Hostname: page-component-78c5997874-t5tsf Total loading time: 0 Render date: 2024-11-14T06:45:11.363Z Has data issue: false hasContentIssue false

The 6dFGS peculiar velocity field

Published online by Cambridge University Press:  26 February 2013

Christopher M. Springob
Affiliation:
International Centre for Radio Astronomy Research, The University of Western Australia, Australia email: christopher.springob@icrar.org ARC Centre of Excellence for All-Sky Astrophysics Australian Astronomical Observatory
Christina Magoulas
Affiliation:
University of Melbourne, Australia
Matthew Colless
Affiliation:
Australian Astronomical Observatory
D. Heath Jones
Affiliation:
Monash University, Australia
Lachlan Campbell
Affiliation:
University of Western Kentucky, USA
John Lucey
Affiliation:
University of Durham, UK
Jeremy Mould
Affiliation:
Swinburne University, Australia
Pirin Erdoğdu
Affiliation:
University College London, UK
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 6dF Galaxy Survey (6dFGS) is an all-southern-sky galaxy survey, including 125,000 redshifts and a Fundamental Plane (FP) subsample of 10,000 peculiar velocities. This makes 6dFGS the largest peculiar-velocity sample to date. We have fitted the FP with a tri-variate Gaussian model using a maximum-likelihood approach, and derive the Bayesian probability distribution of the peculiar velocity for each of the 10,000 galaxies. We fit models of the velocity field, including comparisons to the field predicted from the redshift-survey density field, to derive the values of the redshift-space distortion parameter β, the bulk flow and the residual bulk flow in excess of that predicted from the density field. We compare these results to those derived by other authors and discuss the cosmological implications.

Type
Contributed Papers
Copyright
Copyright © International Astronomical Union 2013

References

Colless, M., Saglia, R. P., Burstein, D., Davies, R. L., McMahan, R. K., & Wegner, G. 2001, MNRAS, 321, 277CrossRefGoogle Scholar
Erdoğdu, P., Lahav, O., Huchra, J. P., et al. 2006, MNRAS, 373, 45CrossRefGoogle Scholar
Fisher, K. B., Lahav, O., Hoffman, Y., Lynden-Bell, D., & Zaroubi, S. 1995, MNRAS, 272, 885Google Scholar
Huchra, J. P., Macri, L. M., Masters, K. L., et al. 2012, ApJS, 199, 26Google Scholar
Jarrett, T. H., Chester, T., Cutri, R., Schneider, S., Skrutskie, M., & Huchra, J. P. 2000, AJ, 119, 2498CrossRefGoogle Scholar
Jones, D. H., Read, M. A., Saunders, W., et al. 2009, MNRAS, 399, 683CrossRefGoogle Scholar
Magoulas, C. 2012, Ph.D. Thesis, University of Melbourne, AustraliaGoogle Scholar
Magoulas, C., Springob, C. M., Colless, M., et al. 2012, MNRAS, 427, 245Google Scholar
Powell, M. J. D. 2006, in: Large-Scale Nonlinear Optimization (Roma, M., & Di Pillo, G., eds), Springer: New York, pp. 255297CrossRefGoogle Scholar