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Real-Time Adaptive Event Detection in Astronomical Data Streams

Thompson, David R. and Burke-Spolaor, Sarah and Deller, Adam T. and Majid, Walid A. and Palaniswamy, Divya and Tingay, Steven J. and Wagstaff, Kiri L. and Wayth, Randall B. (2014) Real-Time Adaptive Event Detection in Astronomical Data Streams. IEEE Intelligent Systems, 29 (1). pp. 48-55. ISSN 1541-1672. doi:10.1109/MIS.2013.10.

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A new generation of observational science instruments is dramatically increasing collected data volumes in a range of fields. These instruments include the Square Kilometer Array (SKA), Large Synoptic Survey Telescope (LSST), terrestrial sensor networks, and NASA satellites participating in "decadal survey"' missions. Their unprecedented coverage and sensitivity will likely reveal wholly new categories of unexpected and transient events. Commensal methods passively analyze these data streams, recognizing anomalous events of scientific interest and reacting in real time. Here, the authors report on a case example: Very Long Baseline Array Fast Transients Experiment (V-FASTR), an ongoing commensal experiment at the Very Long Baseline Array (VLBA) that uses online adaptive pattern recognition to search for anomalous fast radio transients. V-FASTR triages a millisecond-resolution stream of data and promotes candidate anomalies for further offline analysis. It tunes detection parameters in real time, injecting synthetic events to continually retrain itself for optimum performance. This self-tuning approach retains sensitivity to weak signals while adapting to changing instrument configurations and noise conditions. The system has operated since July 2011, making it the longest-running real-time commensal radio transient experiment to date.

Item Type:Article
Related URLs:
URLURL TypeDescription Paper
Thompson, David R.0000-0003-1100-7550
Burke-Spolaor, Sarah0000-0003-4052-7838
Deller, Adam T.0000-0001-9434-3837
Majid, Walid A.0000-0002-4694-4221
Alternate Title:Real Time Adaptive Event Detection in Astronomical Data Streams: Lessons from the VLBA
Additional Information:© 2014 IEEE. Published by the IEEE Computer Society. We thank Peter Hall and J-P Macquart (Curtin University/International Center for Radio Astronomy Research), as well as Dayton Jones, Robert Preston, and Joseph Lazio (Jet Propulsion Laboratory). The International Center for Radio Astronomy Research is a joint venture between Curtin University and The University of Western Australia, funded by the State Government of Western Australia and the joint venture partners. Steven J. Tingay is a Western Australian Premiers Research Fellow. Randall B. Wayth is supported via the Western Australian Center of Excellence in Radio Astronomy Science and Engineering. The National Radio Astronomy Observatory is a facility of the National Science Foundation operated under cooperative agreement by Associated Universities, Incorporated. Part of this research was performed at the Jet Propulsion Laboratory, California Institute of Technology, under the Research and Technology Development Strategic Initiative Program, under a contract with the National Aeronautics and Space Administration. The research was also supported in part by the Keck Institute for Space Studies. Copyright 2013. All rights reserved. US government support acknowledged.
Group:TAPIR, Keck Institute for Space Studies
Funding AgencyGrant Number
State Government of Western AustraliaUNSPECIFIED
Western Australian Centre of Excellence in Radio Astronomy Science and EngineeringUNSPECIFIED
Keck Institute for Space StudiesUNSPECIFIED
Subject Keywords:Radio Astronomy, Pattern Recognition, Real Time Machine Learning, Time Series Analysis, Fast Radio Transients
Issue or Number:1
Record Number:CaltechAUTHORS:20130212-145444554
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Official Citation:D. R. Thompson et al., "Real-Time Adaptive Event Detection in Astronomical Data Streams," in IEEE Intelligent Systems, vol. 29, no. 1, pp. 48-55, Jan.-Feb. 2014. doi: 10.1109/MIS.2013.10
Usage Policy:No commercial reproduction, distribution, display or performance rights in this work are provided.
ID Code:36886
Deposited By: JoAnn Boyd
Deposited On:13 Feb 2013 19:42
Last Modified:09 Nov 2021 23:25

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