Galaxy dynamics in clusters

Abstract

We use high-resolution simulations to study the formation and distribution of galaxies within a cluster that forms hierarchically. We follow both dark matter and a gas component that is subject to thermal pressure, shocks, and radiative cooling. Galaxy formation is identified with the dissipative collapse of the gas into cold, compact knots. We explore two extreme numerical representations of these galaxies during subsequent cluster evolution, one purely gaseous and the other purely stellar, and we fond that the results are quite sensitive to this choice. Simulations in which galaxies remain gaseous appear to suffer from an ''overmerging'' problem, but this problem is much less severe if the gas is allowed to turn into stars. We compare the kinematics of the galaxy population in these two representations to the kinematics of dark halos and of the underlying dark matter distribution. Galaxies in the stellar representation are positively biased (i.e., overrepresented in the cluster) both by number and by mass fraction. Both representations predict the galaxies to be more centrally concentrated than the dark matter, whereas the dark halo population is more extended. A modest velocity bias also exists in both representations, with the largest effect, sigma(gal)/sigma(DM) similar or equal to 0.7, found for the more massive star galaxies. Phase diagrams show that the galaxy population in the stellar case is roughly in hydrostatic equilibrium. Virial mass estimators can underestimate the true cluster mass by up to a factor of 5 because of these various bias effects. The discrepancy is largest if only the most massive galaxies are used, reflecting significant mass segregation. A binding energy analysis suggests that this segregation is primarily a result of dynamical friction. We discuss briefly the relevance of these results both to real clusters and to the general problem of simulating the formation and clustering of galaxies. The incorporation of a realistic star formation algorithm within future simulations is the key to further progress.