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Using User-Guided Development to Teach Complex Scientific Tasks Through a Graphical User Interface

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Part of the book series: Lecture Notes in Computer Science ((LNISA,volume 12766))

Abstract

As more and more data are collected from the night sky, it becomes increasingly important to be able to analyze the data precisely and quickly by using computer programs. Given the importance of data analysis pipelines for telescopes we have developed a photometric pipeline, Photometry+, for the Great Basin Observatory (GBO), a 0.7-m robotic telescope located in the Great Basin National Park in Nevada. This photometric pipeline takes raw images of the night sky and measures the brightness of a star in the image. Studying the changes in the brightness of a star over time is crucial for learning more about variable objects such as supernovae and binary star systems. Photometry+ focuses on human-computer interaction (HCI) in addition to scientific results. The HCI goals of the proposed pipeline are to create a graphical user interface (GUI) that is easy to use, gives astronomers control of and confidence in the results of the program, and teaches students the process of differential photometry through use. User studies show that Photometry+ achieves these goals, cementing it as a new tool for professional astronomers looking to reduce the time they spend on data analysis while still obtaining publication-quality results and for students looking to learn the process alike. The program is publicly available and while its open source code has been designed for the GBO telescope it is flexible enough for use with data from any observatory.

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Acknowledgements

Great thanks to the Great Basin Observatory, Nevada, for their support of this project and the use of their telescope.

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Tudor, A.R., Plotkin, R.M., Shaw, A.W., Covington, A.E., Dascalu, S. (2021). Using User-Guided Development to Teach Complex Scientific Tasks Through a Graphical User Interface. In: Yamamoto, S., Mori, H. (eds) Human Interface and the Management of Information. Information-Rich and Intelligent Environments. HCII 2021. Lecture Notes in Computer Science(), vol 12766. Springer, Cham. https://doi.org/10.1007/978-3-030-78361-7_12

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  • DOI: https://doi.org/10.1007/978-3-030-78361-7_12

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