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Cited by 70Galaxies: Formation, Evolution, PhenomenaCosmology and Gravitation TheoriesModel Reduction and Neural Networks

Super-resolution emulator of cosmological simulations using deep physical models

Open access
Kodi Ramanah, Doogesh, Charnock, Tom, Villaescusa-Navarro, Francisco, Wandelt, Benjamin D·2020·Monthly Notices of the Royal Astronomical Society
DOI 10.1093/mnras/staa1428
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Abstract

We present an extension of our recently developed Wasserstein optimized model to emulate accurate high-resolution (HR) features from computationally cheaper low-resolution (LR) cosmological simulations. Our deep physical modelling technique relies on restricted neural networks to perform a mapping of the distribution of the LR cosmic density field to the space of the HR small-scale structures. We constrain our network using a single triplet of HR initial conditions and the corresponding LR and HR evolved dark matter simulations from the quijote suite of simulations. We exploit the information content of the HR initial conditions as a well-constructed prior distribution from which the network emulates the small-scale structures. Once fitted, our physical model yields emulated HR simulations at low computational cost, while also providing some insights about how the large-scale modes affect the small-scale structure in real space.

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7 of 7 references verified0 citation errors · 0 not checkable

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  • Ramanah et al. (2020) were able to upsample by factor 2 the resolution of the final density field, reproducing power spectrum, density contrast PDF, and bispectrum.

    “With this approach, the authors were able to upsample by a factor of 2 the resolution of the final density field, while reproducing faithfully a number of field properties including the power spectrum, the density contrast probability density function, and the bispectrum.”

    From The Dawes Review 10: The impact of deep learning for the analysis of galaxy surveys (2023) · cites this paper as Ramanah et al. (2020)

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Cites

Flagged first, then by citations

Research works printed but not linked: 28

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Cited by

Sorted by citations

Authors
Kodi Ramanah, D., Charnock, T., Villaescusa-Navarro, F., & Wandelt, B. D.
Affiliations
  • DARK, Niels Bohr Institute, University of Copenhagen, Lyngbyvej 2, DK-2100 Copenhagen, Denmark
  • Centre National de la Recherche Scientifique (FR)
  • Princeton University (US)
ORCID
Venue
Monthly Notices of the Royal Astronomical Society
Publisher
Oxford University Press (OUP)
Published
2020-05-23
Volume
495
Issue
4
Pages
4227-4236
ISSN
  • 0035-8711 (ISSN-L)
  • 1365-2966
DOI
10.1093/mnras/staa1428
OpenAlex
W2998904498
Cited by
70
References
7
Citations by year
2026: 4 · 2025: 9 · 2024: 11 · 2023: 10 · 2022: 10 · 2021: 21 · 2020: 5
FWCI
5.13
Type
journal article
Language
en
Open access
Licence
Locations
  • Monthly Notices of the Royal Astronomical Society (publishedVersion)
  • Research at the University of Copenhagen (University of Copenhagen) (publishedVersion, cc-by-nc)
  • arXiv (Cornell University) (submittedVersion)
  • HAL (submittedVersion, other-oa)
  • arXiv (Cornell University)
Funders
  • ANR (ANR-11-IDEX-0004-02, ANR-16-CE23-0002)
  • Carlsberg Foundation (CF15-0384)
  • Simons Foundation
Topics
  • Physical Sciences › Physics and Astronomy › Astronomy and Astrophysics › Galaxies: Formation, Evolution, Phenomena
  • Physical Sciences › Physics and Astronomy › Astronomy and Astrophysics › Cosmology and Gravitation Theories
  • Physical Sciences › Physics and Astronomy › Statistical and Nonlinear Physics › Model Reduction and Neural Networks
Keywords
cosmological simulations, artificial neural networks, small-scale structure, dark matter halos, cosmic density field, initial conditions, large-scale structure, computational complexity reduction

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