光谱学与光谱分析 |
|
|
|
|
|
Atmospheric Parameter Estimation for LAMOST/GUOSHOUJING Spectra |
LU Yu, LI Xiang-ru*, YANG Tan |
School of Mathematical Sciences, South China Normal University, Guangzhou 510631, China |
|
|
Abstract It is a key task to estimate the atmospheric parameters from the observed stellar spectra in exploring the nature of stars and universe. With our Large Sky Area Multi-Object Fiber Spectroscopy Telescope (LAMOST) which begun its formal Sky Survey in September 2012, we are obtaining a mass of stellar spectra in an unprecedented speed. It has brought a new opportunity and a challenge for the research of galaxies. Due to the complexity of the observing system, the noise in the spectrum is relatively large. At the same time, the preprocessing procedures of spectrum are also not ideal, such as the wavelength calibration and the flow calibration. Therefore, there is a slight distortion of the spectrum. They result in the high difficulty of estimating the atmospheric parameters for the measured stellar spectra. It is one of the important issues to estimate the atmospheric parameters for the massive stellar spectra of LAMOST. The key of this study is how to eliminate noise and improve the accuracy and robustness of estimating the atmospheric parameters for the measured stellar spectra. We propose a regression model for estimating the atmospheric parameters of LAMOST stellar(SVM(lasso)). The basic idea of this model is: First, we use the Haar wavelet to filter spectrum, suppress the adverse effects of the spectral noise and retain the most discrimination information of spectrum. Secondly,We use the lasso algorithm for feature selection and extract the features of strongly correlating with the atmospheric parameters. Finally, the features are input to the support vector regression model for estimating the parameters. Because the model has better tolerance to the slight distortion and the noise of the spectrum, the accuracy of the measurement is improved. To evaluate the feasibility of the above scheme, we conduct experiments extensively on the 33 963 pilot surveys spectrums by LAMOST. The accuracy of three atmospheric parameters is log Teff: 0.006 8 dex, log g: 0.155 1 dex, [Fe/H]: 0.104 0 dex.
|
Received: 2013-10-30
Accepted: 2014-01-29
|
|
Corresponding Authors:
LI Xiang-ru
E-mail: xiangru.li@gmail.com
|
|
[1] Luo Ali, Zhang Haotong, Zhao Yongheng, et al. Research in Astronomy and Astrophysics,2012, 12(9): 1243. [2] Ziao Gang, Zhao Yongheng, Chu Yaoquan, et al. Research in Astronomy and Astrophysics,2012, 12(7): 723. [3] Cui Xiangqun, Zhao Yongheng, Chu Yaoquan, et al. Research in Astronomy and Astrophysics, 2012, 12(9): 1197. [4] Zhang Jifu,Jiang Yiyong, Chang K H, et al. Pattern Recognition Letters, 2009, 30(15): 1434. [5] Posbic H, Katz D, Caffau E, et al. Astronomy & Astrophysics, 2012, 544(A154): 1. [6] Du Wei, Luo Ali, Zhao Yongheng. The Astronomical Journal, 2012, 143(2): 1. [7] Manteiga M, Ordonez D, Dafonte C, et al. Publications of the Astronomical Society of the Pacific, 2010, 122(891): 608. [8] Zhang J, Luo A, Zhao Y. Research in Astronomy and Astrophysics, 2009, 9(6): 712. [9] Willemsen P G, Hilker M, Kayser A, et al. Astronomy & Astrophysics, 2005, 436: 379. [10] Smola A J, Schlkopf B. Statistics and Computing, 2004, 14: 199. [11] Santosh Kumar Mandal, Chan Felix T S, Tiwari M K. Expert. Systems with Applications, 2012, 39(3): 3071. [12] Robert Tibshirani. Journal of the Royal Statistical Society. Series B (Methodological), 1996, 58(1): 267. [13] Hastie T, Taylor J, Tibshirani R, et al. Electronic Journal of Statistics, 2007, 1: 1. [14] Genovese C R, Jin J, Wasserman L, et al. Journal of Machine Learning Research, 2012, 13(1): 2107. [15] Efron B, Hastie T, Johnstone I, et al. The Annals of Statistics, 2004, 32(2): 407. |
[1] |
WEI Si-ye1, 2, FAN Xing-cheng3, MAO Han1, 2, CAO Tao4, 5, CHENG Ao3, FAN Xing-jun3*, XIE Yue3. Abundance and Spectral Characteristics of Molecular Weight Separated Dissolved Organic Matter Released From Biochar at Different Pyrolysis Temperatures[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2022, 42(06): 1809-1815. |
[2] |
LUO Jie1, 2, YUE Su-wei1, 2*, GUO Hong-ying1, LIU Jia-jun3. Spectroscopic Characteristics and Coloring Mechanism of Smithsonite
Jade[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2022, 42(06): 1886-1890. |
[3] |
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. |
[4] |
FENG Rui-jie1, CHEN Zheng-guang1, 2*, YI Shu-juan3. Identification of Corn Varieties Based on Bayesian Optimization SVM[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2022, 42(06): 1698-1703. |
[5] |
LI Quan-lun1, CHEN Zheng-guang1*, SUN Xian-da2. Rapid Detection of Total Organic Carbon in Oil Shale Based on Near
Infrared Spectroscopy[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2022, 42(06): 1691-1697. |
[6] |
MENG Fan-jia1, LUO Shi1, WU Yue-feng1, SUN Hong1, LIU Fei2, LI Min-zan1*, HUANG Wei3, LI Mu3. Characteristic Extraction Method and Discriminant Model of Ear Rot of Maize Seed Base on NIR Spectra[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2022, 42(06): 1716-1720. |
[7] |
PENG Ren-miao1, 2, XU Peng-peng2, ZHAO Yi-mo2, BAO Li-jun1, LI Cheng2*. Identification of Two-Dimensional Material Nanosheets Based on Deep Neural Network and Hyperspectral Microscopy Images[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2022, 42(06): 1965-1973. |
[8] |
HOU Bing-ru1, LIU Peng-hui1, ZHANG Yang1, HU Yao-hua1, 2, 3*. Prediction of the Degree of Late Blight Disease Based on Optical Fiber Spectral Information of Potato Leaves[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2022, 42(05): 1426-1432. |
[9] |
ZHANG Yu-yang, CHEN Mei-hua*, YE Shuang, ZHENG Jin-yu. Research of Geographical Origin of Sapphire Based on Three-Dimensional Fluorescence Spectroscopy: A Case Study in Sri Lanka and Laos Sapphires[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2022, 42(05): 1508-1513. |
[10] |
LIU Mei-chen, XUE He-ru*, LIU Jiang-ping, DAI Rong-rong, HU Peng-wei, HUANG Qing, JIANG Xin-hua. Hyperspectral Analysis of Milk Protein Content Using SVM Optimized by Sparrow Search Algorithm[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2022, 42(05): 1601-1606. |
[11] |
ZHANG Zhao1, 2, 3, 4, YAO Zhi-feng1, 3, 4, WANG Peng1, 3, 4, SU Bao-feng1, 3, 4, LIU Bin3, 4, 5, SONG Huai-bo1, 3, 4, HE Dong-jian1, 3, 4*, XU Yan5, 6, 7, HU Jing-bo2. Early Detection of Plasmopara Viticola Infection in Grapevine Leaves Using Chlorophyll Fluorescence Imaging[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2022, 42(04): 1028-1035. |
[12] |
HE Ya-xiong1, 2, ZHOU Wen-qi1, 2, ZHUANG Bin1, 2, ZHANG Yong-sheng1, 2, KE Chuan3, XU Tao1, 2*, ZHAO Yong1, 2, 3. Study on Time-Resolved Characteristics of Laser-Induced Argon Plasma[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2022, 42(04): 1049-1057. |
[13] |
DONG Rui, TANG Zhuang-sheng, HUA Rui, CAI Xin-cheng, BAO Dar-han, CHU Bin, HAO Yuan-yuan, HUA Li-min*. Research on Classification Method of Main Poisonous Plants in Alpine Meadow Based on Spectral Characteristic Variables[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2022, 42(04): 1076-1082. |
[14] |
LIANG Hao1, XU Wei-xin1*, DUAN Xu-hui1, ZHANG Juan2, DAI Na1, XIAO Qiang-zhi1, WANG Qi-yu1. Threshold Calibration of Key Parameters of Withered Grass Based on PROSAIL Model in Qinghai-Tibet Plateau[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2022, 42(04): 1144-1149. |
[15] |
CHEN Chu-han1, ZHONG Yang-sheng2, WANG Xian-yan3, ZHAO Yi-kun1, DAI Fen1*. Feature Selection Algorithm for Identification of Male and Female
Cocoons Based on SVM Bootstrapping Re-Weighted Sampling[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2022, 42(04): 1173-1178. |
|
|
|
|