kias ssg workshop 2013 2 15 j ae w oo kim snu m im s k lee m hyun snu n.
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High redshift cluster search (SA22 field) PowerPoint Presentation
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High redshift cluster search (SA22 field)

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High redshift cluster search (SA22 field)

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

  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 8.75deg2 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 138deg2 - Y and J band survey using UKIRT - discovering high-z quasars - galaxy cluster at z>1 We try to find galaxy clusters at z>1 using multiwavelength datasets. http://www.vikdhillon.staff.shef.ac.uk/teaching/phy217/telescopes/ukirt.jpg

  5. Mock - GALFORM (Cole+00) - GALEX, SDSS, Subaru, UKIDSS filters - 22.06deg2 & K=24 - i<24.2, J<23.2 Catalogues • Observed - DXS+IMS+CFHTLS • ugrizJK • Swarp, SExtractor - 18.44deg2 - i<24.2, J<23.2 - photo-z : Δz/(1+z)~0.038 z=0.8 z=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 ND300<ND500 • 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 : ngal, nbg, LF • 626 candidates in 0.8<z<1.2 (Muzzin+07) • For the mock catalogue - Apply the same algorithm - to reproduce photo-z, the uncertainty was assigned randomly

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

  8. Preliminary results Normalised richness dist. Clustering of clusters 3.0<logB<3.3 logB>3.3 • 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

  9. 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. Summary

  10. Thank you for listening!!!