Input Beam Matching and Beam Dynamics Design Optimizations of the IsoDAR RFQ Using Statistical and Machine Learning Techniques
<jats:p>We present a novel machine learning-based approach to generate fast-executing virtual radiofrequency quadrupole (RFQ) particle accelerators using surrogate modelling. These could potentially be used as on-line feedback tools during beam commissioning and operation, and to optimize the...
| Main Authors: | , , , , , |
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| Other Authors: | |
| Format: | Article |
| Published: |
Frontiers Media SA
2022
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| Online Access: | https://hdl.handle.net/1721.1/142240 |