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Abstract
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
.