Hostname: page-component-586b7cd67f-gb8f7 Total loading time: 0 Render date: 2024-11-20T12:28:19.434Z Has data issue: false hasContentIssue false
  • English
  • Français

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
Rights & Permissions [Opens in a new window]

Abstract

Core share and HTML view are not available for this content. However, as you have access to this content, a full PDF is available via the ‘Save PDF’ action button.

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