论文标题
贝叶斯删除愉悦:基于抽样的原始CMB和重力镜头推理
Bayesian delensing delight: sampling-based inference of the primordial CMB and gravitational lensing
论文作者
论文摘要
在宇宙微波背景(CMB)中寻找原始引力波很快将受到我们将镜头污染的能力限制在$ b $ mode偏振上。已知镜头的通常使用的二次估计量对于当前正在运行的调查而言是次优的,随着工具噪声的降低,镜头的估计量将继续变得越来越降低。尽管原则上可以通过观察更多的频段来减轻前景,但在删除曲线方面的进展完全基于算法的进步。我们在这里演示了一种新的推理方法,该方法通过对任何所需的宇宙学参数的确切贝叶斯后部,重力透镜势和删除的CMB映射(鉴于温度和极化数据)进行取样。我们使用具有非白噪声的模拟CMB数据来验证该方法,并在650 \,deg $^2 $ patch of Sky patch上进行掩盖。这种方法的独特强度是能够共同估计宇宙学参数,这些参数可以控制原始的CMB和镜头电位,我们在这里首次在这里证明了这是通过对张量与尺度比率,$ r $进行采样,$ r $和镜头潜能的幅度,$ a_cccad $。该方法使我们能够对CMB-S4 $ R $预测的几个重要近似值进行最精确的检查,并确认这些收益到$ r $的正确预期不确定性,比10%更好。
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 $B$-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 non-white noise and masking on up to 650\,deg$^2$ patches of sky. A unique strength of this approach is the ability to jointly estimate 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, $r$, and the amplitude of the lensing potential, $A_ϕ$. The method allows us to perform the most precise check to-date of several important approximations underlying CMB-S4 $r$ forecasting, and we confirm these yield the correct expected uncertainty on $r$ to better than 10%.