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Taxonomic Analysis of Asteroids with Artificial Neural Networks

文献类型：期刊论文

作者Luo NP(罗南平)1,5; Wang XB(王晓彬)1,2,5; Gu SH(顾盛宏)1,2,5; Penttilä, Antti3; Muinonen, Karri3; Liu, Yisi4
刊名ASTRONOMICAL JOURNAL
出版日期2024
卷号167期号:1
ISSN号0004-6256
DOI10.3847/1538-3881/ad0b7a
产权排序第1完成单位
文献子类Article
英文摘要We study the surface composition of asteroids with visible and/or infrared spectroscopy. For example, asteroid taxonomy is based on the spectral features or multiple color indices in visible and near-infrared wavelengths. The composition of asteroids gives key information to understand their origin and evolution. However, we lack compositional information for faint asteroids due to the limits of ground-based observational instruments. In the near future, the Chinese Space Survey Telescope (CSST) will provide multiple colors and spectroscopic data for asteroids of apparent magnitude brighter than 25 and 23 mag, respectively. With the aim of analyzing the CSST spectroscopic data, we applied an algorithm using artificial neural networks (ANNs) to establish a preliminary classification model for asteroid taxonomy according to the design of the survey module of CSST. Using the SMASS II spectra and the Bus-Binzel taxonomic system, our ANN classification tool composed of five individual ANNs is constructed, and the accuracy of this classification system is higher than 92%. As the first application of our ANN tool, 64 spectra of 42 asteroids obtained by us in 2006 and 2007 with the 2.16 m telescope in the Xinglong station (Observatory Code 327) of National Astronomical Observatory of China are analyzed. The predicted labels of these spectra using our ANN tool are found to be reasonable when compared to their known taxonomic labels. Considering its accuracy and stability, our ANN tool can be applied to analyze CSST asteroid spectra in the future.
学科主题天文学
URL标识查看原文
出版地TEMPLE CIRCUS, TEMPLE WAY, BRISTOL BS1 6BE, ENGLAND
WOS关键词SPECTROSCOPIC SURVEY ; PHASE-II ; CLASSIFICATION
资助项目MOST divided by National Natural Science Foundation of China (NSFC)https://doi.org/10.13039/501100001809[11673063]; MOST divided by National Natural Science Foundation of China (NSFC)https://doi.org/10.13039/501100001809[12373069]; National Natural Science Foundation of China[1345115]; National Natural Science Foundation of China[1336546]; Research Council of Finland[CMS-CSST-2021-B08]; China Manned Space Project[G2021039001L]; Foreign Experts Project (FEP) of State Administration of Foreign Experts Affairs of China (SAFEA)[2021VMA0017]; Chinese Academy of Sciences President's International Fellowship Initiative (PIFI)
WOS研究方向Astronomy & Astrophysics
语种英语
WOS记录号WOS:001116566700001
出版者IOP Publishing Ltd
资助机构MOST divided by National Natural Science Foundation of China (NSFC)https://doi.org/10.13039/501100001809[11673063, 12373069] ; National Natural Science Foundation of China[1345115, 1336546] ; Research Council of Finland[CMS-CSST-2021-B08] ; China Manned Space Project[G2021039001L] ; Foreign Experts Project (FEP) of State Administration of Foreign Experts Affairs of China (SAFEA)[2021VMA0017] ; Chinese Academy of Sciences President's International Fellowship Initiative (PIFI)
版本出版稿
源URL[http://ir.ynao.ac.cn/handle/114a53/26443]  
专题云南天文台_系外行星研究组
云南天文台_中国科学院天体结构与演化重点实验室
作者单位1.Yunnan Observatories, CAS, Kunming, 650216, People's Republic of China; luonanping@ynao.ac.cn, wangxb@ynao.ac.cn;
2.Key Laboratory for the Structure and Evolution of Celestial Objects, Chinese Academy of Sciences, Kunming 650216, People's Republic of China;
3.Department of Physics, P.O. box 64, FI-00014 University of Helsinki, Finland;
4.Deep Space Exploration Laboratory, Beijing 100043, People's Republic of China
5.University of Chinese Academy of Sciences, Beijing 100049, People's Republic of China;
推荐引用方式
GB/T 7714		 Luo NP,Wang XB,Gu SH,et al. Taxonomic Analysis of Asteroids with Artificial Neural Networks[J]. ASTRONOMICAL JOURNAL,2024,167(1).
APA 罗南平,王晓彬,顾盛宏,Penttilä, Antti,Muinonen, Karri,&Liu, Yisi.(2024).Taxonomic Analysis of Asteroids with Artificial Neural Networks.ASTRONOMICAL JOURNAL,167(1).
MLA 罗南平,et al."Taxonomic Analysis of Asteroids with Artificial Neural Networks".ASTRONOMICAL JOURNAL 167.1(2024).

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来源：云南天文台

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