Journal
ASTROPHYSICAL JOURNAL
Volume 704, Issue 1, Pages 672-686Publisher
IOP PUBLISHING LTD
DOI: 10.1088/0004-637X/704/1/672
Keywords
cosmology: observations; dark matter; galaxies: clusters: individual (XMMU J2235.3-2557); galaxies: high-redshift; gravitational lensing; X-rays: galaxies: clusters
Categories
Funding
- TABASGO foundation
- NASA [GO-10496, GO-10698, NAS 5-26555]
- GO-10496 and GO-10698
- U. S. Department of Energy [AC02-05CH11231]
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We present a weak-lensing analysis of the z similar or equal to 1.4 galaxy cluster XMMU J2235.3-2557, based on deep Advanced Camera for Surveys images. Despite the observational challenge set by the high redshift of the lens, we detect a substantial lensing signal at the greater than or similar to 8 sigma level. This clear detection is enabled in part by the high mass of the cluster, which is verified by our both parametric and non-parametric estimation of the cluster mass. Assuming that the cluster follows a Navarro-Frenk-White mass profile, we estimate that the projected mass of the cluster within r = 1 Mpc is (8.5 +/- 1.7) x 10(14) M(circle dot), where the error bar includes the statistical uncertainty of the shear profile, the effect of possible interloping background structures, the scatter in concentration parameter, and the error in our estimation of the mean redshift of the background galaxies. The high X-ray temperature 8.6(-1.2)(+1.3) keV of the cluster recently measured with Chandra is consistent with this high lensing mass. When we adopt the 1 sigma lower limit as a mass threshold and use the cosmological parameters favored by the Wilkinson Microwave Anisotropy Probe 5-year (WMAP5) result, the expected number of similarly massive clusters at z greater than or similar to 1.4 in the 11 square degree survey is N similar to 5 x 10(-3). Therefore, the discovery of the cluster within the survey volume is a rare event with a probability less than or similar to 1% and may open new scenarios in our current understanding of cluster formation within the standard cosmological model.
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