P. Ghafour | Search arXiv

arXiv2026

GraphShed: A Data-derived Graph-based waterShed Group Finder

This study introduces GraphShed, a data-derived group-finder that applies top-down watershed segmentation to Voronoi-induced graphs, identifying galaxy systems directly from the density field via a GraphShed-derived linking length, with no density thresholds or tunable parameters. A GraphShed galaxy group catalog compared to a Friends-of-Friends (FoF) catalog built from the IllustrisTNG100-1 simulation with a box size of $\sim 75$ Mpc/h yielding $1067$ and $987$ systems, respectively. The GraphShed-derived linking length adapts to the global clustering power of the sample, varying by $\sim 18 \%$ relative to FoF across different sample selections; thus, catalog differences originate in the watershed segmentation. Cross-catalog comparison shows that $\sim 58 \%$ of FoF systems are identically reproduced by GraphShed, $\sim 5 \%$ are completely removed, and the remainder are partially shaved and/or split into multiple systems. While the $M_{200}$ distributions of the two catalogs are statistically consistent, other structural properties including, $R_{200}$, sphericity, compactness, spin, and centroid shift, differ significantly in at least some richness ranges. The unweighted two-point correlation function of GraphShed systems exhibits a higher amplitude on small scales, $r \lesssim 1$ Mpc/h, and agrees with FoF for $r\gtrsim 5$ Mpc/h. A velocity-based classification reveals that GraphShed resolves more interacting pairs than FoF, demonstrating its ability to distinguish nearby overdense structures that position-only methods merge into single systems. These results demonstrate that GraphShed preserves cosmological statistics while providing a more resolved 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 Mähönen 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↗