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An Effective Method for Modeling Two-dimensional Sky Background of LAMOST

Published online by Cambridge University Press:  30 May 2017

Hasitieer Haerken ,
Fuqing Duan ,
Jiannan Zhang Open the ORCID record for Jiannan Zhang [Opens in a new window]  and
Ping Guo
Hasitieer Haerken
Affiliation:
Image Processing and Pattern Recognition Laboratory, Beijing Normal University, 100875, Beijing, China email: [email protected]@ieee.org National Astronomical Observatories & Chinese Academy of Sciences, 100012 Beijing, China email: [email protected]@bao.ac.cn
Fuqing Duan
Affiliation:
Image Processing and Pattern Recognition Laboratory, Beijing Normal University, 100875, Beijing, China email: [email protected]@ieee.org
Jiannan Zhang
Affiliation:
National Astronomical Observatories & Chinese Academy of Sciences, 100012 Beijing, China email: [email protected]@bao.ac.cn
Ping Guo
Affiliation:
Image Processing and Pattern Recognition Laboratory, Beijing Normal University, 100875, Beijing, China email: [email protected]@ieee.org
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Abstract

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Each CCD of LAMOST accommodates 250 spectra, while about 40 are used to observe sky background during real observations. How to estimate the unknown sky background information hidden in the observed 210 celestial spectra by using the known 40 sky spectra is the problem we solve. In order to model the sky background, usually a pre-observation is performed with all fibers observing sky background. We use the observed 250 skylight spectra as training data, where those observed by the 40 fibers are considered as a base vector set. The Locality-constrained Linear Coding (LLC) technique is utilized to represent the skylight spectra observed by the 210 fibers with the base vector set. We also segment each spectrum into small parts, and establish the local sky background model for each part. Experimental results validate the proposed method, and show the local model is better than the global model.

Keywords

LAMOSTcelestial spectrasky background modeling
Type
Contributed Papers
Information
Proceedings of the International Astronomical Union , Volume 12 , Symposium S325: Astroinformatics , October 2016 , pp. 63 - 66
Copyright
Copyright © International Astronomical Union 2017 

References

Blondin, S., Walsh, J. R., & Leibundgut, B. 2004, AA, 431 (1), 757 CrossRefGoogle Scholar
Bolton, A. S. & Schlegel, D. J. 2010, PASP, 122 (888), 248 Google Scholar
Cuby, J. G. 1994, SPIE, 98Google Scholar
Cui, X., Su, D., & Wang, Y. 2000, Proc Spie, 4003 (3), 347 CrossRefGoogle Scholar
Cui, X. 2006, Proc Spie, 6267Google Scholar
Cui, X., Li, G., Zhang, Y., & Li, Y. 2010, Proc Spie, 7733 (6) Google Scholar
Cui, X., Zhao, Y., Chu, Y., et al. 2012, RAA, 12 (9), 1197 Google Scholar
Horne, K. 1986, PASP, 98 (604), 609 CrossRefGoogle Scholar
Li, G., Zhang, H., & Bai, Z. 2015, PASP, 127 (2) CrossRefGoogle Scholar
Li, G., Zhang, H., Luo, A., et al. 2011, e-Science Technology & Application, 2 (4) Google Scholar
Piskunov, N. E. & Valenti, J. A. 2002, AA, 385 (3), 1095 CrossRefGoogle Scholar
Rahmani, H., Mahmood, A., Huynh, D., et al. 2014, ICPR, 3511 Google Scholar
Robertson, J. G. 1986, PASP, 98 (609), 1220 CrossRefGoogle Scholar
Su, D., Cui, X., Wang, Y., & Yao, Z. Q. 1998, ATI, 3352, 76 Google Scholar
Wang, J., Yang, J., Yu, K., et al. 2010, IEEE, 3360–3367Google Scholar
Watson, F., Offer, A. R., Lewis, I. J., et al. 1998, FSSR, 152, 50 Google Scholar
Wyse, R. F. G.. & Gilmore, G. 1994, MNRAS, 257, 1 CrossRefGoogle Scholar
Yang, Z. & Xiong, H. 2016, VC, 1Google Scholar
Yu, J., Yin, Q., Guo, P., et al. 2014, MNRAS, 443 (2), 1381 CrossRefGoogle Scholar
Zhao, G., Zhao, Y. H., Chu, Y., et al. 2012, RAA, 12 (7), 723 Google Scholar