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

Published online by Cambridge University Press:  01 July 2015

Florent Leclercq  and
Benjamin Wandelt
Florent Leclercq
Affiliation:
Institut d'Astrophysique de Paris (IAP), UMR 7095, CNRS - UPMC Université Paris 6, 98bis boulevard Arago, F-75014 Paris, France Institut Lagrange de Paris (ILP), Sorbonne Universités, 98bis boulevard Arago, F-75014 Paris, France École polytechnique ParisTech, Route de Saclay, F-91128 Palaiseau, France
Benjamin Wandelt
Affiliation:
Institut d'Astrophysique de Paris (IAP), UMR 7095, CNRS - UPMC Université Paris 6, 98bis boulevard Arago, F-75014 Paris, France Institut Lagrange de Paris (ILP), Sorbonne Universités, 98bis boulevard Arago, F-75014 Paris, France Departments of Physics and Astronomy, University of Illinois at Urbana-Champaign, Urbana, IL 61801 emails: florent.leclercq@polytechnique.org, wandelt@iap.fr
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Abstract

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We describe an innovative statistical approach for the ab initio simultaneous 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.

Keywords

large-scale structure of universemethods: statistical

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