Powellsnakes II: a fast Bayesian approach to discrete object detection
  in multi-frequency astronomical data sets
  Powellsnakes is a Bayesian algorithm for detecting compact objects embedded
in a diffuse background, and was selected and successfully employed by the
Planck consortium in the production of its first public deliverable: the Early
Release Compact Source Catalogue (ERCSC). We present the critical foundations
and main directions of further development of PwS, which extend it in terms of
formal correctness and the optimal use of all the available information in a
consistent unified framework, where no distinction is made between point
sources (unresolved objects), SZ clusters, single or multi-channel detection.
An emphasis is placed on the necessity of a multi-frequency, multi-model
detection algorithm in order to achieve optimality.
