Sensitivity of Cosmological Parameter Estimation to Nonlinear Prescription from Galaxy Clustering

Abstract

Next generation large scale surveys probe the nonlinear regime with high resolution. Making viable cosmological inferences based on these observations requires accurate theoretical modeling of the mildly nonlinear regime. In this work we investigate the sensitivity of cosmological parameter measurements from future probes of galaxy clustering to the choice of nonlinear prescription up to $k_{\max }=0.3h{\mathrm{Mpc}}^{-1}$. In particular, we calculate the induced parameter bias when the mildly nonlinear regime is modeled by the Halofit fitting scheme. We find significant ($\sim 5\sigma$) bias for some parameters with a future Euclid-like survey. We also explore the contribution of different scales to the parameter estimation for different observational setups and cosmological scenarios, compared for the two nonlinear prescriptions of Halofit and EFTofLSS. We include in the analysis the free parameters of the nonlinear theory and a blind parametrization for the galaxy bias. We find that marginalization over these nuisance parameters significantly boosts the errors of the standard cosmological parameters. This renders the differences in the predictions of the various nonlinear prescriptions less effective when transferred to the parameter space. More accurate modeling of these nuisance parameters would therefore greatly enhance the cosmological gain from the mildly nonlinear regime.

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• Ultra-large-scale approximations and galaxy clustering: debiasing constraints on cosmological parameters
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