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arXiv · 1603.04441

Revealing the z~2.5 Cosmic Web With 3D Lyman-Alpha Forest Tomography: A Deformation Tensor Approach

Abstract

Studies of cosmological objects should take into account their positions within the cosmic web of large-scale structure. Unfortunately, the cosmic web has only been extensively mapped at low-redshifts ($z<1$), using galaxy redshifts as tracers of the underlying density field. At $z>1$, the required galaxy densities are inaccessible for the foreseeable future, but 3D reconstructions of Lyman-$α$ forest absorption in closely-separated background QSOs and star-forming galaxies already offer a detailed window into $z\sim2-3$ large-scale structure. We quantify the utility of such maps for studying the cosmic web by using realistic $z=2.5$ Ly$α$ forest simulations matched to observational properties of upcoming surveys. A deformation tensor-based analysis is used to classify voids, sheets, filaments and nodes in the flux, which is compared to those determined from the underlying dark matter field. We find an extremely good correspondence, with $70\%$ of the volume in the flux maps correctly classified relative to the dark matter web, and $99\%$ classified to within 1 eigenvalue. This compares favorably to the performance of galaxy-based classifiers with even the highest galaxy densities at low-redshift. We find that narrow survey geometries can degrade the cosmic web recovery unless the survey is $\gtrsim 60\,h^{-1}\,\mathrm{Mpc}$ or $\gtrsim 1\,\mathrm{deg}$ on the sky. We also examine halo abundances as a function of the cosmic web, and find a clear dependence as a function of flux overdensity, but little explicit dependence on the cosmic web. These methods will provide a new window on cosmological environments of galaxies at this very special time in galaxy formation, "high noon", and on overall properties of cosmological structures at this epoch.

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BibTeXRIS

Khee-Gan Lee, Martin White. 2016-11-03. Revealing the z~2.5 Cosmic Web With 3D Lyman-Alpha Forest Tomography: A Deformation Tensor Approach. https://doi.org/10.3847/0004-637x%2F831%2F2%2F181

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