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Deep Source-Counting at 3 GHz

Published online by Cambridge University Press:  01 July 2015

Tessa Vernstrom ,
Jasper Wall  and
Douglas Scott
Tessa Vernstrom
Affiliation:
Department of Physics and Astronomy, University of British Columbia 6224 Agricultural Road, Vancouver, Canada V6T1Z1 emails: [email protected], [email protected], [email protected]
Jasper Wall
Affiliation:
Department of Physics and Astronomy, University of British Columbia 6224 Agricultural Road, Vancouver, Canada V6T1Z1 emails: [email protected], [email protected], [email protected]
Douglas Scott
Affiliation:
Department of Physics and Astronomy, University of British Columbia 6224 Agricultural Road, Vancouver, Canada V6T1Z1 emails: [email protected], [email protected], [email protected]
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Abstract

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We describe an analysis of 3-GHz confusion-limited data from the Karl J. Jansky Very Large Array (VLA). We show that with minimal model assumptions, P(D), Bayesian and Markov-Chain Mone-Carlo (MCMC) methods can define the source count to levels some 10 times fainter than the conventional confusion limit. Our verification process includes a full realistic simulation that considers known information on source angular extent and clustering. It appears that careful analysis of the statistical properties of an image is more effective than counting individual objects.

Keywords

source countconfusion limitP(D)
Type
Contributed Papers
Information
Proceedings of the International Astronomical Union , Volume 10 , Symposium S306: Statistical Challenges in 21st Century Cosmology , May 2014 , pp. 177 - 181
Copyright
Copyright © International Astronomical Union 2015 

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