%0 Generic %1 aviles2021clustering %A Aviles, Alejandro %A Banerjee, Arka %A Niz, Gustavo %A Slepian, Zachary %D 2021 %K library %T Clustering in Massive Neutrino Cosmologies via Eulerian Perturbation Theory %U http://arxiv.org/abs/2106.13771 %X 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 $k0.25 \,h Mpc^-1$. Extending the range of accuracy to higher wave-numbers is possible at the cost of producing an offset in the best-fit linear local bias. 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.

@misc{aviles2021clustering, 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 $k\lesssim 0.25 \,h \text{Mpc}^{-1}$. Extending the range of accuracy to higher wave-numbers is possible at the cost of producing an offset in the best-fit linear local bias. 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.}, added-at = {2021-06-28T13:36:12.000+0200}, author = {Aviles, Alejandro and Banerjee, Arka and Niz, Gustavo and Slepian, Zachary}, biburl = {https://www.bibsonomy.org/bibtex/28aceca969f9f2a8ed3a293ecbc5c5181/gpkulkarni}, description = {Clustering in Massive Neutrino Cosmologies via Eulerian Perturbation Theory}, interhash = {167ce62ad461138e8e5451a8ad8a2d8a}, intrahash = {8aceca969f9f2a8ed3a293ecbc5c5181}, keywords = {library}, note = {cite arxiv:2106.13771Comment: 40 pages, 10 figures}, timestamp = {2021-06-28T13:36:12.000+0200}, title = {Clustering in Massive Neutrino Cosmologies via Eulerian Perturbation Theory}, url = {http://arxiv.org/abs/2106.13771}, year = 2021 }