Fast, high-fidelity Lyman $\alpha$ forests with convolutional neural networks

• URL: http://arxiv.org/abs/2106.12662v1
• Date: Wed, 23 Jun 2021 21:41:47 GMT
• Title: Fast, high-fidelity Lyman $\alpha$ forests with convolutional neural networks
• Authors: Peter Harrington, Mustafa Mustafa, Max Dornfest, Benjamin Horowitz, Zarija Luki\'c
• Abstract summary: We train a convolutional neural network to reconstruct the baryon hydrodynamic variables on scales relevant to the Lyman-$alpha$ (Ly$alpha$) forest. 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.
• Score: 1.0499611180329804
• License: http://creativecommons.org/licenses/by/4.0/
• 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.

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