Hostname: page-component-78c5997874-4rdpn Total loading time: 0 Render date: 2024-11-13T22:46:31.274Z Has data issue: false hasContentIssue false
  • English
  • Français

A Sky-Subtraction Algorithm for LAMOST Using Two-Dimensional Sky-Background Modeling

Published online by Cambridge University Press:  02 January 2013

J. Zhu  and
Z. Ye
J. Zhu
Affiliation:
Institute of Statistical Signal Processing, Department of Electronic Engineering and Information Science, University of Science and Technology of China, Hefei 230027, China
Z. Ye*
Affiliation:
Institute of Statistical Signal Processing, Department of Electronic Engineering and Information Science, University of Science and Technology of China, Hefei 230027, China
*
BCorresponding author. Email: yezf@ustc.edu.cn
Rights & Permissions [Opens in a new window]

Abstract

Core share and HTML view are not available for this content. However, as you have access to this content, a full PDF is available via the ‘Save PDF’ action button.

A novel algorithm is proposed for the sky subtraction of the Large Sky Area Multi-Object Fiber Spectroscopic Telescope (LAMOST) based on two-dimensional sky background modeling. Different from the standard fiber spectrum data processing techniques, a two-dimensional sky background model can be obtained with the new algorithm and sky subtraction can now be performed as an earlier step, before the spectrum extraction. In this study, experiments are performed on simulated data based on the LAMOST project to analyze the accuracy and the effectiveness. The results show that the proposed algorithm can give a more effective sky subtraction than the method that is currently used for LAMOST.

Keywords

instrumentation: spectrographsmethods: data analysistechniques: spectroscopictelescopes
Type
Research Article
Information
Publications of the Astronomical Society of Australia , Volume 29 , Issue 1 , 2012 , pp. 78 - 85
Copyright
Copyright © Astronomical Society of Australia 2012

References

Abazajian, K., et al. , 2004, ApJ, 128, 502CrossRefGoogle Scholar
Bai, Z. R., Zhang, L. & Ye, Z. F., 2008, ChA&A, 32, 109Google Scholar
Barden, S. C., Elston, R., Armandroff, T. & Pryor, C. P., 1993, ASPC, 37, 223Google Scholar
Bolton, A. S. & Burles, S., 2007, NJPh, 9, 443CrossRefGoogle Scholar
Causi, G. L. & De Luca, M., 2005, NA, 11, 81Google Scholar
Cuby, J. & Mignoli, M., 1994, SPIE, 2198, 98Google Scholar
Cui, X. Q., 2009, AAS, 213, 226Google Scholar
de Boor, C., 1978, A Practical Guide to Splines (New York: Springer)CrossRefGoogle Scholar
Elston, R. & Barden, S., 1989, NOAO Newsletter, 19, 21Google Scholar
Gordon, W. & Riesenfeld, R., 1974, in Computer Aided Geometric Design, ed. Barnhill, R. & Riesenfeld, R. (New York: Academic Press), 95CrossRefGoogle Scholar
Kurtz, M. J. & Mink, D. J., 2000, ApJL, 533, L183CrossRefGoogle Scholar
Parry, I. & Carrasco, E., 1990, SPIE, 1235, 702Google Scholar
Sharp, R. & Parkinson, H., 2010, MNRAS, 408, 2495CrossRefGoogle Scholar
Watson, F. G., 1987, PhD Thesis, University of EdinburghGoogle Scholar
Wild, V. & Hewett, P. C., 2005, MNRAS, 358, 1083CrossRefGoogle Scholar
Wynne, C., 1993, MNRAS, 260, 307CrossRefGoogle Scholar
Wyse, R. & Gilmore, G., 1992, MNRAS, 257, 1CrossRefGoogle Scholar
Zhang, H., Chu, Y. & Chen, J., 2002, ChA&A, 26, 97Google Scholar
Zhu, Y. T., Hu, Z. W., Zhang, Q. F., Wang, L. & Wang, J. N., 2006, SPIE, 6269, 20Google Scholar