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Cosmology with photometric redshift surveys | | Chris Blake
; Sarah Bridle
; | | Date: |
26 Nov 2004 | | Subject: | astro-ph | | Abstract: | We explore the utility of future photometric redshift imaging surveys for delineating the large-scale structure of the Universe, and assess the resulting constraints on the cosmological model. We perform two complementary types of analysis: (1) We quantify the statistical confidence and the accuracy with which such surveys will be able to detect and measure characteristic features in the clustering power spectrum such as the acoustic oscillations and the turnover, in a ’model-independent’ fashion. We show for example that a 10,000 deg^2 imaging survey with depth r = 22.5 and photometric redshift accuracy dz/(1+z) = 0.03 will detect the acoustic oscillations with 99.9% confidence, measuring the associated preferred cosmological scale with 2% precision. Such a survey will also detect the turnover with 95% confidence, determining the corresponding scale with 20% accuracy. (2) By assuming a Lambda-CDM model power spectrum we calculate the confidence with which a non-zero baryon fraction can be deduced from such future galaxy surveys. We quantify ’wiggle detection’ by calculating the number of standard deviations by which the baryon fraction is measured, after marginalizing over the shape parameter. This is typically a factor of four more significant (in terms of number of standard deviations) than the ’model-independent’ result. We conclude that the precision with which the clustering pattern may be inferred from future photometric redshift surveys will be competitive with contemporaneous spectroscopic redshift surveys, assuming that systematic effects can be controlled. We also note that an analysis of Luminous Red Galaxies in the Sloan Digital Sky Survey may yield a marginal detection of acoustic oscillations in the imaging survey, in addition to that recently reported for the spectroscopic component. | | Source: | arXiv, astro-ph/0411713 | | Services: | Forum | Review | PDF | Favorites | |
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