Building Merger Trees from Cosmological N-body Simulations

TL;DR

This paper provides a comprehensive analysis of the challenges and best practices for constructing dark matter halo merger trees directly from cosmological N-body simulation data, emphasizing the importance of iterative methods.

Contribution

It introduces a detailed study of the issues in building merger trees from simulation outputs and advocates for an iterative approach over sequential methods.

Findings

Iterative analysis between halo identification and tree construction yields better results.

Most structure finders' limitations do not significantly affect the general conclusions.

Sequential methods are less effective due to the complex, dynamic nature of merger trees.

Abstract

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.