MassiveNuS: Cosmological Massive Neutrino Simulations

TL;DR

MassiveNuS presents a comprehensive set of N-body simulations incorporating massive neutrinos to study their impact on cosmic structure formation, providing valuable data for constraining neutrino masses and understanding their cosmological effects.

Contribution

This work introduces a large suite of neutrino-inclusive N-body simulations using an analytic linear-response approach, enabling detailed analysis of neutrino mass effects on large-scale structure.

Findings

Simulations cover neutrino masses from 0 to 0.6 eV.

Includes diverse data products like lensing maps and halo catalogues.

Validates simulation procedures and code accuracy.

Abstract

The non-zero mass of neutrinos suppresses the growth of cosmic structure on small scales. Since the level of suppression depends on the sum of the masses of the three active neutrino species, the evolution of large-scale structure is a promising tool to constrain the total mass of neutrinos and possibly shed light on the mass hierarchy. In this work, we investigate these effects via a large suite of N-body simulations that include massive neutrinos using an analytic linear-response approximation: the Cosmological Massive Neutrino Simulations (MassiveNuS). The simulations include the effects of radiation on the background expansion, as well as the clustering of neutrinos in response to the nonlinear dark matter evolution. We allow three cosmological parameters to vary: the neutrino mass sum M_nu in the range of 0-0.6 eV, the total matter density Omega_m, and the primordial power spectrum amplitude A_s. The rms density fluctuation in spheres of 8 comoving Mpc/h (sigma_8) is a derived parameter as a result. Our data products include N-body snapshots, halo catalogues, merger trees, ray- traced galaxy lensing convergence maps for four source redshift planes between z_s=1-2.5, and ray-traced cosmic microwave background lensing convergence maps. We describe the simulation procedures and code validation in this paper. The data are publicly available at http://columbialensing.org.