Paper
7 August 2014 Research on key problems for LAMOST optical fiber detection system
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Abstract
The large sky area multi-object fiber spectroscopic telescope (LAMOST) is an innovative reflecting schmidt telescope, promising a very high spectrum acquiring rate of several ten-thousands of spectra per night. By using the parallel controllable fiber positioning technique, LAMOST makes reconfiguration of fibers accurately according to the positions of objects in minutes and fine adjusting the fibers. During telescope observation period, each optical fiber unit positional accuracy directly determines the quality of subsequent spectrum acqusition, yet for real-time optical fiber positional accuracy, there only exists an internal information feedback which focus on the corresponding stepper motor driving conditions, however, this available information is not comprehensive, it can not offer the actual positional information for each fiber unit. Considering the LAMOST on-site environment, a novel real-time optical fiber positional accuracy detection system which can be integrated in the existing observation and control system need to be developed to solve this problem. During the observation interval, this system can offer a comprehensive and effective information feedback about the focal optical fiber positional accuracy. Based on this feedback, the observation assistants can properly adjust the observation strategies to ensure the effectiveness and accuracy of acquired spectrum. Furthermore, this fiber positional accuracy feedback can provide prior spectral quality information to the spectral processing personnel and optimal the spectrum processing efficiency.
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Mengxin Wang, Jianjun Chen, Ali Luo, and Xiaoran Chen "Research on key problems for LAMOST optical fiber detection system", Proc. SPIE 9151, Advances in Optical and Mechanical Technologies for Telescopes and Instrumentation, 91514Q (7 August 2014); https://doi.org/10.1117/12.2052975
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KEYWORDS
Optical fibers

Image processing

CCD cameras

Control systems

Telescopes

Data modeling

Image quality

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