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Characterizing Interference in Radio Astronomy Observations through Active and Unsupervised LearningIn the process of observing signals from astronomical sources, radio astronomers must mitigate the effects of manmade 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.
Document ID
20140003876
Acquisition Source
Jet Propulsion Laboratory
Document Type
Other
Authors
Doran, G.
(Jet Propulsion Lab., California Inst. of Tech. Pasadena, CA, United States)
Date Acquired
April 28, 2014
Publication Date
October 1, 2013
Subject Category
Astronomy
Cybernetics, Artificial Intelligence And Robotics
Report/Patent Number
JPL Publication 13-12
Funding Number(s)
OTHER: 01STCR-R.13.144.006
CONTRACT_GRANT: NNN12AA01C
Distribution Limits
Public
Copyright
Public Use Permitted.
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