Major Mergers Going Notts: Challenges for Modern Halo Finders

TL;DR

This paper compares five halo finders' ability to detect and characterize major mergers in cosmological simulations, highlighting discrepancies and suggesting improvements for future algorithms.

Contribution

It provides a comprehensive comparison of halo finders during major mergers, revealing biases and disagreements, and proposes integrating phase-space and temporal data for better accuracy.

Findings

Halo positions and velocities are generally robust across algorithms.

Mass estimates show biases in all halo finders.

Significant discrepancies exist in detecting merger prevalence and duration, especially at high redshifts.

Abstract

Merging haloes with similar masses (i.e., major mergers) pose significant challenges for halo finders. We compare five halo finding algorithms' (AHF, HBT, Rockstar, SubFind, and VELOCIraptor) recovery of halo properties for both isolated and cosmological major mergers. We find that halo positions and velocities are often robust, but mass biases exist for every technique. The algorithms also show strong disagreement in the prevalence and duration of major mergers, especially at high redshifts (z>1). This raises significant uncertainties for theoretical models that require major mergers for, e.g., galaxy morphology changes, size changes, or black hole growth, as well as for finding Bullet Cluster analogues. All finders not using temporal information also show host halo and subhalo relationship swaps over successive timesteps, requiring careful merger tree construction to avoid problematic mass accretion histories. We suggest that future algorithms should combine phase-space and temporal information to avoid the issues presented.