论文标题
Dayenu：一个简单的光滑前景滤波器，用于强度映射功率光谱
DAYENU: A Simple Filter of Smooth Foregrounds for Intensity Mapping Power Spectra
论文作者
论文摘要
我们介绍了Dayenu，这是一种用于HI强度映射的线性光谱滤波器，可实现逆变异加权加权的理想的前景缓解和误差最小化属性，并使用基础数据的最小建模。超过21厘米的功率光谱估计，我们的滤镜适用于任何分析，其中需要在不规则（或定期）采样的数据中高度动态范围清除光滑的前景，这是许多其他强度映射技术所需的。我们的过滤矩阵通过离散的pr酸球体序列对角度化，这是模拟21 cm强度映射实验中带有带限制前景的最佳基础，因为它们在傅立叶空间有限的区域内最大程度地浓缩了功率。我们表明，Dayenu可以访问锥形DFT估计器无法访问的大规模视线模式。由于这些模式具有最大的SNR，因此Dayenu显着提高了21 cm分析的敏感性，而不是锥形的傅立叶变换。轻微的修改使我们可以使用Dayenu作为迭代延迟清洁（Dayenurest）的线性替换。我们将读者推荐给本文末尾的代码部分，以链接到示例和代码。
We introduce DAYENU, a linear, spectral filter for HI intensity mapping that achieves the desirable foreground mitigation and error minimization properties of inverse co-variance weighting with minimal modeling of the underlying data. Beyond 21 cm power-spectrum estimation, our filter is suitable for any analysis where high dynamic-range removal of spectrally smooth foregrounds in irregularly (or regularly) sampled data is required, something required by many other intensity mapping techniques. Our filtering matrix is diagonalized by Discrete Prolate Spheroidal Sequences which are an optimal basis to model band-limited foregrounds in 21 cm intensity mapping experiments in the sense that they maximally concentrate power within a finite region of Fourier space. We show that DAYENU enables the access of large-scale line-of-sight modes that are inaccessible to tapered DFT estimators. Since these modes have the largest SNRs, DAYENU significantly increases the sensitivity of 21 cm analyses over tapered Fourier transforms. Slight modifications allow us to use DAYENU as a linear replacement for iterative delay CLEANing (DAYENUREST). We refer readers to the Code section at the end of this paper for links to examples and code.
