Oral comunication
M. J. Conceição, A. Krone-Martins, A. d. Silva
Abstract
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.
2024 IEEE 20th International Conference on e-Science
Osaka, Japan
2024 September
