wide/Deep ﬁelds when the seeing is better than FWHM= 0.700 .
• We observe only one of the Deep/Ultradeep ﬁelds in broad-band ﬁlters within a given month; thisoptimizes the cadence for supernova studies.
7
This allocation is not a stringent requirement, and we will be ﬂexible in scheduling our observing nights around open-use
programs of HSC.

Wide-field imaging with Hyper Suprime-Cam

27• The Deep ﬁelds include ﬁve overlapping pointings each. We will not coadd the narrow-band data inthe overlaps, as the ﬁlter responses will be slightly diﬀerent in diﬀerent parts of the ﬁeld.
• We allow no more than two ﬁlter exchanges per night to minimize the ﬁlter-exchange overhead.
• We carry out the redder band imaging when the moon is up (i-band imaging is allowed for ﬁelds > 60◦
from the moon; zy can be done at any moon phase).
However, we would like to note that the survey strategy above is only our current working example.
Depending on the quality of actual HSC data we will soon obtain from the commissioning runs, we willcontinue to explore an optimal survey design to maximize the science return consistent with our science
requirements and goals. We plan to use the ﬁrst year or so of data to write high-impact science papers,
and will use this experience to reﬁne our survey design. Table 9 gives details of the exposure time andoverhead for each layer.
The left panel of Figure 12 shows one example of the survey simulation results, demonstrating that our
strategy gives eﬃcient and homogeneous coverage of the Wide survey area. We have carried out multiple
survey realizations (diﬀering in the random seeds for the seeing and weather conditions). These havedemonstrated that our survey strategy robustly leads to more than 99% completeness on average for all
three layers in all ﬁlters, for a 300 night survey. To be more quantitative, the completeness (observing time
relative to the required total exposure time for our design depth) is: 99 ± 1.5, 99 ± 1.7, and 98 ± 2.1%
for the Wide, Deep and Ultradeep layers, respectively, where the error bars are the 1σ scatters of 300
realizations averaged over ﬁlters. The right-upper panel of Figure 12 shows the expected lightcurves for
SNeIa at diﬀerent redshifts, taken from one realization of the survey simulations. Our cadence allows a
suﬃciently dense sampling in time to follow the lightcurve, which in turn enables us to reliably infer the
luminosity distance based on the standard candle technique. We expect to ﬁnd about 40 SNeIa at z ≥ 1
in the Ultradeep layer, and 120 SNe in all.
Back-up observation plans: Because our photometric calibration is done relative to the dense networkof standards from the SDSS survey (Section 8.2), we will be able to take survey-quality data in mildly
non-photometric conditions. However, when the seeing or weather conditions do not meet our survey
requirements, we will carry out the following backup observations: (1) Observe Deep and Ultradeep ﬁelds
to supplement transient observations; (2) If it is clear but the seeing is bad, we will run multi-scale ditheringobservation for the calibration star ﬁelds to characterize the response (illumination) pattern over the ﬁeldof view, and we will observe ﬁelds with short exposures to extend magnitude overlap with SDSS objects
for photometric calibration.
8

Software and Calibration

8.1 Image processing pipeline
Our goal in processing the HSC images is to allow most science analyses to be carried out using the output
catalogs containing the measured properties of detected objects, without requiring scientists to work with
the images themselves. It was the SDSS experience that suﬃciently sophisticated image processing allowed
most science analyses to be done with the catalogs alone. The image processing pipeline needs to measure
the properties (photometry, astrometry, object shapes, etc) of each detected object, to an accuracy limitedby the depth and seeing of the data themselves, rather than by systematics in the processing or calibration.
We have built a pipeline on the legacy of both the SDSS (Lupton et al. 2001) and Suprime-Cam (Furusawa
et al. 2008), and are working closely with the Data Management Team of the LSST to share software tools
and algorithms. We have carried out extensive tests of our pipeline using both simulated data, and archival
SDSS and Suprime-Cam data.
Our requirements for the software are as follows:• Absolute broad-band photometric calibration per exposure of 1% rms in each ﬁlter, uniform over thesurvey area. This is comparable to the delivered calibration accuracy of SDSS (Padmanabhan et al. 2008)
and Pan-STARRS1 (Schlaﬂy et al. 2012), and as we will have multiple dithered exposures to tie down thesystem, we are conﬁdent that we will reach this requirement. Stable and accurate calibration (Section 8.2)
is key for WL analysis, photometric redshifts, large-scale structure studies, and studies of the physicalproperties of galaxies and stars.
• Astrometric calibration rms accuracy per exposure of 100 mas, allowing all astrometry to be tied
together across the survey.
• Repeat observations of a given star should have an astrometric calibration rms error of 10 mas. This is28

Wide-field imaging with Hyper Suprime-Cam

limited, in practice, by unmodeled distortions in the atmosphere. This requirement determines our ability
to measure proper motions of stars.
• An accurate estimate of both the size and shape of the PSF is necessary to model its eﬀects on galaxy
shapes. Based on the expected S/N of our lensing measurements, we require that the PSF size (ellipticity)
be measured to 0.3% (1% of typical values), which seems achievable based on current data.
• We can use simulations, analytic calculations, and the data themselves to constrain certain wellunderstood types of biases in galaxy shape measurements. We require that any remaining multiplicative
calibration biases be below 0.6%, and that 98% of the typical PSF anisotropy be removed from the galaxy
shapes.
We have already met and exceeded most of these requirements in processing simulated HSC data andarchival Suprime-Cam observations, with a clear path to meeting the rest. We are basing our software
framework on that of the LSST, which allows us to leverage the substantial eﬀort of the LSST data
management team. Like LSST, our challenge is to process in a uniform way multiple images of a given
region of sky, and to optimally combine the detections on the individual exposures. We do so not by simply
coadding the images, which would mix together data with a range of seeing, but rather by working witheach exposure separately and carrying out a ﬁt for the brightness, position, and shape of each detected
object across all exposures simultaneously. This takes full advantage of the best-seeing data, properly
propagates the noise properties of each frame, and keeps the noise on each pixel independent.
The PSF is measured from stars on each exposure, and a model for the spatial dependence of the PSF isﬁt (see Figure 13). This model is used for optimal photometry of stars, and is convolved with model galaxy
proﬁles to ﬁt galaxy properties. Convolving the image with the PSF allows optimal detection of objects.
The stars detected are matched to SDSS for astrometric and photometric calibration (Section 8.2). Finally,
for multiple exposures, each image is subtracted against a coaddition after convolving to a common PSF
to search for variable objects in the diﬀerence image.

0.2

0.2

0.1
0.0

∆m

0.1

0.0

− 0.2

− 0.2
16

17

18

19
mag

20

21

22

30000
25000
20000
15000
10000
5000
0

15

µ = -0.000
σ = 0.009

− 0.1

− 0.1

Figure 13: The ellipticity whisker plot for stars in two CCDs for a single exposure of z-band Suprime-Cam data
before (left panel) and after (middle panel) subtracting the PSF model ellipticity. Stars used to determine the PSF
and a fainter star sample are shown in diﬀerent colors. This shows that stars can be used to make a PSF model that
accurately traces the stellar ellipticities; the fainter star sample contains a few contaminating galaxies with highly
discrepant ellipticities, but coherent PSF anisotropies are largely removed. Ongoing work will improve the modelingof the PSF further, to achieve the requirements for weak lensing. Right panel: The accuracy of relative photometry
between multiple exposures in i of a single stellar ﬁeld (observed with Suprime-Cam in August 2012) after applicationof the photometric calibration procedure described in Section 8.2. The rms diﬀerence is less than 10 millimag. Thisﬁeld was observed with multiple large dithers and rotations between the observations, as we will do with the HSC
survey.

Each detected object will include measurements of its position, brightness (from PSF and galaxy modelﬁtting, e.g., the right panel of Figure 13, as well as within a ﬁxed aperture), scale size, ellipticity and othershape measurements, and estimated errors on these quantities. Information on variability or proper motion
will also be reported. All these quantities will be stored in a database accessible to the collaboration. For
the Deep and Ultradeep layers, we will provide preliminary reductions of the previous night’s data for theidentiﬁcation of transient sources by HSC collaboration scientists.
A quicker version of the processing software will run at Subaru as images come oﬀ the telescope, which

Wide-field imaging with Hyper Suprime-Cam

29will provide the seeing and photometric zero-point in almost real time. This will allow the observers tomake real-time decisions about observing conditions and what ﬁelds to image next. This information will
be stored in the survey operations database to track the completeness of the survey.
A database will archive the scientiﬁc data products from the HSC survey (the processed image data for
individual exposures and mosaiced and stacked images, as well as a comprehensive catalog of the measured
properties of detected sources), and will support data retrieval by our team and the Japanese astronomicalcommunity via a dedicated web interface. To enable fast access by the co-I members, the data products
will be mirrored at partner institutions.
8.2

Photometric and astrometric calibration of the survey

The photometric and astrometric calibration of HSC will be tied to that of SDSS, as the footprint of our
proposed survey falls completely within that of SDSS. SDSS’ photometry was calibrated to of order 1%(Padmanabhan et al. 2008); with our repeat imaging, and careful measurement of ﬂat ﬁelds, we expect
to be able to match this. We will use a natural photometric system—that is, one which is deﬁned by
the wavelength response of the HSC ﬁlter system. The magnitudes we produce will therefore be just the
instrumental magnitudes normalized to AB zero points, derived from the SDSS. This is possible because of
the expected stability of the HSC instrument (both the ﬁlters and the CCDs), whose wavelength response
we will monitor through the survey with a dedicated instrument that can illuminate the focal plane withmonochromatic light when the instrument is oﬀ the telescope.
We will take a 30 second exposure at each pointing in each ﬁlter to tie the HSC photometry to that of
the much shallower SDSS at magnitudes 17 − 20 (the default 3 minute exposures will saturate at r ∼ 19,
where the S/N of the SDSS photometry starts to drop). This will also be used to identify bright PSF
stars for the deeper survey exposures. Given that we are tying to existing photometry, we can take data in
mildly non-photometric conditions, as long as there is not too much spatial variation in the atmospheric
extinction (cf., Ivezić et al. 2007). The HSC ﬁlter set is not identical to that of SDSS, but for main sequencestars, it will be straightforward to deﬁne color terms that allow us to tie the two together, including in
the y band using model atmospheres and UKIDSS photometry. Given these model atmospheres, we cansimilarly deﬁne the zero-point of the narrow-band ﬁlters.
A changing water vapor column has the potential to aﬀect the z and y response functions at the 2%
level. We will use the real-time measurements of this column on Mauna Kea from JCMT and CSO to
calculate and correct for this eﬀect. The eﬀect of water vapor should be smaller for HSC than for SDSS
(because Mauna Kea is much drier than is APO), and given that this eﬀect was undetectable in the SDSS
photometry, we expect the eﬀects to be small in the HSC survey as well.
Doing accurate photometry requires understanding the internal calibration of the instrument, i.e., theratios of the sensitivities of each pixel on each detector to any other. This ﬂat-ﬁelding process is challenging,
due to scattered light in ﬂat-ﬁeld data. The problem is especially pronounced for fast, wide-ﬁeld systems
like HSC, which are essentially impossible to fully baﬄe. We will generate monthly ﬂats taken at zenith
from the illuminated screen on the dome, taken always with exactly the same instrumental and telescope
conﬁguration, using lamps which mimic reasonably the SEDs of the bulk of the SDSS stars, about 4000K.
The scattering contribution to these is spatially smooth and is stable by construction, and will be removed
by ﬁtting repeat stellar photometry over the ﬁeld. We expect of order 600 stars per pointing, and resultsfrom diﬀerent pointings can be combined to get superb statistics on the ﬂat-ﬁeld. Given our large dithers,
the overlap between ﬁelds allows the ﬂat-ﬁeld to be constrained from repeat photometry of stars on diﬀerent
parts of the focal plane; these overlaps allow us to carry out an “uber-calibration”, following Padmanabhan
et al. (2008) and Schlaﬂy et al. (2012).
To do really faint photometry, it is necessary to measure and subtract the sky very accurately, which isan entirely separate problem from the photometric ﬂat-ﬁelding. This will be done by constructing ﬂattened
sky templates (not “ﬂats”) by combining the data from many exposures, which will then be subtracted
from an individual ﬂattened data frame. These images will contaminated by scattered sky light, moonlight,
etc, but our work with Suprime-Cam data shows that a principal component analysis of many sky frames
works well, and takes out the fringing in the redder bands.
Astrometric calibration and stability can be checked by images in a few standard ﬁelds with a high density of stars. Repeat observations of these ﬁelds will also provide an independent check on the photometricstability, since any change in the response functions will produce changes in the relative instrumental colors
for stars of diﬀerent types/colors.

30

Wide-field imaging with Hyper Suprime-Cam

9 Concluding Remarks
Hyper Suprime-Cam, with its enormous ﬁeld of view, and the superb image quality on one of the largest
telescopes in the world, have achieved the most powerful imaging survey capability in the world. The
cosmology and galaxy evolution science we propose to carry out is at the forefront of modern astronomical
research, and will help maintain Subaru’s position as the pre-eminent telescope in the world. The data
will be processed with a state-of-the-art pipeline that will allow scientists to carry out their analyses at thecatalog level.
The data will be distributed to the collaboration, to the Japanese astronomical community, and after asuitable proprietary period, to the world via a sophisticated database, that allows our far-ﬂung collaborationto access the data simultaneously and work together on scientiﬁc projects. Our cadence has been designed
to allow science to be done after the ﬁrst year of data, stimulating our collaboration to work on sciencethroughout the ﬁve years of the program, and drawing attention of the worldwide scientiﬁc community to
our survey. We have organized ourselves in working groups around the broad science themes described inthis proposal, which have developed plans and software tools for carrying out the analyses to reach thescience goals. Our publications will be subject to a survey-wide data policy, with clear-cut guidelines oncommunication and authorship that encourage junior scientists to lead scientiﬁc projects and become ﬁrst
authors on the resulting papers.
In conclusion, the HSC survey will be the most powerful optical imaging survey of the decade, andpromises to have a scientiﬁc impact comparable to that of the great imaging surveys of the past.
References
A. Albrecht et al., arXiv:astro-ph/0609591 (2006); T. Baldauf et al., PRD, 81, 3531 (2010); P. S. Behroozi et al., ApJ, 717, 379 (2010);
V. Belokurov et al., ApJ, 642, L137 (2006); G. Bernstein, & M. Jarvis, AJ, 123, 583 (2002); J. Bernstein et al., ApJ, 753, 152 (2012);
J. Bosch, AJ, 140, 870 (2010); K. Bundy et al., ApJ, 651, 120 (2006); P. Capak et al., ApJS, 172, 99 (2007); C. A. Collins et al.,Nature, 458, 603 (2009); L. L. Cowie, & A. J. Barger, ApJ, 686, 72 (2008); S. Croom et al., MNRAS, 399, 1755 (2009); E. Daddi
et al., ApJ, 670, 156, (2007); K. Dawson et al., arXiv:1208.0022 (2012); G. De Lucia et al., MNRAS, 366, 499 (2006); G. De Lucia,& J. Blaizot, MNRAS, 375, 2 (2007); J. Dunkley et al., ApJS, 180, 306 (2009); R. Ellis, M. Takada et al., arXiv:1206.0737 (2012);
X. Fan, C. L. Carilli, & B. Keating, ARAA, 44, 415 (2006); S. Foucaud et al., A&A, 409, 835 (2003); S. Foucaud et al., MNRAS,
406, 147 (2010); H. Furusawa et al., ApJS, 176, 1 (2008); L. Guaita et al., ApJ, 714, 255 (2010); J. Gunn, & B. Peterson, ApJ,
142, 1633 (1965); J. Guzik, B. Jain, & M. Takada, PRD, 81, 3503 (2010); A. Hearin et al., ApJ, 720, 1351 (2010); C. Heymans etal., arXiv:1210.0032 (2012); C. Hikage, M. Takada, & D. Spergel, MNRAS, 419, 3457 (2012); C. Hirata, & U. Seljak, MNRAS, 343,
459 (2003); G. J. Hill et al., SPIE, 7735, 20 (2010); A. M. Hopkins, & J. F. Beacom, ApJ, 651, 142 (2006a); P. F. Hopkins, et al.,ApJS, 163, 1 (2006b); E. Huﬀ, & G. Graves, arXiv:1111.1070 (2011); H. Ikeda et al., ApJ, 728, L25 (2011); O. Ilbert et al., ApJ,
690, 1236 (2009); Z. Ivezić et al., AJ, 122, 2749 (2001); Z. Ivezić et al., AJ, 134, 973 (2007); M. Iye et al., Nature, 443, 186 (2006);
B. Joachimi, & S. Bridle, A&A, 523, 1 (2010); N. Kaiser, G. Squires, & T. Broadhurst, ApJ, 449, 460 (1995); J. S. Kartaltepe, et al.,ApJS, 172, 320 (2007); N. Kashikawa et al., ApJ, 648, 7 (2006); N. Kashikawa et al., ApJ, 663, 765 (2007); I. Kayo, M. Takada, &B. Jain, arXiv:1207.6322 (2012); M. Kurtz et al., ApJ, 750, 168 (2012); A. Leauthaud et al., ApJS, 172, 219 (2007); A. Leauthaud
et al., ApJ, 744, 159 (2012); K.-S. Lee et al., ApJ, 695, 368 (2009); A. Lidz et al., ApJ, 690, 252 (2009); S. Lilly et al., ApJS, 184,
218 (2009); L. Lin et al., ApJ, 718, 1158 (2010); J. Lotz et al., ApJ, 742, 103 (2011); R. Lupton et al., ASP Conf. Proc., 238 , 269(2001); J. P. Madrid, & D. Macchetto, arXiv:0901.4552 (2009); S. Malhotra, & J. E. Rhoads, ApJ, 617, L5 (2004); R. Mandelbaum
et al., MNRAS, 361, 1287 (2005); R. Mandelbaum et al., MNRAS, 368, 715 (2006); R. Mandelbaum et al., ascl:1108.002 (2011); R.
Mandelbaum et al., arXiv:1207.1120 (2012); Y. Matsuda et al., MNRAS, 410, L13 (2011); Y. Matsuda et al., MNRAS, 425, 878 (2012);
R. J. McLure et al., MNRAS, 395, 2196 (2009); M. McQuinn et al., MNRAS, 377, 1043 (2007); M. McQuinn et al., MNRAS, 388, 1101
(2008); S. Miyatake et al., arXiv:1209.4643 (2012); S. Miyazaki, Y. Komiyama et al., PASJ, 54, 83 (2002a); S. Miyazaki, T. Hamana
et al., ApJ, 580, L97 (2002b); S. Miyazaki et al., ApJ, 669, 714 (2007); T. Morokuma et al., ApJ, 676, 121 (2008); N. Mostek et al.,ApJ, 746, 124 (2012); B. Moster et al., ApJ, 710, 903 (2010); T. Naab et al., ApJ, 699, L178 (2009); J. Newman, ApJ, 684, 88 (2008);
M. Niemack et al., SPIE, 7741, 51 (2010); A. J. Nishizawa et al., ApJ, 718, 1252 (2010); K. Noeske et al., ApJ, 660, L43 (2007); M.
Oguri, & M. Takada, PRD, 83, 023008 (2011); M. Oguri et al., MNRAS, 420, 3213 (2012); N. Okabe, M. Takada et al., PASJ, 62,
811 (2010); Y. Okura, & T. Futamase, ApJ, 748, 112 (2012); Y. Ono et al., MNRAS, 402, 1580 (2010); K. Ota et al., ApJ, 677, 12(2008); M. Ouchi et al., ApJ, 611, 685 (2004); M. Ouchi et al., ApJS, 176, 301 (2008); M. Ouchi et al., ApJ, 706, 1136 (2009); M.
Ouchi et al., ApJ, 723, 869 (2010); N. Padmanabhan et al., ApJ, 674, 1217 (2008); R. Reyes, R. Mandelbaum et al., Nature, 464, 256(2010); R. Reyes, R. Mandelbaum et al., MNRAS, 425, 2610 (2012); G. Richards et al., AJ, 123, 2945 (2002); G. Richards et al., AJ,
131, 49 (2006); B. Robertson et al., Nature, 468, 49 (2010); T. Saito et al., ApJ, 675, 1076 (2008); S. Salim et al., ApJ, 700, 161(2009); M. Sato et al, ApJ, 701, 945 (2009); E. F. Schlaﬂy et al., ApJ, 756, 158 (2012); M. Shirasaki et al., arXiv:1204.4981 (2012);
D. Spergel, ApJS, 191, 58S (2010); C. Steidel et al., ApJ, 736, 160 (2011); M. Strauss et al., ApJ, 522, L61 (1999); M. Strauss et al.,AJ, 124, 1810 (2002); N. Suzuki et al., ApJ, 746, 85 (2012); M. Takada, & B. Jain, MNRAS, 348, 897 (2004); R. Takahashi et al.,ApJ, 700, 479 (2009); R. Takahashi et al., ApJ, 726, 7 (2011); T. Tal, & P. G. van Dokkum, ApJ, 731, 89 (2011); J. Tang, I. Kayo,& M. Takada, MNRAS, 416, 2291 (2011); Y. K. Uchimoto et al., PASJ, 60, 683 (2008); T. Valentinuzzi et al., ApJ, 721, L19 (2010);
P. G. van Dokkum et al., ApJ, 677, L5 (2008); M. White et al., ApJ, 728, 126 (2011); D. G. York et al., AJ, 120, 1579 (2000); S.
Zaroubi et al., MNRAS, 425, 2964 (2012); J. Zhang, MNRAS, 383, 113 (2008).