arXivDaily arXiv每日学术速递 周一至周五更新
arXiv 2001.05519astro-ph.CO
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利用深物理模型进行宇宙学模拟的超分辨率仿真器

Super-resolution emulator of cosmological simulations using deep physical models

Doogesh Kodi Ramanah, Tom Charnock, Francisco Villaescusa-Navarro, Benjamin D. Wandelt

摘要:

我们提出了一个扩展我们最近开发的Wasserstein优化模型,以模拟准确的高分辨率的功能,从计算上更便宜的低分辨率宇宙学模拟。我们的深层物理建模技术依赖于受限神经网络来执行低分辨率宇宙密度场到高分辨率小尺度结构空间的分布映射。我们约束我们的网络使用一个单一的高分辨率的初始条件和相应的低分辨率和高分辨率的演变暗物质模拟的Quijote套件。我们利用高分辨率的初始条件的信息内容作为一个良好的构造先验分布的网络模拟的小规模的结构。一旦拟合,我们的物理模型以低计算成本产生模拟的高分辨率模拟,同时还提供了一些关于大尺度模式如何影响真实空间中小尺度结构的见解。

英文摘要:

We present an extension of our recently developed Wasserstein optimized model to emulate accurate high-resolution features from computationally cheaper low-resolution cosmological simulations. Our deep physical modelling technique relies on restricted neural networks to perform a mapping of the distribution of the low-resolution cosmic density field to the space of the high-resolution small-scale structures. We constrain our network using a single triplet of high-resolution initial conditions and the corresponding low- and high-resolution evolved dark matter simulations from the Quijote suite of simulations. We exploit the information content of the high-resolution initial conditions as a well constructed prior distribution from which the network emulates the small-scale structures. Once fitted, our physical model yields emulated high-resolution simulations at low computational cost, while also providing some insights about how the large-scale modes affect the small-scale structure in real space.

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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