Bayesian approach to radio frequency interference mitigation
arXiv:2211.15448 · doi:10.1103/PhysRevD.108.062006
Abstract
Interfering signals such as Radio Frequency Interference from ubiquitous satellite constellations are becoming an endemic problem in fields involving physical observations of the electromagnetic spectrum. To address this we propose a novel data cleaning methodology. Contamination is simultaneously flagged and managed at the likelihood level. It is modeled in a Bayesian fashion through a piecewise likelihood that is constrained by a Bernoulli prior distribution. The techniques described in this paper can be implemented with just a few lines of code.
6 pages, 4 figures
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- Receiver design for the REACH global 21-cm signal experiment
- Impact of extragalactic point sources on the low-frequency sky spectrum and cosmic dawn global 21-cm measurements
- Radiometer Calibration using Machine Learning
- TABASCAL: Removing multi-satellite interference from radio interferometry observations
- Quantifying the Impact of Lunar and Planetary Occultation on Experimental Global 21 cm Cosmology
- Optimizing Foreground Modelling for Global 21cm Cosmology with GPU-Accelerated Nested Sampling