Two-Dimensional Spectrum Extraction Method Based on Sparse Reconstruction Algorithm for LAMOST Real Data

According to the characteristics of the real data on the Large Sky Area Multi-Object Fiber Spectroscopic Telescope (LAMOST), a two-dimensional (2-D) spectrum extraction method based on sparse reconstruction algorithm is proposed. The proposed method uses the corresponding flat-field images and calibration lamp images, respectively, to build the 2-D Gauss model. Then, a sparse reconstruction algorithm with dual constraints is used to solve the inverse process of the formation of the 2-D observed spectra, restoring the original one-dimensional (1-D) spectrum. The experiments show that the proposed method reduces the extension of the spectra contours, contrasted with the 1-D method, and suppress the ringing problem of the pre-existing methods, getting more accurate extracted results. Besides, the proposed spectrum extraction method can be used on other telescope survey systems as well since the spectra formation mechanism of them are similar.