Mass variance and cluster abundance in the context of a Gaussian + lognormal distribution model

Details Mass variance and cluster abundance in the context of a Gaussian + lognormal distribution model Mass variance and cluster abundance in the context of a Gaussian + lognormal distribution model

Jun 2007

adsabs.harvard.edu/cgi-bin/nph-data_query?bibcode=2007a%26a...468...19r&link_type=abstract

Astronomy and Astrophysics, Volume 468, Issue 1, June II 2007, pp.19-23

Mathematics

Logic

2

Cosmological Parameters, Galaxies: Clusters: General

Scientific paper

Context: We investigate the behavior of the mass variance and the mass function of galaxy clusters in a mixed distribution model. Aims: Our aim is to find a relation between the mass variance at a 8 h-1 Mpc scale, σ_8, and the parameter controlling the Gaussian deviation in the model, α_0, and to constrain the non-Gaussianity using observational data at cluster scales. Methods: By assuming that the statistics of the density field is built as a weighted mixture of two components, a Gaussian + Lognormal distribution, we rewrite the mass variance expression and the mass function for galaxy clusters. Results: We find a relation between the mass variance at a 8 h-1 Mpc scale, σ_8, and the scale parameter controlling the Gaussian deviation in the model, α_0. This result, in conjunction with observational constraints on the mass variance and high-z galaxy clustering, suggests a scenario where structures develop earlier in comparison to strictly Gaussian models, even for α_0≲ 0.003 Mpc. Our model also indicates that only well selected galaxy cluster samples at z≳ 1 can discriminate between Gaussian and non-Gaussian (mixed) distribution models.

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