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Journal Article

Inferring high-redshift large-scale structure dynamics from the Lyman-α forest


Porqueres,  Natalia
Physical Cosmology, MPI for Astrophysics, Max Planck Society;


Enßlin,  Torsten
Computational Structure Formation, MPI for Astrophysics, Max Planck Society;

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Porqueres, N., Jasche, J., Lavaux, G., & Enßlin, T. (2019). Inferring high-redshift large-scale structure dynamics from the Lyman-α forest. Astronomy and Astrophysics, 630: A151. doi:10.1051/0004-6361/201936245.

Cite as: http://hdl.handle.net/21.11116/0000-0006-6FBC-B
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 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.