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The detailed nature of dark energy remains a mystery, leaving the possibility that its effects might be explained by changes to the laws of gravity on large scales. The peculiar velocities of galaxies directly trace the strength of gravity on cosmic scales and provide a means to further constrain such models. We generate constraints on different scenarios of gravitational physics by measuring peculiar velocity (PV) and galaxy clustering two-point correlations, using redshifts and distances from the 6-degree Field Galaxy Survey and the Sloan Digital Sky Survey PV samples, and fitting them against models characteristic of different cosmologies. Our best-fitting results are all found to be in statistical agreement with general relativity, in which context we measure the low-redshift growth of structure to be |$f\sigma _8 = 0.329^{+0.081}_{-0.083}$|⁠ , consistent with the prediction of the standard Lambda cold dark matter model. We also fit the modified gravity scenarios of Dvali–Gabadadze–Porrati and a Hu–Sawicki model of |$f(R)$| gravity, finding the |$2\sigma$| limit of their characteristic parameters to be |$r_{\rm c}H_0/c\gt 6.987$| and |$-\log _{10}(|f_{R0}|)\gt 4.703$|⁠ , respectively. These constraints are comparable to other literature values, though it should be noted that they are significantly affected by the prior adopted for their characteristic parameters. When applied to much larger upcoming PV surveys such as DESI, this method will place rapidly improving constraints on modified gravity models of cosmic expansion and growth. [ABSTRACT FROM AUTHOR]

Measurements of the peculiar velocities of large samples of galaxies enable new tests of the standard cosmological model, including determination of the growth rate of cosmic structure that encodes gravitational physics. With the size of such samples now approaching hundreds of thousands of galaxies, complex statistical analysis techniques and models are required to extract cosmological information. In this paper, we summarize how correlation functions between galaxy velocities, and with the surrounding large-scale structure, may be utilized to test cosmological models. We present new determinations of the analytical covariance between such correlation functions, which may be useful for cosmological likelihood analyses. The statistical model we use to determine these covariances includes the sample selection functions, observational noise, curved-sky effects, and redshift-space distortions. By comparing these covariance determinations with corresponding estimates from large suites of cosmological simulations, we demonstrate that these analytical models recover the key features of the covariance between different statistics and separations, and produce similar measurements of the growth rate of structure. [ABSTRACT FROM AUTHOR]

The local galaxy peculiar velocity field can be reconstructed from the surrounding distribution of large-scale structure and plays an important role in calibrating cosmic growth and expansion measurements. In this paper, we investigate the effect of the stochasticity of these velocity reconstructions on the statistical and systematic errors in cosmological inferences. By introducing a simple statistical model between the measured and theoretical velocities, whose terms we calibrate from linear theory, we derive the bias in the model velocity. We then use lognormal realizations to explore the potential impact of this bias when using a cosmic flow model to measure the growth rate of structure, and to sharpen expansion rate measurements from host galaxies for gravitational wave standard sirens with electromagnetic counterparts. Although our illustrative study does not contain fully realistic observational effects, we demonstrate that in some scenarios these corrections are significant and result in a measurable improvement in determinations of the Hubble constant compared to standard forecasts. [ABSTRACT FROM AUTHOR]

Galaxy peculiar velocities provide an integral source of cosmological information that can be harnessed to measure the growth rate of large-scale structure and constrain possible extensions to General Relativity. In this work, we present a method for extracting the information contained within galaxy peculiar velocities through an ensemble of direct peculiar velocity and galaxy clustering correlation statistics, including the effects of redshift space distortions, using data from the 6-degree Field Galaxy Survey. Our method compares the auto- and cross-correlation function multipoles of these observables, with respect to the local line of sight, with the predictions of cosmological models. We find that the uncertainty in our measurement is improved when combining these two sources of information in comparison to fitting to either peculiar velocity or clustering information separately. When combining velocity and density statistics in the range |$27 \lt s \lt 123 \, h^{-1}$| Mpc we obtain a value for the local growth rate of f σ8 = 0.358 ± 0.075 and for the linear redshift distortion parameter β = 0.298 ± 0.065, recovering both with 20.9 per cent and 21.8 per cent accuracy, respectively. We conclude this work by comparing our measurement with other recent local measurements of the growth rate, spanning different data sets and methodologies. We find that our results are in broad agreement with those in the literature and are fully consistent with ΛCDM cosmology. Our methods can be readily scaled to analyse upcoming large galaxy surveys and achieve accurate tests of the cosmological model. [ABSTRACT FROM AUTHOR]

We present an improved framework for estimating the growth rate of large-scale structure, using measurements of the galaxy–velocity cross-correlation in configuration space. We consider standard estimators of the velocity autocorrelation function, ψ1 and ψ2, the two-point galaxy correlation function, ξgg, and introduce a new estimator of the galaxy–velocity cross-correlation function, ψ3. By including pair counts measured from random catalogues of velocities and positions sampled from distributions characteristic of the true data, we find that the variance in the galaxy–velocity cross-correlation function is significantly reduced. Applying a covariance analysis and χ2 minimization procedure to these statistics, we determine estimates and errors for the normalized growth rate f σ8 and the parameter β = f / b , where b is the galaxy bias factor. We test this framework on mock hemisphere data sets for redshift z < 0.1 with realistic velocity noise constructed from the l-picola simulation code, and find that we are able to recover the fiducial value of f σ8 from the joint combination of ψ1 + ψ2 + ψ3 + ξgg, with 15 per cent accuracy from individual mocks. We also recover the fiducial f σ8 to within 1σ regardless of the combination of correlation statistics used. When we consider all four statistics together we find that the statistical uncertainty in our measurement of the growth rate is reduced by |$59{{\ \rm per\ cent}}$| compared to the same analysis only considering ψ2, by |$53{{\ \rm per\ cent}}$| compared to the same analysis only considering ψ1, and by |$52{{\ \rm per\ cent}}$| compared to the same analysis jointly considering ψ1 and ψ2. [ABSTRACT FROM AUTHOR]

The Galaxy And Mass Assembly Survey (GAMA) covers five fields with highly complete spectroscopic coverage (>95 per cent) to intermediate depths (r < 19.8 or i < 19.0 mag), and collectively spans 250 deg2 of equatorial or southern sky. Four of the GAMA fields (G09, G12, G15, and G23) reside in the European Southern Observatory (ESO) VST KiDS and ESO VISTA VIKING survey footprints, which combined with our GALEX, WISE , and Herschel data provide deep uniform imaging in the |$FUV/NUV/u/g/r/i/Z/Y/J/H/K_s/W1/W2/W3/W4/P100/P160/S250/S350/S500$| bands. Following the release of KiDS DR4, we describe the process by which we ingest the KiDS data into GAMA (replacing the SDSS data previously used for G09, G12, and G15), and redefine our core optical and near-infrared (NIR) catalogues to provide a complete and homogeneous data set. The source extraction and analysis is based on the new ProFound image analysis package, providing matched-segment photometry across all bands. The data are classified into stars, galaxies, artefacts, and ambiguous objects, and objects are linked to the GAMA spectroscopic target catalogue. Additionally, a new technique is employed utilizing ProFound to extract photometry in the unresolved MIR–FIR regime. The catalogues including the full FUV–FIR photometry are described and will be fully available as part of GAMA DR4. They are intended for both standalone science, selection for targeted follow-up with 4MOST, as well as an accompaniment to the upcoming and ongoing radio arrays now studying the GAMA 23h field. [ABSTRACT FROM AUTHOR]