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    Astronomers can now discover new stellar cluster members using Uncover, an interactive tool. This approach replaces manual methods, enabling astronomers to leverage domain expertise for better machine learning model selection in astronomy.

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    Area of Science:

    • Astronomy
    • Machine Learning
    • Data Science

    Background:

    • Identifying member stars in stellar clusters is crucial for astrophysical research.
    • Manual methods for finding new members are time-consuming and inefficient.
    • Novelty detection models offer potential but require careful hyper-parameter tuning.

    Purpose of the Study:

    • To present Uncover, an interactive tool designed to assist astronomers in identifying previously unknown member stars within stellar clusters.
    • To provide a novel approach for selecting appropriate hyper-parameter sets for flexible novelty detection models.
    • To empower astronomers to integrate their domain expertise into the machine learning model selection process.

    Main Methods:

    • Developed a five-step workflow to replace manual trial-and-error hyper-parameter optimization.
    • Incorporated interpretable summary statistics that models must adhere to.
    • Designed an interactive tool, Uncover, for data and task abstraction in astronomy.

    Main Results:

    • The proposed workflow effectively identifies suitable hyper-parameter sets for novelty detection models.
    • Uncover facilitates the use of domain expertise in quantifying model performance.
    • User studies with domain experts confirmed the tool's usability and usefulness.

    Conclusions:

    • Uncover offers a significant improvement over traditional methods for identifying member stars in stellar clusters.
    • The tool promotes a shift towards astronomers actively engaging with and modifying machine learning models.
    • This approach enhances the reliability and interpretability of machine learning applications in astronomical research.