Input beam matching and beam dynamics design optimizations of the IsoDAR RFQ using statistical and machine learning techniques
Abstract
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 RFQ beam dynamics design prior to construction. Since surrogate models execute orders of magnitude faster than corresponding physics beam dynamics simulations using standard tools like PARMTEQM and RFQGen, the computational complexity of the multi-objective optimization problem reduces significantly. Ultimately, this presents a computationally inexpensive and time efficient method to perform sensitivity studies and an optimization of the crucial RFQ beam output parameters like transmission and emittances. Two different methods of surrogate model creation (polynomial chaos expansion and neural networks) are discussed and the achieved model accuracy is evaluated for different study cases with gradually increasing complexity, ranging from a simple FODO cell example to the full RFQ optimization. We find that variations of the beam input Twiss parameters can be reproduced well. The prediction of the beam with respect to hardware changes, e.g., the electrode modulation, are challenging on the other hand. We discuss possible reasons for that and elucidate nevertheless existing benefits of the applied method to RFQ beam dynamics design.
Citation
Koser , D , Waites , L , Winklehner , D , Frey , M , Adelmann , A & Conrad , J 2022 , ' Input beam matching and beam dynamics design optimizations of the IsoDAR RFQ using statistical and machine learning techniques ' , Frontiers in Physics , vol. 10 , 875889 . https://doi.org/10.3389/fphy.2022.875889
Publication
Frontiers in Physics
Status
Peer reviewed
ISSN
2296-424XType
Journal article
Rights
Copyright © 2022 Koser, Waites, Winklehner, Frey, Adelmann and Conrad. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.
Description
This work was supported by NSF grants PHY-1505858 and PHY-1626069 and funding from the Bose Foundation and the Heising-Simons Foundation.Collections
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