论文标题
边缘的贝叶斯校准框架
A Bayesian Calibration Framework for EDGES
论文作者
论文摘要
我们开发了一个贝叶斯模型，该模型共同限制了边缘全局21 \，CM实验的接收器校准，前景和宇宙21cm信号。该模型同时描述了实验室在实验室中获取的校准数据以及边缘低波段天线的Sky-Data。我们将模型应用于用于报告2018年第一星级形成的证据的相同数据（天空和校准）。我们发现，接收器校准并不能对推断的宇宙信号（<1％）产生重大的不确定性，尽管我们的联合模型能够更加稳健地估计宇宙信号对界面模型而言，这些模型否则是无法固执地描述天空数据的前景模型。我们在校准数据中确定了重要的系统，这在我们的分析中很大程度上避免了，但必须在未来的工作中进行更仔细的研究。我们的可能性为将来的分析奠定了基础，其中其他仪器系统（例如梁校正和反射参数）可以以模块化方式添加。
We develop a Bayesian model that jointly constrains receiver calibration, foregrounds and cosmic 21cm signal for the EDGES global 21\,cm experiment. This model simultaneously describes calibration data taken in the lab along with sky-data taken with the EDGES low-band antenna. We apply our model to the same data (both sky and calibration) used to report evidence for the first star formation in 2018. We find that receiver calibration does not contribute a significant uncertainty to the inferred cosmic signal (<1%), though our joint model is able to more robustly estimate the cosmic signal for foreground models that are otherwise too inflexible to describe the sky data. We identify the presence of a significant systematic in the calibration data, which is largely avoided in our analysis, but must be examined more closely in future work. Our likelihood provides a foundation for future analyses in which other instrumental systematics, such as beam corrections and reflection parameters, may be added in a modular manner.
