Weak Lensing Cosmology fromthe Subaru Hyper Suprime-Cam Survey
First Year Data

Hironao Miyatake
Nagoya University
On Behalf of the HSC Collaboration

Outline•

Cosmic acceleration and dark energy•

Weak lensing cosmology•

Subaru Hyper Suprime-Cam (HSC)••

First-year results from HSC survey•

Cosmic shear in real space and Fourier space•

Galaxy-galaxy lensing and clustering (ongoing work)

Future prospects

Ordinary Matter
5%
Dark Matter
26%

Dark Energy
69%

Planck Collaboration (2018)

We Would NOT Exist without Dark Matter

Without Dark MatterWith Dark MatterCredit: N. Yoshida(c) Kobayashi-Maskawa InstituteThe Universe has been evolved under the competition between• Gravity due to dark matter, and• Accelerating expansion due to dark energy.

Dark Sector of the Universe•

Dark Matter• Non-luminous, unknown matter.
• Source of gravity to form galaxies
and galaxy clusters.
• New particle?

•

Dark Energy• Unknown energy.
• Source of cosmic acceleration.
• Breakdown of Einstein’s GR?
• Fifth force?

Cosmology Probes••

Geometry of the Universe•

Type Ia supernovae (SN)•Baryon Acoustic Oscillations (BAO)

Credit: SDSS/BOSS

Growth of structure•

Cluster number count (CL)•Weak gravitational lensing (WL)•

Redshift space distortions (RSD)→ sensitive to modified gravity
Credit: ESA

Cosmology Probes••

Geometry of the Universe•

Type Ia supernovae (SN)•Baryon Acoustic Oscillations (BAO)

Credit: SDSS/BOSS

Growth of structure•

Cluster number count (CL)•Weak gravitational lensing (WL)•

Redshift space distortions (RSD)→ sensitive to modified gravity
Credit: ESA

(Strong) Gravitational Lensing

Abell 2188, Credit: NASA/ESAWeak Gravitational Lensing

WL shear is only a few % of intrinsic shape of galaxies
Credit: Subaru HSC

Weak Gravitational Lensing in a Nutshell

Weak lensing shear

DA(zl, zs)DA(zl)γ∝δ(zl)
DA(zs)
Matter density fluctuation
Geometry of the Universe

DA(zs)DA(zl, zs)DA(zl)

Weak lensing is a unique probe ofthe matter (including dark matter) distribution

Weak Lensing Measurement•

Weak lensing coherently changes
shapes of galaxies

DA(zl, zs)DA(zl)γ∝δ(zl)
DA(zs)

Intrinsic shapes of galaxies (shapenoise) can be beaten down using anumber of galaxies, assuming theposition angle is randomly oriented.

σγ =σγ,intN

With Shape Noise•

LensedWithout Shape NoiseUnlensed→ Need (at least) thousands of galaxies to perform a highsignal-to-noise measurement

Galaxy Shape Measurement

Point Spread Function (PSF)

Superb image quality is required.
Credit: S. Bridle

Extracting Cosmological Information
Observations

TheoryCompare through correlation functions

Need to observe a large volume of the Universe.

What is Necessary for Weak Lensing Cosmology?

•A large number of galaxies to perform a high signal-tonoise measurement•

Superb image quality•

Observations of a large volume of the Universe.

Subaru Hyper Suprime-Cam (HSC) is one of the best
instruments to perform precision weak lensing cosmology.

Subaru Telescope

Summit of Maunakea, Hawaii Island (4200m)

Subaru Telescope
Prime Focus: Wide Field-of-View
8.2m Mirror: Plenty of Galaxies

Adaptive Optics: Superb Image Quality

Hyper Suprime-Cam (HSC)
1 billion pixels
2GB/shot114 2k x 4k back-illuminatedfully-depleted CCDs
developed by Hamamatsu and NAOJ

Miyazaki et al. (2018a)
Komiyama et al. (2018)

Galaxy
Cluster

HubbleSpace
Telescope

Suprime-Cam (predecessor of HSC)

Galaxy
Cluster

HubbleSpace
Telescope

HSC Field-of-View

Suprime-Cam (predecessor of HSC)

A galaxy cluster at z~1 is observed like…
SDSS

HSC

HSC Subaru Strategic Program (SSP) Survey

HSC Subaru Strategic Proposal (SSP) Survey

Wedding-cake-type survey

retuFungoingOstPa
Credit: M. Oguri•

Wide (1400 deg2, ilim~26, grizy)•Deep (28 deg2, ilim~27, grizy + NBs)

•Ultradeep (3 deg2, ilim=27.7, grizy + NBs)

Competitors in the World

Dark Energy Survey (DES) led by USA
5000 deg2, ilim~24

Kilo-Degree Survey (KiDS) led by Europe
1500 deg2, rlim~25
Credit: M. Oguri, Dark Energy Survey

Survey Field•

HSC Survey Fields selected based on overlap with SDSS regions, andother interesting datasets (ACT, PB, VIKING, Spitzer, GAMA, VVDS, etc…)•Spread along the equator•

For details, see a survey overview paper: Aihara et al. (2018a)

A. J. Nishizawa
H. Miyatake
Photo-z WG chair Weak-lensing WG co-chair

Nagoya University is one of the leading institutes!

Matter Density Reconstruction from Observables
Weak lensing shear is measured by averaging galaxy shapes

DA(zl, zs)DA(zl)γ∝δ(zl)
DA(zs)
Source galaxy redshift
Matter density fluctuation

◦

246◦
242◦
RA [deg]

176◦

240◦

Dec [deg]1◦ 00’00’

0◦ 30’-0◦ 30’30’-1◦ 00’

130◦

238◦

225◦

-2◦ 00’-3◦ 00’-4◦ 00’Dec [deg]

Dec [deg]

Dec [deg]

215◦

225◦

220◦

215◦

VVDS

-1◦ 00’176◦

20RA [deg]

32◦
334◦

High number density: ng~23 gal/arcmin2

GAMA09H-3◦ 00’Dec [deg]

2◦

332◦

Dec [deg]

15

XMM-2◦ 00’4◦30◦(c.f., DES~7 gal/arcmin2, KiDS~10 gal/arcmin2)

2500’-0◦ 30’30’

178◦30336◦RA [deg]-1◦ 00’180◦
RA [deg]332◦

effective number density [arcmin−2 ]

00’

334◦

0◦ 30’Dec [deg]0◦ 30’

336◦
RA [deg]

338◦

GAMA15H-0◦ 30’

182◦

338◦

1◦ 00’30’•340◦

WIDE12H

1◦ 00’Dec [deg]238◦

2◦ 00’

240◦

00’

242◦
RA [deg]1◦ 30’

244◦

3500’

RA [deg](c.f., DES~0.9”, KiDS~0.66”)

246◦

XMM

Excellent seeing: FWHM~0.6”00’-0◦ 30’248◦

34◦
RA [deg]•1◦ 30’

GAMA15H36◦

0◦ 30’

42◦ 30’00’00’

VVDS
2◦ 00’

340◦

43◦ 30’0◦ 30’-0◦ 30’HECTOMAP
44◦ 00’220◦

Full-depth full-color: i~26 (5σ)-5◦ 00’•-6◦ 00’WIDE layer: 6 fields, 170deg2

Dec [deg]

38◦•

HECTOMAP

178◦

244◦

WIDE12H180◦
RA [deg]

GAMA09H135◦
RA [deg]

HSC First-year Galaxy Shape Catalog-4◦ 00’

10-5◦ 00’

34◦
RA [deg]

32◦

30◦

3530

Publicly available.

36◦

25

•

130

38◦

◦

20

Calibrations based on image simulations.

135
RA [deg]

15

•

140

◦

10i<24.5, resolution>1/3

5-6◦ 00’◦

5•0◦

effective number density [arcmin−2 ]Mandelbaum, HM et al. (2018a)
Mandelbaum et al. (2018b)

Source Galaxy Redshifts

z=0

z=0.5

z=1.0

Redshift is estimated from photometric colors (called photo-z)

HSC First-year Science Highlights
2D and 3D dark matter map
Cosmic shear in Fourier space and real space
Galaxy-galaxy clustering + lensing

Dark Matter Distributions Reconstructed from WL
2D galaxy light distribution+02.00◦Dec (J2000)+01.50◦+01.00◦+00.50◦+00.00◦−00.50◦340.00◦338.00◦336.00◦RA (J2000)

334.00◦332.00◦0.00080.00070.00060.00050.00040.00030.00020.00010.0000−0.0001−0.0002

3D dark matter distributions

2D dark matter distribution+02.00◦◦Dec (J2000)+01.50+01.00◦+00.50◦+00.00◦−00.50◦340.00◦338.00◦336.00◦RA (J2000)

334.00◦332.00◦

4.0
3.2
2.4
1.6
0.8
0.0
−0.8
−1.6

VVDS

Galaxy clusters selected by dark matter peaks(61, 4.7, 0.3, 35)(06, 6.4 0.1)

(56, 4.8, 0.3,0.8, 17)23)(52, 4.9, 0.3, 54)(65, 4.7, 0.3,0.2, 21)
19)(03, 7.5, 0.2, 48)(59, 4.8)(50, (44,4.9, 5.0,0.1, 0.3,0.4,22) 30)
34)(21, 5.5, 0.3,0.1, 33)
19)

340

 2000335

330

Oguri et al. (2018), Miyazaki et al. (2018b)

How Can We Compare Observations to Theory?
TheoryObservations

Oguri et al. (2018)
Credit: ESA

Initial condition of large scale structure is from quantum fluctuations.
Theories cannot predict where are cluster, galaxies, and dark matter,but can predict their statistical properties.

Correlation Function and Power Spectrum
TheoryObservationsr

Oguri et al. (2018)
Credit: ESA

ξmm(r) = ⟨δm( r′⃗ )δm( r′⃗ + r ⃗)⟩r′⃗

Spatial correlation of matter fluctuations

Cosmological Constraints from Correlation Function

ξmm(r) = ⟨δm( r′⃗ )δm( r′⃗ + r)⟩⃗ r′⃗
Amplitude of matter fluctuations σ8
Growth of matter fluctuations D(z)D(z) can be measured by correlation function measurements inredshift slices. D(z) is a function of• Matter energy density Ωm• Dark energy density ΩΛ , and• Dark energy equation of state parameter p = wρ.

Cosmological Weak Lensing: Cosmic Shear

Cosmic shear can infermatter correlation function.

ξγγ(r) = ⟨γ( r′⃗ )γ( r′⃗ + r ⃗)⟩r′⃗
ξmm(r)
Cosmic shear can bemeasured in Fourier Space.

ξγγ(r)γγClCosmic Shear Tomographic Power Spectra
4-bin tomography in 0.3 < z < 1.5fi thsderrehgih

Focused on 300 < l < 1900 toavoid potential systematic eﬀects••high l : baryon feedback
low l : residual shape noise

S/N of cosmic shear is ~16
BB & EB signals are consistent
with zerobin1: 0.3<z<0.6, bin2: 0.6<z<0.9
bin3: 0.9<z<1.2, bin4: 1.2<z<1.5
Hikage et al. (2019)

Blind Analysis is Essential to Avoid Confirmation BiasBlind Analysis
Galaxy shapes were changed bymultiplying a blinded number.
Our analysis was unblinded when
we are confident on systematic
tests listed in the table.
Cosmology

Intrinsic alignment

PSF modeling error

Baryonic eﬀect

Photo-z uncertainties

Hikage et al. (2019)S8 = σ8(Ωm /0.3)

0.45

amplitude of matter power spectrum
scaled by Ωm

Cosmological Constraint on σ8 and Ωm
HSCPlanck(This work)

DESKiDSΩm matter energy density
No significant inconsistency between Planck and HSC
Hikage et al. (2019)

Constraints on the Nature of Dark Energy

The constraint is still weak, but will be more stringent with thefull HSC data.

Cosmic Shear Tomographic Correlation Functions

Consistent with the power spectra measurement
Hamana et al. (2019)

Galaxy-galaxy Lensing x Galaxy-galaxy Clustering
Combine lensing and clustering signals to extract
cosmological constraints

Galaxy-galaxy Clustering Measurement
Galaxy Distributionr

ξgg(r) = ⟨δng( r′⃗ )δng( r′⃗ + r ⃗)⟩r′⃗

Galaxy galaxy clustering measures how likely a neighboring galaxy exists atdistance r from a galaxy.

Springel et al. (2005), Croton et al. (2006)

Amplitude of Galaxy-galaxy Clustering Signal

Baryon acoustic oscillations (BAO)
Sensitive to geometry of Universe

BAO
Amplitude

Amplitude of g-g clustering signal
Sensitive to Growth of structure, e.g., σ8More signal-to-noise compared to BAO

Eisenstein et al. (2005)

Galaxies: Biased Tracer of Dark Matter
Galaxy Distribution

Dark Matter Distribution≠
Since most of matter in the Universe is dark matter, we need to use dark matter
distribution to extract cosmological information. Galaxy distribution cannot bedirectly used.
Springel et al. (2005), Croton et al. (2006)

How Does Galaxies Populate the Universe?
Dark Matter Distribution

Dark Matter Halo
Dark matter collapses into dark matter halos.
Self-gravitating systems of dark matter
Springel et al. (2005), Croton et al. (2006)

How Does Galaxies Populate the Universe?
Dark Matter Distribution

Dark Matter Halo
Dark matter collapses into dark matter halos.
Galaxies are formed in halos as a result of gas accretion.
Springel et al. (2005), Croton et al. (2006)

Calibration by Galaxy-galaxy Weak Lensing
Dark Matter Distribution

Dark Matter Halo
Dark matter halos are the biased tracer of the underlying dark matter distribution.
Weak lensing measurement around galaxies ( = average dark matter
distributions around galaxies) can be used to calibrate the connection between them.
Springel et al. (2005), Croton et al. (2006)

HSC x BOSS Measurement
SDSS-III/BOSS DR11 spec-z sample•••~8300 deg2z = [0.15, 0.35], [0.47, 0.55], [0.55, 0.70]
Luminosity cut is applied to obtain volumelimited sample.

HSC first-year shape catalog••

137 deg2, <z>~1.0
Galaxy shape catalog is blinded

g-g lensing signal
g-g clustering signal

Measurements

g-g lensinglBdnide

g-g clusteringModeling Lensing Signal

Cross-correlation function of dark matter
halos and surrounding dark matterrξhm(r) = ⟨δnh( r′⃗ )δm( r′⃗ + r ⃗)⟩r′⃗Modeling Lensing Signal

Cross-correlation function of dark matter
halos and surrounding dark matterrξhm(r) = ⟨δnh( r′⃗ )δm( r′⃗ + r ⃗)⟩r′⃗

×How galaxies populate DM halosNg(M)Lensing Signal ξgm(r)Modeling Clustering Signal

Correlation function of dark matter halosξhh(r; M1 . M2) = ⟨δnh( r′⃗ )δnh( r′⃗ + r ⃗)⟩r′⃗r

Modeling Clustering Signal

Correlation function of dark matter halosξhh(r; M1 . M2) = ⟨δnh( r′⃗ )δnh( r′⃗ + r ⃗)⟩r′⃗

r

×

How galaxies populate DM halos

Ng(M)Modeling Clustering Signal

Correlation function of dark matter halosξhh(r; M1 . M2) = ⟨δnh( r′⃗ )δnh( r′⃗ + r ⃗)⟩r′⃗

r

×

How galaxies populate DM halos

Ng(M)Clustering Signal ξgg(r)Major Challenge: Building a Robust ModelCorrelation Functions

Galaxy populations

ξhm(r; Mh, z; θ)How galaxies
populate a dark
matter haloξhh(r; Mh1, Mh2, z; θ)θ: Cosmological parameters×

Lensing signal
Clustering signal

Major Challenge: Building a Robust Model
Correlation Functions

Galaxy populations

ξhm(r; Mh, z; θ)How galaxies
populate a dark
matter haloξhh(r; Mh1, Mh2, z; θ)••×

Lensing signal
Clustering signal

It is not straight forward to model the connection between
halos and underlying dark matter distributions.
In previous studies, analytical fitting formulae have been
used, which can cause systematic bias.

Dark Emulator 


Measure summary statistics in 1 [Gpc/h]3
and 2 [Gpc/h]3 simulations with 20483
particles for 101 cosmological parametersets.

 

 


 


•



Dark Emulator provides the following
summary statistics for a given cosmology• Halo-matter correlation function: ξhm• Halo-halo correlation function: ξhh•θ=(ωb, ωc, ΩΛ, As, ns, w)
•

We no longer rely on fitting formulae!

Interpolate these measurements to agiven cosmology using Gaussianprocess.

 •             

Nishimichi et al. (2018)

Major Challenge: Building a Robust Model
Correlation Functions

Galaxy populations

ξhm(r; Mh, z; θ)How galaxies
populate a dark
matter haloξhh(r; Mh1, Mh2, z; θ)••×

Lensing signal
Clustering signal

Nobody knows what is the “correct” model of galaxies.
We adopt an empirical, standard model (halo occupationdistribution; HOD) and see if it is robust against possible
population models of galaxies.

Cosmology Challenge
Test the robustness of our model by fitting mocks with variants.
GALAXY PARAMS W/VARIANTS‣ DIFFERENT SAT.
DIST.
‣ DIFFERENT HOD
FORM.
‣ OFF-CENTERING‣ BARYONIC EFFECTS‣ ASSEMBLY BIAS,
ETC…

MOCK SIGNALCOSMO. PARAMETERSCOSMO. PARAMETERS

Our “fiducial” model‣ “Standard” HOD (Zheng et al. 2005)
FIT

MODEL
W/ Y1
COVARIANCESCALE CUTS

‣ Off-centering PDF: Gaussian‣ Satellite distribution: NFWCosmology Challenge
Test the robustness of our model by fitting mocks with variants.
GALAXY PARAMS W/VARIANTS‣ DIFFERENT SAT.
DIST.
‣ DIFFERENT HOD
FORM.
‣ OFF-CENTERING‣ BARYONIC EFFECTS‣ ASSEMBLY BIAS,
ETC…

MOCK SIGNALCOSMO. PARAMETERSCOSMO. PARAMETERS

FIT

MODEL
W/ Y1
COVARIANCESCALE CUTSCOSMOGALAXYHM et al. (in prep)

Cosmology Challenge
Test the robustness of our model by fitting mocks with variants.
GALAXY PARAMS W/VARIANTS‣ DIFFERENT SAT.
DIST.
‣ DIFFERENT HOD
FORM.
‣ OFF-CENTERING‣ BARYONIC EFFECTS‣ ASSEMBLY BIAS,ETC…

MOCK SIGNALCOSMO. PARAMETERSCOSMO. PARAMETERSFITMODEL
W/ Y1
COVARIANCESCALE CUTSokc

M
COSMOnAsisyla

GALAXYHM et al. (in prep)

The cosmology challenge is almost done.
Stay tuned for cosmological constraints!

Future Prospects

HSC Third-year Analysis and Beyond•

We are currently creating the galaxy shape catalog basedon the HSC data up to the third year of the survey•Survey area will be extended from 140 deg2 to 270 deg2.

•

We plan to complete the HSC SSP survey in 2020 toreach 1400 deg2.

SuMIRe Subaru Measurement of Images and Redshifts

Prime Focus Spectrograph•••

1.3 deg diameter field of view covered by 2400 fibers.
The SSP survey is expected to start in 2022.
BAO and combined cosmology analysis with HSC is one of
the main science drivers.

Galaxy Imaging Surveys in 2020s

2022-2022-2025-

LSST (USA)15,000 deg2, ilim~27

Euclid (EU)
15,000 deg2
Jlim~24WFIRST (USA)2,200 deg2,Jlim~27

Summary•

Subaru Hyper Suprime-Cam (HSC) started the imagingsurvey in 2014.

•

HSC is a unique instrument that enables a wide and deepsurvey.

•The first-year cosmology results are coming out!

•The Prime Focus Spectrograph (PFS) survey will start in2022, and 3 large imaging surveys are planned in 2020s•

We are living the golden era of observational cosmology!