国立天文台 天文データセンター 白崎裕治
NAOJ, Astronomy Data Center, Yuji Shirasaki
1Contents
1.

Introduction on study of AGN environment
 AGN-AGN auto correlation
 AGN-Galaxy cross correlation

2.

AGN-Galaxy cross correlation study using
Suprime-Cam archive

3.

Perspective on AGN-Galaxy cross correlation
study using HSC

2

Unresolved problem in AGN research Fueling mechanisms of AGN
How is the AGN fueling transferred to the SMBH ?

M major/minor merger
 tidal interaction
 bar instability
 dynamical friction
 stellar disruption
 ...

externalinternalMartini 2004

From recent observations :‒ low/intermediate luminous AGN  internal‒ most luminous AGN (QSO)  external

3What does AGN environment tell us ?
 AGN auto-correlation
 correlation between different DMHs

 large scale structure

Mass of the DMH

 AGN-galaxy cross-correlation
 correlation within the same DMH small (+intermediate) scale structure

Mass of the DMH+ Distribution in the DMH
4

AGN auto-correlation
 Require uniform large area survey

̶̶

2dF Survey
SDSS Survey

 Constant MDMH below z = 3.0
 Larger MDMH at higher redshift (or luminosity ?)MDMH = 2 x 1012 h-1M◎ (z < 3)

Shen et al. (2007)

Ross et al. (2009)5

AGN-galaxy cross correlation (1) Low redshift (z < 0.6)
 SDSS, 2dF Survey
 Low luminosity AGN
 r ~ 6 h-1Mpc

 similar to typical local galaxy

 Intermediate redshift (z = 0.6 ~ 3.0)
 Deep survey / IR observation
 Low/intermediate luminosity AGN
 small sample (a few tens or less) r = 3.7 ~ 6.3 h-1Mpc○ radio AGN > X-ray AGN > IR AGN (Hickox et al. 2009)6

AGN-galaxy cross correlation (2) Subaru Suprime-Cam archive + UKIDSS z = 0.3 ~ 3.0 wide luminosity range (MV = -30 ~ -20)
 1,809 AGNs

Largest samples
ever achieved(at z > 0.6)
Shirasaki et al. (2011)
PASJ 63 S469

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Our method cross correlation function : ξ(r)probability of finding a galaxy at a given separation from an
AGN compared to a random distribution ≈ n(r) / n0 - 1

can be ignored if the two AGN
are well separated

 Stack the number density n(rp) for all theAGNs and derive the average of w(rp)8Merit of this method Doesn’t require redshift measurement forgalaxies
 single band image
 Easy to obtain a large statistical sample

 Precise measurement at small scale (average)
 Distribution of AGN in a DMH

 Free from selection bias for galaxy sample
 all objects detected in the image can be considered9Our result (1) Clustering detected up to z
= 1.8
 Less luminous low-z AGN
 power law
 Luminous high-z AGN flat distribution < 3Mpc

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Our result (2) Difference of the galaxy samples ?

w(r)Our z ~ 1.6 sample shows larger
correlation length than that of theexisting measurements

z~1.6
r0 = ~11 h-1Mpc

z~0.5
r0 = ~5 h-1Mpc
Projected distance (Mpc)

11Summary of our result
 relatively large cross-correlation length wasdetected at z ~ 1.6
 no luminosity dependence was foundbetween the two luminosity groups flat distribution at < 3 Mpc for the bright groupindicates that the AGNs are distributed
uniformly in their DMH
 open question:
 nature of the large clustering found at z ~ 1.6○ redshift, luminosity, galaxy type ?
 the small scale structure should be confirmed withhigher statistic
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HSC Wide Survey + QSO/AGN
Veron QSO&AGN Catalog (13th)Dec

HSC Wide Survey

50,000 AGN/QSO in the HSC Wide30,000 ?
RA13

Expectations for HSC
 extends redshift range beyond 2
 but may be difficult for z > 3. IR can do a better job...
 increase S/N by incorporating photo-z of galaxies
 more precise study on luminosity dependence ofclustering
 known luminous AGNs + HSC low luminosity AGNs

around z ~ 2 find a threshold where clustering becomes large
 precise measurement of cross correlation at asmall scale  distribution of AGN in a DMH
 dependence on the galaxy type needs help of IR data (UKIDSS, VISTA)

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Detection threshold for single bandanalysis

Number of known
AGNs in the HSCwide

HSC Wide 領域に含まれる既知のQSO& AGNの数
to detect
既知のThresholdAGNのみ。
theclustering AGN は未考慮SWANS
で検出される
signal15

Three steps strategy for writing a paper
1.

2.

Cross correlation study using single band data̶simple extension of our study using the Suprime-Camdata with higher statistic.
̶This can be done quickly.

Galaxy selection/rejection by photometric redshift
̶3.

need photo-z code and multi-band catalog

Dependence on galaxy type with the help from IRdata
̶

UKIDSS already there, VISTA will provides deepercatalog
̶MOIRCS archive data might also be useful.
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