Kavli Affiliate: David N. Spergel
| First 5 Authors: Drew Jamieson, Yin Li, Francisco Villaescusa-Navarro, Shirley Ho, David N. Spergel
| Summary:
We present a field-level emulator for large-scale structure, capturing the
cosmology dependence and the time evolution of cosmic structure formation. The
emulator maps linear displacement fields to their corresponding nonlinear
displacements from N-body simulations at specific redshifts. Designed as a
neural network, the emulator incorporates style parameters that encode
dependencies on $Omega_{rm m}$ and the linear growth factor $D(z)$ at
redshift $z$. We train our model on the six-dimensional N-body phase space,
predicting particle velocities as the time derivative of the model’s
displacement outputs. This innovation results in significant improvements in
training efficiency and model accuracy. Tested on diverse cosmologies and
redshifts not seen during training, the emulator achieves percent-level
accuracy on scales of $ksim~1~{rm Mpc}^{-1}~h$ at $z=0$, with improved
performance at higher redshifts. We compare predicted structure formation
histories with N-body simulations via merger trees, finding consistent merger
event sequences and statistical properties.
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