Uncover
- Autor(en)
- Sebastian Ratzenböck, Verena Obermüller, Torsten Möller, Joao Alves, Immanuel Bomze
- Abstrakt
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(en)
- Forschungsverbund Data Science, Forschungsgruppe Visualization and Data Analysis, Institut für Astrophysik, Institut für Statistik und Operations Research, Forschungsplattform Governance of digital practices
- Journal
- IEEE Transactions on Visualization and Computer Graphics
- Band
- 29
- Seiten
- 3855-3872
- Anzahl der Seiten
- 18
- ISSN
- 1077-2626
- DOI
- https://doi.org/10.1109/TVCG.2022.3172560
- Publikationsdatum
- 05-2022
- Peer-reviewed
- Ja
- ÖFOS 2012
- 103003 Astronomie, 103004 Astrophysik, 102019 Machine Learning, 102001 Artificial Intelligence
- Schlagwörter
- ASJC Scopus Sachgebiete
- Software, Signal Processing, Computer Vision and Pattern Recognition, Computer Graphics and Computer-Aided Design
- Link zum Portal
- https://ucrisportal.univie.ac.at/de/publications/5e4b6a30-7b1e-4f5b-8d1c-f400172dc302