CAFe II

X. Chen, X. Qi, C. Su, Y. He, Z. Wang, K. Sun, C. Jin, W. Chen, S. Liu, X. Zhao, D. Jia, M. Yi Chinese Academy of Sciences arXiv Abstract The superconducting linear accelerator is a highly flexiable facility for modern scientific discoveries, necessitating weekly reconfiguration and tuning. Accordingly, minimizing setup time proves essential in affording users with ample experimental time. We propose a trend-based soft actor-critic(TBSAC) beam control method with strong robustness, allowing the agents to be trained in a simulated environment and applied to the real accelerator directly with zero-shot. To validate the effectiveness of our method, two different typical beam control tasks were performed on China Accelerator Facility for Superheavy Elements (CAFe II) and a light particle injector(LPI) respectively. The orbit correction tasks were performed in three cryomodules in CAFe II seperately, the time required for tuning has been reduced to one-tenth of that needed by human experts, and the RMS values of the corrected orbit were all less than 1mm. The other transmission efficiency optimization task was conducted in the LPI, our agent successfully optimized the transmission efficiency of radio-frequency quadrupole(RFQ) to over 85% within 2 minutes. The outcomes of these two experiments offer substantiation that our proposed TBSAC approach can efficiently and effectively accomplish beam commissioning tasks while upholding the same standard as skilled human experts. As such, our method exhibits potential for future applications in other accelerator commissioning fields. ...

X. Chen, Y. Jia, X. Qi, Z. Wang, Y. He Chinese Academy of Sciences Physical Review Accelerators and Beams Abstract Recently, reinforcement learning (RL) algorithms have been applied to a wide range of control problems in accelerator commissioning. In order to achieve efficient and fast control, these algorithms need to be highly efficient, so as to minimize the online training time. In this paper, we incorporated the beam position monitor trend into the observation space of the twin delayed deep deterministic policy gradient (TD3) algorithm and trained two different structure agents, one based on physical prior knowledge and the other using the original TD3 network architecture. Both of the agents exhibit strong robustness in the simulated environment. The effectiveness of the agent based on physical prior knowledge has been validated in a real accelerator. Results show that the agent can overcome the difference between simulated and real accelerator environments. Once the training is completed in the simulated environment, the agent can be directly applied to the real accelerator without any online training process. The RL agent is deployed to the medium energy beam transport section of China Accelerator Facility for Superheavy Elements. Fast and automatic orbit correction is being tested with up to ten degrees of freedom. The experimental results show that the agents can correct the orbit to within 1 mm. Moreover, due to the strong robustness of the agent, when a trained agent is applied to different lattices of different particles, the orbit correction can still be completed. Since there are no online data collection and training processes, all online corrections are done within 30 s. This paper shows that, as long as the robustness of the RL algorithm is sufficient, the offline learning agents can be directly applied to online correction, which will greatly improve the efficiency of orbit correction. Such an approach to RL may find promising applications in other areas of accelerator commissioning. ...