http://arxiv.org/abs/2011.14052
Detection of millikelvin signals from the Cosmic Dawn requires an unprecedented level of sensitivity and systematic calibration. We report the theory behind a novel calibration algorithm developed from the formalism introduced by the EDGES collaboration for use in 21-cm experiments. Our incorporation of a Bayesian framework and machine learning techniques provide significant improvements over previous approaches such as the use of Bayesian evidence to determine the level of noise wave parameter frequency variation supported by the data, the optimisation of individual calibration parameters through maximisation of the evidence and the use of a conjugate-prior based approach that results in a fast algorithm for application in the field. In self-consistency tests using mock data of varying complexity, our methodology is able to calibrate a 50-Ohm ambient-temperature load within 0.03 K of of ambient temperature. The flexibility of our algorithm permits application to any experiment relying on similar methods of calibration and such as REACH, HERA and the SKA.
I. Roque, W. Handley and N. Razavi-Ghods
Tue, 1 Dec 20
46/108
Comments: 7 pages, 6 figures
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