Use of Time Dependent Data in Bayesian Global 21cm Foreground and Signal Modelling
Dominic Anstey, Eloy de Lera Acedo, Will Handley

TL;DR
This paper demonstrates that simultaneous Bayesian fitting of multi-time data improves 21cm signal recovery and foreground modeling accuracy for certain antenna types, enhancing global 21cm cosmology analysis.
Contribution
It introduces a novel Bayesian approach for joint fitting of multi-time data sets, significantly improving signal and foreground modeling for specific antenna configurations.
Findings
Improved 21cm signal recovery for hexagonal dipole antennas.
Foreground modeling accuracy increased by up to 2-3 times.
No significant signal improvement for less chromatic antennas, but foreground modeling still benefits.
Abstract
Global 21cm cosmology aims to investigate the cosmic dawn and epoch of reionisation by measuring the sky averaged HI absorption signal, which requires, accurate modelling of, or correction for, the bright radio foregrounds and distortions arising from chromaticity of the antenna beam. We investigate the effect of improving foreground modelling by fitting data sets from many observation times simultaneously in a single Bayesian analysis, fitting for the same parameter set by performing these fits on simulated data. We find that for a hexagonal dipole antenna, this simultaneous fitting produces a significant improvement in the accuracy of the recovered 21cm signal, relative to fitting a time average of the data. Furthermore, the recovered models of the foreground are also seen to become more accurate by up to a factor of 2-3 relative to time averaged fitting. For a less chromatic…
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Taxonomy
TopicsRadio Astronomy Observations and Technology · Radio Wave Propagation Studies · Antenna Design and Optimization
