Bayesian evidence-driven diagnosis of instrumental systematics for sky-averaged 21-cm cosmology experiments

TL;DR

This paper demonstrates that Bayesian evidence-based analysis effectively diagnoses and disentangles instrumental systematics in sky-averaged 21-cm cosmology experiments, significantly improving signal recovery accuracy.

Contribution

It introduces a Bayesian evidence-driven framework for identifying and modeling instrumental systematics, enhancing the reliability of 21-cm signal detection.

Findings

Poor performance when systematics are unmodeled

Improved signal recovery with systematic models

Bayesian evidence can determine the necessity of systematic models

Abstract

We demonstrate the effectiveness of a Bayesian evidence-based analysis for diagnosing and disentangling the sky-averaged 21-cm signal from instrumental systematic effects. As a case study, we consider a simulated REACH pipeline with an injected systematic. We demonstrate that very poor performance or erroneous signal recovery is achieved if the systematic remains unmodelled. These effects include sky-averaged 21-cm posterior estimates resembling a very deep or wide signal. However, when including parameterised models of the systematic, the signal recovery is dramatically improved in performance. Most importantly, a Bayesian evidence-based model comparison is capable of determining whether or not such a systematic model is needed as the true underlying generative model of an experimental dataset is in principle unknown. We, therefore, advocate a pipeline capable of testing a variety of potential systematic errors with the Bayesian evidence acting as the mechanism for detecting their presence.