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4th of July 2018
ATNF Colloquium
Cleaning radio interferometric images using a spherical wavelet decomposition
by Chris Skipper (Manchester)
Abstract: The deconvolution, or cleaning, of radio interferometric images requires that model visibilities are calculated from a list of clean components, in order that the contribution from the model can be subtracted from the observed visibilities. This step is normally performed using a forward fast Fourier transform (FFT), followed by a ’degridding’ step that interpolates over the uv plane to construct the model visibilities. An alternative approach is to the calculate the model visibilities directly by summing over all the members of the clean component list, which is a more accurate method that can also be much slower. However, if the clean components are used to construct a model image on the surface of the celestial sphere then the model visibilities can be generated directly from the wavelet coefficients, and the sparsity of the model means that most of these coefficients are zero, and can be ignored. We have constructed a prototype imager that uses a spherical-wavelet representation of the model image to generate model visibilities during each major cycle, and in this talk I will discuss the implementation, performance and potential advantages of using such a technique to clean wide-field radio images.

The figure above shows deconvolved, or 'cleaned' radio images (Skipper & Scaife, in prep.), based upon a simulated measurement set, and using a spherical wavelet decomposition of the model image to generate model visibilities at the end of each major cycle. The panels show the effect of increasing the wavelet resolution from J = 1 to J = 3: the full paper shows the effect of increasing this further to J = 9.

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