Machine Learning (ML) techniques are increasingly being adopted in particle accelerator operations to enable efficient control of complex systems. At INFN–LNL, we investigated both offline and real-time ML-driven approaches to enhance beam quality, reduce setup time, and improve reliability across different accelerator facilities. As part of this effort, we developed Adaptive Region Bayesian Optimization (ARBO), a custom Bayesian Optimization algorithm that dynamically expands its search domain when the predicted optimum approaches a boundary. Offline studies applied ARBO to the design optimization of the medium-energy beam transport line of the ANTHEM BNCT facility. Real-time online tests demonstrated the effectiveness of ARBO. At PIAVE–ALPI, the combined transmission improved from 44.2% to 52.6%, corresponding to an ALPI-only increase from approximately 69% to 82%, approaching the theoretical maximum of 93%. At the ESS normal-conducting linac, ARBO enabled the simultaneous tuning of more than 50 control elements while improving transmission and maintaining stable trajectory correction. These results indicate that adaptive optimization strategies can substantially improve accelerator performance and support future advances in ML-assisted accelerator operations.
Ong et al. (Tue,) studied this question.