2007/07/31 by G. Lavaux, R. Mohayaee, S. Colombi +3 · 1 citation
Physics and Astronomy · #Bayesian probability #COSMIC cancer database #Context (archaeology) #Cosmology and Gravitation Theories #Distortion (music) #Galaxies: Formation, Evolution, Phenomena #Galaxy #Measure (data warehouse) #Peculiar velocity #Redshift #astro-ph
paper · pdf · doi:10.1111/j.1365-2966.2007.12539.x
29 pages, 21 figures, 6 tables, Accepted by MNRAS on 2007 October 2. Received 2007 September 30; in original form 2007 July 24
arxiv created 2007/10/12 · openalex publication_date 2008/01/10 · arxiv updated 2009/12/01 · openalex created_date 2016/06/24 · openalex updated_date 2026/08/05
Lagrangian reconstruction of large-scale peculiar velocity fields can be strongly affected by observational biases. We develop a thorough analysis of these systematic effects by relying on specially selected mock catalogues. For the purpose of this paper, we use the Monge–Ampère–Kantorovitch (MAK) reconstruction method, although any other Lagrangian reconstruction method should be sensitive to the same problems. We extensively study the uncertainty in the mass-to-light assignment due to incompleteness (missing luminous mass tracers), and the poorly determined relation between mass and luminosity. The impact of redshift distortion corrections is analysed in the context of MAK and we check the importance of edge and finite-volume effects on the reconstructed velocities. Using three mock catalogues with different average densities, we also study the effect of cosmic variance. In particular, one of them presents the same global features as found in observational catalogues that extend to 80 h−1Mpc scales. We give recipes, checked using the aforementioned mock catalogues, to handle these particular observational effects, after having introduced them into the mock catalogues so as to quantitatively mimic the most densely sampled currently available galaxy catalogue of the nearby Universe. Once biases have been taken care of, the typical resulting error in reconstructed velocities is typically about a quarter of the overall velocity dispersion, and without significant bias. We finally model our reconstruction errors to propose an improved Bayesian approach to measure Ωm in an unbiased way by comparing the reconstructed velocities to the measured ones in distance space, even though they may be plagued by large errors. We show that, in the context of observational data, it is possible to build a nearly unbiased estimator of Ωm using MAK reconstruction.