A New Halo Finding Method for N-Body Simulations
  We have developed a new halo finding method, Physically Self-Bound (PSB) group finding algorithm, which can efficiently identify halos located even at crowded regions. This method combines two physical criteria such as the tidal radius of a halo and the total energy of each particle to find member particles. Two hierarchical meshes are used to increase the speed and the power of halo identification in the parallel computing environments. First, a coarse mesh with cell size equal to the mean particle separation $l_{\rm mean}$ is used to obtain the density field over the whole simulation box. Mesh cells having density contrast higher than a local cutoff threshold $\delta_{\rm LOC}$ are extracted and linked together for those adjacent to each other. This produces local-cell groups. Second, a finer mesh is used to obtain density field within each local-cell group and to identify halos. If a density shell contains only one density peak, its particles are assigned to the density peak. But in the case of a density shell surrounding at least two density peaks, we use both the tidal radii of halo candidates enclosed by the shell and the total energy criterion to find physically bound particles with respect to each halo. Similar to DENMAX and HOP, the \hfind method can efficiently identify small halos embedded in a large halo, while the FoF and the SO do not resolve such small halos. We apply our new halo finding method to a 1-Giga particle simulation of the $\Lambda$CDM model and compare the resulting mass function with those of previous studies. The abundance of physically self-bound halos is larger at the low mass scale and smaller at the high mass scale than proposed by the Jenkins et al. (2001) who used the FoF and SO methods. (abridged) 
Juhan Kim, Changbom Park