VEGA: Voids idEntification using Genetic Algorithm

TL;DR

VEGA is a new algorithm that uses Voronoi tessellation, convex hulls, and genetic algorithms to identify cosmic voids, offering a reliable and flexible method for analyzing large-scale structures in cosmology.

Contribution

The paper introduces VEGA, a novel void-finding algorithm combining Voronoi tessellation, convex hulls, and genetic algorithms, improving the identification of cosmic voids.

Findings

VEGA produces reliable void populations comparable to existing methods.

VEGA effectively identifies voids with diverse shapes and densities.

The method enhances spatial accessibility and seed point detection for void construction.

Abstract

Cosmic voids are large, nearly empty regions that lie between the web of galaxies, filaments and walls, and are recognized for their extensive applications in the field of cosmology and astrophysics. Despite their significance, a universal definition of voids remains unsettled as various void-finding methods identify different types of voids, each differing in shape and density, based on the method that were used. In this paper, we present VEGA, a novel algorithm for void identification. VEGA utilizes Voronoi tessellation to divide the dataset space into spatial cells and applies the Convex Hull algorithm to estimate the volume of each cell. It then integrates Genetic Algorithm analysis with luminosity density contrast to filter out over-dense cells and retain the remaining ones, referred to as void block cells. These filtered cells form the basis for constructing the final void structures. VEGA operates on a grid of points, which increases the algorithm's spatial accessibility to the dataset and facilitates the identification of seed points around which the algorithm constructs the voids. To evaluate VEGA's performance, we applied both VEGA and the Aikio M\"ah\"onen method to the same test dataset. We compared the resulting void populations in terms of their luminosity and number density contrast, as well as their morphological features such as sphericity. This comparison demonstrated that the VEGA void finding method yields reliable results and can be effectively applied to various particle distributions.