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Richard Watkins
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We constrain the velocity power spectrum shape parameter $Gamma$ in linear theory using the nine bulk-flow and shear moments estimated from four recent peculiar velocity surveys. For each survey, a likelihood function for $Gamma$ was found after marginalizing over the power spectrum amplitude $sigma_8Omega_m^{0.6}$ using constraints obtained from comparisons between redshift surveys and peculiar velocity data. In order to maximize the accuracy of our analyses, the velocity noise $sigma_*$ was estimated directly for each survey. A statistical analysis of the differences between the values of the moments estimated from different surveys showed consistency with theoretical predictions, suggesting that all the surveys investigated reflect the same large scale flows. The peculiar velocity surveys were combined into a composite survey yielding the constraint $Gamma=0.13^{+0.09}_{-0.05}$. This value is lower than, but consistent with, values obtained using redshift surveys and CMB data.
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We propose to use spatial correlations of the kinetic Sunyaev-Zeldovich (KSZ) flux as an estimator of the peculiar velocity power spectrum. In contrast with conventional techniques, our new method does not require measurements of the thermal SZ signal or the X-ray temperature. Moreover, this method has the special advantage that the expected systematic errors are always sub-dominant to statistical errors on all scales and redshifts of interest. We show that future large sky coverage KSZ surveys may allow a peculiar velocity power spectrum estimates of an accuracy reaching ~10%.
Redshift-space distortions (RSD) generically affect any spatially-dependent observable that is mapped using redshift information. The effect on the observed clustering of galaxies is the primary example of this. This paper is devoted to another example: the effect of RSD on the apparent peculiar motions of tracers as inferred from their positions in redshift space (i.e. the observed distance). Our theoretical study is motivated by practical considerations, mainly, the direct estimation of the velocity power spectrum, which is preferably carried out using the tracers redshift-space position (so as to avoid uncertainties in distance measurements). We formulate the redshift-space velocity field and show that RSD enters as a higher-order effect. Physically, this effect may be interpreted as a dissipative correction to the usual perfect-fluid description of dark matter. We show that the effect on the power spectrum is a damping on relatively large, quasilinear scales ($k>0.01,h,{rm Mpc}^{-1}$), as was observed, though unexplained, in $N$-body simulations elsewhere. This paper presents two power spectrum models for the the peculiar velocity field in redshift space, both of which can be considered velocity analogues of existing clustering models. In particular, we show that the Finger-of-God effect, while also present in the velocity field, cannot be entirely blamed for the observed damping in simulations. Our work provides some of the missing modelling ingredients required for a density--velocity multi-tracer analysis, which has been proposed for upcoming redshift surveys.
We allow for nonlinear effects in the likelihood analysis of galaxy peculiar velocities, and obtain ~35%-lower values for the cosmological density parameter Om and the amplitude of mass-density fluctuations. The power spectrum in the linear regime is assumed to be a flat LCDM model (h=0.65, n=1, COBE) with only Om as a free parameter. Since the likelihood is driven by the nonlinear regime, we break the power spectrum at k_b=0.2 h/Mpc and fit a power law at k>k_b. This allows for independent matching of the nonlinear behavior and an unbiased fit in the linear regime. The analysis assumes Gaussian fluctuations and errors, and a linear relation between velocity and density. Tests using proper mock catalogs demonstrate a reduced bias and a better fit. We find for the Mark3 and SFI data Om_m=0.32+-0.06 and 0.37+-0.09 respectively, with sigma_8*Om^0.6 = 0.49+-0.06 and 0.63+-0.08, in agreement with constraints from other data. The quoted 90% errors include cosmic variance. The improvement in likelihood due to the nonlinear correction is very significant for Mark3 and moderately so for SFI. When allowing deviations from LCDM, we find an indication for a wiggle in the power spectrum: an excess near k=0.05 and a deficiency at k=0.1 (cold flow). This may be related to the wiggle seen in the power spectrum from redshift surveys and the second peak in the CMB anisotropy. A chi^2 test applied to modes of a Principal Component Analysis (PCA) shows that the nonlinear procedure improves the goodness of fit and reduces a spatial gradient of concern in the linear analysis. The PCA allows addressing spatial features of the data and fine-tuning the theoretical and error models. It shows that the models used are appropriate for the cosmological parameter estimation performed. We address the potential for optimal data compression using PCA.
We investigate the power spectrum of velocity fluctuations in the universe, $V^2(k)$, starting from four different measures of velocity: (1) the power spectrum of velocity fluctuations from peculiar velocities of galaxies; (2) the rms peculiar velocity of galaxy clusters; (3) the power spectrum of velocity fluctuations from the power spectrum of density fluctuations in the galaxy distribution; (4) and the bulk velocity from peculiar velocities of galaxies. We show that measures (1) and (2) are not consistent with each other and either the power spectrum from peculiar velocities of galaxies is overestimated or the rms cluster peculiar velocity is underestimated. The amplitude of velocity fluctuations derived from the galaxy distribution (measure 3) depends on the parameter $beta$. We estimate the parameter $beta$ on the basis of measures (2) and (4). The power spectrum of velocity fluctuations from the galaxy distribution in the Stromlo-APM redshift survey is consistent with the observed rms cluster velocity and with the observed large-scale bulk flow when the parameter $beta$ is in the range 0.4-0.5. In this case the value of the function $V(k)$ at wavelength $lambda=120h^{-1}$Mpc is $sim 350$ km s$^{-1}$ and the rms amplitude of the bulk flow at the radius $r=60h^{-1}$ Mpc is $sim 340$ km s$^{-1}$. The velocity dispersion of galaxy systems originates mostly from the large-scale velocity fluctuations with wavelengths $lambda >100h^{-1}$ Mpc.
We compute the angular power spectrum C_l from 1.5 million galaxies in early SDSS data on large angular scales, l<600. The data set covers about 160 square degrees, with a characteristic depth of order 1 Gpc/h in the faintest (21<r<22) of our four magnitude bins. Cosmological interpretations of these results are presented in a companion paper by Dodelson et al (2001). The data in all four magnitude bins are consistent with a simple flat ``concordance model with nonlinear evolution and linear bias factors of order unity. Nonlinear evolution is particularly evident for the brightest galaxies. A series of tests suggest that systematic errors related to seeing, reddening, etc., are negligible, which bodes well for the sixtyfold larger sample that the SDSS is currently collecting. Uncorrelated error bars and well-behaved window functions make our measurements a convenient starting point for cosmological model fitting.
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