Sussing Merger Trees: The Merger Trees Comparison Project

TL;DR

This paper compares ten different algorithms for constructing dark matter halo merger trees, highlighting their differences and proposing best practices and standard terminology for future research in cosmic structure formation.

Contribution

It introduces a systematic comparison of merger tree algorithms, identifies key features for effective codes, and establishes a common language and output format for the community.

Findings

All algorithms produced distinct results despite similarities.

Effective merger-tree codes should use particle IDs for halo matching.

Codes should handle snapshot skipping and mass fluctuations.

Abstract

Merger trees follow the growth and merger of dark-matter haloes over cosmic history. As well as giving important insights into the growth of cosmic structure in their own right, they provide an essential backbone to semi-analytic models of galaxy formation. This paper is the first in a series to arise from the SUSSING MERGER TREES Workshop in which ten different tree-building algorithms were applied to the same set of halo catalogues and their results compared. Although many of these codes were similar in nature, all algorithms produced distinct results. Our main conclusions are that a useful merger-tree code should possess the following features: (i) the use of particle IDs to match haloes between snapshots; (ii) the ability to skip at least one, and preferably more, snapshots in order to recover subhaloes that are temporarily lost during merging; (iii) the ability to cope with (and ideally smooth out) large, temporary flucuations in halo mass. Finally, to enable different groups to communicate effectively, we defined a common terminology that we used when discussing merger trees and we encourage others to adopt the same language. We also specified a minimal output format to record the results.