失去的地平线：局部地形对全球21厘米宇宙学数据分析的影响的量化
Lost Horizon: Quantifying the Effect of Local Topography on Global 21-cm Cosmology Data Analysis
摘要：
我们提出了一个调查的地平线和全球21厘米观测和分析的影响。我们发现，地平线时，不能忽略建模低频观测。即使准确地知道天空和天线波束，前向模型也不能完全描述波束加权的前景分量，而不能准确地知道地平线。当拟合数据以提取21厘米信号时，单个时间平均光谱或独立的多光谱拟合可能能够补偿由水平线施加的偏差。然而，这些类型的拟合对21 cm信号缺乏约束力，导致信号提取的不确定性很大，在某些情况下，其幅度大于21 cm信号本身。通过执行多光谱拟合，可以显着降低信号不确定性，其中光谱使用公共参数同时建模。然而，这种大大增加的约束能力的代价是，视界效应的时间依赖性，这比它的光谱依赖性更复杂，必须精确地建模，以实现良好的拟合。为了帮助对地平线进行建模，我们提出了一种算法和Python包，用于使用高程数据计算给定观测点的地平线剖面。我们还解决了几个实际问题，如像素化误差，地平线轮廓的不确定性，以及前景障碍物，如周围的建筑物和植被。我们证明，我们基于训练集的分析管道可以考虑所有这些因素，以便对地平线进行足够好的建模，从而从模拟观测中精确提取21 cm信号。
英文摘要：
We present an investigation of the horizon and its effect on global 21-cm observations and analysis. We find that the horizon cannot be ignored when modeling low frequency observations. Even if the sky and antenna beam are known exactly, forward models cannot fully describe the beam-weighted foreground component without accurate knowledge of the horizon. When fitting data to extract the 21-cm signal, a single time-averaged spectrum or independent multi-spectrum fits may be able to compensate for the bias imposed by the horizon. However, these types of fits lack constraining power on the 21-cm signal, leading to large uncertainties on the signal extraction, in some cases larger in magnitude than the 21-cm signal itself. A significant decrease in signal uncertainty can be achieved by performing multi-spectrum fits in which the spectra are modeled simultaneously with common parameters. The cost of this greatly increased constraining power, however, is that the time dependence of the horizon's effect, which is more complex than its spectral dependence, must be precisely modeled to achieve a good fit. To aid in modeling the horizon, we present an algorithm and Python package for calculating the horizon profile from a given observation site using elevation data. We also address several practical concerns such as pixelization error, uncertainty in the horizon profile, and foreground obstructions such as surrounding buildings and vegetation. We demonstrate that our training set-based analysis pipeline can account for all of these factors to model the horizon well enough to precisely extract the 21-cm signal from simulated observations.
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