Asteroid Types, Albedos, and Diameters Catalog from Gaia DR3: Intelligent Inversion Results via Multisource Information Fusion
The Gaia mission’s third data release (Gaia DR3) offers extensive observations of small solar system objects, presenting a key opportunity to expand the asteroid property database. Taxonomy, diameter, and albedo are fundamental physical parameters for characterizing asteroids. In the absence of ther...
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IOP Publishing
2025-01-01
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| Series: | The Astrophysical Journal Supplement Series |
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| Online Access: | https://doi.org/10.3847/1538-4365/adefe1 |
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| author | Jiayi Ge Xiaoming Zhang Juan Li Huijuan Wang Dawei Xu Xiaojun Jiang |
| author_facet | Jiayi Ge Xiaoming Zhang Juan Li Huijuan Wang Dawei Xu Xiaojun Jiang |
| author_sort | Jiayi Ge |
| collection | DOAJ |
| description | The Gaia mission’s third data release (Gaia DR3) offers extensive observations of small solar system objects, presenting a key opportunity to expand the asteroid property database. Taxonomy, diameter, and albedo are fundamental physical parameters for characterizing asteroids. In the absence of thermal infrared observations, diameter estimates primarily rely on the Bowell relationship, with reliability dominated by geometric albedo and absolute magnitude. To improve inversion accuracy using optical data and to obtain parameters for a larger number of asteroids, we propose an asteroid albedo-diameter joint inversion method (AadRF) that integrates low-resolution spectra, orbital dynamics, and physical parameters through artificial intelligence (AI). Cross validation and independent testing show that AadRF reduces the root mean square error for albedo and diameter predictions by 64.0 percentage points and 70.2 percentage points, respectively, compared to the traditional method, with corresponding mean absolute percentage errors of 28.9% and 17.5%. The model’s advantage lies in its fusion of multisource information, embedded domain knowledge, and a hybrid deep learning–random forest architecture, which together enhance generalization. Applied to Gaia DR3, the proposed methods yield a catalog of taxonomic types, albedos, and diameters for 58,168 asteroids. Compared to existing databases, it expands the number of spectrally classified samples by nearly tenfold and adds approximately 18,100 new entries for both albedo and diameter. Further statistics reveal distinct patterns in size distribution, albedo clustering, and taxonomic composition across orbital populations. This study demonstrates the potential of AI-driven, multisource fusion for estimating asteroid parameters from large data sets and broader applications in small body property inversion. |
| format | Article |
| id | doaj-art-4b44e91b79e54329a0ff3c1fdb225eb1 |
| institution | Kabale University |
| issn | 0067-0049 |
| language | English |
| publishDate | 2025-01-01 |
| publisher | IOP Publishing |
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| series | The Astrophysical Journal Supplement Series |
| spelling | doaj-art-4b44e91b79e54329a0ff3c1fdb225eb12025-08-20T05:29:08ZengIOP PublishingThe Astrophysical Journal Supplement Series0067-00492025-01-0128011710.3847/1538-4365/adefe1Asteroid Types, Albedos, and Diameters Catalog from Gaia DR3: Intelligent Inversion Results via Multisource Information FusionJiayi Ge0https://orcid.org/0000-0003-3569-2775Xiaoming Zhang1https://orcid.org/0009-0008-4588-5283Juan Li2Huijuan Wang3https://orcid.org/0000-0003-3271-9709Dawei Xu4Xiaojun Jiang5CAS Key Laboratory of Optical Astronomy, National Astronomical Observatories , Chinese Academy of Sciences, Beijing 100101, People’s Republic of China ; gejy@bao.ac.cn, xiaomingzhang@bao.ac.cn; University of Chinese Academy of Sciences , Beijing 100049, People’s Republic of ChinaCAS Key Laboratory of Optical Astronomy, National Astronomical Observatories , Chinese Academy of Sciences, Beijing 100101, People’s Republic of China ; gejy@bao.ac.cn, xiaomingzhang@bao.ac.cn; University of Chinese Academy of Sciences , Beijing 100049, People’s Republic of ChinaCAS Key Laboratory of Optical Astronomy, National Astronomical Observatories , Chinese Academy of Sciences, Beijing 100101, People’s Republic of China ; gejy@bao.ac.cn, xiaomingzhang@bao.ac.cn; University of Chinese Academy of Sciences , Beijing 100049, People’s Republic of ChinaCAS Key Laboratory of Optical Astronomy, National Astronomical Observatories , Chinese Academy of Sciences, Beijing 100101, People’s Republic of China ; gejy@bao.ac.cn, xiaomingzhang@bao.ac.cn; University of Chinese Academy of Sciences , Beijing 100049, People’s Republic of ChinaUniversity of Chinese Academy of Sciences , Beijing 100049, People’s Republic of China; Key Laboratory of Space Astronomy and Technology, National Astronomical Observatories , Chinese Academy of Sciences, Beijing 100101, People’s Republic of ChinaCAS Key Laboratory of Optical Astronomy, National Astronomical Observatories , Chinese Academy of Sciences, Beijing 100101, People’s Republic of China ; gejy@bao.ac.cn, xiaomingzhang@bao.ac.cn; University of Chinese Academy of Sciences , Beijing 100049, People’s Republic of ChinaThe Gaia mission’s third data release (Gaia DR3) offers extensive observations of small solar system objects, presenting a key opportunity to expand the asteroid property database. Taxonomy, diameter, and albedo are fundamental physical parameters for characterizing asteroids. In the absence of thermal infrared observations, diameter estimates primarily rely on the Bowell relationship, with reliability dominated by geometric albedo and absolute magnitude. To improve inversion accuracy using optical data and to obtain parameters for a larger number of asteroids, we propose an asteroid albedo-diameter joint inversion method (AadRF) that integrates low-resolution spectra, orbital dynamics, and physical parameters through artificial intelligence (AI). Cross validation and independent testing show that AadRF reduces the root mean square error for albedo and diameter predictions by 64.0 percentage points and 70.2 percentage points, respectively, compared to the traditional method, with corresponding mean absolute percentage errors of 28.9% and 17.5%. The model’s advantage lies in its fusion of multisource information, embedded domain knowledge, and a hybrid deep learning–random forest architecture, which together enhance generalization. Applied to Gaia DR3, the proposed methods yield a catalog of taxonomic types, albedos, and diameters for 58,168 asteroids. Compared to existing databases, it expands the number of spectrally classified samples by nearly tenfold and adds approximately 18,100 new entries for both albedo and diameter. Further statistics reveal distinct patterns in size distribution, albedo clustering, and taxonomic composition across orbital populations. This study demonstrates the potential of AI-driven, multisource fusion for estimating asteroid parameters from large data sets and broader applications in small body property inversion.https://doi.org/10.3847/1538-4365/adefe1AsteroidsAstronomy databasesGaia |
| spellingShingle | Jiayi Ge Xiaoming Zhang Juan Li Huijuan Wang Dawei Xu Xiaojun Jiang Asteroid Types, Albedos, and Diameters Catalog from Gaia DR3: Intelligent Inversion Results via Multisource Information Fusion The Astrophysical Journal Supplement Series Asteroids Astronomy databases Gaia |
| title | Asteroid Types, Albedos, and Diameters Catalog from Gaia DR3: Intelligent Inversion Results via Multisource Information Fusion |
| title_full | Asteroid Types, Albedos, and Diameters Catalog from Gaia DR3: Intelligent Inversion Results via Multisource Information Fusion |
| title_fullStr | Asteroid Types, Albedos, and Diameters Catalog from Gaia DR3: Intelligent Inversion Results via Multisource Information Fusion |
| title_full_unstemmed | Asteroid Types, Albedos, and Diameters Catalog from Gaia DR3: Intelligent Inversion Results via Multisource Information Fusion |
| title_short | Asteroid Types, Albedos, and Diameters Catalog from Gaia DR3: Intelligent Inversion Results via Multisource Information Fusion |
| title_sort | asteroid types albedos and diameters catalog from gaia dr3 intelligent inversion results via multisource information fusion |
| topic | Asteroids Astronomy databases Gaia |
| url | https://doi.org/10.3847/1538-4365/adefe1 |
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