arXiv 2012.002402020-12-01astro-ph.COastro-ph.IMcs.LG

使用深度学习快速准确地对宇宙进行非线性预测

Fast and Accurate Non-Linear Predictions of Universes with Deep Learning

PPGCosmo/Flatiron Institute（PPGCosmo/弗拉蒂隆研究所）；Flatiron Institute（弗拉蒂隆研究所）；Princeton University（普林斯顿大学）

Renan Alves de Oliveira, Yin Li, Francisco Villaescusa-Navarro, Shirley Ho, David N. Spergel

备注：: 6 pages, 2 figures, accepted for a poster presentation at Machine Learning and the Physical Sciences Workshop @ NeurIPS 2020

摘要：

宇宙学家的目标是模拟从最初的低振幅高斯密度波动到高度非线性的星系和星系团的“宇宙网”的演化。他们的目标是将这种结构形成过程的模拟与星系追踪的大尺度结构的观测结果进行比较，并推断构成宇宙95%的暗能量和暗物质的性质。这些模拟数十亿星系的集合在计算上要求很高，因此需要更有效的方法来跟踪结构的非线性增长。我们建立了一个基于V-Net的模型，该模型将快速线性预测转换为来自数值模拟的完全非线性预测。我们的NN模型学习模拟小规模的模拟，比目前最先进的近似方法更快，更准确。当在宇宙学参数与训练中使用的宇宙学参数显著不同的宇宙中进行测试时，它也达到了相当的准确性。这表明我们的模型的泛化能力远远超出了我们的训练集。

英文摘要：

Cosmologists aim to model the evolution of initially low amplitude Gaussian density fluctuations into the highly non-linear "cosmic web" of galaxies and clusters. They aim to compare simulations of this structure formation process with observations of large-scale structure traced by galaxies and infer the properties of the dark energy and dark matter that make up 95% of the universe. These ensembles of simulations of billions of galaxies are computationally demanding, so that more efficient approaches to tracing the non-linear growth of structure are needed. We build a V-Net based model that transforms fast linear predictions into fully nonlinear predictions from numerical simulations. Our NN model learns to emulate the simulations down to small scales and is both faster and more accurate than the current state-of-the-art approximate methods. It also achieves comparable accuracy when tested on universes of significantly different cosmological parameters from the one used in training. This suggests that our model generalizes well beyond our training set.

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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英文摘要：

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