Hyper-spectral Compressive Radio-interferometric Imaging in the SKA era
Lead Research Organisation:
Heriot-Watt University
Department Name: Sch of Engineering and Physical Science
Abstract
"This is a PhD research project in Electrical Engineering.
With the advent of the next-generation radio-interferometric telescopes like the Square Kilometre Array, novel signal processing methods are needed to provide the expected imaging resolution and sensitivity from extreme amounts of hyper-spectral data. In this context, the idea is to propose a convex optimisation problem for hyper-spectral data reconstruction and analyse the possible regularisation priors. Posing the problem as a convex optimisation task based on sparsity priors as a regulariser was shown superior to the conventional radio-interferometric imaging techniques like CLEAN. However, extending these methods is essential to process large-scale data. Efficient and fast solvers for the ill-posed inverse problem related to the image reconstruction need also to be tailored. They should employ parallel and distributed computations to achieve scalability, in terms of memory and computational requirements."
With the advent of the next-generation radio-interferometric telescopes like the Square Kilometre Array, novel signal processing methods are needed to provide the expected imaging resolution and sensitivity from extreme amounts of hyper-spectral data. In this context, the idea is to propose a convex optimisation problem for hyper-spectral data reconstruction and analyse the possible regularisation priors. Posing the problem as a convex optimisation task based on sparsity priors as a regulariser was shown superior to the conventional radio-interferometric imaging techniques like CLEAN. However, extending these methods is essential to process large-scale data. Efficient and fast solvers for the ill-posed inverse problem related to the image reconstruction need also to be tailored. They should employ parallel and distributed computations to achieve scalability, in terms of memory and computational requirements."
Organisations
Publications
Abdulaziz A
(2017)
A distributed algorithm for wide-band radio-interferometry
Abdulaziz A
(2019)
Wideband super-resolution imaging in Radio Interferometry via low rankness and joint average sparsity models (HyperSARA)
in Monthly Notices of the Royal Astronomical Society
Dabbech A
(2018)
Cygnus A super-resolved via convex optimization from VLA data
in Monthly Notices of the Royal Astronomical Society
Studentship Projects
| Project Reference | Relationship | Related To | Start | End | Student Name |
|---|---|---|---|---|---|
| EP/N509474/1 | 30/09/2016 | 29/09/2021 | |||
| 1812968 | Studentship | EP/N509474/1 | 31/08/2016 | 29/02/2020 | Abdullah Abdulaziz |
| Description | Upcoming radio telescopes such as the Square Kilometre Array (SKA) will provide sheer amounts of data, allowing large images of the sky to be reconstructed at an unprecedented resolution and sensitivity over thousands of frequency channels. In this regard, wideband radio-interferometric imaging consists in recovering a 3D image of the sky from incomplete and noisy Fourier data, that is a highly ill-posed inverse problem. To regularize the inverse problem, advanced prior image models need to be tailored. Moreover, the underlying algorithms should be highly parallelized to scale with the vast data volumes provided and the Petabyte image cubes to be reconstructed for SKA. The research developed through this award leverages convex optimization techniques to achieve precise and scalable imaging for wideband radio interferometry and further assess the degree of confidence in particular 3D structures present in the reconstructed cube. In the context of image reconstruction, we propose a new approach that decomposes the image cube into regular spatio-spectral facets, each is associated with a sophisticated hybrid prior image model. The approach is formulated as an optimization problem with a multitude of facet-based regularization terms and block-specific data-fidelity terms. The underpinning algorithmic structure benefits from well-established convergence guarantees and exhibits interesting functionalities such as preconditioning to accelerate the convergence speed. Furthermore, it allows for parallel processing of all data blocks and image facets over a multiplicity of CPU cores, allowing the bottleneck induced by the size of the image and data cubes to be efficiently addressed via parallelization. The precision and scalability potential of the proposed approach are confirmed through the reconstruction of a 15 GB image cube of the Cyg A radio galaxy. In addition, we propose a new method that enables analyzing the degree of confidence in particular 3D structures appearing in the reconstructed cube. This analysis is crucial due to the high ill-posedness of the inverse problem. Besides, it can help in making scientific decisions on the structures under scrutiny (\emph{e.g.}, confirming the existence of a second black hole in the Cyg A galaxy). The proposed method is posed as an optimization problem and solved efficiently with a modern convex optimization algorithm with preconditioning and splitting functionalities. The simulation results showcase the potential of the proposed method to scale to big data regimes. |
| Exploitation Route | An important perspective consists in developing a production C++ version of the developed algorithm Faceted HyperSARA, building from the existing C++ version of HyperSARA (see our webpage: https://basp-group.github.io/Puri-Psi/), to achieve maximum performance and scalability of a software implementation. |
| Sectors | Aerospace Defence and Marine Other |
| URL | https://basp-group.github.io/Puri-Psi/ |
| Description | Our developed algorithms are used by astronomers. |
| First Year Of Impact | 2019 |
| Sector | Aerospace, Defence and Marine,Other |