变化环境条件下的全球21厘米信号恢复
Global 21 cm Signal Recovery Under Changing Environmental Conditions
AI总结：
针对环境条件变化时传统方法难以恢复宇宙21厘米信号的问题，提出基于掩蔽自回归流的工具，相比传统流程贝叶斯证据更高，信号恢复均方根误差最高降低45%。
摘要：
宇宙原子氢的红移21厘米谱线是解读早期宇宙最具前景的工具之一。恢复该信号仍是该领域宇宙学家面临的长期难题，因为该信号被比自身亮约五个数量级的前景信号所掩盖。采用贝叶斯数据分析和基于物理的前景模型的传统正向建模拟数据分析流程，在环境条件不变的情况下展现出巨大潜力。但我们在本文中证明，当天线下方土壤的介电特性随时间变化，或观测远场的土壤温度发生变化时，这些传统方法将难以奏效。\n 本文详细介绍了一种使用掩蔽自回归流（Masked Auto-regressive Flows）的工具，在存在环境条件变化的情况下尝试恢复该信号时，它对以往基于物理的前景模型做出了改进。我们证明，在这些参数变化的情况下，我们的工具能始终如一地恢复信号，且贝叶斯证据远高于传统数据分析流程，将注入信号恢复的均方根误差降低了高达45%。
英文摘要：
The redshifted 21 cm line of cosmic atomic hydrogen is one of the most auspicious tools in deciphering the early Universe. Recovering this signal remains an ongoing problem for cosmologists in the field, with the signal being hidden behind foregrounds approximately five orders of magnitude brighter than itself. A traditional forward modelling data analysis pipeline using Bayesian data analysis and a physically motivated foreground model to find this signal shows great promise in the case of unchanging environmental conditions. However we demonstrate in this paper that in the presence of a soil with changing dielectric properties under the antenna over time, or a changing soil temperature in the far field of our observation these traditional methods struggle. In this paper we detail a tool using Masked Auto-regressive Flows that improves upon previous physically motivated foreground models when one is trying to recover this signal in the presence of changing environmental conditions. We demonstrate that with these changing parameters our tool consistently recovers the signal with a much greater Bayesian evidence than the traditional data analysis pipeline, decreasing the root mean square error in the recovery of the injected signal by up to 45 %.
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