Signal separation is a common task in cosmological data analysis. The basic problem is simple to state: a number of signals are mixed together in some manner, either known or unknown, to produce some observed data. The object of signal separation is to infer the underlying signals given the observations.

A large number of techniques have been developed to attack this problem. The approaches adopted depend most crucially on the assumptions made regarding the nature of the signals and how they are mixed. Often methods are split into two broad classes: so-called blind and non-blind methods. Non-blind methods can be applied in cases where we know how the signals were mixed. Conversely, blind methods assume no knowledge of how the signals were mixed, and rely on assumptions about the statistical properties of the signals to make the separation. There are some techniques that straddle the two classes, which we shall refer to as ‘semi-blind’ methods. They assume partial knowledge of how the signals are mixed, or that the mixing properties of some signals are known and those of others are not.

There is a large literature in the field of signal processing about signal separation, using Bayesian techniques or otherwise. For any cosmological signal separation problem, it is almost always the case that someone has already attempted to solve an analogous problem in the signal processing literature. Readers who encounter a problem of this type, which is not already addressed in the cosmological literature, are encouraged to look further afield for existing solutions.