Constraints on a scale-dependent bias from galaxy clustering

TL;DR

This paper forecasts how scale-dependent galaxy bias affects cosmological parameter estimation from galaxy clustering data, showing that certain parameters are more impacted and that bias modeling accuracy is crucial.

Contribution

It introduces a Fisher matrix analysis for future surveys to quantify the impact of scale-dependent bias on cosmological constraints, using mock catalogs calibrated for Euclid-like surveys.

Findings

Allowing for scale-dependent bias increases uncertainties in $\sigma_8$ and $\gamma$ by up to a factor of two.

The linear bias parameter $b_0$ can be estimated within 1-2",

% errors for non-linear bias parameters depend on the model adopted.

Abstract

We forecast the future constraints on scale-dependent parametrizations of galaxy bias and their impact on the estimate of cosmological parameters from the power spectrum of galaxies measured in a spectroscopic redshift survey. For the latter we assume a wide survey at relatively large redshifts, similar to the planned Euclid survey, as baseline for future experiments. To assess the impact of the bias we perform a Fisher matrix analysis and we adopt two different parametrizations of scale-dependent bias. The fiducial models for galaxy bias are calibrated using a mock catalogs of H$\alpha$ emitting galaxies mimicking the expected properties of the objects that will be targeted by the Euclid survey. In our analysis we have obtained two main results. First of all, allowing for a scale-dependent bias does not significantly increase the errors on the other cosmological parameters apart from the rms amplitude of density fluctuations, $\sigma_{8}$, and the growth index $\gamma$, whose uncertainties increase by a factor up to two, depending on the bias model adopted. Second, we find that the accuracy in the linear bias parameter $b_{0}$ can be estimated to within 1-2\% at various redshifts regardless of the fiducial model. The non-linear bias parameters have significantly large errors that depend on the model adopted. Despite of this, in the more realistic scenarios departures from the simple linear bias prescription can be detected with a $\sim2\,\sigma$ significance at each redshift explored. Finally, we use the Fisher Matrix formalism to assess the impact of assuming an incorrect bias model and found that the systematic errors induced on the cosmological parameters are similar or even larger than the statistical ones.