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Joint growth rate measurements from redshift-space distortions and peculiar velocities in the 6dF Galaxy Survey
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abstract
We present a new model for the cross-covariance between galaxy redshift-space distortions and peculiar velocities. We combine this with the auto-covariance models of both probes in a fully self-consistent, maximum-likelihood method, allowing us to extract enhanced cosmological parameter constraints. When applying our method to the 6-degree Field Galaxy Survey (6dFGS), our constraint on the growth rate of structure is $f\sigma_8 = 0.384 \pm 0.052 \rm{(stat)} \pm 0.061 \rm{(sys)}$ and our constraint for the redshift-space distortion parameter is $\beta = 0.289^{+0.044}_{-0.043} \rm{(stat)} \pm 0.049 \rm{(sys)}$. We find that the statistical uncertainty for the growth rate of structure is reduced by 64% when using the complete covariance model compared to the redshift-space distortion auto-covariance model and 50% when compared to using the peculiar velocity auto-covariance model. Our constraints are consistent with those from the literature on combining multiple tracers of large-scale structure, as well as those from other 6dFGS analyses. Our measurement is also consistent with the standard cosmological model.
Forward citations
Cited by 2 Pith papers
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Anomaly detection with spiking neural networks for LHC physics
Claims spiking neural network autoencoders are competitive with conventional autoencoders for LHC anomaly detection across all signal models tested.
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Cosmology with Peculiar Velocity Surveys
This is a review of the peculiar velocity field in cosmology, summarizing methods, surveys, and forecasts for growth-rate constraints.
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