JACoW is a publisher in Geneva, Switzerland that publishes the proceedings of accelerator conferences held around the world by an international collaboration of editors.
@inproceedings{santamariagarcia:fls2023-th3d3,
author = {A. Santamaria Garcia and E. Bründermann and M. Caselle and G. De Carne and A.-S. Müller and L. Scomparin and C. Xu},
% author = {A. Santamaria Garcia and E. Bründermann and M. Caselle and G. De Carne and A.-S. Müller and L. Scomparin and others},
% author = {A. Santamaria Garcia and others},
title = {{How Can Machine Learning Help Future Light Sources?}},
% booktitle = {Proc. FLS'23},
booktitle = {Proc. 67th ICFA Adv. Beam Dyn. Workshop Future Light Sources (FLS'23)},
eventdate = {2023-08-27/2023-09-01},
pages = {249--256},
paper = {TH3D3},
language = {english},
keywords = {controls, operation, electron, laser, feedback},
venue = {Luzern, Switzerland},
series = {ICFA Advanced Beam Dynamics Workshop},
number = {67},
publisher = {JACoW Publishing, Geneva, Switzerland},
month = {01},
year = {2024},
issn = {2673-7035},
isbn = {978-3-95450-224-0},
doi = {10.18429/JACoW-FLS2023-TH3D3},
url = {http://jacow.org/fls2023/papers/th3d3.pdf},
abstract = {{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.}},
}