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ABSTRACT We present a comprehensive simulation-based study of the bayeseor code for 21 cm power spectrum recovery when analytically marginalizing over foreground parameters. To account for covariance between the 21 cm signal and contaminating foreground emission, bayeseor jointly constructs models for both signals within a Bayesian framework. Due to computational constraints, the forward model is constructed using a restricted field of view (FoV) in the image domain. When the only Epoch of Reionization contaminants are noise and foregrounds, we demonstrate that bayeseor can accurately recover the 21 cm power spectrum when the component of sky emission outside this forward-modelled region is downweighted by the beam at the level of the dynamic range between the foreground and 21 cm signals. However, when all-sky foreground emission is included along with a realistic instrument primary beam with sidelobes above this threshold extending to the horizon, the recovered power spectrum is contaminated by unmodelled sky emission outside the restricted FoV model. Expanding the combined cosmological and foreground model to cover the whole sky is computationally prohibitive. To address this, we present a modified version of bayeseor that allows for an all-sky foreground model, while the modelled 21 cm signal remains only within the primary FoV of the telescope. With this modification, it will be feasible to run an all-sky bayeseor analysis on a sizeable compute cluster. We also discuss several future directions for further reducing the need to model all-sky foregrounds, including wide-field foreground subtraction, an image-domain likelihood utilizing a tapering function, and instrument primary beam design. 

ABSTRACT 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 4 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 ∼107 and ∼1010 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∥ ≲ 0.15 hMpc−1). In all other cases, in the completeness regimes studied, those scales are contaminated. 

ABSTRACT High-fidelity radio interferometric data calibration that minimizes spurious spectral structure in the calibrated data is essential in astrophysical applications, such as 21 cm cosmology, which rely on knowledge of the relative spectral smoothness of distinct astrophysical emission components to extract the signal of interest. Existing approaches to radio interferometric calibration have been shown to impart spurious spectral structure to the calibrated data if the sky model used to calibrate the data is incomplete. In this paper, we introduce BayesCal: a novel solution to the sky-model incompleteness problem in interferometric calibration, designed to enable high-fidelity data calibration. The BayesCal data model supplements the a priori known component of the forward model of the sky with a statistical model for the missing and uncertain flux contribution to the data, constrained by a prior on the power in the model. We demonstrate how the parameters of this model can be marginalized out analytically, reducing the dimensionality of the parameter space to be sampled from and allowing one to sample directly from the posterior probability distribution of the calibration parameters. Additionally, we show how physically motivated priors derived from theoretical and measurement-based constraints on the spectral smoothness of the instrumental gains can be used to constrain the calibration solutions. In a companion paper, we apply this algorithm to simulated observations with a HERA-like array and demonstrate that it enables up to four orders of magnitude suppression of power in spurious spectral fluctuations relative to standard calibration approaches.