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

5Citations
Citations of this article
19Readers
Mendeley users who have this article in their library.

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.

Cite

CITATION STYLE

APA

Scheutwinkel, K. H., De Lera Acedo, E., & Handley, W. (2022). Bayesian evidence-driven diagnosis of instrumental systematics for sky-Averaged 21-cm cosmology experiments. Publications of the Astronomical Society of Australia, 39. https://doi.org/10.1017/pasa.2022.49

Register to see more suggestions

Mendeley helps you to discover research relevant for your work.

Already have an account?

Save time finding and organizing research with Mendeley

Sign up for free