Uncover: Toward Interpretable Models for Detecting New Star Cluster Members

Uncover: Toward Interpretable Models for Detecting New Star Cluster Members

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.

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Authors
  • Ratzenbock, Sebastian
  • Obermmüller, Verena
  • Möller, Torsten
  • Alves, Joao
  • Bomze, Immanuel M.
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Shortfacts
Category
Journal Paper
Divisions
Visualization and Data Analysis
Journal or Publication Title
IEEE Transactions on Visualization and Computer Graphics
ISSN
1077-2626
Publisher
IEEE
Page Range
p. 1
Date
5 May 2023
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