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

TL;DR

This paper introduces a Bayesian statistical method for analyzing the formation and structure of the Universe's large-scale structure, applying advanced sampling techniques to survey data to identify cosmic web features.

Contribution

It presents a novel Bayesian approach using Hamiltonian MCMC for large-scale structure inference, enabling simultaneous analysis of formation history and morphology.

Findings

Successful application to SDSS data release 7

Identification of cosmic web structures via tidal shear

Inference of dark matter voids

Abstract

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.