IHEP, Beijing, People’s Republic of China
IHEP CSNS, Guangdong Province, People’s Republic of China
The High Energy Photon Source (HEPS) is a 34-pm, 1360-m storage ring light source being built in the suburb of Beijing, China. The HEPS construction started in mid-2019. While the physics design has been basically determined, modifications on the HEPS accelerator physics design have been made since 2019, in order to deal with challenges emerging from the technical and engineering designs. In this paper, we will introduce the new storage ring lattice and injector design, and also present updated results of related physics issues, including impedance and collective effects, lattice calibration, insertion device effects, injection design studies, etc.
IHEP, Beijing, People’s Republic of China
In recent years, machine learning (ML) has attracted increasing interest among the accelerator field. As a complex collection of multiple physical subsystems, the design and operation of an accelerator can be very nonlinear and complicated, while ML is taken as a powerful tool to solve such nonlinear and complicated problems. In this study, we report on several successful applications of ML to accelerator physics at IHEP. The nonlinear dynamics optimization of the High Energy Photon Source (HEPS) that is a 4th-generation light source is a challenging topic. In this optimization, we use a ML surrogate model to fast select the potentially competitive solutions for a multiobjective genetic algorithm that can significantly improve the convergence rate and the diversity among obtained solutions. Besides, we also tried to apply a generative adversarial net to solve one-to-many problems of longitudinal beam current profile shaping. Unlike most supervised machine learning methods than cannot learn one-to-many maps, the generative adversarial net-based method is able to predict multiple solutions instead of one for a 4-dipole chicane to realize several desired custom current profiles.