Automated spectral classification using template matching |
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| Authors: | Fu-Qing Duan Rong Liu Ping Guo Ming-Quan Zhou Fu-Chao Wu |
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| Institution: | [1]College of Information Science and Technology, Beijing Normal University, Beijing 100875, China [2]Base Department, Beijing Institute of Clothing Technology, Beijing 100029, China [3]National Laboratory of Pattern Recognition, Institute of Automation, Chinese Academy of Sciences,Beijing 100080, China |
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| Abstract: | An automated spectral classification technique for large sky surveys is pro-posed. We firstly perform spectral line matching to determine redshift candidates for an observed spectrum, and then estimate the spectral class by measuring the similarity be-tween the observed spectrum and the shifted templates for each redshift candidate. As a byproduct of this approach, the spectral redshift can also be obtained with high accuracy. Compared with some approaches based on computerized learning methods in the liter-ature, the proposed approach needs no training, which is time-consuming and sensitive to selection of the training set. Both simulated data and observed spectra are used to test the approach; the results show that the proposed method is efficient, and it can achieve a correct classification rate as high as 92.9%, 97.9% and 98.8% for stars, galaxies and quasars, respectively. |
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| Keywords: | methods: data analysis - techniques: spectroscopic - stars: general galaxies: stellar content |
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