21厘米全球实验的贝叶斯噪声波校准
Bayesian noise wave calibration for 21-cm global experiments
摘要：
探测来自“宇宙黎明”的毫开尔文级信号需要前所未有的灵敏度和系统校准。我们报告的理论背后的一种新的校准算法开发的形式主义介绍了在21厘米的实验中使用的EDGES合作。通过结合贝叶斯框架和机器学习技术（例如使用贝叶斯证据来确定由数据支持的校准参数的频率变化水平）来提供对先前方法的改进，在确定校准参数的值时考虑校准参数之间的相关性，并使用共轭-基于先验的方法，其导致用于现场应用的快速算法。在自我一致性测试中，使用不同复杂性的经验数据模型，我们的方法是用来校准50 $Ω$环境温度负载。校准溶液和负载的测量温度之间的RMS误差为8 mK，完全在1 $σ$噪声水平内。虽然这里描述的方法更适用于全球21厘米实验，但它们可以很容易地进行调整并应用于其他应用，包括HERA和SKA等望远镜。
英文摘要：
Detection of millikelvin-level signals from the 'Cosmic Dawn' requires an unprecedented level of sensitivity and systematic calibration. We report the theory behind a novel calibration algorithm developed from the formalism introduced by the EDGES collaboration for use in 21-cm experiments. Improvements over previous approaches are provided through the incorporation of a Bayesian framework and machine learning techniques such as the use of Bayesian evidence to determine the level of frequency variation of calibration parameters that is supported by the data, the consideration of correlation between calibration parameters when determining their values and the use of a conjugate-prior based approach that results in a fast algorithm for application in the field. In self-consistency tests using empirical data models of varying complexity, our methodology is used to calibrate a 50 $Ω$ ambient-temperature load. The RMS error between the calibration solution and the measured temperature of the load is 8 mK, well within the 1$σ$ noise level. Whilst the methods described here are more applicable to global 21-cm experiments, they can easily be adapted and applied to other applications, including telescopes such as HERA and the SKA.
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