FLS2023 - List of Authors (Scomparin, L.)

Paper	Title	Page
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

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)