Large Scale Distribution of Galaxies in The Field HS 47.5-22. I. Data Analysis Technique

TL;DR

This paper introduces new automated methods for analyzing the large-scale distribution of galaxies, including cluster detection and void identification, tested on simulated data to evaluate their effectiveness.

Contribution

It presents novel algorithms using adaptive filtering and Voronoi tessellation for density contrast mapping in galaxy distributions, validated on model catalogs.

Findings

Algorithms successfully identified galaxy clusters and voids.

Statistical parameters like completeness and purity were quantified.

Methods proved effective on simulated datasets.

Abstract

We present the results of methodological works on automated analysis of the large scale distribution of galaxies. Selecting candidates for clusters and groups of galaxies was carried out using two complementary methods of determining the density contrast maps in the narrow layers of the three-dimensional large scale distribution of galaxies: the filtering algorithm with an adaptive core and the Voronoi tesselation. The developed algorithms were tested on 10 data sets of the MICE model catalog; additionally, we determined the statistical parameters of the obtained results (completeness, sample purity, etc.). The constructed density contrast maps were also used to determine voids.