Efficient Parallel Algorithm for Estimating Higher-order Polyspectra

Nonlinearities in the gravitational evolution, galaxy bias, and redshift-space distortion drive the observed galaxy density fields away from the initial near-Gaussian states. Exploiting such a non-Gaussian galaxy density field requires measuring higher-order correlation functions, or, its Fourier co...

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Bibliographic Details
Published in:The Astronomical journal Vol. 158; no. 3; pp. 116 - 126
Main Authors: Tomlinson, Joseph, Jeong, Donghui, Kim, Juhan
Format: Journal Article
Language:English
Published: Madison The American Astronomical Society 01.09.2019
IOP Publishing
Subjects:
Algorithms
Astronomy
Correlation analysis
Density
Estimation
Fast Fourier transformations
Fourier transforms
Galactic evolution
Galaxies
large-scale structure of universe
methods: data analysis
Parallel processing
Red shift
ISSN:0004-6256, 1538-3881, 1538-3881
Online Access:Get full text
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Summary:Nonlinearities in the gravitational evolution, galaxy bias, and redshift-space distortion drive the observed galaxy density fields away from the initial near-Gaussian states. Exploiting such a non-Gaussian galaxy density field requires measuring higher-order correlation functions, or, its Fourier counterpart, polyspectra. Here, we present an efficient parallel algorithm for estimating higher-order polyspectra. Based upon the Scoccimarro estimator, the estimator avoids direct sampling of polygons using the fast Fourier transform, and the parallelization overcomes the large memory requirement of the original estimator. In particular, we design the memory layout to minimize the inter-CPU communications, which excels in the code performance.
Bibliography:Galaxies and Cosmology
AAS17449
ObjectType-Article-1
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ISSN:0004-6256
1538-3881
1538-3881
DOI:10.3847/1538-3881/ab3223