Issue |
A&A
Volume 692, December 2024
|
|
---|---|---|
Article Number | A100 | |
Number of page(s) | 14 | |
Section | Planets, planetary systems, and small bodies | |
DOI | https://doi.org/10.1051/0004-6361/202451971 | |
Published online | 04 December 2024 |
Asteroid material classification based on multi-parameter constraints using artificial intelligence
1
CAS Key Laboratory of Optical Astronomy, National Astronomical Observatories, Chinese Academy of Sciences,
Beijing
100101,
China
2
University of Chinese Academy of Sciences,
Beijing
100049,
China
3
National Space Science Center, Chinese Academy of Sciences,
Beijing
100190,
China
4
School of Physics, Zhejiang University of Technology,
Hangzhou
310014,
China
5
Changchun Observatory, National Astronomical Observatories, Chinese Academy of Sciences,
Changchun
130117,
China
★ Corresponding authors; gejy@bao.ac.cn; xiaomingzhang@bao.ac; xjjiang@bao.ac.cn
Received:
23
August
2024
Accepted:
11
October
2024
Context. Material types of asteroids provide key clues to their evolutionary history and contained resources. The Gaia mission has released extensive low-resolution spectral observation data of small Solar System bodies. However, methods for classifying asteroids based on low-resolution space-based spectra are still inadequate, and do not fully leverage the complementary features of spectra and multiple intrinsic attributes of asteroids to achieve precise material classification.
Aims. Our goal is to propose a method with a higher generalization accuracy for asteroid material classification by integrating multi-source information, identifying optimal feature combinations for model inputs, and deepening the understanding of relationships among asteroid parameters.
Methods. The effective asteroid photometric, physical, and orbital parameters were screened using the information gain ratio and Spearman’s rank correlation coefficient. Then, artificial intelligence techniques were employed to combine asteroid spectra with the selected various parameters for six-class material classification. By comparing five machine learning models, we identified network structures with higher validation accuracy and stable generalization performance. Meanwhile, feature ablation experiments were conducted to determine the input parameter combinations suitable for different scenarios. Finally, based on the statistical results and model outputs, the constraint relationships among asteroid parameters were visualized and analyzed.
Results. The proposed AsterRF model achieved a validation accuracy of 92.2%, an improvement of approximately 7.8 percentage points compared to existing methods that use only spectra. V-type asteroids exhibited the highest classification accuracy, followed by A-type and D-type. X-type asteroids had the lowest precision and recall, and were easily confused with C-type. The model generally showed higher classification confidence for S-type asteroids. The top five attributes that the model focused on are the phase slope parameter (G), orbital type, albedo, H magnitude, and effective diameter. Additionally, the correlations between asteroid materials and other parameters were generally below 0.4.
Conclusions. Incorporating optimal asteroid parameter combinations can significantly enhance classification accuracy based on spectra. A dual-channel network that processes spectra and parameter inputs separately, and employs a self-attention mechanism for feature fusion is effective in combining multi-source asteroid information. Both the statistical correlations and model performance-based importance rankings of parameters contribute to understanding the constraint relationships among asteroid attributes.
Key words: methods: data analysis / techniques: miscellaneous / surveys / minor planets, asteroids: general
© The Authors 2024
Open Access article, published by EDP Sciences, under the terms of the Creative Commons Attribution License (https://creativecommons.org/licenses/by/4.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
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