25–30 Aug 2024
Hilton Chicago
America/Chicago timezone

Machine learning tools to support heavy-ion linac operations

MOPB006
26 Aug 2024, 16:00
2h
Boulevard (Hilton Chicago)

Boulevard

Hilton Chicago

720 South Michigan Ave Chicago, IL 60605 USA
Poster Presentation MC4.5 Other technology Monday Poster Session

Speaker

Brahim Mustapha (Argonne National Laboratory)

Description

At a heavy ion linac facility, such as ATLAS at Argonne National Laboratory, a new ion beam is tuned once or twice a week. The use of machine learning can be leveraged to streamline the tuning process, reducing the time needed to tune a given beam and allowing more beam time for the experimental program. After establishing automatic data collection and two-way communication with the control system, we have developed and deployed machine learning models to tune and control the machine. We have successfully trained online different Bayesian Optimization (BO)-based models for different sections of the linac, including the commissioning of a new beamline. We have demonstrated transfer learning from one ion beam to another allowing fast switching between different ion beams. We have also demonstrated transfer learning from a simulation-based model to an online machine model and used Neural Networks as prior-mean for BO optimization. More recently, we have succeeded in training a Reinforcement Learning (RL) model online for one beam and deployed it for the tuning of another beam. These models are being generalized to other sections of the ATLAS linac and can, in principle, be adapted to control other ion linacs and accelerators with modern control systems.

Funding Agency

This work was supported by the U.S. Department of Energy, under Contract No. DE-AC02-06CH11357. This research used the ATLAS facility, which is a DOE Office of Nuclear Physics User Facility.

Author

Brahim Mustapha (Argonne National Laboratory)

Presentation materials

There are no materials yet.