Statistical super resolution for data analysis and visualization of large scale cosmological simulations

Ko Chih Wang, Jiayi Xu, Jonathan Woodring, Han Wei Shen

Research output: Chapter in Book/Report/Conference proceedingConference contribution

3 Citations (Scopus)

Abstract

Cosmologists build simulations for the evolution of the universe using different initial parameters. By exploring the datasets from different simulation runs, cosmologists can understand the evolution of our universe and approach its initial conditions. A cosmological simulation nowadays can generate datasets on the order of petabytes. Moving datasets from the supercomputers to post data analysis machines is infeasible. We propose a novel approach called statistical super-resolution to tackle the big data problem for cosmological data analysis and visualization. It uses datasets from a few simulation runs to create a prior knowledge, which captures the relation between low-and high-resolution data. We apply in situ statistical down-sampling to datasets generated from simulation runs to minimize the requirements of I/O bandwidth and storage. High-resolution datasets are reconstructed from the statistical down-sampled data by using the prior knowledge for scientists to perform advanced data analysis and render high-quality visualizations.

Original languageEnglish
Title of host publicationProceedings - 2019 IEEE Pacific Visualization Symposium, PacificVis 2019
PublisherIEEE Computer Society
Pages303-312
Number of pages10
ISBN (Electronic)9781538692264
DOIs
Publication statusPublished - 2019 Apr
Externally publishedYes
Event12th IEEE Pacific Visualization Symposium, PacificVis 2019 - Bangkok, Thailand
Duration: 2019 Apr 232019 Apr 26

Publication series

NameIEEE Pacific Visualization Symposium
Volume2019-April
ISSN (Print)2165-8765
ISSN (Electronic)2165-8773

Conference

Conference12th IEEE Pacific Visualization Symposium, PacificVis 2019
Country/TerritoryThailand
CityBangkok
Period2019/04/232019/04/26

Keywords

  • Cosmological data
  • Ensemble data
  • In situ analysis

ASJC Scopus subject areas

  • Computer Graphics and Computer-Aided Design
  • Computer Vision and Pattern Recognition
  • Hardware and Architecture
  • Software

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