Papers › NECOLA: Towards a Universal Field-level Cosmological Emulator
NECOLA: Towards a Universal Field-level Cosmological Emulator
Neerav Kaushal, Francisco Villaescusa-Navarro, Elena Giusarma, Yin Li, Conner Hawry, Mauricio Reyes
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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 sub-percent 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, Ω_b, h, nₛ, σ₈, 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 towards 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.
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