Fast, high-fidelity Lyman $α$ forests with convolutional neural networks
This enables substantial computational savings for studying Lyman-α forests in cosmology, though it is incremental as it builds on existing simulation methods.
The paper tackles the computational expense of full-physics cosmological simulations by training a convolutional neural network to reconstruct baryon hydrodynamic variables from cheaper N-body simulations, achieving rapid estimation at ~20kpc resolution and capturing Lyman-α forest statistics with much greater accuracy than existing approximations.
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-$α$ (Ly$α$) forest, using data from Nyx simulations. We show that our method enables rapid estimation of these fields at a resolution of $\sim$20kpc, and captures the statistics of the Ly$α$ 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$α$ flux directly, it is not limited to a particular choice of ionizing background or mean transmitted flux.