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26 April 2024 |
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Shear Selected Cluster Cosmology I: Tomography and Optimal Filtering | | Joseph F. Hennawi
; David N. Spergel
; | | Date: |
19 Apr 2004 | | Subject: | astro-ph | | Affiliation: | Princeton | | Abstract: | We study the potential of weak lensing surveys to detect clusters of galaxies, using a fast Particle Mesh cosmological N-body simulation algorithm specifically tailored to investigate the statistics of these mass-selected clusters. In particular, we explore the degree to which the radial positions of galaxy clusters can be determined tomographically, by using photometric redshifts of background source galaxies. We quantify the errors in the tomographic redshifts, and study their dependence on mass, redshift, detection significance, and filtering scheme. A Tomographic Matched Filtering (TMF) scheme, which combines tomography and matched filtering, is introduced, which optimally detects clusters of galaxies in weak lensing surveys. The TMF exploits the extra information provided by photometric redshifts of background source galaxies, neglected in previous studies, to optimally weight the sources. The efficacy and reliability of the TMF is investigated using a large ensemble of mock observations from our simulations and detailed comparisons are made to other filters. Using photometric redshift information with the TMF enhances the number of clusters detected with S/N > 4.5 by as much as 76%, and it increases the dynamic range of weak lensing searches for clusters, detecting more high redshift clusters and extending the mass sensitivity down to the scale of large groups. Furthermore, we find that coarsely binning source galaxies in as few as three redshift bins is sufficient to realize the gains of the TMF. Thus, the tomographic filtering techniques presented here can be applied to current ground based weak lensing data in as few as three bands. Cosmological applications of mass selected cluster samples are also discussed. (abridged) | | Source: | arXiv, astro-ph/0404349 | | Services: | Forum | Review | PDF | Favorites | |
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