Title

NECOLA: Toward a Universal Field-level Cosmological Emulator

Document Type

Article

Publication Date

5-10-2022

Department

Department of Physics

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.

Publisher's Statement

© 2022. The Author(s). Published by the American Astronomical Society. Publisher’s version of record: https://doi.org/10.3847/1538-4357/ac5c4a

Publication Title

Astrophysical Journal

Creative Commons License

Creative Commons Attribution 4.0 International License
This work is licensed under a Creative Commons Attribution 4.0 International License.

Version

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