천문학회보 (The Bulletin of The Korean Astronomical Society)
- 제44권2호
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- Pages.53.4-53.4
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- 2019
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- 1226-2692(pISSN)
Matter Density Distribution Reconstruction of Local Universe with Deep Learning
- Hong, Sungwook E. (Natural Science Research Institute, University of Seoul) ;
- Kim, Juhan (Center for Advanced Computation, Korea Institute for Advanced Study) ;
- Jeong, Donghui (Department of Astronomy & Astrophysics, Penn State University) ;
- Hwang, Ho Seong (Korea Astronomy and Space Science Institute)
- 발행 : 2019.10.14
초록
We reconstruct the underlying dark matter (DM) density distribution of the local universe within 20Mpc/h cubic box by using the galaxy position and peculiar velocity. About 1,000 subboxes in the Illustris-TNG cosmological simulation are used to train the relation between DM density distribution and galaxy properties by using UNet-like convolutional neural network (CNN). The estimated DM density distributions have a good agreement with their truth values in terms of pixel-to-pixel correlation, the probability distribution of DM density, and matter power spectrum. We apply the trained CNN architecture to the galaxy properties from the Cosmicflows-3 catalogue to reconstruct the DM density distribution of the local universe. The reconstructed DM density distribution can be used to understand the evolution and fate of our local environment.
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