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{hall:ipac2022-mopotk038,
author = {C.C. Hall and J.P. Edelen and J.A. Einstein-Curtis and M.C. Kilpatrick},
title = {{BPM Analysis with Variational Autoencoders}},
booktitle = {Proc. IPAC'22},
% booktitle = {Proc. 13th International Particle Accelerator Conference (IPAC'22)},
pages = {543--545},
eid = {MOPOTK038},
language = {english},
keywords = {network, focusing, diagnostics, GPU, optics},
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-MOPOTK038},
url = {https://jacow.org/ipac2022/papers/mopotk038.pdf},
abstract = {{In particle accelerators, beam position monitors (BPMs) are used extensively as a non-intercepting diagnostic. Significant information about beam dynamics can often be extracted from BPM measurements and used to actively tune the accelerator. However, common measurement tools, such as measurements of kicked beams, may become more difficult when very strong nonlinearities are present or when data is very noisy. In this work, we examine the use of variational autoencoders (VAEs) as a technique to extract measurements of the beam from simulated turn-by-turn BPM data. In particular, we show that VAEs may have the possibility to outperform other dimensionality reduction techniques that have historically been used to analyze such data. When the data collection period is limited, or the data is noisy, VAEs may offer significant advantages.}},
}