Clustering in Massive Neutrino Cosmologies via Eulerian Perturbation Theory

TL;DR

This paper develops an Eulerian Perturbation Theory framework incorporating EFT, IR-resummation, and biasing to accurately model the clustering of tracers in massive neutrino cosmologies, validated against simulations.

Contribution

It introduces a novel Eulerian approach derived from Lagrangian results, including efficient loop correction approximations and FFTLog acceleration techniques.

Findings

Excellent agreement with simulations up to k≈0.25 h/Mpc

Higher wave-number fits fail to estimate linear bias correctly

Approximate kernels enable faster loop computations using FFTLog

Abstract

We introduce an Eulerian Perturbation Theory to study the clustering of tracers for cosmologies in the presence of massive neutrinos. Our approach is based on mapping recently-obtained Lagrangian Perturbation Theory results to the Eulerian framework. We add Effective Field Theory counterterms, IR-resummations and a biasing scheme to compute the one-loop redshift-space power spectrum. To assess our predictions, we compare the power spectrum multipoles against synthetic halo catalogues from the Quijote simulations, finding excellent agreement on scales $k\lesssim 0.25 \,h \text{Mpc}^{-1}$. One can obtain the same fitting accuracy using higher wave-numbers, but then the theory fails to give a correct estimation of the linear bias parameter. We further discuss the implications for the tree-level bispectrum. Finally, calculating loop corrections is computationally costly, hence we derive an accurate approximation wherein we retain only the main features of the kernels, as produced by changes to the growth rate. As a result, we show how FFTLog methods can be used to further accelerate the loop computations with these reduced kernels.