An Automated Galaxy Spectra Recognition Method Basing on Spectral Lines Information. Issue Volume 12:Issue S325(2016) (30th May 2017)

• Record Type: Journal Article
• Authors: Zhang, Jiannan; Chen, Xiaoyan; Wu, Yue; Li, Xiangru
• Editors: Brescia, M.; Djorgovski, S.G.; Feigelson, E.; Longo, G.; Cavuoti, S.
• Is Part Of: Proceedings of the International Astronomical Union. Volume 12:Issue S325(2016)
• Page Start: 221
• Page End: 224
• Publication Date: 2017-05-30
• Subjects: methods: data analysis, -- techniques: spectroscopic, -- galaxies: fundamental parameters (classification, colors, luminosities, masses, radii, etc.), -- telescopes: LAMOST; Astronomy -- Congresses; Astronomy -- Periodicals; 520
• DOI: 10.1017/S1743921316013193
• Languages: English
• ISSNs: 1743-9213

Abstract

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.