Robust Morphological Measures for Large-Scale Structure in the Universe

TL;DR

This paper introduces a robust morphological analysis method using Minkowski functionals to characterize large-scale cosmic structures, providing detailed shape and connectivity information from galaxy data.

Contribution

It presents a new, statistically robust approach for morphological analysis of galaxy distributions based on Minkowski functionals, applicable to various point processes.

Findings

Effective in small samples

Applicable to different galaxy cluster datasets

Provides both global and local morphological insights

Abstract

We propose a novel method for the description of spatial patterns formed by a coverage of point sets representing galaxy samples. This method is based on a complete family of morphological measures known as Minkowski functionals, which includes the topological Euler characteristic and geometric descriptors to specify the content, shape and connectivity of spatial sets. The method is numerically robust even for small samples, independent of statistical assumptions, and yields global as well as local morphological information. We illustrate the method by applying it to a Poisson process, a `double-Poisson' process, and to the Abell catalogue of galaxy clusters.