Speaker
Kilean Hwang
(Facility for Rare Isotope Beams)
Description
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.
Funding Agency
Work supported by the U.S. Department of Energy, Office of Science, Office of Nuclear Physics, under Award Number DE-SC0024707 and used resources of the FRIB Operations, which is a DOE Office of Scien
Primary author
Kilean Hwang
(Facility for Rare Isotope Beams)
Co-authors
Alexander Plastun
(Facility for Rare Isotope Beams, Michigan State University)
Kei Fukushima
(Facility for Rare Isotope Beams, Michigan State University)
Peter Ostroumov
(Facility for Rare Isotope Beams, Michigan State University)
Qiang Zhao
(Michigan State University)
Tomofumi Maruta
(Facility for Rare Isotope Beams, Michigan State University)
Tong Zhang
(Facility for Rare Isotope Beams, Michigan State University)
