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Register a new userConsistently Large Cosmic Flows on Scales of 100 Mpc/h: a Challenge for the Standard LCDM Cosmology
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Richard Watkins
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Peculiar velocity surveys have non-uniform spatial distributions of tracers, so that the bulk flow estimated from them does not correspond to that of a simple volume such as a sphere. Thus bulk flow estimates are generally not strictly comparable between surveys, even those whose effective depths are similar. In addition, the sparseness of typical surveys can lead to aliasing of small scale power into what is meant to be a probe of the largest scales. Here we introduce a new method of calculating bulk flow moments where velocities are weighted to give an optimal estimate of the bulk flow of an idealized survey, with the variance of the difference between the estimate and the actual flow being minimized. These minimum variance estimates can be designed to estimate the bulk flow on a particular scale with minimal sensitivity to small scale power, and are comparable between surveys. We compile all major peculiar velocity surveys and apply this new method to them. We find that most surveys we studied are highly consistent with each other. Taken together the data suggest that the bulk flow within a Gaussian window of radius 50 Mpc/h is 407 km/s toward l=287 and b=8. The large-scale bulk motion is consistent with predictions from the local density field. This indicates that there are significant density fluctuations on very large scales. A flow of this amplitude on such a large scale is not expected in the WMAP5-normalized LCDM cosmology, for which the predicted one-dimensional r.m.s. velocity is ~110 km/s. The large amplitude of the observed bulk flow favors the upper values of the WMAP5 error-ellipse, but even the point at the top of the WMAP5 95% confidence ellipse predicts a bulk flow which is too low compared to that observed at >98% confidence level.
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To study galactic motions on the largest available scales, we require bulk flow moments whose window functions have as narrow a peak as possible and having as small an amplitude as possible outside the peak. Typically the moments found using the maximum likelihood estimate weights do not meet these criteria. We present a new method for calculating weights for moments that essentially allow us to design the moments window function, subject, of course, to the distribution and uncertainties of the available data.
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