A deconvolution method based on adaptive Landweber iteration to extract LAMOST one-dimensional spectra. Issue 2 (16th January 2019) - Li, Min; Li, Guangwei; Lv, Ke; Duan, Fuqing; Haerken, Hasitieer; Zhao, Yongheng
Abstract: In this paper, we present a deconvolution algorithm based on adaptive Landweber iteration (ALI) to extract the one-dimensional (1D) spectra from Large Sky Area Multi-Object Fiber Spectroscopy Telescope (LAMOST) multi-fibre spectrum images. The algorithm can eliminate serious crosstalk and depress the noise from two-dimensional (2D) images by using adaptively selected regularization parameters. Experiments on simulated images illustrate that 2D residuals reach CCD noise level and that the signal-to-noise (S/N) ratios of the resulting 1D spectra are higher than those from traditional methods. Application on a real LAMOST image shows that the resolutions of 1D spectra extracted from our algorithm are ∼25 per cent higher than those extracted using the traditional method, and it is obvious that the S/N ratio is also improved.