2020/12/10 by Chang Chen, Yin Li, Chen, Chang +8 · 1 citation
Computer Science · Physics and Astronomy · #Astronomy and Astrophysical Research #Computational Physics and Python Applications #Cosmology and Nongalactic Astrophysics (astro-ph.CO) #FOS: Computer and information sciences #FOS: Physical sciences #Galaxies: Formation, Evolution, Phenomena #Machine Learning (cs.LG) #astro-ph.CO #cs.LG
paper · pdf · doi:10.48550/arxiv.2012.05472
6 pages, 3 figures, paper accepted by the NeurIPS 2020 Machine Learning and the Physical Science Workshop
arxiv created 2020/12/10 · openalex publication_date 2020/12/10 · arxiv updated 2021/05/05 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28
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.