Clustering in massive neutrino cosmologies via Eulerian Perturbation Theory

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 less than or similar to 0.25 h 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.

Automated summary

An Eulerian Perturbation Theory is introduced to study the clustering of tracers in cosmologies with massive neutrinos, incorporating Effective Field Theory counterterms, IR-resummations, and biasing scheme for one-loop redshift-space power spectrum calculation. Comparisons with synthetic halo catalogues show good agreement on scales up to 0.25 h Mpc(-1), but higher wave-numbers lead to inaccurate estimation of the linear bias parameter. An accurate approximation method is derived to reduce computational cost for loop corrections and accelerate calculations with FFTLoG methods.