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David Thompson , Jet Propulsion Laboratory, California Institute of Technology, Pasadena
Sarah Burke-Spolaor , Jet Propulsion Laboratory, California Institute of Technology, Pasadena
Adam Deller , ASTRON, Dwingeloo
Walid Majid , Jet Propulsion Laboratory, California Institute of Technology, Pasadena
Divya Palaniswamy , Curtin University, Perth
Steven Tingay , Curtin University, Perth
Kiri Wagstaff , Jet Propulsion Laboratory, California Institute of Technology, Pasadena
Randall Wayth , Curtin University, Perth
ABSTRACT
A new generation of observational science instruments is dramatically increasing collected data volumes in a range of fields. These instruments include the Square Kilometre 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. We report on a case example: 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.
CITATION
David Thompson, Sarah Burke-Spolaor, Adam Deller, Walid Majid, Divya Palaniswamy, Steven Tingay, Kiri Wagstaff, Randall Wayth, "Real Time Adaptive Event Detection in Astronomical Data Streams: Lessons from the Very Long Baseline Array", IEEE Intelligent Systems, , no. 1, pp. 1, PrePrints PrePrints, doi:10.1109/MIS.2013.10
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