Neutrino mass from the Lyman-Alpha forest

TL;DR

This paper develops hydrodynamical simulations of the Lyman-Alpha forest to improve constraints on neutrino mass from high-redshift cosmological data, aiding future survey analyses.

Contribution

It provides a suite of neutrino-inclusive hydrodynamical simulations tailored for BOSS and upcoming surveys, enhancing modeling accuracy of the intergalactic medium.

Findings

Simulations span neutrino masses from 0.1 to 0.8 eV.

Models are compatible with Planck 2013 cosmological parameters.

Simulation resolution matches SDSS-III/BOSS data quality.

Abstract

The quest for the neutrino mass is a central goal in contemporary cosmology, subject to intense scrutiny, and among different large-scale structure tracers the Lyman-Alpha forest is re-emerging as a unique tool to probe the neutrino mass at high-redshift - through characteristic imprints on the transmitted Lyman-Alpha flux. A detailed modeling of the low-density regions of the intergalactic medium in presence of massive neutrinos on scale ranging from a few to hundreds of megaparsecs is required, if one wants to interpret state-of-the-art data from observations of quasar spectra. To this end, we provide a suite of hydrodynamical simulations made with Gagdet-3, spanning different volumes and having a range of resolutions and neutrino masses (from M_nu=0.1 to 0.8 eV, assuming 3 degenerate species), specifically designed to meet the requirements of the Baryon Acoustic Spectroscopic Survey (BOSS). We adopted a particle-type implementation of massive neutrinos, and chose cosmological parameters compatible with the latest Planck (2013) results. While the resolution requirements match the quality of the SDSS-III/BOSS data, our numerical simulations will also establish a useful theoretical ground for upcoming surveys such as SDSS-IV/eBOSS and DESI. In the very near future, data from leading spectroscopic surveys will allow measuring the absolute mass scale of neutrinos, and determining the exact nature of the neutrino mass hierarchy; hence, we expect that this modeling will become increasingly useful.