1. High redshift cluster search (SA22 field) KIAS SSG workshop 2013. 2. 15. Jae-Woo Kim (SNU) M. Im, S.-K. Lee & M. Hyun (SNU)

2. Galaxy cluster A few words describing galaxy clusters - Gravitationally bound - The most massive object - Laboratory for investigating the galaxy evolution - Density peak : halo abundance -Large-scale structure What we need - Wide & deep surveys : clusters are rare objects Many clusters have been found from various surveys Main science of future wide surveys. (e.g., DES) From NASA APOD

3. Galaxy cluster @ z>1 At 1<z<2, the Universe changed and completed various phenomena. Important period to understand the evolution of Universe. Galaxy clusters? Cluster galaxies? BUT not many known clusters at z>1 – lack of wide NIR A NIR dataset plays an important role to identify z>1 clusters due to the redshifted stellar emission. Madau 1998 Darkenergysurvey.org

4. NIR surveys UKIDSS Deep eXtragalactic Survey - four 8.75deg 2 with J and K band using UKIRT - clustering of galaxies at z>1 -searching for galaxy clusters at z>1 -LAS, GPS, GCS, DXS & UDS Infrared Medium-deep Survey - mapping 138deg 2 - Y and J band survey using UKIRT - discovering high-z quasars - galaxy cluster at z>1 http://www.vikdhillon.staff.shef.ac.uk/t eaching/phy217/telescopes/ukirt.jpg We try to find galaxy clusters at z>1 using multiwavelength datasets.

5. Catalogues Observed - DXS+IMS+CFHTLS -ugrizJK -Swarp, SExtractor - 18.44deg 2 - i<24.2, J<23.2 - photo-z : Δz/(1+z)~0.038 Mock - GALFORM (Cole+00) - GALEX, SDSS, Subaru, UKIDSS filters - 22.06deg 2 & K=24 - i<24.2, J<23.2 z=0.8z=1.2

6. Algorithm Select galaxies in redshift bins : z±(1+z)0.038 Count the number of galaxies within 500h -1 kpc Gaussian fit & select >3σ galaxies Reject galaxies if ND 300 <ND 500 Merge candidates - connected z-bins - separation < 0.0238 deg Properties of candidates - Centre : mean of candidates in connect bins - Redshift : median of gals. within 500 h -1 kpc and in photo-z uncertainty - Richness : n gal, n bg, LF 626 candidates in 0.8<z<1.2 For the mock catalogue - Apply the same algorithm - to reproduce photo-z, the uncertainty was assigned randomly (Muzzin+07)

7. Based on Mock z dependence Mass vs. richnessRecovery rate previous result - Low z - No z dependence We found the z dependence But the slope is independent on mass ~100% for logM=14.2 ~10% for logM=13.5 Richness is a good proxy for a cluster mass. BUT large scatter. 100% 10%

8. Preliminary results Normalised richness dist.Clustering of clusters Overall, they look similar. BUT the observation shows more halos with larger richness. Angular correlation fn. Similar shape with ΛCDM on the linear regime Bias=9.84 with logB>3.3 Bias=8.98 with 3.0<logB<3.3 logB>3.3 3.0<logB<3.3

9. Summary We try to discover new galaxy cluster at z~1. The algorithm is successful. Probably more massive halos than ΛCDM Clustering shape is matched to that by ΛCDM on the linear regime with bias~9 The wide, deep survey provides an opportunity to identify galaxy clusters. The NIR dataset is important to find clusters at z>1.

10. Thank you for listening!!!