Abstract
One of the major science goals over the coming decade is to test fundamental physics with probes of the cosmic large-scale structure out to high redshift. Here we present a fully Bayesian approach to infer the three-dimensional cosmic matter distribution and its dynamics atz > 2 from observations of the Lyman-αforest. We demonstrate that the method recovers the unbiased mass distribution and the correct matter power spectrum at all scales. Our method infers the three-dimensional density field from a set of one-dimensional spectra, interpolating the information between the lines of sight. We show that our algorithm provides unbiased mass profiles of clusters, becoming an alternative for estimating cluster masses complementary to weak lensing or X-ray observations. The algorithm employs a Hamiltonian Monte Carlo method to generate realizations of initial and evolved density fields and the three-dimensional large-scale flow, revealing the cosmic dynamics at high redshift. The method correctly handles multi-modal parameter distributions, which allow constraining the physics of the intergalactic medium with high accuracy. We performed several tests using realistic simulated quasar spectra to test and validate our method. Our results show that detailed and physically plausible inference of three-dimensional large-scale structures at high redshift has become feasible.
Highlights
In this work, we present a novel and statistically rigorous approach to extract cosmologically relevant and significant information from highredshift Lyman-α forest observations tracing the dynamic evolution of cosmic structures
Our approach builds upon the algorithm for Bayesian Origin Reconstruction from Galaxies (BORG, Jasche & Wandelt 2013; Jasche & Lavaux 2019), which employs physical models of structure formation and sophisticated Markov chain Monte Carlo (MCMC) techniques to optimally extract large-scale structure information from data and quantify corresponding uncertainties. To make such inferences feasible, we developed a likelihood based on the fluctuating Gunn-Peterson approximation (FGPA, Gunn & Peterson 1965) and jointly constrained the astrophysical properties of the intergalactic medium (IGM)
In fitting dynamical structure growth models to data, the task of inferring non-linear matter density fields turns into a statistical initial conditions problem aiming at inferring the spatial distribution of primordial matter fluctuations from which present structures formed via gravitational structure growth
Summary
Delubac et al 2015; du Mas des Bourboux et al 2017) The resolution of these tensions may encompass systematic effects, but may be the first signs of new physics indicated by novel cosmological data of increasing quality. New observations and better control on systematic effects in data analyses are inevitable to gain new insights into the physical processes driving the evolution of the universe. For this reason, in this work, we present a novel and statistically rigorous approach to extract cosmologically relevant and significant information from highredshift Lyman-α forest observations tracing the dynamic evolution of cosmic structures
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