Motivation

Voids are vast underdense regions embedded within the large-scale structure of the universe. Studying these structures provides valuable insights into the history, evolution, and geometry of the cosmos. Two key statistical tools in void research — the Void-Galaxy Cross-Correlation Function (VGCF) and the Void Size Function (VSF) — enable the use of voids as powerful cosmological probes, helping to constrain models of the universe's formation and expansion.

There are many methods designed to identify cosmic voids, commonly known as void finders. These algorithms are built on different assumptions about what defines a void, and they implement a variety of search strategies. As a result, different void finders often produce significantly different outcomes. The Void Finder Toolkit was developed to provide a unified framework for comparing void catalogs using the Void-Galaxy Cross-Correlation Function (VGCF) and the Void Size Function (VSF) as standard statistical tools. In this framework, a void is fundamentally characterized by the set of tracers it contains. The project bundles a set of tools for void finding, catalog cleaning, and statistical analysis. The toolkit currently includes three integrated void finders, all accessible through a simple, easy-to-use interface.

Finders

The current status of VFT integrates 3 public algorithms:

• ZOBOV (Neyrinck, 2008): ZOBOV works in analogy with a watershed method with water filling basins in a density field. It looks for voids as density minima with surrounding depressions and requires no free parameters. Each Void grows in density starting from a local minimum up to a link density where particles start falling into a deeper minimum.
• Spherical (POPCORN)(Paz et al., 2023): This method searches regions of low density in a Voronoi tessellation. For each minimum density region the algorithm then grows a sphere around each candidate until the average density inside reaches a specific threshold.
• POPCORN (Paz et al., 2023): The algorithm targets low-density regions by adding layers on spherical void shapes. Each layer strategically places seeds that expand while maintaining density. Only the best seed merges, and a refined process ensures full coverage. This continues until small spheres can’t be added, capturing the entire void effectively.

Installation:

Follow this steps:

$ pip install voidfindertk

Dev Installation

Clone this repo and then inside the local directory execute

$ git clone https://github.com/FeD7791/voidFinderProject.git
$ cd voidFinderProject
$ pip install -e .

You can find the package here, alongside with the installing instructions:

1 Install ZOBOV

(ZOBOV)

2 Install POPCORN

(POPCORN)

3 (OPTIONAL) Install Cosmobolognia Lib

(CBL)

About our dependencies

VoidFinder Toolkit is built on top of many codes provided by the cosmological community.

We thank and recognize the efforts and contributions of all of them.

For more information check dependencies.md

Requeriments

python 3.9+

DOCS!

See the available documentation DOCS

Authors

• Federico Bustillos (federicobustillos@mi.unc.edu.ar)
• Sebastian Gualpa (srgualpa@unc.edu.ar)