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Field-Based Physical Inference From Peculiar Velocity Tracers by Prideaux-Ghee, James; Leclercq, Florent; Lavaux, Guilhem; Heavens, Alan; Jasche, Jens is a scholarly article available to read on EtoBox.

What is Field-Based Physical Inference From Peculiar Velocity Tracers about?

We present a Bayesian hierarchical modelling approach to reconstruct the initial cosmic matter density field constrained by peculiar velocity observations. As our approach features a model for the gravitational evolution of dark matter to connect the initial conditions to late-time observations, it reconstructs the final density and velocity fields as natural byproducts. We implement this field-based physical inference approach by adapting the Bayesian Origin Reconstruction from Galaxies (BORG) algorithm, which explores the high-dimensional posterior through the use of Hamiltonian Monte Carlo sampling. We test the self-consistency of the method using random sets of mock tracers, and assess its accuracy in a more complex scenario where peculiar velocity tracers are non-linearly evolved mock haloes. We find that our framework self-consistently infers the initial conditions, density and velocity fields, and shows some robustness to model mis-specification. As compared to the state-of-the-art approach of constrained Gaussian random fields/Wiener filtering, our method produces more accurate final density and velocity field reconstructions. It also allows us to constrain the initial cond

Author

Prideaux-Ghee, James; Leclercq, Florent; Lavaux, Guilhem; Heavens, Alan; Jasche, Jens

Published

2022

Language

EN