Field-based physical inference from peculiar velocity tracers
File(s)
Accepted version
Author(s)
Prideaux-Ghee, James
Leclercq, Florent
Lavaux, Guilhem
Heavens, Alan
Jasche, Jens
Type
Journal Article
Abstract
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 conditions by
peculiar velocity observations, complementing in this aspect previous
field-based approaches based on other cosmological observables.
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 conditions by
peculiar velocity observations, complementing in this aspect previous
field-based approaches based on other cosmological observables.
Date Issued
2023-01-01
Date Acceptance
2022-11-11
Citation
Monthly Notices of the Royal Astronomical Society, 2023, 518 (3), pp.4191-4213
ISSN
0035-8711
Publisher
Royal Astronomical Society
Start Page
4191
End Page
4213
Journal / Book Title
Monthly Notices of the Royal Astronomical Society
Volume
518
Issue
3
Copyright Statement
© 2022 The Author(s) Published by Oxford University Press on behalf of Royal Astronomical Society. his is a pre-copy-editing, author-produced version of an article accepted for publication in Monthly Notices of the Royal Astronomical Society following peer review. The definitive publisher-authenticated version is available online at: https://doi.org/10.1093/mnras/stac3346
Identifier
http://arxiv.org/abs/2204.00023v3
Subjects
astro-ph.CO
astro-ph.CO
Notes
23 pages, 15 figures. Accepted for publication in MNRAS
Publication Status
Published
Date Publish Online
2022-11-21