AI-assisted super-resolution cosmological simulations

TL;DR

This paper introduces a deep learning approach to enhance low-resolution cosmological simulations, producing high-resolution realizations that accurately reproduce key statistical properties, enabling rapid large-scale galaxy formation modeling.

Contribution

The authors develop a neural network-based super-resolution method that generates high-resolution cosmological simulations from low-resolution data, requiring minimal training data and enabling large-scale rapid simulation generation.

Findings

Achieved percent-level accuracy in matter power spectrum reproduction.

Reproduced halo mass function within 10% down to $10^{11} M_igodot$.

Successfully generated high-resolution simulations in a box 1000 times larger than training data.

Abstract

Cosmological simulations of galaxy formation are limited by finite computational resources. We draw from the ongoing rapid advances in Artificial Intelligence (specifically Deep Learning) to address this problem. Neural networks have been developed to learn from high-resolution (HR) image data, and then make accurate super-resolution (SR) versions of different low-resolution (LR) images. We apply such techniques to LR cosmological N-body simulations, generating SR versions. Specifically, we are able to enhance the simulation resolution by generating 512 times more particles and predicting their displacements from the initial positions. Therefore our results can be viewed as new simulation realizations themselves rather than projections, e.g., to their density fields. Furthermore, the generation process is stochastic, enabling us to sample the small-scale modes conditioning on the large-scale environment. Our model learns from only 16 pairs of small-volume LR-HR simulations, and is then able to generate SR simulations that successfully reproduce the HR matter power spectrum to percent level up to $16\,h^{-1}\mathrm{Mpc}$, and the HR halo mass function to within $10 \%$ down to $10^{11} \, M_\odot$. We successfully deploy the model in a box 1000 times larger than the training simulation box, showing that high-resolution mock surveys can be generated rapidly. We conclude that AI assistance has the potential to revolutionize modeling of small-scale galaxy formation physics in large cosmological volumes.