An Active Flexure Compensation Method for LAMOST spectrograph based on BP-Neural Network  

作　　者：Shuo Huang Hua Zou Tian-Jiao Liu Shi-Yu Zhao Hang Zhang 

机构地区：[1]College of Science,Hohai University,Nanjing 211100,China

出　　处：《Research in Astronomy and Astrophysics》2020年第3期42-49,共8页天文和天体物理学研究（英文版）

基　　金：support of the National Natural Science Foundation of China(Grant No.11503005);Jiangsu Students’ Innovation and Entrepreneurship Training Program(201910294155Y and 201810294059X);the National Undergraduate Training Program for Innovation and Entrepreneurship(201810294099).

摘　　要：The Large sky Area Multi-Object fiber Spectroscopic Telescope(LAMOST)is a Chinese national scientific research facility operated by National Astronomical Observatories,Chinese Academy of Sciences(NAOC).The LAMOST survey for the Milky Way Galaxy and extra-galactic objects has been carried out for several years.The accuracies in measuring radial velocity are expected to be 5 km s-1 for the low resolution spectroscopic survey(R=1800),and 1 km s-1 for the medium resolution mode.The stability of spectrograph is the main factor affecting the accuracies in measuring radial velocity,so an Active Flexure Compensation Method(AFCM)based on Back Propagation Neural Network(BPNN)is proposed in this paper.It utilizes a deep BP(4-layer,5-layer etc.)model of thermal-induced flexure to periodically predict and apply flexure corrections by commanding the corresponding tilt and tip motions to the camera.The spectrograph camera system is adjusted so that the positions of these spots match those in a reference image.The simulated calibration of this compensation method analytically illustrates its performance on LAMOST spectrograph.The Large sky Area Multi-Object fiber Spectroscopic Telescope(LAMOST) is a Chinese national scientific research facility operated by National Astronomical Observatories, Chinese Academy of Sciences(NAOC). The LAMOST survey for the Milky Way Galaxy and extra-galactic objects has been carried out for several years. The accuracies in measuring radial velocity are expected to be 5 km s-1 for the low resolution spectroscopic survey(R = 1800), and 1 km s-1 for the medium resolution mode. The stability of spectrograph is the main factor affecting the accuracies in measuring radial velocity, so an Active Flexure Compensation Method(AFCM) based on Back Propagation Neural Network(BPNN) is proposed in this paper. It utilizes a deep BP(4-layer, 5-layer etc.) model of thermal-induced flexure to periodically predict and apply flexure corrections by commanding the corresponding tilt and tip motions to the camera. The spectrograph camera system is adjusted so that the positions of these spots match those in a reference image. The simulated calibration of this compensation method analytically illustrates its performance on LAMOST spectrograph.

关 键 词：instrumentation:spectrographs methods:data analysis techniques:imaging SPECTROSCOPY telescopes 

分 类 号：P111.23[天文地球—天文学]