arXivDaily arXiv每日学术速递 周一至周五更新
arXiv 2007.10340astro-ph.CO
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HInet: Generating neutral hydrogen from dark matter with neural networks

Digvijay Wadekar, Francisco Villaescusa-Navarro, Shirley Ho, Laurence Perreault-Levasseur

英文摘要:

Upcoming 21cm surveys will map the spatial distribution of cosmic neutral hydrogen (HI) 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 non-linear 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 HI from the state-of-the-art hydrodynamic simulation IllustrisTNG. Our model performs better than the widely used theoretical model: Halo Occupation Distribution (HOD) for all statistical properties up to the non-linear scales $k\lesssim1$ h/Mpc. Our method allows the generation of 21cm mocks over very big cosmological volumes with similar properties as hydrodynamic simulations.

机构、总结、中文摘要均由AI分析而来,可能存在错误,请以论文正文为准。

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