Hostname: page-component-78c5997874-fbnjt Total loading time: 0 Render date: 2024-11-04T21:33:06.605Z Has data issue: false hasContentIssue false
  • English
  • Français

Classification of ASKAP VAST Radio Light Curves†

Published online by Cambridge University Press:  20 April 2012

Umaa Rebbapragada ,
Kitty Lo ,
Kiri L. Wagstaff ,
Colorado Reed ,
Tara Murphy  and
David R. Thompson
Umaa Rebbapragada
Affiliation:
Jet Propulsion Laboratory, Pasadena, CA, 91109USA email: [email protected]
Kitty Lo
Affiliation:
Sydney Institute for Astronomy, University of Sydney, Sydney, NSW 2006, Australia
Kiri L. Wagstaff
Affiliation:
Jet Propulsion Laboratory, Pasadena, CA, 91109USA email: [email protected]
Colorado Reed
Affiliation:
Department of Physics, University of Iowa, Iowa City, IA 52242, USA
Tara Murphy
Affiliation:
Sydney Institute for Astronomy, University of Sydney, Sydney, NSW 2006, Australia
David R. Thompson
Affiliation:
Jet Propulsion Laboratory, Pasadena, CA, 91109USA email: [email protected]
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.

The VAST survey is a wide-field survey that observes with unprecedented instrument sensitivity (0.5 mJy or lower) and repeat cadence (a goal of 5 seconds) that will enable novel scientific discoveries related to known and unknown classes of radio transients and variables. Given the unprecedented observing characteristics of VAST, it is important to estimate source classification performance, and determine best practices prior to the launch of ASKAP's BETA in 2012. The goal of this study is to identify light-curve characterization and classification algorithms that are best suited for archival VAST light-curve classification. We perform our experiments on light-curve simulations of eight source types and achieve best-case performance of approximately 90% accuracy. We note that classification performance is most influenced by light-curve characterization rather than classifier algorithm.

Keywords

ASKAPVASTradio astronomyclassificationdata analysis
Type
Contributed Papers
Information
Proceedings of the International Astronomical Union , Volume 7 , Symposium S285: New Horizons in Time-Domain Astronomy , September 2011 , pp. 397 - 399
Copyright
Copyright © International Astronomical Union 2012

References

Breiman, L. 2001, Machine Learning, 45, p. 5CrossRefGoogle Scholar
Cortes, C. & Vapnik, V. 1995, Machine Learning 20, p. 273Google Scholar
Hall, M., et al. , 2009, SIGKDD Explorations 11, 1CrossRefGoogle Scholar
Murphy, T. & Chatterjee, S. 2009, http://www.physics.usyd.edu.au/sifa/vast/index.php/Main/DocumentsGoogle Scholar
Quinlan, J. R. 1986, Machine Learning, 1, 1Google Scholar
Richards, J. W., et al. , 2011, ApJ 733, 1CrossRefGoogle Scholar
Scargle, J. D. 1982, ApJ 263, p. 835CrossRefGoogle Scholar
Wachman, G., Khardon, R., Protopapas, P., & Alcock, C. R. 2009, in: Buntine, W.L., Grobelnik, M., Mladenic, D. & Shawe-Taylor, J. (eds.), Kernels for Periodic Time Series Arising in Astronomy, Proc. ECML (Bled, Slovenia), p. 489Google Scholar