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The Exoplanet Citizen Science Pipeline:Human Factors and Machine Learning

Creaner, Oisín, Preis, Anna, Ryan, Cormac and Gorchakova, Nika (2026) The Exoplanet Citizen Science Pipeline:Human Factors and Machine Learning. Proceedings of the International Astronomical Union, 20 (S387). pp. 97-101. ISSN 1743-9213

Abstract
We present the progress of work to streamline and simplify the process of exoplanet observation by citizen scientists. International collaborations such as ExoClock and Exoplanet Watch enable citizen scientists to use small telescopes to carry out transit observations. These studies provide essential supports for space missions Such as JWST and ARIEL. Contributions include maintenance or recovery of ephemerides, follow up confirmation and transit time variations. Ongoing observation programs benefit from a large pool of observers, with a wide variety of experience levels. Our projects work closely with these communities to streamline their observation pipelines and enable wider participation. Two complementary approaches are taken: Star Guide applies human-centric design and community consultation to identify points of friction within existing systems and provide complementary online tools and resources to reduce barriers to entry to the observing community. Machine Learning is used to accelerate data processing and automate steps which are currently manual, providing a streamlined tool for citizen science and a scalable solution for large-scale archival research.
Metadata
Item Type:Article (Published)
Refereed:Yes
Additional Information:Publisher Copyright: © The Author(s), 2026.
Uncontrolled Keywords:Astronomy and Astrophysics, Space and Planetary Science, Citizen Science, Exoplanet, Machine Learning
Subjects:Physics and Astronomy > Astronomy and Astrophysics
Earth and Planetary Sciences > Space and Planetary Science
School, Faculty or Unit:Faculty of Science & Health > School of Physical Sciences
Publisher:Cambridge University Press
Official URL:https://doi.org/10.1017/S1743921324001625
ID Code:32708
Deposited On:19 Oct 2026 11:11 by Pure Test . Last Modified 19 Oct 2026 11:11
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