• Intelligent Systems
  • PrePrints
  • Abstract - Real Time Adaptive Event Detection in Astronomical Data Streams: Lessons from the Very Long Baseline Array
 This Article 
   
 Share 
   
 Bibliographic References 
   
 Add to: 
 
Digg
Furl
Spurl
Blink
Simpy
Google
Del.icio.us
Y!MyWeb
 
 Search 
   
Real Time Adaptive Event Detection in Astronomical Data Streams: Lessons from the Very Long Baseline Array
PrePrint
ISSN: 1541-1672
ASCII Text
x 
 
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, vol. 99, no. 1, pp. 1, , 5555.
 
 
BibTex
x
 
@article{ 10.1109/MIS.2013.10,
author = {David Thompson and Sarah Burke-Spolaor and Adam Deller and Walid Majid and Divya Palaniswamy and Steven Tingay and Kiri Wagstaff and Randall Wayth},
title = {Real Time Adaptive Event Detection in Astronomical Data Streams: Lessons from the Very Long Baseline Array},
journal ={IEEE Intelligent Systems},
volume = {99},
number = {1},
issn = {1541-1672},
year = {5555},
pages = {1},
doi = {http://doi.ieeecomputersociety.org/10.1109/MIS.2013.10},
publisher = {IEEE Computer Society},
address = {Los Alamitos, CA, USA},
}
 
 
RefWorks Procite/RefMan/Endnote
x 
 
TY - MGZN
JO - IEEE Intelligent Systems
TI - Real Time Adaptive Event Detection in Astronomical Data Streams: Lessons from the Very Long Baseline Array
IS - 1
SN - 1541-1672
SP
EP
EPD - 1
A1 - David Thompson,
A1 - Sarah Burke-Spolaor,
A1 - Adam Deller,
A1 - Walid Majid,
A1 - Divya Palaniswamy,
A1 - Steven Tingay,
A1 - Kiri Wagstaff,
A1 - Randall Wayth,
PY - 5555
VL - 99
JA - IEEE Intelligent Systems
ER -
 
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
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, 24 Jan. 2013. IEEE computer Society Digital Library. IEEE Computer Society, <http://doi.ieeecomputersociety.org/10.1109/MIS.2013.10>
Usage of this product signifies your acceptance of the Terms of Use.