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We implement EuclidEmulator (version 1), an emulator for the non-linearcorrection of the matter power spectrum, into the MCMC forecasting codeMontePython. We compare the performance of Halofit, HMCode, andEuclidEmulator1, both at the level of power spectrum prediction and at thelevel of posterior probability distributions of the cosmological parameters,for different cosmological models and different galaxy power spectrum wavenumber cut-offs. We confirm that the choice of the power spectrum predictor hasa non-negligible effect on the computed sensitivities when doing cosmologicalparameter forecasting, even for a conservative wave number cut-off of$0.2\,h\,{\rm Mpc}^{-1}$. We find that EuclidEmulator1 is on average up to$17\%$ more sensitive to the cosmological parameters than the other two codes,with the most significant improvements being for the Hubble parameter of up to$42\%$ and the equation of state of dark energy of up to $26\%$, depending onthe case. In addition, we point out that the choice of the power spectrumpredictor contributes to the risk of computing a significantly biased meancosmology when doing parameter estimations. For the four tested scenarios wefind biases, averaged over the cosmological parameters, of between 0.5 and2$\sigma$ (from below $1\sigma$ up to $6\sigma$ for individual parameters).This paper provides a proof of concept that this risk can be mitigated bytaking a well-tailored theoretical uncertainty into account as this allows toreduce the bias by a factor of 2 to 5, depending on the case underconsideration, while keeping posterior credibility contours small: the standarddeviations are amplified by a factor of $\leq1.4$ in all cases.

Parameter inference with non-linear galaxy clustering: accounting for theoretical uncertainties