Get started
Work

Bayesian large-scale structure inference: initial conditions and the cosmic web

Year: 2014
Type: article
Abstract: We describe an innovative statistical approach for theab initiosimultaneous analysis of the formation history and morphology of the large-scale structure of the inhomogeneous Universe. Our algorithm explores the joint posterior distribution of the many millions of parameters involved via efficient Hamiltonian Markov Chain Monte Carlo sampling. We describe its application to the Sloan Digital Sky Survey data release 7 and an additional non-linear filtering step. We illustrate the use of our findings for cosmic web analysis: identification of structures via tidal shear analysis and inference of dark matter voids.
DOI: 10.1017/s1743921314011120
Institutions: Centre National de la Recherche Scientifique, École Polytechnique, ParisTech, Institut d'Astrophysique de Paris, Sorbonne Université, Université Pierre-et-Marie-Curie, Institut Lagrange de Paris, University of Illinois Urbana-Champaign
Language: English
Open Access status: bronze
Awards: G2647246237, G4325526357, G4820567559, G4866930200, G5615659713, G6578895096, Sorbonne Universités à Paris pour l'Enseignement et la Recherche, G7240651115, G7376787254, G7512206404
OpenAlex ID: W2149991505
FWCI:
Cites: 16
Cited by: 2
Related to: 10