TimeTubes: Automatic extraction of observable blazar features from long-term, multi-dimensional datasets

Naoko Sawada, Masanori Nakayama, Makoto Uemura, Issei Fujishiro

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

Abstract

Blazars are attractive objects for astronomers to observe in order to demystify the relativistic jet. Astronomers need to classify characteristic temporal variation patterns and correlations of multidimensional time-dependent observed blazar datasets. Our visualization scheme, called TimeTubes, allows them to easily explore and analyze such datasets geometrically as a 3D volumetric tube. Even with TimeTubes, however, data analysis over such long-term datasets costs them so much labor and may cause a biased analysis. This paper, therefore, attempts to incorporate into the current prototype of TimeTubes, a new functionality: feature extraction, which supports astronomers' efficient data analysis by automatically extracting characteristic spatiotemporal subspaces.

Original languageEnglish
Title of host publication2018 IEEE Scientific Visualization Conference, SciVis 2018 - Proceedings
EditorsBerk Geveci, Gordon Kindlmann, Luis Gustavo Nonato
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages67-71
Number of pages5
ISBN (Electronic)9781538668825
DOIs
Publication statusPublished - 2018 Oct
Event2018 IEEE Scientific Visualization Conference, SciVis 2018 - Berlin, Germany
Duration: 2018 Oct 212018 Oct 26

Publication series

Name2018 IEEE Scientific Visualization Conference, SciVis 2018 - Proceedings

Conference

Conference2018 IEEE Scientific Visualization Conference, SciVis 2018
CountryGermany
CityBerlin
Period18/10/2118/10/26

Keywords

  • Empirical studies in visualization
  • Human-centered computing
  • Scientific visualization
  • Visualization
  • Visualization application domains

ASJC Scopus subject areas

  • Computer Graphics and Computer-Aided Design
  • Media Technology

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  • Cite this

    Sawada, N., Nakayama, M., Uemura, M., & Fujishiro, I. (2018). TimeTubes: Automatic extraction of observable blazar features from long-term, multi-dimensional datasets. In B. Geveci, G. Kindlmann, & L. G. Nonato (Eds.), 2018 IEEE Scientific Visualization Conference, SciVis 2018 - Proceedings (pp. 67-71). [8823802] (2018 IEEE Scientific Visualization Conference, SciVis 2018 - Proceedings). Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/SciVis.2018.8823802