Learning to predict the cosmological structure formation
Abstract
Matter evolved under the influence of gravity from minuscule density fluctuations. Nonperturbative structure formed hierarchically over all scales and developed non-Gaussian features in the Universe, known as the cosmic web. To fully understand the structure formation of the Universe is one of the holy grails of modern astrophysics. Astrophysicists survey large volumes of the Universe and use a large ensemble of computer simulations to compare with the observed data to extract the full information of our own Universe. However, to evolve billions of particles over billions of years, even with the simplest physics, is a daunting task. We build a deep neural network, the Deep Density Displacement Model ( ), which learns from a set of prerun numerical simulations, to predict the nonlinear large-scale structure of the Universe with the Zel’dovich Approximation (ZA), an analytical approximation based on perturbation theory, as the input. Our extensive analysis demonstrates that outperforms the second-order perturbation theory (2LPT), the commonly used fast-approximate simulation method, in predicting cosmic structure in the nonlinear regime. We also show that is able to accurately extrapolate far beyond its training data and predict structure formation for significantly different cosmological parameters. Our study proves that deep learning is a practical and accurate alternative to approximate 3D simulations of the gravitational structure formation of the Universe.
- Authors:
-
- Carnegie Mellon Univ., Pittsburgh, PA (United States); Flatiron Inst. New York, NY (United States); Univ. of Tokyo (Japan)
- Univ. of California, Berkeley, CA (United States); Lawrence Berkeley National Lab. (LBNL), Berkeley, CA (United States); Univ. of Tokyo (Japan)
- Univ. of California, Berkeley, CA (United States); Lawrence Berkeley National Lab. (LBNL), Berkeley, CA (United States)
- Carnegie Mellon Univ., Pittsburgh, PA (United States); Flatiron Inst. New York, NY (United States); Univ. of Tokyo (Japan); Univ. of California, Berkeley, CA (United States); Lawrence Berkeley National Lab. (LBNL), Berkeley, CA (United States)
- Univ. of British Columbia, Vancouver, BC (Canada)
- Flatiron Inst. New York, NY (United States)
- Carnegie Mellon Univ., Pittsburgh, PA (United States)
- Publication Date:
- Research Org.:
- Lawrence Berkeley National Lab. (LBNL), Berkeley, CA (United States)
- Sponsoring Org.:
- USDOE Office of Science (SC); National Aeronautics and Space Administration (NASA); Simons Foundation
- OSTI Identifier:
- 1561927
- Grant/Contract Number:
- AC02-05CH11231; 15-WFIRST15-0008; 12-EUCLID12-0004
- Resource Type:
- Accepted Manuscript
- Journal Name:
- Proceedings of the National Academy of Sciences of the United States of America
- Additional Journal Information:
- Journal Volume: 116; Journal Issue: 28; Journal ID: ISSN 0027-8424
- Publisher:
- National Academy of Sciences, Washington, DC (United States)
- Country of Publication:
- United States
- Language:
- English
- Subject:
- 79 ASTRONOMY AND ASTROPHYSICS; cosmology; deep learning; simulation
Citation Formats
He, Siyu, Li, Yin, Feng, Yu, Ho, Shirley, Ravanbakhsh, Siamak, Chen, Wei, and Póczos, Barnabás. Learning to predict the cosmological structure formation. United States: N. p., 2019.
Web. doi:10.1073/pnas.1821458116.
He, Siyu, Li, Yin, Feng, Yu, Ho, Shirley, Ravanbakhsh, Siamak, Chen, Wei, & Póczos, Barnabás. Learning to predict the cosmological structure formation. United States. doi:10.1073/pnas.1821458116.
He, Siyu, Li, Yin, Feng, Yu, Ho, Shirley, Ravanbakhsh, Siamak, Chen, Wei, and Póczos, Barnabás. Mon .
"Learning to predict the cosmological structure formation". United States. doi:10.1073/pnas.1821458116. https://www.osti.gov/servlets/purl/1561927.
@article{osti_1561927,
title = {Learning to predict the cosmological structure formation},
author = {He, Siyu and Li, Yin and Feng, Yu and Ho, Shirley and Ravanbakhsh, Siamak and Chen, Wei and Póczos, Barnabás},
abstractNote = {Matter evolved under the influence of gravity from minuscule density fluctuations. Nonperturbative structure formed hierarchically over all scales and developed non-Gaussian features in the Universe, known as the cosmic web. To fully understand the structure formation of the Universe is one of the holy grails of modern astrophysics. Astrophysicists survey large volumes of the Universe and use a large ensemble of computer simulations to compare with the observed data to extract the full information of our own Universe. However, to evolve billions of particles over billions of years, even with the simplest physics, is a daunting task. We build a deep neural network, the Deep Density Displacement Model (D3M), which learns from a set of prerun numerical simulations, to predict the nonlinear large-scale structure of the Universe with the Zel’dovich Approximation (ZA), an analytical approximation based on perturbation theory, as the input. Our extensive analysis demonstrates that D3M outperforms the second-order perturbation theory (2LPT), the commonly used fast-approximate simulation method, in predicting cosmic structure in the nonlinear regime. We also show that D3M is able to accurately extrapolate far beyond its training data and predict structure formation for significantly different cosmological parameters. Our study proves that deep learning is a practical and accurate alternative to approximate 3D simulations of the gravitational structure formation of the Universe.},
doi = {10.1073/pnas.1821458116},
journal = {Proceedings of the National Academy of Sciences of the United States of America},
number = 28,
volume = 116,
place = {United States},
year = {2019},
month = {6}
}
Web of Science
Figures / Tables:

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conference, November 2018
- Mathuriya, Amrita; Bard, Deborah; Mendygral, Peter
- SC18: International Conference for High Performance Computing, Networking, Storage and Analysis
Simulation as an engine of physical scene understanding
journal, October 2013
- Battaglia, P. W.; Hamrick, J. B.; Tenenbaum, J. B.
- Proceedings of the National Academy of Sciences, Vol. 110, Issue 45
Bayesian physical reconstruction of initial conditions from large-scale structure surveys
journal, April 2013
- Jasche, Jens; Wandelt, Benjamin D.
- Monthly Notices of the Royal Astronomical Society, Vol. 432, Issue 2
Predicting the sequence specificities of DNA- and RNA-binding proteins by deep learning
journal, July 2015
- Alipanahi, Babak; Delong, Andrew; Weirauch, Matthew T.
- Nature Biotechnology, Vol. 33, Issue 8
Fast automated analysis of strong gravitational lenses with convolutional neural networks
journal, August 2017
- Hezaveh, Yashar D.; Levasseur, Laurence Perreault; Marshall, Philip J.
- Nature, Vol. 548, Issue 7669
The 6dF Galaxy Survey: final redshift release (DR3) and southern large-scale structures
journal, October 2009
- Jones, D. Heath; Read, Mike A.; Saunders, Will
- Monthly Notices of the Royal Astronomical Society, Vol. 399, Issue 2
Sdss-Iii: Massive Spectroscopic Surveys of the Distant Universe, the Milky way, and Extra-Solar Planetary Systems
journal, August 2011
- Eisenstein, Daniel J.; Weinberg, David H.; Agol, Eric
- The Astronomical Journal, Vol. 142, Issue 3
Newtonian Image Understanding: Unfolding the Dynamics of Objects in Static Images
conference, June 2016
- Mottaghi, Roozbeh; Bagherinezhad, Hessam; Rastegari, Mohammad
- 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR)
Cosmology and fundamental physics with the Euclid satellite
journal, April 2018
- Amendola, Luca; Appleby, Stephen; Avgoustidis, Anastasios
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Mastering the game of Go with deep neural networks and tree search
journal, January 2016
- Silver, David; Huang, Aja; Maddison, Chris J.
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Lagrangian theory of gravitational instability of Friedman-Lematre cosmologies - a generic third-order model for non-linear clustering
journal, April 1994
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Planning chemical syntheses with deep neural networks and symbolic AI
journal, March 2018
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The Zel'dovich approximation
journal, February 2014
- White, Martin
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DeepCMB: Lensing reconstruction of the cosmic microwave background with deep neural networks
journal, July 2019
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Deep learning
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Exploring the posterior surface of the large scale structure reconstruction
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- Springel, Volker; Yoshida, Naoki; White, Simon D. M.
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Photometric Supernova Classification with Machine Learning
journal, August 2016
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FastPM: a new scheme for fast simulations of dark matter and haloes
journal, August 2016
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Physics 101: Learning Physical Object Properties from Unlabeled Videos
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The initial conditions of the Universe from constrained simulations
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A volumetric deep Convolutional Neural Network for simulation of mock dark matter halo catalogues
journal, November 2018
- Berger, Philippe; Stein, George
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DeepSphere: Efficient spherical convolutional neural network with HEALPix sampling for cosmological applications
journal, April 2019
- Perraudin, N.; Defferrard, M.; Kacprzak, T.
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- Ade, P. A. R.; Aghanim, N.; Arnaud, M.
- Astronomy & Astrophysics, Vol. 594
Galaxy And Mass Assembly (GAMA): end of survey report and data release 2
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- Monthly Notices of the Royal Astronomical Society, Vol. 452, Issue 2
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- Hand, Nick; Feng, Yu; Beutler, Florian
- The Astronomical Journal, Vol. 156, Issue 4
Human-level control through deep reinforcement learning
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- Mnih, Volodymyr; Kavukcuoglu, Koray; Silver, David
- Nature, Vol. 518, Issue 7540
CMU DeepLens: deep learning for automatic image-based galaxy–galaxy strong lens finding
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- Lanusse, François; Ma, Quanbin; Li, Nan
- Monthly Notices of the Royal Astronomical Society, Vol. 473, Issue 3
V-Net: Fully Convolutional Neural Networks for Volumetric Medical Image Segmentation
conference, October 2016
- Milletari, Fausto; Navab, Nassir; Ahmadi, Seyed-Ahmad
- 2016 Fourth International Conference on 3D Vision (3DV)
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Figures / Tables found in this record: