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

Abstract

We address the problem of inferring the three-dimensional matter distribution from a sparse set of one-dimensional quasar absorption spectra of the Lyman-α forest. Using a Bayesian forward modelling approach, we focus on extending the dynamical model to a fully self-consistent hierarchical field-level prediction of redshift-space quasar absorption sightlines. Our field-level approach rests on a recently developed semiclassical analogue to Lagrangian perturbation theory (LPT), which improves over noise problems and interpolation requirements of LPT. It furthermore allows for a manifestly conservative mapping of the optical depth to redshift space. In addition, this new dynamical model naturally introduces a coarse-graining scale, which we exploited to accelerate the Markov chain Monte-Carlo (MCMC) sampler using simulated annealing. By gradually reducing the effective temperature of the forward model, we were able to allow it to first converge on large spatial scales before the sampler became sensitive to the increasingly larger space of smaller scales. We demonstrate the advantages, in terms of speed and noise properties, of this field-level approach over using LPT as a forward model, and, using mock data, we validated its performance to reconstruct three-dimensional primordial perturbations and matter distribution from sparse quasar sightlines.

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.…”

“…Recent extensions to the Borg framework have led to substantial improvements in cosmological parameter inference (Kodi Ramanah et al 2019;Elsner et al 2020;Schmidt et al 2020), Lyman-α (Porqueres et al 2019a(Porqueres et al , 2020 and cosmic shear (Porqueres et al 2021) reconstructions, with the field-level treatment transcending the capabilities of conventional cosmological analyses. Novel sophisticated additions to the forward model include a robust likelihood to account for unknown foreground contamination and systematics (Porqueres et al 2019b) and machine learning-based galaxy bias models (Charnock et al 2020).…”

“…Therefore, the inference carried out in previous studies by Jasche & Wandelt (2013), , transcends the limitations of contemporary analyses based on statistical summaries in order to yield detailed characterizations of individual 3D structures. Presently, apart from our framework, several research groups are developing the technology to perform full 3D characterizations of the cosmic structures probed by galaxy surveys (Wang et al 2014;Modi et al 2018;Kitaura et al 2021;Porqueres et al 2020Porqueres et al , 2021.…”

Searching for the shadows of giants – II. The effect of local ionization on the Ly α absorption signatures of protoclusters at redshift z ∼ 2.4

“…An alternative technique for protocluster identification that also makes use of intergalactic Ly α absorption is IGM or Ly α forest tomography (Pichon et al 2001;Caucci et al 2008;Lee et al 2014;Stark et al 2015;Horowitz et al 2019;Porqueres et al 2020;Li, Horowitz & Cai 2021). If a sufficient number of individual Ly α forest sight-lines sample a given volume, a three-dimensional map of the Ly α transmission from the IGM may be reconstructed.…”

“…The goal of this work is not to test the efficacy of tomographic reconstruction techniques; this is already discussed in the literature in some detail (see e.g. Stark et al 2015;Horowitz et al 2019;Porqueres et al 2020;Li et al 2021). In this work, rather than using a full forward model, we instead create idealised, noiseless Ly α transmission maps by degrading our simulations to match the resolution of the tomographically reconstructed observations from Lee et al (2018) and Newman et al (2020).…”