RSD排序帽：在投影中分解径向尺度
The RSD Sorting Hat: Unmixing Radial Scales in Projection
摘要：
未来的数据集将使红移空间失真（RSD）和弱透镜（WL）之间的互相关。虽然光度透镜和聚类互相关提供了一些迄今为止最严格的宇宙学约束，但如何以无损的方式最佳地执行类似的RSD/WL联合分析还没有得到很好的理解。RSD通常在$3D$红移空间中测量，但WL本质上是投影信号，使得角度统计成为组合分析的自然选择。因此，我们确定可以使用投影统计量提取的RSD信息量。具体来说，我们进行Fisher分析，预测约束和模型偏差比较两个不同的上帝之指（FoG）模型，使用两个，$3D$功率谱，$P(k, μ)$，和断层$C(\ell)$。我们发现，由于天真的层析投影混合大尺度与建模不良的非线性径向模式，它不提供竞争性的约束，$3D$ RSD功率谱没有模型偏差变得不可接受的大。这是真实的，即使在狭窄的断层摄影箱的限制。鉴于此，我们提出了一种新的径向加权方案，该方案在投影中对径向RSD尺度进行解混，从而对$3D$ RSD功率谱产生竞争性约束，同时保持模型偏差较小。这项工作奠定了RSD和宇宙剪切的最佳联合分析的基础。
英文摘要：
Future data sets will enable cross-correlations between redshift space distortions (RSD) and weak lensing (WL). While photometric lensing and clustering cross-correlations have provided some of the tightest cosmological constraints to date, it is not well understood how to optimally perform similar RSD/WL joint analyses in a lossless way. RSD is typically measured in $3D$ redshift space, but WL is inherently a projected signal, making angular statistics a natural choice for the combined analysis. Thus, we determine the amount of RSD information that can be extracted using projected statistics. Specifically we perform a Fisher analysis to forecast constraints and model bias comparing two different Fingers-of-God (FoG) models using both, the $3D$ power spectrum, $P(k, μ)$, and tomographic $C(\ell)$. We find that because naïve tomographic projection mixes large scales with poorly modelled nonlinear radial modes, it does not provide competitive constraints to the $3D$ RSD power spectrum without the model bias becoming unacceptably large. This is true even in the limit of narrow tomographic bins. In light of this we propose a new radial weighting scheme which unmixes radial RSD scales in projection yielding competitive constraints to the $3D$ RSD power spectrum, while keeping the model bias small. This work lays the groundwork for optimal joint analyses of RSD and cosmic shear.
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