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An Automated Galaxy Spectra Recognition Method Basing on Spectral Lines Information

  • Jiannan Zhang (a1), Xiaoyan Chen (a1), Yue Wu (a1) and Xiangru Li (a2)

Abstract

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.

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References

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