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arXiv · astro-ph/9812137

The Durham/UKST Galaxy Redshift Survey - VI. Power spectrum analysis of clustering

Abstract

We present the power spectrum analysis of clustering in the Durham/UKST Galaxy Redshift Survey, which covers 1450 square degrees and consists of 2501 galaxy redshifts sampled at a rate of 1 in 3 to b_J = 17. The fluctuations that we measure can be expressed as sigma_{8} = 1.01 +/- 0.17 . We find remarkably good agreement between the power spectrum measured for the Durham/UKST Survey and those from other optical studies, in spite of the very different sampling rates, magnitude limits and survey geometries employed, on scales up to lambda = (2 pi)/k = 80/h Mpc. On scales larger than this we find good agreement with the power measured from the Stromlo-APM Survey (Tadros & Efstathiou), but find more power than estimated from the Las Campanas Redshift Survey (Lin et al). We apply a simple model for redshift space distortions to the real space APM Galaxy Survey power spectrum and find a shape and amplitude that is in very good agreement with the power spectrum of the Durham/UKST Survey. This implies beta=Omega^{0.6}/b=0.60 +/- 0.35, where Omega is the density parameter and b is the bias between fluctuations in the galaxy and mass distributions, and also suggests a one dimensional velocity dispersion of sigma = 320 +/-140 km/s. We find that for any choice of normalisation, the standard CDM model has a shape that cannot be reconciled with the Durham/UKST Survey power spectrum, unless either unacceptably high values of the one dimensional velocity dispersion are adopted or the assumption that bias is constant is invalid on scales greater than 20/h Mpc. Over the range of wavenumbers for which we have a robust measurement of the power spectrum, we find the best agreement is obtained for a critical density CDM model in which the shape of the power spectrum is modified. (Abridged)

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BibTeXRIS

F. Hoyle, C. M. Baugh, T. Shanks, A. Ratcliffe. 1999-10-04. The Durham/UKST Galaxy Redshift Survey - VI. Power spectrum analysis of clustering. https://doi.org/10.1046/j.1365-8711.1999.02904.x

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