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Beam based alignment using a neural network
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AI
DOI：10.1007/s41365-024-01436-y.png)
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Abstract
En 中文
Beams typically do not travel through the magnet centers because of errors in storage rings. The beam deviating from the quadrupole centers is affected by additional dipole fields due to magnetic field feed-down. Beam-based alignment (BBA) is often performed to determine a golden orbit where the beam circulates around the quadrupole center axes. For storage rings with many quadrupoles, the conventional BBA procedure is time-consuming, particularly in the commissioning phase, because of the necessary iterative process. In addition, the conventional BBA method can be affected by strong coupling and the nonlinearity of the storage ring optics. In this study, a novel method based on a neural network was proposed to determine the golden orbit in a much shorter time with reasonable accuracy. This golden orbit can be used directly for operation or adopted as a starting point for conventional BBA. The method was demonstrated in the HLS-II storage ring for the first time through simulations and online experiments. The results of the experiments showed that the golden orbit obtained using this new method was consistent with that obtained using the conventional BBA. The development of this new method and the corresponding experiments are reported in this paper.
Keywords:
Golden orbit
Beam-based alignment
Neural network
Storage ring
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