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Large-Scale Clustering in Bubble Models

Luca Amendola·Stefano Borgani
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Abstract

We analyze the statistical properties of bubble models for the large-scale distribution of galaxies. To this aim, we realize static simulations, in which galaxies are mostly randomly arranged in the regions surrounding bubbles. As a first test, we realize simulations of the Lick map, by suitably projecting the three-dimensional simulations. In this way, we are able to safely compare the angular correlation function implied by a bubbly geometry to that of the APM sample. We find that several bubble models provide an adequate amount of large-scale correlation, which nicely fits that of APM galaxies. Further, we apply the statistics of the count-in-cell moments to the three-dimensional distribution and compare them with available observational data on variance, skewness and kurtosis. Based on our purely geometrical constructions, we find that a well defined hierarchical scaling of higher order moments up to scales $\sim 70\hm$. The overall emerging picture is that the bubbly geometry is well suited to reproduce several aspects of large-scale clustering.

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Luca Amendola, Stefano Borgani. 1993-05-07. Large-Scale Clustering in Bubble Models. https://doi.org/10.1093/mnras%2F266.1.191

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