基于训练集的全局21-cm宇宙学分析的鲁棒性保证
Ensuring Robustness in Training Set Based Global 21-cm Cosmology Analysis
摘要：
我们提出了一种方法，以确保我们的分析管道的鲁棒性分离的全球21厘米氢宇宙学信号从大型系统的基础上奇异值分解（SVD）的训练集。我们展示了如何传统的拟合优度指标，如$χ^2$统计，评估适合完整的数据可能无法检测到一个次优提取的21厘米的信号时，它是适合旁边的一个或多个额外的组件，由于它们之间的显着协方差。然而，我们发现，将针对给定拟合由流水线选择的每个分量的SVD本征模的数量与针对从训练集曲线创建的合成数据实现选择的本征模的分布进行比较，可以检测何时一个或多个训练集不足以最佳地提取信号。此外，该测试可以区分哪些训练集（例如前景，21 cm信号）需要修改，以便更好地描述数据并提高21 cm信号提取的质量。我们还扩展了这种拟合优度测试的情况下，来自训练集的先验分布的应用，并发现，在这种情况下，$χ^2$统计以及最近推出的$ψ^2$统计能够检测到不足的训练集，由于增加了限制所施加的前。至关重要的是，本文中描述的测试可以在使用我们的管道分析任何类型的观测时执行。
英文摘要：
We present a methodology for ensuring the robustness of our analysis pipeline in separating the global 21-cm hydrogen cosmology signal from large systematics based on singular value decomposition (SVD) of training sets. We show how traditional goodness-of-fit metrics such as the $χ^2$ statistic that assess the fit to the full data may not be able to detect a suboptimal extraction of the 21-cm signal when it is fit alongside one or more additional components due to significant covariance between them. However, we find that comparing the number of SVD eigenmodes for each component chosen by the pipeline for a given fit to the distribution of eigenmodes chosen for synthetic data realizations created from training set curves can detect when one or more of the training sets is insufficient to optimally extract the signal. Furthermore, this test can distinguish which training set (e.g. foreground, 21-cm signal) needs to be modified in order to better describe the data and improve the quality of the 21-cm signal extraction. We also extend this goodness-of-fit testing to cases where a prior distribution derived from the training sets is applied and find that, in this case, the $χ^2$ statistic as well as the recently introduced $ψ^2$ statistic are able to detect inadequacies in the training sets due to the increased restrictions imposed by the prior. Crucially, the tests described in this paper can be performed when analyzing any type of observations with our pipeline.
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。
