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:用神经网络从暗物质中产生中性氢

HInet: Generating neutral hydrogen from dark matter with neural networks

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

摘要:

即将进行的21厘米巡天将绘制宇宙中性氢(HI)在非常大的宇宙学体积上的空间分布。为了最大限度地提高这些调查的科学回报,需要准确的理论预测。流体动力学模拟目前是最准确的工具,以提供这些预测在温和的非线性制度。不幸的是,它们的计算成本非常高:数千万CPU小时。我们使用卷积神经网络来找到来自N体模拟的物质空间分布与来自最先进的流体动力学模拟IllustrisTNG的HI之间的映射。我们的模型比广泛使用的理论模型:Halo Occupation Distribution(HOD)的所有统计特性的非线性尺度$k\lesssim1$ h/Mpc更好。我们的方法允许在非常大的宇宙学体积上产生21厘米的模拟,具有与流体动力学模拟相似的性质。

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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