Forecasts on neutrino mass constraints from the redshift-space two-point correlation function

TL;DR

This paper develops a model to forecast how accurately future galaxy surveys can measure neutrino mass by analyzing redshift-space distortions, considering survey parameters like density, bias, and volume.

Contribution

It introduces a fitting function that predicts neutrino mass measurement errors based on survey parameters, aiding future survey design and analysis.

Findings

Error decreases with increasing bias and volume.

Error decreases with density up to a saturation point.

Euclid-like survey can measure neutrino mass fraction with high precision.

Abstract

We provide constraints on the accuracy with which the neutrino mass fraction, $f_{\nu}$, can be estimated when exploiting measurements of redshift-space distortions, describing in particular how the error on neutrino mass depends on three fundamental parameters of a characteristic galaxy redshift survey: density, halo bias and volume. In doing this, we make use of a series of dark matter halo catalogues extracted from the BASICC simulation. The mock data are analysed via a Markov Chain Monte Carlo likelihood analysis. We find a fitting function that well describes the dependence of the error on bias, density and volume, showing a decrease in the error as the bias and volume increase, and a decrease with density down to an almost constant value for high density values. This fitting formula allows us to produce forecasts on the precision achievable with future surveys on measurements of the neutrino mass fraction. For example, a Euclid-like spectroscopic survey should be able to measure the neutrino mass fraction with an accuracy of $\delta f_{\nu} \approx 6.7\times10^{-4}$, using redshift-space clustering once all the other cosmological parameters are kept fixed to the $\Lambda$CDM case.