Transformer-based Approach for Accurate Asteroid Spectra taxonomy and albedo estimation
Fuente:
arXiv
Enregistré dans:
| Auteurs principaux: | , , , , |
|---|---|
| Format: | Preprint |
| Publié: |
2025
|
| Sujets: | |
| Accès en ligne: | |
| Tags: |
Ajouter un tag
Pas de tags, Soyez le premier à ajouter un tag!
|
| _version_ | 1866912242339938304 |
|---|---|
| author | Tang, Yijun Yunxiao, Jiang Feng, Yuxiang Zhang, Xiaoming Jiang, Xiaojun |
| author_facet | Tang, Yijun Yunxiao, Jiang Feng, Yuxiang Zhang, Xiaoming Jiang, Xiaojun |
| contents | China plans to launch a probe (Tianwen-2) around 2025, mainly for exploring the near-Earth asteroid 2016 HO3 . The mission involves close-range exploration, landing, and mining operations that require three-dimensional modeling of the asteroid, which requires prior knowledge of its material composition and uniformity. This information is crucial in progressive or ground exploration processes. Our research focuses on high-precision intelligent inversion of complex physical properties of asteroids based on spectral data, providing support for further analysis of aster oid materials, density, and structure. We have developed a platform for asteroid spectral classification, albedo estimation, and composition analysis, which includes three types of neural networks based on Transformer attention mechanism: One for spectral classification, achieving a four-class classification accuracy of 97.28% and an eleven-class classification accuracy of 95.69%; second one for albedo estimation, with an average absolute error of 0.0308 in S-type asteroid albedo estimation, and the third one for composition analysis, with a predicted spectral angular distance of only 0.0340 and a root mean square error of 0.1759 for the abundance of end members. These results indicate that our network can provide high-precision asteroid spectral classification, albedo estimation, and composition analysis results. In addition, we utilized the platform to analyze and provide results for six asteroids. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2502_16458 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Transformer-based Approach for Accurate Asteroid Spectra taxonomy and albedo estimation Tang, Yijun Yunxiao, Jiang Feng, Yuxiang Zhang, Xiaoming Jiang, Xiaojun Earth and Planetary Astrophysics Instrumentation and Methods for Astrophysics China plans to launch a probe (Tianwen-2) around 2025, mainly for exploring the near-Earth asteroid 2016 HO3 . The mission involves close-range exploration, landing, and mining operations that require three-dimensional modeling of the asteroid, which requires prior knowledge of its material composition and uniformity. This information is crucial in progressive or ground exploration processes. Our research focuses on high-precision intelligent inversion of complex physical properties of asteroids based on spectral data, providing support for further analysis of aster oid materials, density, and structure. We have developed a platform for asteroid spectral classification, albedo estimation, and composition analysis, which includes three types of neural networks based on Transformer attention mechanism: One for spectral classification, achieving a four-class classification accuracy of 97.28% and an eleven-class classification accuracy of 95.69%; second one for albedo estimation, with an average absolute error of 0.0308 in S-type asteroid albedo estimation, and the third one for composition analysis, with a predicted spectral angular distance of only 0.0340 and a root mean square error of 0.1759 for the abundance of end members. These results indicate that our network can provide high-precision asteroid spectral classification, albedo estimation, and composition analysis results. In addition, we utilized the platform to analyze and provide results for six asteroids. |
| title | Transformer-based Approach for Accurate Asteroid Spectra taxonomy and albedo estimation |
| topic | Earth and Planetary Astrophysics Instrumentation and Methods for Astrophysics |
| url | https://arxiv.org/abs/2502.16458 |