Predicting Gas in 3D Dark Matter N-body Simulations with Convolutional Neural Networks

We propose a fast and accurate methodology for prediction of hydrodynamic gas distributions from Dark Matter N-body simulations. We perform fast emulations of 3D gas density cubes from the Dark Matter counterparts, comparing different Convolutional Neural Network architectures. Our method achieves an accuracy above 95% for the pixel density contrast distributions within the middle regions of the density contrast domain, and 98% accuracy in the matter power spectrum throughout the entire k domain. Moreover, our method provides a gain of 4 orders of magnitude in CPU run times compared to running the full hydrodynamic N-body simulation on a slightly newer computer system. Most importantly, our methodology provides a scalable and generalizable approach to the problem of N-body emulation, with the potential to be applied to simulations of arbitrary sizes and various scalar quantities.