[Paper Review] VIDE: The Void IDentification and Examination toolkit

P. M. Sutter, Guilhem Lavaux+8 more|arXiv (Cornell University)|Jun 4, 2014

Galaxies: Formation, Evolution, Phenomena60 references4 citations

TL;DR

VIDE is an open-source Python/C++ toolkit for identifying and analyzing cosmic voids in galaxy redshift surveys and N-body simulations using an enhanced Voronoi-based watershed algorithm. It enables robust void detection, hierarchical cataloging, and comprehensive analysis of void properties, member particles, and clustering statistics, with applications in cosmology and astrophysics.

ABSTRACT

We present VIDE, the Void IDentification and Examination toolkit, an open-source Python/C++ code for finding cosmic voids in galaxy redshift surveys and N-body simulations, characterizing their properties, and providing a platform for more detailed analysis. At its core, VIDE uses a substantially enhanced version of ZOBOV (Neyinck 2008) to calculate a Voronoi tessellation for estimating the density field and a performing a watershed transform to construct voids. Additionally, VIDE provides significant functionality for both pre- and post-processing: for example, vide can work with volume- or magnitude-limited galaxy samples with arbitrary survey geometries, or dark matter particles or halo catalogs in a variety of common formats. It can also randomly subsample inputs and includes a Halo Occupation Distribution model for constructing mock galaxy populations. VIDE uses the watershed levels to place voids in a hierarchical tree, outputs a summary of void properties in plain ASCII, and provides a Python API to perform many analysis tasks, such as loading and manipulating void catalogs and particle members, filtering, plotting, computing clustering statistics, stacking, comparing catalogs, and fitting density profiles. While centered around ZOBOV, the toolkit is designed to be as modular as possible and accommodate other void finders. VIDE has been in development for several years and has already been used to produce a wealth of results, which we summarize in this work to highlight the capabilities of the toolkit. VIDE is publicly available at http://bitbucket.org/cosmicvoids/vide public and http://www.cosmicvoids.net.

Motivation & Objective

• To provide a scalable, extensible, and community-driven toolkit for identifying and analyzing cosmic voids in cosmological datasets.
• To address the lack of publicly available, robust, and well-documented void-finding tools for both observational and simulated data.
• To support diverse data formats, including galaxy catalogs, dark matter particles, and halo catalogs, with flexible survey geometry handling.
• To enable detailed post-processing analysis such as clustering statistics, profile fitting, stacking, and cross-correlation with external datasets.
• To serve as a modular platform that can integrate alternative void finders beyond the core ZOBOV-based algorithm.

Proposed method

• Employs a substantially enhanced version of the ZOBOV algorithm to compute Voronoi tessellations for accurate density field estimation.
• Applies a watershed transform to segment the density field and identify voids based on local minima in the potential field.
• Uses watershed levels to organize voids into a hierarchical tree structure, enabling multi-scale analysis.
• Supports volume- and magnitude-limited galaxy samples with arbitrary survey geometries, including complex footprints.
• Provides a Python API for loading, filtering, plotting, and analyzing void catalogs and their member particles.
• Integrates with Halo Occupation Distribution (HOD) models to generate mock galaxy populations for synthetic void studies.

Experimental results

Research questions

• RQ1 How can cosmic voids be robustly identified in complex, inhomogeneous survey geometries with varying depth and completeness?
• RQ2 What are the key statistical and morphological properties of voids in both dark matter and galaxy distributions across different redshifts?
• RQ3 How do void properties such as size, shape, and density profile correlate with large-scale structure and cosmological parameters?
• RQ4 To what extent can voids serve as cosmological probes through their clustering, lensing, or ISW signals?
• RQ5 How can void-finding tools be designed to be modular, extensible, and community-extensible for future research needs?

Key findings

• VIDE successfully identifies voids in both real galaxy redshift surveys and N-body simulations with high speed and robustness, even in complex survey geometries.
• The toolkit enables accurate characterization of void properties including sky positions, shapes, sizes, and hierarchical tree structures.
• VIDE supports the extraction of member particles and their spatial volumes, facilitating detailed studies of galaxy properties in low-density environments.
• The platform allows for advanced analysis such as clustering statistics, stacking, and fitting of universal HSW density profiles to theoretical predictions.
• VIDE has already been used to produce a range of scientific results, including studies of void-galaxy cross-correlations and cosmological parameter constraints.
• The toolkit is extensible and modular, with support for future integration of alternative void finders and enhanced functionality like angular selection functions and 2D clustering statistics.