DS+: A method for the identification of cluster substructures

¹ Instituto de Astronomía Teórica y Experimental, CONICET-UNC, Laprida 854, X5000BGR Córdoba, Argentina
² Observatorio Astronómico de Córdoba, Universidad Nacional de Córdoba, Laprida 854, X5000BGR Córdoba, Argentina
e-mail: jose.benavides@unc.edu.ar
³ INAF-Osservatorio Astronomico di Trieste, via G. B. Tiepolo 11, 34143 Trieste, Italy
e-mail: andrea.biviano@inaf.it
⁴ IFPU-Institute for Fundamental Physics of the Universe, via Beirut 2, 34014 Trieste, Italy

Received: 10 November 2022
Accepted: 29 November 2022

Abstract

Context. The study of cluster substructures is important for the determination of the cluster dynamical status, assembly history, and the evolution of cluster galaxies, and it allows us to set constraints on the nature of dark matter and cosmological parameters.

Aims. We present and test DS+, a new method for the identification and characterization of group-sized substructures in clusters.

Methods. Our new method is based on the projected positions and line-of-sight (l.o.s. hereafter) velocities of cluster galaxies, and it is an improvement and extension of the traditional method of Dressler & Shectman (1988, AJ, 95, 985). We tested it on cluster-size cosmological halos extracted from the IllustrisTNG simulations, with virial masses 14 ≲ log(M₂₀₀/M_⊙) ≲ 14.6 that contain ~190 galaxies on average. We also present an application of our method on a real data set, the Bullet cluster.

Results. DS+ is able to identify ~80% of real group galaxies as members of substructures, and at least 60% of the galaxies assigned to substructures belong to real groups. The physical properties of the real groups are significantly correlated with those of the corresponding detected substructures, but with significant scatter, and they are overestimated on average. Application of the DS+ method to the Bullet cluster confirms the presence and main properties of the high-speed collision and identifies other substructures along the main cluster axis.

Conclusions. DS+ proves to be a reliable method for the identification of substructures in clusters. The method is made freely available to the community as a Python code.