HInet: Generating Neutral Hydrogen from Dark Matter with Neural Networks
Open accessAbstract
Upcoming 21 cm surveys will map the spatial distribution of cosmic neutral hydrogen (H i) over very large cosmological volumes. In order to maximize the scientific return of these surveys, accurate theoretical predictions are needed. Hydrodynamic simulations currently are the most accurate tool to provide those predictions in the mildly to nonlinear regime. Unfortunately, their computational cost is very high: tens of millions of CPU hours. We use convolutional neural networks to find the mapping between the spatial distribution of matter from N-body simulations and H i from the state-of-the-art hydrodynamic simulation IllustrisTNG. Our model performs better than the widely used theoretical model: halo occupation distribution for all statistical properties up to the nonlinear scales k ≲ 1 h Mpc−1. Our method allows the generation of 21 cm mocks over very big cosmological volumes with similar properties to hydrodynamic simulations.
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“Our results can be further improved if we train the U-net on a simulation with a larger volume,21 which would have a larger number of high-mass halos, or using a set of zoom-in simulations focused on galaxy clusters (Thiele et al. 2020).”
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Mishra, S., Trotta, R., & Viel, M. (2025) · Monthly Notices of the Royal Astronomical Society · doi:10.1093/mnras/staf2071 · Cited by 2 · see citing works
EMBER-2: emulating baryons from dark matter across cosmic time with deep modulation networks
Bernardini, M., Feldmann, R., Gensior, J., Anglés‐Alcázar, D., Bassini, L., Bieri, R., Cenci, E., Tortora, L., & Faucher‐Giguère, C. (2025) · Monthly Notices of the Royal Astronomical Society · doi:10.1093/mnras/staf341 · Cited by 1 · see citing works
Recovering Cosmic Structure with a Simple Physical Constraint
Luan, T., Wang, X., Ding, J., Li, Q., Li, X.-D., & Zhu, W. (2025) · The Astrophysical Journal · doi:10.3847/1538-4357/adc1c6 · Cited by 1 · see citing works
Prediction of Individual Halo Concentrations Across Cosmic Time Using Neural Networks
Zhang, T., Mao, T., Xu, W., & Li, G. (2025) · Universe · doi:10.3390/universe11020037
Deep learning for Lyman-alpha based cosmology
Boonkongkird, C. (2023) · doi:10.70675/2006dd76z218fz4b48zaa1az760d33b3f076
- Authors
- Wadekar, D., Villaescusa-Navarro, F., Ho, S., & Perreault-Levasseur, L.
- Affiliations
- New York University (US)
- Princeton University (US)
- Mila - Quebec Artificial Intelligence Institute (CA)
- Corresponding author
- Wadekar, Digvijay
- Venue
- The Astrophysical Journal
- Publisher
- American Astronomical Society
- Published
- 2021-07-01
- Volume
- 916
- Issue
- 1
- Pages
- 42
- ISSN
- 0004-637X (ISSN-L)
- 1538-4357
- OpenAlex
- W3044997459
- Cited by
- 32
- References
- 69
- Citations by year
- 2025: 5 · 2024: 2 · 2023: 8 · 2022: 9 · 2021: 6 · 2020: 2
- FWCI
- 3.36
- Type
- journal article
- Language
- en
- Open access
- bronze
- doi.org
- Licence
- Locations
- The Astrophysical Journal (publishedVersion)
- arXiv (Cornell University) (submittedVersion)
- arXiv (Cornell University)
- Funders
- WFIRST (NNG26PJ30C, NNN12AA01C)
- Topics
- Physical Sciences › Physics and Astronomy › Astronomy and Astrophysics › Galaxies: Formation, Evolution, Phenomena
- Physical Sciences › Physics and Astronomy › Astronomy and Astrophysics › Radio Astronomy Observations and Technology
- Physical Sciences › Physics and Astronomy › Astronomy and Astrophysics › Cosmology and Gravitation Theories
- Keywords
- neutral hydrogen, dark matter, convolutional neural networks, IllustrisTNG, halo occupation distribution, mock catalogues, hydrodynamic modeling, N-body simulations, non-linear scales
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