Bayesian Data Analysis for Sky-averaged 21-cm Experiments in the Presence of Ionospheric Effects

Abstract

The ionosphere introduces chromatic distortions on low frequency radio waves, and thus poses a hurdle for 21-cm cosmology. In this paper we introduce time-varying chromatic ionospheric effects on simulated antenna temperature data of a global 21-cm data analysis pipeline, and try to detect the injected global signal. We demonstrate that given turbulent ionospheric conditions, more than 5\% error in our knowledge of the ionospheric parameters could lead to comparatively low evidence and high root-mean-square error (RMSE), suggesting a false or null detection. When using a constant antenna beam for cases that include data at different times, the significance of the detection lowers as the number of time samples increases. It is also shown that for observations that include data at different times, readjusting beam configurations according to the time-varying ionospheric conditions should greatly improve the significance of a detection, yielding higher evidences and lower RMSE, and that it is a necessary procedure for a successful detection when the ionospheric conditions are not ideal.

8 pages, 7 figures, 7 tables

References in corpus (3)

• Testing for calibration systematics in the EDGES low-band data using Bayesian model selection
• Quantifying Ionospheric Effects on Global 21-cm Observations
• On the detection of a cosmic dawn signal in the radio background

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• Ionospheric contributions to the excess power in high-redshift 21-cm power-spectrum observations with LOFAR
• A general Bayesian model-validation framework based on null-test evidence ratios, with an example application to global 21-cm cosmology
• Accounting for Noise and Singularities in Bayesian Calibration Methods for Global 21-cm Cosmology Experiments
• Detecting Cosmic 21 cm Global Signal Using an Improved Polynomial Fitting Algorithm
• Optimizing Foreground Modelling for Global 21cm Cosmology with GPU-Accelerated Nested Sampling