Blooming Trees: Substructures and Surrounding Groups of Galaxy Clusters

We develop the Blooming Tree Algorithm, a new technique that uses spectroscopic redshift data alone to identify the substructures and the surrounding groups of galaxy clusters, along with their member galaxies. Based on the estimated binding energy of galaxy pairs, the algorithm builds a binary tree that hierarchically arranges all the galaxies in the field of view. The algorithm searches for buds, corresponding to gravitational potential minima on the binary tree branches; for each bud, the algorithm combines the number of galaxies, their velocity dispersion and their average pairwise distance into a parameter that discriminates between the buds that do not correspond to any substructure or group, and thus eventually die, and the buds that correspond to substructures and groups, and thus bloom into the identified structures. We test our new algorithm with a sample of 300 mock redshift surveys of clusters in different dynamical states; the clusters are extracted from a large cosmological $N$-body simulation of a $\Lambda$CDM model. We limit our analysis to substructures and surrounding groups identified in the simulation with mass larger than $10^{13} h^{-1} M_{\odot}$. With mock redshift surveys with 200 galaxies within 6 $h^{-1}$~Mpc from the cluster center, the technique recovers $80$\% of the real substructures and $60$\% of the surrounding groups; in $57$\% of the identified structures, at least 60\% of the member galaxies of the substructures and groups belong to the same real structure. These results improve by roughly a factor of two the performance of the best substructure identification algorithm currently available, the $\sigma$ plateau algorithm, and suggest that our Blooming Tree Algorithm can be an invaluable tool for detecting substructures of galaxy clusters and investigating their complex dynamics.