FLS2023 - List of Authors (Mueller, A.-S.)

Paper	Title	Page
TU4P34	Recent Developments of the cSTART Project	155
	M. Schwarz, A. Bernhard, E. Bründermann, D. El Khechen, B. Härer, A. Malygin, A.-S. Müller, M.J. Nasse, G. Niehues, A.I. Papash, R. Ruprecht, J. Schäfer, M. Schuh, N.J. Smale, P. Wesolowski, C. Widmann KIT, Karlsruhe, Germany
	The combination of a compact storage ring and a laser-plasma accelerator (LPA) can serve as the basis for future compact light sources. One challenge is the large momentum spread (~ 2%) of the electron beams delivered by the LPA. To overcome this challenge, a very large acceptance compact storage ring (VLA-cSR) was designed as part of the compact STorage ring for Accelerator Research and Technology (cSTART) project. The project will be realized at the Karlsruhe Institute of Technology (KIT, Germany). Initially, the Ferninfrarot Linac- Und Test-Experiment (FLUTE), a source of ultra-short bunches, will serve as an injector for the VLA-cSR to benchmark and emulate LPA-like beams. In a second stage, a laser-plasma accelerator will be used as an injector, which is being developed as part of the ATHENA project in collaboration with DESY and the Helmholtz Institute Jena (HIJ). The small facility footprint, the large-momentum spread bunches with charges from 1 pC to 1 nC and lengths from few fs to few ps pose challenges for the lattice design, RF system and beam diagnostics. This contribution summarizes the latest results on these challenges.
DOI •	reference for this paper ※ doi:10.18429/JACoW-FLS2023-TU4P34
About •	Received ※ 21 August 2023 — Revised ※ 22 August 2023 — Accepted ※ 31 August 2023 — Issued ※ 02 December 2023
Cite •	reference for this paper using ※ BibTeX, ※ LaTeX, ※ Text/Word, ※ RIS, ※ EndNote (xml)

TH3D3	How Can Machine Learning Help Future Light Sources?	249
	A. Santamaria Garcia, E. Bründermann, M. Caselle, A.-S. Müller, L. Scomparin, C. Xu KIT, Karlsruhe, Germany G. De Carne Karlsruhe Institute of Technology (KIT), Eggenstein-Leopoldshafen, Germany
	Machine learning (ML) is one of the key technologies that can considerably extend and advance the capabilities of particle accelerators and needs to be included in their future design. Future light sources aim to reach unprecedented beam brightness and radiation coherence, which require challenging beam sizes and accelerating gradients. The sensitive designs and complex operation modes that arise from such demands will impact the beam availability and flexibility for the users, and can render future accelerators inefficient. ML brings a paradigm shift that can re-define how accelerators are operated. In this contribution we introduce the vision of ML-driven facilities for future accelerators, address some challenges of future light sources, and show an example of how such methods can be used to control beam instabilities.
	Slides TH3D3 [5.398 MB]
DOI •	reference for this paper ※ doi:10.18429/JACoW-FLS2023-TH3D3
About •	Received ※ 23 August 2023 — Revised ※ 25 August 2023 — Accepted ※ 31 August 2023 — Issued ※ 02 December 2023
Cite •	reference for this paper using ※ BibTeX, ※ LaTeX, ※ Text/Word, ※ RIS, ※ EndNote (xml)

Paper

Title

Page

Recent Developments of the cSTART Project

155

M. Schwarz, A. Bernhard, E. Bründermann, D. El Khechen, B. Härer, A. Malygin, A.-S. Müller, M.J. Nasse, G. Niehues, A.I. Papash, R. Ruprecht, J. Schäfer, M. Schuh, N.J. Smale, P. Wesolowski, C. Widmann
KIT, Karlsruhe, Germany

The combination of a compact storage ring and a laser-plasma accelerator (LPA) can serve as the basis for future compact light sources. One challenge is the large momentum spread (~ 2%) of the electron beams delivered by the LPA. To overcome this challenge, a very large acceptance compact storage ring (VLA-cSR) was designed as part of the compact STorage ring for Accelerator Research and Technology (cSTART) project. The project will be realized at the Karlsruhe Institute of Technology (KIT, Germany). Initially, the Ferninfrarot Linac- Und Test-Experiment (FLUTE), a source of ultra-short bunches, will serve as an injector for the VLA-cSR to benchmark and emulate LPA-like beams. In a second stage, a laser-plasma accelerator will be used as an injector, which is being developed as part of the ATHENA project in collaboration with DESY and the Helmholtz Institute Jena (HIJ). The small facility footprint, the large-momentum spread bunches with charges from 1 pC to 1 nC and lengths from few fs to few ps pose challenges for the lattice design, RF system and beam diagnostics. This contribution summarizes the latest results on these challenges.

DOI •

reference for this paper ※ doi:10.18429/JACoW-FLS2023-TU4P34

About •

Received ※ 21 August 2023 — Revised ※ 22 August 2023 — Accepted ※ 31 August 2023 — Issued ※ 02 December 2023

Cite •

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

TH3D3

How Can Machine Learning Help Future Light Sources?

249

A. Santamaria Garcia, E. Bründermann, M. Caselle, A.-S. Müller, L. Scomparin, C. Xu
KIT, Karlsruhe, Germany
G. De Carne
Karlsruhe Institute of Technology (KIT), Eggenstein-Leopoldshafen, Germany

Machine learning (ML) is one of the key technologies that can considerably extend and advance the capabilities of particle accelerators and needs to be included in their future design. Future light sources aim to reach unprecedented beam brightness and radiation coherence, which require challenging beam sizes and accelerating gradients. The sensitive designs and complex operation modes that arise from such demands will impact the beam availability and flexibility for the users, and can render future accelerators inefficient. ML brings a paradigm shift that can re-define how accelerators are operated. In this contribution we introduce the vision of ML-driven facilities for future accelerators, address some challenges of future light sources, and show an example of how such methods can be used to control beam instabilities.

Slides TH3D3 [5.398 MB]

DOI •

reference for this paper ※ doi:10.18429/JACoW-FLS2023-TH3D3

About •

Received ※ 23 August 2023 — Revised ※ 25 August 2023 — Accepted ※ 31 August 2023 — Issued ※ 02 December 2023

Cite •

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