IPAC2021 - List of Authors (Kaiser, J.)

Paper	Title	Page
TUPAB298	First Steps Toward an Autonomous Accelerator, a Common Project Between DESY and KIT	2182
	A. Eichler, F. Burkart, J. Kaiser, W. Kuropka, O. Stein DESY, Hamburg, Germany E. Bründermann, A. Santamaria Garcia, C. Xu KIT, Eggenstein-Leopoldshafen, Germany
	Funding: Helmholtz Artificial Cooperation Unit Reinforcement Learning algorithms have risen in popularity in recent years in the accelerator physics community, showing potential in beam control and in the optimization and automation of tasks in accelerator operation. The Helmholtz AI project "Machine Learning toward Autonomous Accelerators" is a collaboration between DESY and KIT that works on investigating and developing RL applications for the automatic start-up of electron linear accelerators. The work is carried out in parallel at two similar research accelerators: ARES at DESY and FLUTE at KIT, giving the unique opportunity of transfer learning between facilities. One of the first steps of this project is the establishment of a common interface between the simulations and the machine, in order to test and apply various optimization approaches interchangeably between the two accelerators. In this paper we present the first results on the common interface and its application to beam focusing in ARES, and the idea of laser shaping with spatial light modulators at FLUTE.
DOI •	reference for this paper ※ https://doi.org/10.18429/JACoW-IPAC2021-TUPAB298
About •	paper received ※ 19 May 2021 paper accepted ※ 02 August 2021 issue date ※ 17 August 2021
Export •	reference for this paper using ※ BibTeX, ※ LaTeX, ※ Text/Word, ※ RIS, ※ EndNote (xml)

Paper

Title

Page

First Steps Toward an Autonomous Accelerator, a Common Project Between DESY and KIT

2182

A. Eichler, F. Burkart, J. Kaiser, W. Kuropka, O. Stein
DESY, Hamburg, Germany
E. Bründermann, A. Santamaria Garcia, C. Xu
KIT, Eggenstein-Leopoldshafen, Germany

Funding: Helmholtz Artificial Cooperation Unit
Reinforcement Learning algorithms have risen in popularity in recent years in the accelerator physics community, showing potential in beam control and in the optimization and automation of tasks in accelerator operation. The Helmholtz AI project "Machine Learning toward Autonomous Accelerators" is a collaboration between DESY and KIT that works on investigating and developing RL applications for the automatic start-up of electron linear accelerators. The work is carried out in parallel at two similar research accelerators: ARES at DESY and FLUTE at KIT, giving the unique opportunity of transfer learning between facilities. One of the first steps of this project is the establishment of a common interface between the simulations and the machine, in order to test and apply various optimization approaches interchangeably between the two accelerators. In this paper we present the first results on the common interface and its application to beam focusing in ARES, and the idea of laser shaping with spatial light modulators at FLUTE.

DOI •

reference for this paper ※ https://doi.org/10.18429/JACoW-IPAC2021-TUPAB298

About •

paper received ※ 19 May 2021 paper accepted ※ 02 August 2021 issue date ※ 17 August 2021

Export •

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