Painting with baryons: augmenting N-body simulations with gas using deep generative models
Open accessAbstract
Running hydrodynamical simulations to produce mock data of large-scale structure and baryonic probes, such as the thermal Sunyaev–Zeldovich (tSZ) effect, at cosmological scales is computationally challenging. We propose to leverage the expressive power of deep generative models to find an effective description of the large-scale gas distribution and temperature. We train two deep generative models, a variational auto-encoder and a generative adversarial network, on pairs of matter density and pressure slices from the BAHAMAS hydrodynamical simulation. The trained models are able to successfully map matter density to the corresponding gas pressure. We then apply the trained models on 100 lines of sight from SLICS, a suite of N-body simulations optimized for weak lensing covariance estimation, to generate maps of the tSZ effect. The generated tSZ maps are found to be statistically consistent with those from BAHAMAS. We conclude by considering a specific observable, the angular cross-power spectrum between the weak lensing convergence and the tSZ effect and its variance, where we find excellent agreement between the predictions from BAHAMAS and SLICS, thus enabling the use of SLICS for tSZ covariance estimation.
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GANs led to more accurate results but were harder to train and less stable than VAEs in Tröster et al. (2019).
“but did report a tradeoff of GANs leading to more accurate results but being harder to train and less stable than VAEs.”
From The Dawes Review 10: The impact of deep learning for the analysis of galaxy surveys (2023) · cites this paper as Tröster et al. (2019)
Tröster et al. (2019) found excellent agreement with ground truth simulations at different redshifts for conditional VAEs and GANs mapping 2D dark matter density slices to 2D tSZ maps.
“They found excellent agreement with ground truth simulations, at different redshifts”
From The Dawes Review 10: The impact of deep learning for the analysis of galaxy surveys (2023) · cites this paper as Tröster et al. (2019)
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Rigo, M., Trotta, R., & Viel, M. (2025) · Monthly Notices of the Royal Astronomical Society · doi:10.1093/mnras/staf948 · Cited by 1 · see citing works
Deconfusing intensity maps with neural networksPreprint
Pfeffer, D. N., Breysse, P. C., & Stein, G. (2019) · arXiv (Cornell University) · doi:10.48550/arxiv.1905.10376 · Cited by 1 · see citing works
Dark energy survey year 3 results: Cosmology with peaks using an emulator approach
Zürcher, D., Fluri, J., Sgier, R., Kacprzak, T., Gatti, M., Doux, C., Whiteway, L., Refregier, A. R., Chang, C., Jeffrey, N., et al. (2022) · Monthly Notices of the Royal Astronomical Society · doi:10.1093/mnras/stac078
Forecasts and simulations for relativistic corrections to the Sunyaev-Zeldovich effect
Kuhn, L., Li, Z., & Coulton, W. R. (2026) · Journal of Cosmology and Astroparticle Physics · doi:10.1088/1475-7516/2026/02/059
Replicating weak-lensing summary-statistic covariances with normalizing flows
Anonymous, Thiele, L., & Liu, J. (2026) · Physical review. D/Physical review. D. · doi:10.1103/lbb5-2kbt
- Authors
- Tröster, T., Ferguson, C., Harnois-Déraps, J., & McCarthy, I. G.
- Affiliations
- Royal Observatory (GB)
- Liverpool John Moores University (GB)
- Corresponding author
- Tröster, Tilman
- Venue
- Monthly Notices of the Royal Astronomical Society: Letters
- Publisher
- Oxford University Press (OUP)
- Published
- 2019-05-29
- Volume
- 487
- Issue
- 1
- Pages
- L24-L29
- ISSN
- 1745-3925 (ISSN-L)
- 1745-3933
- OpenAlex
- W2923557884
- Cited by
- 77
- References
- 23
- Citations by year
- 2026: 3 · 2025: 11 · 2024: 7 · 2023: 10 · 2022: 15 · 2021: 15 · 2020: 9 · 2019: 7
- FWCI
- 6.14
- Type
- journal article
- Language
- en
- Open access
- green
- arxiv.org
- Licence
- academic.oup.com/journals/pages/open_access/funder_policies/chorus/standard_publication_model (vor)
- Locations
- Monthly Notices of the Royal Astronomical Society Letters (publishedVersion)
- arXiv (Cornell University) (submittedVersion)
- Edinburgh Research Explorer (University of Edinburgh) (submittedVersion, other-oa)
- Liverpool John Moores University (submittedVersion)
- arXiv (Cornell University)
- Funders
- Marie Skłodowska-Curie (797794)
- European Research Council (647112, 769130)
- Science and Technology Facilities Council (ST/K00042X/1, ST/P002293/1, ST/R002371/1, ST/S002502/1, ST/R000832/1)
- Topics
- Physical Sciences › Physics and Astronomy › Astronomy and Astrophysics › Cosmology and Gravitation Theories
- Physical Sciences › Physics and Astronomy › Astronomy and Astrophysics › Galaxies: Formation, Evolution, Phenomena
- Physical Sciences › Physics and Astronomy › Nuclear and High Energy Physics › Dark Matter and Cosmic Phenomena
- Keywords
- thermal Sunyaev-Zel'dovich effect, large-scale structure, deep generative models, variational autoencoder, generative adversarial networks, N-body simulations, Bahamas
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
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