Machine Learning-Driven Beam Tuning Using Adaptive Region Bayesian Optimization at INFN-LNL

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 d...

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Bibliografske podrobnosti

Principais autores:	Ysabella Kassandra Ong, Luca Bellan, Damiano Bortolato, Maurizio Montis, Michele Comunian, Natalia Milas, Ryoichi Miyamoto, Domenic Nicosia, Francesco Grespan, Enrico Fagotti, Andrea Pisent
Format:	Artigo
Jezik:	Inglês
Izdano:	MDPI AG 2026-06-01
Serija:	Instruments
Teme:	machine learning particle accelerator operations INFN-LNL Bayesian optimization ANTHEM BNCT facility ESS
Online dostop:	https://www.mdpi.com/2410-390X/10/2/33
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