A Method of Stellar Spectral Classification Based on Map/Reduce Distributed Computing
文献类型：期刊论文
| 作者 | Pan Jing-chang1; Wang Jie1 ; Jiang Bin1; Luo A-li1,2; Wei Peng2; Zheng Qiang3
|
| 刊名 | SPECTROSCOPY AND SPECTRAL ANALYSIS
 |
| 出版日期 | 2016-08-01 |
| 卷号 | 36期号:8页码:2651-2654 |
| 关键词 | Lick line index Stellar spectral classification Hadoop |
| 英文摘要 | Celestial spectrum contains a great deal of astrophysical information. Through the analysis of spectra, people can get the physical information of celestial bodies, as well as their chemical composition and atmospheric parameters. With the implementation of LAMOST, SDSS telescopes and other large-scale surveys, massive spectral data will be produced, especially along with the formal operation of LAMOST, 2 000 to 4 000 spectral data will be generated each observation night. It requires more efficient processing technology to cope with such massive spectra. Automatic classification of stellar spectra is a basic content of spectral processing. The main purpose of this paper is to research the automatic classification of massive stellar spectra. The Lick index is a set of standard indices defined in astronomical spectra to describe the spectral intensity of spectral lines, which represent the physical characteristics of spectra. Lick index is a relatively wide spectral characteristics, each line index is named after the most prominent absorption line. In this paper, the Bayesian method is used to classify stellar spectra based on Lick line index, which divides stellar spectra to three subtypes: F, G, K. First of all, Lick line index of spectra is calculated as the characteristic vector of spectra, and then Bayesian method is used to classify these spectra. For massive spectra, the computation of Lick indices and the spectral classification using Bayesian decision method are implemented on Hadoop. With use of the high throughput and good fault tolerance of HDFS, combined with the advantages of MapReduce parallel programming model, the efficiency of analysis and processing for massive spectral data have been improved significantly. The main innovative contributions of this thesis are as follows. (1) Using Lick indices as the characteristic to classify stellar spectra based on Bayesian decision method. (2) Implementing parallel computation of Lick indices and parallel classification of stellar spectra using Bayesian based on Hadoop MapReduce distributed computing framework. |
| WOS标题词 | Science & Technology ; Technology |
| 类目[WOS] | Spectroscopy |
| 研究领域[WOS] | Spectroscopy |
| 关键词[WOS] | ABSORPTION-LINE INDEXES ; LAMOST ; STARS |
| 收录类别 | SCI |
| 语种 | 英语 |
| WOS记录号 | WOS:000382096600052 |
| 源URL | [http://ir.bao.ac.cn/handle/114a11/6796]  |
| 专题 | 国家天文台_光学天文研究部 |
| 作者单位 | 1.Shandong Univ, Sch Mech Elect & Informat Engn, Weihai 264209, Weihai, Peoples R China 2.Chinese Acad Sci, Key Lab Opt Astron, Natl Astron Observ, Beijing 100012, Peoples R China 3.Yantai Univ, Coll Comp & Control Engn, Yantai 264005, Peoples R China |
| 推荐引用方式 GB/T 7714 | Pan Jing-chang,Wang Jie,Jiang Bin,et al. A Method of Stellar Spectral Classification Based on Map/Reduce Distributed Computing[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS,2016,36(8):2651-2654. |
| APA | Pan Jing-chang,Wang Jie,Jiang Bin,Luo A-li,Wei Peng,&Zheng Qiang.(2016).A Method of Stellar Spectral Classification Based on Map/Reduce Distributed Computing.SPECTROSCOPY AND SPECTRAL ANALYSIS,36(8),2651-2654. |
| MLA | Pan Jing-chang,et al."A Method of Stellar Spectral Classification Based on Map/Reduce Distributed Computing".SPECTROSCOPY AND SPECTRAL ANALYSIS 36.8(2016):2651-2654. |
入库方式： OAI收割
来源：国家天文台
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