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Fast Transient Detection as a Prototypical “Big Data” Problem

Published online by Cambridge University Press:  20 April 2012

Dayton L. Jones ,
Kiri Wagstaff ,
David Thompson ,
Larry D'Addario ,
Robert Navarro ,
Chris Mattmann ,
Walid Majid ,
Umaa Rebbapragada ,
Joseph Lazio  and
Robert Preston
Dayton L. Jones
Affiliation:
Jet Propulsion Laboratory, California Institute of Technology, Pasadena, CA 91109, USA email: [email protected]
Kiri Wagstaff
Affiliation:
Jet Propulsion Laboratory, California Institute of Technology, Pasadena, CA 91109, USA email: [email protected]
David Thompson
Affiliation:
Jet Propulsion Laboratory, California Institute of Technology, Pasadena, CA 91109, USA email: [email protected]
Larry D'Addario
Affiliation:
Jet Propulsion Laboratory, California Institute of Technology, Pasadena, CA 91109, USA email: [email protected]
Robert Navarro
Affiliation:
Jet Propulsion Laboratory, California Institute of Technology, Pasadena, CA 91109, USA email: [email protected]
Chris Mattmann
Affiliation:
Jet Propulsion Laboratory, California Institute of Technology, Pasadena, CA 91109, USA email: [email protected]
Walid Majid
Affiliation:
Jet Propulsion Laboratory, California Institute of Technology, Pasadena, CA 91109, USA email: [email protected]
Umaa Rebbapragada
Affiliation:
Jet Propulsion Laboratory, California Institute of Technology, Pasadena, CA 91109, USA email: [email protected]
Joseph Lazio
Affiliation:
Jet Propulsion Laboratory, California Institute of Technology, Pasadena, CA 91109, USA email: [email protected]
Robert Preston
Affiliation:
Jet Propulsion Laboratory, California Institute of Technology, Pasadena, CA 91109, USA email: [email protected]
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Abstract

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The detection of fast (< 1 second) transient signals requires a challenging balance between the need to examine vast quantities of high time-resolution data and the impracticality of storing all the data for later analysis. This is the epitome of a “big data” issue—far more data will be produced by next generation-astronomy facilities than can be analyzed, distributed, or archived using traditional methods. JPL is developing technologies to deal with “big data” problems from initial data generation through real-time data triage algorithms to large-scale data archiving and mining. Although most current work is focused on the needs of large radio arrays, the technologies involved are widely applicable in other areas.

Keywords

methods: data analysisastronomical data bases: miscellaneous
Type
Contributed Papers
Information
Proceedings of the International Astronomical Union , Volume 7 , Symposium S285: New Horizons in Time-Domain Astronomy , September 2011 , pp. 340 - 341
Copyright
Copyright © International Astronomical Union 2012

References

D'Addario, L. 2011 Square Kilometre Array Memo, 133, http://www.skatelescope.org/Google Scholar
Brisken, W., et al. , 2011, Proc. National Radio Science Meeting, Boulder, CO, 2011 January 6Google Scholar
Thompson, D. R., et al. , 2011, ApJ, 735, 98Google Scholar
Wayth, R. B., et al. , 2011, ApJ, 735, 97CrossRefGoogle Scholar