Stochastic Super-resolution of Cosmological Simulations with Denoising
  Diffusion Models
Andreas Schanz Florian List Oliver Hahn
In recent years, deep learning models have been successfully employed for
augmenting low-resolution cosmological simulations with small-scale
information, a task known as "super-resolution". So far, these cosmological
super-resolution models have relied on generative adversarial networks (GANs),
which can achieve highly realistic results, but suffer from various
shortcomings (e.g. low sample diversity). We introduce denoising diffusion
models as a powerful generative model for super-resolving cosmic large-scale
structure predictions (as a first proof-of-concept in two dimensions). To
obtain accurate results down to small scales, we develop a new "filter-boosted"
training approach that redistributes the importance of different scales in the
pixel-wise training objective. We demonstrate that our model not only produces
convincing super-resolution images and power spectra consistent at the percent
level, but is also able to reproduce the diversity of small-scale features
consistent with a given low-resolution simulation. This enables uncertainty
quantification for the generated small-scale features, which is critical for
the usefulness of such super-resolution models as a viable surrogate model for
cosmic structure formation.