Three-dimensional spatial join count exploiting CPU optimized STR R-tree

Mitsuhashi, Ryuya; Kawashima, Hideyuki; Nishimichi, Takahiro; Tatebe, Osamu

doi:10.1109/bigdata.2016.7840944

Cited by 1 publication

(1 citation statement)

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“…We count pairs only in the same or the adjacent cells from which pairs can be closer than the matching scale. The code is then updated to employ a more sophisticated sort-tile-recursive (STR) R-tree scheme for a more efficient spatial indexing (Mitsuhashi et al 2016). Note that these different versions give identical results, and the difference lies only in the speed to perform the exact pair counting.…”

Section: Halo Catalogsmentioning

confidence: 99%

Dark Quest. I. Fast and Accurate Emulation of Halo Clustering Statistics and Its Application to Galaxy Clustering

Nishimichi

Takada

Takahashi

et al. 2019

ApJ

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We perform an ensemble of N-body simulations with 2048 3 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 auto-correlation as a function of halo masses, redshift, separations and cosmological models, based on the Principal Component Analysis and the 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 auto-correlation, 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 mass-concentration 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 the galaxy-galaxy weak lensing and the projected correlation function for any model within the wCDM cosmologies, in a few CPU seconds. Subject headings: large-scale structure of the universe -numerical simulations -machine learning

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Section: Halo Catalogsmentioning

confidence: 99%