The point spread function depends on the observing frequency. For broad band image with multiple spectral windows, the deconvolution can utilize the multi-frequency synthesis (MFS) technique. Also, in the situation of steep spectral index (can be as large as 4 in some dust emission regions) and large frequency separation, simple convolution relationship no longer applies. A special algorithm mfclean is needed to handle multiple frequency synthesis deconvolution. Miriadtask taskmfclean supports handling SMA data with a large number of frequency windows (chunks).
In this section, a use of the traditional CLEAN algorithm by Hogbom/Clark is shown and discussed. The Miriad task clean performs a hybrid Hogbom/Clark Clean algorithm, which takes a dirty map and beam, and produces an output map which consists of the clean components. However, mfclean is MFS version of clean, which is useful to the SMA data with a large number of chunks.
mfclean% inp Task: mfclean map = sgra-star.map beam = sgra-star.beam out = sgra-star.icmp gain = 0.1 cutoff = 0 niters = 500 region = quarter
Using the dirty map and dirty beam, clean or mfclean (see above setup) can make a clean image. The clean component image is convolved with a model by using a gaussian beam (clean beam). Miriad task restor restores clean components to make the CLEAN map.
Fig. 4.3 shows the restored clean image. Since significant residual errors remain in the uv data, the ``clean image'' appears to be distorted. We need Self-calibration to correct for the residual errors.
restor% inp Task: restor model = sgra-star.icmp beam = sgra-star.beam map = sgra-star.map out = sgra-star.icln
