| 光谱学与光谱分析 |
|
|
|
|
|
| A Method to Estimate Metal Abundance from Stellar Spectra Using Ca Line Index |
| PAN Jing-chang1, LUO A-li1, 2, LI Xiang-ru3, WEI Peng2 |
1. School of Mechanical, Electrical & Information Engineering, Shandong University, Weihai, Weihai 264209, China
2. Key Laboratory of Optical Astronomy, National Astronomical Observatories, Chinese Academy of Sciences, Beijing 100012, China
3. South China Normal University, Guangzhou 510631, China |
|
|
|
|
Abstract This paper presents a method to estimate stellar metallicity based on BP neural network and Ca line index. This method trains a BP ANN model from SDSS/SEGUE stellar spectra and parameters provided by SSPP. The values of Teff and the line index of Ca lines are the input of network while the [Fe/H] values are the oputput of the network. A set of samples are resampled from the set of all and then a network model Is trained. The network can be used to predict the stellar metallicity from low-resolution spsectra. The experiment shows that the proposed method can accurately and effectively measure the [Fe/H] from the stellar spectra.
|
|
Received: 2014-04-07
Accepted: 2014-08-05
|
|
|
|
Corresponding Authors:
PAN Jing-chang
E-mail: pjc@sdu.edu.cn
|
|
[1] Lee Y S, Beers T C, Sivarani T, et al. The Astronomical Journal, 2008, 136(5): 2022.
[2] Prieto C A, Beers T C, Wilhelm R, et al. The Astrophysical Journal, 2006, 636(2): 804.
[3] Wilhelm R, Beers T C, Gray R O. The Astronomical Journal, 1999, 117(5): 2308.
[4] Beers T C, Rossi S, Norris J E, et al. The Astronomical Journal, 1999, 117(2): 981.
[5] Fuentes O. Experimental Astronomy, 2001, 12(1): 21.
[6] Bailer-Jones C A, Irwin M, Gilmore G, et al. Monthly Notices of the Royal Astronomical Society, 1997, 292(1): 157.
[7] Zhao J K, Zhao G, Chen Y Q, et al. Research in Astronomy and Astrophysics, 2011, 11(5): 563.
[8] Zhang J N, Luo A L, Zhao Y H. Research in Astronomy and Astrophysics, 2009, 9(6): 712.
[9] Wu Y, Luo A L, Li H N, et al. Research in Astronomy and Astrophysics, 2011, 11(8): 924.
[10] Cenarro A, Cardiel N, Gorgas J, et al. Monthly Notices of the Royal Astronomical Society, 2001, 326(3): 959.
[11] Tolstoy E, Irwin M J, Cole A A, et al. Monthly Notices of the Royal Astronomical Society, 2001, 327(3): 918.
[12] Du W, Luo A L, Zhao Y H. The Astronomical Journal, 2012, 143(2): 44. |
| [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] |
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. |
| [8] |
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. |
| [9] |
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. |
| [10] |
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. |
| [11] |
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. |
| [12] |
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. |
| [13] |
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. |
| [14] |
WANG Guang-pei1, PAN Jing-chang1*, YI Zhen-ping1, WEI Peng2, JIANG Bin1 . Outlier Data Mining and Analysis of LAMOST Stellar Spectra in Line Index Feature Space[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2016, 36(10): 3364-3368. |
| [15] |
WANG Guang-pei1, PAN Jing-chang1*, YI Zhen-ping1, WEI Peng2, JIANG Bin1 . Research on the Clustering of Massive Stellar Spectra Based on Line Index [J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2016, 36(08): 2646-2650. |
|
|
|
|
