Convergence properties of halo merger trees; halo and substructure merger rates across cosmic history
Gregory B. Poole, Simon J. Mutch, Darren J. Croton, Stuart Wyithe

TL;DR
This paper introduces gbpTrees, an algorithm for constructing accurate halo merger trees from cosmological simulations, analyzing convergence properties, and providing analytic fits for merger rates across cosmic history.
Contribution
The paper presents gbpTrees, a novel algorithm that improves halo merger tree accuracy by correcting for pathologies and analyzing convergence across different resolutions and snapshots.
Findings
Merger counts converge within 5% for FoF haloes at specified resolutions.
Substructure merger rates converge within 10% at higher particle counts.
Analytic fits for merger rates match observed galactic history up to redshift 8.5.
Abstract
We introduce gbpTrees: an algorithm for constructing merger trees from cosmological simulations, designed to identify and correct for pathological cases introduced by errors or ambiguities in the halo finding process. gbpTrees is built upon a halo matching method utilising pseudo-radial moments constructed from radially-sorted particle ID lists (no other information is required) and a scheme for classifying merger tree pathologies from networks of matches made to-and-from haloes across snapshots ranging forward-and-backward in time. Focusing on Subfind catalogs for this work, a sweep of parameters influencing our merger tree construction yields the optimal snapshot cadence and scanning range required for converged results. Pathologies proliferate when snapshots are spaced by dynamical times; conveniently similar to that needed for convergence of semi-analytical…
Peer Reviews
No public reviews on file for this paper yet. If you reviewed it on a platform where reviews are public (OpenReview, ICLR, NeurIPS, ICML), you can paste yours below so the community can read it here.
Videos
No videos yet. Explain this paper in a talk, walkthrough, or lecture? Add one.
