AI-assisted super-resolution cosmological simulations IV: An emulator for deterministic realizations
Super-resolution (SR) models in cosmological simulations use deep learning (DL) to rapidly enhance low-resolution (LR) runs with statistically correct fine details. These models preserves large-scale structures by conditioning on an LR version of the simulation. On smaller scales, the generative pro...
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| Main Authors: | , , , , , , , |
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| Format: | Article |
| Language: | English |
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Maynooth Academic Publishing
2025-02-01
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| Series: | The Open Journal of Astrophysics |
| Online Access: | https://doi.org/10.33232/001c.129471 |
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| author | Xiaowen Zhang Patrick Lachance Ankita Dasgupta Rupert A. C. Croft Tiziana Di Matteo Yueying Ni Simeon Bird Yin Li |
| author_facet | Xiaowen Zhang Patrick Lachance Ankita Dasgupta Rupert A. C. Croft Tiziana Di Matteo Yueying Ni Simeon Bird Yin Li |
| author_sort | Xiaowen Zhang |
| collection | DOAJ |
| description | Super-resolution (SR) models in cosmological simulations use deep learning (DL) to rapidly enhance low-resolution (LR) runs with statistically correct fine details. These models preserves large-scale structures by conditioning on an LR version of the simulation. On smaller scales, the generative process is inherently stochastic, producing multiple possible SR realizations with distinct small-scale structures. Validation of reconstructed SR runs from LR simulations requires ensuring that specific statistics of interest are accurately reproduced by comparing SR outputs with target high resolution (HR) runs. In this study, we develop an emulator designed to reproduce the small-scale structures of target HR simulation with high fidelity. By processing an SR realization alongside the high-resolution initial condition (HRIC), we transform the SR output to emulate the result of a full simulation with that HRIC. By comparing various metrics, from visualization to individual halo measurements, we demonstrate that the emulated SR runs closely align with the target HR simulation, even at length scales an order of magnitude smaller than the corresponding LR run. These results show the potential of this method for efficiently generating accurate simulations and mock observations for large galaxy surveys. |
| format | Article |
| id | doaj-art-0868ee7dd6d3408a9eafc7ea60ea0854 |
| institution | Kabale University |
| issn | 2565-6120 |
| language | English |
| publishDate | 2025-02-01 |
| publisher | Maynooth Academic Publishing |
| record_format | Article |
| series | The Open Journal of Astrophysics |
| spelling | doaj-art-0868ee7dd6d3408a9eafc7ea60ea08542025-02-10T08:09:32ZengMaynooth Academic PublishingThe Open Journal of Astrophysics2565-61202025-02-018AI-assisted super-resolution cosmological simulations IV: An emulator for deterministic realizationsXiaowen ZhangPatrick LachanceAnkita DasguptaRupert A. C. CroftTiziana Di MatteoYueying NiSimeon BirdYin LiSuper-resolution (SR) models in cosmological simulations use deep learning (DL) to rapidly enhance low-resolution (LR) runs with statistically correct fine details. These models preserves large-scale structures by conditioning on an LR version of the simulation. On smaller scales, the generative process is inherently stochastic, producing multiple possible SR realizations with distinct small-scale structures. Validation of reconstructed SR runs from LR simulations requires ensuring that specific statistics of interest are accurately reproduced by comparing SR outputs with target high resolution (HR) runs. In this study, we develop an emulator designed to reproduce the small-scale structures of target HR simulation with high fidelity. By processing an SR realization alongside the high-resolution initial condition (HRIC), we transform the SR output to emulate the result of a full simulation with that HRIC. By comparing various metrics, from visualization to individual halo measurements, we demonstrate that the emulated SR runs closely align with the target HR simulation, even at length scales an order of magnitude smaller than the corresponding LR run. These results show the potential of this method for efficiently generating accurate simulations and mock observations for large galaxy surveys.https://doi.org/10.33232/001c.129471 |
| spellingShingle | Xiaowen Zhang Patrick Lachance Ankita Dasgupta Rupert A. C. Croft Tiziana Di Matteo Yueying Ni Simeon Bird Yin Li AI-assisted super-resolution cosmological simulations IV: An emulator for deterministic realizations The Open Journal of Astrophysics |
| title | AI-assisted super-resolution cosmological simulations IV: An emulator for deterministic realizations |
| title_full | AI-assisted super-resolution cosmological simulations IV: An emulator for deterministic realizations |
| title_fullStr | AI-assisted super-resolution cosmological simulations IV: An emulator for deterministic realizations |
| title_full_unstemmed | AI-assisted super-resolution cosmological simulations IV: An emulator for deterministic realizations |
| title_short | AI-assisted super-resolution cosmological simulations IV: An emulator for deterministic realizations |
| title_sort | ai assisted super resolution cosmological simulations iv an emulator for deterministic realizations |
| url | https://doi.org/10.33232/001c.129471 |
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