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We have derived estimators for the linear growth rate of density fluctuations using the cross-correlation function of voids and haloes in redshift space, both directly and in Fourier form. In linear theory, this cross-correlation contains only monopole and quadrupole terms. At scales greater than the void radius, linear theory is a good match to voids traced out by haloes in N-body simulations; small-scale random velocities are unimportant at these radii, only tending to cause small and often negligible elongation of the redshift-space cross-correlation function near its origin. By extracting the monopole and quadrupole from the cross-correlation function, we measure the linear growth rate without prior knowledge of the void profile or velocity dispersion. We recover the linear growth parameter $beta$ to 9% precision from an effective volume of 3(Gpc/h)^3 using voids with radius greater than 25Mpc/h. Smaller voids are predominantly sub-voids, which may be more sensitive to the random velocity dispersion; they introduce noise and do not help to improve the measurement. Adding velocity dispersion as a free parameter allows us to use information at radii as small as half of the void radius. The precision on $beta$ is reduced to approximately 5%. Contrary to the simple redshift-space distortion pattern in overdensities, voids show diverse shapes in redshift space, and can appear either elongated or flattened along the line of sight. This can be explained by the competing amplitudes of the local density contrast, plus the radial velocity profile and its gradient, with the latter two factors being determined by the cumulative density profile of voids. The distortion pattern is therefore determined solely by the void profile and is different for void-in-cloud and void-in-void. This diversity of redshift-space void morphology complicates measurements of the Alcock-Paczynski effect using voids.
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We perform a comprehensive redshift-space distortion analysis based on cosmic voids in the large-scale distribution of galaxies observed with the Sloan Digital Sky Survey. To this end, we measure multipoles of the void-galaxy cross-correlation function and compare them with standard model predictions in cosmology. Merely considering linear-order theory allows us to accurately describe the data on the entire available range of scales and to probe void-centric distances down to about $2h^{-1}{rm Mpc}$. Common systematics, such as the Fingers-of-God effect, scale-dependent galaxy bias, and nonlinear clustering do not seem to play a significant role in our analysis. We constrain the growth rate of structure via the redshift-space distortion parameter $beta$ at two median redshifts, $beta(bar{z}=0.32)=0.599^{+0.134}_{-0.124}$ and $beta(bar{z}=0.54)=0.457^{+0.056}_{-0.054}$, with a precision that is competitive with state-of-the-art galaxy-clustering results. While the high-redshift constraint perfectly agrees with model expectations, we observe a mild $2sigma$ deviation at $bar{z}=0.32$, which increases to $3sigma$ when the data is restricted to the lowest available redshift range of $0.15<z<0.33$.
Cosmic voids in the large-scale structure of the Universe affect the peculiar motions of objects in their vicinity. Although these motions are difficult to observe directly, the clustering pattern of their surrounding tracers in redshift space is influenced in a unique way. This allows to investigate the interplay between densities and velocities around voids, which is solely dictated by the laws of gravity. With the help of $N$-body simulations and derived mock-galaxy catalogs we calculate the average density fluctuations around voids identified with a watershed algorithm in redshift space and compare the results with the expectation from general relativity and the $Lambda$CDM model. We find linear theory to work remarkably well in describing the dynamics of voids. Adopting a Bayesian inference framework, we explore the full posterior of our model parameters and forecast the achievable accuracy on measurements of the growth rate of structure and the geometric distortion through the Alcock-Paczynski effect. Systematic errors in the latter are reduced from $sim15%$ to $sim5%$ when peculiar velocities are taken into account. The relative parameter uncertainties in galaxy surveys with number densities comparable to the SDSS MAIN (CMASS) sample probing a volume of $1h^{-3}{rm Gpc}^3$ yield $sigma_{f/b}left/(f/b)right.sim2%$ ($20%$) and $sigma_{D_AH}/D_AHsim0.2%$ ($2%$), respectively. At this level of precision the linear-theory model becomes systematics dominated, with parameter biases that fall beyond these values. Nevertheless, the presented method is highly model independent; its viability lies in the underlying assumption of statistical isotropy of the Universe.
Redshift space distortions (RSD) in the void-galaxy correlation $xi^s$ provide information on the linear growth rate of structure in low density environments. Accurate modelling of these RSD effects can also allow the use of voids in competitive Alcock-Paczynski measurements. Linear theory models of $xi^s$ are able to provide extremely good descriptions of simulation data on all scales provided the real space void positions are known. However, by reference to simulation data we demonstrate the failure of the assumptions implicit in current models of $xi^s$ for voids identified directly in redshift space, as would be simplest using real observational data. To overcome this problem we instead propose using a density-field reconstruction method based on the Zeldovich approximation to recover the real space void positions from redshift space data. We show that this recovers the excellent agreement between theory and data for $xi^s$. Performing the reconstruction requires an input cosmological model so, to be self-consistent, we have to perform reconstruction for every model to be tested. We apply this method to mock galaxy and void catalogues in the Big MultiDark $N$-body simulation and consistently recover the fiducial growth rate to a precision of $3.4%$ using the simulation volume of $(2.5;h^{-1}mathrm{Gpc})^3$.
Euclid will survey galaxies in a cosmological volume of unprecedented size, providing observations of more than a billion objects distributed over a third of the full sky. Approximately 20 million of these galaxies will have spectroscopy available, allowing us to map the three-dimensional large-scale structure of the Universe in great detail. This paper investigates prospects for the detection of cosmic voids therein, and the unique benefit they provide for cosmology. In particular, we study the imprints of dynamic and geometric distortions of average void shapes and their constraining power on the growth of structure and cosmological distance ratios. To this end, we make use of the Flagship mock catalog, a state-of-the-art simulation of the data expected to be observed with Euclid. We arrange the data into four adjacent redshift bins, each of which contains about 11000 voids, and estimate the void-galaxy cross-correlation function in every bin. Fitting a linear-theory model to the data, we obtain constraints on $f/b$ and $D_M H$, where $f$ is the linear growth rate of density fluctuations, $b$ the galaxy bias, $D_M$ the comoving angular diameter distance, and $H$ the Hubble rate. In addition, we marginalize over two nuisance parameters included in our model to account for unknown systematic effects in the analysis. With this approach Euclid will be able to reach a relative precision of about 4% on measurements of $f/b$ and 0.5% on $D_M H$ in each redshift bin. Better modeling or calibration of the nuisance parameters may further increase this precision to 1% and 0.4%, respectively. Our results show that the exploitation of cosmic voids in Euclid will provide competitive constraints on cosmology even as a stand-alone probe. For example, the equation-of-state parameter $w$ for dark energy will be measured with a precision of about 10%, consistent with earlier more approximate forecasts.
We compute a general expression for the contribution of vector perturbations to the redshift-space distortion of galaxy surveys. We show that they contribute to the same multipoles of the correlation function as scalar perturbations and should thus in principle be taken into account in data analysis. We derive constraints for next-generation surveys on the amplitude of two sources of vector perturbations, namely non-linear clustering and topological defects. While topological defects leave a very small imprint on redshift-space distortions, we show that the multipoles of the correlation function are sensitive to vorticity induced by non-linear clustering. Therefore future redshift surveys such as DESI or the SKA should be capable of measuring such vector modes, especially with the hexadecapole which appears to be the most sensitive to the presence of vorticity.
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