光谱学与光谱分析 |
|
|
|
|
|
Searching for WDMS Candidates In SDSS-DR10 With Automatic Method |
JIANG Bin, WANG Cheng-you*, WANG Wen-yu, WANG Wei |
School of Mechanical, Electrical & Information Engineering, Shandong University, Weihai, Weihai 264209, China |
|
|
Abstract The Sloan Digital Sky Survey (SDSS) has released the latest data (DR10) which covers the first APOGEE spectra. The massive spectra can be used for large sample research including the structure and evolution of the Galaxy and multi-waveband identi cation. In addition, the spectra are also ideal for searching for rare and special objects like white dwarf main-sequence star (WDMS). WDMS consist of a white dwarf primary and a low-mass main-sequence (MS) companion which has positive significance to the study of evolution and parameter of close binaries. WDMS is generally discovered by repeated imaging of the same area of sky, measuring light curves for objects or through photometric selection with follow-up observations. These methods require significant manual processing time with low accuracy and the real-time processing requirements can not be satisfied. In this paper, an automatic and efficient method for searching for WDMS candidates is presented. The method Genetic Algorithm (GA) is applied in the newly released SDSS-DR10 spectra. A total number of 4 140 WDMS candidates are selected by the method and 24 of them are new discoveries which prove that our approach of finding special celestial bodies in massive spectra data is feasible. In addition, this method is also applicable to mining other special celestial objects in sky survey telescope data. We report the identfication of 24 new WDMS with spectra. A compendium of positions, mjd, plate and fiberid of these new discoveries is presented which enrich the spectral library and will be useful to the research of binary evolution models.
|
Received: 2014-04-17
Accepted: 2014-08-08
|
|
Corresponding Authors:
WANG Cheng-you
E-mail: wangchengyou@sdu.edu.cn
|
|
[1] Dawson Kyle S, Schlegel David J, Ahn Christopher P, et al. The Astronomical Journal, 2013, 145(1): 41. [2] Anderson Lauren, Aubourg Eric, Bailey Stephen, et al. Monthly Notices of the Royal Astronomical Society, 2012, 427: 3435. [3] Smith Verne V, Cunha Katia, Shetrone Matthew D, et al. The Astrophysical Journal, 2013, 765(1):15. [4] Thomas D, Steele O, Maraston C, et al. Monthly Notices of the Royal Astronomical Society, 2013, 431(2):1383. [5] Ren J, Luo A, Li Y, et al. The Astronomical Journal, 2013, 146(4): 82. [6] Rebassa-Mansergas A, Nebot Gomez-Moran A, Schreiber M R, et al. Monthly Notices of the Royal Astronomical Society, 2011, 402:620. [7] Aggarwal C C, Yu P S. Outlier Detection for High-Dimensional Data. ACM SIGMOD Conference Proceedings, 2001. 37. [8] Aggarwal C C, Yu P S. The VLDB Journal, 2005, 14(2): 211. [9] CHEN Guang-ping, YE Dong-yi(陈光平, 叶东毅). Journal of Fuzhou University·Natural Science Edition(福州大学学报·自然科学版), 2007, 30(4): 376. [10] SHI Dong-dong, JIA Rui-yu, HUANG Yi-tang(施冬冬, 贾瑞玉, 黄义堂). Computer Technology and Development(计算机技术与发展), 2009, 19(3): 141. [11] Heller R, Homeier D, Dreizler S, et al. Astronomy and Astrophysics, 2009, 496(1):191. |
[1] |
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. |
[2] |
KONG Qing-qing, GONG Hui-li*, DING Xiang-qian, LIU Ming. Research on Genetic Algorithm Based on Mutual Information in the Spectrum Selection[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2018, 38(01): 31-35. |
[3] |
MA Yue1, JIANG Qi-gang1*, MENG Zhi-guo1, 2, LIU Hua-xin1. Black Soil Organic Matter Content Estimation Using Hybrid Selection Method Based on RF and GABPSO[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2018, 38(01): 181-187. |
[4] |
TAN Nian1,SUN Yi-dan1,WANG Xue-shun1*,HUANG An-min2,XIE Bing-feng1. Research on Near Infrared Spectrum with Principal Component Analysis and Support Vector Machine for Timber Identification[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2017, 37(11): 3370-3374. |
[5] |
LI Xue-ying, FAN Ping-ping*, HOU Guang-li, Lü Mei-rong, WANG Qian, LIU Yan. Rapid Detection of Soil Nutrients Based on Visible and Near Infrared Spectroscopy[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2017, 37(11): 3562-3566. |
[6] |
JIANG Bin, LIU Shu-hui, WANG Wen-yu, GAO Jun*. Research on Automatic Parameter Measurement of WDMS Spectra[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2017, 37(09): 2914-2918. |
[7] |
LIANG Hao1, CAO Jun1, LIN Xue2, ZHANG Yi-zhuo1*. Surface Defects Detection of Solid Wood Board Using Near-Infrared Spectroscopy Based on Bayesian Neural Network[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2017, 37(07): 2041-2045. |
[8] |
ZHU Wei-hua1, CHEN Guo-qing2, ZHU Zhuo-wei2, ZHU Feng1, GENG Ying1, HE Xiang1,TANG Chun-mei1. Year Prediction of a Mild Aroma Chinese Liquors Based on Fluorescence Spectra and Quantum Genetic Algorithm[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2017, 37(05): 1431-1436. |
[9] |
MA Shuang-shuang, MA Qiu-lin, HAN Lu-jia, HUANG Guang-qun*. Modelling of Calcuim Content in Manure Using Laser-Induced Breakdown Spectroscopy and Genetic Algorithm Combined with Partial Least Squares[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2017, 37(05): 1530-1534. |
[10] |
WANG Bing-yu1, SUN Wei-jiang2,3*, HUANG Yan2, YU Wen-quan4, WU Quan-jin1, LIN Fu-ming1, XIA Jin-mei1. Rapid Quality Evaluation of Anxi Tieguanyin Tea Based on Genetic Algorithm[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2017, 37(04): 1100-1104. |
[11] |
ZHOU Xing-fan1, SONG Xiang-zhong2, FU Zhao-hui1, ZHAO Peng1, XU Zhi-zhen1, TANG Shi-chuan1* . Rapid Detection of Atrazine at Workplace with Near-Infrared Spectroscopy [J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2017, 37(03): 755-759. |
[12] |
WU De-cao1, 2, WEI Biao1*, XIONG Shuang-fei1, FENG Peng1, TANG Ge1, TANG Yuan1, LIU Juan1, CHEN Wei3, QIU Yu2, CHEN Yuan-yuan2, YE Xin4. An Optimized Ultraviolet-Visible Spectrum Dual Optical Path Length Fusion Algorithm for Water Quality Monitoring[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2017, 37(03): 799-805. |
[13] |
DUAN Hong-wei1, ZHU Rong-guang1*, XU Wei-dong1, QIU Yuan-yuan1, YAO Xue-dong1, XU Cheng-jian2 . Hyperspectral Imaging Detection of Total Viable Count from Vacuum Packing Cooling Mutton Based on GA and CARS Algorithms [J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2017, 37(03): 847-852. |
[14] |
ZHANG Hai-liang1,2, LUO Wei2, LIU Xue-mei2, HE Yong1* . Measurement of Soil Organic Matter with Near Infrared Spectroscopy Combined with Genetic Algorithm and Successive Projection Algorithm [J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2017, 37(02): 584-587. |
[15] |
LI Jia-yu1, SUN Ping1*, ZOU Yun1, LIU Wei2, WANG Wen-ai2 . Comparison and Analysis of Iterative and Genetic Algorithms Used to Extract the Optical Parameters of Glucose Polycrystalline[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2016, 36(12): 3875-3880. |
|
|
|
|