论文标题
贝叶斯的高保真性干涉校准ii：与模拟数据的演示
A Bayesian approach to high fidelity interferometric calibration II: demonstration with simulated data
论文作者
论文摘要
在同伴论文中，我们提出了贝叶斯卡尔（Bayescal），这是一种数学形式主义，用于减轻干涉校准中的天空模型不完整。在本文中，我们证明了使用贝叶斯量的使用校准全孔的归化增益参数模拟观测值，并使用HERA样六角形的封闭式冗余阵列进行校准，对于校准天空模型的A先验成分的三个假定完整性水平。我们将贝叶斯量表校准解决方案与通过校准归化的增益参数恢复的贝叶斯量表校准解决方案，仅具有校准天空模型的先验已知组件，无论有没有在有和没有在增益幅度振幅解决方案上施加物理动机的先验和两个基线长度范围的选择。 We find that BayesCal provides calibration solutions with up to four orders of magnitude lower power in spurious gain amplitude fluctuations than the calibration solutions derived for the same data set with the alternate approaches, and between $\sim10^7$ and $\sim10^{10}$ times smaller than in the mean degenerate gain amplitude on the full range of spectral scales accessible in the data.此外，我们发现，在场景中，仅建模的贝叶斯夸符具有足够高的忠诚度校准解决方案，可在大光谱尺度上无偏恢复21 cm的功率谱（$ k_ \ parallel \ lissal \ sillsim 0.15〜h \ h \ mathrm {mpc}^{ - 1} $）。在所有其他情况下，在所研究的完整度方案中，这些量表受到污染。
In a companion paper, we presented BayesCal, a mathematical formalism for mitigating sky-model incompleteness in interferometric calibration. In this paper, we demonstrate the use of BayesCal to calibrate the degenerate gain parameters of full-Stokes simulated observations with a HERA-like hexagonal close-packed redundant array, for three assumed levels of completeness of the a priori known component of the calibration sky model. We compare the BayesCal calibration solutions to those recovered by calibrating the degenerate gain parameters with only the a priori known component of the calibration sky model both with and without imposing physically motivated priors on the gain amplitude solutions and for two choices of baseline length range over which to calibrate. We find that BayesCal provides calibration solutions with up to four orders of magnitude lower power in spurious gain amplitude fluctuations than the calibration solutions derived for the same data set with the alternate approaches, and between $\sim10^7$ and $\sim10^{10}$ times smaller than in the mean degenerate gain amplitude on the full range of spectral scales accessible in the data. Additionally, we find that in the scenarios modelled only BayesCal has sufficiently high fidelity calibration solutions for unbiased recovery of the 21 cm power spectrum on large spectral scales ($k_\parallel \lesssim 0.15~h\mathrm{Mpc}^{-1}$). In all other cases, in the completeness regimes studied, those scales are contaminated.
