Volume Statistics as a Probe of Large-Scale Structure

TL;DR

This paper explores the use of volume statistics derived from tessellation methods to analyze large-scale cosmic structures, revealing their potential to detect features like BAO without bias.

Contribution

It introduces a novel application of volume statistics using tessellation techniques to study cosmic structures and demonstrates their effectiveness in identifying BAO features.

Findings

Volume statistics show negative clustering bias.

BAO features are detectable in volume power spectra.

Tessellation methods effectively analyze large-scale structure.

Abstract

We investigate the application of volume statistics to probe the distribution of underdense regions in the large-scale structure of the Universe. This statistic measures the distortion of Eulerian volume elements relative to Lagrangian ones and can be built from tracer particles using tessellation methods. We apply Voronoi and Delaunay tessellation to study the clustering properties of density and volume statistics. Their level of shot-noise contamination is similar, as both methods take into account all available tracer particles in the field estimator. The tessellation causes a smoothing effect in the power spectrum, which can be approximated by a constant window function on large scales. The clustering bias of the volume statistic with respect to the dark matter density field is determined and found to be negative. We further identify the Baryon Acoustic Oscillation (BAO) feature in the volume statistic. Apart from being smoothed out on small scales, the BAO is present in the volume power spectrum as well, without any systematic bias. These observations suggest that the exploitation of volume statistics as a complementary probe of cosmology is very promising.