Angular Distribution of Clustersin Skewed CDM Models

Abstract

We perform a detailed investigation of the statistical properties of the projected distribution of galaxy clusters obtained in Cold Dark Matter (CDM) models with both Gaussian and skewed primordial density fluctuations. We use N-body simulations to construct a set artificial Lick maps. An objective cluster--finding algorithm is used to identify clusters of different richness. For Gaussian models, the overall number of clusters is too small in the standard CDM case, but a model with higher normalisation fares much better; non--Gaussian models with negative skewness also fit faily well. We apply several statistical tests to compare real and simulated cluster samples, such as the 2-point correlation function, the minimal spanning tree construction, the multifractal analysis and the skewness of cell counts. The emerging picture is that Gaussian models, even with a higher normalization, are in trouble. Skew-positive models are also ruled out, while skew-negative models can reproduce the observed clustering of galaxy clusters in the CDM framework.