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@inproceedings{koser:ipac2021-wepab203,
author = {D. Koser and A. Adelmann and J.M. Conrad and M. Frey and S. Mayani and L.H. Waites and D. Winklehner},
% author = {D. Koser and A. Adelmann and J.M. Conrad and M. Frey and S. Mayani and L.H. Waites and others},
% author = {D. Koser and others},
title = {{RFQ Beam Dynamics Optimization Using Machine Learning}},
booktitle = {Proc. IPAC'21},
pages = {3100--3102},
eid = {WEPAB203},
language = {english},
keywords = {rfq, simulation, focusing, network, quadrupole},
venue = {Campinas, SP, Brazil},
series = {International Particle Accelerator Conference},
number = {12},
publisher = {JACoW Publishing, Geneva, Switzerland},
month = {08},
year = {2021},
issn = {2673-5490},
isbn = {978-3-95450-214-1},
doi = {10.18429/JACoW-IPAC2021-WEPAB203},
url = {https://jacow.org/ipac2021/papers/wepab203.pdf},
note = {https://doi.org/10.18429/JACoW-IPAC2021-WEPAB203},
abstract = {{To efficiently inject a high-current H²⁺ beam into the 60 MeV driver cyclotron for the proposed IsoDAR project in neutrino physics, a novel direct-injection scheme is planned to be implemented using a compact radio-frequency quadrupole (RFQ) as a pre-buncher, being partially inserted into the cyclotron yoke. To optimize the RFQ beam dynamics design, machine learning approaches were investigated for creating a surrogate model of the RFQ. The required sample datasets are generated by standard beam dynamics simulation tools like PARMTEQM and RFQGen or more sophisticated PIC simulations. By reducing the computational complexity of multi-objective optimization problems, surrogate models allow to perform sensitivity studies and an optimization of the crucial RFQ beam output parameters like transmission and emittances. The time to solution might be reduced by up to several orders of magnitude. Here we discuss different methods of surrogate model creation (polynomial chaos expansion and neural networks) and identify present limitations of surrogate model accuracy.}},
}