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Characterizing Interference in Radio Astronomy Observations through Active and Unsupervised Learning

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dc.contributor.author Doran, G.
dc.date.accessioned 2013-11-13T16:02:45Z
dc.date.available 2013-11-13T16:02:45Z
dc.date.issued 2013-10
dc.identifier.clearanceno 13-12
dc.identifier.uri http://hdl.handle.net/2014/44018
dc.description.abstract In the process of observing signals from astronomical sources, radio astronomers must mitigate the effects of man-made radio sources such as cell phones, satellites, aircraft, and observatory equipment. Radio frequency interference (RFI) often occurs as short bursts (< 1 ms) across a broad range of frequencies, and can be confused with signals from sources of interest such as pulsars. With ever-increasing volumes of data being produced by observatories, automated strategies are required to detect, classify, and characterize these short “transient” RFI events. We investigate an active learning approach in which an astronomer labels events that are most confusing to a classifier, minimizing the human effort required for classification. We also explore the use of unsupervised clustering techniques, which automatically group events into classes without user input. We apply these techniques to data from the Parkes Multibeam Pulsar Survey to characterize several million detected RFI events from over a thousand hours of observation en_US
dc.description.sponsorship NASA/JPL en_US
dc.language.iso en_US en_US
dc.publisher Pasadena, CA : Jet Propulsion Laboratory, National Aeronautics and Space Administration, 2013. en_US
dc.relation.ispartofseries JPL Publication en_US
dc.subject machine learning en_US
dc.subject radio astronomy en_US
dc.subject radio frequency interferences en_US
dc.title Characterizing Interference in Radio Astronomy Observations through Active and Unsupervised Learning en_US
dc.type Technical Report en_US
dc.subject.NASATaxonomy Cybernetics, Artificial Intelligence, and Robotics. en_US


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