Stochastic Super-resolution of Cosmological Simulations with Denoising Diffusion Models
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arXiv
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| Natura: | Preprint |
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2023
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| author | Schanz, Andreas List, Florian Hahn, Oliver |
| author_facet | Schanz, Andreas List, Florian Hahn, Oliver |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2310_06929 |
| institution | arXiv |
| publishDate | 2023 |
| record_format | arxiv |
| spellingShingle | Stochastic Super-resolution of Cosmological Simulations with Denoising Diffusion Models Schanz, Andreas List, Florian Hahn, Oliver Cosmology and Nongalactic Astrophysics Instrumentation and Methods for Astrophysics Machine Learning 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. |
| title | Stochastic Super-resolution of Cosmological Simulations with Denoising Diffusion Models |
| topic | Cosmology and Nongalactic Astrophysics Instrumentation and Methods for Astrophysics Machine Learning |
| url | https://arxiv.org/abs/2310.06929 |