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西蒙斯天文台:大尺度B模的管线比较与验证

The Simons Observatory: pipeline comparison and validation for large-scale B-modes

K. Wolz, S. Azzoni, C. Hervias-Caimapo, J. Errard, N. Krachmalnicoff, D. Alonso, C. Baccigalupi, A. Baleato Lizancos, M. L. Brown, E. Calabrese, J. Chluba, J. D… 展开作者

K. Wolz, S. Azzoni, C. Hervias-Caimapo, J. Errard, N. Krachmalnicoff, D. Alonso, C. Baccigalupi, A. Baleato Lizancos, M. L. Brown, E. Calabrese, J. Chluba, J. Dunkley, G. Fabbian, N. Galitzki, B. Jost, M. Morshed, F. Nati

AI总结:

针对西蒙斯天文台小口径望远镜的原初张量-标量比$r$高精度约束需求,提出三种分析管线并对比验证,发现默认版本在复杂前景下会产生$>2σ$偏差,扩展方案可将偏差降至统计不确定度以下。

摘要:

即将建成的西蒙斯天文台小口径望远镜旨在实现对原初张量-标量比$r$的约束,精度达到$σ(r=0)\lesssim0.003$,其观测将在部分天区覆盖、宇宙方差、非均匀非白噪声以及银河系前景的条件下开展偏振宇宙微波背景(CMB)观测。我们提出了三种不同的分析管线,它们能够基于最新的仪器性能数据约束$r$,并在一组天空模拟数据上比较了它们的预测结果,这些模拟数据可用于探索多种银河系前景模型以及与西蒙斯天文台相关的仪器噪声要素。三种管线在地图和功率谱层面采用了参数化与非参数化成分分离的不同组合,并使用B模纯化方法来估计CMB B模功率谱。我们将它们应用于一组共同的模拟真实频率地图,重点围绕其提取张量-标量比$r$的稳健约束的能力,对其进行了比较与验证。我们通过该参数的偏差和统计不确定性来评估它们的性能。在大多数场景下,三种方法都能达到相近的性能。然而,在使用这些管线的默认版本分析若干带有复杂前景信号的模拟数据时,会导致$r$出现大于$2σ$的偏差,这凸显了开发更复杂的、可对前景残差进行边缘化处理的管线组件的必要性。我们展示了两种此类扩展方案,分别采用基于功率谱和基于地图的方法,它们能够在所有已探索的前景模型中将$r$的偏差完全降低至统计不确定性以下,且仅带来$σ(r)$方面的适度代价。

英文摘要:

The upcoming Simons Observatory Small Aperture Telescopes aim at achieving a constraint on the primordial tensor-to-scalar ratio $r$ at the level of $σ(r=0)\lesssim0.003$, observing the polarized CMB in the presence of partial sky coverage, cosmic variance, inhomogeneous non-white noise, and Galactic foregrounds. We present three different analysis pipelines able to constrain $r$ given the latest available instrument performance, and compare their predictions on a set of sky simulations that allow us to explore a number of Galactic foreground models and elements of instrumental noise, relevant for the Simons Observatory. The three pipelines employ different combinations of parametric and non-parametric component separation at the map and power spectrum levels, and use B-mode purification to estimate the CMB B-mode power spectrum. We applied them to a common set of simulated realistic frequency maps, and compared and validated them with focus on their ability to extract robust constraints on the tensor-to-scalar ratio $r$. We evaluated their performance in terms of bias and statistical uncertainty on this parameter. In most of the scenarios the three methodologies achieve similar performance. Nevertheless, several simulations with complex foreground signals lead to a $>2σ$ bias on $r$ if analyzed with the default versions of these pipelines, highlighting the need for more sophisticated pipeline components that marginalize over foreground residuals. We show two such extensions, using power-spectrum-based and map-based methods, that are able to fully reduce the bias on $r$ below the statistical uncertainties in all foreground models explored, at a moderate cost in terms of $σ(r)$.

机构、总结、中文摘要均由AI分析而来,可能存在错误,请以论文正文为准。

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