Computer Science – Performance
Scientific paper
Sep 2011
adsabs.harvard.edu/cgi-bin/nph-data_query?bibcode=2011acasn..52..413w&link_type=abstract
Acta Astronomica Sinica, vol. 52, no.5, p.413-426
Computer Science
Performance
Telescopes, Methods: Numerical
Scientific paper
The initial wavelength calibration procedure of Large Sky Area Multi-Object Fiber Spectroscopic Telescope (LAMOST) consists of three steps. Firstly, for each certain point in the search space near the prior calibration coefficients, its corresponding simulation arc spectrum could be obtained with the interpolation method. Then, the cross correlation between the simulation arc spectrum and the observed one will be calculated. Finally, the result of initial wavelength calibration is the calibration coefficient corresponding to the maximum correlation coefficient. Thus, multi-parameter optimization problem is essential in the calibration procedure. Particle swarm optimization (PSO) is a stochastic global optimization algorithm that is based on swarm intelligence. It has the advantages of easy to implement, high accuracy and fast convergence. Considering the excellent performance of PSO, we propose an optimization method for initial wavelength calibration of LAMOST based on PSO, and design the corresponding algorithm and the initial wavelength calibration test experiments. The experimental results show that the proposed PSO-based algorithm outperforms the improved genetic algorithm in terms of convergence speed, solution quality and CPU time. Therefore, the proposed method is a more effective method for initial wavelength calibration.
Wang Shuang
Ye G. H.
Ye Zhong-Fu
Zhu Jia-Ji
Zhu Zi-Qiang
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