Taxonomic Analysis of Asteroids with Artificial Neural Networks

doi:10.3847/1538-3881/ad0b7a

	Taxonomic Analysis of Asteroids with Artificial Neural Networks
	Luo NP(罗南平)1,5; Wang XB(王晓彬)1,4,5; Gu SH(顾盛宏)1,4,5; Penttilä, Antti 3; Muinonen, Karri 3; Liu, Yisi 2
刊名	ASTRONOMICAL JOURNAL
	2024
卷号	167 期号:1
ISSN号	0004-6256
DOI	10.3847/1538-3881/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
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关键词	SPECTROSCOPIC SURVEY ; PHASE-II ; CLASSIFICATION
WOS研究方向	Astronomy & Astrophysics
语种	英语
出版者	IOP Publishing Ltd
WOS记录号	WOS:001116566700001
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)
内容类型	期刊论文
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
作者单位	1.University of Chinese Academy of Sciences, Beijing 100049, People's Republic of China; 2.Deep Space Exploration Laboratory, Beijing 100043, People's Republic of China 3.Department of Physics, P.O. box 64, FI-00014 University of Helsinki, Finland; 4.Key Laboratory for the Structure and Evolution of Celestial Objects, Chinese Academy of Sciences, Kunming 650216, People's Republic of China; 5.Yunnan Observatories, CAS, Kunming, 650216, People's Republic of China; luonanping@ynao.ac.cn, wangxb@ynao.ac.cn;
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).