Speaker
Description
The Blooming Tree (BT) algorithm is an optimized hierarchical clustering method designed to identify clusters, groups, and substructures. We evaluate the performance of this method using a compiled wide-field ($10° \times 10°$) spectroscopic dataset centered on a supercluster of galaxies, A2029. This algorithm effectively identifies all X-ray luminous clusters, many groups, and even filaments around clusters within the field. By adjusting the detection threshold, this algorithm can identify superclusters with explicit membership. It also provides hierarchical relationships between clusters and groups that make up superclusters. This capability could be helpful for understanding the inner structure of superclusters and the evolution of large-scale structures. Our results show that the BT method is a powerful tool for fully utilizing spectroscopic redshift survey data to analyze the hierarchical universe.
