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Physics and Astronomy


We have studied the galaxy-group cross-correlations in redshift space for the Galaxy And Mass Assembly (GAMA) Survey. We use a set of mock GAMA galaxy and group catalogues to develop and test a novel ‘halo streaming’ model for redshift-space distortions. This treats 2-halo correlations via the streaming model, plus an empirical 1-halo term derived from the mocks, allowing accurate modelling into the non-linear regime. In order to probe the robustness of the growth rate inferred from redshift-space distortions, we divide galaxies by colour, and divide groups according to their total stellar mass, calibrated to total mass via gravitational lensing. We fit our model to correlation data, to obtain estimates of the perturbation growth rate, fσ8, validating parameter errors via the dispersion between different mock realizations. In both mocks and real data, we demonstrate that the results are closely consistent between different subsets of the group and galaxy populations, considering the use of correlation data down to some minimum projected radius, rmin. For the mock data, we can use the halo streaming model to below rmin = 5 h−1 Mpc, finding that all subsets yield growth rates within about 3 per cent of each other, and consistent with the true value. For the actual GAMA data, the results are limited by cosmic variance: fσ8 = 0.29 ± 0.10 at an effective redshift of 0.20; but there is every reason to expect that this method will yield precise constraints from larger data sets of the same type, such as the Dark Energy Spectroscopic Instrument (DESI) bright galaxy survey


© 2022 The Author(s) Published by Oxford University Press on behalf of Royal Astronomical Society

Original Publication Information

Qianjun Hang, John A Peacock, Shadab Alam, Yan-Chuan Cai, Katarina Kraljic, Marcel van Daalen, M Bilicki, B W Holwerda, J Loveday, Galaxy and Mass Assembly (GAMA): probing galaxy-group correlations in redshift space with the halo streaming model, Monthly Notices of the Royal Astronomical Society, Volume 517, Issue 1, November 2022, Pages 374–392.