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
â€™degriddingâ€™ 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. |