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• W3175280507 abstract "Full-physics cosmological simulations are powerful tools for studying the formation and evolution of structure in the universe but require extreme computational resources. Here, we train a convolutional neural network to use a cheaper N-body-only simulation to reconstruct the baryon hydrodynamic variables (density, temperature, and velocity) on scales relevant to the Lyman-$alpha$ (Ly$alpha$) forest, using data from Nyx simulations. We show that our method enables rapid estimation of these fields at a resolution of $sim$20kpc, and captures the statistics of the Ly$alpha$ forest with much greater accuracy than existing approximations. Because our model is fully-convolutional, we can train on smaller simulation boxes and deploy on much larger ones, enabling substantial computational savings. Furthermore, as our method produces an approximation for the hydrodynamic fields instead of Ly$alpha$ flux directly, it is not limited to a particular choice of ionizing background or mean transmitted flux." @default.
• W3175280507 created "2021-07-05" @default.
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• W3175280507 date "2021-06-23" @default.
• W3175280507 modified "2023-09-27" @default.
• W3175280507 title "Fast, high-fidelity Lyman α forests with convolutional neural networks." @default.
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