| 光谱学与光谱分析 |
|
|
|
|
|
| Automatic Measurement of the Stellar Atmospheric Parameters Based Mass Estimation |
| TU Liang-ping1, 2, WEI Hui-ming1, LUO A-li2, ZHAO Yong-heng2 |
1. School of Science, University of Science and Technology Liaoning, Anshan 114051, China
2. Key Laboratory of Optical Astronomy, National Astronomical Observatories, Chinese Academy of Sciences, Beijing 100012, China |
|
|
|
|
Abstract We have collected massive stellar spectral data in recent years, which leads to the research on the automatic measurement of stellar atmospheric physical parameters (effective temperature Teff, surface gravity log g and metallic abundance [Fe/H]) become an important issue. To study the automatic measurement of these three parameters has important significance for some scientific problems, such as the evolution of the universe and so on. But the research of this problem is not very widely, some of the current methods are not able to estimate the values of the stellar atmospheric physical parameters completely and accurately. So in this paper, an automatic method to predict stellar atmospheric parameters based on mass estimation was presented, which can achieve the prediction of stellar effective temperature Teff, surface gravity log g and metallic abundance [Fe/H]. This method has small amount of computation and fast training speed. The main idea of this method is that firstly it need us to build some mass distributions, secondly the original spectral data was mapped into the mass space and then to predict the stellar parameter with the support vector regression (SVR) in the mass space. we choose the stellar spectral data from the United States SDSS-DR8 for the training and testing. We also compared the predicted results of this method with the SSPP and achieve higher accuracy. The predicted results are more stable and the experimental results show that the method is feasible and can predict the stellar atmospheric physical parameters effectively.
|
|
Received: 2014-08-19
Accepted: 2014-12-06
|
|
|
|
Corresponding Authors:
TU Liang-ping
E-mail: tlp_kd@163.com
|
|
[1] Wei Peng, Luo Ali, Li Yingbing, et al. The Astrophysics Journal, 2014, 147: 101.
[2] Zhao G，et al. Research in Astronomy and Astrophysics, 2012, 12(7): 723.
[3] Soubiran C, Katz D, Cayrel R, et al. Astronomy and Astrophysics Supplement Series, 1988, 133(2): 221.
[4] Katz D, Soubiran C, Cayrel R, et al. Astronomy and Astrophysics, 1988, 338(1): 151.
[5] Fuentes O, Gulati R K. The Seventh Texas-Mexico Conference on Astrophysics: Flows, Blows and Glows, 2001, 10: 209.
[6] TAN Xin, PAN Jing-chang, WANG Jie，et al(谭 鑫，潘景昌，王 杰，等). Spectroscopy and Spectral Analysis(光谱学与光谱分析), 2013, 33(5): 1397.
[7] Ting K M, Zhou Guangtong, Liu Feitong, et al. In Proceedings of ACM SIGKDD, 2010: 989.
[8] Ting K M, Zhou Guangtong, Liu Feitong, et al. Mach. Learn, 2013, 90: 127. |
| [1] |
FAN Ya-wen, LIU Yan-ping*, QIU Bo, JIANG Xia, WANG Lin-qian, WANG Kun. Research on Spectral Classification of Stellar Subtypes Based on
SSTransformer[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2023, 43(08): 2523-2528. |
| [2] |
JIANG Xia*, QIU Bo, WANG Lin-qian, GUO Xiao-yu. Automatic Classification Method of Star Spectra Based on
Semi-Supervised Mode[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2023, 43(06): 1875-1880. |
| [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] |
WANG Tian-xiang1, 2, FAN Yu-feng1*, WANG Xiao-li1, LONG Qian1, WANG Chuan-jun1. Prediction of Stellar Spectrum Categories Based on Deep Residual Network[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2021, 41(05): 1602-1606. |
| [5] |
JIANG Bin, ZHAO Zi-liang, WANG Shu-ting, WEI Ji-yu, QU Mei-xia*. Decomposition and Classification of Stellar Spectra Based on t-SNE[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2020, 40(09): 2913-2917. |
| [6] |
WANG Nan-nan1, QIU Bo1*, MA Jie1*, SHI Chao-jun1, SONG Tao1, GUO Ping2*. Fast Classification Method of Star Spectra Data Based on Convolutional Neural Network[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2019, 39(10): 3297-3301. |
| [7] |
JIANG Bin, ZHAO Yong-jian, WANG Lu-yao, WEI Ji-yu, QU Mei-xia*. Research on Parameter Measurement of Cataclysmic Variable Stars[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2019, 39(09): 2935-2939. |
| [8] |
XUE Ren-zheng1, CHEN Shu-xin1*, HUANG Hong-ben2. Line Index of A-Type Stellar Astronomical Spectrum Predict Effective Temperature by Ridge Regression Model[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2019, 39(08): 2624-2629. |
| [9] |
LIU Zhong-bao1, 2, QIN Zhen-tao1, LUO Xue-gang1, ZHOU Fang-xiao1, ZHANG Jing1. Stellar Spectra Classification by Support Vector Machine with Spectral Distribution Properties[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2019, 39(04): 1307-1311. |
| [10] |
SHI Chao-jun1, QIU Bo1*, ZHOU Ya-tong1, DUAN Fu-qing2*. Automatic Classification Method of Star Spectra Data Based on Convolutional Neural Network[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2019, 39(04): 1312-1316. |
| [11] |
LIU Zhong-bao1,2, LEI Yu-fei1, SONG Wen-ai2, ZHANG Jing2, WANG Jie3, TU Liang-ping4. Stellar Spectra Classification by Support Vector Machine with Unlabeled Data[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2019, 39(03): 948-952. |
| [12] |
ZHANG Jing, LIU Zhong-bao*, SONG Wen-ai, FU Li-zhen, ZHANG Yong-lai. Stellar Spectra Classification Method Based on Multi-Class Support Vector Machine[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2018, 38(07): 2307-2310. |
| [13] |
CHEN Shu-xin1, 2，SUN Wei-min1*，KONG Xiao3. Similarity Measurement Among Classification Templates for LAMOST Stellar Spectra[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2018, 38(06): 1922-1925. |
| [14] |
LIU Zhong-bao1, REN Juan-juan2, SONG Wen-ai1*, ZHANG Jing1, KONG Xiao2, FU Li-zhen1. Stellar Spectra Classification with Entropy-Based Learning Machine[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2018, 38(02): 660-664. |
| [15] |
GOU Yu-dan1, LU Peng-fei1, HE Jiu-ning1, ZHANG Chang-hua1*, LI Ping1, LI Xiang-yuan2. Measurement of H2O Spectroscopic Parameters near 1.39 μm and Application in Combustion Kinetics[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2018, 38(01): 176-180. |
|
|
|
|
