Uncover
- Author(s)
- Sebastian Ratzenböck, Verena Obermüller, Torsten Möller, Joao Alves, Immanuel Bomze
- Abstract
In this design study, we present Uncover, an interactive tool aimed at astronomers to find previously unidentified member stars in stellar clusters. We contribute data and task abstraction in the domain of astronomy and provide an approach for the non-trivial challenge of finding a suitable hyper-parameter set for highly flexible novelty detection models. We achieve this by substituting the tedious manual trial and error process, which usually results in finding a small subset of passable models with a five-step workflow approach. We utilize ranges of a priori defined, interpretable summary statistics models have to adhere to. Our goal is to enable astronomers to use their domain expertise to quantify model goodness effectively. We attempt to change the current culture of blindly accepting a machine learning model to one where astronomers build and modify a model based on their expertise. We evaluate the tools' usability and usefulness in a series of interviews with domain experts.
- Organisation(s)
- Research Network Data Science, Research Group Visualization and Data Analysis, Department of Astrophysics, Department of Statistics and Operations Research, Research Platform Governance of digital practices
- External organisation(s)
- Universität Wien
- Journal
- IEEE Transactions on Visualization and Computer Graphics
- Volume
- 29
- Pages
- 3855-3872
- No. of pages
- 18
- ISSN
- 1077-2626
- DOI
- https://doi.org/10.1109/TVCG.2022.3172560
- Publication date
- 05-2022
- Peer reviewed
- Yes
- Austrian Fields of Science 2012
- 103003 Astronomy, 103004 Astrophysics, 102019 Machine learning, 102001 Artificial intelligence
- Keywords
- ASJC Scopus subject areas
- Software, Signal Processing, Computer Vision and Pattern Recognition, Computer Graphics and Computer-Aided Design
- Portal url
- https://ucris.univie.ac.at/portal/en/publications/uncover(5e4b6a30-7b1e-4f5b-8d1c-f400172dc302).html