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

Abstract

We implement EuclidEmulator (version 1), an emulator for the non-linear correction of the matter power spectrum, into the Markov chain Monte Carlo forecasting code MontePython. We compare the performance of HALOFIT, HMCode, and EuclidEmulator1, both at the level of power spectrum prediction and at the level 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 has a non-negligible effect on the computed sensitivities when doing cosmological parameter forecasting, even for a conservative wavenumber cut-off of 0.2 h Mpc−1. We find that EuclidEmulator1 is on average up to $17{{\ \rm per\ cent}}$ more sensitive to the cosmological parameters than the other two codes, with the most significant improvements being for the Hubble parameter of up to $42{{\ \rm per\ cent}}$ and the equation of state of dark energy of up to $26{{\ \rm per\ cent}}$, depending on the case. In addition, we point out that the choice of the power spectrum predictor contributes to the risk of computing a significantly biased mean cosmology when doing parameter estimations. For the four tested scenarios we find biases, averaged over the cosmological parameters, of between 0.5 and 2σ (from below 1σ up to 6σ for individual parameters). This paper provides a proof of concept that this risk can be mitigated by taking a well-tailored theoretical uncertainty into account as this allows to reduce the bias by a factor of 2 to 5, depending on the case under consideration, while keeping posterior credibility contours small: the standard deviations are amplified by a factor of ≤1.4 in all cases.

Dark Energy Survey Year 3 results: cosmology with moments of weak lensing mass maps – validation on simulations

“…We validate the Gower St simulations by comparing the power spectra measured on the full-sky convergence maps, weighted by the DES redshift distributions, against theory predictions obtained using halofit [99]. Note that we did not use the more recent (and more accurate) EuclidEmu [31] for this comparison, as Eu-clidEmu covers only a very limited portion of our parameter space. We generally do not expect an agreement better than 2 per cent, as this is the typical relative error between different non-linear power spectrum prescriptions or other modelling implementations (e.g.…”