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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.
Real-time systems, Antennas, Telescopes, Event detection, Time frequency analysis,intelligent systems, radio astronomy, pattern recognition, real-time machine learning, time series analysis, fast radio transients
David R. Thompson, Sarah Burke-Spolaor, Adam T. Deller, Walid A. Majid, Divya Palaniswamy, Steven J. Tingay, Kiri L. Wagstaff, Randall B. Wayth, "Real-Time Adaptive Event Detection in Astronomical Data Streams", IEEE Intelligent Systems, vol. 29, no. , pp. 48-55, Jan.-Feb. 2014, doi:10.1109/MIS.2013.10
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