光谱学与光谱分析 |
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A Novel Method to Determine the Redshifts of Active Galaxies Based on Wavelet Transform |
TU Liang-ping1, 2, LUO A-li2*, JIANG Bin2, 3, WEI Peng2, ZHAO Yong-heng2, LIU Rong4 |
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 3. School of Mechanical, Electrical & Information Engineering, Shandong University at Weihai, Weihai 264209, China 4. Base Department, Beijing Institute of Clothing Technology, Beijing 100029, China |
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Abstract Automatically determining redshifts of galaxies is very important for astronomical research on large samples, such as large-scale structure of cosmological significance. Galaxies are generally divided into normal galaxies and active galaxies, and the spectra of active galaxies mostly have more obvious emission lines. In the present paper, the authors present a novel method to determine spectral redshifts of active galaxies rapidly based on wavelet transformation mainly, and it does not need to extract line information accurately. This method includes the following steps: Firstly, we denoised a spectrum to be processed; Secondly, the low-frequency spectrum was extracted based on wavelet transform, and then we could get the residual spectrum through the denoised spectrum subtracting the low-frequency spectrum; Thirdly, the authors calculated the standard deviation of the residual spectrum and determined a threshold value T, then retained the wavelength set whose corresponding flux was greater than T; Fourthly, according to the wavelength form of all the standard lines, we calculated all the candidate redshifts; Finally, utilizing the density estimation method based on Parzen window, we determined the redshift point with maximum density, and the average value of its neighborhood would be the final redshift of this spectrum. The experiments on simulated data and real data from SDSS-DR7 show that this method is robust and its correct rate is encouraging. And it can be expected to be applied in the project of LAMOST.
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Received: 2012-03-30
Accepted: 2012-06-20
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Corresponding Authors:
LUO A-li
E-mail: lal@lamost.org
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[1] Lewis I, Cannon R, Taylor K, et al. Monthly Notices of the Royal Astronomical Society, 2002, 333(2): 279. [2] York D G, Adelman J, Anderson J E, et al. The Astronomical Journal, 2000, 120(3): 1579. [3] Su Dingqiang, Cui Xiangqun, Wang Yanan, et al. Proc. SPIE,1998,3352:76. [4] Tonry J, Davis M. The Astronomical Journal,1979, 84: 1511. [5] Glazebrook K, Offer A R,Deeley K. The Astrophysical Journal, 1998, 492(1): 98. [6] Jolliffe I T. Principal Component Analysis, Series: Springer Series in Statistics, 2nd ed., Springer, NY, 2002. 63. [7] ZHOU Hong, HUANG Ling-yun, LUO Man-li(周 虹, 黄凌云, 罗曼丽). Journal of Electronics(电子科学学刊), 2000, 22(4): 529. [8] LIU Rong, DUAN Fu-qing, LIU San-yang, et al(刘 蓉,段福庆,刘三阳,等). Journal of Electronics & Information Technology(电子与信息学报), 2006, 28(1): 76. [9] Donoho D L. Trans. Inform. Theory, 1995, 41(3): 613. [10] Abazajian K N, Adelman-McCarthy, Jennifer K, et al. The Astrophysical Journal Supplement, 2009, 182(2): 543. [11] Duan Fuqing, Liu Rong, Guo Ping, et al. Research in Astronomy and Astrophysics, 2009, 9(3): 341. |
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