论文标题
黑暗能源调查3年结果：协方差建模及其对参数估计和拟合质量的影响
Dark Energy Survey Year 3 Results: Covariance Modelling and its Impact on Parameter Estimation and Quality of Fit
论文作者
论文摘要
我们描述并测试了深色能源调查3年（DES-Y3）数据集的联合2点功能分析的基准协方差矩阵模型。使用各种新的Ansatzes进行协方差建模和测试，我们验证了该模型的假设和近似值。其中包括假设高斯的可能性，三光谱对协方差的贡献，以错误的参数评估模型的影响，掩盖和调查几何形状的影响，对Poissonian Shot-Noise的偏差，星系加权方案和其他副作用。我们发现我们的协方差模型是强大的，并且其近似值对拟合优度和参数估计的影响很小。对处理有限的调查区域的所谓$ f _ {\ mathrm {sky}} $近似，对最佳合并的最佳影响产生了最大的影响。超出此近似值的标准方法对于DES-Y3失败，但我们得出了处理这些功能的近似方案。对于参数估计，我们对评估协方差模型的确切参数的无知会导致主要效果。我们发现，它将$ω_m$的最大后验值和$σ_8$的最大后验散布增加$ 3 \％$，而状态参数的暗能量方程则增加了约$ 5 \％$。
We describe and test the fiducial covariance matrix model for the combined 2-point function analysis of the Dark Energy Survey Year 3 (DES-Y3) dataset. 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 a 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-fit figure-of-merit arises from the so-called $f_{\mathrm{sky}}$ approximation for dealing with finite survey area, which on average increases the $χ^2$ between maximum posterior model and measurement by $3.7\%$ ($Δχ^2 \approx 18.9$). 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 $Ω_m$ and $σ_8$ by about $3\%$ and for the dark energy equation of state parameter by about $5\%$.
