|
|
|
|
|
|
| An Automated Stellar Spectra Classification System Basing on Non-Parameter Regression and Adaboost |
| LIU Rong1, QIAO Xue-jun2*, ZHANG Jian-nan3, DUAN Fu-qing4 |
1. Base Department, Beijing Institute Of Fashion Technology, Beijing 100029, China
2. School of Science, Xi’an University of Architecture and Technology, Xi’an 710055, China
3. The National Astronomical Observatories, Chinese Academy of Sciences, Beijing 100012, China
4. College of Information Science and Technology, Beijing Normal University, Beijing 100875, China |
|
|
|
|
Abstract With the analysis of stellar spectra, the evolution and structure of the Milky Way galaxy is studied. Spectral classification is one of the basic tasks of stellar spectral analysis. In this paper, a method of MK classification based on non parametric regression and Adaboost for stellar spectra is proposed, and the stars are classified according to the luminosity type, spectral type as well as the spectral subtype. The spectral type of the stellar spectrum and its sub type represent the effective temperature of the star, while the luminosity type represents the luminous intensity of the star. In the same spectral type, the luminosity type reflects the variation of the shape details of the spectral line, so the classification of the photometric type must be based on the spectral type classification. The spectral type classification is transformed as a regression problem of class label, and the type and subtype of the stellar spectra are recognized with non parametric regression method. The luminosity type of the stellar spectra is recognized using Adaboost method which combines a group of K nearest neighbor classifiers. Adaboost generates a strong classifier with weighted combination of a group of weak classifiers to improve the recognition rate of the luminosity type. Experimental results validate the proposed method. The accuracy of spectral subtype recognition is up to 0.22, and the correct rate of the luminosity type classification is 84% above. Two KNN methods are compared with Adaboost method on luminosity recognition. The results show that the recognition rate can be greatly enhanced with the Adaboost method and using KNN.
|
|
Received: 2016-08-13
Accepted: 2016-12-19
|
|
|
|
Corresponding Authors:
QIAO Xue-jun
E-mail: xjqiao1@163.com
|
|
[1] Li X R, Lu Y, Comte G, et. al. The Astrophysical Journal Supplement Series, 2015, 218(1): 3.
[2] Li X R, Wu Q M J, Luo A L, et al. The Astrophysical Journal, 2015, 790(2): 85.
[3] Kheirdastan S, Bazarghan M. Astrophysics and Space Science, 2016, 361(9)：304.
[4] Liu C, Cui, W Y, Zhang B, et. al. Research in Astronomy and Astrophysics, 2015, 15(8)：1137.
[5] Gray R O，Corbally C J. The Astronomical Journal, 2014, 147(4): 80.
[6] McGurk R C, Kimball A E, Ivezic . The Astronomical Journal, 2010, 139(3)：1261.
[7] Daniel S, Connolly A, Schneider J, et al. The Astronomical Journal, 2011, 142(6)：203.
[8] Yang T, Li X R. Monthly Notices of the Royal Astronomical Society, 2015, 452(1): 158.
[9] Lu Y, Li X R. Monthly Notices Royal Astronomical Society, 2015, 452(2): 1394.
[10] YU Shuang, LIU Guo-hai, XIA Rong-sheng, et al(于 霜, 刘国海, 夏荣盛, 等). Spectroscopy and Spectral Analysis(光谱学与光谱分析), 2016, 36(1): 51.
[11] Rakhlin A，Sridharan K. Computer Science, 2014, 35: 1232.
[12] Zhang Z，Xie X. International Conference on Computer Engineering and Technology, 2010, 1: 647.
[13] Jacoby G H, Hunter D A, Christian C A. The Astrophysical Journal Supplement Series, 1984, 56: 257.
[14] Silva D R, Cornell M E. The Astrophysical Journal Supplement Series, 1992, 81: 865.
[15] Pickles A J. Publications of the Astronomical Society of the Pacific, 1998, 110: 863. |
| [1] |
ZENG Si-xian1, REN Xin1, HE Hao-xuan1, NIE Wei1, 2*. Influence Analysis of Spectral Line-Shape Models on Spectral Diagnoses Under High-Temperature Conditions[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2023, 43(09): 2715-2721. |
| [2] |
ZHANG Rong1, 2, DUAN Ning1, 3, JIANG Lin-hua1, 3*, XU Fu-yuan3, JIN Wei3, LI Jian-hui1. Study on Optical Path Optimization for Direct Determination of
Spectrophotometry of High Concentration Hexavalent Chromium
Solution by Ultraviolet Visible Spectroscopy[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2023, 43(06): 1829-1837. |
| [3] |
WU Chao1, QIU Bo1*, PAN Zhi-ren1, LI Xiao-tong1, WANG Lin-qian1, CAO Guan-long1, KONG Xiao2. Application of Spectral and Metering Data Fusion Algorithm in Variable Star Classification[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2023, 43(06): 1869-1874. |
| [4] |
LI Shuang-chuan, TU Liang-ping*, LI Xin, WANG Li-li. Besvm: A-Type Star Spectral Subtype Classification Algorithm Based on Transformer Feature Extraction[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2023, 43(05): 1575-1581. |
| [5] |
ZHANG Xue-fei1, DUAN Ning1, 2*, JIANG Lin-hua1, 2*, CHENG Wen2, YU Zhao-sheng3, LI Wei-dong2, ZHU Guang-bin4, XU Yan-li2. Study on Stability and Sensitivity of Deep Ultraviolet Spectrophotometry Detection System[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2022, 42(12): 3802-3810. |
| [6] |
LI Peng-hui, JIANG Zheng-wei, LI Jia-quan, REN Jian-peng, WU Wen-juan*. Research Progress in Quantitative Determination of Phenolic Hydroxyl Groups in Lignin[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2022, 42(09): 2666-2671. |
| [7] |
LU Ya-kun1, QIU Bo1*, LUO A-li2, GUO Xiao-yu1, WANG Lin-qian1, CAO Guan-long1, BAI Zhong-rui2, CHEN Jian-jun2. Classification of 2D Stellar Spectra Based on FFCNN[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2022, 42(06): 1881-1885. |
| [8] |
ZHANG Jie1, 2, XU Bo1, FENG Hai-kuan1, JING Xia2, WANG Jiao-jiao1, MING Shi-kang1, FU You-qiang3, SONG Xiao-yu1*. Monitoring Nitrogen Nutrition and Grain Protein Content of Rice Based on Ensemble Learning[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2022, 42(06): 1956-1964. |
| [9] |
YAO Shan1, ZHANG Xuan-ling1, CAI Yu-xin1, HE Lian-qiong1, LI Jia-tong1, WANG Xiao-long1, LIU Ying1, 2*. Study on Distribution Characteristics of Different Nitrogen and
Phosphorus Fractions by Spectrophotometry in Baiyangdian
Lake and Source Analysis[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2022, 42(04): 1306-1312. |
| [10] |
LIU Zhong-bao1, WANG Jie2*. Research on the Improvement of Spectra Classification Performance With the High-Performance Hybrid Deep Learning Network[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2022, 42(03): 699-703. |
| [11] |
LIU Qin-rong1, DU Zi-wei1, LI Jia-zhen1, WANG Yi-shuo1, 3*, GU Xuan2, CUI Xiu-mei2. Analysis and Evaluation of Inorganic Elements in Salvia miltiorrhiza and Rhizosphere Soils From Different Areas[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2021, 41(11): 3618-3624. |
| [12] |
WANG Da-xin1，2, FU Li-ping1,3,4*, JIANG Fang1，3，4, JIA Nan1，2，3，4, WANG Tian-fang1,2,3,4, DOU Shuang-tuan1,2. Inversion of Ionospheric O/N2 by Using FY-3D Ionospheric Photometer Data[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2021, 41(04): 1004-1010. |
| [13] |
LI Ling-qiao1,2, LI Yan-hui2, YIN Lin-lin4, YANG Hui-hua1,2*, FENG Yan-chun3, YIN Li-hui3, HU Chang-qin3. Data Augmentation of Raman Spectral and Its Application Research Based on DCGAN[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2021, 41(02): 400-407. |
| [14] |
HE Jin-xin1, REN Xiao-yu1, CHEN Sheng-bo2*, XIONG Yue1, XIAO Zhi-qiang1, ZHOU Hai1. Automatic Classification of Rock Spectral Features Based on Fusion Learning Model[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2021, 41(01): 141-144. |
| [15] |
LIN Nan1,2, LIU Hai-qi3, YANG Jia-jia4, WU Meng-hong1, LIU Han-lin1. Hyperspectral Estimation of Soil Nutrient Content in the Black Soil Region Based on BA-Adaboost[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2020, 40(12): 3825-3831. |
|
|
|
|
