Prof. Benjamin Wandelt (IAP, UPMC)
We present a new Bayesian semi-blind approach for foreground removal in observations of the 21-cm signal with interferometers. The technique, which we call HIEMICA (HI Expectation-Maximization Independent Component Analysis), is an extension of the Independent Component Analysis (ICA) technique developed for two-dimensional (2D) CMB maps to three-dimensional (3D) 21-cm cosmological signals measured by interferometers. This technique provides a fully Bayesian inference of power spectra and maps and separates the foregrounds from signal based on the diversity of their power spectra and frequency dependence. Only relying on the statistical independence of the components, this approach can jointly estimate the 3D power spectrum of the 21-cm signal, the 2D angular power spectrum and the frequency dependence of each foreground component, with very mild prior assumptions about foregrounds. This approach has been tested extensively by applying it to mock data from interferometric 21-cm intensity mapping observations, demonstrating much better performance for power spectrum recovery over all scales than the commonly used Principal Component Analysis (PCA).