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- W4226504895 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 sub-percent accuracy down to $ksimeq1~h{rm Mpc}^{-1}$. 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 $Omega_{rm m}$, $Omega_{rm b}$, $h$, $n_s$, $sigma_8$, $w$, and $M_nu$ with very high accuracy: the power spectrum and the cross-correlation coefficients are within $simeq1%$ down to $k=1~h{rm Mpc}^{-1}$. 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 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." @default.
- W4226504895 created "2022-05-05" @default.
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- W4226504895 date "2021-11-03" @default.
- W4226504895 modified "2023-09-29" @default.
- W4226504895 title "NECOLA: Towards a Universal Field-level Cosmological Emulator" @default.
- W4226504895 hasPublicationYear "2021" @default.
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