IPAC2021 - List of Authors (Xu, C.)

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
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WEPAB289	Machine Learning Based Spatial Light Modulator Control for the Photoinjector Laser at FLUTE	3332
	C. Xu, E. Bründermann, A.-S. Müller, M.J. Nasse, A. Santamaria Garcia, C. Sax, C. Widmann KIT, Karlsruhe, Germany A. Eichler DESY, Hamburg, Germany
	Funding: C. Xu acknowledges the support by the DFG-funded Doctoral School "Karlsruhe School of Elementary and Astroparticle Physics: Science and Technology". FLUTE (Ferninfrarot Linac- und Test-Experiment) at KIT is a compact linac-based test facility for novel accelerator technology and a source of intense THz radiation. FLUTE is designed to provide a wide range of electron bunch charges from the pC- to nC-range, high electric fields up to 1.2 GV/m, and ultra-short THz pulses down to the fs-timescale. The electrons are generated at the RF photoinjector, where the electron gun is driven by a commercial titanium sapphire laser. In this kind of setup the electron beam properties are determined by the photoinjector, but more importantly by the characteristics of the laser pulses. Spatial light modulators can be used to transversely and longitudinally shape the laser pulse, offering a flexible way to shape the laser beam and subsequently the electron beam, influencing the produced THz pulses. However, nonlinear effects inherent to the laser manipulation (transportation, compression, third harmonic generation) can distort the original pulse. In this paper we propose to use machine learning methods to manipulate the laser and electron bunch, aiming to generate tailor-made THz pulses. The method is demonstrated experimentally in a test setup.
DOI •	reference for this paper ※ https://doi.org/10.18429/JACoW-IPAC2021-WEPAB289
About •	paper received ※ 19 May 2021 paper accepted ※ 06 July 2021 issue date ※ 26 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)

WEPAB289

Machine Learning Based Spatial Light Modulator Control for the Photoinjector Laser at FLUTE

3332

C. Xu, E. Bründermann, A.-S. Müller, M.J. Nasse, A. Santamaria Garcia, C. Sax, C. Widmann
KIT, Karlsruhe, Germany
A. Eichler
DESY, Hamburg, Germany

Funding: C. Xu acknowledges the support by the DFG-funded Doctoral School "Karlsruhe School of Elementary and Astroparticle Physics: Science and Technology".
FLUTE (Ferninfrarot Linac- und Test-Experiment) at KIT is a compact linac-based test facility for novel accelerator technology and a source of intense THz radiation. FLUTE is designed to provide a wide range of electron bunch charges from the pC- to nC-range, high electric fields up to 1.2 GV/m, and ultra-short THz pulses down to the fs-timescale. The electrons are generated at the RF photoinjector, where the electron gun is driven by a commercial titanium sapphire laser. In this kind of setup the electron beam properties are determined by the photoinjector, but more importantly by the characteristics of the laser pulses. Spatial light modulators can be used to transversely and longitudinally shape the laser pulse, offering a flexible way to shape the laser beam and subsequently the electron beam, influencing the produced THz pulses. However, nonlinear effects inherent to the laser manipulation (transportation, compression, third harmonic generation) can distort the original pulse. In this paper we propose to use machine learning methods to manipulate the laser and electron bunch, aiming to generate tailor-made THz pulses. The method is demonstrated experimentally in a test setup.

DOI •

reference for this paper ※ https://doi.org/10.18429/JACoW-IPAC2021-WEPAB289

About •

paper received ※ 19 May 2021 paper accepted ※ 06 July 2021 issue date ※ 26 August 2021

Export •

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