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TimeTubes: Visual Fusion for Ameliorating Uncertainty of Blazar Datasets from Different Observatories

• Naoko Sawada
  Keio University, Yokohama, Kanagawa, Japan
• Masanori Nakayama nakayama (at) fj.ics.keio.ac.jp
  Keio University, Yokohama, Kanagawa, Japan
• Hsing-Yun Wu hsiang.yun.wu (at) acm.org
  Keio University, Yokohama, Kanagawa, Japan
• Makoto Uemura uemuram (at) hiroshima-u.ac.jp
  Hiroshima University, Higashi-Hiroshima, Hiroshima, Japan
• Issei Fujishiro fuji (at) ics.keio.ac.jp
  Keio University, Yokohama, Kanagawa, Japan

Abstract

Blazars are active galactic nuclei whose relativistic jets ejected from the central black hole are pointing toward the Earth. Astronomers have attempted to classify blazars, whereas it is difficult to analyze the time-dependent multivariate datasets with the conventional visualization methods, such as animeted scatterplot matrices. In our previous study, a new visualization scheme, called TimeTubes, was proposed, which allows the astronomers to analyze dynamic changes in and feature causality among the multiple time-varying variables. In this poster, we present a core idea to ameliorate data-inherent and mapping-inherent uncertainty through visual fusion of datasets for the same blazar from two different observatories.