The CARIBU-matic project: automation for the transport of radioactive beams from the CARIBU source

ORAL

Abstract

The CARIBU source at the ATLAS user facility provides beams of radioactive isotopes made from the spontaneuos fission of a 252Cf source. Such radioactive beams allow our users to carry out their cutting-edge experiments. However, the stardard approach at extracting and transporting the beams to the user's end stations is based on time-consuming expert-driven manual tuning methods. In this talk, we will give an update on the CARIBU-matic project, which is an effort to automate components of the radioactive beam delivery process through machine-learning methods and hardware improvements. Results from an early implementation of the automation of CARIBU beam transport via Bayesian optimization, using Xopt [1] and Badger [2], will be presented.

[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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Presenters

  • Daniel Santiago-Gonzalez

    • Argonne National Laboratory

Authors

  • Daniel Santiago-Gonzalez

    • Argonne National Laboratory

  • Sergio Lopez-Caceres

    • Louisiana State University
    • Argonne National Laboratory

  • Brahim Mustapha

    • Argonne National Laboratory