论文标题
基于半盲PCA的前景减法方法，用于21 cm强度映射
A Semi-blind PCA-based Foreground Subtraction Method for 21 cm Intensity Mapping
论文作者
论文摘要
主成分分析（PCA）方法和奇异值分解（SVD）方法被广泛用于21 cm强度映射实验中的前景减法。我们显示了它们的等效性，并指出使用PCA/SVD完全清洁前景和宇宙21 cm信号的条件是不现实的。我们提出了一种基于PCA的前景减法方法，称为“单数矢量投影（SVP）”方法，该方法利用了前景的左和/或右奇异向量的先验信息。我们通过仿真测试证明，这种新的半盲方法可以通过数量级来减少恢复的21 cm信号的误差，即使只利用了最大模式中的左和/或右单数向量。 SVP估计器为21 cm观测值提供了一种新的，有效的方法，以去除前景并发现宇宙21 cm信号中的物理。
The Principal Component Analysis (PCA) method and the Singular Value Decomposition (SVD) method are widely used for foreground subtraction in 21 cm intensity mapping experiments. We show their equivalence, and point out that the condition for completely clean separation of foregrounds and cosmic 21 cm signal using the PCA/SVD is unrealistic. We propose a PCA-based foreground subtraction method, dubbed "Singular Vector Projection (SVP)" method, which exploits a priori information of the left and/or right singular vectors of the foregrounds. We demonstrate with simulation tests that this new, semi-blind method can reduce the error of the recovered 21 cm signal by orders of magnitude, even if only the left and/or right singular vectors in the largest few modes are exploited. The SVP estimators provide a new, effective approach for 21 cm observations to remove foregrounds and uncover the physics in the cosmic 21 cm signal.
