Abstract

We train convolutional neural networks to correct the output of fast and approximate N-body simulations at the field level. Our model, Neural Enhanced COLA (NECOLA), takes as input a snapshot generated by the computationally efficient COLA code and corrects the positions of the cold dark matter particles to match the results of full N-body Quijote simulations. We quantify the accuracy of the network using several summary statistics, and find that NECOLA can reproduce the results of the full N-body simulations with subpercent accuracy down to k ≃ 1 hMpc−1. Furthermore, the model that was trained on simulations with a fixed value of the cosmological parameters is also able to correct the output of COLA simulations with different values of Ωm, Ωb, h, n s , σ 8, w, and M ν with very high accuracy: the power spectrum and the cross-correlation coefficients are within ≃1% down to k = 1 hMpc−1. Our results indicate that the correction to the power spectrum from fast/approximate simulations or field-level perturbation theory is rather universal. Our model represents a first step toward the development of a fast field-level emulator to sample not only primordial mode amplitudes and phases, but also the parameter space defined by the values of the cosmological parameters.