Redshift-Space Distortions in Lagrangian Perturbation Theory

TL;DR

This paper develops a precise Lagrangian perturbation theory model for redshift-space distortions, accurately predicting galaxy clustering statistics and enabling unbiased cosmological parameter estimation from large-volume surveys.

Contribution

It introduces a full resummation approach in Lagrangian perturbation theory for redshift-space distortions, improving accuracy over previous methods.

Findings

Achieves percent-level accuracy in power spectrum predictions for halos and mock galaxies.

Effectively models baryon acoustic oscillation damping due to galaxy bulk motions.

Provides a fast Python code for practical computation of clustering statistics.

Abstract

We present the one-loop 2-point function of biased tracers in redshift space computed with Lagrangian perturbation theory, including a full resummation of both long-wavelength (infrared) displacements and associated velocities. The resulting model accurately predicts the power spectrum and correlation function of halos and mock galaxies from two different sets of N-body simulations at the percent level for quasi-linear scales, including the damping of the baryon acoustic oscillation signal due to the bulk motions of galaxies. We compare this full resummation with other, approximate, techniques including the moment expansion and Gaussian streaming model. We discuss infrared resummation in detail and compare our Lagrangian formulation with the Eulerian theory augmented by an infrared resummation based on splitting the input power spectrum into "wiggle" and "no-wiggle" components. We show that our model is able to recover unbiased cosmological parameters in mock data encompassing a volume much larger than what will be available to future galaxy surveys. We demonstrate how to efficiently compute the resulting expressions numerically, making available a fast Python code capable of rapidly computing these statistics in both configuration and Fourier space.