Speaker
Jitao Sun
(Dalian Institute of Chemical Physics)
Description
High-dimensional phase space reconstruction is an important tool for achieving precise beam simulation and optimization. We adopt a machine learning approach with a polarizable transverse deflecting cavity to reconstruct the multi-dimensional phase space of electron beam. By scanning the strength of the quadrupole magnets and the polarizations of the deflecting cavity, projections of the multi-dimensional phase space in different directions are obtained. A neural network is first trained with a large dataset, and the trained model is then applied to reconstruct the phase space. The result shows that the reconstructed phase space is good agreement with the original one. This report will describe the methods and results in detail.
| Region represented | Asia |
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Author
Jitao Sun
(Dalian Institute of Chemical Physics)
Co-authors
Xinmeng Li
(Dalian Institute of Chemical Physics)
Qizhang Huang
(Institute of Advanced Science Facilities, Shenzhen)
Zongbin Li
(Institute of Advanced Science Facilities, Shenzhen)
Jiahang Shao
(Institute of Advanced Science Facilities, Shenzhen)
Yong Yu
(Dalian Institute of Chemical Physics)
Jia Yang
(Dalian Institute of Chemical Physics)
Weiqing Zhang
(Institute of Advanced Science Facilities, Shenzhen)
