Clusters of galaxies in Subaru
Hyper Suprime-Cam survey
Masamune Oguri (University of Tokyo)

2016/11/2 Cosmology and Structure Formation@KIAS

Clusters as a multifaceted
cosmological probe• nature of dark matter

proof of existence, cold/warm, self-interaction,annihilation/decay signals, ....

• gravity theory

GR or modified gravity?

• cosmological parameters

σ8, Ωm, dark energy, primordial NG, ...

Cluster of galaxies in optical(from SDSS)

Coma cluster (Dean Rowe)• tight color-magnitude relation for member galaxies(red-sequence) → find clusters & derive photo-z’sOptical cluster finder: CAMIRA• “red-sequence” cluster finder with arbitrary
set of filters• fit all photometric galaxies with SPS model(BC03) to derive likelihood of being cluster
members as a function of redshift• construct a 3D richness map to find clusters
as peaks in the map

Oguri MNRAS 444(2014)147

CAMIRA SDSS DR8 catalogue
Cluster finding algorithm

9• applied this method to SDSS
DR8 data

redshift
10M. Oguri

Figure 11. Comparison between photometric cluster redshifts
zcl (see Section 2.5) and spectroscopic redshifts of clusters fromvarious external catalogues. See Section 4.1 for descriptions ofindividual external catalogues.

4.3• 0.1<z<0.6, N>20, ~70,000 2
clusters from ~10,000 deg(catalog publicly available)• performance comparable to
redMaPPer

Comparison with X-ray properties

The comparison between richness and X-ray properties suchas X-ray luminosity (LX ) and temperature (TX ) is useful
because these X-ray properties are thought
X to correlate better with cluster masses than optical richness. This suggeststhat the tightness of the mass-richness relation can be inferred from the scaling relation between richness and X-rayproperties. For instance Rykoﬀ et al. (2012) has used thisapproach to refine their richness estimates. Here we compare our richness estimates with eternal X-ray cluster cata-T -richness

Figure 12. Comparison between corrected richness N̂cor and Xray luminosities LX for MCXC clusters. The solid and dashedlines show the best-fit LX -N̂cor relation and 1σ scatter, respectively. The lower panel shows the residual of fitting as a functionof cluster redshift zcl .

PI: Satoshi Miyazaki (NAOJ)

Hyper Suprime-Cam(HSC)
HSC Survey Fields
HSC-D

R.A.

HSC-W

DEC

HSC-D

HSC-D/UD
Galactic Extinction E(B-V)• new wide-field (1.7 deg ) camera at Subaru telescope
see also Masayuki Tanaka’s talk• 3-layer survey (2014-2019?)
2• The HSC 2fields are selected based on …!

– Synergy with other data sets: SDSS/BOSS, The Atacama
Cosmology Telescope CMB survey (from Chile), X-ray (XMMLSS), spectroscopic data sets!
– Spread in RA!
lim– Low dust extinction!

− Wide(1400 deg , r ~ 26, grizy)− Deep(27 deg2, rlim ~ 27, grizy+3NBs)− Ultra-Deep (3.5 deg2, rlim ~ 28, grizy+3NBs)

The power of HSC survey
HSC WideDES SV (Rykoff et al.)SDSSall members(>0.2L*) out
to z~1.1!

Oguri et al., in prep.

CAMIRA HSC cluster catalogue• clusters frominternal releaseof HSC data(S16A) covering2~232 degOguri et al., in prep.

CAMIRA HSC cluster catalogue
2x1014Msun/hredshiftnum density

richness

1014Msun/h

5x1013Msun/hredshift

N>15 at 0.1<zcl<1.1• 1921 clusters with
14
• N=15 → M~10 Msun/hN=64.7 z=0.812

N=43.6 z=1.074

Oguri et al., in prep.

Photometric redshift accuracy• comparison withcluster spec-z

spec-z of BCGs

• accurate photo-z:(zcl-zspec)/(1+z)
bias−0.0013
scatter 0.0081
foutlier
0.017(use 4σ clipping)cluster photo-z

X-ray temperature

X-richness correlation• comparison with X-ray
clusters from XXL andXMM-LSS• richness correlates wellwith X-ray properties!

richnesscluster photo-z• small intrinsic scatterof 0.12 comparable toSDSS CAMIRA andredMaPPer results

Mass calibration: stacked lensing• much higher S/N bystacking WL for
many clusters• accurate average

mass profile ofa sample of clusters(e.g., Oguri & Takada 2011)+++…

HSC stacked weak lensing signals• photo-z PDF
included• background(preliminary!)• significant detection even at z~1!
→ accurate mass calibration

gal. selection
for eliminating
dilution effectWeak lensing selected clusters• direct reconstruction of mass distributions

with weak lensing is possible (Kaiser & Squires 1993)• clusters from peaks of mass maps
• totally different from traditional cluster finding(no baryon info)

Selection function• selection function ofWL-selected cluster
can be derived
easily and accurately• selection bias, e.g.,orientation bias

(Hamana, Oguri+ 2012)

can also be derived
accurately(assuming HSC-like survey)

Importance of ngal
HSCDES• high galaxy number
density of HSC is
crucial for massselected clusters

w/ Satoshi Miyazaki, Takashi Hamana (NAOJ), et al.

WL-selected clusters in HSC
with S/N>5 from• ~100 mass-selected clusters
2
weak lensing of ~200 deg HSC Wide images

G15, ~40 deg2

w/ Satoshi Miyazaki, Takashi Hamana (NAOJ), et al.

WL-selected clusters in HSC
with S/N>5 from• ~100 mass-selected clusters
2
weak lensing of ~200 deg HSC Wide images

G15, ~40 deg2

HSC (full area)
HSC (now)

Summary• HSC survey is powerful for cluster studies!
• optical clusters are identified successfully out
to z~1.1• masses of these optical clusters are calibratedusing stacked weak lensing• first large sample of mass-selected clusters

from weak lensing maps is being constructed