用于 Ly-α 模拟的 TensorFlow 流体动力学分析

IF 16.4 1区化学 Q1 CHEMISTRY, MULTIDISCIPLINARY

Accounts of Chemical Research Pub Date : 2024-07-01 DOI:10.1016/j.ascom.2024.100858

J. Ding , B. Horowitz , Z. Lukić

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引用次数: 0

摘要

我们介绍了 Python 程序 THALAS（TensorFlow Hydrodynamics Analysis for Lyman-Alpha Simulations），它将重子场（重子密度、温度和速度）映射到实际空间和红移空间的 Lyα 光学深度场。与以前的Lyα代码不同，THALAS是完全可微分的，因此可以广泛应用于流体力学模拟的一般分析和宇宙学推断。为了证明THALAS的能力，我们将其应用于Lyα森林反演问题：给定一个Lyα光学深度场，我们重建相应的真实空间暗物质密度场。这种应用与莱曼阿尔法森林数据的宇宙学和三维层析分析都有关系。

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TensorFlow Hydrodynamics Analysis for Ly-α Simulations

We introduce the Python program THALAS (TensorFlow Hydrodynamics Analysis for Lyman-Alpha Simulations), which maps baryon fields (baryon density, temperature, and velocity) to Ly $α$ optical depth fields in both real space and redshift space. Unlike previous Ly $α$ codes, THALAS is fully differentiable, enabling a wide variety of potential applications for general analysis of hydrodynamical simulations and cosmological inference. To demonstrate THALAS’s capabilities, we apply it to the Ly $α$ forest inversion problem: given a Ly $α$ optical depth field, we reconstruct the corresponding real-space dark matter density field. Such applications are relevant to both cosmological and three-dimensional tomographic analyses of Lyman Alpha forest data.

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来源期刊

Accounts of Chemical Research 化学-化学综合

CiteScore

31.40

自引率

1.10%

发文量

312

审稿时长

2 months

期刊介绍： Accounts of Chemical Research presents short, concise and critical articles offering easy-to-read overviews of basic research and applications in all areas of chemistry and biochemistry. These short reviews focus on research from the author’s own laboratory and are designed to teach the reader about a research project. In addition, Accounts of Chemical Research publishes commentaries that give an informed opinion on a current research problem. Special Issues online are devoted to a single topic of unusual activity and significance. Accounts of Chemical Research replaces the traditional article abstract with an article "Conspectus." These entries synopsize the research affording the reader a closer look at the content and significance of an article. Through this provision of a more detailed description of the article contents, the Conspectus enhances the article's discoverability by search engines and the exposure for the research.