论文标题

内部降级偏见对CMB极化原始B模型的搜索的影响

Impact of internal-delensing biases on searches for primordial B-modes of CMB polarisation

论文作者

Lizancos, Antón Baleato, Challinor, Anthony, Carron, Julien

论文摘要

搜索度尺度CMB $ b $ mmode极化数据中原始重力波的烙印必须占重力镜头的重大污染。幸运的是,可以通过将高分辨率$ e $ mmode测量与预计物质分布的估计相结合,可以部分消除镜头效应。在不久的将来,实验特征将是可以从观察到的CMB的高保真度中重建后者的,而$ eb $ $ $二次估计仪可提供大量信噪比。在这种情况下,这是一个众所周知的现象,即在要删除的$ b $字段与$ b $ field之间的任何重叠,从中得出重建的$ b $ field都会导致抑制超出范围的删除功率,这可以归因于透镜效应的缓解。更重要的是,与此频谱相关的差异也减少了,提出了一个问题,即额外的功率抑制是否可以帮助更好地限制张量与尺度比率,即$ r $。在本文中,我们表明事实并非如此,如先前的工作中所建议但未量化。我们为有偏见的$ b $ mmode角功率谱开发了一个分析模型,该模型提出了一种简单的重态化处方,以避免对推断的张量表与标准比率偏差。通过这种方法,我们了解到,与“无偏删除”相比,原始成分上的信噪比必须导致信噪比的降解。接下来,我们评估从镜头重建中删除任何重叠的$ b $ modes对我们约束$ r $的能力的影响,这通常表明这样做是有利的,而不是对偏见进行建模或重新构成偏差。最后,我们在应用于模拟的最大可能推理框架中验证这些结果。

Searches for the imprint of primordial gravitational waves in degree-scale CMB $B$-mode polarisation data must account for significant contamination from gravitational lensing. Fortunately, the lensing effects can be partially removed by combining high-resolution $E$-mode measurements with an estimate of the projected matter distribution. In the near future, experimental characteristics will be such that the latter can be reconstructed internally with high fidelity from the observed CMB, with the $EB$ quadratic estimator providing a large fraction of the signal-to-noise. It is a well-known phenomenon in this context that any overlap in modes between the $B$-field to be delensed and the $B$-field from which the reconstruction is derived leads to a suppression of delensed power going beyond that which can be attributed to a mitigation of the lensing effects. More importantly, the variance associated with this spectrum is also reduced, posing the question of whether the additional power suppression could help better constrain the tensor-to-scalar ratio, $r$. In this paper, we show this is not the case, as suggested but not quantified in previous work. We develop an analytic model for the biased delensed $B$-mode angular power spectrum, which suggests a simple renormalisation prescription to avoid bias on the inferred tensor-to-scalar ratio. With this approach, we learn that the bias necessarily leads to a degradation of the signal-to-noise on a primordial component compared to "unbiased delensing". Next, we assess the impact of removing from the lensing reconstruction any overlapping $B$-modes on our ability to constrain $r$, showing that it is in general advantageous to do this rather than modeling or renormalising the bias. Finally, we verify these results within a maximum-likelihood inference framework applied to simulations.

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