DEMNUni: the clustering of large-scale structures in the presence of massive neutrinos

We analyse the clustering features of Large Scale Structures (LSS) in the presence of massive neutrinos, employing a set of large-volume, high-resolution cosmological N-body simulations, where neutrinos are treated as separate collisionless particles. The volume of 8 h-3 Gpc3, combined with a resolution of about 8×1010h-1M⊚ for the cold dark matter (CDM) component, represents a significant improvement over previous N-body simulations in massive neutrino cosmologies. In this work we focus, in the first place, on the analysis of nonlinear effects in CDM and neutrinos perturbations contributing to the total matter power spectrum. We show that most of the nonlinear evolution is generated exclusively by the CDM component. We therefore compare mildly nonlinear predictions from Eulerian Perturbation Theory (PT), and fully nonlinear prescriptions (HALOFIT) with the measurements obtained from the simulations. We find that accounting only for the nonlinear evolution of the CDM power spectrum allows to recover the total matter power spectrum with the same accuracy as the massless case. Indeed, we show that, the most recent version of the (HALOFIT) formula calibrated on ΛCDM simulations can be applied directly to the linear CDM power spectrum without requiring additional fitting parameters in the massive case. As a second step, we study the abundance and clustering properties of CDM halos, confirming that, in massive neutrino cosmologies, the proper definition of the halo bias should be made with respect to the cold rather than the total matter distribution, as recently shown in the literature. Here we extend these results to the redshift space, finding that, when accounting for massive neutrinos, an improper definition of the linear bias can lead to a systematic error of about 1-2 % in the determination of the linear growth rate from anisotropic clustering. This result is quite important if we consider that future spectroscopic galaxy surveys, as e.g. Euclid, are expected to measure the linear growth-rate with statistical errors less than about 3 % at z≲1.