Journal
ASTRONOMICAL JOURNAL
Volume 158, Issue 3, Pages -Publisher
IOP Publishing Ltd
DOI: 10.3847/1538-3881/ab3223
Keywords
large-scale structure of universe; methods: data analysis
Categories
Funding
- NSF [AST-1517363]
- NASA ATP program [80NSSC18K1103]
- National Research Foundation of Korea [KG039603] Funding Source: Korea Institute of Science & Technology Information (KISTI), National Science & Technology Information Service (NTIS)
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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.
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