Robustness of cosmic void statistics: insights from SDSS DR7 and the ELUCID simulation

TL;DR

This study systematically compares cosmic void properties from SDSS DR7 and ELUCID simulation, revealing that void morphology is robust while size and density profiles depend on identification methods and tracer bias.

Contribution

It provides a comprehensive analysis of void robustness across different data reconstructions, algorithms, and tracer biases, highlighting which properties are stable and which are sensitive.

Findings

Void morphology remains stable across reconstructions and tracer selections.

Void size and density profiles depend on the identification algorithm used.

Tracer bias mainly affects void density profiles, especially for massive subhaloes.

Abstract

We present a systematic analysis of the statistical properties of cosmic voids using galaxies from the Sloan Digital Sky Survey Data Release 7 (SDSS DR7) and subhaloes from the ELUCID constrained simulation. By comparing voids identified in redshift space, real space, and reconstructed volumes, we assess the impact of redshift-space distortions (RSD) and tracer bias. Using the \texttt{VAST} toolkit, we apply both the geometry-based \texttt{VoidFinder} algorithm and watershed-based methods. We find that void properties are not equally robust. The three-dimensional morphology of voids, quantified by their sphericity and triaxiality, remains stable across different reconstructions and tracer selections. In contrast, void size distributions and radial density profiles depend strongly on the identification algorithm, with watershed-based methods systematically producing larger voids and higher compensation walls than \texttt{VoidFinder}. Using the full ELUCID simulation box, we show that tracer bias mainly affects void density profiles, with noticeable changes only for the most massive subhaloes ($>10^{11.5}\,h^{-1}{\rm M}_\odot$). The agreement between SDSS observations, the ELUCID reconstruction, and the full simulation box demonstrates the high fidelity of constrained simulations and reveals a clear hierarchy in the robustness of void statistics.