Constructing high-fidelity halo merger trees in AbacusSummit

TL;DR

This paper introduces a new algorithm for constructing detailed halo merger trees from the AbacusSummit simulations, improving the fidelity of halo catalogues for galaxy formation modeling and survey mock-ups.

Contribution

The paper presents a novel core-tracking algorithm for building halo merger trees from the AbacusSummit simulations, enhancing halo catalogue accuracy by post-processing cleaning techniques.

Findings

The merger trees enable identification of non-monotonic merger histories.

Cleaning halo catalogues removes unphysical features caused by halo finder artifacts.

Resulting catalogues are more robust for galaxy formation and survey simulations.

Abstract

Tracking the formation and evolution of dark matter haloes is a critical aspect of any analysis of cosmological $N$-body simulations. In particular, the mass assembly of a halo and its progenitors, encapsulated in the form of its merger tree, serves as a fundamental input for constructing semi-analytic models of galaxy formation and, more generally, for building mock catalogues that emulate galaxy surveys. We present an algorithm for constructing halo merger trees from AbacusSummit, the largest suite of cosmological $N$-body simulations performed to date consisting of nearly 60 trillion particles, and which has been designed to meet the Cosmological Simulation Requirements of the Dark Energy Spectroscopic Instrument (DESI) survey. Our method tracks the cores of haloes to determine associations between objects across multiple timeslices, yielding lists of halo progenitors and descendants for the several tens of billions of haloes identified across the entire suite. We present an application of these merger trees as a means to enhance the fidelity of AbacusSummit halo catalogues by flagging and "merging" haloes deemed to exhibit non-monotonic past merger histories. We show that this cleaning technique identifies portions of the halo population that have been deblended due to choices made by the halo finder, but which could have feasibly been part of larger aggregate systems. We demonstrate that by cleaning halo catalogues in this post-processing step, we remove potentially unphysical features in the default halo catalogues, leaving behind a more robust halo population that can be used to create highly-accurate mock galaxy realisations from AbacusSummit.