Choose to smooth: Gaussian streaming with the truncated Zel'dovich approximation
Michael Kopp, Cora Uhlemann, Ixandra Achitouv

TL;DR
This paper enhances the Gaussian streaming model for predicting the redshift space correlation function of dark matter halos by introducing a smoothing technique based on the truncated Zel'dovich approximation, validated against N-body simulations.
Contribution
It introduces a truncated CLPT approach with smoothing of initial conditions, improving the accuracy of halo correlation predictions in redshift space.
Findings
Optimal smoothing scale of 1 Mpc/h at z=0 for real space correlation functions.
Pairwise velocity dispersion best predicted by smoothing at the halo's Lagrangian size.
Significant improvement over previous models in matching N-body simulation results.
Abstract
We calculate the dark matter halo correlation function in redshift space using the Gaussian streaming model (GSM). To determine the scale dependent functions entering the streaming model we use local Lagrangian bias together with Convolution Lagrangian perturbation theory (CLPT) which constitutes an approximation to the Post-Zel'dovich approximation. On the basis of N-body simulations we demonstrate that a smoothing of the initial conditions with the Lagrangian radius improves the Zel'dovich approximation and its ability to predict the displacement field of proto-halos. Based on this observation we implement a "truncated" CLPT by smoothing the initial power spectrum and investigate the dependence of the streaming model ingredients on the smoothing scale. We find that the real space correlation functions of halos and their mean pairwise velocity are optimised if the coarse graining scale…
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