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 because it is truly difficult to obtain a reliable deconvolution of interferometric data alone when short-spacing information is important. Indeed, a multiplicative interferometer filters 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). When we add the short-spacing information (and in particular the zero spacing) through the pseudo-visibility method, we enforce 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's assume an intensity distribution made of a large scale structure (e.g. a uniform intensity) superimposed with a small scale distribution both in emission and absorption. A multiplicative interferometer will filter out the uniform intensity distribution. If there is no additional zero spacing information, the uniform intensity 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. Algorithms of the MEM family enforce positivity in the deconvolved image. In addition, the presence of large negative structures makes instable 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 succeed to stabilize the CLEAN algorithms with the drawback of biasing the result.
Both kind of algorithms (hybridization and pseudo-visibility) are implemented in GILDAS. However, we strongly recommend to use the pseudo-visibility algorithm. That's why only the pseudo-visibility method is packaged in a user-friendly way (e.g. through the Short Space Processing widget). Pety et al. (2001b); Pety et al. (2001a); Pety et al. (2001c) 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.