All Sky Modelling Requirements for Bayesian 21 cm Power Spectrum Estimation with BayesEoR
Jacob Burba, Peter H. Sims, Jonathan C. Pober

TL;DR
This paper evaluates the BayesEoR code for 21 cm power spectrum recovery, addressing challenges of foreground contamination and computational constraints, and proposes a modified approach for all-sky modeling within feasible computational limits.
Contribution
It introduces a modified BayesEoR method enabling all-sky foreground modeling with limited 21 cm signal modeling, improving power spectrum recovery under realistic observational conditions.
Findings
BayesEoR accurately recovers the 21 cm power spectrum with limited sky modeling.
Including all-sky foregrounds without modification leads to contamination.
The modified method enables feasible all-sky analysis on large compute clusters.
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 EoR 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…
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