Finding Evidence for Massive Neutrinos using 3D Weak Lensing

TL;DR

This study demonstrates that 3D cosmic shear combined with Planck data can significantly improve constraints on neutrino mass and number, potentially providing decisive evidence for massive neutrinos and distinguishing between different models.

Contribution

The paper introduces a Bayesian evidence approach to forecast the ability of 3D cosmic shear surveys to detect and differentiate massive neutrino models, enhancing current constraints.

Findings

3D cosmic shear + Planck can constrain neutrino parameters with high precision.

The method can provide substantial evidence for massive neutrinos.

It can decisively distinguish models with no massive neutrinos from those with significant masses.

Abstract

In this paper we investigate the potential of 3D cosmic shear to constrain massive neutrino parameters. We find that if the total mass is substantial (near the upper limits from LSS, but setting aside the Ly alpha limit for now), then 3D cosmic shear + Planck is very sensitive to neutrino mass and one may expect that a next generation photometric redshift survey could constrain the number of neutrinos N_nu and the sum of their masses m_nu to an accuracy of dN_nu ~ 0.08 and dm_nu ~ 0.03 eV respectively. If in fact the masses are close to zero, then the errors weaken to dN_nu ~ 0.10 and dm_nu~0.07 eV. In either case there is a factor 4 improvement over Planck alone. We use a Bayesian evidence method to predict joint expected evidence for N_nu and m_nu. We find that 3D cosmic shear combined with a Planck prior could provide `substantial' evidence for massive neutrinos and be able to distinguish `decisively' between many competing massive neutrino models. This technique should `decisively' distinguish between models in which there are no massive neutrinos and models in which there are massive neutrinos with |N_nu-3| > 0.35 and m_nu > 0.25 eV. We introduce the notion of marginalised and conditional evidence when considering evidence for individual parameter values within a multi-parameter model.