A Bayesian calibration framework for EDGES
A Bayesian calibration framework for EDGES
复制标题
EDGES 的贝叶斯校准框架
DOI：
10.1093/mnras/stac2600
复制
发表时间：
2022
影响因子：
4.8
通讯作者：
Vydula, Akshatha Konakondula
中科院分区：
文献类型：
--
作者：
Murray, Steven G.;Bowman, Judd D.;Sims, Peter H.;Mahesh, Nivedita;Rogers, Alan E. E.;Monsalve, Raul A.;Samson, Titu;Vydula, Akshatha Konakondula
我们开发了一个贝叶斯模型，共同约束接收器校准，前景，和宇宙21厘米的信号的EDGES全球21厘米实验。该模型同时描述了在实验室中沿着的校准数据和使用EDGES低波段天线采集的天空数据。我们将我们的模型应用于用于报告2018年第一颗星星形成证据的相同数据（包括天空和校准）。我们发现，接收器校准不会对推断的宇宙信号（）产生显著的不确定性，尽管我们的联合模型能够更鲁棒地估计前景模型的宇宙信号，否则前景模型太不灵活，无法描述天空数据。我们确定存在一个显着的系统的校准数据，这在很大程度上是避免在我们的分析，但必须在未来的工作中更密切地检查。我们的可能性提供了一个基础，在未来的分析中，其他仪器系统，如光束校正和反射参数，可以添加在一个模块化的方式。
We develop a Bayesian model that jointly constrains receiver calibration, foregrounds, and cosmic 21 cm 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 (), 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.