Stochastic Super-resolution of Cosmological Simulations with Denoising Diffusion Models

Autor(en)

Andreas Schanz, Florian List, Oliver Hahn

Abstrakt

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.

Organisation(en)

Institut für Astrophysik, Institut für Mathematik

Journal

The Open Journal of Astrophysics

Anzahl der Seiten

9

ISSN

2565-6120

Publikationsdatum

10-2023

Peer-reviewed

Ja

ÖFOS 2012

103004 Astrophysik, 102009 Computersimulation, 103044 Kosmologie, 103043 Computational Physics

Schlagwörter

Link zum Portal

https://ucrisportal.univie.ac.at/de/publications/fafbc627-9ea8-4cb6-977b-a87edcfad7b7