An investigation on space debris of unknown origin using proper elements and neural networks
Abstract Proper elements represent a dynamical fingerprint of an object’s inherent state and have been used by small-body taxonomists in characterizing asteroid families. Being linked to the underlying dynamical structure of orbits, Celletti, Pucacco, and Vartolomei have recently adop...
| Main Authors: | , |
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| Other Authors: | |
| Format: | Article |
| Language: | English |
| Published: |
Springer Netherlands
2023
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| Online Access: | https://hdl.handle.net/1721.1/151718 |
| _version_ | 1826216958686658560 |
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| author | Wu, Di Rosengren, Aaron J. |
| author2 | Massachusetts Institute of Technology. Department of Aeronautics and Astronautics |
| author_facet | Massachusetts Institute of Technology. Department of Aeronautics and Astronautics Wu, Di Rosengren, Aaron J. |
| author_sort | Wu, Di |
| collection | MIT |
| description | Abstract
Proper elements represent a dynamical fingerprint of an object’s inherent state and have been used by small-body taxonomists in characterizing asteroid families. Being linked to the underlying dynamical structure of orbits, Celletti, Pucacco, and Vartolomei have recently adopted these innate orbital parameters for the association of debris from breakup or collision into its parent satellite. Building from this rich astronomical heritage and recent foundations, we introduce an unsupervised learning method—density-based spatial clustering of applications with noise (DBSCAN)—to determine clusters of orbital debris in the space of proper elements. Data is taken from the space-object catalog of trackable Earth-orbiting objects in the form of two-line element sets. Proper elements for debris fragments in low-Earth orbit are computed using an ad hoc numerical scheme, akin to the state-of-the-art Fourier-series-based synthetic method for the asteroid domain. Given the heuristic nature of classical DBSCAN, we investigate the use of neural networks, trained on known families, to augment DBSCAN into a classification problem and apply it to analyst objects of unknown origin. |
| first_indexed | 2024-09-23T16:55:52Z |
| format | Article |
| id | mit-1721.1/151718 |
| institution | Massachusetts Institute of Technology |
| language | English |
| last_indexed | 2024-09-23T16:55:52Z |
| publishDate | 2023 |
| publisher | Springer Netherlands |
| record_format | dspace |
| spelling | mit-1721.1/1517182024-01-10T18:25:21Z An investigation on space debris of unknown origin using proper elements and neural networks Wu, Di Rosengren, Aaron J. Massachusetts Institute of Technology. Department of Aeronautics and Astronautics Abstract Proper elements represent a dynamical fingerprint of an object’s inherent state and have been used by small-body taxonomists in characterizing asteroid families. Being linked to the underlying dynamical structure of orbits, Celletti, Pucacco, and Vartolomei have recently adopted these innate orbital parameters for the association of debris from breakup or collision into its parent satellite. Building from this rich astronomical heritage and recent foundations, we introduce an unsupervised learning method—density-based spatial clustering of applications with noise (DBSCAN)—to determine clusters of orbital debris in the space of proper elements. Data is taken from the space-object catalog of trackable Earth-orbiting objects in the form of two-line element sets. Proper elements for debris fragments in low-Earth orbit are computed using an ad hoc numerical scheme, akin to the state-of-the-art Fourier-series-based synthetic method for the asteroid domain. Given the heuristic nature of classical DBSCAN, we investigate the use of neural networks, trained on known families, to augment DBSCAN into a classification problem and apply it to analyst objects of unknown origin. 2023-08-01T16:59:50Z 2023-08-01T16:59:50Z 2023-07-25 2023-07-30T03:14:59Z Article http://purl.org/eprint/type/JournalArticle https://hdl.handle.net/1721.1/151718 Celestial Mechanics and Dynamical Astronomy. 2023 Jul 25;135(4):44 PUBLISHER_CC en https://doi.org/10.1007/s10569-023-10157-0 Creative Commons Attribution http://creativecommons.org/licenses/by/4.0/ The Author(s) application/pdf Springer Netherlands Springer Netherlands |
| spellingShingle | Wu, Di Rosengren, Aaron J. An investigation on space debris of unknown origin using proper elements and neural networks |
| title | An investigation on space debris of unknown origin using proper elements and neural networks |
| title_full | An investigation on space debris of unknown origin using proper elements and neural networks |
| title_fullStr | An investigation on space debris of unknown origin using proper elements and neural networks |
| title_full_unstemmed | An investigation on space debris of unknown origin using proper elements and neural networks |
| title_short | An investigation on space debris of unknown origin using proper elements and neural networks |
| title_sort | investigation on space debris of unknown origin using proper elements and neural networks |
| url | https://hdl.handle.net/1721.1/151718 |
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