BayesEoR: Bayesian 21-cm Power Spectrum Estimation fromInterferometric Visibilities
BayesEoR is a GPU-accelerated, MPI-compatible Python package for estimating the power spectrum of redshifted 21-cm emission from interferometric observations of the Epoch of Reionization (EoR).Utilizing a Bayesian framework, BayesEoR jointly fits for the 21-cm EoR power spectrum and a "foreground" model, referring to bright, contaminating emission between us and the cosmological signal, and forward models the instrument with which these signals are observed.To perform the sampling, we use MultiNest (Buchner et al., 2014), which calculates the Bayesian evidence as part of the analysis.Thus, BayesEoR can also be used as a tool for model selection (see e.g.Sims et al., 2019).
Authors
- Jacob Burba (ORCID: https://orcid.org/0000-0002-8465-9341)
- Peter Sims (ORCID: https://orcid.org/0000-0002-2871-0413)
- Jonathan C. Pober (ORCID: https://orcid.org/0000-0002-3492-0433)
Institutions
- John Brown University (US)
- University of Manchester (GB)
- Arizona State University (US)
Publication Details
- Journal
- The Journal of Open Source Software
- Published
- 2024-11-18
- DOI
- https://doi.org/10.21105/joss.06667
- Primary Topic
- Radio Astronomy Observations and Technology
- Type
- article
- Field-Weighted Citation Impact
- 0.00
Funders
- National Science Foundation
- National Aeronautics and Space Administration
- Rhode Island Space Grant Consortium
- Brown University
- McGill University
- European Commission
- McGill Space Institute