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Nonlinear reconstruction
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abstract
We present a direct approach to nonparametrically reconstruct the linear density field from an observed nonlinear map. We solve for the unique displacement potential consistent with the nonlinear density and positive definite coordinate transformation using a multigrid algorithm. We show that we recover the linear initial conditions up to the nonlinear scale ($r_{\delta_r\delta_L}>0.5$ for $k\lesssim1\ h/\mathrm{Mpc}$) with minimal computational cost. This reconstruction approach generalizes the linear displacement theory to fully nonlinear fields, potentially substantially expanding the baryon acoustic oscillations and redshift space distortions information content of dense large scale structure surveys, including for example SDSS main sample and 21cm intensity mapping initiatives.
Forward citations
Cited by 2 Pith papers
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Standard Reconstruction Shifts the Optimal Input Scale for CNN-Based Density-Field Reconstruction
Applying standard reconstruction before a CNN shifts the optimal input cube for z=10 density reconstruction from ~150-200 h^-1 Mpc to ~38-114 h^-1 Mpc, and a single post-reconstruction CNN beats dual-scale CNN inputs.
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Restoring Missing Modes of 21cm Intensity Mapping with Deep Learning: Impact on BAO Reconstruction
A U-Net restores foreground-removed Fourier modes in simulated 21cm intensity maps, preserves BAO reconstruction performance, and transfers from coarse to fine resolutions.
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