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TARDIS Paper I: A Constrained Reconstruction Approach to Modeling the z~2.5 Cosmic Web Probed by Lyman-alpha Forest Tomography
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abstract
Recent Lyman-$\alpha$ forest tomography measurements of the intergalactic medium (IGM) have revealed a wealth of cosmic structures at high redshift ($z\sim 2.5$). In this work, we present the Tomographic Absorption Reconstruction and Density Inference Scheme (TARDIS), a new chrono-cosmographic analysis tool for understanding the formation and evolution of these observed structures. We use maximum likelihood techniques with a fast non-linear gravitational model to reconstruct the initial density field of the observed regions. We find that TARDIS allows accurate reconstruction of smaller scale structures than standard Wiener filtering techniques. Applying this technique to mock Lyman-$\alpha$ forest data sets that simulate ongoing and future surveys such as CLAMATO, Subaru-PFS or the ELTs, we are able to infer the underlying matter density field at observed redshift and classify the cosmic web structures. We find good agreement with the underlying truth both in the characteristic eigenvalues and eigenvectors of the pseudo-deformation tensor, with the eigenvalues inferred from 30m-class telescopes correlated at $r=0.95$ relative to the truth. As an output of this method, we are able to further evolve the inferred structures to late time ($z=0$), and also track the trajectories of coeval $z=2.5$ galaxies to their $z=0$ cosmic web environments.
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DeepCHART: Mapping the 3D dark matter density field from Ly$\alpha$ forest surveys using deep learning
A 3D neural network reconstructs the dark matter density field at redshift 2.5 from simulated Lyman-alpha forest spectra, reaching voxel-wise correlation of roughly 0.77 for current surveys and 0.90 for denser future ...
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