Visualization in astrophysics: developing new methods, discovering our universe, and educating the earth

Typology categories used in this survey paper.
A typology of task-driven (primary), technique-driven (secondary), and topic-driven (tertiary) categories used in this survey paper.
Abstract
We present a state-of-the-art report on visualization in astrophysics. We survey representative papers from both astrophysics and visualization and provide a taxonomy of existing approaches based on data analysis tasks. The approaches are classified based on five categories: data wrangling, data exploration, feature identification, object reconstruction, as well as education and outreach. Our unique contribution is to combine the diverse viewpoints from both astronomers and visualization experts to identify challenges and opportunities for visualization in astrophysics. The main goal is to provide a reference point to bring modern data analysis and visualization techniques to the rich datasets in astrophysics.
Materials
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Authors
Fangfei Lan
Michael Young
Lauren Anderson
Anders Ynnerman
Alexander Bock
Angus G. Forbes
Juna A. Kollmeier
Bei Wang
Citation
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Visualization in astrophysics: developing new methods, discovering our universe, and educating the earth

Fangfei Lan, Michael Young, Lauren Anderson, Anders Ynnerman, Alexander Bock, Michelle A. Borkin, Angus G. Forbes, Juna A. Kollmeier, and Bei Wang. Computer Graphics Forum—CGF. 2021. DOI: 10.1111/cgf.14332

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