Sampling-based inference of the primordial CMB and gravitational lensing
Sampling-based inference of the primordial CMB and gravitational lensing
复制标题
基于采样的原始宇宙微波背景和引力透镜推理
DOI：
10.1103/physrevd.102.123542
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发表时间：
2020
影响因子：
5
通讯作者：
Wandelt, Benjamin D.
中科院分区：
文献类型：
--
作者：
Millea, Marius;Anderes, Ethan;Wandelt, Benjamin D.
在宇宙微波背景辐射（CMB）中寻找原初引力波的工作很快就会受到我们去除透镜污染的能力的限制。通常使用的二次估计透镜已知是次优的调查，目前正在运作，并将继续变得越来越不有效的仪器噪声降低。虽然原则上可以通过在更多频段进行观察来减轻前景，但去透镜化的进展完全取决于算法的进步。我们在这里展示了一种新的推理方法，解决了这个问题，通过采样的精确贝叶斯后验的任何所需的宇宙学参数，引力透镜的潜力，和delensed CMB地图，给定的透镜温度和偏振数据。我们使用模拟的CMB数据与非白噪声和遮蔽的天空上的toppatches的方法进行验证。这种方法的一个独特的优势是能够进行宇宙学参数的联合推断，这些参数控制着原始CMB和透镜势，我们在这里首次通过采样张量与标量比和透镜势的振幅来证明这一点。该方法使我们能够执行最精确的检查到目前为止的几个重要的近似CMB-S4预测的基础，我们确认这些产生正确的预期不确定性到优于10%。
The search for primordial gravitational waves in the cosmic microwave background (CMB) will soon be limited by our ability to remove the lensing contamination to-mode polarization. The often-used quadratic estimator for lensing is known to be suboptimal for surveys that are currently operating and will continue to become less and less efficient as instrumental noise decreases. While foregrounds can, in principle, be mitigated by observing in more frequency bands, progress in delensing hinges entirely on algorithmic advances. We demonstrate here a new inference method that solves this problem by sampling the exact Bayesian posterior of any desired cosmological parameters, of the gravitational lensing potential, and of the delensed CMB maps, given lensed temperature and polarization data. We validate the method using simulated CMB data with nonwhite noise and masking on up topatches of sky. A unique strength of this approach is the ability to perform joint inference of cosmological parameters, which control both the primordial CMB and the lensing potential, which we demonstrate here for the first time by sampling both the tensor-to-scalar ratio,, and the amplitude of the lensing potential,. The method allows us to perform the most precise check to-date of several important approximations underlying CMB-S4forecasting, and we confirm these yield the correct expected uncertainty onto better than 10%.