DES Y3结果：图像模拟中的混合剪切和红移偏差
DES Y3 results: Blending shear and redshift biases in image simulations
摘要：
由于星系弱透镜效应的统计能力达到了百分之一级的精度，因此需要进行大型、逼真和鲁棒的模拟来校准观测系统，特别是考虑到随着调查深度的增加，物体混合的重要性越来越大。为了捕捉剪切和光度红移校准混合的耦合效应，我们定义了有效的红移分布透镜，$n_γ(z)$，并描述了如何估计它使用图像模拟。我们使用一套广泛的定制图像模拟来表征应用于暗能量调查（DES）第3年数据集的剪切估计管道的性能。我们描述了多波段，多历元的模拟，并通过与真实DES数据的比较证明了其高水平的现实主义。我们通过在基准模拟上运行变化来隔离产生剪切校准偏差的效应，并发现混合相关效应是对近似$-2\%$的平均乘法偏差的主要贡献。通过生成与输入剪切信号随红移而变化的模拟，我们校准我们的估计的有效redshfit分布的偏差，并证明了这种方法的重要性时，混合存在。我们提供校正后的有效红移分布，纳入统计和系统的不确定性，准备用于DES年3弱透镜分析。
英文摘要：
As the statistical power of galaxy weak lensing reaches percent level precision, large, realistic and robust simulations are required to calibrate observational systematics, especially given the increased importance of object blending as survey depths increase. To capture the coupled effects of blending in both shear and photometric redshift calibration, we define the effective redshift distribution for lensing, $n_γ(z)$, and describe how to estimate it using image simulations. We use an extensive suite of tailored image simulations to characterize the performance of the shear estimation pipeline applied to the Dark Energy Survey (DES) Year 3 dataset. We describe the multi-band, multi-epoch simulations, and demonstrate their high level of realism through comparisons to the real DES data. We isolate the effects that generate shear calibration biases by running variations on our fiducial simulation, and find that blending-related effects are the dominant contribution to the mean multiplicative bias of approximately $-2\%$. By generating simulations with input shear signals that vary with redshift, we calibrate biases in our estimation of the effective redshfit distribution, and demonstrate the importance of this approach when blending is present. We provide corrected effective redshift distributions that incorporate statistical and systematic uncertainties, ready for use in DES Year 3 weak lensing analyses.
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