Galactic foregrounds are the main obstacle to observations of the cosmic microwave background (CMB) B-mode polarization. In addition to obscuring the inflationary B-mode signal by several orders of magnitude, Galactic foregrounds have non-trivial spectral signatures that are partially unknown and distorted by averaging effects along the line of sight, within the pixel/beam window, and by various analysis choices (e.g. spherical harmonic transforms and filters). Statistical moment expansion methods provide a powerful tool for modelling the effective Galactic foreground emission resulting from these averaging effects in CMB observations, while blind component separation treatments can handle unknown foregrounds. In this work, we combine these two approaches to develop a new semiblind component separation method at the intersection of parametric and blind methods, called constrained moment ILC (cMILC). This method adds several constraints to the standard ILC method to deproject the main statistical moments of the Galactic foreground emission. Applications to maps are performed in needlet space and when compared to the NILC method, this helps in significantly reducing residual foreground contamination (bias, variance, and skewness) in the reconstructed CMB B-mode map, power spectrum, and tensor-to-scalar ratio. We consider sky simulations for experimental settings similar to those of LiteBIRD and PICO, illustrating which trade-offs between residual foreground biases and degradation of the constraint on r can be expected within the new cMILC framework. We also outline several directions that require more work in preparation for the coming analysis challenges.

中文翻译：

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用约束矩 ILC 方法剥离前景以揭示原始 CMB B 模式

银河前景是观测宇宙微波背景 (CMB) B 模式极化的主要障碍。除了将暴胀 B 模式信号模糊几个数量级之外，银河前景还具有重要的光谱特征，这些特征部分是未知的，并且由于沿视线、像素/光束窗口内的平均效应以及各种分析而失真选择（例如球谐变换和滤波器）。统计矩扩展方法为模拟由 CMB 观测中的这些平均效应产生的有效银河前景发射提供了强大的工具，而盲分量分离处理可以处理未知前景。在这项工作中，我们将这两种方法结合起来，在参数方法和盲方法的交叉点上开发了一种新的半盲分量分离方法，称为约束矩 ILC (cMILC)。该方法在标准 ILC 方法中增加了几个约束，以对银河前景发射的主要统计矩进行去投影。地图应用是在针状空间中执行的，与 NILC 方法相比，这有助于显着减少重建的 CMB B 模式地图、功率谱和张量到标量中的残余前景污染（偏差、方差和偏斜）比率。我们考虑了类似于 LiteBIRD 和 PICO 的实验设置的天空模拟，说明了在新的 cMILC 框架内可以预期残余前景偏差和 r 约束退化之间的权衡。