Research on tune feedback of the Hefei Light Source II based on machine learning

Abstract

The theory of tune feedback correction and the principle of a feedback algorithm based on machine learning are introduced, with a focus on the application of lasso regression for tune feedback correction. Simulation verification and online feedback correction results are presented. The results show that, after applying machine learning, the feedback accuracy of the tune feedback system was higher, and the betatron tune stability was further improved.

Machine learning in nuclear physics at low and intermediate energies

“…ML is widely used in the control of complex equipment. In the Hefei Light Source II (HLS-II) storage ring, machine leading method is used to improve the feedback accuracy of the tune feedback system, and the betatron tune stability [154]. Firmware base ML applications are promising in improving the performance of data acquisitions.…”

Neural network structure optimization for Hefei Light Source II β function correction

“…Genetic algorithms have been successfully used in many fields to solve various optimization problems, taking advantage of their global optimality-seeking properties [3][4][5]. In the field of optical parameter optimization of particle accelerators, neural networks are widely utilized [6][7][8][9]. However, challenges remain in constructing and optimizing neural networks for these applications.…”