Hostname: page-component-78c5997874-lj6df Total loading time: 0 Render date: 2024-11-20T02:42:26.081Z Has data issue: false hasContentIssue false
  • English
  • Français

Comparison of Potential ASKAP Hi Survey Source Finders

Published online by Cambridge University Press:  02 January 2013

A. Popping ,
R. Jurek ,
T. Westmeier ,
P. Serra ,
L. Flöer ,
M. Meyer  and
B. Koribalski
A. Popping*
Affiliation:
International Centre for Radio Astronomy Research (ICRAR), The University of Western Australia, 35 Stirling Hwy, Crawley, WA 6009, Australia
R. Jurek
Affiliation:
Australia Telescope National Facility, CSIRO Astronomy and Space Science, PO Box 76, Epping, NSW 1710, Australia
T. Westmeier
Affiliation:
International Centre for Radio Astronomy Research (ICRAR), The University of Western Australia, 35 Stirling Hwy, Crawley, WA 6009, Australia
P. Serra
Affiliation:
Netherlands Institute for Radio Astronomy (ASTRON), Postbus 2, 7990 AA Dwingeloo, The Netherlands
L. Flöer
Affiliation:
Argelander-Institut für Astronomie, Auf dem Hügel 71, 53121 Bonn, Germany
M. Meyer
Affiliation:
International Centre for Radio Astronomy Research (ICRAR), The University of Western Australia, 35 Stirling Hwy, Crawley, WA 6009, Australia
B. Koribalski
Affiliation:
Australia Telescope National Facility, CSIRO Astronomy and Space Science, PO Box 76, Epping, NSW 1710, Australia
*
ECorresponding author. 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 large size of the ASKAP Hi surveys DINGO and WALLABY necessitates automated 3D source finding. A performance difference of a few percent corresponds to a significant number of galaxies being detected or undetected. As such, the performance of the automated source finding is of paramount importance to both of these surveys. We have analysed the performance of various source finders to determine which will allow us to meet our survey goals during the DINGO and WALLABY design studies. Here we present a comparison of the performance of five different methods of automated source finding. These source finders are duchamp, gamma-finder, a cnhi finder, a 2d–1d wavelet reconstruction finder and a sigma clipping method (s + c finder). Each source finder was applied to the same three-dimensional data cubes containing (a) point sources with a Gaussian velocity profile and (b) spatially extended model-galaxies with inclinations and rotation profiles. We focus on the completeness and reliability of each algorithm when comparing the performance of the different source finders.

Keywords

methods: data analysis
Type
Research Article
Information
Publications of the Astronomical Society of Australia , Volume 29 , Special Issue 3: Source Finding and Visualisation , 2012 , pp. 318 - 339
Copyright
Copyright © Astronomical Society of Australia 2012

References

Boyce, P., 2003, MSc Dissertation, University of CardiffGoogle Scholar
Deboer, D. R., et al. , 2009, IEEEP, 97, 1507Google Scholar
Dewdney, P. E., Hall, P. J., Schilizzi, R. T. & Lazio, T. J. L., 2009, IEEEP, 97, 1482Google Scholar
Driver, S. P., et al. , 1999, A&G, 50, 12Google Scholar
Flöer, L. & Winkel, B., 2011, PASA Special Issue on Source Finding and Visualization, arXiv1112.3807FGoogle Scholar
Haynes, M. P., et al. , 2011, AJ, 142, 170CrossRefGoogle Scholar
Hibbard, J. E., van Gorkom, J. H., Rupen, M. P. & Schiminovich, D., 2001, ASPC, 240, 657Google Scholar
Johnston, S., et al. , 2008, ExA, 22, 151Google Scholar
Jonas, J. L., 2009, IEEEP, 97, 1522Google Scholar
Jones, A. J., Evans, D., Margetts, S. & Durrant, P. J., 2002, in Heuristic and Optimization for Knowledge Discovery, ed. Abbass, H. A., Newton, C. S. & Sarker, R. (Hershey: Idea Group Publishing), 142CrossRefGoogle Scholar
Jurek, R., 2011, PASA Special Issue on Source Finding and Visualization, arXiv1112.1561JGoogle Scholar
Koribalski, B. S. & Staveley-Smith, L., 2009, ASKAP Survey Science ProposalGoogle Scholar
Koribalski, B. S., et al. , 2004, AJ, 128, 16CrossRefGoogle Scholar
Meyer, M. J., et al. , 2004, MNRAS, 350, 1195CrossRefGoogle Scholar
Meyer, M., 2009, in Panoramic Radio Astronomy: Wide-field 1–2 GHz Research on Galaxy Evolution, ed. Heald, G. & Serra, P., Proceedings of Science, PoS(PRA2009)015Google Scholar
Minchin, R. F., et al. , 2003, MNRAS, 346, 787CrossRefGoogle Scholar
Putman, M. E., et al. , 2002, AJ, 123, 873CrossRefGoogle Scholar
Serra, P., et al. , 2011a, MNRAS, submittedGoogle Scholar
Serra, P., Jurek, R. & Flöer, L., 2011b, PASA Speci al Issue on Source Finding and Visualization, arXiv1112.3162SGoogle Scholar
Springob, C. M., et al. , 2005, ApJS, 160, 149CrossRefGoogle Scholar
Starck, J. L., Fadili, J. M., Digel, S., Zhang, B. & Chiang, J., 2009, A&A, 504, 641Google Scholar
Stefansson, A., Koncar, N. & Jones, A. J., 1997, Neural Computing and Applications, 5, 131CrossRefGoogle Scholar
Verheijen, M. A. W., Oosterloo, T. A., van Cappellen, W. A., Bakker, L., Ivashina, M. V. & van der Hulst, J. M., 2008, AIPC, 1035, 265Google Scholar
Westmeier, T., Popping, A. & Serra, P., 2011, PASA Special Issue on Source Finding and Visualization, arXiv1112.3093WGoogle Scholar
Whiting, M. T., 2011, MNRAS, arXiv1201.2710Google Scholar