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Generative machine learning-based 6-dimensional phase space reconstruction using bunch compressors at PAL-XFEL by Kim, Seongyeol; Gonzalez-Aguilera, Juan Pablo; Roussel, Ryan; Kim, Gyujin; Edelen, Auralee; Cho, Myung-Hoon; Kim, Young-Kee; Shim, Chi Hyun; Heo, Hoon; Yang, Haeryong is a scholarly article available to read on EtoBox.

What is Generative machine learning-based 6-dimensional phase space reconstruction using bunch compressors at PAL-XFEL about?

Understanding the distribution of particles in 6-dimensional position-momentum phase space of an accelerated beam is one of the essential tasks of accelerator operations. Conventional beam phase space characterization methods in accelerator facilities, such as longitudinal phase space measurements, require specialized diagnostic elements like radio-frequency transverse deflecting cavities to determine the phase space distribution of particles in a beam. Recently, generative phase space reconstruction (GPSR) techniques have shown orders-of-magnitude speed up in resolving detailed phase distributions from experimental measurements. However, these methods still required the use of specialized diagnostic elements to effectively measure the 6-dimensional phase space distribution of beams. In this work, we show that the same machine learning technique can be used in conjunction with longitudinal bunch compressor elements found in many linear accelerators to resolve the 6-dimensional phase space distribution. We demonstrate in both simulation and experiment at the Pohang Accelerator Laboratory X-ray Free Electron Laser facility that the reconstruction technique can accurately resolve deta

Author

Kim, Seongyeol; Gonzalez-Aguilera, Juan Pablo; Roussel, Ryan; Kim, Gyujin; Edelen, Auralee; Cho, Myung-Hoon; Kim, Young-Kee; Shim, Chi Hyun; Heo, Hoon; Yang, Haeryong

Published

2025

Language

EN