Blind foreground subtraction for intensity mapping experiments
  We make use of a large set of fast simulations of an intensity mapping
experiment with characteristics similar to those expected of the Square
Kilometre Array (SKA) in order to study the viability and limits of blind
foreground subtraction techniques. In particular, we consider different
approaches: polynomial fitting, principal component analysis (PCA) and
independent component analysis (ICA). We review the motivations and algorithms
for the three methods, and show that they can all be described, using the same
mathematical framework, as different approaches to the blind source separation
problem. We study the efficiency of foreground subtraction both in the angular
and radial (frequency) directions, as well as the dependence of this efficiency
on different instrumental and modelling parameters. For well-behaved
foregrounds and instrumental effects we find that foreground subtraction can be
successful to a reasonable level on most scales of interest. We also quantify
the effect that the cleaning has on the recovered signal and power spectra.
Interestingly, we find that the three methods yield quantitatively similar
results, with PCA and ICA being almost equivalent.
