ESS DTL Tuning Using Machine Learning Methods
(2021) 12th International Particle Accelerator Conference, IPAC 2021 In International Particle Accelerator Conference p.1872-1875- Abstract
- The European Spallation Source, currently under construction in Lund, Sweden, will be the world's most powerful neutron source. It is driven by a proton linac with a current of 62.5 mA, 2.86 ms long pulses at 14 Hz. The final section of its normal-conducting front-end consists of a 39 m long drift tube linac (DTL) divided into five tanks, designed to accelerate the proton beam from 3.6 MeV to 90 MeV. The high beam current and power impose challenges to the design and tuning of the machine and the RF amplitude and phase have to be set within 1% and 1 ∘ of the design values. The usual method used to define the RF set-point is signature matching, which can be a time consuming and challenging process, and new techniques to meet the growing... (More)
- The European Spallation Source, currently under construction in Lund, Sweden, will be the world's most powerful neutron source. It is driven by a proton linac with a current of 62.5 mA, 2.86 ms long pulses at 14 Hz. The final section of its normal-conducting front-end consists of a 39 m long drift tube linac (DTL) divided into five tanks, designed to accelerate the proton beam from 3.6 MeV to 90 MeV. The high beam current and power impose challenges to the design and tuning of the machine and the RF amplitude and phase have to be set within 1% and 1 ∘ of the design values. The usual method used to define the RF set-point is signature matching, which can be a time consuming and challenging process, and new techniques to meet the growing complexity of accelerator facilities are highly desirable. In this paper we study the usage of Machine Learning to determine the RF optimum amplitude and phase. The data from a simulated phase scan is fed into an artificial neural network in order to identify the needed changes to achieve the best tuning. Our test for the ESS DTL1 shows promising results, and further development of the method will be outlined. (Less)
Please use this url to cite or link to this publication:
https://lup.lub.lu.se/record/3c83c571-e53e-4854-8feb-2ef9f8d17772
- author
- Lundquist, J S LU ; Werin, S LU ; Milas, Natalia and Nilsson, E
- organization
- publishing date
- 2021
- type
- Chapter in Book/Report/Conference proceeding
- publication status
- published
- subject
- keywords
- DTL, cavity, linac, network, proton
- host publication
- Proceedings of IPAC2021
- series title
- International Particle Accelerator Conference
- pages
- 4 pages
- publisher
- JACoW Publishing
- conference name
- 12th International Particle Accelerator Conference, IPAC 2021
- conference location
- Brazil
- conference dates
- 2021-05-24
- ISSN
- 2673-5490
- ISBN
- 978-3-95450-214-1
- DOI
- 10.18429/JACoW-IPAC2021-TUPAB198
- language
- English
- LU publication?
- yes
- id
- 3c83c571-e53e-4854-8feb-2ef9f8d17772
- date added to LUP
- 2022-03-09 11:49:37
- date last changed
- 2022-03-23 08:04:20
@inproceedings{3c83c571-e53e-4854-8feb-2ef9f8d17772,
abstract = {{The European Spallation Source, currently under construction in Lund, Sweden, will be the world's most powerful neutron source. It is driven by a proton linac with a current of 62.5 mA, 2.86 ms long pulses at 14 Hz. The final section of its normal-conducting front-end consists of a 39 m long drift tube linac (DTL) divided into five tanks, designed to accelerate the proton beam from 3.6 MeV to 90 MeV. The high beam current and power impose challenges to the design and tuning of the machine and the RF amplitude and phase have to be set within 1% and 1 ∘ of the design values. The usual method used to define the RF set-point is signature matching, which can be a time consuming and challenging process, and new techniques to meet the growing complexity of accelerator facilities are highly desirable. In this paper we study the usage of Machine Learning to determine the RF optimum amplitude and phase. The data from a simulated phase scan is fed into an artificial neural network in order to identify the needed changes to achieve the best tuning. Our test for the ESS DTL1 shows promising results, and further development of the method will be outlined.}},
author = {{Lundquist, J S and Werin, S and Milas, Natalia and Nilsson, E}},
booktitle = {{Proceedings of IPAC2021}},
isbn = {{978-3-95450-214-1}},
issn = {{2673-5490}},
keywords = {{DTL; cavity; linac; network; proton}},
language = {{eng}},
pages = {{1872--1875}},
publisher = {{JACoW Publishing}},
series = {{International Particle Accelerator Conference}},
title = {{ESS DTL Tuning Using Machine Learning Methods}},
url = {{http://dx.doi.org/10.18429/JACoW-IPAC2021-TUPAB198}},
doi = {{10.18429/JACoW-IPAC2021-TUPAB198}},
year = {{2021}},
}