Network Theory in Galaxy Distributions: The Coma Supercluster Neighborhood

TL;DR

This paper applies spatial network theory to galaxy distributions in the Coma Supercluster, revealing correlations between network parameters and galaxy environment properties, and proposing new tools for large-scale structure analysis.

Contribution

It introduces a novel approach using network parameters to analyze galaxy environments and structures, linking them to traditional density measures.

Findings

Degree centrality correlates with environmental density.

High degrees are associated with elliptical galaxies, confirming the density-morphology relation.

Network parameters effectively trace filamentary structures and superclusters.

Abstract

In this work, we use the theory of spatial networks to analyze galaxy distributions. The aim is to develop new approaches to study the spatial galaxy environment properties by means of the network parameters. We investigate how each of the network parameters (degree, closeness and betweeness centrality; diameter; giant component; transitivity) map the cluster structure and properties. We measure the network parameters of galaxy samples comprising the Coma Supercluster and 4 regions in their neighborhood ($z<0.0674$) using the catalog produced by \citet{tempel2014flux}. For comparison we repeat the same procedures for Random Geometric Graphs and Segment Cox process, generated with the same dimensions and mean density of nodes. We found that there is a strong correlation between degree centrality and the normalized environmental density. Also, at high degrees there are more elliptical than spiral galaxies, which confirms the density-morphology relation. The mean degree as a function of the connection radius is an estimator of the count-of-spheres and consequently provides the correlation dimension as a function of the connection radius. The correlation dimension indicates high clustering at scales indicated by the network diameter. Further, at this scales, high values of betweeness centrality characterize galaxy bridges connecting dense regions, tracing very well the filamentary structures. Then, since galaxies with the highest closeness centrality belongs to the largest components of the network, associated to supercluster regions, we can produce a catalog of superclusters only by extracting the largest connected components of the network. Establishing the correlation between the well-studied normalized environmental densities and the parameters of the network theory allows us to develop alternative tools to the study of the large-scale structures.