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arXiv 2108.01663astro-ph.CO
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用于CMB透镜化的最佳多频加权

Optimal multifrequency weighting for CMB lensing

Noah Sailer, Emmanuel Schaan, Simone Ferraro, Omar Darwish, Blake Sherwin

摘要:

在宇宙微波背景(CMB)温度图的河外前景导致显着的偏差在CMB透镜重建,如果没有适当的考虑。多频率数据的组合已被用于最小化总体地图方差(内部线性组合,或ILC),或具体地使给定前景无效,但这些并不适合于CMB透镜效应。在本文中,我们推导出一个最佳的多频率组合,以共同最小化CMB透镜噪声和偏差。我们专注于标准的透镜二次估计,以及“剪切”和源硬化估计,其响应前景不同。我们发现,一个最佳的多频组合是ILC和联合解投影之间的妥协,它使热Sunyaev-Zel'dovich(tSZ)和宇宙红外背景(CIB)的贡献。特别是,对于一个西蒙斯观测器样实验与$\ell_{\text{max},T}=3000$,我们发现,单独的轮廓硬化(与标准的ILC)减少了偏见的透镜功率幅度由$40\%$,在噪声的$20\%$成本,而偏差的互相关与LSST样样本减少了近一个数量级的$10\%$噪声成本,相对于标准的二次估计。通过少量的联合解投影,相对于具有标准ILC权重的轮廓硬化估计器,在分别用于自相关和互相关的$20\%$和$5\%$噪声成本下,对轮廓硬化估计器的偏差可以进一步减小到小于相应幅度上的统计不确定性的一半。最后,我们探讨了更积极的掩蔽和不同的$\ell_{\text{max,}T}$可能的改进。

英文摘要:

Extragalactic foregrounds in Cosmic Microwave Background (CMB) temperature maps lead to significant biases in CMB lensing reconstruction if not properly accounted for. Combinations of multi-frequency data have been used to minimize the overall map variance (internal linear combination, or ILC), or specifically null a given foreground, but these are not tailored to CMB lensing. In this paper, we derive an optimal multi-frequency combination to jointly minimize CMB lensing noise and bias. We focus on the standard lensing quadratic estimator, as well as the "shear-only" and source-hardened estimators, whose responses to foregrounds differ. We show that an optimal multi-frequency combination is a compromise between the ILC and joint deprojection, which nulls the thermal Sunyaev-Zel'dovich (tSZ) and Cosmic Infrared Background (CIB) contributions. In particular, for a Simons Observatory-like experiment with $\ell_{\text{max},T}=3000$, we find that profile hardening alone (with the standard ILC) reduces the bias to the lensing power amplitude by $40\%$, at a $20\%$ cost in noise, while the bias to the cross-correlation with a LSST-like sample is reduced by nearly an order of magnitude at a $10\%$ noise cost, relative to the standard quadratic estimator. With a small amount of joint deprojection the bias to the profile hardened estimator can be further reduced to less than half the statistical uncertainty on the respective amplitudes, at a $20\%$ and $5\%$ noise cost for the auto- and cross-correlation respectively, relative to the profile hardened estimator with the standard ILC weights. Finally, we explore possible improvements with more aggressive masking and varying $\ell_{\text{max,}T}$.

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