Mass and Light distributions in Clusters of Galaxies - Henry A ...

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WL Determination of the Distance-Redshift Relation the well studied deep field surveys, we may reliably define several galaxy populations of differing mean depths in the CC-space covered by the filters used for our cluster. Three colour selection has also been applied in a similar context to simulations aimed at forecasting the capabilities of WL tomograghy (Jain et al. 2007; Medezinski et al. 2010). These simulations convincingly demonstrate that greater efficieny is likely by using limited 3-band imaging for WL tomorgraphy, rather than by investing greater imaging time in additional bands to improve photometric redshift precision. For our purposes too we show here that a judicious choice of non-orthogonal boundries in CC-space allows the definition of several distinct redshift samples of differing mean depth, with relatively little overlap in redshift. We rely on well studied deep field surveys to estimate the redshift distribution of these different background populations, using the wide-field COSMOS 30-band photometric redshift survey (Ilbert et al. 2009) and also the deeper GOODS-MUSIC survey which has wide multi-wavelength coverage in 14 bands (from the U band to the Spitzer 8 µm band) (Grazian et al. 2006). In § 4.2 we present the cluster observations and data reduction and in § 4.3 we explain the selection of background galaxy samples and in § 4.4 we describe the WL analysis and outline the formalism. In § 4.5 we derive the WL amplitude and mean redshift information of the background samples, presenting our results regarding the lensing distance-redshift relation. We discuss the requirements for constraining the cosmological model with this method in 4.6. We summarize and conclude in § 4.7. 4.2 Subaru data reduction We analyze deep images of the intermediate-redshift cluster, A370, observed with the wide-field camera Suprime-Cam (Miyazaki et al. 2002) in three optical bands, at the prime focus of the 8.3m Subaru telescope. The cluster data are publicly available from the Subaru archive, SMOKA 5 . Subaru 5 http://smoka.nao.ac.jp 116
4.3 Sample selection from the color-color diagram Table 4.1 – The A370 Subaru Data Filters used 1 Exposure Time Seeing (sec) (arcsec) B J 7200 0.7 R C 8340 0.6 z ′ 14221 0.7 1 Detection band marked in bold. reduction software (SDFRED) developed by Yagi et al. (2002) is used for flat-fielding, instrumental distortion correction, differential refraction, sky subtraction, and stacking. Photometric catalogs are created using SExtractor (Bertin & Arnouts 1996). Since our work relies much on the colors of galaxies, we prefer using isophotal magnitudes. We use the Colorpro (Coe et al. 2006) program to detect in the R C -band and measure colors through matched isophotes in the other two bands. Astrometric correction is done with Scamp (Bertin 2006) using reference objects in the NOMAD catalog (Zacharias et al. 2004) and the SDSS-DR6 (Adelman-McCarthy et al. 2008) where available. The observational details are listed in Table 4.1. 4.3 Sample selection from the color-color diagram We use Subaru observations in three broad optical passbands and all observations are of good seeing, representing some of the highest quality imaging by Subaru in terms of depth, resolution, and color coverage. We first describe how we separate the background galaxies from foreground and cluster galaxies, combining WL measurements and the distribution of objects in the CC plane and their clustering relative to the center of the cluster. We then examine the redshift distribution of objects selected to lie in the background using the CC plane with reference to the COSMOS field where deep photometric redshifts are established to faint limits using 30 independent passbands 117
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Chapter 1 Introduction Clusters of
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