Large-Scale Clustering of Cosmic Voids

Abstract

We study the clustering of voids using $N$-body simulations and simple theoretical models. The excursion-set formalism describes fairly well the abundance of voids identified with the watershed algorithm, although the void formation threshold required is quite different from the spherical collapse value. The void cross bias $b_{\mathrm{c}}$ is measured and its large-scale value is found to be consistent with the peak background split results. A simple fitting formula for $b_{\mathrm{c}}$ is found. We model the void auto-power spectrum taking into account the void biasing and exclusion effect. A good fit to the simulation data is obtained for voids with radii $\gtrsim$ 30 Mpc/$h$, especially when the void biasing model is extended to 1-loop order. However, the best-fit bias parameters do not agree well with the peak-background split results. Being able to fit the void auto-power spectrum is particularly important not only because it is the direct observable in galaxy surveys, but also our method enables us to treat the bias parameters as nuisance parameters, which are sensitive to the techniques used to identify voids.

20 pages, 14 figures, minor changes to match published version

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Cited by in corpus (11)

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• Non-fiducial cosmological test from geometrical and dynamical distortions around voids
• The halo bias inside cosmic voids
• The clustering of critical points in the evolving cosmic web
• Gravitational collapse in the Schrödinger-Poisson system
• Identification of Cosmic Voids as Massive Cluster Counterparts
• On the Abundance of Extreme Voids
• Spatial correlations of extended cosmological structures
• Ignorance-Aware Approaches and Algorithms for Prototype Selection in Machine Learning
• Cosmological constraints from the density gradient weighted correlation function