Stochastic Super-resolution of Cosmological Simulations with Denoising Diffusion Models

• URL: http://arxiv.org/abs/2310.06929v2
• Date: Tue, 12 Nov 2024 08:24:28 GMT
• Title: Stochastic Super-resolution of Cosmological Simulations with Denoising Diffusion Models
• Authors: Andreas Schanz, Florian List, Oliver Hahn,
• Abstract summary: We introduce denoising diffusion models as a powerful generative model for super-resolving cosmic large-scale structure predictions. 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.
• Score: 3.2771631221674333
• License:
• Abstract: 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.

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