PKU, Beijing, People’s Republic of China
Ultra-intense laser-plasma interactions can produce TV/m acceleration gradients, making them promising for compact accelerators. Peking University is constructing a proton radiotherapy system prototype based on PW laser accelerators, but transient processes challenge stability control, critical for medical applications. This work demonstrates artificial intelligence’s (AI) application in laser accelerator control systems. To achieve micro-precision alignment between the ultra-intense laser and target, we propose an automated positioning program using the YOLO algorithm. This real-time method employs a convolutional neural network, directly predicting object locations and class probabilities from input images. It enables precise, automatic solid target alignment in about a hundred milliseconds, reducing experimental preparation time. The YOLO algorithm is also integrated into the safety interlocking system for anti-tailing, allowing quick emergency response. The intelligent control system also enables convenient, accurate beam tuning. We developed high-performance virtual accelerator software using "OpenXAL" and GPU-accelerated multi-particle beam transport simulations. The software allows real-time or custom parameter simulations and features control interfaces compatible with optimization algorithms. By designing tailored objective functions, desired beam size and distribution can be achieved in a few iterations.
PKU, Beijing, People’s Republic of China
Peking University, Beijing, Haidian District, People’s Republic of China
China’s Ministry of Science and Technology supports Peking University in constructing a proton radiotherapy device based on a petawatt (PW) laser accelerator. The control system’s functionality and performance are vital for the accelerator’s reliability, stability, and efficiency. The PW laser accelerator control system has a three-layer distributed architecture, including device control, front-end (input/output) control and central control (data management, and human-machine interface) layers. The software platform primarily uses EPICS, supplemented by PLC, Python, and Java, while the hardware platform comprises industrial control computers, servers, and private cloud configurations. The control system incorporates various subsystems that manage the laser, target field, beamline, safety interlocks, conditions, synchronization, and functionalities related to data storage, display, and more. This paper presents a control system implementation suitable for laser accelerators, providing valuable insights for future laser accelerator control system development.