Dark Energy Survey year 3 results: covariance modelling and its impact on parameter estimation and quality of fit. Issue 3 (27th August 2021) - Friedrich, O; Andrade-Oliveira, F; Camacho, H; Alves, O; Rosenfeld, R; Sanchez, J; Fang, X; Eifler, T F; Krause, E; Chang, C; Omori, Y; Amon, A; et al.
ABSTRACT: We describe and test the fiducial covariance matrix model for the combined two-point function analysis of the Dark Energy Survey Year 3 (DES-Y3) data set. Using a variety of new ansatzes for covariance modelling and testing, we validate the assumptions and approximations of this model. These include the assumption of Gaussian likelihood, the trispectrum contribution to the covariance, the impact of evaluating the model at a wrong set of parameters, the impact of masking and survey geometry, deviations from Poissonian shot noise, galaxy weighting schemes, and other sub-dominant effects. We find that our covariance model is robust and that its approximations have little impact on goodness of fit and parameter estimation. The largest impact on best-fitting figure-of-merit arises from the so-called f sky approximation for dealing with finite survey area, which on average increases the χ 2 between maximum posterior model and measurement by $3.7{{\ \rm per\ cent}}$ (Δχ 2 ≈ 18.9). Standard methods to go beyond this approximation fail for DES-Y3, but we derive an approximate scheme to deal with these features. For parameter estimation, our ignorance of the exact parameters at which to evaluate our covariance model causes the dominant effect. We find that it increases the scatter of maximum posterior values for Ωm and σ8 by about $3{{\ \rm per\ cent}}$ and for the dark energy equation-of-state parameter by about $5{{\ \rm per\ cent}}$ .