Constructing Merger Trees of Density Peaks Using Phase-Space Watershed Segmentation Algorithm

TL;DR

This paper introduces a novel phase-space watershed algorithm for identifying density peaks in cosmological simulations and constructs merger trees to track their evolution over time, improving structure analysis in complex environments.

Contribution

It presents a new watershed-based method focusing on density peaks and a merger tree algorithm using boosted potential to track peaks across simulation timesteps.

Findings

Effective identification of complex structures in simulations

Improved tracking of density peaks over time

Enhanced analysis of cosmological structures

Abstract

Structure identification in cosmological simulations plays an important role in analysing simulation outputs. The definition of these structures directly impacts the inferred properties derived from these simulations. This paper proposes a more straightforward definition and model of structure by focusing on density peaks rather than halos and clumps. It introduces a new watershed algorithm that uses phase-space analysis to identify structures, especially in complex environments where traditional methods may struggle due to spatially overlapping structures. Additionally, a merger tree code is introduced to track density peaks across timesteps, making use of the boosted potential for identifying the most bound particles for each peak.