Using Contour Trees in the Analysis and Visualization of Radio Astronomy Data Cubes
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Abstract (arXiv)
The current generation of radio and millimeter telescopes, particularly the Atacama Large Millimeter Array (ALMA), offers enormous advances in observing capabilities. While these advances represent an unprecedented opportunity to facilitate scientific understanding, the increased complexity in the spatial and spectral structure of these ALMA data cubes lead to challenges in their interpretation. In this paper, we perform a feasibility study for applying topological data analysis and visualization techniques never before tested by the ALMA community. Through techniques based on contour trees, we seek to improve upon existing analysis and visualization workflows of ALMA data cubes, in terms of accuracy and speed in feature extraction. We review our application development process in building effective analysis and visualization capabilities for the astrophysicists. We also summarize effective design practices by identifying domain-specific needs of simplicity, integrability, and reproducibility, in order to best target and service the large astrophysics community.
Key facts. Cited by 4 papers in the map (data through 7 Oct 2026). First posted on arXiv 15 Apr 2017.
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Citing papers per year: 2021: 2; 2022: 0; 2023: 0; 2024: 1; 2025: 0; 2026: 1.
4 papers.
- 2024Position: Topological Deep Learning is the New Frontier for Relational Learningcited by 65
- 2021Topological Deep Learning: Classification Neural Networkscited by 5
- 2021Persistent Homology and Graphs Representation Learningcited by 5
- 2026TopoPilot: Reliable Conversational Workflow Automation for Topological Data Analysis and Visualizationcited by 3
- 2026TopoPilot: Reliable Conversational Workflow Automation for Topological Data Analysis and Visualization26 Mar 2026
- 2024Position: Topological Deep Learning is the New Frontier for Relational Learning14 Feb 2024
- 2021Persistent Homology and Graphs Representation Learning25 Feb 2021
- 2021Topological Deep Learning: Classification Neural Networks16 Feb 2021