Identifying Telescope Usage in Astrophysics Publications: A Machine Learning Framework for Institutional Research Management at Observatories
Large scientific institutions, such as the Space Telescope Science Institute, track the usage of their facilities to understand the needs of the research community. Astrophysicists incorporate facility usage data into their scientific publications, embedding this information in plain text. Tradition...
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| Format: | Article |
| Language: | English |
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IOP Publishing
2024-01-01
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| Series: | The Astronomical Journal |
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| Online Access: | https://doi.org/10.3847/1538-3881/ad9026 |
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| author | Vicente Amado Olivo Wolfgang Kerzendorf Brian Cherinka Joshua V. Shields Annie Didier Katharina von der Wense |
| author_facet | Vicente Amado Olivo Wolfgang Kerzendorf Brian Cherinka Joshua V. Shields Annie Didier Katharina von der Wense |
| author_sort | Vicente Amado Olivo |
| collection | DOAJ |
| description | Large scientific institutions, such as the Space Telescope Science Institute, track the usage of their facilities to understand the needs of the research community. Astrophysicists incorporate facility usage data into their scientific publications, embedding this information in plain text. Traditional automatic search queries prove unreliable for accurate tracking due to the misidentification of facility names in plain text. As automatic search queries fail, researchers are required to manually classify publications for facility usage, which consumes valuable research time. In this work, we introduce a machine learning classification framework for the automatic identification of facility usage of observation sections in astrophysics publications. Our framework identifies sentences containing telescope mission keywords (e.g., Kepler and TESS) in each publication. Subsequently, the identified sentences are transformed using term frequency–inverse document frequency and classified with a support vector machine. The classification framework leverages the context surrounding the identified telescope mission keywords to provide relevant information to the classifier. The framework successfully classifies the usage of MAST-hosted missions with a 92.9% accuracy. Furthermore, our framework demonstrates robustness when compared to other approaches, considering common metrics and computational complexity. The framework’s interpretability makes it adaptable for use across observatories and other scientific facilities worldwide. |
| format | Article |
| id | doaj-art-c7e9e03b0394436b829d296167a0eccb |
| institution | OA Journals |
| issn | 1538-3881 |
| language | English |
| publishDate | 2024-01-01 |
| publisher | IOP Publishing |
| record_format | Article |
| series | The Astronomical Journal |
| spelling | doaj-art-c7e9e03b0394436b829d296167a0eccb2025-08-20T02:34:39ZengIOP PublishingThe Astronomical Journal1538-38812024-01-0116914210.3847/1538-3881/ad9026Identifying Telescope Usage in Astrophysics Publications: A Machine Learning Framework for Institutional Research Management at ObservatoriesVicente Amado Olivo0https://orcid.org/0000-0003-2248-0941Wolfgang Kerzendorf1https://orcid.org/0000-0002-0479-7235Brian Cherinka2https://orcid.org/0000-0002-4289-7923Joshua V. Shields3https://orcid.org/0000-0002-1560-5286Annie Didier4Katharina von der Wense5Department of Computational Mathematics, Science, and Engineering, Michigan State University , East Lansing, MI 48824, USA ; amadovic@msu.eduDepartment of Computational Mathematics, Science, and Engineering, Michigan State University , East Lansing, MI 48824, USA ; amadovic@msu.edu; Department of Physics and Astronomy, Michigan State University , East Lansing, MI 48824, USASpace Telescope Science Institute , Baltimore, MD 21218, USADepartment of Physics and Astronomy, Michigan State University , East Lansing, MI 48824, USAOutrider, Golden, CO 80403, USADepartment of Computer Science, University of Colorado Boulder , Boulder, CO 80309, USA; Institute of Computer Science, Johannes Gutenberg University Mainz , 55128 Mainz, GermanyLarge scientific institutions, such as the Space Telescope Science Institute, track the usage of their facilities to understand the needs of the research community. Astrophysicists incorporate facility usage data into their scientific publications, embedding this information in plain text. Traditional automatic search queries prove unreliable for accurate tracking due to the misidentification of facility names in plain text. As automatic search queries fail, researchers are required to manually classify publications for facility usage, which consumes valuable research time. In this work, we introduce a machine learning classification framework for the automatic identification of facility usage of observation sections in astrophysics publications. Our framework identifies sentences containing telescope mission keywords (e.g., Kepler and TESS) in each publication. Subsequently, the identified sentences are transformed using term frequency–inverse document frequency and classified with a support vector machine. The classification framework leverages the context surrounding the identified telescope mission keywords to provide relevant information to the classifier. The framework successfully classifies the usage of MAST-hosted missions with a 92.9% accuracy. Furthermore, our framework demonstrates robustness when compared to other approaches, considering common metrics and computational complexity. The framework’s interpretability makes it adaptable for use across observatories and other scientific facilities worldwide.https://doi.org/10.3847/1538-3881/ad9026Support vector machineRandom ForestsClassificationOpen source software |
| spellingShingle | Vicente Amado Olivo Wolfgang Kerzendorf Brian Cherinka Joshua V. Shields Annie Didier Katharina von der Wense Identifying Telescope Usage in Astrophysics Publications: A Machine Learning Framework for Institutional Research Management at Observatories The Astronomical Journal Support vector machine Random Forests Classification Open source software |
| title | Identifying Telescope Usage in Astrophysics Publications: A Machine Learning Framework for Institutional Research Management at Observatories |
| title_full | Identifying Telescope Usage in Astrophysics Publications: A Machine Learning Framework for Institutional Research Management at Observatories |
| title_fullStr | Identifying Telescope Usage in Astrophysics Publications: A Machine Learning Framework for Institutional Research Management at Observatories |
| title_full_unstemmed | Identifying Telescope Usage in Astrophysics Publications: A Machine Learning Framework for Institutional Research Management at Observatories |
| title_short | Identifying Telescope Usage in Astrophysics Publications: A Machine Learning Framework for Institutional Research Management at Observatories |
| title_sort | identifying telescope usage in astrophysics publications a machine learning framework for institutional research management at observatories |
| topic | Support vector machine Random Forests Classification Open source software |
| url | https://doi.org/10.3847/1538-3881/ad9026 |
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