Abstract
Abstract
We study the use of U-Nets in reconstructing the linear dark matter density field and its
consequences for constraining cosmological parameters, in particular primordial
non-Gaussianity. Our network is able to reconstruct the initial conditions of redshift z = 0
density fields from N-body simulations with 90% accuracy out to k ≤ 0.4 h/Mpc, competitive
with state-of-the-art reconstruction algorithms at a fraction of the computational cost. We study
the information content of the reconstructed z = 0 density field with a Fisher analysis using the QUIJOTE simulation suite, including non-Gaussian initial conditions. Combining the pre-
and post-reconstructed power spectrum and bispectrum data up to k
max = 0.52 h/Mpc, we
find significant improvements in all parameters. Most notably, we find a factor 3.65 (local),
3.54 (equilateral), and 2.90 (orthogonal) improvement on the marginalized errors of f
NL as compared to only using the pre-reconstructed data. We show that these improvements can
be attributed to a combination of reduced data covariance and parameter degeneracy. The results
constitute an important step towards a more optimal inference of primordial non-Gaussianity from
non-linear scales.
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