JACoW is a publisher in Geneva, Switzerland that publishes the proceedings of accelerator conferences held around the world by an international collaboration of editors.
@inproceedings{saoulis:ipac2022-tupopt057,
author = {A.A. Saoulis and K.R.L. Baker and S. Basak and H.V. Cavanagh and J. Cha and J. Thiyagalingam and R.E. Williamson},
% author = {A.A. Saoulis and K.R.L. Baker and S. Basak and H.V. Cavanagh and J. Cha and J. Thiyagalingam and others},
% author = {A.A. Saoulis and others},
title = {{Using Surrogate Models to Assist Accelerator Tuning at ISIS}},
booktitle = {Proc. IPAC'22},
% booktitle = {Proc. 13th International Particle Accelerator Conference (IPAC'22)},
pages = {1133--1136},
eid = {TUPOPT057},
language = {english},
keywords = {simulation, synchrotron, controls, operation, injection},
venue = {Bangkok, Thailand},
series = {International Particle Accelerator Conference},
number = {13},
publisher = {JACoW Publishing, Geneva, Switzerland},
month = {07},
year = {2022},
issn = {2673-5490},
isbn = {978-3-95450-227-1},
doi = {10.18429/JACoW-IPAC2022-TUPOPT057},
url = {https://jacow.org/ipac2022/papers/tupopt057.pdf},
abstract = {{High intensity hadron accelerator performance is often dominated by the need to minimise and control beam losses. Operator efforts to tune the machine during live operation are often restricted to local parameter space searches, while existing physics-based simulations are generally too computationally expensive to aid tuning in real-time. To this end, Machine Learning-based surrogate models can be trained on data produced by physics-based simulations, and serve to produce fast, accurate predictions of key beam properties, such as beam phase and bunch shape over time. These models can be used as a virtual diagnostic tool to explore the parameter space of the accelerator in real-time, without making changes on the live machine. At the ISIS Neutron and Muon source, major beam losses in the synchrotron are caused by injection and longitudinal trapping processes, as well as high intensity effects. This paper describes the training and inference performance of a neural network surrogate model of the longitudinal beam dynamics in the ISIS synchrotron, from injection at 70 MeV to 800 MeV extraction, and evaluates the model’s ability to assist accelerator tuning.}},
}