Study on the Environment ofQSOs with Redshift 1~3
using the JVO SystemYuji Shirasaki
National Astronomical Observatory of Japanand JVO collaboration

Objectives
 QSO-Galaxy correlation:
 Formation and Fuelling mechanism of QSO
 History of the large scale structure formation

 Observations so far:z < 1.0
 Poor statistics (~tens of QSOs)
 Goal of our study:
 Explore z > 1.0
 High statistics (~hundreds or more) QSO/AGN catalog (SDSS, 2dF, …) 100,000 known QSOs/AGNs
 Archived deep images by large telescopes (Subaru).
 Opt ~ IR (SuprimeCam, MORICS)

Data Discovery through JVO
 Two VO data services are constructed:
 QSO Catalog data service (Veron et al. 2006 from VizieR)
 Subaru SuprimeCam image data service
 Both services are developed by using the JVO SkyNodetoolkit.
 http://jvo.nao.ac.jp/download/skynode-toolkit
 SuprimeCam images containing cataloged QSOs aresearched on JVO portal service.
 Three kinds of data search methods

SQL Search mode
General interface for data accessUser I/FDedicatedfor a specificpurpose

Workflow (Script) mode
Automate the procedure: data discovery, data retrieval, imageanalysis (source extraction), photo-Z estimation …

Procedure of QSO image search
1. Request QSO Coordinates (ADQL)
2. QSO Coordinates (VOTable)

QSO Catalog
108080 records

JVO Portal

3. Request images of QSOs
(ADQL+VOTable)
Subaru SuprimeCam
4. Metadata of Images (VOTable)
Image Data
30444 exposures
5. Request an image (HTTP Get) 291 mosaic images
6. A QSO image (FITS)

SuprimeCam QSO Fields
Three wide band filters, Exposure > 1000 sec
 ~60 out of 1599 fields (17 Fields alanlyzed)
 ~700 out of 108080 QSOs/AGNs (100 QSOs/AGNs analyzed)
Number of QSOs in each field (#QSO>=3)Field name|#filter| #QSO-------------------+-------+----COSMOS|
4 | 140
LHNW|
3 | 92HDF|
4 | 62
F1052N|3 | 50J0053+1234|
3 | 19F1052S|
3 | 19MS2054|
3 | 17Lynx|
3 | 12
RXJ0153|
4 | 12Daddi|
3 | 11
GRB030723|
4 | 10
SDSS J0338+0021|
4 | 10SXDS_1|
5 |8SDSSp J 104433.0… |4 |8MS0451.6-0305|
3 |8XMM_deep|
3 |6MS0016|5 |6SIRTF HDF-N Field |4 |6F0230E|
3 |6Field name|#filter| #QSO---------------+-------+----SXDF|
3 |6A2390|
5 |6SSA13|4 |5NGC4486_1|
3 |5F0230W|
3 |5Chandra deep |3 |52309-00
|
3 |
5M51
|
3 |
5NGC2419|
3 |4z5qsos|
3 |4F0848W|
3 |4CL1604_3|
3 |4CL1604_2|
3 |3
CL1604_5|
3 |3
CL1604_4|
4 |30918+06|
3 |30127-00|
3 |3CL1604_8|
3 |3NGC4486_3|
3 |3Analysis
 Reject images whose coverage is less than 50% of
10x10 armin2 area centered at QSO position.
 Check if there are at least three-bands images.
 Source Catalog around QSOs (SExtractor)
 Calculate redshift probability P(z) (HyperZ)
 Select objects with P(zQSO) > 0.5
 Radial distribution. (dead pix, bright objects)
 Spatial QSO-Galaxy cross-correlation amplitude Bgq

Average Radial Profile forz = 0.5 ~ 1.534 QSOs

20 QSOsAverage Radial Profile forz = 1.5 ~ 4.0
22 QSOs

20 QSOs

4 QSOsSpatial QSO-Galaxy cross-correlation
amplitude Bgq
Number density of Galaxy at a distance r from a QSO

Average Number density of Galaxy at QSO redshift

Total Number within 60 arcsec (0.5 Mpc)

Number expected from BG region(200~280 arcsec)

Number density brighter than limiting magnitude at QSO redshiftBgq vs Redshift

(4)

(34)

(20)

(22)(20)

Summary Data discovery of deep images around known

QSOs is easily achieved.

 700 QSOs are imaged by Subaru SuprimeCam 100 QSOs are analyzed.

 Bgq is derived for z = 0.5 ~ 4.0
 Tendency of increase of Bgq at higher redshifts
 Clustering seems to be significant for M < -25 Future Work
 Analyze more data (only 20% of data is anlyzed ) Incorporate IR data (Subaru MOIRCS etc.)
 Better Photometric redshift estimate.
 Early type galaxies.