Extreme-scale precision imaging in radio astronomy

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Because of superior angular resolutions and sensitivities, next-generation radio interferometers based on aperture synthesis are expected to make breakthroughs and bring answers to essential questions in astronomy. However, the sensing strategy of interferometers only provides incomplete linear information of the original sky. Recovering the clean image from the blur and noisy received data forms a complicated ill-posed inverse imaging problem. Additionally, the gigantic scales of the new radio telescopes, such as Square Kilometres Array (SKA), also bring enormous data flows, commensurate to the target scale, unprecedented precision and sensitivity. The wide-band image cubes generated by SKA will reach the size of 1 Petabyte. To meet the capabilities of such powerful equipment, every section in the image processing pipeline needs to be tuned. Nowadays, optimisation is suggested to be one of the promising frameworks in designing deconvolution algorithms for astronomical imaging. And the objective function can be seen as the sum combination of fidelity term and regularisation term. Thanks to relevant research into faceted processing of regularisation terms, the large data volumes can be divided into small and overlapped blocks first. Then parallel processing becomes possible, and the scalability is greatly improved. Another noticeable trend is introducing deep learning frameworks into astronomical imaging to take the advantages of scalability and parallelism in neural networks. An exciting progress is using neural networks to approximate regularisation operators and integrate into image recovery process. This PhD project will start by extending current optimisation astronomical imaging algorithms and leverage the power of neural networks to improve the resolution and dynamic range of the reconstructed images. Then, the parallelism and scalability will be investigated and optimised, aiming to scale these algorithms up to meet the requirements of next-generation radio interferometers. Feeding into this, the calibration problems and uncertainty qualification problems in astronomical imaging will be considered. The ultimate goal of this project is implementing those optimised algorithms and deploying them on production HPC systems to fine tune the implementations and algorithms and making them performant and scalable for real world applications. One key feature in enabling this is the introduction of heterogeneous computing into the implementations. Besides CPU nodes, there is a range of computing hardware, such as GPUs, FPGAs, DSPs, etc., that can be integrated into this process to further improve processing speeds. Additionally, the algorithms for radio astronomical imaging are also applicable in solving medical imaging problems. As such, the data sets used to evaluate and optimise our algorithms and implementations will be extended to medical imaging after the results have been tested in astronomical imaging. Lastly, I will further apply the experience in extreme scale astronomical imaging and HPC system to other fields if possible, such as designing specific processing units for astronomical imaging and optimising large sale rendering problems on HPC machines.