Abstract
Abstract Back-illuminated spot mapping is essential for observations with modern spectroscopic survey telescopes. In the fiber position detection system, after the fiber view camera (FVC) detects the positions of the light spots from the robotic fiber positioners (RFPs), these positions must be mapped to the RFPs individually to complete subsequent calculations of their operational data. The back-illuminated images captured by the FVC show thousands of small light spots, each only hundreds of microns in size, randomly distributed on a completely black background, making accurate one-to-one mapping to the actual RFP positions highly challenging due to the lack of additional RFP features. This study constructs an artificial probability potential field to calculate the likelihood of each light spot appearing in the patrol area of an RFP, employing a global probability potential energy optimization method to establish the optimal mapping relationship. The proposed method successfully maps all light spots in the closed-loop fiber positioning task and is ultimately applied to the fiber positioning system of the Large Sky Area Multi-Object Fiber Spectroscopic Telescope, achieving accurate mapping of 4000 RFPs on the focal plane. This algorithm effectively addresses challenges such as multitarget interference, uniform background, and RFP failures, providing a reliable approach for mapping the back-illuminated points of tens of thousands of dual-rotation RFPs without requiring special backlight designs in future spectral survey telescopes.
Published Version
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