Input Beam Matching and Beam Dynamics Design Optimizations of the IsoDAR RFQ Using Statistical and Machine Learning Techniques

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...

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Bibliographic Details
Main Authors: Koser, Daniel, Waites, Loyd, Winklehner, Daniel, Frey, Matthias, Adelmann, Andreas, Conrad, Janet
Other Authors: Massachusetts Institute of Technology. Laboratory for Nuclear Science
Format: Article
Published: Frontiers Media SA 2022
Online Access:https://hdl.handle.net/1721.1/142240

Internet

https://hdl.handle.net/1721.1/142240

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