A Bayesian approach to modelling spectrometer data chromaticity corrected using beam factors -- I. Mathematical formalism
Peter H. Sims, Judd D. Bowman, Nivedita Mahesh, Steven G. Murray, John, P. Barrett, Rigel Cappallo, Raul A. Monsalve, Alan E. E. Rogers, Titu Samson,, and Akshatha K. Vydula

TL;DR
This paper develops a mathematical formalism for a Bayesian model of spectrometer data that accounts for instrumental chromaticity corrections, enabling unbiased detection of the 21-cm signal.
Contribution
It introduces a Bayesian approach to model spectrometer data with beam-factor chromaticity correction, addressing partial correction issues for the first time.
Findings
BFCC data model enables unbiased 21-cm signal recovery in simulations.
The formalism accounts for residual chromaticity effects.
Simulation results validate the model's effectiveness.
Abstract
Accurately accounting for spectral structure in spectrometer data induced by instrumental chromaticity on scales relevant for detection of the 21-cm signal is among the most significant challenges in global 21-cm signal analysis. In the publicly available EDGES low-band data set, this complicating structure is suppressed using beam-factor based chromaticity correction (BFCC), which works by dividing the data by a sky-map-weighted model of the spectral structure of the instrument beam. Several analyses of this data have employed models that start with the assumption that this correction is complete. However, while BFCC mitigates the impact of instrumental chromaticity on the data, given realistic assumptions regarding the spectral structure of the foregrounds, the correction is only partial. This complicates the interpretation of fits to the data with intrinsic sky models (models that…
Peer Reviews
No public reviews on file for this paper yet. If you reviewed it on a platform where reviews are public (OpenReview, ICLR, NeurIPS, ICML), you can paste yours below so the community can read it here.
Videos
No videos yet. Explain this paper in a talk, walkthrough, or lecture? Add one.
Taxonomy
TopicsSpectroscopy and Chemometric Analyses · Water Quality Monitoring and Analysis · Analytical Chemistry and Chromatography
