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

Naoko Sawada, Masanori Nakayama, Makoto Uemura, Issei Fujishiro

研究成果: Conference contribution

抄録

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.

本文言語English
ホスト出版物のタイトル2018 IEEE Scientific Visualization Conference, SciVis 2018 - Proceedings
編集者Berk Geveci, Gordon Kindlmann, Luis Gustavo Nonato
出版社Institute of Electrical and Electronics Engineers Inc.
ページ67-71
ページ数5
ISBN(電子版)9781538668825
DOI
出版ステータスPublished - 2018 10
イベント2018 IEEE Scientific Visualization Conference, SciVis 2018 - Berlin, Germany
継続期間: 2018 10 212018 10 26

出版物シリーズ

名前2018 IEEE Scientific Visualization Conference, SciVis 2018 - Proceedings

Conference

Conference2018 IEEE Scientific Visualization Conference, SciVis 2018
国/地域Germany
CityBerlin
Period18/10/2118/10/26

ASJC Scopus subject areas

  • コンピュータ グラフィックスおよびコンピュータ支援設計
  • メディア記述

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