LARGE-SCALE CLUSTERING IN BUBBLE MODELS

We analyse the statistical properties of bubble models for the large-scale distribution of galaxies. To this end, we carry out static simulations, in which galaxies are mostly randomly arranged in the regions surrounding bubbles. As a first test, we simulate the Lick map, by suitably projecting the three-dimensional simulations. In this way, we are able to compare the angular correlation function implied by a bubbly geometry to that of the APM sample. Quite remarkably, we find that several bubble models provide adequate large-scale correlation, which fits well 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. From our purely geometrical constructions, we find a well-defined hierarchical scaling of higher-order moments up to scales approximately 70 h-1 Mpc. We show that this must be expected for any non-Gaussian distribution in the weak-coherence regime. The overall emerging picture is that the bubbly geometry is well suited to reproduce several aspects of large-scale clustering. Furthermore, the statistical tests we apply are able to discriminate between different models. We find that models with fixed bubble radius have problems in accounting for the angular correlation of APM galaxies, while a shallow spectrum fails to reproduce the observed three-dimensional skewness. A model with a steep spectrum of bubble radii accounts for the observational tests that we consider.