http://arxiv.org/abs/2005.12928
We address the problem of inferring the three-dimensional matter distribution from a sparse set of one-dimensional quasar absorption spectra of the Lyman-$\alpha$ 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 exploit to accelerate the MCMC sampler using simulated annealing. By gradually reducing the effective temperature of the forward model, we can allow it to converge first on large spatial scales before the sampler becomes 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, validate its performance to reconstruct three-dimensional primordial perturbations and matter distribution from sparse quasar sightlines.
N. Porqueres, O. Hahn, J. Jasche, et. al.
Thu, 28 May 20
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