Bulletin of the American Physical Society
2024 Fall Meeting of the APS Division of Nuclear Physics
Sunday–Thursday, October 6–10, 2024; Boston, Massachusetts
Session F11: Mini-Symposium: From Data to Discovery: Machine Learning III (Followed by Panel Discussion)
2:00 PM–3:36 PM,
Tuesday, October 8, 2024
Hilton Boston Park Plaza
Room: Arlington, Mezzanine Level
Chair: Kyle Godbey, Michigan State University
Abstract: F11.00003 : The CARIBU-matic project: automation for the transport of radioactive beams from the CARIBU source*
2:48 PM–3:00 PM
Presenter:
Daniel Santiago-Gonzalez
(Argonne National Laboratory)
Authors:
Daniel Santiago-Gonzalez
(Argonne National Laboratory)
Sergio Lopez-Caceres
(Argonne National Laboratory)
Brahim Mustapha
(Argonne National Laboratory)
[1] R. Roussel., et al., "Xopt: A simplified framework for optimization of accelerator problems using advanced algorithms", in Proc. IPAC'23, Venezia.doi:https://doi.org/10.18429/JACoW-14th International Particle Accelerator Conference-THPL164
[2] Zhang, Z., et al. "Badger: The missing optimizer in ACR", in Proc. IPAC'22, Bangkok. doi:10.18429/JACoW-IPAC2022-TUPOST058
*This material is based upon work supported by the U.S. Department of Energy, Office of Science, Office of Nuclear Physics, under contract number DE-AC02-06CH11357, and an "Artificial Intelligence, and Machine Learning for Autonomous Optimization and Control of Accelerators and Detectors" grant from the DOE's Office of Nuclear Physics. This research used resources of ANL's ATLAS facility, which is a DOE Office of Science User Facility.
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