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arXiv 1804.06403astro-ph.CO
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前景免疫CMB透镜与仅剪切重建

Foreground-immune CMB lensing with shear-only reconstruction

Emmanuel Schaan, Simone Ferraro

摘要:

来自当前和即将到来的宽场CMB实验(如AdvACT,SPT-3G和西蒙斯天文台)的CMB透镜在很大程度上依赖于温度(与偏振)。在这种情况下,对温度图的前景污染产生显著的透镜偏置,这不能通过多频率分量分离、掩蔽或偏置硬化来完全控制。在这封信中,我们分裂成一组新的最佳“多极”估计标准CMB透镜二次估计。在大尺度上,这些多极估计减少到已知的放大和剪切估计,和一个新的剪切B-模式估计。我们利用不同的对称性的透镜CMB和河外前景认为,剪切估计应该是大约免疫河外前景。我们建立了一个新的方法来计算单独和无噪声的初级,次级和三光谱偏差CMB透镜从前景模拟。使用这种方法,我们证明了剪切估计确实是不敏感的河外前景,即使当应用到一个单频温度图污染CIB,tSZ,kSZ和无线电点源。前景偏差的这种显著降低使我们能够包括比标准二次估计器更高的温度多极,从而增加了超过二次估计器的总透镜信噪比。此外,仅放大和剪切B模式估计提供了有用的诊断潜在的残差。我们的Python代码LensQuEst可以预测各种估计器的信噪比,生成模拟映射,对它们进行建模,并将各种透镜估计器应用于它们,这些代码可以在https://github.com/EmmanuelSchaan/LensQuEst上公开获得。

英文摘要:

CMB lensing from current and upcoming wide-field CMB experiments such as AdvACT, SPT-3G and Simons Observatory relies heavily on temperature (vs. polarization). In this regime, foreground contamination to the temperature map produces significant lensing biases, which cannot be fully controlled by multi-frequency component separation, masking or bias hardening. In this letter, we split the standard CMB lensing quadratic estimator into a new set of optimal "multipole" estimators. On large scales, these multipole estimators reduce to the known magnification and shear estimators, and a new shear B-mode estimator. We leverage the different symmetries of the lensed CMB and extragalactic foregrounds to argue that the shear-only estimator should be approximately immune to extragalactic foregrounds. We build a new method to compute separately and without noise the primary, secondary and trispectrum biases to CMB lensing from foreground simulations. Using this method, we demonstrate that the shear estimator is indeed insensitive to extragalactic foregrounds, even when applied to a single-frequency temperature map contaminated with CIB, tSZ, kSZ and radio point sources. This dramatic reduction in foreground biases allows us to include higher temperature multipoles than with the standard quadratic estimator, thus increasing the total lensing signal-to-noise beyond the quadratic estimator. In addition, magnification-only and shear B-mode estimators provide useful diagnostics for potential residuals. Our Python code LensQuEst to forecast the signal-to-noise of the various estimators, generate mock maps, lense them, and apply the various lensing estimators to them is publicly available at https://github.com/EmmanuelSchaan/LensQuEst .

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