ExoSpotter: Few Shot Relevance Feedback For Learning High Recall Exoplanet Search
Transit photometry is a widely used method for searching exoplanets. For example, NASA’s Transiting Exoplanet Survey Satellite (TESS) utilizes this technique. However, identifying exoplanet candidates requires significant human effort to process light curves; a workflow with minimal human input is d...
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| Format: | Thesis |
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Massachusetts Institute of Technology
2024
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| Online Access: | https://hdl.handle.net/1721.1/153896 |
| _version_ | 1826197973471592448 |
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| author | Živanović, Goran |
| author2 | Ravela, Sai Czander |
| author_facet | Ravela, Sai Czander Živanović, Goran |
| author_sort | Živanović, Goran |
| collection | MIT |
| description | Transit photometry is a widely used method for searching exoplanets. For example, NASA’s Transiting Exoplanet Survey Satellite (TESS) utilizes this technique. However, identifying exoplanet candidates requires significant human effort to process light curves; a workflow with minimal human input is desirable. Unfortunately, very few labeled training data are available (i.e., light curves labeled as planet candidates), which makes automatic classification di cult. Here, we propose a new approach to identify planet candidates using relevance-feedback accelerated few-shot learning. We generate many labeled synthetic light curves with and without transits by combining a simple physics-based transit injection model with a statistics-based generative model seeded with abundant non-transiting (“noise”) light curve data. After comparing multiple methods, we selected and trained a generic XGBoost classifier offline on only unfolded and diffused synthetic light curves. We adapted it online by feeding back a few observed and misclassified light curves. The result is an exoplanet classifier with the currently best-known recall and precision. |
| first_indexed | 2024-09-23T10:56:40Z |
| format | Thesis |
| id | mit-1721.1/153896 |
| institution | Massachusetts Institute of Technology |
| last_indexed | 2024-09-23T10:56:40Z |
| publishDate | 2024 |
| publisher | Massachusetts Institute of Technology |
| record_format | dspace |
| spelling | mit-1721.1/1538962024-03-22T03:49:10Z ExoSpotter: Few Shot Relevance Feedback For Learning High Recall Exoplanet Search Živanović, Goran Ravela, Sai Czander Massachusetts Institute of Technology. Department of Electrical Engineering and Computer Science Transit photometry is a widely used method for searching exoplanets. For example, NASA’s Transiting Exoplanet Survey Satellite (TESS) utilizes this technique. However, identifying exoplanet candidates requires significant human effort to process light curves; a workflow with minimal human input is desirable. Unfortunately, very few labeled training data are available (i.e., light curves labeled as planet candidates), which makes automatic classification di cult. Here, we propose a new approach to identify planet candidates using relevance-feedback accelerated few-shot learning. We generate many labeled synthetic light curves with and without transits by combining a simple physics-based transit injection model with a statistics-based generative model seeded with abundant non-transiting (“noise”) light curve data. After comparing multiple methods, we selected and trained a generic XGBoost classifier offline on only unfolded and diffused synthetic light curves. We adapted it online by feeding back a few observed and misclassified light curves. The result is an exoplanet classifier with the currently best-known recall and precision. E.E. 2024-03-21T19:14:22Z 2024-03-21T19:14:22Z 2024-02 2024-02-21T18:02:04.036Z Thesis https://hdl.handle.net/1721.1/153896 In Copyright - Educational Use Permitted Copyright retained by author(s) https://rightsstatements.org/page/InC-EDU/1.0/ application/pdf Massachusetts Institute of Technology |
| spellingShingle | Živanović, Goran ExoSpotter: Few Shot Relevance Feedback For Learning High Recall Exoplanet Search |
| title | ExoSpotter: Few Shot Relevance Feedback For Learning High Recall Exoplanet Search |
| title_full | ExoSpotter: Few Shot Relevance Feedback For Learning High Recall Exoplanet Search |
| title_fullStr | ExoSpotter: Few Shot Relevance Feedback For Learning High Recall Exoplanet Search |
| title_full_unstemmed | ExoSpotter: Few Shot Relevance Feedback For Learning High Recall Exoplanet Search |
| title_short | ExoSpotter: Few Shot Relevance Feedback For Learning High Recall Exoplanet Search |
| title_sort | exospotter few shot relevance feedback for learning high recall exoplanet search |
| url | https://hdl.handle.net/1721.1/153896 |
| work_keys_str_mv | AT zivanovicgoran exospotterfewshotrelevancefeedbackforlearninghighrecallexoplanetsearch |