Testing the minimum variance method for estimating large-scale velocity moments

Abstract

The estimation and analysis of large-scale bulk flow moments of peculiar velocity surveys is complicated by non-spherical survey geometry, the non-uniform sampling of the matter velocity field by the survey objects and the typically large measurement errors of the measured line-of-sight velocities. Previously, we have developed an optimal `minimum variance' (MV) weighting scheme for using peculiar velocity data to estimate bulk flow moments for idealized, dense and isotropic surveys with Gaussian radial distributions, that avoids many of these complications. These moments are designed to be easy to interpret and are comparable between surveys. In this paper, we test the robustness of our MV estimators using numerical simulations. Using MV weights, we estimate the bulk flow moments for various mock catalogues extracted from the LasDamas and the Horizon Run numerical simulations and compare these estimates to the moments calculated directly from the simulation boxes. We show that the MV estimators are unbiased and negligibly affected by non-linear flows.

11 pages, 8 figures, 2 tables, updated to match version accepted by MNRAS

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Cited by in corpus (10)

• Analyzing the Large-Scale Bulk Flow using CosmicFlows4: Increasing Tension with the Standard Cosmological Model
• The 6dF Galaxy Survey: Bulk Flows on $50-70h^{-1}$ Mpc scales
• An Unbiased Estimator of Peculiar Velocity with Gaussian Distributed Errors for Precision Cosmology
• Evaluating bulk flow estimators for CosmicFlows-4 measurements
• HectoMAP and Horizon Run 4: Dense Structures and Voids in the Real and Simulated Universe
• Easily Interpretable Bulk Flows: Continuing Tension with the Standard Cosmological Model
• On methods of estimating cosmological bulk flows
• The Peculiar Velocity Correlation Function
• The Cosmic Mach Number: Comparison from Observations, Numerical Simulations and Nonlinear Predictions
• Improved Methods for Estimating Peculiar Velocity Correlation Functions Using Volume Weighting