%0 Generic %1 sims2022bayesian %A Sims, Peter H. %A Bowman, Judd D. %A Mahesh, Nivedita %A Murray, Steven G. %A Barrett, John P. %A Cappallo, Rigel %A Monsalve, Raul A. %A Rogers, Alan E. E. %A Samson, Titu %A Vydula, Akshatha K. %D 2022 %K library %T A Bayesian approach to modelling spectrometer data chromaticity corrected using beam factors -- I. Mathematical formalism %U http://arxiv.org/abs/2212.03875 %X 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 (Bowman et al. 2018), 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, which complicates the interpretation of fits to the data with intrinsic sky models. In this paper, we derive a BFCC data model from an analytic treatment of BFCC and demonstrate using simulated observations that the BFCC data model enables unbiased recovery of a simulated global 21-cm signal from beam factor chromaticity corrected data.

@misc{sims2022bayesian, 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 (Bowman et al. 2018), 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, which complicates the interpretation of fits to the data with intrinsic sky models. In this paper, we derive a BFCC data model from an analytic treatment of BFCC and demonstrate using simulated observations that the BFCC data model enables unbiased recovery of a simulated global 21-cm signal from beam factor chromaticity corrected data.}, added-at = {2022-12-09T08:48:42.000+0100}, author = {Sims, Peter H. and Bowman, Judd D. and Mahesh, Nivedita and Murray, Steven G. and Barrett, John P. and Cappallo, Rigel and Monsalve, Raul A. and Rogers, Alan E. E. and Samson, Titu and Vydula, Akshatha K.}, biburl = {https://www.bibsonomy.org/bibtex/27a7b5b03e30c0bef30d7e60084f39c8a/gpkulkarni}, description = {A Bayesian approach to modelling spectrometer data chromaticity corrected using beam factors -- I. Mathematical formalism}, interhash = {cf14dc11281873ad40bb7910a8bf463d}, intrahash = {7a7b5b03e30c0bef30d7e60084f39c8a}, keywords = {library}, note = {cite arxiv:2212.03875Comment: 26 pages, 8 figures}, timestamp = {2022-12-09T08:48:42.000+0100}, title = {A Bayesian approach to modelling spectrometer data chromaticity corrected using beam factors -- I. Mathematical formalism}, url = {http://arxiv.org/abs/2212.03875}, year = 2022 }