Accurate image reconstruction in radio interferometry
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This thesis is concerned with accurate imaging from radio interferometry data and with subsequent analysis so as to determine source positions and fluxes in the radio sky. The thesis makes proposals and implementations of new algorithms, which substantially improve the accuracy of image products and the results of source extraction. These improvements in accuracy promise to assist scientific research into astronomical objects and phenomena in radio astronomy.
The thesis contains six chapters, beginning with an overview of the imaging process in radio interferometry in Chapter 1.
Chapter 2 focuses on improving the accuracy of source extraction, by utilising the Bayesian methodology. The proposed Bayesian method has been implemented in a software package called 'BaSC' which uses the Markov Chain Monte Carlo (MCMC) technique. By design, it works with intermediate radio interferometry image products, such as dirty images, rather than with reconstructed images. BaSC achieves greater precision in source location and better resolving power than mainstream source extraction software such as SExtractor, which works with reconstructed images. This finding confirms that reconstructed images are not a true representation of the radio sky, whereas dirty images already contain full information about the observations. This piece of work has been accepted by Monthly Notices of the Royal Astronomical Society (Hague et al. 2018). Chapter 2 is based on this paper, but has been rewritten and expanded.
Based on this conclusion, Chapter 3 seeks to optimise the gridding process so as to make accurate dirty images. Since the Fast Fourier transform (FFT) produces dirty images with a much lower computational cost than the Direct Fourier transform (DFT), a new gridding function is needed which minimises the difference between DFT and FFT dirty images. The 'Least-misfit' gridding function is proposed, so as to minimise the image misfit between the DFT and FFT dirty images, and this is implemented and tested. Given an identical support width, it outperforms the main-stream spheroidal function in the image misfit by a factor of at least 100, it also suppresses aliasing in the image plane better. Aliasing is essentially a part of the image misfit, so there is no need to consider it separately. The least-misfit gridding function, with a support width of 7 and an image cropping rate of 0.5, is recommended for application to both the gridding and degridding processes, and makes it realistic to achieve the limit of single precision arithmetic in the image misfit and visibility misfit.
With the new gridding function in place, Chapter 4 proposes two novel wide-field imaging algorithms: improved W-Stacking and N-Faceting. The improved W-Stacking method uses a three-dimensional gridding, rather than two-dimensional gridding as in the original W-Stacking method. This renders possible the calculation and application of the correcting function on the
The N-Faceting method involves imaging of multiple
Chapter 5 applies the improved W-Stacking method to two real sets of observational data, comprising one GMRT dataset and one VLA dataset. The dirty images on the celestial sphere and the reconstructed images are shown. The improved W-Stacking method successfully removes non-coplanar effects. For the observation with a larger range of
Finally, Chapter 6 sets out conclusions drawn from the present work.
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Nikolic, Bojan