The Exoplanet Citizen Science Pipeline: Human Factors and Machine Learning
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Zenodo
2024
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| _version_ | 1866902088128135168 |
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| author | Creaner, Oisin Preis, Anna Ryan, Cormac Gorchakova, Nika |
| author_facet | Creaner, Oisin Preis, Anna Ryan, Cormac Gorchakova, Nika |
| contents | <p>Exoplanets transits occur when an exoplanet passes between us and its star, blocking some of its from reaching us. Some of this light passes through the atmosphere of the planet. Space telescopes such as JWST and ARIEL can observe the faint effects this has on the starlight. However, transit timing can be uncertain, and this uncertainty<br>multiplies every time the planet completes an orbit. </p> <p>International collaborations such as ExoClock and Exoplanet Watch enable citizen scientists to use small telescopes to complement these observations by carrying out ongoing transit observations to maintain up-to-date transit timing records. Our projects work closely with these communities to streamline their observation pipelines and enable wider participation.</p> <p>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.</p> |
| format | Recurso digital |
| id | zenodo_https___doi_org_10_5281_zenodo_15237240 |
| institution | Zenodo |
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| publishDate | 2024 |
| publisher | Zenodo |
| record_format | zenodo |
| spellingShingle | The Exoplanet Citizen Science Pipeline: Human Factors and Machine Learning Creaner, Oisin Preis, Anna Ryan, Cormac Gorchakova, Nika <p>Exoplanets transits occur when an exoplanet passes between us and its star, blocking some of its from reaching us. Some of this light passes through the atmosphere of the planet. Space telescopes such as JWST and ARIEL can observe the faint effects this has on the starlight. However, transit timing can be uncertain, and this uncertainty<br>multiplies every time the planet completes an orbit. </p> <p>International collaborations such as ExoClock and Exoplanet Watch enable citizen scientists to use small telescopes to complement these observations by carrying out ongoing transit observations to maintain up-to-date transit timing records. Our projects work closely with these communities to streamline their observation pipelines and enable wider participation.</p> <p>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.</p> |
| title | The Exoplanet Citizen Science Pipeline: Human Factors and Machine Learning |
| url | https://doi.org/10.5281/zenodo.15237240 |