Identification of galaxy cluster substructures with the Caustic method

TL;DR

This paper evaluates the caustic technique's effectiveness in identifying galaxy cluster substructures using optical redshift data, demonstrating its potential despite some limitations in accuracy.

Contribution

It introduces and tests the caustic method as a tool for detecting galaxy cluster substructures from redshift data, extending its application beyond mass profile estimation.

Findings

Recovers 30-50% of real substructures in mock surveys.

15-20% of caustic-identified substructures are true cluster substructures.

Shows promise for studying galaxy cluster dynamics.

Abstract

We investigate the power of the caustic technique for identifying substructures of galaxy clusters from optical redshift data alone. The caustic technique is designed to estimate the mass profile of galaxy clusters to radii well beyond the virial radius, where dynamical equilibrium does not hold. Two by-products of this technique are the identification of the cluster members and the identification of the cluster substructures. We test the caustic technique as a substructure detector on two samples of 150 mock redshift surveys of clusters; the clusters are extracted from a large cosmological $N$-body simulation of a $\Lambda$CDM model and have masses of $M_{200} \sim 10^{14} h^{-1} M_{\odot}$ and $M_{200} \sim 10^{15} h^{-1} M_{\odot}$ in the two samples. We limit our analysis to substructures identified in the simulation with masses larger than $10^{13} h^{-1} M_{\odot}$. With mock redshift surveys with 200 galaxies within $3R_{200}$, (1) the caustic technique recovers $\sim 30-50$\% of the real substructures, and (2) $\sim 15-20$\% of the substructures identified by the caustic technique correspond to real substructures of the central cluster, the remaining fraction being low-mass substructures, groups or substructures of clusters in the surrounding region, or chance alignments of unrelated galaxies. These encouraging results show that the caustic technique is a promising approach for investigating the complex dynamics of galaxy clusters.