An inpainting approach to tackle the kinematic and thermal SZ induced biases in CMB-cluster lensing estimators
An inpainting approach to tackle the kinematic and thermal SZ induced biases in CMB-cluster lensing estimators
复制标题
一种解决 CMB 簇透镜估计器中运动学和热 SZ 引起的偏差的修复方法
DOI：
10.1088/1475-7516/2019/11/037
复制
发表时间：
2019
影响因子：
6.4
通讯作者：
Whitehorn, Nathan
中科院分区：
文献类型：
--
作者：
Raghunathan, Srinivasan;Holder, Gilbert P.;Bartlett, James G.;Patil, SanjayKumar;Reichardt, Christian L.;Whitehorn, Nathan
星系团自身的Sunyaev-Zel'dovich（SZ）信号是利用宇宙微波背景（CMB）温度图重建星系团潜在透镜势时的主要污染物。在这项工作中，我们开发了一个修改的二次估计（QE），旨在减轻由于运动学和热SZ效应的透镜偏差。该方法背后的想法是使用修复技术从大规模CMB梯度图中消除集群自身的发射。在这个修复的梯度图中，我们使用约束高斯实现基于来自周围区域的信息填充聚类位置处的像素值。我们表明，与即将进行的调查的其他噪声源相比，由于修复过程引起的噪声很小，并且对最终的透镜信噪比的影响可以忽略不计。在没有任何前景清理的情况下，我们发现CMB-S4实验在z= 0.7时包含5000个M200 c = 2× 1014 M的团簇样品的堆积质量不确定度为6.5%。除了SZ引起的透镜偏置，我们还量化了低质量的偏见所产生的由于污染的CMB梯度集群收敛。对于在这项工作中考虑的基准集群样本，我们发现，这种偏见是可以忽略不计的统计不确定性的标准和修改后的QE，即使当模式高达2700用于梯度估计。随着更多的梯度模式，我们证明，灵敏度可以增加14%相比，上述基准结果使用梯度模式高达2000。
A galaxy cluster's own Sunyaev-Zel'dovich (SZ) signal is known to be a major contaminant when reconstructing the cluster's underlying lensing potential using cosmic microwave background (CMB) temperature maps. In this work, we develop a modified quadratic estimator (QE) that is designed to mitigate the lensing biases due to the kinematic and thermal SZ effects. The idea behind the approach is to use inpainting technique to eliminate the cluster's own emission from the large-scale CMB gradient map. In this inpainted gradient map, we fill the pixel values at the cluster location based on the information from surrounding regions using a constrained Gaussian realization. We show that the noise induced due to inpainting process is small compared to other noise sources for upcoming surveys and has negligible impact on the final lensing signal-to-noise. Without any foreground cleaning, we find a stacked mass uncertainty of 6.5% for the CMB-S4 experiment on a cluster sample containing 5000 clusters with M 200c= 2× 10 14 M⊙ at z= 0.7. In addition to the SZ-induced lensing biases, we also quantify the low mass bias arising due to the contamination of the CMB gradient by the cluster convergence. For the fiducial cluster sample considered in this work, we find that this bias is negligible compared to the statistical uncertainties for both the standard and the modified QE even when modes up to∼ 2700 are used for the gradient estimation. With more gradient modes, we demonstrate that the sensitivity can be increased by 14% compared to the fiducial result quoted above using gradient modes up to 2000.