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Radio observations of the neutral hydrogen signal from the Cosmic Dawn andEpoch of Reionisation have helped to provide constraints on the properties ofthe first stars and galaxies. Since this global 21-cm cosmological signal fromthe Cosmic Dawn is effectively constant on observing timescales and sinceeffects resulting from systematics will vary with time, the effects of thesesystematics can be mitigated without the need for a model of the systematic. Wepresent a method to account for unmodelled time-varying systematics in 21-cmradio cosmology experiments using a squared-exponential Gaussian process kernelto account for correlations between time bins in a fully Bayesian way. We findby varying the model parameters of a simulated systematic that the Gaussianprocess method improves our ability to recover the signal parameters bywidening the posterior in the presence of a systematic and reducing the bias inthe mean fit parameters. When varying the amplitude of a model sinusoidalsystematic between 0.25 and 2.00 times the 21-cm signal amplitude and theperiod between 0.5 and 4.0 times the signal width, we find on average a 5%improvement in the root mean squared error of the fitted signal. We can use thefitted Gaussian process hyperparameters to identify the presence of asystematic in the data, demonstrating the method's utility as a diagnostictool. Furthermore, we can use Gaussian process regression to calculate a meanfit to the residuals over time, providing a basis for producing a model of thetime-varying systematic.

A Bayesian Method to Mitigate the Effects of Unmodelled Time-Varying Systematics for 21-cm Cosmology Experiments