Building Merger Trees from Cosmological N-body Simulations
  Although a fair amount of work has been devoted to growing Monte-Carlo merger
trees which resemble those built from an N-body simulation, comparatively
little effort has been invested in quantifying the caveats one necessarily
encounters when one extracts trees directly from such a simulation. To somewhat
revert the tide, this paper seeks to provide its reader with a comprehensive
study of the problems one faces when following this route. The first step to
building merger histories of dark matter haloes and their subhaloes is to
identify these structures in each of the time outputs (snapshots) produced by
the simulation. Even though we discuss a particular implementation of such an
algorithm (called AdaptaHOP) in this paper, we believe that our results do not
depend on the exact details of the implementation but extend to most if not all
(sub)structure finders. We then highlight different ways to build merger
histories from AdaptaHOP haloes and subhaloes, contrasting their various
advantages and drawbacks. We find that the best approach to (sub)halo merging
histories is through an analysis that goes back and forth between
identification and tree building rather than one which conducts a
straightforward sequential treatment of these two steps. This is rooted in the
complexity of the merging trees which have to depict an inherently dynamical
process from the partial temporal information contained in the collection of
instantaneous snapshots available from the N-body simulation.
