Towards an optimal sampling of peculiar velocity surveys for Wiener Filter reconstructions
Résumé
The Wiener Filter (WF) technique enables the reconstruction of density and velocity fields from observed radial peculiar velocities. This paper aims at identifying the optimal design of peculiar velocity surveys within the WF framework. The prime goal is to test the dependence of the reconstruction quality on the distribution and nature of data points. Mock data sets, extending to 250 h^−1 Mpc, are drawn from a constrained simulation that mimics the local Universe to produce realistic mock catalogues. Reconstructed fields obtained with these mocks are compared to the reference simulation. Comparisons, including residual distributions, cell-to-cell and bulk velocities, imply that the presence of field data points is essential to properly measure the flows. The fields reconstructed from mocks that consist only of galaxy cluster data points exhibit poor-quality bulk velocities. In addition, the reconstruction quality depends strongly on the grouping of individual data points into single points to suppress virial motions in high-density regions. Conversely, the presence of a Zone of Avoidance hardly affects the reconstruction. For a given number of data points, a uniform sample does not score any better than a sample with decreasing number of data points with the distance. The best reconstructions are obtained with a grouped survey containing field galaxies: assuming no error, they differ from the simulated field by less than 100 km s^−1 up to the extreme edge of the catalogues or up to a distance of three times the mean distance of data points for non-uniform catalogues. The overall conclusions hold when errors are added.