arXiv subjects

P. Ghafour

Publications and source records attributed to P. Ghafour.

2 recordsLinked to original sources

arXiv2026

GraphShed: a parameter-free Graph-based waterShed group finder

In this study, a parameter-free group-finding method named GraphShed is introduced and evaluated using the IllustrisTNG100-1 simulation. The method utilizes top-down watershed segmentation applied to the set of separated Voronoi-induced graphs, facilitating the recognition of aggregations directly from the density field without tunable parameters or density thresholds. A galaxy group catalog constructed with GraphShed is compared with a Friends-of-Friends catalog generated from the same dataset. The $M_{200}$ distributions of the two catalogs are statistically consistent; nevertheless, other structural properties, including $R_{200}$, sphericity, compactness, spin, and centroid shift show significant differences, suggesting that GraphShed could improve several internal characteristics of the identified systems. Conversely, the two-point correlation function and the mass function of the identified galaxy systems, derived from the aforementioned methods, show consistency. A velocity-based classification of interacting pairs indicates that GraphShed provides improved separation of nearby over-densities which might otherwise be considered as components of a single larger system in position-only methods due to their positional proximity. These results demonstrate that GraphShed effectively preserves cosmological statistics while offering a more refined detection of galaxy systems and their dynamical interactions.

astro-ph.CO↗

arXiv2025

VEGA: Voids idEntification using Genetic Algorithm

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.

astro-ph.IM↗