Automated classification of MESSENGER plasma observations via unsupervised transfer learning
Our methodology demonstrates a proof of concept of the applicability of transfer learning for heliophysics, a machine learning technique where knowledge learned from one task is reused to perform a similar unsupervised learning task with additional fine tuning. We applied an unsupervised clustering...
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| Format: | Article |
| Language: | English |
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Frontiers Media S.A.
2025-07-01
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| Series: | Frontiers in Astronomy and Space Sciences |
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| Online Access: | https://www.frontiersin.org/articles/10.3389/fspas.2025.1608091/full |
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| author | Vicki Toy-Edens Wenli Mo Robert C. Allen Sarah K. Vines Savvas Raptis |
| author_facet | Vicki Toy-Edens Wenli Mo Robert C. Allen Sarah K. Vines Savvas Raptis |
| author_sort | Vicki Toy-Edens |
| collection | DOAJ |
| description | Our methodology demonstrates a proof of concept of the applicability of transfer learning for heliophysics, a machine learning technique where knowledge learned from one task is reused to perform a similar unsupervised learning task with additional fine tuning. We applied an unsupervised clustering algorithm, initially trained on data from the Magnetospheric Multiscale (MMS) mission at Earth, to MErcury Surface, Space ENvironment, GEochemistry, and Ranging (MESSENGER) observationsat Mercury to identify three distinct plasma regions: magnetosphere, magnetosheath, and solar wind. While our method requires modifications to the model from post-cleaning rules due to instrument effects, it allows for rapid classification using just a few examples to generate post-cleaning rules. Since there is no ground truth or standardized validation set to compare with, we compare our model’s result with published magnetopause and bow shock lists and find that the clustering algorithm is agreement with 67% of bow shock crossings and 74% of magnetopause crossings. These findings highlight the potential use of clustering algorithms across multiple planetary environments. |
| format | Article |
| id | doaj-art-4b802267cc694c0eba9be0f641882adc |
| institution | Kabale University |
| issn | 2296-987X |
| language | English |
| publishDate | 2025-07-01 |
| publisher | Frontiers Media S.A. |
| record_format | Article |
| series | Frontiers in Astronomy and Space Sciences |
| spelling | doaj-art-4b802267cc694c0eba9be0f641882adc2025-08-20T03:51:08ZengFrontiers Media S.A.Frontiers in Astronomy and Space Sciences2296-987X2025-07-011210.3389/fspas.2025.16080911608091Automated classification of MESSENGER plasma observations via unsupervised transfer learningVicki Toy-Edens0Wenli Mo1Robert C. Allen2Sarah K. Vines3Savvas Raptis4Johns Hopkins University Applied Physics Laboratory, Laurel, MD, United StatesJohns Hopkins University Applied Physics Laboratory, Laurel, MD, United StatesSouthwest Research Institute, San Antonio, TX, United StatesSouthwest Research Institute, San Antonio, TX, United StatesJohns Hopkins University Applied Physics Laboratory, Laurel, MD, United StatesOur methodology demonstrates a proof of concept of the applicability of transfer learning for heliophysics, a machine learning technique where knowledge learned from one task is reused to perform a similar unsupervised learning task with additional fine tuning. We applied an unsupervised clustering algorithm, initially trained on data from the Magnetospheric Multiscale (MMS) mission at Earth, to MErcury Surface, Space ENvironment, GEochemistry, and Ranging (MESSENGER) observationsat Mercury to identify three distinct plasma regions: magnetosphere, magnetosheath, and solar wind. While our method requires modifications to the model from post-cleaning rules due to instrument effects, it allows for rapid classification using just a few examples to generate post-cleaning rules. Since there is no ground truth or standardized validation set to compare with, we compare our model’s result with published magnetopause and bow shock lists and find that the clustering algorithm is agreement with 67% of bow shock crossings and 74% of magnetopause crossings. These findings highlight the potential use of clustering algorithms across multiple planetary environments.https://www.frontiersin.org/articles/10.3389/fspas.2025.1608091/fullmessengermachine learningunsupervised learningtransfer learningplasmaMMS |
| spellingShingle | Vicki Toy-Edens Wenli Mo Robert C. Allen Sarah K. Vines Savvas Raptis Automated classification of MESSENGER plasma observations via unsupervised transfer learning Frontiers in Astronomy and Space Sciences messenger machine learning unsupervised learning transfer learning plasma MMS |
| title | Automated classification of MESSENGER plasma observations via unsupervised transfer learning |
| title_full | Automated classification of MESSENGER plasma observations via unsupervised transfer learning |
| title_fullStr | Automated classification of MESSENGER plasma observations via unsupervised transfer learning |
| title_full_unstemmed | Automated classification of MESSENGER plasma observations via unsupervised transfer learning |
| title_short | Automated classification of MESSENGER plasma observations via unsupervised transfer learning |
| title_sort | automated classification of messenger plasma observations via unsupervised transfer learning |
| topic | messenger machine learning unsupervised learning transfer learning plasma MMS |
| url | https://www.frontiersin.org/articles/10.3389/fspas.2025.1608091/full |
| work_keys_str_mv | AT vickitoyedens automatedclassificationofmessengerplasmaobservationsviaunsupervisedtransferlearning AT wenlimo automatedclassificationofmessengerplasmaobservationsviaunsupervisedtransferlearning AT robertcallen automatedclassificationofmessengerplasmaobservationsviaunsupervisedtransferlearning AT sarahkvines automatedclassificationofmessengerplasmaobservationsviaunsupervisedtransferlearning AT savvasraptis automatedclassificationofmessengerplasmaobservationsviaunsupervisedtransferlearning |