A Bayesian Calibration Framework for EDGES

TL;DR

This paper introduces a Bayesian framework that jointly models calibration, foregrounds, and the cosmic 21cm signal in EDGES data, improving robustness and reducing uncertainties in the signal detection process.

Contribution

It presents a novel Bayesian model that combines calibration and sky data for EDGES, enabling more reliable cosmic signal estimation and systematic identification.

Findings

Calibration uncertainty is less than 1%.

The joint model better fits complex foreground models.

Identifies a significant systematic in calibration data.

Abstract

We develop a Bayesian model that jointly constrains receiver calibration, foregrounds and cosmic 21cm signal for the EDGES global 21\,cm experiment. This model simultaneously describes calibration data taken in the lab along with sky-data taken with the EDGES low-band antenna. We apply our model to the same data (both sky and calibration) used to report evidence for the first star formation in 2018. We find that receiver calibration does not contribute a significant uncertainty to the inferred cosmic signal (<1%), though our joint model is able to more robustly estimate the cosmic signal for foreground models that are otherwise too inflexible to describe the sky data. We identify the presence of a significant systematic in the calibration data, which is largely avoided in our analysis, but must be examined more closely in future work. Our likelihood provides a foundation for future analyses in which other instrumental systematics, such as beam corrections and reflection parameters, may be added in a modular manner.