Radio interferometric imaging with compressive sensing

Jason McEwen (Mullard Space Science Laboratory (MSSL) - University College London (UCL))

APPLIED SIGNAL PROCESSING SERIES ACT Chapter of the IEEE Signal Processing and Communications Societies seminar

DATE: 2015-08-26
TIME: 11:00:00 - 12:00:00
LOCATION: RSISE Seminar Room, ground floor, building 115, cnr. North and Daley Roads, ANU
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We are about to enter a new era of radio astronomy with new radio interferometric telescopes under design and construction, such as the Square Kilometre Array (SKA), planned for construction in Australia and South Africa. While such telescopes would provide many scientific opportunities, they will also present considerable modelling and data processing challenges. Novel modelling and imaging techniques will be required to overcome these challenges. The theory of compressive sensing is a recent, revolutionary development in the field of information theory, which goes beyond the standard Nyquist-Shannon sampling theorem by exploiting the sparsity of natural images. Compressive sensing suggests a powerful framework for solving linear inverse problems (through sparse regularisation), such as recovering images from the incomplete Fourier measurements taken by radio interferometric telescopes. I will present recent developments in compressive sensing techniques for radio interferometric imaging, which have shown a great deal of promise. Furthermore, by appealing to the theoretical foundations of compressive sensing, I will discuss how telescope configurations can be optimised to further enhance imaging fidelity via the spread spectrum effect that arises in non-coplanar baseline and wide field-of-view settings.
Jason McEwen is a lecturer in the Mullard Space Science Laboratory (MSSL) at University College London (UCL). He is a Core Team member of the ESA Planck satellite mission, a member of the Square Kilometre Array (SKA) Science Data Processor (SDP) working group, a member of the ESA Euclid satellite Science Consortium, and a member of the Large Synoptic Survey Telescope (LSST) Dark Energy Science Collaboration (DESC), co-leading the LSST:UK informatics and statistics working group. After graduating with a B.E. (Hons) in Electrical and Electronic Engineering from the University of Canterbury in 2002, he completed a Ph.D. in Astrophysics at the University of Cambridge in 2007. Following his Ph.D. he held a Research Fellowship at Clare College, Cambridge, followed by a Leverhulme Trust Early Career Fellowship and then a Newton International Fellowship, both at University College London. His research interests are focused on astrostatistics and astroinformatics, combing his interest in signal processing, including sampling theory, wavelet theory, compressed sensing and Bayesian statistics, with applications in cosmology and radio interferometry.

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