Orateur
Prof.
Benjamin Wandelt
(IAP, UPMC)
Description
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).
Auteurs principaux
Prof.
Benjamin Wandelt
(IAP, UPMC)
Dr
Le Zhang
(University of Wisconsin, Madison)
Co-auteurs
Prof.
Andrei Korotkov
(Brown)
Dr
Ata Karakci
(APC)
Prof.
Emory Bunn
(University of Richmond)
Prof.
Gregory Tucker
(Brown)
Dr
Paul Sutter
(Trieste)
Prof.
Peter Timbie
(U. Wisconsin, Madison)
