The Rockstar Phase-Space Temporal Halo Finder and the Velocity Offsets
  of Cluster Cores
  We present a new algorithm for identifying dark matter halos, substructure,
and tidal features. The approach is based on adaptive hierarchical refinement
of friends-of-friends groups in six phase-space dimensions and one time
dimension, which allows for robust (grid-independent, shape-independent, and
noise-resilient) tracking of substructure; as such, it is named Rockstar
(Robust Overdensity Calculation using K-Space Topologically Adaptive
Refinement). Our method is massively parallel (up to 10^5 CPUs) and runs on the
largest current simulations (>10^10 particles) with high efficiency (10 CPU
hours and 60 gigabytes of memory required per billion particles analyzed). A
previous paper (Knebe et al 2011) has shown Rockstar to have class-leading
recovery of halo properties; we expand on these comparisons with more tests and
higher-resolution simulations. We show a significant improvement in
substructure recovery as compared to several other halo finders and discuss the
theoretical and practical limits of simulations in this regard. Finally, we
present results which demonstrate conclusively that dark matter halo cores are
not at rest relative to the halo bulk or satellite average velocities and have
coherent velocity offsets across a wide range of halo masses and redshifts. For
massive clusters, these offsets can be up to 350 km/s at z=0 and even higher at
high redshifts. Our implementation is publicly available at
http://code.google.com/p/rockstar .
