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{baker:ipac2022-tupost048,
author = {K.R.L. Baker and S. Basak and J. Cha and I.D. Finch and S.R. Lawrie and A.A. Saoulis and J. Thiyagalingam},
% author = {K.R.L. Baker and S. Basak and J. Cha and I.D. Finch and S.R. Lawrie and A.A. Saoulis and others},
% author = {K.R.L. Baker and others},
title = {{Development of a Virtual Diagnostic for Estimating Key Beam Descriptors}},
booktitle = {Proc. IPAC'22},
% booktitle = {Proc. 13th International Particle Accelerator Conference (IPAC'22)},
pages = {969--972},
eid = {TUPOST048},
language = {english},
keywords = {simulation, diagnostics, MEBT, real-time, controls},
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-TUPOST048},
url = {https://jacow.org/ipac2022/papers/tupost048.pdf},
abstract = {{Real-time beam descriptive data such as emittance, envelope and loss, are central to accelerator operations, including online diagnostics, maintenance and beam quality control. However, these cannot always be obtained without disrupting user runs. Physics-based simulations, such as particle tracking codes, can be leveraged to provide estimates of these beam descriptors. However, such simulation-based methods are computationally intensive requiring access to high performance computing facilities, and hence, they are often non-realistic for real-time purposes. The proposed work explores the feasibility of using machine learning to replace these simulations with fast-executing inference models based on surrogate modelling. The approach is intended to provide the operators with estimates of key beam properties in real time. Bayesian optimisation is used to generate a synthetic dataset to ensure the input space is efficiently sampled and representative of operating conditions. This is used to train a surrogate model to predict beam envelope, emittance and loss. The methodology is applied to the ISIS MEBT as a case study to evaluate the performance of the surrogate model.}},
}