Machine learning for FEL optimization
Abstract/Contents
- Abstract
- With improvements in linear accelerator (Linac) technology, high-energy electron beams accelerated and compressed by Linac systems can radiate in a high-gain X-ray Free Electron Laser (XFEL). After more than a decade of successful operation at the LINAC Coherent Light Source (LCLS) and other facilities around the world, the scientific community from various fields has a strong demand for further increase in the peak and average power of the next generation of XFELs. Reaching higher peak power is critical for coherent diffraction imaging of complex molecules, a key scientific motivation for the LCLS, which could lead to medical discoveries and drug development, for example, by imaging the structure of a complex virus in situ. In addition, higher power X-rays can facilitate fundamental research in strong eld physics and other fields. The first part of the work to be discussed in this dissertation is the theoretical study of achieving higher XFEL power by implementing undulator tapering and related optimization algorithms. Second, simulation and experimental results of taper optimization are presented, showing that with the new devices in the LCLS-II including phase shifters and the variable gap undulator, and with the advent of different kinds of Machine Learning (ML) algorithms, FEL power can be successfully optimized. The next part of this work investigates methods of retrieving transverse and longitudinal information of the X-ray pulses including both power and phase and illustrates the impact of the optimized FEL power. Finally, the longitudinal eld reconstruction research is carried out for explanation of the longitudinal pulses, including both power and phase. This research establishes novel techniques for FEL optimization and demonstrates that the improved devices and ML algorithms are promising experimental techniques to maximize FEL power.
Description
| Type of resource | text |
|---|---|
| Form | electronic resource; remote; computer; online resource |
| Extent | 1 online resource. |
| Place | California |
| Place | [Stanford, California] |
| Publisher | [Stanford University] |
| Copyright date | 2021; ©2021 |
| Publication date | 2021; 2021 |
| Issuance | monographic |
| Language | English |
Creators/Contributors
| Author | Zhang, Xiao |
|---|---|
| Degree supervisor | Huang, Zhirong, 1968- |
| Degree supervisor | Raubenheimer, Tor O |
| Thesis advisor | Huang, Zhirong, 1968- |
| Thesis advisor | Raubenheimer, Tor O |
| Thesis advisor | Aiken, Alex |
| Thesis advisor | Wu, Juhao |
| Degree committee member | Aiken, Alex |
| Degree committee member | Wu, Juhao |
| Associated with | Stanford University, Department of Applied Physics |
Subjects
| Genre | Theses |
|---|---|
| Genre | Text |
Bibliographic information
| Statement of responsibility | Xiao Zhang. |
|---|---|
| Note | Submitted to the Department of Applied Physics. |
| Thesis | Thesis Ph.D. Stanford University 2021. |
| Location | https://purl.stanford.edu/js994xd8093 |
Access conditions
- Copyright
- © 2021 by Xiao Zhang
- License
- This work is licensed under a Creative Commons Attribution Non Commercial 3.0 Unported license (CC BY-NC).
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