Bayesian estimation of cross-coupling and reflection systematics in 21cm array visibility data

TL;DR

This paper introduces a Bayesian method using Hamiltonian Monte Carlo to model and mitigate systematic effects like reflections and couplings in 21cm array data, improving the robustness of EoR signal detection.

Contribution

It presents a novel Bayesian approach with uncertainty quantification for systematics mitigation in 21cm cosmology data analysis.

Findings

Effectively constrains systematics in high-noise scenarios

Reduces residual systematics below noise level in low-noise cases

Maintains signal integrity while mitigating systematics

Abstract

Observations with radio arrays that target the 21-cm signal originating from the early Universe suffer from a variety of systematic effects. An important class of these are reflections and spurious couplings between antennas. We apply a Hamiltonian Monte Carlo sampler to the modelling and mitigation of these systematics in simulated Hydrogen Epoch of Reionisation Array (HERA) data. This method allows us to form statistical uncertainty estimates for both our models and the recovered visibilities, which is an important ingredient in establishing robust upper limits on the Epoch of Reionisation (EoR) power spectrum. In cases where the noise is large compared to the EoR signal, this approach can constrain the systematics well enough to mitigate them down to the noise level for both systematics studied. Incoherently averaging the recovered power spectra can further reduce the noise and improve recovery. Where the noise level is lower than the EoR, our modelling can mitigate the majority of the reflections and coupling with there being only a minor level of residual systematics. Our approach performs similarly to existing filtering/fitting techniques used in the HERA pipeline, but with the added benefit of rigorously propagating uncertainties. In all cases it does not significantly attenuate the underlying signal.