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{fujii:ipac2022-mopopt034,
author = {H. Fujii and N. Fukunishi and M. Yamakita},
title = {{Surrogate-Based Bayesian Inference of Transverse Beam Distribution for Non-Stationary Accelerator Systems}},
booktitle = {Proc. IPAC'22},
% booktitle = {Proc. 13th International Particle Accelerator Conference (IPAC'22)},
pages = {324--327},
eid = {MOPOPT034},
language = {english},
keywords = {controls, experiment, beam-transport, framework, simulation},
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-MOPOPT034},
url = {https://jacow.org/ipac2022/papers/mopopt034.pdf},
abstract = {{Constraints on the beam diagnostics available in real-time and time-varying beam source conditions make it difficult to provide users with high-quality beams for long periods without interrupting experiments. Although surrogate model-based inference is useful for inferring the unmeasurable, the system states can be incorrectly inferred due to manufacturing errors and neglected higher-order effects when creating the surrogate model. In this paper, we propose to adaptively assimilate the surrogate model for reconstructing the transverse beam distribution with uncertainty and underspecification using a sequential Monte Carlo from the measurements of quadrant beam loss monitors. The proposed method enables sample-efficient and training-free inference and control of the time-varying transverse beam distribution.}},
}