An AI super-resolution field emulator for cosmological hydrodynamics: the Lyman-α forest
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| Format: | Preprint |
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2025
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| _version_ | 1866916855702093824 |
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| author | Hafezianzadeh, Fatemeh Zhang, Xiaowen Ni, Yueying Croft, Rupert A. C. DiMatteo, Tiziana Qezlou, Mahdi Bird, Simeon |
| author_facet | Hafezianzadeh, Fatemeh Zhang, Xiaowen Ni, Yueying Croft, Rupert A. C. DiMatteo, Tiziana Qezlou, Mahdi Bird, Simeon |
| contents | We extend our super-resolution and emulation framework for cosmological dark matter simulations to include hydrodynamics. We present a two-stage deep learning model to emulate high-resolution (HR-HydroSim) baryonic fields from low-resolution (LR-HydroSim) simulations at redshift $z = 3$. The method takes as inputs an LR-HydroSim and the high-resolution initial conditions (HR-HydroICs). First, the model stochastically generates high-resolution baryonic fields from the LR-HydroSim. Second, a deterministic emulator refines these fields using HR-HydroICs to reconstruct small-scale structures including displacement, velocity, internal energy, and gas/star classification. Trained on paired low- and high-resolution simulations produced with \texttt{MP-Gadget}, the model captures small-scale structures of the intergalactic medium and %Lyman-$α$ forest observables down to the 100 kpc pressure smoothing scale relevant to the Lyman-$α$ forest. The model achieves subpercent error for overdensity, temperature, velocity, and optical depth fields, a mean relative error of 1.07\% in the large-scale flux power spectrum (\(k < 3 \times 10^{-2}\ \mathrm{s/km}\)), and less than 10\% error in the flux probability distribution function. Notably, the two-stage model reduces the compute time by a factor of $\sim$450 compared to full smoothed particle hydrodynamics at the same resolution. This work demonstrates the potential of this framework as a powerful and efficient tool for generating high-resolution fields offering fast and accurate alternatives to traditional cosmological hydrodynamic simulations and enabling large-volume mock datasets for next-generation cosmological surveys. |
| format | Preprint |
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arxiv_https___arxiv_org_abs_2507_16189 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | An AI super-resolution field emulator for cosmological hydrodynamics: the Lyman-α forest Hafezianzadeh, Fatemeh Zhang, Xiaowen Ni, Yueying Croft, Rupert A. C. DiMatteo, Tiziana Qezlou, Mahdi Bird, Simeon Cosmology and Nongalactic Astrophysics Astrophysics of Galaxies Instrumentation and Methods for Astrophysics Computational Physics We extend our super-resolution and emulation framework for cosmological dark matter simulations to include hydrodynamics. We present a two-stage deep learning model to emulate high-resolution (HR-HydroSim) baryonic fields from low-resolution (LR-HydroSim) simulations at redshift $z = 3$. The method takes as inputs an LR-HydroSim and the high-resolution initial conditions (HR-HydroICs). First, the model stochastically generates high-resolution baryonic fields from the LR-HydroSim. Second, a deterministic emulator refines these fields using HR-HydroICs to reconstruct small-scale structures including displacement, velocity, internal energy, and gas/star classification. Trained on paired low- and high-resolution simulations produced with \texttt{MP-Gadget}, the model captures small-scale structures of the intergalactic medium and %Lyman-$α$ forest observables down to the 100 kpc pressure smoothing scale relevant to the Lyman-$α$ forest. The model achieves subpercent error for overdensity, temperature, velocity, and optical depth fields, a mean relative error of 1.07\% in the large-scale flux power spectrum (\(k < 3 \times 10^{-2}\ \mathrm{s/km}\)), and less than 10\% error in the flux probability distribution function. Notably, the two-stage model reduces the compute time by a factor of $\sim$450 compared to full smoothed particle hydrodynamics at the same resolution. This work demonstrates the potential of this framework as a powerful and efficient tool for generating high-resolution fields offering fast and accurate alternatives to traditional cosmological hydrodynamic simulations and enabling large-volume mock datasets for next-generation cosmological surveys. |
| title | An AI super-resolution field emulator for cosmological hydrodynamics: the Lyman-α forest |
| topic | Cosmology and Nongalactic Astrophysics Astrophysics of Galaxies Instrumentation and Methods for Astrophysics Computational Physics |
| url | https://arxiv.org/abs/2507.16189 |