|Title:||Dark Quest. I. Fast and Accurate Emulation of Halo Clustering Statistics and Its Application to Galaxy Clustering|
|Authors:||Nishimichi, Takahiro https://orcid.org/0000-0002-9664-0760 (unconfirmed)|
|Author's alias:||西道, 啓博|
|Keywords:||large-scale structure of universe|
|Publisher:||American Astronomical Society|
|Journal title:||The Astrophysical Journal|
|Abstract:||We perform an ensemble of N-body simulations with 20483 particles for 101 flat wCDM cosmological models sampled based on a maximin distance sliced Latin hypercube design. By using the halo catalogs extracted at multiple redshifts in the range of z = [0, 1.48], we develop Dark Emulator, which enables fast and accurate computations of the halo mass function, halo–matter cross-correlation, and halo autocorrelation as a function of halo masses, redshift, separations, and cosmological models based on principal component analysis and Gaussian process regression for the large-dimensional input and output data vector. We assess the performance of the emulator using a validation set of N-body simulations that are not used in training the emulator. We show that, for typical halos hosting CMASS galaxies in the Sloan Digital Sky Survey, the emulator predicts the halo–matter cross-correlation, relevant for galaxy–galaxy weak lensing, with an accuracy better than 2% and the halo autocorrelation, relevant for galaxy clustering correlation, with an accuracy better than 4%. We give several demonstrations of the emulator. It can be used to study properties of halo mass density profiles such as the concentration–mass relation and splashback radius for different cosmologies. The emulator outputs can be combined with an analytical prescription of halo–galaxy connection, such as the halo occupation distribution at the equation level, instead of using the mock catalogs to make accurate predictions of galaxy clustering statistics, such as galaxy–galaxy weak lensing and the projected correlation function for any model within the wCDM cosmologies, in a few CPU seconds.|
|Description:||宇宙の大規模構造の複雑な統計パターンを高速予言する人工知能(AI)ツールを開発 --宇宙ビッグデータのAI分析に向けて--. 京都大学プレスリリース. 2019-10-08.|
Artificial Intelligence tool developed to predict the structure of the Universe. 京都大学プレスリリース. 2019-10-08.
|Rights:||© 2019. The American Astronomical Society. All rights reserved.|
|Appears in Collections:||Journal Articles|
Items in DSpace are protected by copyright, with all rights reserved, unless otherwise indicated.