Machine-learning-assisted beam tuning at FRIB
Year: 2024
Type: conference-paper
Abstract: Facility for Rare Isotope Beams (FRIB) requires diverse primary ion species beams to produce rare isotopes. The beam tuning time can be reduced by employing Machine Learning (ML) techniques. In this presentation, we aim to explore practical perspectives on shortening beam tuning time. Specifically, we discuss customization of Bayesian Optimization for maximum beam time utilization, and virtual diagnostics that are currently under development.
Source: JACOW
Authors: Hwang,Kilean, Plastun,Alexander, Fukushima,Kei, Ostroumov,Peter, Zhao,Qiang, Maruta,Tomofumi, Zhang,Tong
Institutions: Facility for Rare Isotope Beams, Michigan State University
Language: English
Subfield: Aerospace Engineering
Field: Engineering
Domain: Physical Sciences
Open Access status: hybrid
OpenAlex ID: W6944385242