Impact of Galactic dust non-Gaussianity on searches for B -modes from inflation
Impact of Galactic dust non-Gaussianity on searches for B -modes from inflation
复制标题
银河尘埃非高斯性对暴胀 B 模式搜索的影响
DOI：
10.1093/mnras/stad3529
复制
发表时间：
2024
影响因子：
4.8
通讯作者：
Abril-Cabezas I
中科院分区：
文献类型：
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作者：
Abril-Cabezas I
寻找原始B模式的一个关键挑战是存在偏振的银河系前景，特别是热尘埃发射。基于功率谱的分析方法在构造似然和计算协方差矩阵时，一般假设前景为高斯随机场。在这篇文章中，我们研究了尘埃场中的非高斯性如何在膨胀B模式搜索的背景下影响CMB和前景参数推断，通过修改尘埃功率谱协方差矩阵来捕捉这种影响。对于即将到来的实验，如西蒙斯天文台，我们发现张量标量比的不确定性不依赖于尘埃的非高斯性程度或尘埃协方差矩阵的性质。我们对这一结果进行了解释，指出当频率去相关可以忽略时，中频通道中的尘埃以一种与尘埃的空间统计无关的方式使用高频数据进行清理。我们表明，我们的结果也适用于与现有数据兼容的非零频率去相关水平。然而，我们发现，忽略协方差矩阵中尘埃非高斯性的影响可能会导致拟合优度度量的不准确。因此，当使用这种度量来测试B模式频谱和模型时，必须小心，尽管我们表明，在计算拟合优度统计时，只使用清洁的频谱组合可以缓解任何此类问题。
A key challenge in the search for primordialB-modes is the presence of polarized Galactic foregrounds, especially thermal dust emission. Power-spectrum-based analysis methods generally assume the foregrounds to be Gaussian random fields when constructing a likelihood and computing the covariance matrix. In this paper, we investigate how non-Gaussianity in the dust field instead affects CMB and foreground parameter inference in the context of inflationaryB-mode searches, capturing this effect via modifications to the dust power-spectrum covariance matrix. For upcoming experiments such as the Simons Observatory, we findnodependence of the tensor-to-scalar ratio uncertaintyon the degree of dust non-Gaussianity or the nature of the dust covariance matrix. We provide an explanation of this result, noting that when frequency decorrelation is negligible, dust in mid-frequency channels is cleaned using high-frequency data in a way that is independent of the spatial statistics of dust. We show that our results hold also for non-zero levels of frequency decorrelation that are compatible with existing data. We find, however, that neglecting the impact of dust non-Gaussianity in the covariance matrix can lead to inaccuracies in goodness-of-fit metrics. Care must thus be taken when using such metrics to testB-mode spectra and models, although we show that any such problems can be mitigated by using only cleaned spectrum combinations when computing goodness-of-fit statistics.