论文标题
地面CMB制图中的大规模功率损失
Large-scale power loss in ground-based CMB mapmaking
论文作者
论文摘要
CMB映射依赖于数据模型来求解天空图，如果数据模型无法捕获信号的全部行为，则此过程容易受到偏差的影响。我们证明，这种偏见不仅限于在天空的高对比度区域中的小规模效应，而且可以表现为$ \ Mathcal {O}（1）$在地图中大尺度上的功率损失在地图和地面上对地面CMB望远镜的现实性。这种偏见是基于模拟的测试看不见的，这些测试不会明确地对其进行建模，从而容易错过。我们确定了两种不同的机制，这些机制都引起长波长模式的抑制：子像素误差和检测器获得校准不匹配。我们表明，可以使用双线性指向矩阵消除子像素偏差的特定情况，但也提供了用于测试大规模模型误差偏差的简单方法。
CMB mapmaking relies on a data model to solve for the sky map, and this process is vulnerable to bias if the data model cannot capture the full behavior of the signal. We demonstrate that this bias is not just limited to small-scale effects in high-contrast regions of the sky, but can manifest as $\mathcal{O}(1)$ power loss on large scales in the map under conditions and assumptions realistic for ground-based CMB telescopes. This bias is invisible to simulation-based tests that do not explicitly model them, making it easy to miss. We identify two different mechanisms that both cause suppression of long-wavelength modes: sub-pixel errors and detector gain calibration mismatch. We show that the specific case of subpixel bias can be eliminated using bilinear pointing matrices, but also provide simple methods for testing for the presence of large-scale model error bias in general.
