Modeling cosmic void statistics

TL;DR

This paper reviews recent advances in modeling the internal structure and spatial distribution of cosmic voids using simulations, highlighting their potential as probes for understanding dark energy, dark matter, and modified gravity.

Contribution

It introduces new models for void density and velocity profiles and two-point statistics in redshift space based on state-of-the-art simulations and catalogs.

Findings

Void statistics exhibit universal, self-similar characteristics.

Cosmic voids are promising for probing fundamental cosmological physics.

Models show consistency across different simulation datasets.

Abstract

Understanding the internal structure and spatial distribution of cosmic voids is crucial when considering them as probes of cosmology. We present recent advances in modeling void density- and velocity-profiles in real space, as well as void two-point statistics in redshift space, by examining voids identified via the watershed transform in state-of-the-art $\Lambda$CDM n-body simulations and mock galaxy catalogs. The simple and universal characteristics that emerge from these statistics indicate the self-similarity of large-scale structure and suggest cosmic voids to be among the most pristine objects to consider for future studies on the nature of dark energy, dark matter and modified gravity.