NECOLA: Toward a Universal Field-level Cosmological Emulator
Neerav Kaushal, Francisco Villaescusa-Navarro, Elena Giusarma, Yin Li, Conner Hawry, Mauricio Reyes
Abstract
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 h Mpc −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 Ω m , Ω b , 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 h Mpc −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.