Article

Testing the minimum variance method for estimating large-scale velocity moments

Monthly Notices of the Royal Astronomical Society (Impact Factor: 5.23). 12/2011; 424(4). DOI: 10.1111/j.1365-2966.2012.21345.x
Source: arXiv

ABSTRACT The estimation and analysis of large-scale bulk flow moments of peculiar
velocity surveys is complicated by non-spherical survey geometry, the
non-uniform sampling of the matter velocity field by the survey objects and the
typically large measurement errors of the measured line-of-sight velocities.
Previously, we have developed an optimal `minimum variance' (MV) weighting
scheme for using peculiar velocity data to estimate bulk flow moments for
idealized, dense and isotropic surveys with Gaussian radial distributions, that
avoids many of these complications. These moments are designed to be easy to
interpret and are comparable between surveys. In this paper, we test the
robustness of our MV estimators using numerical simulations. Using MV weights,
we estimate the bulk flow moments for various mock catalogues extracted from
the LasDamas and the Horizon Run numerical simulations and compare these
estimates to the moments calculated directly from the simulation boxes. We show
that the MV estimators are unbiased and negligibly affected by non-linear
flows.

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    ABSTRACT: We calculate the cosmic Mach number M - the ratio of the bulk flow of the velocity field on scale R to the velocity dispersion within regions of scale R. M is effectively a measure of the ratio of large-scale to small-scale power and can be a useful tool to constrain the cosmological parameter space. Using a compilation of existing peculiar velocity surveys, we calculate M and compare it to that estimated from mock catalogues extracted from the Large Suite of Dark Matter Simulations (LasDamas, a Λ cold dark matter cosmology) numerical simulations. We find agreement with expectations for the LasDamas cosmology at ˜1.5σ confidence level. We also show that our Mach estimates for the mocks are not biased by selection function effects. To achieve this, we extract dense and nearly isotropic distributions using Gaussian selection functions with the same width as the characteristic depth of the real surveys, and show that the Mach numbers estimated from the mocks are very similar to the values based on Gaussian profiles of the corresponding widths. We discuss the importance of the survey window functions in estimating their effective depths. We investigate the non-linear matter power spectrum interpolator PKANN as an alternative to numerical simulations, in the study of Mach number.
    Monthly Notices of the Royal Astronomical Society 06/2013; 432(1):307-317. DOI:10.1093/mnras/stt464 · 5.23 Impact Factor

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