Improvement On LAMOST Data Acquisition And Preprocessing Methods For Spectra

Large Sky Area Multi-Object Fiber Spectroscopy Telescope(LAMOST) is one of the National Major Scientific Projects undertaken by the Chinese Academy of Sciences. The set-up of it will make our country a leading role in the research field of large scale spectrum observation and large field astronomy.LAMOST will obtain several ten-thousands of spectra per night. Therefore an automatically observational controlling and data processing system is in urgent need. This paper firstly introduces the process of observation and data processing as well as methods to improve the algorithm of spectrum flux extraction which solves the disadvantage of processing slowly, making the calculation more effective. Then the difficult issues—spectrum de-nosing and continuum fitting are analyzed in detail. The concrete works are described as follows:1. A new wavelet de-noising scheme based on sparse representation is presented in this paper. This method removes noise by means of dealing with the wavelet coefficients of each scale based on sparse representation. The method not only takes the structure properties in the wavelet coefficients into consideration, but also can maintain the local characteristics of wavelet coefficients. Therefore it can effectively keep the information of featured spectral lines during the process of de-noising.2. A combined method of wavelet transform and spine fitting is proposed for continuum fitting. This method removes strong spectral lines during the process of wavelet transform, trying to approximate it to real continuum spectrum.3. The traditional methods for sky spectrum subtraction and spectral line extraction are improved reasonably and effectively by means of the presented algorithms for de-noising and continuum fitting. Experimental results show the superiority of our improved algorithms.