The simplicity of the hybridization technique is its main advantage. It is
simple to understand and simple to implement. However, this method works
badly in practice because it is truly difficult to obtain a
reliable deconvolution of interferometric data alone when short-spacing
information is important. An interferometer is a spatial pass-band
filter, filtering in particular the zero spacing. This implies that the total flux in the
dirty image is zero (i.e. as much negative as positive flux in the dirty
image) but that the dirty beam integral is also zero (i.e. as much negative
as positive sidelobes). Adding the short-spacing information (and in
particular the zero spacing) through the pseudo-visibility method, we
enforces the positivity of the dirty image total flux and of the dirty beam
integral. It is well-known that trying to deconvolve a mosaic built only
with interferometric data is quite difficult. It almost always requires the
definition of support where the CLEAN algorithms can search for clean
components with the clear risk to bias the final result. In contrast,
adding the short-spacing information through pseudo-visibilities enables an
almost straightforward CLEAN deconvolution without the need of
any support.
For the sake of illustration, let us assume an intensity distribution
made of a large scale structure (e.g. a smoothly varying intensity)
superimposed with a small scale distribution both in emission and
absorption. An interferometer will filter out the smooth
distribution. If there is no additional zero spacing
information, the smooth distribution is completely lost with
the important consequence that the final deconvoled image will have
positive (emission) and negative (absorption) structures. Trying to
reproduce both negative and positive structures is one of the most
difficult task for deconvolution algorithms. In addition, the
presence of large negative structures create instabilities in the
algorithms of the CLEAN family (because it is difficult to
distinguish between negative absorption structures and negative
sidelobes of emission structures). Only the definition of support
around positive emission peaks may succeed to stabilize the CLEAN
algorithms with the drawback of biasing the result.
Both kind of algorithms are implemented in [rgb]1,0,0IMAGER, under commands
magentaFEATHER for the hybridization and magentaUV_SHORT for the
pseudo-visibilities. However, we strongly recommend to use the
pseudo-visibility algorithm.
(,,) showed through
simulations that 1) the pseudo-visibility algorithm implemented in
GILDAS enable extremely reliable results (fidelities of a few thousands)
on ideal observations and 2) the accuracy of the wide-field imaging is
limited by pointing errors, amplitude calibration errors and atmospheric
phase noise (and not by the used algorithms), even for ALMA.
Next: Hybridization technique and ALMA
Up: Algorithms to merge single-dish
Previous: Single-dish vs interferometer weight
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Gildas manager
2023-06-01