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We have studied the galaxy-group cross-correlations in redshift space for theGalaxy And Mass Assembly (GAMA) Survey. We use a set of mock GAMA galaxy andgroup catalogues to develop and test a novel 'halo streaming' model forredshift-space distortions. This treats 2-halo correlations via the streamingmodel, plus an empirical 1-halo term derived from the mocks, allowing accuratemodelling into the nonlinear regime. In order to probe the robustness of thegrowth rate inferred from redshift-space distortions, we divide galaxies bycolour, and divide groups according to their total stellar mass, calibrated tototal mass via gravitational lensing. We fit our model to correlation data, toobtain estimates of the perturbation growth rate, $f\sigma_8$, validatingparameter errors via the dispersion between different mock realizations. Inboth mocks and real data, we demonstrate that the results are closelyconsistent between different subsets of the group and galaxy populations,considering the use of correlation data down to some minimum projected radius,$r_{\rm min}$. For the mock data, we can use the halo streaming model to below$r_{\rm min} = 5h^{-1}$ Mpc, finding that all subsets yield growth rates withinabout 3% of each other, and consistent with the true value. For the actual GAMAdata, the results are limited by cosmic variance: $f\sigma_8=0.29\pm 0.10$ atan effective redshift of 0.20; but there is every reason to expect that thismethod will yield precise constraints from larger datasets of the same type,such as the DESI bright galaxy survey.

Galaxy and Mass Assembly (GAMA): Probing galaxy-group correlations in redshift space with the halo streaming model