  We present PPMAP, a Bayesian procedure that uses images of dust continuum
emission at multiple wavelengths to produce resolution-enhanced image cubes of
differential column-density as a function of dust temperature and position.
PPMAP is based on the generic 'point process' formalism, whereby the system of
interest (in this case, a dusty astrophysical structure such as a filament or
prestellar core) is represented by a collection of points in a suitably defined
state space. It can be applied to a variety of observational data, such as
Herschel images, provided only that the image intensity is delivered by
optically thin dust in thermal equilibrium. PPMAP takes full account of the
instrumental point spread functions and does not require all images to be
degraded to the same resolution. We present the results of testing using
simulated data for a prestellar core and a fractal turbulent cloud, and
demonstrate its performance with real data from the Hi-GAL survey.
Specifically, we analyse observations of a large filamentary structure in the
CMa OB1 giant molecular cloud. Histograms of differential column-density
indicate that the warm material (T > 13 K) is distributed log-normally,
consistent with turbulence, but the column-densities of the cooler material are
distributed as a high density tail, consistent with the effects of
self-gravity. The results illustrate the potential of PPMAP to aid in
distinguishing between different physical components along the line of sight in
star-forming clouds, and aid the interpretation of the associated PDFs of
column density.
