Emulating Hydrodynamics from Dark Matter 3D Density Fields

In this work, we propose an efficient methodology for emulating hydrodynamic gas structures in Dark Matter Nbody simulations. We perform fast emulations of 3D gas density cubes from the Dark Matter counterparts, comparing different machine learning approaches, namely Principal Component Analysis + Random Forest ( $\text{PCA}+\text{RF}$ ) and a Convolutional Neural Network (CNN). The method provides a gain of 5 orders of magnitude in CPU run times compared to running the full hydrodynamic N-body simulation in the same computer system. Using $\text{PCA}+\text{RF}$ , the method achieves 98% accuracy in the matter power spectrum compared to the full hydrodynamic simulation throughout most of the k domain $(k < 1.0\ Mpc\ h^{-1})$ . Finally, CNNs offer increased accuracy (∼ 99%), showing a striking improvement in performance at the extremes of the k domain.