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{hwang:ipac2022-tupost053,
author = {K. Hwang and K. Fukushima and T. Maruta and S. Nash and P.N. Ostroumov and A.S. Plastun and T. Zhang and Q. Zhao},
% author = {K. Hwang and K. Fukushima and T. Maruta and S. Nash and P.N. Ostroumov and A.S. Plastun and others},
% author = {K. Hwang and others},
title = {{Beam Tuning at the FRIB Front End Using Machine Learning}},
booktitle = {Proc. IPAC'22},
% booktitle = {Proc. 13th International Particle Accelerator Conference (IPAC'22)},
pages = {983--986},
eid = {TUPOST053},
language = {english},
keywords = {simulation, operation, rfq, controls, status},
venue = {Bangkok, Thailand},
series = {International Particle Accelerator Conference},
number = {13},
publisher = {JACoW Publishing, Geneva, Switzerland},
month = {07},
year = {2022},
issn = {2673-5490},
isbn = {978-3-95450-227-1},
doi = {10.18429/JACoW-IPAC2022-TUPOST053},
url = {https://jacow.org/ipac2022/papers/tupost053.pdf},
abstract = {{The Facility for Rare Isotope Beams (FRIB) at Michigan State University produced and identified the first rare isotopes demonstrating the key performance parameter and completion of the project. An important next step toward FRIB user operation includes fast tuning of the Front End (FE) decision parameters to maintain optimal beam optics. The FE consists of the ion source, charge selection system, LEBT, RFQ, and MEBT. The strong coupling of many ion source parameters, strong space-charge effects in multi-component ion beams, and a not well-known neutralization factor in the beamline from the ion source to the charge selection system make the FE modeling difficult. In this paper, we present our first effort toward the Machine Learning (ML) application for automatic control of the beam exiting the FE.}},
}