AI Method for LAMOST Fiber Detection Based on Front Illumination

Abstract The double revolving fiber positioning technology employed in the Large Sky Area Multi-Object Fiber Spectroscopic Telescope (LAMOST) represents one of the most successful advancements in large-scale multi-objective spectroscopy. The precision of fiber positioning is crucial, as it directly impacts the observational efficiency of LAMOST. A critical component of the fiber positioning system is the closed-loop control system, which traditionally utilizes the light spot generated at fiber end. However, this study introduces a novel approach based on front-illuminated LAMOST focal plane image measurements. Unlike back-illumination, front-illumination does not necessitate internal lighting in the spectrograph, thus reducing light pollution and eliminating the need for additional photography. This method employs an artificial intelligence model to analyze images captured at the focal plane unit (FPU), using the image of the white ceramic head on the FPU as the data set for training, the model is capable of accurately measuring the fiber positions solely through front-illumination. Preliminary trials indicate that the measurement accuracy achieved using the front-illumination method is approximately <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML" overflow="scroll"> <mml:mn>0</mml:mn> <mml:mover accent="true"> <mml:mrow> <mml:mi>.</mml:mi> </mml:mrow> <mml:mrow> <mml:mi>″</mml:mi> </mml:mrow> </mml:mover> <mml:mn>13</mml:mn> </mml:math> . This level of precision meets the stringent fiber positioning accuracy requirement of LAMOST, set at <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML" overflow="scroll"> <mml:mn>0</mml:mn> <mml:mover accent="true"> <mml:mrow> <mml:mi>.</mml:mi> </mml:mrow> <mml:mrow> <mml:mi>″</mml:mi> </mml:mrow> </mml:mover> <mml:mn>2</mml:mn> </mml:math> . Furthermore, this novel approach demonstrates compatibility with LAMOST’s existing closed-loop fiber control system, offering potential for seamless integration and enhanced operational efficiency.