Interloper treatment in dynamical modelling of galaxy clusters

¹ Nicolaus Copernicus Astronomical Center, Bartycka 18, 00-716 Warsaw, Poland e-mail: wojtak@camk.edu.pl
² Institut d'Astrophysique de Paris (UMR 7095: CNRS and Université Pierre & Marie Curie), 98 bis Bd Arago, 75014 Paris, France
³ GEPI (UMR 8111: CNRS and Université Denis Diderot), Observatoire de Paris, 92195 Meudon, France
⁴ Astrophysikalisches Institut Potsdam, An der Sternwarte 16, 14482 Potsdam, Germany
⁵ Instituto de Astrofísica de Andalucia (CSIC), Apartado Correos 3005, 18080 Granada, Spain

Received: 24 November 2006
Accepted: 1 February 2007

Abstract

Aims. The aim of this paper is to study the efficiency of different approaches to interloper treatment in dynamical modelling of galaxy clusters.

Methods.Using cosmological N-body simulation of standard ΛCDM model, we select 10 massive dark matter haloes and use their particles to emulate mock kinematic data in terms of projected galaxy positions and velocities as they would be measured by a distant observer. Taking advantage of the full 3D information available from the simulation, we select samples of interlopers defined with different criteria. The interlopers thus selected provide means to assess the efficiency of different interloper removal schemes found in the literature.

Results. We study direct methods of interloper removal based on dynamical or statistical restrictions imposed on ranges of positions and velocities available to cluster members. In determining these ranges, we use either the velocity dispersion criterion or a maximum velocity profile. We also generalize the common approaches taking into account both the position and velocity information. Another criterion is based on the dependence of the commonly used virial mass and projected mass estimators on the presence of interlopers. We find that the direct methods exclude on average 60–70 percent of unbound particles producing a sample with contamination as low as 2–4 percent. Next, we consider indirect methods of interloper treatment which are applied to the data stacked from many objects. In these approaches, interlopers are treated in a statistical way as a uniform background which modifies the distribution of cluster members. Using a Bayesian approach, we reproduce the properties of composite clusters and estimate the probability of finding an interloper as a function of distance from the object centre.