ICALEPCS2019 - List of Authors (Schirmer, D.)

Paper	Title	Page
WEPHA137	Integration of a Model Server into the Control System of the Synchrotron Light Source DELTA	1421
	D. Schirmer, A. Althaus DELTA, Dortmund, Germany
	During the past decades, a variety of particle optics programs have been applied for accelerator studies at the storage ring facility DELTA. Depending on the application, most programs were used offline without dynamic machine synchronisation. In order to centralize and standardize storage ring modeling capabilities, a dedicated online model server was developed and integrated into the EPICS-based control system. The core server is based on Python/EPICS service modules using OCELOT and COBEA as simulation tools. All data, actual machine readings/settings, conversion coefficients, results of simulation calculations as well as manual parameter settings, are handled via EPICS process variables. Thus, the data are transparently available in the entire control system for further processing or visualisation. To improve maintainability and adaptability, the remote presentation model controller concept was realized in the implementation. The paper explains the setup of the model server and discusses first use cases.
DOI •	reference for this paper ※ https://doi.org/10.18429/JACoW-ICALEPCS2019-WEPHA137
About •	paper received ※ 01 October 2019 paper accepted ※ 20 October 2019 issue date ※ 30 August 2020
Export •	reference for this paper using ※ BibTeX, ※ LaTeX, ※ Text/Word, ※ RIS, ※ EndNote (xml)

WEPHA138	Orbit Correction With Machine Learning Techniques at the Synchrotron Light Source DELTA	1426
	D. Schirmer DELTA, Dortmund, Germany
	In the last years, artificial intelligence (AI) has experienced a renaissance in many fields. AI-based concepts are nature-inspired and can also be used in the field of accelerator controls. At DELTA, various studies on this subject were conducted in the past. Among other possible applications, the use of neural networks for automated correction of the electron beam position (orbit control) is of interest. Machine learning (ML) simulations with a DELTA storage ring model were already successful. Recently, conventional Feed-Forward Neural Networks (FFNN) were trained on measured orbits to apply local and global beam position corrections to the 1.5 GeV storage ring DELTA. First experimental results are presented and compared with other orbit control methods.
DOI •	reference for this paper ※ https://doi.org/10.18429/JACoW-ICALEPCS2019-WEPHA138
About •	paper received ※ 30 September 2019 paper accepted ※ 09 October 2019 issue date ※ 30 August 2020
Export •	reference for this paper using ※ BibTeX, ※ LaTeX, ※ Text/Word, ※ RIS, ※ EndNote (xml)

Paper

Title

Page

Integration of a Model Server into the Control System of the Synchrotron Light Source DELTA

1421

D. Schirmer, A. Althaus
DELTA, Dortmund, Germany

During the past decades, a variety of particle optics programs have been applied for accelerator studies at the storage ring facility DELTA. Depending on the application, most programs were used offline without dynamic machine synchronisation. In order to centralize and standardize storage ring modeling capabilities, a dedicated online model server was developed and integrated into the EPICS-based control system. The core server is based on Python/EPICS service modules using OCELOT and COBEA as simulation tools. All data, actual machine readings/settings, conversion coefficients, results of simulation calculations as well as manual parameter settings, are handled via EPICS process variables. Thus, the data are transparently available in the entire control system for further processing or visualisation. To improve maintainability and adaptability, the remote presentation model controller concept was realized in the implementation. The paper explains the setup of the model server and discusses first use cases.

DOI •

reference for this paper ※ https://doi.org/10.18429/JACoW-ICALEPCS2019-WEPHA137

About •

paper received ※ 01 October 2019 paper accepted ※ 20 October 2019 issue date ※ 30 August 2020

Export •

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

WEPHA138

Orbit Correction With Machine Learning Techniques at the Synchrotron Light Source DELTA

1426

D. Schirmer
DELTA, Dortmund, Germany

In the last years, artificial intelligence (AI) has experienced a renaissance in many fields. AI-based concepts are nature-inspired and can also be used in the field of accelerator controls. At DELTA, various studies on this subject were conducted in the past. Among other possible applications, the use of neural networks for automated correction of the electron beam position (orbit control) is of interest. Machine learning (ML) simulations with a DELTA storage ring model were already successful. Recently, conventional Feed-Forward Neural Networks (FFNN) were trained on measured orbits to apply local and global beam position corrections to the 1.5 GeV storage ring DELTA. First experimental results are presented and compared with other orbit control methods.

DOI •

reference for this paper ※ https://doi.org/10.18429/JACoW-ICALEPCS2019-WEPHA138

About •

paper received ※ 30 September 2019 paper accepted ※ 09 October 2019 issue date ※ 30 August 2020

Export •

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