Laser beam control using machine learning technology for particle accelerator

Kai Jin; Yimin Hu; Wei Lu; Shukui Zhang

doi:10.1117/12.2608311

4 March 2022 Laser beam control using machine learning technology for particle accelerator

Kai Jin, Yimin Hu, Wei Lu, Shukui Zhang

Proceedings Volume 11997, Optical Components and Materials XIX; 119970M (2022) https://doi.org/10.1117/12.2608311
Event: SPIE OPTO, 2022, San Francisco, California, United States

Abstract

The Pockels Cells play important role in generating helicity-flipping polarized laser beam to be used in high energy electron beam accelerator facility. Due to exceptional requirements for ultra-stable electron beam in modern nuclear physics experiment, the operation of Pockels Cells which are key components to generate stable electron beam becomes critical. However, since the operation of Pockels Cell, which usually work in pair, involves beam alignments up to 12 degrees of freedom, it requires extremely complicated controls to maintain the stable output beam through whole operation time of accelerator. In this paper, we combined the machine learning method with the Pockels Cells control system, automatically collected data of Pockels cells optical properties such as polarization extinction ratio (PER), beam position, optical intensity asymmetry, etc., at different orientation angles and physical potions, and built an artificial neural network which can determine the optimal position of Pockels Cells. The trained artificial neural network can predict the PER, intensity asymmetry, beam position difference with a mean agreement around 95%, which makes it possible to find the optimal yaw/pitch/roll angles and physical positions of the Pockels cells in a short time. This technology can also be translated to alignments of devices in other laser systems such as high energy ultrafast oscillators and amplifiers.

Citation Download Citation

Kai Jin, Yimin Hu, Wei Lu, and Shukui Zhang "Laser beam control using machine learning technology for particle accelerator", Proc. SPIE 11997, Optical Components and Materials XIX, 119970M (4 March 2022); https://doi.org/10.1117/12.2608311

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