Speaker
Michele Comunian
(Istituto Nazionale di Fisica Nucleare)
Description
Based on the operational experience on the superconducting heavy ion linac ALPI, we introduced Machine Learning techniques to improve the performances and speed up the tuning of the ion linac at LNL.
The technique proved to be particularly effective to overcome issues on the longitudinal matching.
The algorithm has been adapted to be compatible with high intensity linacs, in terms of transverse and longitudinal acceptance, available diagnostics and machine protection prescriptions.
After a detailed preparatory simulation work, the algorithm has been tested during the 2025 commissioning shift of the ESS linac.
In this paper the results obtained in the ALPI and ESS campaigns.
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Authors
Francesco Grespan
(Istituto Nazionale di Fisica Nucleare)
Luca Bellan
(Istituto Nazionale di Fisica Nucleare)
Co-authors
Andrea Pisent
(Istituto Nazionale di Fisica Nucleare)
Damiano Bortolato
(Istituto Nazionale di Fisica Nucleare)
Domenic Nicosia
(European Spallation Source)
Enrico Fagotti
(Istituto Nazionale di Fisica Nucleare)
Maurizio Montis
(Istituto Nazionale di Fisica Nucleare)
Michele Comunian
(Istituto Nazionale di Fisica Nucleare)
Natalia Milas
(European Spallation Source)
Ryoichi Miyamoto
(European Spallation Source)
Ysabella Kassandra Ong
(Istituto Nazionale di Fisica Nucleare)