TimeTubes: Visual fusion and validation for ameliorating uncertainties of blazar datasets from different observatories

Naoko Sawada, Masanori Nakayama, Hsiang Yun Wu, Makoto Uemura, Issei Fujishiro

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

2 Citations (Scopus)

Abstract

Astronomers have been observing blazars to solve the mystery of the relativistic jet. A technique called TimeTubes uses a 3D volumetric tube to visualize the time-dependent multivariate observed datasets and allows astronomers to interactively analyze the dynamic behavior of and relationship among those variables. However, the observed datasets themselves exhibit uncertainty due to their errors and missing periods, whereas periods interpolated by TimeTubes result in a different type of uncertainty. In this paper, we present a technique for ameliorating such data-and mappinginherent uncertainties: visual fusion of datasets for the same blazar from two different observatories. Visual data fusion with Time-Tubes enables astronomers to validate the datasets in a meticulous manner.

Original languageEnglish
Title of host publicationCGI 2017 - Proceedings of the 2017 Computer Graphics International Conference
PublisherAssociation for Computing Machinery
VolumePart F128640
ISBN (Electronic)9781450352284
DOIs
Publication statusPublished - 2017 Jun 27
Event2017 Computer Graphics International Conference, CGI 2017 - Yokohama, Japan
Duration: 2017 Jun 272017 Jun 30

Other

Other2017 Computer Graphics International Conference, CGI 2017
CountryJapan
CityYokohama
Period17/6/2717/6/30

Fingerprint

Observatories
Data fusion
Uncertainty

Keywords

  • Astrophysics
  • Blazer
  • Time-varying multivariate visualization
  • Uncertainty visualization

ASJC Scopus subject areas

  • Human-Computer Interaction
  • Computer Networks and Communications
  • Computer Vision and Pattern Recognition
  • Software

Cite this

Sawada, N., Nakayama, M., Wu, H. Y., Uemura, M., & Fujishiro, I. (2017). TimeTubes: Visual fusion and validation for ameliorating uncertainties of blazar datasets from different observatories. In CGI 2017 - Proceedings of the 2017 Computer Graphics International Conference (Vol. Part F128640). [a14] Association for Computing Machinery. https://doi.org/10.1145/3095140.3095154

TimeTubes : Visual fusion and validation for ameliorating uncertainties of blazar datasets from different observatories. / Sawada, Naoko; Nakayama, Masanori; Wu, Hsiang Yun; Uemura, Makoto; Fujishiro, Issei.

CGI 2017 - Proceedings of the 2017 Computer Graphics International Conference. Vol. Part F128640 Association for Computing Machinery, 2017. a14.

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

Sawada, N, Nakayama, M, Wu, HY, Uemura, M & Fujishiro, I 2017, TimeTubes: Visual fusion and validation for ameliorating uncertainties of blazar datasets from different observatories. in CGI 2017 - Proceedings of the 2017 Computer Graphics International Conference. vol. Part F128640, a14, Association for Computing Machinery, 2017 Computer Graphics International Conference, CGI 2017, Yokohama, Japan, 17/6/27. https://doi.org/10.1145/3095140.3095154
Sawada N, Nakayama M, Wu HY, Uemura M, Fujishiro I. TimeTubes: Visual fusion and validation for ameliorating uncertainties of blazar datasets from different observatories. In CGI 2017 - Proceedings of the 2017 Computer Graphics International Conference. Vol. Part F128640. Association for Computing Machinery. 2017. a14 https://doi.org/10.1145/3095140.3095154
Sawada, Naoko ; Nakayama, Masanori ; Wu, Hsiang Yun ; Uemura, Makoto ; Fujishiro, Issei. / TimeTubes : Visual fusion and validation for ameliorating uncertainties of blazar datasets from different observatories. CGI 2017 - Proceedings of the 2017 Computer Graphics International Conference. Vol. Part F128640 Association for Computing Machinery, 2017.
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