Research on particle accelerator control network deformation prediction algorithm with machine learning

Abstract

Regular measurement of particle accelerators is imperative to ensure their long-term, stable operation and to meet future construction needs. The aim of this study is to minimise unplanned downtime and to provide data analysis and predictive insights for the remeasurement of similar particle accelerators. The focus is on the three-dimensional coordinate prediction and performance evaluation of a control network for a particle accelerator. The study uses observational data from the laser tracker in the Hefei Light Source storage ring control network, gathered from 2013 to 2023. Innovative use of the linear regression model, hyperbolic model, grey prediction model, and backpropagation neural network model has been made in the alignment of particle accelerators. A comparative analysis with measured data reveals that various deformation prediction methods can achieve a prediction accuracy of 0.25 mm. We recommend using the linear regression model for predicting the plumb direction, and the grey prediction model for predicting the horizontal direction. In addition, reducing the number of backward iteration steps can further improve the prediction accuracy. This study offers guidance for the deformation prediction of particle accelerators, and provides scientific data support and a basis for decision making on their long-term, stable operation.