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An Automated Galaxy Spectra Recognition Method Basing on Spectral Lines Information

Published online by Cambridge University Press:  30 May 2017

Jiannan Zhang Open the ORCID record for Jiannan Zhang [Opens in a new window] ,
Xiaoyan Chen ,
Yue Wu  and
Xiangru Li
Jiannan Zhang
Affiliation:
National Astronomical Observatory, Chinese Academy of Sciences, 100012, Beijing, China email: jnzhang@bao.ac.cnChenxy@bao.ac.cnWuyue@bao.ac.cn
Xiaoyan Chen
Affiliation:
National Astronomical Observatory, Chinese Academy of Sciences, 100012, Beijing, China email: jnzhang@bao.ac.cnChenxy@bao.ac.cnWuyue@bao.ac.cn
Yue Wu
Affiliation:
National Astronomical Observatory, Chinese Academy of Sciences, 100012, Beijing, China email: jnzhang@bao.ac.cnChenxy@bao.ac.cnWuyue@bao.ac.cn
Xiangru Li
Affiliation:
School of Mathematical Sciences, South China Normal University 510631, Guangzhou, China email: xiangru.li@qq.com
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Abstract

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For the vast amounts of spectra produced by LAMOST, the pipeline basing on PCAZ method is limited by the bad flux calibration and low S/N data. This work focuses on the study of the efficient recognition methods of galaxy spectra of LAMOST basing on spectral lines information. The new method searches spectral lines and extracts the information of spectral lines (position, height, and width et al.) automatically. Using the spectral lines information which are less influenced by the quality of flux calibration and the S/N ratio, galaxy spectra are recognized with the redshift measured through spectral lines matching method. The experiment verified it is feasible for the LAMOST galaxy spectra: the correct recognition rate > 80% for the data with SNR_g > 5, and > 90% for the data with SNR_r > 5. Compared with the redshift of SDSS, the systematic error of our method is 0, and the standard deviation of the error is 0.0002.

Keywords

methods: data analysistechniques: spectroscopicgalaxies: fundamental parameters (classification, colors, luminosities, masses, radii, etc.)telescopes: LAMOST
Type
Contributed Papers
Information
Proceedings of the International Astronomical Union , Volume 12 , Symposium S325: Astroinformatics , October 2016 , pp. 221 - 224
Copyright
Copyright © International Astronomical Union 2017 

References

Bolton, S., Schlegel, J., Aubourg, E., Bailey, S., et al. 2012, AJ, 144, 144 CrossRefGoogle Scholar
Baldry, K., Alpaslan, M., Bauer, E., Bland-Hawthorn, J., et al. 2014, MNRAS, 442, 2440 Google Scholar
Wu, Y., Luo, A-Li, Li, H., Shi, J., et al. 2011, RAA, 11, 924 Google Scholar
Lee, Y., Beers, C., Sivarani, T., Allende, C., et al. 2008, AJ, 136, 2022 Google Scholar