Hostname: page-component-cd9895bd7-jkksz Total loading time: 0 Render date: 2024-12-31T20:28:18.689Z Has data issue: false hasContentIssue false
  • English
  • Français

Application of a Bayesian Method to Absorption Spectral-Line Finding in Simulated ASKAP Data

Published online by Cambridge University Press:  02 January 2013

J. R. Allison ,
E. M. Sadler  and
M. T. Whiting
J. R. Allison*
Affiliation:
Sydney Institute for Astronomy, School of Physics A28, University of Sydney, NSW 2006, Australia
E. M. Sadler
Affiliation:
Sydney Institute for Astronomy, School of Physics A28, University of Sydney, NSW 2006, Australia ARC Centre of Excellence for All-sky Astrophysics (CAASTRO)
M. T. Whiting
Affiliation:
CSIRO Astronomy & Space Science, P.O. Box 76, Epping, NSW 1710, Australia
*
DCorresponding 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 spectral bandwidth and wide field of view of the Australian SKA Pathfinder radio telescope will open up a completely new parameter space for large extragalactic HI surveys. Here we focus on identifying and parametrising HI absorption lines which occur in the line of sight towards strong radio continuum sources. We have developed a method for simultaneously finding and fitting HI absorption lines in radio data by using multi-nested sampling, a Bayesian Monte Carlo algorithm. The method is tested on a simulated ASKAP data cube, and is shown to be reliable at detecting absorption lines in low signal-to-noise data without the need to smooth or alter the data. Estimation of the local Bayesian evidence statistic provides a quantitative criterion for assigning significance to a detection and selecting between competing analytical line-profile models.

Keywords

methods: data analysismethods: statisticalradio lines: galaxies
Type
Research Article
Information
Publications of the Astronomical Society of Australia , Volume 29 , Special Issue 3: Source Finding and Visualisation , 2012 , pp. 221 - 228
Copyright
Copyright © Astronomical Society of Australia 2012

References

Allison, J. R., Taylor, A. C., Jones, M. E., Rawlings, S. & Kay, S. T., 2011, MNRAS, 410, 341CrossRefGoogle Scholar
Condon, J. J., Cotton, W. D., Greisen, E. W., Yin, Q. F., Perley, R. A., Taylor, G. B. & Broderick, J. J., 1998, AJ, 115, 1693CrossRefGoogle Scholar
Curran, S. J., Whiting, M. T., Wiklind, T., Webb, J. K., Murphy, M. T. & Purcell, C. R., 2008, MNRAS, 391, 765CrossRefGoogle Scholar
Deboer, D. R. et al. , 2009, IEEE Proceedings, 97, 1507CrossRefGoogle Scholar
Feroz, F. & Hobson, M. P., 2008, MNRAS, 384, 449CrossRefGoogle Scholar
Feroz, F., Hobson, M. P., Zwart, J. T. L., Saunders, R. D. E. & Grainge, K. J. B., 2009a, MNRAS, 398, 2049CrossRefGoogle Scholar
Feroz, F., Hobson, M. P. & Bridges, M., 2009b, MNRAS, 398, 1601CrossRefGoogle Scholar
Gupta, N., Srianand, R., Bowen, D.V., York, D.G. & Wadadekar, Y., 2010, MNRAS, 408, 849CrossRefGoogle Scholar
Johnston, S. et al. , 2007, PASA, 24, 174CrossRefGoogle Scholar
Kanekar, N., Prochaska, J. X., Ellison, S. L. & Chengalur, J. N., 2009, MNRAS, 396, 385CrossRefGoogle Scholar
Marshall, P. J., Hobson, M. P. & Slosar, A., 2003, MNRAS, 346, 489CrossRefGoogle Scholar
Mauch, T., Murphy, T., Buttery, H. J., Curran, J., Hunstead, R. W., Piestrzynski, B., Robertson, J. G. & Sadler, E. M., 2003, MNRAS, 342, 1117CrossRefGoogle Scholar
Sivia, D. S., 2006, Data Analysis: A Bayesian Tutorial (2nd ed.; New York: Oxford University Press)CrossRefGoogle Scholar
Skilling, J., 2004, in Bayesian Inference and Maximum Entropy Methods in Science and Engineering: 24th International Workshop on Bayesian Inference and Maximum Entropy Methods in Science and Engineering Vol. 735, Nested Sampling, 395–405Google Scholar
Wells, D. C., Greisen, E. W. & Harten, R. H., 1981, AAPS, 44, 363Google Scholar
Whiting, M. T., 2008, in Galaxies in the Local Volume Astronomers! Do You Know Where Your Galaxies are? ed. Koribalski, B. S. & Jerjen, H., 343Google Scholar
Wilman, R. J. et al. , 2008, MNRAS, 388, 1335Google Scholar