发表机构
Université Paris Cité; CNRS; Astroparticule et Cosmologie; ILANCE, CNRS – University of Tokyo International Research Laboratory; Kavli Institute for the Physics and Mathematics of the Universe (Kavli IPMU, WPI), UTIAS, The University of Tokyo; Université Paris-Saclay; IJCLab（巴黎西岱大学; 法国国家科学研究中心; 亚原子粒子与宇宙学; ILANCE，日本东京大学-法国国家科学研究中心国际研究实验室; 东京大学宇宙物质前沿研究所（Kavli IPMU）; 巴黎萨克雷大学; 伊夫·让勒默物理研究所）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
本研究针对卫星测量CMB B模式偏振的前景分离问题，通过增强SMICA分量和分区域运行，并结合模板边缘化，在模拟LiteBIRD观测中实现了对张量-标量比r的无偏估计，不确定性约10^{-3}。
AI 中文摘要
原初CMB B模式偏振的测量从根本上受限于我们能否将这一微弱信号从明亮且复杂的银河系前景中分离出来。谱匹配独立分量分析（SMICA）为这一前景清理问题提供了一种灵活、半盲的方法。然而，其标准实现依赖于一个物理上不切实际的假设，即前景辐射在空间上不变化，这降低了方法的效率。在本工作中，我们开发了两种互补策略来解决分量分离层面的这一局限性。首先，我们在物理的尘埃和同步辐射自由度之外，为SMICA增加了有效的前景分量。其次，我们将SMICA扩展为在天空子集上独立运行，以更好地捕捉前景辐射的局部变化。在分量分离之外，我们还进一步探索了一种使用内部重建前景模板的似然级边缘化程序。我们在模拟的类似LiteBIRD的观测上展示了该流程的性能，使用了基于\ exttt{d1s1}和\ exttt{d10s5}的复杂度递增的前景模型。将我们的分量分离改进与模板边缘化相结合，使得恢复的张量-标量比$r$达到有效无偏的水平，统计不确定性约为$10^{-3}$或更低。
英文摘要
The measurement of primordial CMB B-mode polarization is fundamentally limited by our ability to separate this faint signal from bright and complex Galactic foregrounds. Spectral Matching Independent Component Analysis (SMICA) offers a flexible, semi-blind approach to this foreground-cleaning problem. However, its standard implementation relies on a physically unrealistic assumption of non-spatially varying foreground emission, reducing the efficiency of the method. In this work, we develop two complementary strategies to address this limitation at the component-separation level. First, we augment SMICA with effective foreground components beyond the physical dust and synchrotron degrees of freedom. Second, we extend SMICA to operate independently on subsets of the sky, better capturing local variations in the foreground emission. Beyond component separation, we further explore a likelihood-level marginalization procedure using internally reconstructed foreground templates. We demonstrate the performance of the pipeline on simulated LiteBIRD-like observations, using foreground models of increasing complexity based on $\texttt{d1s1}$ and $\texttt{d10s5}$. Combining our component-separation improvements with template marginalization brings the recovered tensor-to-scalar ratio $r$ to effectively unbiased levels, with statistical uncertainties on the order of $10^{-3}$ or below.
Comments44 pages, 20 figures, prepared for submission to JCAP
