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Research Abstracts Online
January 2010 - March 2011

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University of Minnesota Twin Cities
College of Science and Engineering
School of Physics and Astronomy

PI: Attila Kovacs

Astronomical Data Reduction and Imaging

Large astronomical detector arrays are producing increasingly large data volumes. Fortunately, the algorithms for reducing such data can be massively parallelized, and are well-suited for cluster computing with a large number of computing nodes. Additionally GPU processing can be used for even greater speeds. This research focuses on pioneering more capable and faster approaches for ground-based imaging applications.