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arXiv 2012.05472astro-ph.COcs.LG
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在N体模拟中学习宇宙的演化

Learning the Evolution of the Universe in N-body Simulations

New York University(纽约大学);Flatiron Institute(熨斗研究所);Princeton University(普林斯顿大学)

Chang Chen, Yin Li, Francisco Villaescusa-Navarro, Shirley Ho, Anthony Pullen

摘要:

理解大规模宇宙学调查的物理学,直到小尺度(非线性),将大大提高我们对宇宙的认识。大型N体模拟已经建成,以获得在非线性政权的预测。然而,N体模拟在计算上是昂贵的,并且生成大量数据,从而给存储带来负担。这些数据是模拟宇宙在不同时间的快照,精细采样是准确保存其整个历史所必需的。我们使用深度神经网络模型来预测中间时间步的非线性N体模拟,给出两个相距甚远的快照。我们的结果优于三次Hermite插值基准方法插值N体模拟。这项工作可以大大减少存储需求,并允许我们从更少的宇宙快照中重建宇宙历史。

英文摘要:

Understanding the physics of large cosmological surveys down to small (nonlinear) scales will significantly improve our knowledge of the Universe. Large N-body simulations have been built to obtain predictions in the non-linear regime. However, N-body simulations are computationally expensive and generate large amount of data, putting burdens on storage. These data are snapshots of the simulated Universe at different times, and fine sampling is necessary to accurately save its whole history. We employ a deep neural network model to predict the nonlinear N-body simulation at an intermediate time step given two widely separated snapshots. Our results outperform the cubic Hermite interpolation benchmark method in interpolating N-body simulations. This work can greatly reduce the storage requirement and allow us to reconstruct the cosmic history from far fewer snapshots of the universe.

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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