Kavli Affiliate: David N. Spergel
| First 5 Authors: Drew Jamieson, Yin Li, Renan Alves de Oliveira, Francisco Villaescusa-Navarro, Shirley Ho
| Summary:
We build a field level emulator for cosmic structure formation that is
accurate in the nonlinear regime. Our emulator consists of two convolutional
neural networks trained to output the nonlinear displacements and velocities of
N-body simulation particles based on their linear inputs. Cosmology dependence
is encoded in the form of style parameters at each layer of the neural network,
enabling the emulator to effectively interpolate the outcomes of structure
formation between different flat $Lambda$CDM cosmologies over a wide range of
background matter densities. The neural network architecture makes the model
differentiable by construction, providing a powerful tool for fast field level
inference. We test the accuracy of our method by considering several summary
statistics, including the density power spectrum with and without redshift
space distortions, the displacement power spectrum, the momentum power
spectrum, the density bispectrum, halo abundances, and halo profiles with and
without redshift space distortions. We compare these statistics from our
emulator with the full N-body results, the COLA method, and a fiducial neural
network with no cosmological dependence. We find our emulator gives accurate
results down to scales of $k sim 1 mathrm{Mpc}^{-1}, h$, representing a
considerable improvement over both COLA and the fiducial neural network. We
also demonstrate that our emulator generalizes well to initial conditions
containing primordial non-Gaussianity, without the need for any additional
style parameters or retraining.
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