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Title |
Other Keywords |
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| WE4P12 |
Upgrades of High Level Applications at Shanghai Soft X-Ray FEL Facility |
FEL, electron, MMI, laser |
171 |
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- H. Luo, D. Gu, T. Liu, Z. Wang
SARI-CAS, Pudong, Shanghai, People’s Republic of China
- K.Q. Zhang
SSRF, Shanghai, People’s Republic of China
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The Shanghai soft X-ray free-electron laser(SXFEL) facility has made significant progress in recent years with the rapid, upgraded iterations of the high level software, including but not limited to energy matching, orbit feedback and load, beam optimization, etc. These tools are key components in operation and experiment of free electron laser facility. Some key applications are presented in this paper.
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| DOI • |
reference for this paper
※ doi:10.18429/JACoW-FLS2023-WE4P12
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| About • |
Received ※ 21 August 2023 — Revised ※ 29 August 2023 — Accepted ※ 30 August 2023 — Issued ※ 02 December 2023 |
| Cite • |
reference for this paper using
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| WE4P20 |
Alignment Results of Tandem EPUs at the Taiwan Photon Source |
photon, electron, alignment, synchrotron |
192 |
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- Y.-C. Liu, C.M. Cheng, T.Y. Chung, Y.M. Hsiao, F.H. Tseng
NSRRC, Hsinchu, Taiwan
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Taiwan Photon Source (TPS) has been open to user operation since 2016. We report the alignment results of tandem EPUs in one double mini-beta y long straight section. The goal is to increase the brilliance of the synchrotron lights produced by the tandem EPUs through well-alignment and using a phase shifter to achieve both spatial and temporal coherence. The calculated brilliance gain of the tandem EPUs is compared, and the difference between the measured and numerical results is analyzed.
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Poster WE4P20 [4.435 MB]
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| DOI • |
reference for this paper
※ doi:10.18429/JACoW-FLS2023-WE4P20
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| About • |
Received ※ 16 August 2023 — Revised ※ 30 August 2023 — Accepted ※ 31 August 2023 — Issued ※ 02 December 2023 |
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reference for this paper using
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| TH3D3 |
How Can Machine Learning Help Future Light Sources? |
controls, operation, electron, laser |
249 |
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- A. Santamaria Garcia, E. Bründermann, M. Caselle, A.-S. Müller, L. Scomparin, C. Xu
KIT, Karlsruhe, Germany
- G. De Carne
Karlsruhe Institute of Technology (KIT), Eggenstein-Leopoldshafen, Germany
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Machine learning (ML) is one of the key technologies that can considerably extend and advance the capabilities of particle accelerators and needs to be included in their future design. Future light sources aim to reach unprecedented beam brightness and radiation coherence, which require challenging beam sizes and accelerating gradients. The sensitive designs and complex operation modes that arise from such demands will impact the beam availability and flexibility for the users, and can render future accelerators inefficient. ML brings a paradigm shift that can re-define how accelerators are operated. In this contribution we introduce the vision of ML-driven facilities for future accelerators, address some challenges of future light sources, and show an example of how such methods can be used to control beam instabilities.
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Slides TH3D3 [5.398 MB]
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| DOI • |
reference for this paper
※ doi:10.18429/JACoW-FLS2023-TH3D3
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| About • |
Received ※ 23 August 2023 — Revised ※ 25 August 2023 — Accepted ※ 31 August 2023 — Issued ※ 02 December 2023 |
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reference for this paper using
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