  The aim of this paper is to study the efficiency of different approaches to
interloper treatment in dynamical modelling of galaxy clusters. Using
cosmological N-body simulation of standard LCDM 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. 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.
