Constructing Merger Trees that Mimic N-Body Simulations
Eyal Neistein, Avishai Dekel

TL;DR
This paper introduces an empirical algorithm that constructs dark-matter halo merger trees closely matching N-body simulation results, improving over EPS trees and providing insights into merger-tree statistics.
Contribution
The paper presents a simple, Markovian algorithm that accurately reproduces the distribution of merger trees from N-body simulations, including the main progenitor and joint progenitor distributions.
Findings
The algorithm outperforms EPS trees in reproducing merger tree distributions.
Main progenitor distribution is log-normal in the variance of linear density fluctuations.
Provides analytic approximations for halo merger rates and main progenitor accretion histories.
Abstract
We present a simple and efficient empirical algorithm for constructing dark-matter halo merger trees that reproduce the distribution of trees in the Millennium cosmological -body simulation. The generated trees are significantly better than EPS trees. The algorithm is Markovian, and it therefore fails to reproduce the non-Markov features of trees across short time steps, except for an accurate fit to the evolution of the average main progenitor. However, it properly recovers the full main progenitor distribution and the joint distributions of all the progenitors over long-enough time steps, , where is the self-similar time variable and refers to the linear growth of density fluctuations. We find that the main progenitor distribution is log-normal in the variable , the variance of linear density…
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