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

Published by Issei FUJISHIRO on

Naoko SawadaMasanori Nakayama, Makoto Uemura, Issei Fujishiro

in Proceedings of IEEE Scientific Visualization Conference (SciVis), October 2018

[doi: 10.1109/SciVis.2018.8823802]

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 multi-dimensional 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.

Publication page in 2018 is here


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