4.7 Article

Clustering of galaxy clusters in cold dark matter universes

Journal

MONTHLY NOTICES OF THE ROYAL ASTRONOMICAL SOCIETY
Volume 319, Issue 1, Pages 209-214

Publisher

BLACKWELL SCIENCE LTD
DOI: 10.1046/j.1365-8711.2000.03832.x

Keywords

gravitation; methods : numerical; galaxies : clusters : general; cosmology : theory; dark matter

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We use very large cosmological N-body simulations to obtain accurate predictions for the two-point correlations and power spectra of mass-limited samples of galaxy clusters. We consider two currently popular cold dark matter (CDM) cosmogonies, a critical density model (tau CDM) and a flat low density model with a cosmological constant (Lambda CDM). Our simulations each use 10(9) particles to follow the mass distribution within cubes of side 2 h(-1) Gpc (tau CDM) and 3 h(-1) Gpc (Lambda CDM) with a force resolution better than 10(-4) of the cube side. We investigate how the predicted cluster correlations increase for samples of increasing mass and decreasing abundance. Very similar behaviour is found in the two cases. The correlation length increases from r(0) = 12-13 h(-1) Mpc for samples with mean separation d(c) = 30 h(-1) Mpc to r(0) = 22-27 h(-1) Mpc for samples with d(c) = 100 h(-1) Mpc. The lower value here corresponds to tau CDM and the upper to Lambda CDM. The power spectra of these cluster samples are accurately parallel to these of the mass over more than a decade in scale. Both correlation lengths and power spectrum biases can be predicted to better than 10 per cent using the simple model of Sheth, Mo & Tormen. This prediction requires only the linear mass power spectrum and has no adjustable parameters. We compare our predictions with published results for the automated plate measurement (APM) cluster sample. The observed variation of correlation length with richness agrees well with the models, particularly for Lambda CDM. The observed power spectrum (for a cluster sample of mean separation d(c) = 31 h(-1) Mpc) lies significantly above the predictions of both models.

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