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arXiv · 1903.07626

Constraining Cosmology with Big Data Statistics of Cosmological Graphs

Sungryong Hong·Donghui Jeong·Ho Seong Hwang·Juhan Kim·Sungwook E. Hong·Changbom Park·Arjun Dey·Milos Milosavljevic·Karl Gebhardt·Kyoung-Soo Lee
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Abstract

By utilizing large-scale graph analytic tools implemented in the modern Big Data platform, Apache Spark, we investigate the topological structure of gravitational clustering in five different universes produced by cosmological $N$-body simulations with varying parameters: (1) a WMAP 5-year compatible $\Lambda$CDM cosmology, (2) two different dark energy equation of state variants, and (3) two different cosmic matter density variants. For the Big Data calculations, we use a custom build of stand-alone Spark/Hadoop cluster at Korea Institute for Advanced Study (KIAS) and Dataproc Compute Engine in Google Cloud Platform (GCP) with the sample size ranging from 7 millions to 200 millions. We find that among the many possible graph-topological measures, three simple ones: (1) the average of number of neighbors (the so-called average vertex degree) $\alpha$, (2) closed-to-connected triple fraction (the so-called transitivity) $\tau_\Delta$, and (3) the cumulative number density $n_{s\ge5}$ of subcomponents with connected component size $s \ge 5$, can effectively discriminate among the five model universes. Since these graph-topological measures are in direct relation with the usual $n$-points correlation functions of the cosmic density field, graph-topological statistics powered by Big Data computational infrastructure opens a new, intuitive, and computationally efficient window into the dark Universe.

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Sungryong Hong, Donghui Jeong, Ho Seong Hwang, Juhan Kim, Sungwook E. Hong, Changbom Park, Arjun Dey, Milos Milosavljevic, Karl Gebhardt, Kyoung-Soo Lee. 2019-03-18. Constraining Cosmology with Big Data Statistics of Cosmological Graphs. https://doi.org/10.1093/mnras%2Fstaa566

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