An experimental application of machine learning algorithms to optimize the FEL lasing via beam trajectory tuning at Dalian Coherent Light Source

The lasing optimization of Free-Electron Laser (FEL) facilities is a time-consuming and challenging task. Instead of operating manually by experienced operators, implementation of machine learning algorithms offers a rapid and adaptable approach for FEL lasing optimization. Recently, such an experiment has been conducted at the vacuum ultraviolet FEL facility - Dalian Coherent Light Source (DCLS). Four algorithms, namely the standard and the neural network-based genetic algorithms, the deep deterministic policy gradient and the soft actor critic reinforcement learning algorithms, have been employed to enhance the FEL intensity by optimizing the electron beam trajectories. These algorithms have shown notable efficacy in enhancing the FEL lasing, especially the reinforcement learning ones which achieved convergence within only approximately 400 iterations. This study demonstrates the validity of machine learning algorithms for FEL lasing optimization, providing a forward-looking perspective on the automatic operation of DCLS.