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Real-Time Classification of Transient Events in Synoptic Sky Surveys

Published online by Cambridge University Press:  20 April 2012

Ashish A. Mahabal ,
C. Donalek ,
S. G. Djorgovski ,
A. J. Drake ,
M. J. Graham ,
R. Williams ,
Y. Chen ,
B. Moghaddam  and
M. Turmon
Ashish A. Mahabal
Affiliation:
California Institute of Technology, Pasadena, CA 91125, USA email: [email protected]
C. Donalek
Affiliation:
California Institute of Technology, Pasadena, CA 91125, USA email: [email protected]
S. G. Djorgovski
Affiliation:
California Institute of Technology, Pasadena, CA 91125, USA email: [email protected] Distinguished visiting professor, King Abdulaziz Univ., Jeddah, Saudi Arabia.
A. J. Drake
Affiliation:
California Institute of Technology, Pasadena, CA 91125, USA email: [email protected]
M. J. Graham
Affiliation:
California Institute of Technology, Pasadena, CA 91125, USA email: [email protected]
R. Williams
Affiliation:
California Institute of Technology, Pasadena, CA 91125, USA email: [email protected]
Y. Chen
Affiliation:
California Institute of Technology, Pasadena, CA 91125, USA email: [email protected]
B. Moghaddam
Affiliation:
Jet Propulsion Laboratory, Pasadena, CA 91109, USA
M. Turmon
Affiliation:
Jet Propulsion Laboratory, Pasadena, CA 91109, USA
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Abstract

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An automated rapid classification of the transient events detected in modern synoptic sky surveys is essential for their scientific utility and effective follow-up when resources are scarce. This problem will grow by orders of magnitude with the next generation of surveys. We are exploring a variety of novel automated classification techniques, mostly Bayesian, to respond to those challenges, using the ongoing CRTS sky survey as a testbed. We describe briefly some of the methods used.

Type
Contributed Papers
Information
Proceedings of the International Astronomical Union , Volume 7 , Symposium S285: New Horizons in Time-Domain Astronomy , September 2011 , pp. 355 - 357
Copyright
Copyright © International Astronomical Union 2012

References

Djorgovski, S. G., et al. , 2011a, in: Srivasatva, A. & Chawla, N. (eds.), Stati. Anal. Data Mining (CIDU 2011 conf.), in press.Google Scholar
Djorgovski, S. G., et al. , 2011b, in: Mihara, T. & Kawai, N. (eds.), The First Year of MAXI: Monitoring Variable X-ray Sources (Tokyo: JAXA Special Publ.), in pressGoogle Scholar
Donalek, C., et al. , 2008, in: Bailer-Jones, (ed.), Classification and Discovery in Large Astronomical Surveys, AIPC, 1082, 252.Google Scholar
Drake, A. J., et al. , 1999, ApJ, 521, 602CrossRefGoogle Scholar
Drake, A. J., et al. , 2009, ApJ, 696, 870CrossRefGoogle Scholar
Mahabal, A. A., et al. , 2008, AN, 329, 3, 288Google Scholar
Mahabal, A. A., et al. , 2010, ASPCS, 434, 115, in: Mizumoto, Y., Morita, K. I. & Ohishi, M. (eds.), ADASS XIXGoogle Scholar
Mahabal, A. A., et al. , 2011, BASI, 39, 387Google Scholar
Prieto, J., et al. , 2011, ApJ, submitted (arXiv:1107.5043)Google Scholar