| 光谱学与光谱分析 |
|
|
|
|
|
| An Automated Method to Fit Stellar Continuum Based on Statistic Windows |
| PAN Jing-chang1, WANG Xing-xing1, WEI Peng2, JIANG Bin1,2, TU Liang-ping2,3, LUO A-li1,2 |
1. School of Mechanical, Electrical & Information Engineering, Shandong University at Weihai, Weihai 264209, China
2. Key Laboratory of Optical Astronomy, National Astronomical Observatories, Chinese Academy of Sciences, Beijing 100012, China
3. School of Science，Liaoning University of Science and Technology, Anshan 144051, China |
|
|
|
|
Abstract A novel statistic window based method to fit stellar continuum is proposed. First a stellar spectrum is divided into a series of statistic windows in which a certain percent of flux points is selected according to S/N ratio; then low order polynomial iteration fitting is carried out based on the selected flux points to obtain the stellar continuum. Experimental results show that the continuum obtained by the proposed method is more close to the real continuum, compared to other existed methods. This method has a better practical applicability and robustness to all kinds of spectra (except M-type spectrum) in SDSS. It also works well for Guoshoujing Telescope (LAMOST) pilot survey spectra.
|
|
Received: 2012-02-18
Accepted: 2012-05-20
|
|
|
|
Corresponding Authors:
PAN Jing-chang
E-mail: pjc@sdu.edu.cn
|
|
[1] Su D, Cui X, Wang Y, et al. SPIE, 1998, 3352: 76.
[2] Newberg H J, Bai Z, Beers T, et al. AAS, 2012, 219.
[3] Zhao G, Chen Y Q, Shi J R, et al. Chinese Journal of Astronomy and Astrophysics, 2006, 6: 265.
[4] Gulati R K, Gupta R, Gothoskar P, et al. Bulletin of the Astronomical Society of India, 1996, 24: 21.
[5] Soubiran C, Katz D, Cayrel R. Arxiv Preprint Astro-ph/9806234, 1998.
[6] Luo A L, Yongheng Z. Chinese Journal of Astronomy and Astrophysics, 2000, 2(4): 427.
[7] Lee Y S, Beers T C, Sivarani T, et al. The Astronomical Journal, 2008, 136: 2022.
[8] HUANG Fu-zhen，SU Jian-bo(黄福珍, 苏剑波). Chinese Journal of Computers(计算机学报), 2003, 26(4)：491. |
| [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] |
CHEN Chuan-jie1, 2, FAN Yong-sheng3, FANG Zhong-qing1, 2, WANG Yuan-yuan1, 2, KONG Wei-bin1, 2, ZHOU Feng1, 2*, WANG Ru-gang1, 2. Research on the Electron Temperature in Nanosecond Pulsed Argon Discharges Based on the Continuum Emission[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2021, 41(08): 2337-2342. |
| [5] |
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. |
| [6] |
MA Yang, ZHANG Ji-fu, CAI Jiang-hui, YANG Hai-feng, ZHAO Xu-jun*. Parallel Extraction and Analysis of Abnormal Features of QSO Spectra Based on Sparse Subspace[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2021, 41(04): 1086-1091. |
| [7] |
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. |
| [8] |
QU Cai-Xia, YANG Hai-feng*, CAI Jiang-hui, XUN Ya-ling. P-Cygni Profile Analysis of the Spectrum: LAMOST J152238.11+333136.1[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2020, 40(04): 1304-1308. |
| [9] |
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. |
| [10] |
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. |
| [11] |
QU Cai-xia1, YANG Hai-feng1*, CAI Jiang-hui1*, LUO A-li2, ZHANG Ji-fu1, NIE Yao-yao1. Feature Extraction and Analysis of Double-Peaked Emission Line Spectra Based on Relevant Subspace[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2019, 39(06): 1677-1682. |
| [12] |
CAI Jiang-hui1, YANG Yu-qing1, YANG Hai-feng1*, LUO A-li2, KONG Xiao2, ZHANG Ji-fu1. Spectral Analysis of Sky Light Based on Trajectory Clustering[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2019, 39(04): 1301-1306. |
| [13] |
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. |
| [14] |
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. |
| [15] |
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. |
|
|
|
|
