The Blooming Tree Algorithm at Work: Clusters, Filaments and Superclusters in the Field of A2029

TL;DR

The Blooming Tree algorithm uses hierarchical clustering to identify and analyze galaxy structures like clusters, filaments, and superclusters in large redshift surveys, revealing their formation and evolution.

Contribution

This paper introduces the Blooming Tree algorithm, a novel hierarchical clustering method tailored for detecting cosmic structures in galaxy redshift data.

Findings

Effectively identifies X-ray and optical clusters

Detects filaments and superclusters in survey data

Provides insights into the hierarchical assembly of cosmic structures

Abstract

The Blooming Tree (BT) algorithm, based on the hierarchical clustering method, is designed to identify clusters, groups, and substructures from galaxy redshift surveys. We apply the BT algorithm to a wide-field ($10\times 10$ deg$^2$) spectroscopic dataset centered on the galaxy cluster A2029. The BT algorithm effectively identifies all the X-ray luminous clusters and most of the optical clusters known in the literature, numerous groups, and the filaments surrounding the clusters, associating a list of galaxy members to each structure. By lowering the detection threshold, the BT algorithm also identifies the three superclusters in the field. The BT algorithm arranges the clusters and groups that make up the superclusters in a hierarchical tree according to their pairwise binding energy: the algorithm thus unveils the possible accretion history of each supercluster and their future evolution. These results show how the BT algorithm can represent a crucial tool to investigate the formation and evolution of cosmic structures on non-linear and mildly non-linear scales.