Speaker
Description
The High Energy Photon Source (HEPS) is a fourth-generation synchrotron radiation facility under construction in Beijing, China. Since the beam commissioning of the storage ring commenced in July 2024, progress has proceeded smoothly, and the first light was achieved in October. During the construction and beam commissioning of HEPS, we explored machine learning to address critical technical challenges. In the offline optimization phase, we applied serval machine learning methods, focusing on data-driven evaluation and optimization of Touschek lifetime. With approximately 95% evaluation accuracy, we reduced computation time by about 90% compared to traditional methods, thereby significantly improving computational efficiency. In the beam commissioning phase, we employed unsupervised learning methods to identify abnormal states in the power supplies, successfully detecting anomalies. Furthermore, we are optimizing beam lifetime by applying machine learning algorithms to accelerator parameters and Beam Loss Monitor (BLM) data in an ongoing study. In recognition of the importance of high-quality data for machine learning applications, we developed the FAIR-Compliant AI-Ready Accelerator Data Platform (FARAD), aimed at generating AI-ready datasets to advance research in the accelerator field. All the beam experiments described above were conducted on the FARAD platform, greatly enhancing research efficiency. This paper will provide a detailed description of the progress in the research outlined above, along with future plans.
