IPAC2021 - List of Authors (Schirmer, D.)

Paper	Title	Page
WEPAB079	Optics Studies on the Operation of a New Wiggler and Bunch Shortening at the DELTA Storage Ring	2772
	B. Büsing, P. Hartmann, A. Held, S. Khan, C. Mai, D. Schirmer, G. Schmidt DELTA, Dortmund, Germany
	Funding: Work supported by Deutsche Forschungsgemeinschaft via project INST 212/330-1 AOBJ: 619186 The 1.5-GeV electron storage ring DELTA is a synchrotron light source operated by the TU Dortmund University. Radiation from hard X-rays to the THz regime is provided by dipole magnets and insertion devices like undulators and wigglers. To provide even shorter wavelengths, a new 22-pole superconducting 7-T wiggler has been installed. The edge focusing of the wiggler has a large impact on the linear optics of the storage ring. Measurements regarding its influence and simulations were performed. In addition, a second radiofrequency (RF) cavity has been installed to compensate the increased energy loss per turn due to the new wiggler. As a consequence of the higher RF power, the electron bunches are shorter compared to the old setup with only one cavity. In view of reducing the bunch length even more, studies of the storage ring optics with reduced momentum compaction factor were performed.
DOI •	reference for this paper ※ https://doi.org/10.18429/JACoW-IPAC2021-WEPAB079
About •	paper received ※ 19 May 2021 paper accepted ※ 24 June 2021 issue date ※ 01 September 2021
Export •	reference for this paper using ※ BibTeX, ※ LaTeX, ※ Text/Word, ※ RIS, ※ EndNote (xml)

WEPAB303	Machine Learning Applied to Automated Tunes Control at the 1.5 GeV Synchrotron Light Source DELTA	3379
	D. Schirmer DELTA, Dortmund, Germany
	Machine learning (ML) driven algorithms are finding more and more use cases in the domain of accelerator physics. Apart from correlation analysis in large data volumes, low and high level controls, like beam orbit correction, also non-linear feedback systems are possible application fields. This also includes monitoring the storage ring betatron tunes, as an important task for stable machine operation. For this purpose classical, shallow (non-deep), feed-forward neural networks (NNs) were investigated for automated adjusting the storage ring tunes. The NNs were trained with experimental machine data as well as with simulated data based on a lattice model of the DELTA storage ring. With both data sources comparable tune correction accuracies were achieved, both, in real machine operation and for the simulated storage ring model. In contrast to conventional PID methods, the trained NNs were able to approach the desired target tunes in fewer steps. The report summarizes the current status of this machine learning project and points out possible future improvements as well as other possible applications.
	Poster WEPAB303 [1.575 MB]
DOI •	reference for this paper ※ https://doi.org/10.18429/JACoW-IPAC2021-WEPAB303
About •	paper received ※ 19 May 2021 paper accepted ※ 05 July 2021 issue date ※ 25 August 2021
Export •	reference for this paper using ※ BibTeX, ※ LaTeX, ※ Text/Word, ※ RIS, ※ EndNote (xml)

Paper

Title

Page

Optics Studies on the Operation of a New Wiggler and Bunch Shortening at the DELTA Storage Ring

2772

B. Büsing, P. Hartmann, A. Held, S. Khan, C. Mai, D. Schirmer, G. Schmidt
DELTA, Dortmund, Germany

Funding: Work supported by Deutsche Forschungsgemeinschaft via project INST 212/330-1 AOBJ: 619186
The 1.5-GeV electron storage ring DELTA is a synchrotron light source operated by the TU Dortmund University. Radiation from hard X-rays to the THz regime is provided by dipole magnets and insertion devices like undulators and wigglers. To provide even shorter wavelengths, a new 22-pole superconducting 7-T wiggler has been installed. The edge focusing of the wiggler has a large impact on the linear optics of the storage ring. Measurements regarding its influence and simulations were performed. In addition, a second radiofrequency (RF) cavity has been installed to compensate the increased energy loss per turn due to the new wiggler. As a consequence of the higher RF power, the electron bunches are shorter compared to the old setup with only one cavity. In view of reducing the bunch length even more, studies of the storage ring optics with reduced momentum compaction factor were performed.

DOI •

reference for this paper ※ https://doi.org/10.18429/JACoW-IPAC2021-WEPAB079

About •

paper received ※ 19 May 2021 paper accepted ※ 24 June 2021 issue date ※ 01 September 2021

Export •

reference for this paper using ※ BibTeX, ※ LaTeX, ※ Text/Word, ※ RIS, ※ EndNote (xml)

WEPAB303

Machine Learning Applied to Automated Tunes Control at the 1.5 GeV Synchrotron Light Source DELTA

3379

D. Schirmer
DELTA, Dortmund, Germany

Machine learning (ML) driven algorithms are finding more and more use cases in the domain of accelerator physics. Apart from correlation analysis in large data volumes, low and high level controls, like beam orbit correction, also non-linear feedback systems are possible application fields. This also includes monitoring the storage ring betatron tunes, as an important task for stable machine operation. For this purpose classical, shallow (non-deep), feed-forward neural networks (NNs) were investigated for automated adjusting the storage ring tunes. The NNs were trained with experimental machine data as well as with simulated data based on a lattice model of the DELTA storage ring. With both data sources comparable tune correction accuracies were achieved, both, in real machine operation and for the simulated storage ring model. In contrast to conventional PID methods, the trained NNs were able to approach the desired target tunes in fewer steps. The report summarizes the current status of this machine learning project and points out possible future improvements as well as other possible applications.

Poster WEPAB303 [1.575 MB]

DOI •

reference for this paper ※ https://doi.org/10.18429/JACoW-IPAC2021-WEPAB303

About •

paper received ※ 19 May 2021 paper accepted ※ 05 July 2021 issue date ※ 25 August 2021

Export •

reference for this paper using ※ BibTeX, ※ LaTeX, ※ Text/Word, ※ RIS, ※ EndNote (xml)