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Learning to predict the cosmological structure formation.

Siyu He1,2,3, Yin Li4,5,6, Yu Feng4,5

  • 1Department of Physics, Carnegie Mellon University, Pittsburgh, PA 15213; shirleyho@flatironinstitute.org siyuh@andrew.cmu.edu.

Proceedings of the National Academy of Sciences of the United States of America
|June 26, 2019
PubMed
Summary
This summary is machine-generated.

We developed a deep neural network, the Deep Density Displacement Model, to accurately predict the Universe's cosmic structure. This AI model surpasses traditional methods, offering a faster and more precise way to study cosmic web formation.

Keywords:
cosmologydeep learningsimulation

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Area of Science:

  • Cosmology
  • Astrophysics
  • Computational Physics

Background:

  • The evolution of matter under gravity from initial density fluctuations forms the cosmic web.
  • Understanding the hierarchical, non-Gaussian structure formation is a key challenge in modern astrophysics.
  • Current methods involve large-scale surveys and computationally intensive simulations.

Purpose of the Study:

  • To develop a novel, efficient method for predicting the nonlinear large-scale structure of the Universe.
  • To assess the performance of a deep learning model against established approximation techniques.
  • To explore the model's capability for extrapolation to different cosmological parameters.

Main Methods:

  • A deep neural network, the Deep Density Displacement Model (DDM), was constructed.
  • The DDM was trained on data from pre-run numerical simulations.
  • The Zel'dovich Approximation (ZA) served as the input for the DDM to predict cosmic structure.

Main Results:

  • The Deep Density Displacement Model significantly outperforms second-order perturbation theory (2LPT).
  • DDM accurately predicts cosmic structure in the nonlinear regime.
  • The model demonstrates strong extrapolation capabilities for different cosmological parameters.

Conclusions:

  • Deep learning offers a practical and accurate alternative to approximate 3D simulations for gravitational structure formation.
  • The DDM provides a powerful new tool for astrophysical research and cosmological simulations.
  • This approach accelerates the study of the Universe's large-scale structure.