Bayesian inference of the initial conditions from large-scale structure surveys

TL;DR

This paper presents a Bayesian method to infer the initial conditions of the universe's large-scale structure from survey data, enabling detailed analysis of the cosmic web and early universe physics.

Contribution

It introduces a fully Bayesian statistical framework for reconstructing primordial conditions from large-scale structure surveys, applied to SDSS data.

Findings

Detailed characterization of the cosmic web's tidal environment.

Reconstruction of initial density fields consistent with observed galaxy distributions.

Insights into early universe physics from large-scale structure analysis.

Abstract

Analysis of three-dimensional cosmological surveys has the potential to answer outstanding questions on the initial conditions from which structure appeared, and therefore on the very high energy physics at play in the early Universe. We report on recently proposed statistical data analysis methods designed to study the primordial large-scale structure via physical inference of the initial conditions in a fully Bayesian framework, and applications to the Sloan Digital Sky Survey data release 7. We illustrate how this approach led to a detailed characterization of the dynamic cosmic web underlying the observed galaxy distribution, based on the tidal environment.