Testing Bayesian reconstruction methods from peculiar velocities

Aurélien Valade, Noam I Libeskind, Yehuda Hoffman, Simon Pfeifer

Submitted on 13 September 2022


Reconstructing the large scale density and velocity fields from surveys of galaxy distances, is a major challenge for cosmography. The data is very noisy and sparse. Estimated distances, and thereby peculiar velocities, are strongly affected by the Malmquist-like lognormal bias. Two algorithms have been recently introduced to perform reconstructions from such data: the Bias Gaussian correction coupled with the Wiener filter (BGc/WF) and the HAMLET implementation of the Hamiltonian Monte Carlo forward modelling. The two methods are tested here against mock catalogs that mimic the Cosmicflows-3 data. Specifically the reconstructed cosmography and moments of the velocity field (monopole, dipole) are examined. A comparison is made to the ``exact'' wiener filter as well - namely the Wiener Filter in the unrealistic case of zero observational errors. This is to understand the limits of the WF method. The following is found. In the nearby regime (d40Mpc/h) the two methods perform roughly equally well. HAMLET does slightly better in the intermediate regime (40d120Mpc/h). The main differences between the two appear in the most distant regime (d120Mpc/h), close to the edge of the data. The HAMLET outperforms the BGc/WF in terms of better and tighter correlations, yet in the distant regime the HAMLET yields a somewhat biased reconstruction. Such biases are missing from the BGc/WF reconstruction. In sum, both methods perform well and create reliable reconstructions with significant differences apparent when details are examined.


Comment: 13 pages, 12 figures

Subject: Astrophysics - Cosmology and Nongalactic Astrophysics