Inferring high-redshift large-scale structure dynamics from the Lyman-αforest

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 at z > 2 from observations of the Lyman-α forest. 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 (IGM) 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.

A hierarchical field-level inference approach to reconstruction from sparse Lyman-α forest data

“…We note that this plot is not comparable to Fig. 11 in Porqueres et al (2019a) since the signal-to-noise of the mock data, the distribution of lines of sight and the width of the slice are different. A more detailed analysis of the impact of the mean line of sight separation on the accuracy of the results will be included in future work.…”

“…In this section, we provide a summary of the algorithm. A more detailed description of the BORG framework can be found in Jasche & Wandelt (2013), Jasche et al (2015), Lavaux & Jasche (2016), and, more specifically, the extension of BORG to the Lyα forest analysis is described in Porqueres et al (2019a).…”

“…These approaches fail to reproduce the high-order statistics of the filamentary matter distribution. To reproduce the high-order statistics, Porqueres et al (2019a) and Horowitz et al (2019) recently used a large-scale optimisation approach to fit a gravitational structure growth model to simulated Ly-α data.…”

“…In this work, we employed for the first time PPT in a Bayesian forward model to infer the dark matter density from the Ly-α forest. In particular, we use the extension of the BORG framework (Jasche & Kitaura 2010;Jasche & Wandelt 2013;Lavaux et al 2019) to the analysis of the Ly-α forest presented in Porqueres et al (2019a), combined with a redshift-space optical depth field obtained from our extension of PPT presented here.…”

Optimal machine-driven acquisition of future cosmological data

“…Our results demonstrate the value of going beyond state-of-the-art analyses of the cosmic large-scale structures that are limited to summary statistics and ignore this richness of the 3D cosmic structures. Even though we consider one particular observable, namely galaxy counts, in this study, our proposed framework can be seamlessly applied to other tracers, such as Lyman-α forest (Porqueres et al 2019a(Porqueres et al , 2020, which would bear complementary information.…”