Closing the resolution gap in Lyman alpha simulations with deep learning

In recent years, super-resolution and related approaches powered by deep neural networks have emerged as a compelling option to accelerate computationally expensive cosmological simulations, which require modeling complex multi-physics systems in large spatial volumes. However, training such models in a physically consistent way is not always feasible or well-defined, as the data volume output by a super-resolution model may be too large, and the spatiotemporal dynamics of the simulation as well as the statistics of key observables like Lyman alpha flux are very sensitive to changes in resolution. In this work we address both challenges simultaneously, training neural networks to synthesize \Lya{} and other hydrodynamic fields with correct statistics on the relevant length scales but represented on the coarse grid of the input simulations. Effectively, our method is capable of 8x super-resolving a coarse simulation in-place without increasing memory footprint, using just a single pair of simulations for training. With chunked inference, we are able to apply the model to simulations of arbitrary size after training, and demonstrate this capability on a very large volume simulation spanning 600 Mpc/$h$.