Unveiling Cosmic Dawn and the Epoch of Reionisation with HERA

该项目的目的是促进开发和应用先进的统计方法来分析再电离阵列(HERA)氢纪元的现有和未来数据。关键问题包括：在存在明亮前景发射的情况下，高效而准确地估计21厘米波动的功率谱。纠正由于去除无线电频率干扰而造成的数据切割造成的“泄漏”。通过严格应用零测试和通过分析管道提供模拟数据，分析结果在各种分析假设下的稳定性。准确估计数据的协方差矩阵，这是最佳提取微弱21厘米信号所需的。以计算高效的方式对物理EoR和宇宙黎明信号进行建模/模拟。其中一些工作将与总部设在伯克利、麦吉尔、西开普州、喷气推进实验室、宾夕法尼亚大学和华盛顿大学的HERA协作的其他成员合作进行。学生将获得分析和解释射电天文数据的技能，将高维推理技术应用于大量数据，应用结构化和无监督机器学习技术来检测数据中的虚假特征，以及开发可在高性能计算机上运行的快速并行数据分析代码。

The aim of this project is to contribute to the development and application of advanced statistical methods to the analysis of existing and future data from the Hydrogen Epoch of Reionization Array (HERA). Key problems include: Efficiently and precisely estimating the power spectrum of 21cm fluctuations, in the presence of bright foreground emission. Correcting for "leakage" due to cuts in the data, imposed by excising radio frequency interference. Analysing the stability of the results to various analysis assumptions, by rigorously applying null tests and feeding simulated data through the analysis pipeline. Accurately estimating the covariance matrix of the data, which is needed to optimally extract the faint 21cm signal. Modelling/simulating the physical EoR and Cosmic Dawn signal in a computationally-efficient way.Some of this work will be carried out in collaboration with other members of the HERA Collaboration based in Berkeley, McGill, U. Western Cape, JPL, U. Penn, and U. Washington. The student will gain skills in analysis and interpretation of radio astronomical data, the application of high-dimensional inference techniques to large volumes of data, the application of structured and unsupervised machine learning techniques to detect spurious features in data, and the development of fast parallel data analysis codes to be run on high-performance computers.