Large-Scale Clustering of Cosmic Voids

TL;DR

This paper investigates the clustering properties of cosmic voids using simulations and theoretical models, providing new insights into void biasing, exclusion effects, and their implications for galaxy survey observations.

Contribution

It introduces a comprehensive modeling approach for void auto-power spectrum that accounts for biasing and exclusion, and compares theoretical predictions with simulation data.

Findings

Void abundance well described by excursion-set formalism with adjusted thresholds

Void cross bias consistent with peak background split predictions

Accurate modeling of void auto-power spectrum for large radii with biasing and exclusion effects

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_{\rm 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_{\rm 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.