盲地图级系统清洗：二次估计方法
Blind Map Level Systematics Cleaning: A Quadratic Estimator Approach
摘要：
我们提出了第一个详细的案例研究，使用二次估计（QE）诊断和删除系统存在于观测到的宇宙微波背景（CMB）地图。在这项工作中，我们专注于温度极化泄漏。我们使用迭代QE分析来去除系统，类似于去透镜，恢复原始B模式信号和系统图。我们引入了一个新的高斯滤波方案的迭代清洗过程的稳定收敛至关重要，并验证与半分析预测比较。我们研究了这种方法的局限性，通过研究其性能的理想化模拟和更现实的，非理想的模拟，我们假设不同的去透镜效率。最后，我们通过对张量与标量之比$r$进行似然分析来量化系统清洁效率，并证明盲清洁导致$r$的无偏测量，从而将系统诱导的B模式功率降低近两个数量级。
英文摘要：
We present the first detailed case study using quadratic estimators (QE) to diagnose and remove systematics present in observed Cosmic Microwave Background (CMB) maps. In this work we focus on the temperature to polarization leakage. We use an iterative QE analysis to remove systematics, in analogy to de-lensing, recovering the primordial B-mode signal and the systematic maps. We introduce a new Gaussian filtering scheme crucial to stable convergence of the iterative cleaning procedure and validate with comparisons to semi-analytical forecasts. We study the limitations of this method by examining its performance both on idealized simulations and on more realistic, non-ideal simulations, where we assume varying de-lensing efficiencies. Finally, we quantify the systematic cleaning efficiency by presenting a likelihood analysis on the tensor to scalar ratio, $r$, and demonstrate that the blind cleaning results in an un-biased measurement of $r$, reducing the systematic induced B-mode power by nearly two orders of magnitude.
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