The Gaussian streaming model and Lagrangian effective field theory

TL;DR

This paper enhances the Gaussian streaming model for redshift-space clustering by integrating Lagrangian perturbation theory, EFT, and advanced bias expansion, improving theoretical predictions against N-body simulations.

Contribution

It introduces a generalized bias expansion and EFT counter terms into the GSM, providing more accurate modeling of biased tracers in redshift space.

Findings

Improved agreement of GSM with N-body simulations for low multipoles

Demonstrated importance of advanced biasing schemes

Showed EFT corrections are crucial for accurate modeling

Abstract

We update the ingredients of the Gaussian streaming model (GSM) for the redshift-space clustering of biased tracers using the techniques of Lagrangian perturbation theory, effective field theory (EFT) and a generalized Lagrangian bias expansion. After relating the GSM to the cumulant expansion, we present new results for the real-space correlation function, mean pairwise velocity and pairwise velocity dispersion including counter terms from EFT and bias terms through third order in the linear density, its leading derivatives and its shear up to second order. We discuss the connection to the Gaussian peaks formalism. We compare the ingredients of the GSM to a suite of large N-body simulations, and show the performance of the theory on the low order multipoles of the redshift-space correlation function and power spectrum. We highlight the importance of a general biasing scheme, which we find to be as important as higher-order corrections due to non-linear evolution for the halos we consider on the scales of interest to us.